Abstract: We present grey-box concolic testing, a novel path-based test case generation method that combines the best of both white-box and grey-box fuzzing. At a high level, our technique systematically explores execution paths of a program under test as in white-box fuzzing, a.k.a. concolic testing, while not giving up the simplicity of grey-box fuzzing: it only uses a lightweight instrumentation, and it does not rely on an SMT solver. We implemented our technique in a system called Eclipser, and compared it to the state-of-the-art grey-box fuzzers (including AFLFast, LAF-intel, Steelix, and VUzzer) as well as a symbolic executor (KLEE). In our experiments, we achieved higher code coverage and found more bugs than the other tools.

Abstract: Recent developments in sensor technology and computational analysis methods enable new strategies to measure and interpret lung acoustic signals that originate internally, such as breathing or vocal sounds, or are externally introduced, such as in chest percussion or airway insonification. A better understanding of these sounds has resulted in a new instrumentation that allows for highly accurate as well as portable options for measurement in the hospital, in the clinic, and even at home. This review outlines the instrumentation for acoustic stimulation and measurement of the lungs. We first review the fundamentals of acoustic lung signals and the pathophysiology of the diseases that these signals are used to detect. Then, we focus on different methods of measuring and creating signals that have been used in recent research for pulmonary disease diagnosis. These new methods, combined with signal processing and modeling techniques, lead to a reduction in noise and allow improved feature extraction and signal classification. We conclude by presenting the results of human subject studies taking advantage of both the instrumentation and signal processing tools to accurately diagnose common lung diseases. This paper emphasizes the active areas of research within modern lung acoustics and encourages the standardization of future work in this field.

Abstract: Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor. Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.

Abstract: Ensuring correctness of smart contracts is paramount to ensuring trust in blockchain-based systems. This paper studies the safety and security of smart contracts in the Azure Blockchain Workbench, an enterprise Blockchain-as-a-Service offering from Microsoft. In particular, we formalize semantic conformance of smart contracts against a state machine workflow with access-control policy and propose an approach to reducing semantic conformance checking to safety verification using program instrumentation. We develop a new Solidity program verifier VeriSol that is based on translation to Boogie, and have applied it to analyze all application contracts shipped with the Azure Blockchain Workbench and found previously unknown bugs in these published smart contracts. After fixing these bugs, VeriSol was able to successfully perform full verification for all of these contracts.

Abstract: Ethereum is the largest blockchain platform supporting smart contracts with the second biggest market capitalization. Ethereum data can yield many useful insights because of the large volume of transactions, accounts and blocks as well as the popular applications developed as smart contracts. Studying Ethereum data can also reveal many new attacks to the platform and its smart contracts. Unfortunately, it is non-trivial to systematically explore Ethereum because it involves massive heterogeneous data, which are produced and stored in different ways. Although a few recent studies report some interesting observations about Ethereum, they are limited by their data acquisition methods which cannot provide comprehensive and precise data. In this paper, to fill the gap, we propose DataEther, a systematic and high-fidelity data exploration framework for Ethereum by exploiting its internal mechanisms. Besides supporting the analyses in existing studies, DataEther further empowers users to explore unknown phenomena and obtain in-depth understandings. We first describe how we tackle the challenging issues in developing DataEther, and then use four data-centric applications to demonstrate its usage and report many new observations.

Abstract: One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. These files need to be digitized for purposes of inventory management and updation, and easy reference to different components of the schematics. There are several challenging vision problems associated with digitizing real world P&ID diagrams. Real world P&IDs come in several different resolutions, and often contain noisy textual information. Extraction of instrumentation information from these diagrams involves accurate detection of symbols that frequently have minute visual differences between them. Identification of pipelines that may converge and diverge at different points in the image is a further cause for concern. Due to these reasons, to the best of our knowledge, no system has been proposed for end-to-end data extraction from P&ID diagrams. However, with the advent of deep learning and the spectacular successes it has achieved in vision, we hypothesized that it is now possible to re-examine this problem armed with the latest deep learning models. To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols. This is followed by association of the detected components with the appropriate pipeline. The extracted pipeline information is used to populate a tree-like data-structure for capturing the structure of the piping schematics. We evaluated proposed method on a real world dataset of P&ID sheets obtained from an oil firm and have obtained promising results.

Abstract: WebAssembly is the new low-level language for the web and has now been implemented in all major browsers since over a year. To ensure the security, performance, and correctness of future web applications, there is a strong need for dynamic analysis tools for WebAssembly. However, building such tools from scratch requires knowledge of low-level details of the language and its runtime environment. This paper presents Wasabi, the first general-purpose framework for dynamically analyzing WebAssembly. Wasabi provides an easy-to-use, high-level API that supports heavyweight dynamic analyses. It is based on binary instrumentation, which inserts calls to analysis functions written in JavaScript into a WebAssembly binary. Dynamically analyzing WebAssembly comes with several unique challenges, such as the problem of tracing type-polymorphic instructions with analysis functions that have a fixed type, which we address through on-demand monomorphization. Our evaluation on compute-intensive benchmarks and real-world applications shows that Wasabi (i) faithfully preserves the original program behavior, (ii) imposes an overhead that is reasonable for heavyweight dynamic analysis, and (iii) makes it straightforward to implement various dynamic analyses, including instruction counting, call graph extraction, memory access tracing, and taint analysis.

Abstract: Instruction on the design of analytical instrumentation is a critical component of the analytical chemistry curriculum. To simplify this process and enable students to directly see how the instruments that are in their own laboratory setting work, the use of augmented reality technology can be implemented. In this report, the HP Reveal platform was used to create digital overlays that are triggered when students view an analytical instrument through their smartphone camera. From here, further information on the components and operation of the instrument can be presented to students. To demonstrate this technology, various overlays were created for four analytical instruments commonly taught in second-year undergraduate analytical chemistry courses: flame atomic absorption spectrometer, gas chromatograph–mass spectrometer, liquid chromatograph, and double-beam UV–vis spectrophotometer.

Abstract: Operating directly from raw high dimensional sensory inputs like images is still a challenge for robotic control. Recently, Reinforcement Learning methods have been proposed to solve specific tasks end-to-end, from pixels to torques. However, these approaches assume the access to a specified reward which may require specialized instrumentation of the environment. Furthermore, the obtained policy and representations tend to be task specific and may not transfer well. In this work we investigate completely self-supervised learning of a general image embedding and control primitives, based on finding the shortest time to reach any state. We also introduce a new structure for the state-action value function that builds a connection between model-free and model-based methods, and improves the performance of the learning algorithm. We experimentally demonstrate these findings in three simulated robotic tasks.

Abstract: A new class of chemical instrumentation seeks to alleviate the tedium and complexity of organic syntheses. A new class of chemical instrumentation seeks to alleviate the tedium and complexity of organic syntheses.

Abstract: Meanwhile, a high fraction of vehicles, driven by diesel engines, is equipped with very efficient particle filters in Europe The filters have been enforced by the particle number limit for type approval testing If the filter works properly, the emissions are very low However, a small fraction of vehicles having broken or manipulated particle filters and therefore very high emissions can dominate the average fleet emissions of vehicles The on-board diagnostic systems do not detect most of these filter defects In several studies, failure rates in the order of some percent up to 20% have been observed Therefore, identifying these high polluters is urgently needed An option to achieve this is a periodical technical inspection Gasoline engines may also have very high particle emissions, if there is a malfunction Nevertheless, as a first step, the focus of current work is on diesel engines Some data for gasoline engines will be presented, but the suggested test procedure has only been verified for diesel engines For these, it has been shown that a test measurement can be done at low idle, which allows a fast and cheap procedure Requirements for the test instrumentation are specified and a test procedure is suggested A limit value is proposed which on the one hand is higher than requirements in type approval, but low enough to detect high polluters The method suggested can be applied to passenger vehicles, as well as to heavy-duty engines and off-road applications

Abstract: Attacks on transactions of Ethereum could be dangerous because they could lead to a big loss of money. There are many tools detecting vulnerabilities in smart contracts trying to avoid potential attacks. However, we found that there are still many missed vulnerabilities in contracts. Motivated by this, we propose a methodology to reinforce EVM to stop dangerous transactions in real time even when the smart contract contains vulnerabilities. Basically, the methodology consists of three steps: monitoring strategy definition, opcode-structure maintenance and EVM instrumentation. Monitoring strategy definition refers to the specific rule to test whether there is a dangerous operation during transaction execution. Opcode-structure maintenance is to maintain a structure to store the rule related opcodes and analyze it before an operation execution. EVM instrumentation inserts the monitoring strategy, interrupting mechanism and the opcode-structure operations in EVM source code. For evaluation, we implement EVM* on js-evm, a widely-used EVM platform written in javascript. We collect 10 contracts online with known bugs and use each contract to execute a dangerous transaction, all of them have been interrupted by our reinforced EVM*, while the original EVM permits all attack transactions. For the time overhead, the reinforced EVM* is slower than the original one by 20-30%, which is tolerable for the financial critical applications.

Abstract: Recent advances in portability of analytical equipment have been considered to enlighten the advantages offered by portable instrumentation on greening the analytical methods. Their use drastically reduces sampling, sample stockage, and transport, thus avoiding environmental side effects and risks, also improving decision-making. The fact that portable instrumentation is, in general, less expensive than bench instruments and apparatuses makes also available the analytical tools for extended sectors of the population, thus making accessible the advantages derived from analytical methods. The role of sensor technology and portable miniaturized systems has been considered to be paid special attention to the portable sample treatment systems based on microwave and ultrasound technologies and the use of image processing systems.

Abstract: Procedural knowledge describes actions and manipulations that are carried out to complete programming tasks. An effective way to document procedural knowledge is programming video tutorials. Unlike text-based software artifacts and tutorials that can be effectively searched and linked using information retrieval techniques, the streaming nature of programming videos limits the ways to explore the captured workflows and interact with files, code and program output in the videos. Existing solutions to adding interactive workflow and elements to programming videos have a dilemma between the level of desired interaction and the efforts required for authoring tutorials. In this work, we tackle this dilemma by designing and building a programming video tutorial authoring system that leverages operating system level instrumentation to log workflow history while tutorial authors are creating programming videos, and the corresponding tutorial watching system that enhances the learning experience of video tutorials by providing programming-specific workflow history and timeline-based browsing interactions. Our tutorial authoring system does not incur any additional burden on tutorial authors to make programming videos interactive. Given a programming video accompanied by synchronously-logged workflow history, our tutorial watching system allows tutorial watchers to freely explore the captured workflows and interact with files, code and program output in the tutorial. We conduct a user study of 135 developers to evaluate the design and effectiveness of our system in helping developers learn programming knowledge in video tutorials.

Abstract: This paper describes system identification of a nonlinear flight dynamic model for a small, low-cost, fixed-wing unmanned aircraft with a limited instrumentation system. Unique challenges include g...

Abstract: Existing directed fuzzers are not efficient enough. Directed symbolic-execution-based whitebox fuzzers, e.g. BugRedux, spend lots of time on heavyweight program analysis and constraints solving at runtime. Directed greybox fuzzers, such as AFLGo, perform well at runtime, but considerable calculation during instrumentation phase hinders the overall performance. In this paper, we propose Sequence-coverage Directed Fuzzing (SCDF), a lightweight directed fuzzing technique which explores towards the user-specified program statements efficiently. Given a set of target statement sequences of a program, SCDF aims to generate inputs that can reach the statements in each sequence in order and trigger bugs in the program. Moreover, we present a novel energy schedule algorithm, which adjusts on demand a seed's energy according to its ability of covering the given statement sequences calculated on demand. We implement the technique in a tool LOLLY in order to achieve efficiency both at instrumentation time and at runtime. Experiments on several real-world software projects demonstrate that LOLLY outperforms two well-established tools on efficiency and effectiveness, i.e., AFLGo-a directed greybox fuzzer and BugRedux-a directed symbolic-execution-based whitebox fuzzer.

Abstract: Broadly, Internet of Things (IoT) paradigm represents an evolving technology which tries to reach its main goal, where each and every ‘thing’ (i.e. device) can be: (i) connected through a network and (ii) controllable from any remote station. Activity sectors like intelligent manufacturing, farms, e-agriculture, real-time traffic controls, environment monitoring, camera security systems, health-care, etc., are going to be governed by the IoT paradigm as backbone in the near future. In this context, ensuring security during ‘thing’ operation and information exchange becomes a critical task. Therefore, secured IoT applies equally to: (i) IoT device-to-device communication, (ii) IoT sensing/actuating, and (iii) IoT information exchange. In this paper, a brief literature survey regarding the research pointing out the Hardware Security (HS) for IoT is presented. Furthermore, in the paper, a short analysis regarding the existing trends for measurements and instrumentation is reported.

Abstract: This paper presents a thorough review of the initiatives carried out in the last 10 years toward the development of active knee prostheses (AKP) for transfemoral amputees. Three selection criteria were employed to filter the works to be considered in the review: (1) a prototype of the prosthesis is available; (2) the mechanical design, instrumentation, and control strategy of such a prototype have been presented in a scientific disclosure media; and (3) the prototype has been subjected to clinical assessment at least in a preliminary way. After applying such criteria, 16 projects were selected and further reviewed through a total of 31 scientific papers, considering the following six aspects: (1) actuators, (2) instrumentation, (3) control, (4) testing trials, (5) performance metrics, and (6) limitations. Then, in addition, the chronological appearance of the aforesaid papers is also shown and quantified regarding each of the previously mentioned issues, to initiate discussion on the related topics. Thus, the present review results in a specialized summary of all these developments in a structured format, offering additional understanding of the recent advances achieved in this field.

Abstract: As a blockchain platform that has developed vigorously in recent years, Ethereum is different from Bitcoin in that it introduces smart contracts into blockchain.Solidity is one of the most mature and widely used smart contract programming language,which is used to write smart contracts and deploy them on blockchain. However, once the data in the blockchain is written, it cannot be modified. Ethereum smart contract is stored in the block chain, which makes the smart contract can no longer repair the code problems such as re-entrancy vulnerabilities or integer overflow problems. Currently, there still lacks of an efficient and effective approach for detecting these problems in Solidity. In this paper, we first classify all the possible problems in Solidity, then propose a smart contract problem detection approach for Solidity, namely SolidityCheck. The approach uses regular expressions to define the characteristics of problematic statements and uses regular matching and program instrumentation to prevent or detect problems. Finally, a large number of experiments is performed to show that SolidityCheck is superior to existing approaches.

Abstract: Solidity is an object-oriented and high-level language for writing smart contracts that are used to execute, verify and enforce credible transactions on permissionless blockchains. In the last few years, analysis of smart contracts has raised considerable interest and numerous techniques have been proposed to check the presence of vulnerabilities in them. Current techniques lack traceability in source code and have widely differing work flows. There is no single unifying framework for analysis, instrumentation, optimisation and code generation of Solidity contracts at the source code level. In this paper, we present SIF, a comprehensive framework for Solidity contract analysis, query, instrumentation, and code generation. SIF provides support for Solidity contract developers and testers to build source level techniques for analysis, understanding, diagnostics, optimisations and code generation. We show feasibility and applicability of the framework by building practical tools on top of it and running them on 1838 real smart contracts deployed on the Ethereum network.

Abstract: Introduction: Total knee arthroplasty (TKA) is one of the most commonly performed orthopedic procedures. During the past decade, patient-specific instrumentation (PSI) has been commercially introdu...

Abstract: This paper presents a polymer optical fiber (POF)-based sensor system for smart walker (SW) instrumentation. The system comprises an oximetry sensor working in reflectance mode, which is embedded in a 3D printed handle for the SW. In addition, there is also a POF-based smart textile that can be integrated in the users’ clothes and is placed on their chest. In this case, the smart textile can detect the displacement caused by the respiration, vibration due to heartbeat and bending induced in the gait. Results show that the proposed sensor system can detect the oxygen saturation, breathing rate, gait cadence, and heart rate with errors of about 1%. In addition, it is proposed an architecture for the SW in which the POF-based sensor system can be applied not only for remote healthcare applications, but also as sensor feedback in cloud robotics for some control paradigms for human–robot interaction. Furthermore, the proposed instrumentation presents the possibility of novel SW control, where the health conditions of the user are considered in the human–robot interaction.

Abstract: Over the years companies have accumulated large amounts of legacy data. With modern data mining and machine learning techniques the data is increasingly valuable. Therefore being able to convert legacy data into a computer understandable form is important. In this work, we investigate how to convert schematic diagrams, such as process and instrumentation diagrams (P&I diagrams). We use modern machine learning based approaches, in particular, the Yolo neural network system, to detect high-level objects, e.g. pumps or valves, in diagrams which are scanned from paper archives or stored in pixel or vector form. Together with connection detection and OCR this is an essential step for the reuse of old planning data. Our results show that Yolo, as an instance of modern machine learning based object detection systems, works well with schematic diagrams. In our concept, we use a simulator to automatically generate labeled training material to the system. We then retrain a previously trained network to detect the components of our interest. Detection of large components is accurate but small components with sizes below 15% of page size are missed. However, this can be worked around by dividing a big diagram into a set of smaller subdiagrams with different scales, processing them separately, and combining the results.

Abstract: In collaborative settings such as multi-investigator laboratories, data scientists need improved tools to manage not their data records but rather their data sets and data products, to facilitate both provenance tracking and data (and code) reuse within their data lakes and file systems. We demonstrate the Juneau System, which extends computational notebook software (Jupyter Notebook) as an instrumentation and data management point for overseeing and facilitating improved dataset usage, through capabilities for indexing, searching, and recommending "complementary" data sources, previously extracted machine learning features, and additional training data. This demonstration focuses on how we help the user find related datasets via search.

Abstract: Prototyping hyperspectral imaging devices in current biomedical optics research requires taking into consideration various issues regarding optics, imaging, and instrumentation. In summary, an ideal imaging system should only be limited by exposure time, but there will be technological limitations (e.g., actuator delay and backlash, network delays, or embedded CPU speed) that should be considered, modeled, and optimized. This can be achieved by constructing a multiparametric model for the imaging system in question. The article describes a rotating-mirror scanning hyperspectral imaging device, its multiparametric model, as well as design and calibration protocols used to achieve its optimal performance. The main objective of the manuscript is to describe the device and review this imaging modality, while showcasing technical caveats, models and benchmarks, in an attempt to simplify and standardize specifications, as well as to incentivize prototyping similar future designs.

Abstract: Aim To develop a scientifically current and evidence based protocol on the efficacy of rotary and hand root canal instrumentation in primary teeth. Materials and methods Previous randomized control trials were used for the current review. Hand search and online search engines of PUBMED and Google Scholar were used to search English language articles with human subjects published up to December 2016. Results After screening of the abstracts and articles, based on the inclusion and exclusion criteria a total of 13 articles were included in the systematic review. Conclusion Rotary instrumentation shows equivalent cleaning efficiency than hand files depending on the system of instrumentation and techniques used. However, use of rotary in primary teeth leads to improved shaping of canals providing better quality of treatment in less time. How to cite this article Panchal V, Jeevanandan G, et al. Comparison between the Effectiveness of Rotary and Manual Instrumentation in Primary Teeth: A Systematic Review. Int J Clin Pediatr Dent 2019;12(4):340-346.