Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generic Static Analysis Framework for Domain-specific Languages

Software used to monitor and control operations within an automation system is defined using domain-specific languages. Latent errors in the control code, if left undetected, can lead to unexpected system failures compromising the safety and the security of the automation system. Traditional analysis techniques are insufficient to detect such errors as they do not cater specifically to the underlying domain-specific language. However, given the diversity of different automation domains, there is no standard platform for analysis of these languages. This paper proposes a generic static analysis framework for domain-specific languages used in the automation domain. The analysis approach exhaustively detects runtime errors in control code and ensures compliance to good programming practices. These runtime errors and coding violations are checked against abstract syntax trees and control flow graphs derived from the code. Data Flow Analysis (DFA), Abstract interpretation and pattern-based matching techniques are used to identify domain specific errors and coding violations for control languages.
Highly Sensitive Mass Sensing by Means of the Optomechanical Nonlinearity

Nanomechanical oscillator has attracted considerable attention in mass sensing applications because of its ability to connect mass variation and frequency change on mechanical oscillator. Here, using the exact same parameters with previous work based on the linearized dynamics of the optomechanical interactions, we show that the frequency shift of optomechanically induced second-order sideband is more sensitive to the mass change of mechanical oscillator. The proposed method may offer an approach to open up a broad application prospect for on-chip optomechanical devices in sensors by virtue of the achievable intrinsic optomechanical nonlinearity in the weak coupling regime under the currently existing experimental technique.
Fritz: A Tool for Spreadsheet Quality Assurance

While spreadsheets are widely used for business-related tasks, they are mostly handled by novice users instead of professional programmers. Consequently, those users often are not aware of quality issues in their spreadsheet programs that may lead to faults with significant adverse effects. In this work, we therefore present a tool, called Fritz, to support users in checking and improving the quality of their spreadsheets. The tool enriches the traditional spreadsheet visualization scheme by including visual feedback about certain structural and quality aspects. This allows for easier cognition of a spreadsheet's layout, and helps users to detect and comprehend irregularities within it. Furthermore, Fritz highlights suspicious (smelly) cells, such as complex formula cells or empty input cells, that are prone to introduce errors. In contrast to other smell detection tools, Fritz also warns against smells that point out structural irregularities.
Keyframe-based Local Normal Distribution Transform Occupancy Maps for Environment Mapping

In this paper, we propose a new mapping method based on Normal Distribution Transform Occupancy Maps (NDT-OM) for environment exploration. Our goal is to propose a new architecture which can be used by an industrial mobile robot in a priori unknown environment. The mobile robot introduced in a new environment has to explore the workspace, localize itself and build a map. Current state of the art methods require storing all data collected during this stage and finally build a dense model of the environment. We propose a method which allows building local dense maps of the environment which are organized in a graph-like structure. The change in the registered trajectory of the robot, which may occur after loop closure detection, can be easily utilized by our architecture. Finally, we build a global map which can be later used for collision checking and motion planning.
Performance of Spectrum Sensing Based on Energy Detection for Cognitive Radios

Cognitive radios have been proposed for opportunistic users to make use of the frequency bands allocated to licensed users. For this to be possible, spectrum sensing should be performed in the search for free frequency bands. In this work, the spectrum sensing is investigated based on energy detection. Scenarios are studied where the noise distribution is Gaussian and scenarios considering the noise of uncertainty. The behavior of the probability of detection as a function of the probability of a false alarm for a parameterized number of samples and signal-to-noise ratio (SNR) is presented.
Ontology Based Similarity for Information Technology Skills

Online job search and talent procurement have given rise to challenging match and search problems in the e-recruitment domain. Existing systems perform direct keyword matching of technical skills which misses out a closely matching candidate on account of it not having the exact skills. This results in substandard results which ignores the relationships between technical skills. In an attempt to improve relevancy, this paper proposes a semantic similarity measure between IT skills using a knowledge based approach. The approach builds an ontology using DBpedia and uses it to derive a similarity score using feature based similarity measures. The proposed approach performs better than the Resumatcher system in finding the similarity between skills.
A 141 UW, 2.46 PJ/Neuron Binarized Convolutional Neural Network Based Self-Learning Speech Recognition Processor in 28NM CMOS

An ultra-low power speech recognition processor is implemented in 28 nm CMOS technology, which is based on an optimized binary convolutional neural network (BCNN). A tailored self-learning mechanism is implemented to learn the features of users and improve recognition accuracy on the fly. Measurement results show that this processor supports real time speech recognition with power consumption of 141 uW and energy efficiency of 2.46 pJ/Neuron when working at 2.5 MHz, while achieving at most 98.6% recognition accuracy.
A Graph-Based Database Partitioning Method for Parallel OLAP Query Processing

As the amount of data to process increases, a scalable and efficient horizontal database partitioning method becomes more important for OLAP query processing in parallel database platforms. Existing partitioning methods have a few major drawbacks such as a large amount of data redundancy and not supporting join processing without shuffle in many cases despite their large data redundancy. We elucidate the drawbacks arise from their tree-based partitioning schemes and propose a novel graph-based database partitioning method called GPT that improves query performance with lower data redundancy. Through extensive experiments using three benchmarks, we show that GPT significantly outperforms the state-of-the-art method in terms of both storage overhead and query performance.
HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion

Many important real-world applications—such as social networks or distributed data bases—can be modeled as hypergraphs. In such a model, vertices represent entities—such as users or data records—whereas hyperedges model a group membership of the vertices—such as the authorship in a specific topic or the membership of a data record in a specific replicated shard. To optimize such applications, we need an efficient and effective solution to the NP-hard balanced k-way hypergraph partitioning problem. However, existing hypergraph partitioners that scale to very large graphs do not effectively exploit the hy-pergraph structure when performing the partitioning decisions. We propose HYPE, a hypergraph partitionier that exploits the neighborhood relations between vertices in the hypergraph using an efficient implementation of neighborhood expansion. HYPE improves partitioning quality by up to 95% and reduces runtime by up to 39% compared to streaming partitioning.
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates

Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lack the capability of jointly modeling these two factors. To alleviate this problem, this paper proposes a hybrid neural attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. Experimental results on three (dis)agreement inference datasets show that our model outperforms the state-of-the-art models.
How well are regular expressions tested in the wild?

Developers report testing their regular expressions less than the rest of their code. In this work, we explore how thoroughly tested regular expressions are by examining open source projects. Using standard metrics of coverage, such as line and branch coverage, gives an incomplete picture of the test coverage of regular expressions. We adopt graph-based coverage metrics for the DFA representation of regular expressions, providing fine-grained test coverage metrics. Using over 15,000 tested regular expressions in 1,225 Java projects on GitHub, we measure node, edge, and edge-pair coverage. Our results show that only 17% of the regular expressions in the repositories are tested at all. For those that are tested, the median number of test inputs is two. For nearly 42% of the tested regular expressions, only one test input is used. Average node and edge coverage levels on the DFAs for tested regular expressions are 59% and 29%, respectively. Due to the lack of testing of regular expressions, we explore whether a string generation tool for regular expressions, Rex, achieves high coverage levels. With some exceptions, we found that tools such as Rex can be used to write test inputs with similar coverage to the developer tests.
Extracting User Habits from Google Maps History Logs

The exponential growth in the usage of smart devices, such as smartphones, interconnected wearables etc., creates a huge amount of information to manage and many research and business opportunities. Such smart devices become a useful tool for user movement recognition, since they are equipped with different types of sensors and processors that can process sensor data and extract useful knowledge. Taking advantage of the GPS sensor, they can collect the timestamped geographical coordinates of the user, which can then be used to extract the geographical location and movement of the user. Our work, takes this analysis one step ahead and attempts to identify the user's behavior and habits, based on the analysis of user's location data. This type of information can be valuable for many other domains such as Recommender Systems, targeted/personalized advertising etc. In this paper, we present a methodology for analyzing user location information in order to identify user habits. To achieve this, we analyze user's GPS logs provided through his Google location history, we find locations that user usually spends more time, and after identifying the user's frequently preferred transportation types and trajectories, we find what type of places the user visits in a regular base (such as cinemas, restaurants, gyms, bars etc) and extract the habits that the user is most likely to have.
A Biconvex Analysis for Lasso $\ell _1$  Reweighting

Iterative <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> reweighting algorithms are very popular in sparse signal recovery and compressed sensing, since in the practice they have been observed to outperform classical <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> methods. Nevertheless, the theoretical analysis of their convergence is a critical point, and generally is limited to the convergence of the functional to a local minimum or to subsequence convergence. In this letter, we propose a new convergence analysis of a Lasso <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> reweighting method, based on the observation that the algorithm is an alternated convex search for a biconvex problem. Based on that, we are able to prove the numerical convergence of the sequence of the iterates generated by the algorithm. Furthermore, we propose an alternative iterative soft thresholding procedure, which is faster than the main algorithm.
Low-Cost Submersible Turbidity Sensors Using Low-Frequency Source Light Modulation

Submersible turbidimeter and fluorometer design generally involves tradeoffs between ambient light rejection, sensitivity, and cost. We introduce an optical backscatter transducer in which the light source is modulated with a low-frequency (tens of Hz) square wave, with a detection scheme that mitigates the effects of external interference from ambient light and from 50- and 60-Hz sources. With this design, high-pass filtering and subsequent amplification enable sensitive measurements using inexpensive circuitry. Additional accuracy-enhancing features include: a rail-splitting virtual ground, which eliminates the potential for clipping at low signal levels; a reference output, which provides correction for the intensity of the internal light source; and error-tracking provisions, which flag the output if circuit voltages reach saturation limits during measurement. We have designed, constructed, and evaluated submersible turbidimeters which incorporate this transducer. These sensors were inexpensive to fabricate (U.S. $70 of materials and 4 h of labor) and were proof tested to 7 atm of water pressure. They responded linearly and reproducibly to formazin turbidity standard, with negligible inherent offset. A calibrated sensor exhibited bias ${\le} 0.11$ NTU in suspensions ranging from 0 to 8 NTU. A similar sensor with reduced circuit gain exhibited a one-to-one response to increasing turbidity in suspensions ranging from 0 to 815 NTU.
Automated Phase-Noise-Aware Design of RF Clock Distribution Circuits

This paper presents a methodology for phase-noise-aware design of RF clock distribution networks. The methodology is embedded within a commercial digital toolchain. It consists of a method for phase noise analysis based on lookup tables, a buffer insertion algorithm providing an optimal buffering for large interconnect trees and a gate sizing algorithm that offers fast power minimization, subject to noise constraints. The phase noise estimate produced by the proposed method on average deviates by less than 3.45% from SPICE simulation results; however, it can be computed three to five orders of magnitude faster. The proposed buffer insertion methodology can easily handle very large industrial examples. It completes within seconds, compared to days for methodologies used in industry today. The gate sizing optimization offers three to four orders of magnitude speedup, compared to commercial products, while producing comparable results. The proposed design methodology reduces the design time of RF clock distribution networks from weeks to a few hours by eliminating the need for manual circuit design, analog simulations, and manual layout. To the extent of our knowledge, this is the first paper on the design automation of RF clock distribution networks.
Design and Implementation of Time Synchronization Experimental System for Wireless Sensor Networks

The basic principle of the reference broadcast synchronization protocol using in wireless sensor networks is present，and a compact reference broadcast synchronization experimental system is designed and implemented in this paper. The experimental results show that the receivers of the system can successfully capture the timing packets issued by the broadcast beacon within 200 meters and simultaneously adjust their local clocks according the timing packets. The time synchronization error between the receivers is less than 410 microseconds. The circuit design scheme of the time synchronization experimental system proposed in this paper may be taken as technical reference for the planning and designing of low cost, miniaturization and high accuracy nodes in wireless sensor networks.
Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors

Gestures are a natural communication modality for humans. The ability to interpret gestures is fundamental for robots aiming to naturally interact with humans. Wearable sensors are promising to monitor human activity, in particular the usage of triaxial accelerometers for gesture recognition have been explored. Despite this, the state of the art presents lack of systems for reliable online gesture recognition using accelerometer data. The article proposes SLOTH, an architecture for online gesture recognition, based on a wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic classifier and a procedure for continuous gesture detection, relying on modelling gesture probabilities, that guarantees (i) good recognition results in terms of precision and recall, (ii) immediate system reactivity.
Towards Reliable Missing Truth Discovery in Online Social Media Sensing Applications

Social media sensing has emerged as a new application paradigm to collect observations from online social media users about the physical environment. A fundamental problem in social media sensing applications lies in estimating the evolving truth of the measured variables and the reliability of data sources without knowing either of them a priori. This problem is referred to as dynamic truth discovery. Two major limitations exist in current truth discovery solutions: i) existing solutions cannot effectively address the missing truth problem where the measured variables do not have any reported measurements from the data sources; ii) the latent correlations among the measured variables were not fully captured and utilized in current solutions. In this paper, we proposed a Reliable Missing Truth Finder (RMTF) to address the above limitations in social media sensing applications. In particular, we develop a novel data-driven technique to identify the lagged and latent correlations among measured variables, and incorporate such correlation information into a holistic spatiotemporal inference model to infer the missing truth. We evaluated the RMTF using the real-world Twitter data feeds. The results show that the RMTF scheme significantly outperforms the state-of-the-art truth discovery solutions by correctly inferring the missing truth of the measured variables.
Investigation of AC Copper and Iron Losses in High-Speed High-Power Density PMSM

This paper presents a detailed investigation of AC copper losses in high-speed high-power density permanent magnet synchronous machine. For parallel strands winding, it is not easy to accurately estimate the AC copper losses using finite element model (FEM), due to the random distribution of the conductors inside the slots. Therefore, this work is focused on the separation of the AC copper losses component from the total measured losses. In order to obtain more precise results, the effect of manufacturing process on the stator core has also been considered. A 3D FEM has been used to evaluate the total power losses including the effect of the end-windings. Finally, the influence of the temperature on the AC losses is taken into account by monitoring the winding temperature during the experiments.
A 100MS/S 12-bit Coarse-Fine SAR ADC with Shared Split-CDAC

A 100MS/s 12-bit SAR ADC designed for terahertz imaging sensor is presented. The proposed ADC utilizes coarse-fine SAR architecture to relax reference requirement and improve operation rate. Split-CDAC structure is applied to reduce chip area, and its LSBs capacitors are reused as a coarse DAC. Redundant capacitor arrays are applied in both coarse and fine ADCs. A power-and-area-efficient ADC with high sampling rate can be realized with these techniques combined. The prototype is implemented in TSMC 1P9M 65nm-CMOS process. The simulation result with noise and other on board non-ideal effects shows that the ADC consumes 1.8 mW with an SNDR 65.7 dB and an SFDR of 77.0 dB with a Nyquist input at 1.2V power supply, achieving an ENOB of 10.6-bit and a FoM of 11.6 fJ/conv-step.
Some Local Stability Properties of an Autonomous Long Short-Term Memory Neural Network Model

In this paper some local stability results for an autonomous Long Short-Term Memory neural network model with respect to the origin are provided. In particular, it is shown through linearization that the local asymptotic stability conditions with respect to the origin only depend on one of the weight matrices. Simulations indicate that these local stability conditions greatly influence the behavior of the autonomous four-dimensional neural network in the region where each variable's values vary between minus one and one. Finally, some sufficient stability conditions for the nonlinear model are formulated as a convex program involving linear matrix inequalities.
Ordinal Network Measures — Quantifying Determinism in Data

The ordinal network construction method was proposed at ISCAS2013. Since then a great deal of theoretical and experimental work has developed these methods to the point where they can be applied as a diagnostic tool to understand changing system behaviour from observed time series. We now also have a solid theoretical understanding of what these methods measure and why this is useful. This talk will review the method and demonstrate its application to computation and experimental systems with the aim of detecting changes or bifurcations in the underlying system. We will conclude with applications to biosignal processing for cardiac interbeat interval time series and electroencephalograms.
The Internal Coherence of Breast Cancer Patients Is Associated with the Decision-Making for Chemotherapy and Viscum album L. Treatment

Objective In the present observational study, the influence of internal coherence on shared decision-making for chemotherapy (CTX) and Viscum album L. extracts (VA) treatment in breast cancer patients was evaluated. Methods Breast cancer patients with a guideline-oriented advice from the tumor board for CTX were included in the study. At first diagnosis (T0) and 6 months later (T1), a questionnaire, the internal coherence scale (ICS), was administered and evaluated. Prior to analysis, patients were classified retrospectively depending on their treatment decision. Results 64 primary nonmetastasized breast cancer patients (median age 54.8 years, IQR: 46.3-65.3) were analyzed in this study. At T0, adjusted multivariable linear regression analyses revealed significant low ICS scores in patients rejecting CTX, especially in the ICS subscale “thermo coherence” (p = 0.006). The decision for add-on VA-therapy was associated with low scores for the ICS subscale “inner resilience coherence”, in particular low for the item “courage”. At T1, in the CTX+VA-group the thermo coherence increased significantly (p(d) < 0.01), while in contrast, in the CTX-only group the thermo coherence decreased significantly (p(d) = 0.02). Conclusion Add-on VA-applications in CTX treatment support the thermo coherence of breast cancer patients, revealing a decision option to encourage patients to undergo CTX in combination with additional VA-treatments.
Characterization of Brain Cortical Morphology Using Localized Topology-Encoding Graphs

The human brain cortical layer has a convoluted morphology that is unique to each individual. Characterization of the cortical morphology is necessary in longitudinal studies of structural brain change, as well as in discriminating individuals in health and disease. A method for encoding the cortical morphology in the form of a graph is presented. The design of graphs that encode the global cerebral hemisphere cortices as well as localized cortical regions is proposed. Spectral features of these graphs are then studied and proposed as descriptors of cortical morphology. As proof-of-concept of their applicability in characterizing cortical morphology, the descriptors are studied in the context of discriminating individuals based on their sex.