Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Feature Learning via Dual Sequences for Defect Prediction

Software defect prediction (SDP) can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts. Existing methods often serialize an Abstract Syntax Tree (AST) obtained from the program source code into a token sequence, which is then inputted into the deep learning model to learn the semantic features. However, there are different ASTs with the same token sequence, and it is impossible to distinguish the tree structure of the ASTs only by a token sequence. To solve this problem, this paper proposes a framework called Semantic Feature Learning via Dual Sequences (SFLDS), which can capture the semantic and structural information in the AST for feature generation. Specifically, based on the AST, we select the representative nodes in the AST and convert the program source code into a simplified AST (S-AST). Our method introduces two sequences to represent the semantic and structural information of the S-AST, one is the result of traversing the S-AST node in pre-order, and another is composed of parent nodes. Then each token in the dual sequences is encoded as a numerical vector via mapping and word embedding. Finally, we use a bi-directional long short-term memory (BiLSTM) based neural network to automatically generate semantic features from the dual sequences for SDP. In addition, to leverage the statistical characteristics contained in the handcrafted metrics, we also propose a framework called Defect Prediction via SFLDS (DP-SFLDS) which combines the semantic features generated from SFLDS with handcrafted metrics to perform SDP. In our empirical studies, eight open-source Java projects from the PROMISE repository are chosen as our empirical subjects. Experimental results show that our proposed approach can perform better than several state-of-the-art baseline SDP methods.
Mapping Natural Language Questions to Medical Specialties

Many real-world applications handle large amounts of data, which needs to be preprocessed and classified before it can be used. Performing this classification using human agents is usually slow and costly, thus motivating the need for automation. One of the areas that can benefit from an automatized process includes online health-care platforms that connect people to doctors remotely. This type of platform usually offers an asynchronous messaging service, and can receive up to hundreds or thousands of medical questions every day. The questions need to be assigned to the appropriate specialists in a timely manner. This paper offers an automatic solution to this problem. Its purpose is to address the issue of mapping natural language texts to medical specialties. Two solutions are proposed and compared, one based on the Naive Bayes classifier and the other on a Linear classifier implemented using TensorFlow. The former obtains an accuracy of over 95% for 23% of the questions while the latter obtains the same accuracy for 60% of the questions. The results motivate the use of these solutions in real-world applications.
Learning to See Through a Few Pixels: Multi Streams Network for Extreme Low-Resolution Action Recognition

Human action recognition is one of the most pressing questions in societal emergencies of any kind. Technology is helping to solve such problems at the cost of stealing human privacy. Several approaches have considered the relevance of privacy in the pervasive process of observing people. New algorithms have been proposed to deal with low-resolution images hiding people identity. However, many of these methods do not consider that social security asks for real-time solutions: active cameras require flexible distributed systems in sensible areas as airports, hospitals, stations, squares and roads. To conjugate both human privacy and real-time supervision, we propose a novel deep architecture, the Multi Streams Network. This model works in real-time and performs action recognition on extremely low-resolution videos, exploiting three sources of information: RGB images, optical flow and slack mask data. Experiments on two datasets show that our architecture improves the recognition accuracy compared to the two-streams approach and ensure real-time execution on Edge TPU (Tensor Processing Unit).
Adapting the Learning Rate of the Learning Rate in Hypergradient Descent

Gradient descent is a widely used optimization method. The adjustment of the learning rate is an important factor in improving its performance, and many researchers have investigated methods for automatically adjusting the learning rate. One such method, hypergradient descent, automatically adjusts the learning rate by using gradient descent. However, it introduces the “learning rate of the learning rate,” and an appropriate value for the learning rate of the learning rate must be chosen in order to effectively adjust the learning rate. We investigated the use of two datasets and two optimization methods for doing this and achieved an effective adjustment of the learning rate when the objective function was convex and $L$-smooth.
Research on the Bi-Level Optimization Model of Distribution Network Based on Distributed Cooperative Control

The uncertainty of high-permeability renewable energy output brings about great challenges to the operation control in distribution network. The traditional control mode has lower reliability, higher calculation and greater communication pressure. It is more difficult to realize the technical requirements of “plug and play” of power components. Therefore, a bi-level coordinated control model is proposed for the operation and energy management optimization of active distribution network with multi-microgrid in this paper. The consensus algorithm which exists in the multi-agent system is applied in the upper-level model and the capacity utilization ratio of the different microgrids is set as the consistent variable because the ratio of resistance and reactance is usually larger in distribution network and the interests in the different microgrid entities should be distributed fairly. Moreover, in the lower-level model, the lowest operating cost is the final goal and the model predictive control rolling optimization is utilized to reasonably allocate the output of the equipments in the microgrid according to the changing trend of renewable energy. Thereby the tracking of the upper-level control commands is realized. Finally, the simulations are conducted in the IEEE-33 nodes distribution system connected to a multi-microgrid to verify the feasibility and effectiveness of the proposed method, in which a reference for the optimal operation of higher penetration renewable energy to distribution network is provided.
Decimeter Level Indoor Localization Using Hybrid Measurements of a Distributed Single Receiver

Recently, indoor localization using a single receiver has received significant attention. Despite the improvement of this, developing an accurate and universal system is still heavily required. In this article, we design and implement BiLoc, a decimeter level indoor localization system with hybrid measurements of a distributed single receiver. Specifically, different from the existing single receiver-based localization system, in BiLoc, some antennas of the array on the single receiver are distributed at different locations. To this end, BiLoc is based on the fact that the paths recorded on the same receiver incur common phase errors that are caused by nonsynchronization between the transmitter and the receiver. In BiLoc, first, some antennas of the antenna array are moved to different locations to locate the target. Then, BiLoc uses the 2-D matrix pencil (MP) to estimate the Angle of Arrival (AoA) and Time of Flight (ToF) of the multipath signals, which reduces the computational complexity effectively. After that, BiLoc constructs a nonlinear localization model by using the ToFs of the paths received at different antennas and AoAs measured at the antenna array, and then, an iterative algorithm based on the gradient of the localization objective function is used to solve this nonlinear problem. Because of combining hybrid AoA and ToF measurements, the proposed localization method is no longer sensitive to the selection of the initial guess of the target location. Finally, we implement BiLoc on the software-defined radio (SDR) X310 devices, and the designed prototype has six antennas with a maximum signal bandwidth of 100 MHz. The extensive experiments conducted in the practical indoor environments show that BiLoc achieves a median accuracy of 5 dm, which is comparable to the existing localization systems with decimeter level accuracy, and the localization delay is less than 100 ms. We believe that BiLoc can perform better in the emerging networks, such as 5G, because wider bandwidth and more antennas can be provided.
Quantitative Comparative Analysis in Parallel Translation Corpus: building author’s and translator’s statistical profiles : (a case study of Lucy Maud Montgomery)

This paper deals with man-machine interaction issues in parallel translation corpus with reference to English and Ukrainian languages. Computational linguistics tools are applied with the intention to create parallel translation corpus data and for statistical analysis of the data acquired. The limited evidence of practical application of linguostatistical methods in translation and Translation Studies makes the chosen topic up- to-date and novel. Prior results ventilate into how corpora may be involved in the pre study of author’s idiostyle. Thus, quantitative comparative analysis in translation best presents itself from the viewpoint of statistics on text structure (form) level.
Automation Recognition Technology of Controls in Human-Machine Interface for Manufacturing Enterprises

The automation equipment and software with Human-Machine interaction interface are gradually popularized and applied in manufacturing enterprises. Due to the limitation of manpower level of the enterprise and the complicated and tedious operation methods, this promotion process is greatly hindered. Aiming at the automatic detection of controls, especially those with label images in Human-Machine interaction interface, a method to classify and identify the label images by combining image processing and deep learning is proposed in this paper. A dataset containing 20 types of control labels was collected and the traditional CNN network and Siamese neural network (SNN) were combined to cluster and classify them. According to the experimental results, the classification and identification accuracy of SNN on the test set can reach near 97%, which proved that the method proposed in this paper could classify and recognize controls with label images with a high accuracy.
Automatic acquisition of immune cells location using deep learning for automated analysis

Immune cells are analyzed for the elucidation of the human immune system by researchers. They take a cells' movie and track manually the cells to analyze. But the manual process is hard for them. The aim of this study was reducing the load of theirs with automated cells' picking up. To pick up cells, “Recognition Frequency Space” was generated using immune cells' classifier that was made by CNN that a method of deep learning. The classifier was trained while comparing several conditions to decide the best performance classifier. Recognition Frequency Space was indicated the number of times cells are recognized from the frame image of the movie. Then, it was checked if cells were picked up from high recognized points of the space. As the result, 9 immune cells were picked up from the first frame image. The result indicates that multiple immune cells can be picked up automatically if it is used the space, and the method can reduce the load of the cell's analyst.
Towards Distribution Feeders Frequency Response via Solid State Transformers

Although Solid State Transformers have shown to be improve energy quality in distribution systems, its massive integration is a risk for the frequency stability as they prevent the loads from providing inertia and frequency respond to the system. Synthetic or virtual inertia strategies have been proposed to solve this problems in the case of wind and solar converters, but they are not directly applicable to solid state transformers. This paper propose a simple control strategy based on loads dynamic, SST and power system dynamics to provide the solid state transformer with frequency response. Study cases are presented using an ERCOT power system model and a Serbian distribution feeder model to show numerical results.
How Much Can Radio Resource Allocation Help to Improve Secrecy Capacity of V2V Underlay Cellular Networks?

Vehicle-to-vehicle (V2V) underlay cellular communication plays an important role in the fifth-generation (5G) and beyond communications and its security issues attract a lot of attention. Physical layer security, which explores channel characteristics to protect wireless communications, is very attractive due to its low latency. This paper focuses on secrecy capacity maximization in V2V underlay cellular communications, where the secrecy capacity of vehicular user equipments (VUEs) is optimized first, followed by sum proportional fairness function optimization for both VUEs and cellular user equipments (CUEs). The optimization problems can be solved in two sub-problems, e.g., power and subcarrier allocation problems. The power allocation can be done with the first strategy using a one-dimensional searching algorithm, whereas a genetic algorithm (GA) is used to solve the second optimization problem. In addition, as VUEs share uplink resources with CUEs, the co-channel interference exists and the subcarrier allocation sub-problem is a 3D search problem, which is solved by a two-step iterative Hungarian algorithm (IHA). Finally, simulations are performed to validate the performance of the proposed schemes.
Integrated Intellectual Approach to the Diagnostics of Defects of Operations of Induction Motors

This paper will show the procedures for the intelligent assessment of the state of the electric motors in order to identity a set of defects. Procedures involve the use of speech processing methods. Initially, an attribute vector is formed on the basis of which standards are created that are subsequently used as average performances of the technical condition. Further, using the trained artificial neural networks, the condition of an object is recognized. The obtained performances of the object are also used to form the knowledge base of the expert system with the help of which decisions are subsequently made on the condition of the electric motor.
Forecasting hourly electricity demand of Uruguay for the next day using artificial neural networks

This article presents different models applying computational intelligence to forecast the total hourly electricity demand of Uruguay for the next day. Short term electricity demand forecasting is crucial to optimize the economic dispatch of electricity generation, improving the rational use of resources. It also allows improving energy efficiency and demand response policies related with smart grids. Classical statistical models have been applied to predict electricity demand but with the recent development of computational hardware and the vast amount of data available from various sources, computational intelligence models have emerged as successful methods for prediction. In this article, two artificial neural network architectures are presented and applied to forecast the total electricity demand of Uruguay for the next day. The first architecture combines Long Short Term Memory units (LSTM) with fully connected neural networks layers, and the second architecture improves the first by adding a Convolutional Neural Network as first layer (CNN+LSTM). Both architectures use a dropout technique to avoid overfitting. An ExtraTreesRegressor model is used as benchmark to evaluate both architectures. Three steps of data preprocessing are carried out, including treating missing values, removing outliers, and standardization. Considering the high computing demands of the applied techniques, they are developed and executed on the high performance computing platform provided by National Supercomputing Center (Cluster-UY), Uruguay. Standard performance metrics are applied to evaluate the proposed models. The experimental evaluation reports successful forecasting results: the CNN+LSTM model has a mean absolute percentage error of 4.3% when applied to the prediction of unseen data.
A review on depression detection and diagnoses based on visual facial cues

Computerized artificial intelligent machines and devices are invading all human life details rapidly. Verities of algorithms have been proposed to achieve results that can match or exceed the human results. The automatic visual facial cues based depression assessment is a fast-growing research area since it can provide help to psychiatrists and physicians in some cases when traditional other ways fail. The system has many stages, and different kinds of strategies have been used over the past years. Summarizing the previous works, going over the system stages and how the system works are needed to give a brief overview on the topic. This can provide a helpful start for researchers that tend to explore such a domain. Accordingly, the work demonstrates the topic from many aspects, touches on the general parts, and details the important associated points of it. The contribution of this paper is to make a review for the techniques used to detect depression from facial cues, as well as the established system for signal processing and extraction of features for previous studies in this field; which is new in the field of detect depression, so this review is not investigation has been done, but it's a discussion of researches that Issued and comparing the techniques utilized and proposing the best technology.
Using Anisotropic Insulators to Engineer the Electrostatics of Conventional and Tunnel Field-Effect Transistors

Materials scientists have developed a wide variety of anisotropic insulators that could offer avenues to separately manipulate lateral and perpendicular electric fields if implemented as the gate insulators or spacers in field-effect transistors (FETs). However, there have been no works that have studied how the electrostatics of an FET can be engineered by using anisotropic insulators. We address this gap in knowledge by simulating metal–oxide–semiconductor FETs (MOSFETs) and tunnel FETs (TFETs) while separately varying the in-plane and out-of-plane permittivities of their insulators. Our results show that MOSFETs should have gate insulators and spacers with small in-plane and large out-of-plane permittivities to maximize their performance, and we demonstrate that an FET with a single anisotropic insulator that covers both the gate dielectric and spacers can outperform a similar structure that uses hafnium dioxide for the gate dielectric and air for the spacers. We also demonstrate that anisotropic gate insulators can greatly improve the subthreshold swing and ON-currents of TFETs, and we introduce a new type of heterogate dielectric for TFETs by manipulating the gate insulator’s in-plane permittivity. We anticipate that the results of this study will motivate experimental research toward developing FETs with anisotropic insulators.
University Research Graph Database For Efficient Multi-Perspective Data Analysis Using Neo4j

In general, research-related data are modeled using a relational database optimized for transaction processing. In many cases, this solution is effective and efficient enough to answer basic queries and simple reporting requirements. However, when users request a more-in-depth, more expansive, multi-perspective, and sometimes more abstract analysis, the relational database struggles to provide answers. This study proposes a research graph database implemented using neo4j as an effort to answer the problems. The database consists of a core model and an extension model. The core model represents scientific articles-related data loaded with real data scraped from Google Scholar. The extension model indicates research and community engagement activities done by researchers loaded manually. The database enables the university to analyze researchers’ individual and collaborative performances with fellow researchers inside and outside universities. The study concludes that the research graph database implementation is more efficient in answering similar questions than the relational database implementation.
Evaluation of Correlation Method for Monitoring of Localized Corrosion in Water Environment

The use of correlation-based video inspection system for evaluation of development of a localized corrosion process requires validation of its inaccuracy. This investigation was provided for laboratory testing of speckle correlation method, developed for evaluation of localized corrosion degree on metal surface in water environment. Stainless steel sample with rough surface in water salt solution was tested. Localized corrosion degree was monitored during experiment with correlation method and its error value was evaluated.
Experimental Demonstration of Gate-Level Logic Camouflaging and Run-Time Reconfigurability Using Ferroelectric FET for Hardware Security

Outsourcing of integrated circuit (IC) manufacturing and increasing sophistication of IC reverse engineering techniques have unleashed security threats such as intellectual property (IP) theft and insertion of hardware Trojans. In this article, we propose and implement a run-time reconfigurable camouflage logic technology based on ferroelectric field-effect transistor (FeFET). The technology simultaneously obfuscates design IP from zero-trust foundry and untrusted testing facility and thwarts reverse engineering and counterfeiting threat. We fabricate for the first time an eight FeFET-based circuit block and demonstrate <italic>in silico</italic> gate-level camouflaging by implementing three Boolean logic functions—NOR, NAND, and XNOR—in the same circuit topology using threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula>) programming of FeFET. We perform circuit simulations using empirically calibrated models to estimate power, latency, and area overhead. Compared with recent proposal of programmable camouflage logic using hot-carrier injection (HCI), FeFET offers an overhead reduction of <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$2.8\times $ </tex-math></inline-formula> in area, power, and delay, respectively.
Electromagnetic Induction Imaging at Multiple Depths With a Single Coil

Electromagnetic induction imaging (EII) is an attractive imaging modality due to its contactless operation, low cost, and good penetration depth. To implement real-time imaging, in applications such as object tracking or functional biomedical imaging, fast data acquisition is required. To proceed toward this goal, in this article, we propose the use of a single coil for 3-D EII along with a fast inversion approach. To collect sufficient data that allow 3-D imaging, we propose two approaches: collecting data at multiple coil’s drive current levels and collecting data with multiple tank circuit’s capacitor values. We show that the use of such data when using a single coil allows for 3-D EII. For proof-of-concept experiments, we use a commercial data acquisition board, that is, LDC1614EVM module, and multilayer planar coils made by Texas Instruments.