Reviewerly/exHarmony huggingface dataset:
Logic Gate Integrated Circuit Identification Through Augmented Reality and a Smartphone

The basic concepts of logic gates are now taught in high school and university. In laboratories, it is essential to understand the integrated circuits (ICs) that make digital systems and computers possible. This paper proposes an augmented reality (AR) system based on smart phones that automatically identifies 7 basic logic gate integrated circuits that are: AND gate, OR gate, NOT gate, NAND gate, NOR gate, XOR gate and XNOR gate. Starting from the IC image taken by the smartphone, it digitally generates a layer of virtual objects that are then mixed with the original image. Under the markerless paradigm, 2 virtual objects are placed over the IC: IC identification and pin information. Finally, the smartphone screen shows the image of the logic gate obtained. The entire evaluation of the AR system is presented with a technical efficiency of 100%.
Sparsity in function and derivative approximation via the empirical feature space

Several practical applications require estimation of the values of a function and its derivative at specific sample locations.This is a challenging task particularly when the explicit forms of the function and its derivative are not known. There have been a few methods proposed in the literature to learn an approximant that simultaneously uses values of a function as well as values of its derivatives or partial derivatives. These methods typically use Support Vector Regression (SVR) and solve a Quadratic Programming Problem (QPP) for the task, which results in a learning model that can estimate the function and derivative values. In this paper, we propose an alternative novel approach that focuses on introducing sparsity in such a learning model, that is based on minimizing the model complexity in the Empirical Feature Space (EFS). Sparsity in such a model is useful when it needs to be evaluated a large number of times as it entails lower computational cost compared to a dense model. The proposed approach, called the EFSRD (EFS Regression for Function and Derivative approximation), involves solving a Linear Programming Problem (LPP). On a number of benchmark examples, EFSRD learns models that offer comparable or better performance, while learning models that are nearly a fourth the size of those obtained by existing approaches.
Hybrid Artificial Intelligence B2B2C Business Application – Online Travel Services

Expert systems are commonly used artificial intelligence AI applications in business. They aim to solve complex problems with expert-level knowledge and rule-based techniques. However, a narrowly defined scope and existing structured knowledge base are necessary for an expert system. The case study concerns the deployment of a hybrid AI model in a business application that tries to overcome the limitations of a predefined domain of knowledge stored in the expert system. In addition to the data set or the algorithm, the case illustrates the successful use of a hybrid AI application as an effective business solution in B2B2C industries.
Automating the Operation of a 3D-Printed Unmanned Ground Vehicle in Indoor Environments

The United States Department of Defense anticipates unmanned systems will be integrated into most defense operations by 2030 to reduce risk to human life, enhance reliability, and ensure operation consistency and efficiency. However, current technology requires human operation for ethical decision-making, leaving an opportunity to automate some tasks to assist operators. Previously, a University of Virginia capstone team designed an unmanned ground vehicle (“the rover”) to aid intelligence, surveillance, and reconnaissance missions in adversarial environments. However, a lack of GPS connectivity indoors and system latency limited the rover's performance and created a lag in the operator's view compared to the rover's true position, occasionally causing the operator to inadvertently crash the rover into obstacles. The objectives of this project are to mitigate operational risks by equipping the rover with functionalities to autonomously avoid obstacles, map an unknown indoor space, and navigate itself back to a predetermined location (“base”). Obstacle avoidance is accomplished through an algorithm that stops the rover a safe distance away from a detected obstacle, but still allows the human operator to navigate the rover away from the obstacle prior to continuing the mission. Algorithms are implemented to perform Simultaneous Localization And Mapping and to determine best-route navigation to the base. Laser rangefinder data, an improved processor, and, potentially, visual odometry sensors are used to aid in the navigation algorithms. Testing has confirmed that the rover successfully stops in front of laser-detected obstacles, builds digital maps of an unknown indoor space, and can navigate back to a base, though the performance has room for improvement. It is anticipated that incorporating visual odometry can enhance the rover's mapping implementation and obstacle avoidance performance.
SMT-Based Modeling and Verification of Cloud Applications

Cloud applications have been rapidly evolving and gained more and more attention in the past decade. Formal modeling and verification of cloud services are necessarily needed to guarantee their correctness and reliability of complex cloud applications. In this paper, we present a formal framework for modeling and verification of cloud applications based on the SMT solver Z3. Simple cloud services are specified as the basis for the modeling of composition and more complex cloud services. Three different classes Service, Composition and Cloud indicating simple cloud services, composition patterns and composed cloud services are defined, which facilitates the further development of attributes and methods. We also propose an approach to check the refinement and equivalence relations between cloud services, in which counter examples can be automatically generated when the relation is not valid.
Research on Electronic Lock Structure Evaluation Based on User Experience

An electronic lock is different from a mechanical lock in that it uses an induction card, a digital password and a fingerprint to unlock the lock. In the big wave of smart home, electronic lock is considered as the entry level product of smart home. With the upgrade of consumption and the change of people’s consumption concept, excellent user experience will be the future development trend of electronic lock. However, in view of the current development status of electronic lock products, there is still a lack of research on user experience of electronic lock. This paper will evaluate the current electronic lock product structure from the perspective of user experience, to improve the user experience of electronic lock products in the future.
Hierarchical extended collaborative representation based classification for single‐sample face recognition

Collaborative representation based classification (CRC) has been widely used and shown good performance in face recognition (FR). Afterwards, hierarchical representation based classification has recently been proposed and aims to enhance the classification performance of the CRC method. However, these methods highly depend on the over‐complete dictionary comprised of sufficient training samples, and cannot be directly applied for single‐sample FR. In this study, the authors propose a novel CRC‐based FR framework to address this issue, which is named hierarchical extended collaborative representation based classification (HECRC). Firstly, they integrate hierarchical representation based model with low‐rank constrained variation dictionaries. Secondly, they select training samples that are the nearest neighbours of test images to obtain a more discriminative training dictionary, where an adaptive scheme is introduced to select proper samples automatically instead of setting a predefined number in traditional methods. Finally, the refined training dictionary and the learned variation dictionaries are jointly utilised to represent the test sample. Moreover, they combined the proposed HECRC with deep features to further improve the recognition rate. Experiments have been conducted on the AR and FERET datasets, and the results show that the proposed method has a substantial improvement over existing algorithms for single‐sample FR.
The Use of Mathematical Tasks for Development by Younger Schoolchildren Skills of Research Activities

The article deals with the formation of research competence of primary schoolchildren through the use of heterogeneous mathematical tasks. The author analyses the types of classifications of mathematical tasks of different types that contribute to the activation and development of thinking in younger schoolchildren: creative tasks and tasks, the solution of which leads to the emergence of a new idea. It justifies the conclusion that the work on the development of logical thinking and the formation of a non-standard approach to solving problems in the course of teaching mathematics can and should start from the first grade.
New discretization of total variation functional for image processing tasks

In this paper, a new discretization of total variation model is proposed to solve mathematical image problems. By using difference operators in the four directions (i.e. horizontal, vertical and two diagonal directions) an estimation of derivative amplitude is found. Based on the new obtained estimation, a new regularization term will be defined, which can be viewed as a new discretized total variation ( TVprn) model. By improving TVprn, a more effective discrete total variation is introduced. By finding conjugate of TVprn and producing vector fields with special constraints, a new discretized TV for two dimensional discrete functions is proposed (TVnew). The capability of the new TV model to solve mathematical image problems is examined in some numerical experiments. For some denoising and upscaling problems, the new model is compared with three famous total variation models. Generally, the proposed model has the best performance in comparison with all the other models in the experiments in sense of reconstructing the edges, smoothing, denoising and accuracy.
Modeling the Software Execution of CEP in DCEP-Sim

DCEP-Sim facilitates simulation of distributed CEP where the latency and bandwidth limitations in the network are well reflected, but it currently lacks models to simulate the temporal behavior of event processing. In this demonstration, we use a modeling methodology to model the software execution of a CEP system called T-Rex. We instrument and trace T-Rex to parameterize a software execution model that is integrated into DCEP-Sim. Furthermore, we use this instance of DCEP-Sim to run simulations and see how significant the processing delay introduced by the model is compared to the transmission delay.
DIGITAL ECONOMY AND PROSPECTS OF INNOVATIVE DEVELOPMENT OF M.A. KARTSEV COMPUTING SYSTEM RESEARCH AND DEVELOPMENT INSTITUTE

The article deals with some areas of innovative activity of M. A. Kartsev Computing System Research and Development Institute from the point of view of implementation of the program «Digital economy of the Russian Federation». 2019 will be a year of priority activities within the areas of «Information infrastructure», «Formation of research competencies and technological reserves» and «Information security». At the beginning of the article the activity of the enterprise for the purpose of preservation of the leading positions in the market of hardware and software complexes and information technologies is considered. It is noted that the development of computing devices, systems and technologies is carried out taking into account the strict requirements for operating conditions and mass ‑ dimensional characteristics of the equipment. Next, we consider the work on the creation of computing devices and systems, in particular multi‑heterogeneous multiprocessor computing platform M‑17. Attention is paid to security systems, technological and emergency information recorders. In conclusion, attention is paid to the development of the methodology of computer‑aided design of multilayer printed circuit boards based on the use of methods and algorithms for modeling printed circuit boards with analysis and taking into account the topological characteristics, noise immunity parameters and ensure the integrity of the transmission of logic signals of the subnanosecond range.
A Simple Framework for the Annotation of Small Corpora

This paper describes a framework for the annotation of discourse which consists of the combination of software tools and a tagset.Its main purpose is to create small corpora for action-research projects conducted by Second of Foreign Language teachers, including Content and Language Integrated Learning, in their classrooms.The framework intends to be a solution for this particular situation, which requires a high level of simplicity.Although in the literature of Corpus Linguistics there are some good frameworks and tagsets for the annotation of corpora, they are usually too complex for teachers who are not experts either in Corpus Linguistics or Discourse Analysis.
Arbitrary norm semi-supervised extreme learning machine

Applying semi-supervised learning to extreme learning machine (ELM), we propose a semi-supervised extreme learning machine classification framework (SSELM) with arbitrary norm (q-norm, q=0,1 and 2). However, the SSELM involves nonconvex and nonsmooth problem. In this work, two types of optimizatio n methods are developed to solve the proposed SSELM. The first one is an exact solution approach that reformulates SSELM as mixed integer programming. The second is an approximation approach that approximates the SSELM framework by DC (difference of convex functions) programming. Several formulations for SSELM are presented with different norm. Furthermore, the proposed methods are applied in a practical medical dataset using near-infrared spectral technology. Experimental results in different spectral regions show that incorporating unlabeled samples in training improves the generalization compared with the supervised ELM when insufficient training information is available. Moreover, the proposed methods achieve equivalent performance in benchmark data sets compared to the supervised ELM algorithms and other semi-supervised methods. These results show the feasibility and effectiveness of the proposed algorithms.
Research on keyword indexing algorithm based on big data

Abstract Keyword retrieval is widely used in various aspects of Web information, such as information processing, data mining, etc. Keyword search has been studied for a long time. Many research results have been obtained from keyword search of early relational database, keyword search of semi-structured data and keyword search of graph data. Given multiple keywords, the goal of keyword retrieval is to find the matching node of keywords and find the most compact data fragment containing all keywords. For traditional retrieval model exists in the retrieval of data redundancy, the disadvantage of fuzzy matching, retrieval information deviation, combined with the current hot, discusses the heterogeneous data integration and redundant data, efficient data classification, keyword retrieval model and the method, such as to big data environment, make full use of the traditional technology combined with spatial retrieval model and other technical storage model, improved classification algorithm, and optimize the retrieval algorithm, thus improve the operation efficiency of the algorithm, to provide users with a set of data storage, classification and retrieval in the integration of large data retrieval platform.
Lattice Metric Space Application to Grain Defect Detection

We propose a new model for grain defect detection based on the theory of lattice metric space [7]. The lattice metric space $$(\mathscr {L},d_{\mathscr {L}})$$ shows outstanding advantages in representing lattices. Utilizing this advantage, we propose a new algorithm, Lattice clustering algorithm (LCA). After over-segmentation using regularized k-means, the merging stage is built upon the lattice equivalence relation. Since LCA is built upon $$(\mathscr {L},d_{\mathscr {L}})$$ , it is robust against missing particles, deficient hexagonal cells, and can handle non-hexagonal lattices without any modification. We present various numerical experiments to validate our method and investigate interesting properties.
Modern Approaches to Building Recommender Systems for Online Stores

Abstract The article presents current approaches to solving the problem of building recommender systems designed to intellectualize the user interface of online stores. Much attention is paid to modern methods of building recommender systems, analysing their strengths and weaknesses. Of greatest interest are the criteria for selecting effective methods for specific online stores and the authors’ concept of a typical recommender system of electronic commerce.
Comparison of algorithms determining sign of Bessel function in time averaging interferometry

Time averaging interferometry is one of the techniques that allows to investigate a dynamic behaviour of MEMS elements. The information of the maximum amplitude of vibration is encoded in the argument of Bessel function. Many different approaches enable evaluation of this value. Due to the fact that accuracy of the result depends on the quality of the input data, Bessel function of a good quality must be calculated first. Wide range of modulation distribution calculation methods enable to obtain absolute or square value of J<sub>0</sub>. These results are more difficult in processing than pure Bessel function because of gradient function discontinuity or poor SNR. For ensuring smaller errors one may normalize absolute values of J<sub>0</sub> by the sign of the function. In this paper, several different algorithms for determining the sign of Bessel function were investigated and compared. For each approach accuracy of the method was calculated. In the end, the best solution was found.
Discriminative Structured Dictionary Learning for Face Recognition

For a few years, plenty of face recognition algorithms, such as deep learning, have been in hot pursuit with the trend of the technology, while dictionary learning algorithm is still out of the woods for the sake of its higher robustness to occlusion and light. In this paper, we propose to learn a discriminative structured dictionary with constraint named as multi-label to suppress representations for different classes, as well as Laplacian Eigenmaps to encourage the representations for the same class to be close to each other. Demonstrated by the results of the experiments, our proposed dictionary learning methods intend to achieve better classification performance and higher computational efficiency compared to the existing algorithms.
Inlining External Sources in Answer Set Programs

HEX-programs are an extension of answer set programs (ASP) with external sources. To this end, external atoms provide a bidirectional interface between the program and an external source. The traditional evaluation algorithm for HEX-programs is based on guessing truth values of external atoms and verifying them by explicit calls of the external source. The approach was optimized by techniques that reduce the number of necessary verification calls or speed them up, but the remaining external calls are still expensive. In this paper we present an alternative evaluation approach based on inlining of external atoms, motivated by existing but less general approaches for specialized formalisms such as DL-programs. External atoms are then compiled away such that no verification calls are necessary. The approach is implemented in the dlvhex reasoner. Experiments show a significant performance gain. Besides performance improvements, we further exploit inlining for extending previous (semantic) characterizations of program equivalence from ASP to HEX-programs, including those of strong equivalence, uniform equivalence and H, B -equivalence. Finally, based on these equivalence criteria, we characterize also inconsistency of programs wrt. extensions. Since well-known ASP extensions (such as constraint ASP) are special cases of HEX, the results are interesting beyond the particular formalism. Under consideration in Theory and Practice of Logic Programming (TPLP).
An Intelligent and Autoadaptive System of Virtual Identities Based on Deep Learning for the Analysis of Online Advertising Networks

Marketing is one of the areas that benefits the most from web platforms. Several online marketing techniques have been developed throughout the years that focus on determining what is the most effective displayable content depending on the population target. In order to analyze the advertisements displayed to each population segment, a system based on Virtual Identities is proposed. In this system, each population target is represented by a virtual identity, that navigates the internet according to a scripted behavior. In each of the visited webpages, advertising content is displayed, that needs to be detected, located and subsequently analyzed to extract patterns. In this work, a Natural Language Processing model is presented, where advertising detection is treated as a binary classification problem, predicting for each block composing the webpage whether it is, or is not, commercial content. Two different approaches are considered for the input, depending on whether the HTML markup text is removed or not. Furthermore, several text embedding and predictive models are evaluated in order to select the best model according to the presented input.
Sonar: Writing Testbenches through Python

Design verification is an important though time-consumingaspect of hardware design. A good testbench should supportperforming functional coverage of a design by making it easy to implement tests and determine which tests are being performed. However, for complex designs, creating and main-taining effective testbenches can take increasing amounts of time away from actual design. A further complication is there may be two development flows: conventional hardware written in a hardware description language (HDL) such as Verilog orVHDL and high-level synthesis (HLS). In the HLS approach, the hardware is specified in a higher-level language (HLL) and then converted to an HDL through HLS tools. In this flow, testbenches for the design are written in the same HLLand cosimulation is used to verify the generated HDL. Due totool restrictions, cosimulation may not always work. In VivadoHLS [1] for example, the design must contain control signals to define when to start and stop the module or the initiation interval for new data must be one cycle. Without cosimulation, the user must write an HDL testbench manually in addition to a testbench in the HLL for preliminary verification. To simplify writing testbenches, we present Sonar: an open-source Python library to write cross-language testbenches. From a common source script, Sonar can generate testbenches written in SystemVerilog (SV) and C++. These files can then be imported into standard simulation tools such as ModelSim[2] or Vivado HLS and run. The use of Python makes it easy to extend Sonar with higher layers of abstraction for testbenches and integrate it with other software platforms.Sonar is available at https://github.com/UofT-HPRC/sonar.
Modeling and Control of Engine Knock Using Gaussian Process Regression

Knock limit control is necessary for high efficient operation of automotive engines. In this paper, we propose an identification and control method of the engine knock using a Gaussian process. Feedforward input is designed by the Gaussian process model approximating knock probability, and its model is updated online by a feedback of the knock intensity value. As modeling methods using the Gaussian process, we apply a heteroscedastic Gaussian process and a recursive Gaussian process. The effectiveness of the proposed method is demonstrated by a numerical simulation.