Reviewerly/exHarmony huggingface dataset:
Lost and Found!

We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multi-modal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network.
Krylov Subspace Method Using Quantum Computing

The Krylov subspace algorithm uses iterative methods to solve bulky linear equations. It has a time complexity of O(n2) when run on a classical computing machine. The proposed algorithm has an exponential speedup over the classical algorithm. It takes O(n√k) time complexity when run on a quantum computer, where k is the condition number. In this paper, we will discuss an in-depth comparison between classical Krylov subspace algorithm and quantum Krylov subspace algorithm. A brief introduction of quantum computing is presented. Further, we will understand quantum Krylov subspace algorithm through quantum entanglement feature of quantum computing. The proposed algorithm using quantum phase estimation will provide deterministic solutions to the linear vector subspace. The paper will be closed by a quantum logic gate implementation of the quantum algorithm and will be concluded by the results.
Exploring Novice Programming Behavior over Time

This work focuses on the effect that programming time has on novice programmers' interaction with two versions of the BlueJ programming environment that differ in compilation mechanism and error message presentation. We utilize programming process data from users of both BlueJ versions, and those who used each one exclusively. Results indicate there is a threshold of approximately twenty hours of programming below and above which compilation and error message frequency changes. This indicates that being exposed to an environment for prolonged time can influence the programming interaction between novices and environments. This phenomenon is not yet understood.
Multi-objective optimization of item selection in computerized adaptive testing

Computerized-adaptive testing (CAT) is a form of assessment in which items/questions are administered based upon a test taker's ability (i.e., their estimated proficiency, such as a knowledge, skill, or personality characteristic). CAT is regularly used in psychological studies, medical exams, and standardized testing to reduce test length and improve measurement accuracy and precision. A key challenge in CAT is item selection. Past algorithms have been designed based on criteria such as item difficulty and maximum Fisher information. However, these only consider a fixed-length test, which may result in it being longer or less precise. To address this problem, we formulate a new multi-objective optimization problem to model the trade-off between test length and precision. A binary population-based genetic algorithm, NSGA-II, is used to obtain the set of Pareto-optimal solutions by maximizing precision and minimizing the number of questions. We evaluate our approach with a simulated study using four standard personality assessments. We also investigate the influence of test response types (e.g., binary versus categorical response) and number of variables (i.e., the number of possible items) on performance. The results obtained show multi-objective optimization can be used in CAT to minimize overall test length and improve measurement precision and overall accuracy.
Verifiable Secret Sharing Scheme Based on Certain Projective Transformation

The main purpose of verifiable secret sharing scheme is to solve the honesty problem of participants.In this paper, the concept of nonzero k-submatrix and theresidual vector of system of hyperplane intersecting line equations is proposed.Based on certain projective transformations in projective space, a verifiable (t, n)-threshold secret sharing scheme is designed by using the structure of solutions of linear equations and the difficulty of solving discrete logarithm problems.The results show that this scheme can verify the correctness of the subkey provided by each participant before the reconstruction of the master key, and can effectively identify the fraudster.The fraudster can only cheat by guessing and the probability of success is only 1/p.The design of the scheme is exquisite and the calculation complexity is small.Each participant only needs to hold a subkey, which is convenient for management and use.The analysis shows that the scheme in this paper meets the security requirements and rules of secret sharing, and it is a computationally secure and effective scheme with good practical value.
A Summary Evaluation Method Combining Linguistic Quality and Semantic Similarity

Summary evaluation method is crucial to promote the development of text summarization technologies. However, most of the existing summary evaluation methods seldom consider the content integrity and readability of summaries simultaneously. This paper proposed a Linguistic Quality and Semantic Similarity Model (LQSSM) to evaluate generated summaries more comprehensively. Considering the readability of summaries, a linguistic quality evaluation network (LQEN) is proposed to evaluate summaries automatically. Meanwhile, a semantic similarity evaluation network (SSEN) is introduced to directly measure the informativeness between the summary and original text. Additionally, there is also a comprehensive evaluation using fusion indicators. The LQSSM and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) methods were used to score the summaries generated by five models. The results showed that the proposed method gave further feedback from different angles on the basis of reaching the level of other methods.
Energy Efficient Computing with Heterogeneous DNN Accelerators

The exploration of custom deep neural network (DNN) based accelerators for highly energy constrained edge devices with on-device intelligence is gaining traction in the research community. Despite the superior throughout and performance of custom accelerators as compared to CPUs or GPUs, the energy efficiency and versatility of state-of-the-art DNN accelerators is constrained due to the limited scope of monolithic architectures, where the entire accelerator executes only one model at any given time. In this paper, a multi-voltage domain heterogeneous DNN accelerator architecture is proposed that simultaneously executes multiple models with different power-performance operating points. The proposed architecture and circuits are evaluated with SPICE simulation in a 65 nm CMOS technology. The simulation results indicate that the proposed heterogeneous architecture improves the energy efficiency to 2.04 TOPS/W, while the conventional monolithic and single voltage domain architecture exhibits an energy efficiency of 0.0458 TOPS/W. In addition, the total power consumption of the accelerator SoC is reduced to 1.34 W as compared to the 3.72 W consumed by the baseline architecture when all multiply-and-accumulate (MACs) units operate at a voltage of 0.45 V.
Blockchain-Based Infrastructure for Artificial Intelligence with Quantum Resistant

It is undeniable that the concepts of artificial intelligence and blockchain are spreading at an alarming speed. These two technologies have different degrees of technical complexity and multi-dimensional business meaning. The convergence of artificial intelligence and blockchain holds the promise of creating smarter, safer, efficient, and more secure systems. However, with the surprising development of quantum technology, most existing blockchain systems are vulnerable to quantum attacks. In this paper, we accordingly propose a lattice-based blockchain infrastructure for artificial intelligence with quantum resistant, aiming at tackling the critical issue of throughput, our infrastructure based on lattice-based aggregate signature which can effectively the proportion of signatures in block size. The proposed scheme can be proved secure in random oracle model, and it is also more efficient than similar literatures. In addition, we also give the detailed description of the post-quantum blockchain transaction. Furthermore, this work can help to enrich the research on the future post-quantum blockchain (PQB).
Reduced CNN Model for Face Image Detection with GAN Oversampling

In this paper, we propose a reduced convolution neural network (RedNet) model for face image detection implemented using SqueezeNet and AlexNet. The proposed RedNet model shows about 12.5% lower storage efficiency than AlexNet. Compared to SqueezeNet, the RedNet model can improve accuracy by 2.3809% and reduce loss by 0.05986. Eventually, the RedNet model predicts age and gender based on face images. To do this, the RedNet model that is lighter than AlexNet and more accurate than SqueezeNet is proposed, but it still shows a disadvantage to the imbalance of the dataset. Therefore, we solve the overfitting and data imbalance problem in the learning process through oversampling of GAN and apply the proposed RedNet model.
Detection of XSS Attack and Defense of REST Web Service – Machine Learning Perspective

The paper presents a machine learning approach for detection of stored XSS attack and for defense of REST web service. For this purpose, a XML-based REST web service is developed in JAVA, which is tested and attacked in specially created test-bed simulation environment, consisting of IntelliJ IDEA environment, Postman and web browser. The obtained data sets are processed resulting in the selection of 30 out of 171 features for further treatment. Supervised machine learning classifiers: Random Forest, Random Tree, Decision Tree and Gradient Boosted Tree are used for the detection of known attacks and clustering algorithm k-Means for the identification of unknown threats. The efficiency of implementing machine learning algorithms is evaluated and the results confirm their high accuracy. In addition fuzzy sets and fuzzy logic theory is utilized for solving multi-criteria task in support of decision making for web service defense.
Binary Optimal Control of Single-Flux-Quantum Pulse Sequences

We introduce a binary, relaxed gradient, trust-region method for optimizing pulse sequences for single flux quanta control of a quantum computer. The pulse sequences are optimized with the goal of realizing unitary gate transformations. Each pulse has a fixed amplitude and duration. We model this process as a binary optimal control problem, constrained by Schrödinger’s equation, where the binary variables indicate whether each pulse is on or off. We introduce a first-order trust-region method, which takes advantage of a relaxed gradient to determine an optimal pulse sequence that minimizes the gate infidelity, while also suppressing leakage to higher energy levels. The proposed algorithm has a computational complexity of , where is the number of pulses in the sequence. We present numerical results for the H and X gates, where the optimized pulse sequences give gate fidelities better than 99.9%, in trust-region iterations.
VEHICLE: Validation and Exploration of the Hierarchical Integration of Conflict Event Data

Abstract The exploration of large‐scale conflicts, as well as their causes and effects, is an important aspect of socio‐political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi‐automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user‐defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed VEHICLE , a web‐based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map‐based depictions as well as advanced interaction techniques. We showed the usefulness of VEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.
Storytelling in human–centric software engineering research

BACKGROUND: Software engineering is a human activity. People naturally make sense of their activities and experience through storytelling. But storytelling does not appear to have been properly studied by software engineering research. AIM: We explore the question: what contribution can storytelling make to human–centric software engineering research? METHOD: We define concepts, identify types of story and their purposes, outcomes and effects, briefly review prior literature, identify several contributions and propose next steps. RESULTS: Storytelling can, amongst other contributions, contribute to data collection, data analyses, ways of knowing, research outputs, interventions in practice, and advocacy, and can integrate with evidence and arguments. Like all methods, storytelling brings risks. These risks can be managed. CONCLUSION: Storytelling provides a potential counter–balance to abstraction, and an approach to retain and honour human meaning in software engineering.
Preserving and Scrambling of Health Records with Multiple Owner Access Using Enhanced Break-Glass Algorithm

Cloud computing servers gives a stage for the clients to remotely store information and offer the information to everyone. Healthcare record management system (HRMS) has been developed as a tolerant-driven model for health-related data exchange. Classification of the common information, i.e., visit details, report details, etc., remains as a serious issue when patients utilize public cloud servers since it might be viewed by everyone. To guarantee the patient’s control over admittance to their own uncommon well-being records, it is a promising technique to encode the reports before redistributing and pick up the gain power to that data. Security introduction, adaptability in key association, and flexible access experience have remained the most essential difficulties toward accomplishing fine-grained, cryptographically endorsed data get the opportunity to control. This research work proposes an arrangement of systems for information get to control to healthcare record management system (HRMS) put away in outsider servers with classification of data into public and private data. In HRMS for accomplishing smooth and adaptable information get the chance to control, attribute-based encryption (ABE) methodologies are utilized to encode each patient’s HRMS record for getting to. While providing secure data outsourcing, the main concentration is the multiple owners of data. On account of this framework significantly diminishes the key administration unpredictability for information owners and clients, HRMS ensured a high level of patient security. With respect to emergency situations, a break-glass extraction method is introduced here to access the data in emergency situations with authorized keys.
Constraint-based learning for non-parametric continuous bayesian networks

Modeling high-dimensional multivariate distributions is a computationally challenging task. In the discrete case, Bayesian networks have been successfully used to reduce the complexity and to simplify the problem. However, they lack of a general model for continuous variables. In order to overcome this problem, Elidan (2010) proposed the model of copula Bayesian networks that parametrizes Bayesian networks using copula functions. We propose a new learning algorithm for this model based on a PC algorithm and a conditional independence test proposed by Bouezmarni et al. (2009). This test being non-parametric, no model assumptions are made allowing it to be as general as possible. This algorithm is compared on generated data with the parametric method proposed by Elidan (2010) and proves to have better results.
Prediction of Wide Area Road State Using Measurement Sensor Data and Meteorological Mesh Data

In this paper, prediction of wide area road state using actual measurement data on on-board environment sensors and meteorological mesh data are introduced. Actual sensor data are sampled from running vehicles gathered to roadside edge computer. On the other hand, those meteorological mesh data are collected into the cloud computing from Meteorological Agency as open data. By integrating those actual data and the meteorological mesh data and applying Kalman filter technology, temporal and spatial prediction of wide area road state can be realized and visualized on GIS viewer system. In this paper, the fundamental system configuration and both temporal and spatial prediction algorithms are discussed.
Full-field optical metrology in polar and cylindrical coordinates

Abstract There are a large number of engineering measurement demands that can be very well met with optical methods. Among them, there are some that deal with circular or cylindrical geometries, for which Cartesian coordinates are not the ideal. Measurements in cylindrical and polar coordinates are much more natural choices. This paper presents an overview of various principles and configurations to measure textures, wear, geometry and deformations of external and internal cylindrical parts, displacements, deformations, stresses and residual stresses in cylindrical or polar coordinates. Optical measurement techniques such as triangulation, photogrammetry, deflectometry, white light interferometry, speckle interferometry, shearography in cylindrical or polar coordinates, are addressed. Most of the configurations presented here involve internal or external conical mirrors. These special optical components have several very interesting properties capable of performing transformations between coordinate systems. Examples of real engineering demands are presented and briefly discussed. The results obtained met the demands for which they were developed. Our expectation is that the configurations presented here can serve as a reference for already established applications and as a source of inspiration for new demands and possibilities.
Complex Dynamic Method of Web Applications Verification by the Criterion of Time Minimization

This article presents the complex dynamic method of Web application verification according to the criterion of minimum time with a further review of related tools.Dynamic verification methods use the results of real work, tested by the software system or its prototypes, to verify the compliance of these results with the requirements and design decisions. The review includes methods of dynamic verification - testing and verification monitoring. Software verification support tools - jUnit (testing) and AppDynamics (monitoring) are analyzed. The classification of methods is based on the list of important features, such as functionality, performance, reliability, and ease of use. Dynamic verification can be real or simulated, depending on whether it is used in its course, the software, its prototype or executable model.The experimental part presented as results of monitoring and test running on pre-prepared software, which is based on the following technology stack: programming language (PL) - Java, Database Management System (DBMS) - PostgreSQL, monitoring service (MS) - AppDynamics, test framework (TF) - JUnit and operating system (OS) Ubuntu 15.4 - a closed system that will act as a testing ground, which in turn will host the software package.
Digital Modelling of Educational Content in the Professional Training for Mining Industry

The creation of high-quality educational content is a necessary condition for obtaining professional education and training in mining. The article examines the relevance of modeling and refining the components of educational content in solving the problems of training highly qualified personnel. The role of MOOCs (Massive Open Online Courses, or MOOC – Massive Open Online Courses) in the system of professional education is considered for mining training. Theoretical analysis thesaurus phenomenon of digital culture, the definition of the concept of digital culture, identified the principles of its creation and functioning. The influence of digital technologies on culture, innovations and traditions in public structures is investigated. Digital culture and digital transformation of society, creating an artificial environment, carries out the transfer of socio-cultural objects into virtual reality. Social changes in connection with the emergence of digital technologies and processes in mining education are analyzed.
MNGAN: Detecting Anomalies with Memorized Normal Patterns

Anomaly detection is an significant problem in machine learning and has been well-studied in a wide range of applications. To model complex and high dimensional data distributions, existing methods, trained with an auto-encoder architecture either directly or indirectly, usually attempt to produce higher reconstruction error for anomalies than normal samples. However, lacking constrains on the latent representation of input data results in an unexpected performance that anomalies can be reconstructed well too, leading to the “missed alarm”. In this work, we propose to reconstruct input data with typical patterns of normal data learned through adversarial networks. Our approach, called MNGAN, which employs an encoder-decoder-encoder architecture with a memory network, learns to memorize prototypical patterns of normal data and simultaneously preserve details of data style for better reconstruction. In test phase, given a input data, the model will reconstruct it with the most relevant memory item, which indicates one normal pattern. Thus, reconstructions of anomalous data are similar to normal samples, resulting in effective detection for anomalies due to the high reconstruction error. Experiments over several benchmark datasets, from varying domains, shows that our proposed method outperforms previous state-of-the-art anomaly detection approaches.
MTS Algorithms for Cardinal UAV Motion Models

This chapter proposes the use of Ant Colony Optimization (ACO) techniques for a discrete version of the MTS problem with UAVs moving according to the eight cardinal directions. ACO is a nature inspired metaheuristic that has shown good performance solving combinatorial problems with high computational complexity, such as TSP [5]. It is also an iterative algorithm whose initial population of solutions is improved iteration by iteration using the information of the best found solutions in previous iterations. A distinguishing feature of ACO from other population based algorithms (such as genetic algorithms) is the inclusion of problem specific heuristic information during the solution construction process. In this chapter, we propose a specific MTS heuristic that guides the UAVs toward the higher and closer areas of the target probability map. The main objective of the chapter is to analyze the adequacy of ACO for MTS, and its advantages and disadvantages versus other approaches.