Reviewerly/exHarmony huggingface dataset:
Deep Learning-based Trajectory Estimation of Vehicles in Crowded and Crossroad Scenarios

The rapid developments in the field of Artificial Intelligence are bringing enhancements in the area of intelligent transport systems by overcoming the challenges of safety concerns. Traffic surveillance systems based on CCTV cameras can help us to achieve safe and sustainable transport systems. Trajectory estimation of vehicles is an important part of traffic surveillance systems and self-driving cars. The task is challenging due to the variations in illumination intensities, object sizes and real-time detection. We propose tracking by detection based trajectory estimation pipeline which consists of two stages: The first stage is the detection and localization of vehicles and the second stage is building associations in bounding boxes and track the associated bounding boxes. We analyze the performance of the Mask RCNN benchmark and YOLOv3 on the UA-DETRAC dataset and evaluate certain metrics like Intersection over Union, Precision-Recall curve, and Mean Average Precision. Experiments show that Mask RCNN Benchmark outperforms YOLOv3 in terms of accuracy. SORT tracker is applied on detected bounding boxes to estimate trajectories. The tracker is evaluated using mean absolute error. We demonstrate that the developed technique works successfully in crowded and crossroad scenarios.
Value-Based Reasoning in Autonomous Agents

The issue of decision-making of autonomous agents constitutes the current work topic for many researchers.In this paper we propose to extend the existing model of value-based teleological reasoning by a new, numerical manner of representation of the level of value promotion.The authors of the paper present and discuss proofs of compatibility of both previous and current models, a formal mechanism of conversion of the parameters of the autonomous device into the levels of promotion of values, the mechanism of integration with machine learning approaches, and a comprehensive argumentation-based reasoning mechanism allowing for making decisions.
Parallel Implementations of Candidate Solution Evaluation Algorithm for N-Queens Problem

The N-Queens problem plays an important role in academic research and practical application. Heuristic algorithm is often used to solve variant 2 of the N-Queens problem. In the process of solving, evaluation of the candidate solution, namely, fitness function, often occupies the vast majority of running time and becomes the key to improve speed. In this paper, three parallel schemes based on CPU and four parallel schemes based on GPU are proposed, and a serial scheme is implemented at the baseline. The experimental results show that, for a large-scale N-Queens problem, the coarse-grained GPU scheme achieved a maximum 307-fold speedup over a single-threaded CPU counterpart in evaluating a candidate solution. When the coarse-grained GPU scheme is applied to simulated annealing in solving N-Queens problem variant 2 with a problem size no more than 3000, the speedup is up to 9.3.
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate

Understanding the algorithmic regularization effect of stochastic gradient descent (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works, however, focus on very small or even infinitesimal learning rate regime, and fail to cover practical scenarios where the learning rate is moderate and annealing. In this paper, we make an initial attempt to characterize the particular regularization effect of SGD in the moderate learning rate regime by studying its behavior for optimizing an overparameterized linear regression problem. In this case, SGD and GD are known to converge to the unique minimum-norm solution; however, with the moderate and annealing learning rate, we show that they exhibit different directional bias: SGD converges along the large eigenvalue directions of the data matrix, while GD goes after the small eigenvalue directions. Furthermore, we show that such directional bias does matter when early stopping is adopted, where the SGD output is nearly optimal but the GD output is suboptimal. Finally, our theory explains several folk arts in practice used for SGD hyperparameter tuning, such as (1) linearly scaling the initial learning rate with batch size; and (2) overrunning SGD with high learning rate even when the loss stops decreasing.
<i>RAP</i>: A Light-Weight Privacy-Preserving Framework for Recommender Systems

In today's Internet, recommender systems play an indispensable role in helping users discover items of interests, such as products, books, movies and so on. However, a higher recommendation accuracy is commonly at the cost of more disclosure of user privacy. Thus, a wider adoption of recommender systems poses significant security and privacy concerns to users. In this article, we propose a light-weight privacy-preserving framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAP</i> for recommender systems, which can protect user privacy while still ensuring a high recommendation accuracy. Instead of directly sending users’ private ratings to the recommender, users first conduct a local perturbation operation on private ratings, and then send the perturbed ratings to the recommender. The recommender can run recommendation algorithms directly over the perturbed ratings and return the results to users. Different from crypto-based methods, our perturbation and de-perturbation methods are linear operations. Thus, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAP</i> is light-weight and highly efficient in privacy protection. To be more rigorous, we formally prove that the order of recommendation accuracy will not decrease when our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAP</i> framework is applied to any MF (Matrix Factorization)-based recommender systems. We also derive the closed-form expression for the degree of privacy preservation of our framework. Finally, we conduct extensive evaluations using large-scale real-world datasets to verify the effectiveness of our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAP</i> framework and compare with other baseline algorithms. The results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAP</i> framework can improve the degree of privacy preservation from zero to over 0.5 for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Movielens</i> dataset and 4 for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Jester</i> dataset, and still maintain the approaching level of recommendation accuracy.
МODERN METHODOLOGY OF EDUCATION SCIENCE: IMPORTANCE, ROLE AND POSITION OF REFLECTION THEORY

The article covers the correlation between methodology of education science and theory of reflection. It defines the importance, role and position of reflection in educational research cognition and educational practice. The comprehensive analysis of the idea of reflection in philosophy, psychology, scientology and education science is carried out. It was established that reflection is considered from the perspectives of many aspects in educational research cognition: at the level of cognitive phenomenon, cognitive activity, cognitive process, method of scientific cognition, at the thematic and metatheoretic levels, as the tool and form of cognition. It was shown that in pedagogical cognition reflection plays an important role in methodology of education science. Currently it is an integral part of educational researches and systemic educational practice. The theory of reflection, its methods and reflective activity provide opportunities to show the actual pedagogical reality and pedagogical activity directly and indirectly, to gain and explain new scientific pedagogical knowledge, components and structure of pedagogical theory, to study own scientific nature and status of education science, to apply improvements for the system of training and education and educational process. Moreover, it has a direct relationship to education science methodology and is systemically important for it. The reflective activity and its capacity is a part of the scientific and pedagogical, teaching and cognitive activity of each participant of the educational process. It is also included in the educational objective and planned educational results. It was concluded that the components of the theory of reflection can be found at all four levels of general system of methodological knowledge in education science: philosophical, general scientific, specific scientific and technological. The capacity for organization and arrangement of pedagogical conditions for the reflection of students’ cognitive activity along with the reflection of own pedagogical activity is an essential component of methodological competence and methodological culture of a teacher. The theory of reflection should be presented completely and integrally in the very definition, content and structure of modern methodology of education science.
Research on Data Security Protection Method Based on Improved K-means Clustering Algorithm

Data security is a severe challenges in the era of Internet. The traditional K-means clustering algorithm can protect the data security properly when it is applied in the field of information security, but the accuracy and availability of its clustering process are difficult to meet the needs of reality. In order to effectively achieve the high availability and accuracy of clustering algorithm, aiming at the shortcomings of traditional K-means algorithm, this paper studies the improvement of K-means clustering algorithm based on differential privacy protection. The core theory of K-modes is used to select the initial point of clustering, and the shortest distance from the current point to the original cluster center point is found by using Euclidean distance, so as to get the cluster again. The differential privacy protection technology is introduced and improved based on Laplace mechanism. By calculating the distance between the data sample and the center point, the specific location of sensitive attributes in the data sample is obtained, so as to change the order of adding noise and obtain more accurate clustering results. The experimental results show that compared with other data security protection algorithms, the algorithm proposed in this paper has obvious advantages in clustering effect, clustering accuracy and time complexity.
Analysis of a Dynamical System Modeling Lasers and Applications for Optical Neural Networks

An analytical study of dynamical properties of a semiconductor laser with optical injection of arbitrary polarization is presented. It is shown that if the injected field is sufficiently weak, then the laser has nine equilibrium points; however, only one of them is stable. Even if the injected field is linearly polarized, six of the equilibrium points have a state of polarization that is elliptical. Dependence of the equilibrium points on the injected field is described, and it is shown that as the intensity of the injected field increases, the number of equilibrium points decreases, with only a single equilibrium point remaining for strong enough injected fields. As an application, a complex-valued optical neural network with working principle based on injection locking is proposed.
Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech

This paper addresses an automatic proficiency evaluation and speech recognition for second language (L2) speech. The proposed method recognizes the speech uttered by the L2 speaker, measures a variety of fluency scores, and evaluates the proficiency of the speaker’s spoken English. Stress and rhythm scores are one of the important factors used to evaluate fluency in spoken English and are computed by comparing the stress patterns and the rhythm distributions to those of native speakers. In order to compute the stress and rhythm scores even when the phonemic sequence of the L2 speaker’s English sentence is different from the native speaker’s one, we align the phonemic sequences based on a dynamic time-warping approach. We also improve the performance of the speech recognition system for non-native speakers and compute fluency features more accurately by augmenting the non-native training dataset and training an acoustic model with the augmented dataset. In this work, we augment the non-native speech by converting some speech signal characteristics (style) while preserving its linguistic information. The proposed variational autoencoder (VAE)-based speech conversion network trains the conversion model by decomposing the spectral features of the speech into a speaker-invariant content factor and a speaker-specific style factor to estimate diverse and robust speech styles. Experimental results show that the proposed method effectively measures the fluency scores and generates diverse output signals. Also, in the proficiency evaluation and speech recognition tests, the proposed method improves the proficiency score performance and speech recognition accuracy for all proficiency areas compared to a method employing conventional acoustic models.
High-performance AES-128 algorithm implementation by FPGA-based SoC for 5G communications

&lt;p&gt;In this research work, a fast and lightweight AES-128 cypher based on the Xilinx ZCU102 FPGA board is presented, suitable for 5G communications. In particular, both encryption and decryption algorithms have been developed using a pipelined approach, so enabling the simultaneous processing of the rounds on multiple data packets at each clock cycle. Both the encryption and decryption systems support an operative frequency up to 220 MHz, reaching 28.16 Gbit/s maximum data throughput; besides, the encryption and decryption phases last both only ten clock periods. To guarantee the interoperability of the developed encryption/decryption system with the other sections of the 5G communication apparatus, synchronization and control signals have been integrated. The encryption system uses only 1631 CLBs, whereas the decryption one only 3464 CLBs, ascribable, mainly, to the Inverse Mix Columns step. The developed cypher shows higher efficiency (8.63 Mbps/slice) than similar solutions present in literature.&lt;/p&gt;
Controlled Sharing Mechanism of Data Based on the Consortium Blockchain

In the process of sharing data, the costless replication of electric energy data leads to the problem of uncontrolled data and the difficulty of third-party access verification. This paper proposes a controlled sharing mechanism of data based on the consortium blockchain. The data flow range is controlled by the data isolation mechanism between channels provided by the consortium blockchain by constructing a data storage consortium chain to achieve trusted data storage, combining attribute-based encryption to complete data access control and meet the demands for granular data accessibility control and secure sharing; the data flow transfer ledger is built to record the original data life cycle management and effectively record the data transfer process of each data controller. Taking the application scenario of electric energy data sharing as an example, the scheme is designed and simulated on the Linux system and Hyperledger Fabric. Experimental results have verified that the mechanism can effectively control the scope of access to electrical energy data and realize the control of the data by the data owner.
XOR-Based Progressively Secret Image Sharing

Secret image sharing technology is a strategy for jointly protecting secret images. The (n, n) secret image sharing problem can be solved by conventional Boolean calculation easily. However, how to recover secret images with progressive steps is not addressed. In this study, we proposed an XOR-based (m, t, Ti) multi-secret image sharing scheme that shares m secret images among m participants and recovers m shared images progressively with t thresholds. The proposed secret images partition strategy (SIPS) partitions m secret images to generate intermediate images for different thresholds in the sharing procedure. Based on progressive recovery property, the proposed recovery method recovers parts of the secret images by gathering consecutive shared images. Moreover, gathering all shared images can perfectly recover all secret images. The experimental results show that the proposed XOR-based multi-secret image sharing method has high security and efficiency.
The Use of Smartphones in Learning English Language Skills: A Study of University Students’ Perspectives

This study investigated students’ perspectives about the use of smartphones in learning English language skills. It discussed the benefits and difficulties of using smartphones in the process of learning. The sample consisted of one hundred and fifty-one students enrolled in the first semester of the academic year 2019/2020 in different English courses taught at Taibah University (Badr Branch). The researcher designed a questionnaire depending on the previous studies investigated the role of smartphones in the process of teaching and learning. The participants were asked to decide at which level they agreed with each statement. The researcher analyzed data through Statistical Package for Social Sciences by applying mean scores and standard deviations. The results according to the students’ answers were positive with moderate usage of their smartphones in their attempt to learn English. Moreover, the results showed that there were no difficulties faced by students during the process of learning through smartphones.
Use of Artificial Intelligence and Blockchain in Banking Sector: A Study of Scheduled Commercial Banks in India

Artificial Intelligence (AI) is rapidly transforming the global financial services industry. Artificial Intelligence studying the thought processes of human beings and it deals with representing those processes via machines (like computers, robots, etc.) On the other side the Block-chain Technology is a technology in which digital information is stored in a public shared data-base. This technology got famous mainly after introducing the first cryptocurrency which is the Bitcoin. The study was conducted to know the importance of using Artificial Intelligence and Block Chain especially in the Banks to reduce the dependency on the human element also to understand what can be the possible implications of the use of artificial intelligence and Block Chain.
SLID: Exploiting Spatial Locality in Input Data as a Computational Reuse Method for Efficient CNN

Convolutional Neural Networks (CNNs) revolutionized computer vision and reached the state-of-the-art performance for image processing, object recognition, and video classification. Even though CNN inference is notoriously compute-intensive, as convolutions account for >90% of the total operation tasks, the ability to tradeoff between accuracy, performance, power, and latency to meet target application makes it an open research topic. This paper proposes the Spatial Locality Input Data (SLID) method for computational reuse during the inference stage for a pre-trained network. The method exploits input data spatial locality via skipping partial processing of the multiply-and-accumulate (MAC) operations for adjacent data and equating its value to previously computed ones. SLID improves the throughput of resource-constrained devices (Internet-of-Things, edge devices) and accelerates computations during the inference phase by reducing the number of MAC operations. Such approximate computing schema does not require a similarity quantification step nor any modification for the training stage. The computational data reuse was evaluated on three well-known distinctive CNN structures and data sets with alternating layer selections: LeNet, CIFAR-10, and AlexNet. The computational data reuse method saves up to 34.9%, 49.84%, and 31.5% of MAC operations while reducing the accuracy by 8%, 3.7%, and 5.0% for the three models mentioned earlier, respectively. Besides, the proposed method saves on memory access by eliminating data fetching of skipped inputs. Furthermore, filter size, strides, and padding on the accuracy and savings of operations are analyzed. SLID is the first work to exploit the input spatial locality for savings on CNN convolution operations with minimal accuracy loss and without memory or computational overhead. This makes it a great option to support intelligence at the edge.
A visual analytic approach for the identification of ICU patient subpopulations using ICD diagnostic codes

A large number of clinical concepts are categorized under standardized formats that ease the manipulation, understanding, analysis, and exchange of information. One of the most extended codifications is the International Classification of Diseases (ICD) used for characterizing diagnoses and clinical procedures. With formatted ICD concepts, a patient profile can be described through a set of standardized and sorted attributes according to the relevance or chronology of events. This structured data is fundamental to quantify the similarity between patients and detect relevant clinical characteristics. Data visualization tools allow the representation and comprehension of data patterns, usually of a high dimensional nature, where only a partial picture can be projected. In this paper, we provide a visual analytics approach for the identification of homogeneous patient cohorts by combining custom distance metrics with a flexible dimensionality reduction technique. First we define a new metric to measure the similarity between diagnosis profiles through the concordance and relevance of events. Second we describe a variation of the Simplified Topological Abstraction of Data (STAD) dimensionality reduction technique to enhance the projection of signals preserving the global structure of data. The MIMIC-III clinical database is used for implementing the analysis into an interactive dashboard, providing a highly expressive environment for the exploration and comparison of patients groups with at least one identical diagnostic ICD code. The combination of the distance metric and STAD not only allows the identification of patterns but also provides a new layer of information to establish additional relationships between patient cohorts. The method and tool presented here add a valuable new approach for exploring heterogeneous patient populations. In addition, the distance metric described can be applied in other domains that employ ordered lists of categorical data.
A Service Recommendation Method Based on Requirements for the Cloud Environment

In the cloud computing environment, there are huge amounts of functionally similar cloud services. Additionally, user requirements can change. Therefore, it is difficult to recommend services that meet users’ requirements. To overcome the problems, a service recommendation method based on requirements is proposed. First, we form user communities by clustering to reduce the recommended range. Second, we use the reported QoS (Quality of Service) values and the evaluated QoS values to predict the QoS requirements of users. Third, based on the requirements, the matching degree of users to services is obtained. Finally, based on the similarity between the target user and the user’s neighbors and the difference in their matching degree of service and the ratings of services by the neighbors, we can obtain a list of service recommendations for the target user. Compared to the traditional collaborative filtering method and the deviation-based method, our method improves the recommendation accuracy without lowering the efficiency.