Reviewerly/exHarmony huggingface dataset:
An Improved Particle Swarm Optimization for Antenna Design

An improved Particle Swarm Optimization (PSO) with high efficiency and low time consumption is presented in this paper. The optimization adopts the method of interval prediction, and it combines multiple parameters into a new fitness value to judge the optimization direction. In order to verify the proposed optimization, S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</inf> and bandwidth of a classic patch Yagi antenna are simultaneously optimized by the improved PSO method. It shows that the optimization speed is increased by 80% comparing to traditional PSO.
Evaluation of Machine Learning Models for Sign Language Digit Recognition

Basically, sign language helps to define communication in many ways. It is useful for hearing challenged people as well as for children of earlier ages to understand the concepts in a better way. In the context of human and machine interaction, sign language can help in the process of training for larger groups and training at any pace or time. When computer applications must understand the signs of people, they must be trained with suitable machine learning models. This paper discusses the evaluation of various learning models for sign language digits recognition. Classifiers based on decision tree, regression, and deep learning are considered for the recognition of digits from various sign images. Performances were observed by training the models over raw and skin segmented images from publicly available digit datasets. Among machine learning models over unsegmented images, Support Vector Machines returned the highest test accuracy whereas Random Forest classifier returned the highest accuracy over segmented images. Deep learning-based convolutional neural network with more parameters and elaborate training process achieved better accuracy over the machine learning models. The performances were found to improve when trained and tested over segmented images. Also, the accuracy was found to improve with an improvement in segmentation accuracy.
Deconstructing Whiteness: Visualizing Racial Bias in a Face Recognition Algorithm

Deconstructing Whiteness is an art performance which examines the visibility of race through the lens of AI face recognition technology. The artist interacts with an off-the-shelf face recognition algorithm, while specifically examining the confidence level by which it recognizes her as ‘White’. During the performance she changes her appearance by modifying her facial expressions and hair style and by doing so is able to ‘trick’ the algorithm and affect its confidence regarding her racial visibility.
Enhancement of PSNR based Anomaly Detection in Surveillance Videos using Penalty Modules

One of the desirable features of a surveillance system is the automatic identification of anomalous events in surveillance videos. The recent approaches for anomalous events identification utilize the difference between the predicted future frame and the current frame to detect the frames with an anomalous event. However, these approaches fare poorly if there is an overlap between multiple objects present in the scene. This work proposes to incorporate two modules to the future frame prediction-based anomalous activity detection approach. The first module penalizes the frame-wise PSNR value if there is an overlap between a normal and an anomalous object. In contrast, the second module penalizes the PSNR value if there is a sudden deviation of the vehicles from its trajectory. This object-centric approach ensures that the anomalous events are correctly identified even in the presence of occlusion. The proposed method is evaluated on two standard datasets Ped 2 and CUHK Avenue. The proposed method outperforms the existing approaches, and an AUC of 96.2% and 85.22% is obtained on Ped2 and CUHK, respectively.
A More Efficient Microgrid Operation Through Theintegration of Probabilistic Solar Forecasts

This work proposes a methodology based on the probabilistic dynamic programming (PDP) to integrate operational probabilistic forecasts of a photovoltaic (PV) plant into the optimization of the day-ahead schedule of an energy storage system (ESS). The proposed approach is tested on a micro grid based on a real educational building, a PV farm and Li-ion batteries.The objective is to minimize the operating cost of the microgrid. The operational day-ahead forecasts are derived from the Ensemble Prediction System (EPS) provided by a well-known Numerical Weather Prediction (NWP) model. Contrary to the classical use of deterministic forecasts, we demonstrate that the integration of the probabilistic forecasts in the optimizationprocess leads to a more efficient microgrid management and to a reduction of up to 38% of the operating costs. Besides, it is shown that the non-linearity of the inverters must be taken into account in order to yield relevantoptimization results.
Weighted Multi-task Sparse Representation Classifier for 3D Face Recognition

Rapid development of 3D face recognition can help people overcome some bottlenecks in 2D recognition. But still susceptible to changes in facial expressions. At the same time, due to the large number of 3D point clouds, the calculation speed is also greatly affected. This paper mainly proposes a method to classify 3D human faces according to the characteristics of their semi-rigid and non-rigid regions to enhance the robustness of recognition of 3D facial expression changes. At the same time, improve the expression of the 3D point cloud face, reduce the number of points involved in the calculation, and increase the speed of the algorithm. Experimental results show that the algorithm not only has a higher recognition rate but also has stronger robustness to changes in facial expression.
Balletic VR

Art-integrated education has many positive impacts on various educational disciplines. In addition, teaching STEM in art related courses also benefits students with critical thinking. We present an art-integrated collaborative project for dance science education. This project aims to provide a Virtual Reality-based educational application, Balletic VR that can provide students with simulated 360 views of fundamental movements. It allows students to scrub through movements, rotate the virtual instructor, choose from several different dance options, and change the speed of the currently viewed dance movement. Motion capture was used to provide the initial data for fundamental dance positions, to portray these movements accurately. A “muscle mode” is also provided in the interface, highlighting key muscle groups used in specific dance positions and providing information about how they are being used and how to train such muscles to better enhance their ability. Through the iterative process, we experimented how artists, anatomists, dancers, virtual reality developers collaborate and succeed to create an environment that provides artistic and anatomical contents and experiences.
DeCaptcha: Cracking captcha using Deep Learning Techniques

CAPTCHA or Completely Automated Public Turing test to Tell Computers and Humans Apart is a technique to distinguish between humans and computers by generating and evaluating tests that can be passed by humans but not computer bots. However, captchas are not foolproof, and they can be bypassed which raises security concerns. Hence, sites over the internet remain open to such vulnerabilities. This research paper identifies the vulnerabilities found in some of the commonly used captcha schemes by cracking them using Deep Learning techniques. It also aims to provide solutions to safeguard against these vulnerabilities and provides recommendations for the generation of secure captchas.
SmartCLIDE: Shortening the Toolchain of SOA-based Cloud Software Development by Automating Service Creation, Composition, Testing, and Deployment

Nowadays the majority of cloud applications are developed based on the Service-Oriented Architecture (SOA) paradigm. Large-scale applications are structured as a collection of well-integrated services that are deployed in public, private or hybrid cloud. Despite the inherent benefits that service-based cloud development provides, the process is far from trivial, in the sense that it requires the software engineer to be (at least) comfortable with the use of various technologies in the long cloud development toolchain: programming in various languages, testing tools, build / CI tools, repositories, deployment mechanisms, etc. In this paper, we propose an approach and corresponding toolkit (termed SmartCLIDE—as part of the results of an EU-funded research project) for facilitating SOA-based software development for the cloud, by extending a well-known cloud IDE from Eclipse. The approach aims at shortening the toolchain for cloud development, hiding the process complexity and lowering the required level of knowledge from software engineers. The approach and tool underwent an initial validation from professional cloud software developers. The results underline the potential of such an automation approach, as well as the usability of the research prototype, opening further research opportunities and providing benefits for practitioners.
Intelligent Visual Path Selection for Health Industry Robots Based on Data Mining

Abstract The robot intelligent visual path selection method is of great importance to improving the performance of industrial robots, including in the health industry. An optimization design method for robot visual path selection system is proposed. The application of this tool also can be in relation to the health industry for medical images. Based on data mining, the intelligent visual image is preprocessed by grayscale, histogram equalization, denoising, and so on. According to the different environments of image detection, an appropriate method is chosen to extract the edge of the image and calculate the regression of data mining. The coordinate system in the path recognition system is transformed, and the robot motion path model is established. Based on the analysis of the optimal path data, the visual path planning of the robot is realized. The experimental results show that the visual path selection method can realize the path planning of the robot efficiently and accurately. The same came be extended to preamble path planning for surgical procedures in order to assist doctors.
Multi-pedestrian Tracking and Person Re-identification

In the recent years, it is noticed that most road accidents are caused due to the negligence and ignorance of humans. Tracking can be used to predict the motion and/or position of objects. Tracking can help locate specific motion or specific objects. Trackers like MIL, Boosting and KCF, have been implemented and analyzed to come up with the most efficient tracking technique. Re-Identification is very useful when a particular object(s) are to be tracked and each of them are given a unique tracking identification number. This can be done with various features like texture and color. DeepSORT and YOLO can track the multiple pedestrians in a given frame. These trackers can track pedestrians at various speeds and of various sizes. Re-Identification of a pedestrian is done by identifying pedestrians uniquely with an individual identification number that is done by using different texture and color parameters of an image. If a given pedestrian goes out the frame of the video and comes back into the frame later, he/she is identified with the same identification number and also tracked successfully.
Allocation Optimization of Medical Samples For Distributed Testing

The world is witnessing the COVID-19 pandemic, which originated in the city of Wuhan, China, and has quickly spread to the whole world, with many cases having been reported in India as well. The healthcare system is going through unprecedented load on its resources while the available infrastructure is inadequate.COVID-19 samples are being tested at a massive scale and even small optimizations at this scale can save time, huge amounts of money, and resources. Particularly, the manual approach or even baseline greedy approach being used to allocate COVID-19 samples to medical labs across a state can lead to underutilization of resources. Hence, this work proposes a system to optimize the problem of allocation of medical samples to medical testing laboratories with high efficiency and minimal economic penalty. We use the Mixed Integer Programming (MIP) Model using high-performance MIP based solvers for custom applications by providing a tight integration with the branch-and-cut algorithms of the supported solvers to improve the results compared to baseline greedy approach. The system provides a transportation schedule optimized with respect to capacity of different labs and COVID-19 cases across the state of Karnataka. We tested the model on various datasets and observed significant improvement over the baseline greedy model.
Implementing a Multitenant System using a Document-based NoSQL Database

Software- as-a-service applications in cloud computing are generally multitenant in nature. Higher performance gains can be expected if the underlying database of an application is also multi-tenant. Traditionally, relational databases have been utilized in shared-database and shared schema manner to store all tenants' data within the same tables of common database. This results in problems while extending or modifying an existing schema as well as inflexibility while retrieving a specific tenant's data. Therefore, this paper presents an approach whereby a document-oriented database system is used to implement multi-tenancy in SaaS applications. The proposed approach uses the flexibility of schema design in document databases along with the sharding feature to implement efficient tenant data storage while maintaining isolation between tenants. A case study is also presented to illustrate the proposed approach.
Intelligent Digitalization of the Sinhala Form Templates

In Sri Lanka, most of the population uses the Sinhala Language as their first language to communicate and for documentation in most government departments. It is evident that the digitalization of the Sinhala Language is essential in a country like Sri Lanka. The specialty of Sinhalese characters is that they have very tiny differences in feature, and the number of different characters formed from the letters of the Sinhala alphabet and its elements is relatively high, leading to the classification among the Sinhala letters becoming quite a complex task. Previous proposed research case studies involved machine learning based feature detections related to rule-based theories and geometry features that had average accuracy rates, which indicate that further improvement is required with new features. Consequently, in this research paper, a Deep Learning Character Classification method for Sinhala OCR is proposed, which is for both Printed and Handwritten Sinhala texts as well as an Intelligent Sinhala Form Automation technique to read both answers and questions in an application to convert them into e-texts. The converted e-texts will be sharpened and fixed through a Sinhala Spelling & Grammar checking feature that is developed in the system more intelligently. In this research work, it was a success to obtain an overall accuracy level of more than 90% considering all components.
A hidden challenge of link prediction: which pairs to check?

The traditional setup of link prediction in networks assumes that a test set of node pairs, which is usually balanced, is available over which to predict the presence of links. However, in practice, there is no test set: the ground truth is not known, so the number of possible pairs to predict over is quadratic in the number of nodes in the graph. Moreover, because graphs are sparse, most of these possible pairs will not be links. Thus, link prediction methods, which often rely on proximity-preserving embeddings or heuristic notions of node similarity, face a vast search space, with many pairs that are in close proximity, but that should not be linked. To mitigate this issue, we introduce LinkWaldo, a framework for choosing from this quadratic, massively skewed search space of node pairs, a concise set of candidate pairs that, in addition to being in close proximity, also structurally resemble the observed edges. This allows it to ignore some high-proximity but low-resemblance pairs, and also identify high-resemblance, lower-proximity pairs. Our framework is built on a model that theoretically combines stochastic block models (SBMs) with node proximity models. The block structure of the SBM maps out where in the search space new links are expected to fall, and the proximity identifies the most plausible links within these blocks, using locality sensitive hashing to avoid expensive exhaustive search. LinkWaldo can use any node representation learning or heuristic definition of proximity and can generate candidate pairs for any link prediction method, allowing the representation power of current and future methods to be realized for link prediction in practice. We evaluate LinkWaldo on 13 networks across multiple domains and show that on average it returns candidate sets containing 7–33% more missing and future links than both embedding-based and heuristic baselines’ sets. Our code is available at https://github.com/GemsLab/LinkWaldo .
DCUIP Poisoning Attack in Intel x86 Processors

Cache prefetching technique brings huge benefits to performance improvement, but it comes at the cost of microarchitectural security in processors. In this letter, we deep dive into internal workings of a DCUIP prefetcher, which is one of prefetchers equipped in Intel processors. We discover that a DCUIP table is shared among different execution contexts in hyperthreading-enabled processors, which leads to another microarchitectural vulnerability. By exploiting the vulnerability, we propose a DCUIP poisoning attack. We demonstrate an AES encryption key can be extracted from an AES-NI implementation by mounting the proposed attack.
NameChecker: Detecting Inconsistency between Method Names and Method Bodies

Methods are basic elements for functional organization in software applications. A high-quality method name should clearly express its function, and help developers understand its usages quickly without reading through the lengthy and complex method body. However, in some cases, method names could be inconsistent with their functional implementations. The inconsistency in turn may result in inaccurate interpretation of methods, and even buggy method invocations. To this end, in this paper, we propose a deep learning-based approach, called NameChecker, to detecting the inconsistency between method names and their corresponding method bodies. NameChecker extracts lexical and structural features of source code by static code analysis. Based on the extracted features, NameChecker employs deep learning techniques (i.e., LSTM, and Attention mechanism) to predict whether the given method name is consistent with its implementation. Different from other deep learning based approaches to inconsistency detection, NameChecker avoids the generation (recommendation) of method names. Empirical studies suggested that generated method names are often incorrect, and thus avoiding method name generation may significantly improve the accuracy of NameChecker. We evaluate NameChecker on open-source applications, and our evaluation results suggest that NameChecker improves the state of the art by increasing the F1-score from 66.7% to 73.4%.
Federated Learning for 6G Edge Intelligence: Concepts, Challenges and Solutions

Along with continuous evolution, the future 6G network will become a converged “Cloud-Edge-Terminal” ecosystem which can carry various crucial AI applications on edge computing units, formulating an ubiquitous “Edge Intelligence” paradigm to enable differentiated service innovations and empower intelligent transformation of vertical industries. However, due to issues of data security, user privacy, wireless network transmission capability and etc., it is not feasible for conventional machine learning methods to build AI models by directly collecting massive distributed edge data together, and hence resulting a large number of “isolated data islands” in the edge units. In order to break the data sharing barrier and drive cross-edge data cooperation, this paper studies a federated learning based AI model training method by which sensitive raw data can be maintained and protected in its original edge units. Based on the general scheme, some challenging problems are discussed to implement this new paradigm in practical scenarios, and the corresponding promising solutions and key techniques are proposed to inspire further researches.
Revisiting System Noise in Side-Channel Attacks: Mutual Assistant SCA vs. Genetic Algorithm

For side-channel attacks (SCA) against parallel S-boxes such as AES, side-channel leakage that is not related to the targeted byte becomes a system noise for the key recovery of the target byte. With a recovered key byte for an S-box calculation, one can mitigate the corresponding system noise to assist the recovery of other key bytes. Following this idea, this paper proposes a mutual assistant side-channel attack (MA-SCA) that improves SCA efficiency using partially recovered keys. MA-SCA is inspired by the simple genetic algorithm CPA (SGA-CPA) and multiple sieve CPA (MS-CPA) attack that combines genetic algorithm with correlation power analysis. In genetic algorithm-based CPA, the partial key pieces are randomly combined and sieved repeatedly to solve the optimization problem of the correlation coefficient. In MA-SCA, the partial key pieces are combined in an organized manner to achieve a compact balance between the computation and the data complexities. In the evaluation, compared with MS-CPA, mutual MA-CPA archived a similar success rate using only 1–5% of the calculation. This work also discusses the boundaries of SCA improvement by reducing system noise.
Software change‐proneness prediction based on deep learning

Abstract Software change‐proneness prediction can help reduce software maintenance costs. Thus, it has drawn the attention of many researchers. In this paper, we propose a CNN (convolutional neural network)‐based method for software change‐proneness prediction, aiming to utilize the powerful prediction ability to make score of the performance measure higher than other baseline methods. Moreover, to alleviate the effect of the class imbalance problem, resampling methods are employed with the CNN. To validate the performance of the proposed CNN‐based method, an empirical study was conducted. The experimental results show that the CNN‐based method together with the resampling method performs better than the baseline methods, and the scores of performance measure of CNN with the ROS (random oversampling) method are higher than other method, especially the important performance measure.