Reviewerly/exHarmony huggingface dataset:
Discrete Baby Search Algorithm for Combinatorial Optimization Problems

Combinatorial optimization problems widely exist in many real-world engineering projects and artificial intelligence applications. There are many effective methods to deal with combinatorial optimization problems, of which the approaches based on evolutionary algorithms are a generally used class of methods. We proposed a novelty fabulous evolutionary algorithm called Baby Search Algorithm (BSA) which simulates the behavior of infants searching for interesting toys. BSA has a powerful ability to balance exploration and exploitation, but it is designed for handling continuous optimization issues. We develop a variant of BSA named Discrete Baby Search Algorithm (DBSA) for handling combinatorial optimization problems in this paper. There are two versions of the DBSA, one using constant value binarization strategy and the other using function discretization strategy. Besides, DBSA introduces an interesting factor to advance its performance. We take feature selection as the testing problem, and use six binary classification datasets with six classic methods to evaluate DBSA. The results demonstrate the effectiveness and superiority of our method.
Real coded self-organising migrating genetic algorithm for nonlinear constrained optimisation problems

The objective of this article is to propose a new hybrid algorithm named as real coded self-organising migrating genetic algorithm (C-RCSOMGA) by combining real coded genetic algorithm (RCGA) and modified self-organising migrating algorithm (SOMA) for solving the nonlinear constrained optimisation problems. In RCGA, a modified mutation operator called as double mutation operator has been introduced combining two different existing mutation operators, whereas in SOMA, a modified strategy has been proposed. To test the performance of the proposed algorithm, a set of test problems taken from the existing literature has been solved and the simulated results have been compared numerically as well as graphically with the existing algorithms. In the graphical comparison, a modification of performance index (PI) has been made. Finally, with the help of modified performance index (MPI), it has been shown that the proposed hybrid algorithm has performed much better than the existing algorithms.
The Effects of Solar Panel Temperature on the Power Output Efficiency in Calabar, Nigeria

Background: The current push for adoption of alternative energysources necessitates research into parameters that would enhancethe optimum harvest of the benefits of such efforts.Purpose: To determine the effects of solar panel temperature on itsefficiency in Calabar, Nigeria, by determining the output currentand voltage changes due to solar panel temperature.Materials and Methods: A modern digital thermometer, Model220k type, and Alda ADV 890C digital multimeter, were used toobtain measurements of solar panel temperatures, current andvoltage, respectively, in Calabar, Nigeria. Comparisons were madeof the values of current and voltage obtained with solar paneltemperature changes, and efficiency of the output of the solarpanels determined by calculation using efficiency as given byKachhava (2003).Results: Results indicate that a maximum output current of18.4x10-1A was recorded at 42.8oC. Beyond this temperature theoutput current dropped down to 12x10-1A at 54.8oC. The voltageoutput remained relatively stable as solar panel temperatureincreased. Power output efficiency dropped from 82.3% at 42.8oCto 52.4% at 54.8oC.Conclusion: High solar panel temperatures may have adverseeffects on solar panel efficiency. The result obtained in this studysuggest a maximum operating temperature of solar panel inCalabar, Nigeria.
Development of Contents Maker for mobile terminals in Active Textbook System and examination of cloud-based content server

The authors have proposed an active text system (A-txt), which is a next-generation electronic teaching material that can make general books digitally more extensible with a general-purpose mobile terminal using augmented reality (AR). A-txt content is created by software called Contents Maker (C-mkr). This paper discusses the development of C-mkr for mobile terminals and this development can realize new learning methods using it by students. A-txt is our original system that can easily add and show digital contents by using figures and sentences written on papers as augmented reality (AR) markers. Since Unity is used for the development of A-txt, users can use it regardless of the OS type of the mobile terminal. C-mkr used to need PCs exclusively so far, but now that we have developed an application for mobile terminals, it is possible to create contents on smartphones and tablet computers owned by every user. Along with that, A-txt itself has undergone various improvements. In fact, the A-txt system cannot be used by anyone at present because it is operated by an intra server, but we considered how to solve the problem by reconstructing the server using cloud type. With the above development, users can easily create and upload content anytime and anywhere. We are planning to use this system for students to make textbooks into reference books by themselves. This method is named "Self-decoration (Sel-Deco)", and its effectiveness will be verified in the future.
Text2Pos: Text-to-Point-Cloud Cross-Modal Localization

Natural language-based communication with mobile devices and home appliances is becoming increasingly popular and has the potential to become natural for communicating with mobile robots in the future. Towards this goal, we investigate cross-modal text-to-point-cloud localization that will allow us to specify, for example, a vehicle pick-up or goods delivery location. In particular, we propose Text2Pos, a cross-modal localization module that learns to align textual descriptions with localization cues in a coarse-to-fine manner. Given a point cloud of the environment, Text2Pos locates a position that is specified via a natural language-based description of the immediate surroundings. To train Text2Pos and study its performance, we construct KITTI360Pose, the first dataset for this task based on the recently introduced KITTI360 dataset. Our experiments show that we can localize 65% of textual queries within 15m distance to query locations for top-10 retrieved locations. This is a starting point that we hope will spark future developments towards language-based navigation. “Alexa, hand me over my special delivery at the sidewalk in front of the yellow building next to the blue bus stop.”
Doppler

Selecting the optimal cloud target to migrate SQL estates from on-premises to the cloud remains a challenge. Current solutions are not only time-consuming and error-prone, requiring significant user input, but also fail to provide appropriate recommendations. We present Doppler, a scalable recommendation engine that provides right-sized Azure SQL Platform-as-a-Service (PaaS) recommendations without requiring access to sensitive customer data and queries. Doppler introduces a novel price-performance methodology that allows customers to get a personalized rank of relevant cloud targets solely based on low-level resource statistics, such as latency and memory usage. Doppler supplements this rank with internal knowledge of Azure customer behavior to help guide new migration customers towards one optimal target. Experimental results over a 9-month period from prospective and existing customers indicate that Doppler can identify optimal targets and adapt to changes in customer workloads. It has also found cost-saving opportunities among over-provisioned cloud customers, without compromising on capacity or other requirements. Doppler has been integrated and released in the Azure Data Migration Assistant v5.5, which receives hundreds of assessment requests daily.
Non-Contrastive Nearest Neighbor Identity-Guided Method for Unsupervised Object Re-Identification

Recently, self-paced contrastive learning has emerged as a promising method for unsupervised object re-identification. These methods generate pseudo labels, store centroid features in the memory bank, and periodically update them. However, affected by the performance of the clustering method, within each cluster exists inevitably noisy instances, and self-paced contrastive learning usually requires a large number of negative samples from various classes, where false-negative samples give rise to the class collision issue. These lead to performing incorrect model optimization. In this paper, we propose a non-contrastive nearest neighbor identity-guided (NNNI) method to overcome these challenges. The advantage of NNNI is to provide the model with a highly accurate prior. Specifically, this method relies on the random identity sampler commonly used in re-identification tasks to provide the network with a regression target of the nearest neighbors of the same identity within a mini-batch. It encodes more and more information in an iterative process through a Siamese network with an exponential moving average to train high-quality representations. NNNI alleviates the negative effects of noise instances and corrects class collision issues during training. Extensive experiments show that our method is effective on unsupervised object re-identification and achieves state-of-the-art performance on three large-scale person re-identification datasets and one large-scale vehicle re-identification dataset, which is competitive with even supervised methods.
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors

Conventional knowledge distillation (KD) methods for object detection mainly concentrate on homogeneous teacher-student detectors. However, the design of a lightweight detector for deployment is often significantly different from a high-capacity detector. Thus, we investigate KD among heterogeneous teacher-student pairs for a wide application. We observe that the core difficulty for heterogeneous KD (hetero-KD) is the significant semantic gap between the backbone features of heterogeneous detectors due to the different optimization manners. Conventional homogeneous KD (homo-KD) methods suffer from such a gap and are hard to directly obtain satisfactory performance for hetero-KD. In this paper, we propose the HEtero-Assists Distillation (HEAD) framework, leveraging heterogeneous detection heads as assistants to guide the optimization of the student detector to reduce this gap. In HEAD, the assistant is an additional detection head with the architecture homogeneous to the teacher head attached to the student backbone. Thus, a hetero-KD is transformed into a homo-KD, allowing efficient knowledge transfer from the teacher to the student. Moreover, we extend HEAD into a Teacher-Free HEAD (TF-HEAD) framework when a well-trained teacher detector is unavailable. Our method has achieved significant improvement compared to current detection KD methods. For example, on the MS-COCO dataset, TF-HEAD helps R18 RetinaNet achieve 33.9 mAP ( $$+2.2$$ ), while HEAD further pushes the limit to 36.2 mAP ( $$+4.5$$ ).
A Survey of Personalized and Incentive Mechanisms for Federated Learning

Federated learning (FL) provides a higher privacy guarantee for data sharing in a multi-party computation environment. However, how to invite participants to federated training if they already have a self-sanitized dataset? What is more, FL can not be directly applied to Non-IID data, and the global model can not meet the different feature requirements of clients. Personalized and incentive mechanisms are very necessary to build a good learning environment for FL. However, there has been little discussion about personalized and incentive mechanisms schemes so far, while more attention is focused on the optimization, efficiency and effectiveness improvement, and security aspects. Thus, in this paper, we make a review of personalized and incentive mechanisms of federated learning with different techniques.
SPARVIS: Combining Smartphone and Augmented Reality for Visual Data Analytics

We present SPARVIS, a conceptual framework that combines the strength of both AR and smartphones to enhance visual data analyt-ics. The framework uses AR to display coordinated visualizations and a smartphone to interact with and organize them. This design enables users to take advantage of their surrounding space to exter-nalize and organize their intermediate analysis results in an AR environment and manipulate the visualizations via familiar smartphone interactions. We identify four interactions that are fundamental to vi-sual analytics tasks and propose alternative implementations of these techniques in the SPARVIS framework following existing design guidelines. We run an empirical evaluation to quantitatively test the efficiency and physical demand of two alternative implementations of the “click” mechanism (the most fundamental one among the four) and provide design suggestions based on the study result. We further develop a prototype application based on the framework to illustrate SPARVIS's usage and conduct a user study with 22 participants to validate its usability. Based on the finding, we provide implications for research and design of integrating AR and smartphone for visual data analytics.
CONDITIONS FOR IMPLEMENTATION MODULAR APPROACH TO PHYSICAL EDUCATION LESSONS V RURAL AND URBAN INSTITUTIONS OF GENERAL SECONDARY EDUCATION

AnnotationIntroduction.The article is devoted to the study of new physical culture programs and existing conditions for the implementation of a modular approach in physical education lessons in rural and urban institutions of general secondary education.Study purpose.Determine the conditions for the implementation of a modular approach in physical education lessons in rural and urban institutions of general secondary education.Material and methods.The studies were carried out in EIA No. 17 in Kyiv, No. 1 in Chernivtsi, Voloshinevsky UVK of Baryshevsky District and Kodryansky Lyceum of Makarovsky Settlement Council of Buchansky District of Kyiv Region.The following methods were used in the course of the study: theoretical analysis and generalization of scientific literature; questioning; methods of mathematical statistics.Results.Among the reasons that prevent choosing variable modules for physical education classes in urban schools is a large number of students in one lesson, several classes in the gym at the same time.In rural schools, students determined the lack of the necessary sports equipment for studying selected sports.Together with a fairly high percentage of respondents in rural and urban schools is the lack of necessary conditions for conducting lessons of a favorite sport.The assessment of the material and technical support of the subject by markers: the quality of the equipment of the gym, sports grounds, inventory is determined by the students of urban and rural schools as mediocre, much is missing.The bank of variable modules that middle school students would like to master is quite diverse, but the implementation requires appropriate work on the ground in institutions of general secondary education.Conclusions.The relevant criteria for selecting variable modules are undoubtedly availability of material and technical infrastructure, environmental friendliness of educational environment, sports traditions of a region, provision of personnel and desires of students, which determine the characteristic trends that are reflected in our data, taking into account different territorial locations.
Learning Robust Image-Based Rendering on Sparse Scene Geometry via Depth Completion

Recent image-based rendering (IBR) methods usually adopt plenty of views to reconstruct dense scene geometry. However, the number of available views is limited in prac-tice. When only few views are provided, the performance of these methods drops off significantly, as the scene geometry becomes sparse as well. Therefore, in this paper, we propose Sparse-IBRNet (SIBRNet) to perform robust IBR on sparse scene geometry by depth completion. The SIBR-Net has two stages, geometry recovery (GR) stage and light blending (LB) stage. Specifically, GR stage takes sparse depth map and RGB as input to predict dense depth map by exploiting the correlation between two modals. As in-accuracy of the complete depth map may cause projection biases in the warping process, LB stage first uses a bias-corrected module (BCM) to rectify deviations, and then ag-gregates modified features from different views to render a novel view. Extensive experimental results demonstrate that our method performs best on sparse scene geometry than re-cent IBR methods, and it can generate better or comparable results as well when the geometric information is dense. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Legal Power-Subjection Relations: Ontological Analysis and Modeling Pattern

The development of dependable information systems in legal contexts requires a precise understanding of the subtleties of the underlying legal phenomena. According to a modern understanding in philosophy of law, much of these phenomena are relational in nature. In this paper, we employ a theoretically well-grounded legal core ontology (UFO-L) to conduct an ontological analysis focused on a fundamental legal relation, namely, the power-subjection relation. We show that in certain cases, power-subjection relations are primitive, i.e., by means of institutional acts, other legal relations can be generated from them. Examples include relations of rights and duties, permissions and non-rights, liberties, secondary power-subjection, etc. As a contribution to the practice of conceptual modeling, and leveraging on the result of our analysis, we propose a conceptual modeling pattern, which is then applied to model a real-world case in tax law.
A Survey on Image Splice Forgery Detection and Localization Techniques

In this research, image splice detection and localization are explored. A spliced image is a digital image that has been copied and recreated on another image. The cropped image may undergo resizing, rotation, blurring, etc., before being forged. With the advancement of many photo-shop tools and software, image manipulation, and splicing have become a significant issue of concern. It is being misused nowadays in many fields like changing will, court orders, official documents, photo morphing, etc. Excellent forgeries are so deceptive that they go undetected by the naked eye. Traditional picture tamper detection algorithms many times fail to detect innovative image splicing. In the past few years, solutions based on deep learning have been proposed for tampered image detection and are found to be more efficient than other techniques. In this research paper, we review relevant research articles on image splicing, both detection, and localization, for the period 2003 to 2022.
An Attention Based Chinese Sign Language Recognition Method Using sEMG Signal

This paper proposes a Chinese sign language recognition method based on attention mechanism. We use multimodal information from sEMG, ACC and GYR channels to explore the problem of Chinese sign language isolated word recognition based on EMG armbands. Our network adopts a CNN-RNN architecture to extract features of the data itself and time-domain features. the attention mechanism is also introduced into our method, and we use SE-Block to set the corresponding weights for different channels of the data. In the experimental part, we recruited 10 volunteers to collect a total of 50 kinds of 2780 data for us. We validated the effectiveness of our model with the dataset and achieved an overall accuracy of 89% and an F1 score of 86%. in terms of timeliness, our model analyzes individual sign languages in about 0.01 seconds, which is computationally low. Finally, we analyze the application scenarios and feasibility of this method.
The final security problem in IOT: Don’t count on the canary!

Memory-based vulnerabilities are becoming more and more common in low-power and low-cost devices in IOT. We study several low-level vulnerabilities that lead to memory corruption in C and C++ programs, and how to use stack corruption and format string attack to exploit these vulnerabilities. Automatic methods for resisting memory attacks, such as stack canary and address space layout randomization ASLR, are studied. These methods do not need to change the source program. However, a return-oriented programming (ROP) technology can bypass them. Control flow integrity (CFI) can resist the destruction of ROP technology. In fact, the security design is holistic. Finally, we summarize the rules of security coding in embedded devices, and propose two novel methods of software anomaly detection process for IOT devices in the future.
Differential Oriented Image Foresting Transform Segmentation by Seed Competition

The Image Foresting Transform (IFT) is a graph-based framework to develop image operators based on optimum connectivity between a root set and the remaining nodes, according to a given path-cost function. Oriented Image Foresting Transform (OIFT) was proposed as an extension of some IFT-based segmentation methods to directed graphs, enabling them to support the processing of global object properties, such as connectedness, shape constraints, boundary polarity, and hierarchical constraints, allowing their customization to a given target object. OIFT lies in the intersection of the Generalized Graph Cut and the General Fuzzy Connectedness frameworks, inheriting their properties. Its returned segmentation is optimal, with respect to an appropriate graph cut measure, among all segmentations satisfying the given constraints. In this work, we propose the Differential Oriented Image Foresting Transform (DOIFT), which allows multiple OIFT executions for different root sets, making the processing time proportional to the number of modified nodes. Experimental results show considerable efficiency gains over the sequential flow of OIFTs in image segmentation, while maintaining a good treatment of tie zones. We also demonstrate that the differential flow makes it feasible to incorporate area constraints in OIFT segmentation of multi-dimensional images.
Musique concrète and the Aesthetic Regime of Art

The use of noise, defined as an indeterminate complex of sound, initiated a radical shift of the field of “music.” Between Pierre Schaeffer and John Cage there is a common point : against the hierarchical principals that rule music, they both pose equality of all sound material. Examining the case of Pierre Schaeffer and musique concrète, this essay asks how noise, as a raw material, breaks the logic of mimesis. How does this material imply new ways of composing or listening, a new “partage du sensible”?
BCT-CS: Blockchain Technology Applications for Cyber Defense and Cybersecurity: A Survey and Solutions

Blockchain technology has now emerged as a ground-breaking technology with possible solutions to applications from securing smart cities to e-voting systems.Although it started as a digital currency or cryptocurrency, bitcoin, there is no doubt that blockchain is influencing and will influence business and society more in the near future.We present a comprehensive survey of how blockchain technology is applied to provide security over the web and to counter ongoing threats as well as increasing cybercrimes and cyber-attacks.During the review, we also investigate how blockchain can affect cyber data and information over the web.Our contributions included the following: (i) summarizing the Blockchain architecture and models for cybersecurity (ii) classifying and discussing recent and relevant works for cyber countermeasures using blockchain (iii) analyzing the main challenges and obstacles of blockchain technology in response to cyber defense and cybersecurity and (iv) recommendations for improvement and future research on the integration of blockchain with cyber defense.