Reviewerly/exHarmony huggingface dataset:
Hand Gesture Input Interface as Native Function of IntelligentBox Using Leap Motion Controller

In this paper, the authors treat a native interface of IntelligentBox for two hands’ gesture input. IntelligentBox is a 3D graphics software development system that provides 3D primitive components. Users can develop 3D applications as composite components composed from such 3D primitive components through direct manipulations of a mouse device on a computer screen. In the situation like COVID-19 pandemic, contactless input interfaces are significant. The authors have already proposed one dedicated component of IntelligentBox works as the input interface of two hands’ gesture for its 3D applications. In this paper, the authors propose a new native interface for two hands’ gesture input of IntelligentBox that allows users to create 3D applications without using any contact device like a mouse device.
A Mobile-Oriented GPU Implementation of a Convolutional Neural Network for Object Detection

Realtime image detection and recognition on low-power edge devices are key to implementing many social applications such as automated traffic guard systems. This paper proposes mobile-oriented convolutional neural network implementation for object detection, focusing on computational reduction techniques such as depthwise separable convolution and grouped convolution on an edge GPU device. Through empirical evaluation and analysis, it is shown that the use of grouped convolution and half-precision floating point arithmetic is effective for the calculation of $$3 \times 3$$ convolution, and the grouped convolution with 8 groups for YOLOv3-tiny achieves 16 FPS with 86% detection accuracy in a 5 W low power mode on Jetson Nano. We also discuss how the processing time and recognition accuracy are affected by the floating arithmetic types and power consumption modes that the GPU device offers.
NWP in the Middle: College-, Career-, and Community-Ready Writing with English Learners

Preview this article: NWP in the Middle: College-, Career-, and Community-Ready Writing with English Learners, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/vm/27/3/voicesfromthemiddle30542-1.gif
Study of vertical solar irradiance and local scale climate to assess passive cooling potential in Tangerang of Indonesia

Solar radiation information is very important in green building design, namely for the daylighting, solar heat gain prevention as well for solar energy utilization. This study uses solar radiation data from six pyranometer sensors for measuring the horizontal component (global horizontal irradiance (GHI) and infrared irradiance) and the vertical component (pyranometer sensors to the north, south, east, and west) located in Tangerang, Indonesia. Measurements have been filtered for one year from January 2021 to December 2021. The solar radiation observation is complemented with other measurement of climate elements, such as wind speed and direction, dew point temperature, relative humidity, and air temperature. The diurnal and seasonal patterns of solar irradiance and other climatic elements have been observed using the visualization technique of heat maps. The results show that sensors pointing north experience an increase in solar intensity in May, June, July, and August. Meanwhile, sensors that point to the south experience the increases in solar intensity in November, December, January, and February. The increase in radiation intensity towards the east occurs from 07.00 to 10.00 local time (LT), while the increase in sensor intensity towards the west occurs from 14.00 to 16.00 LT. The results of solar radiation analysis in horizontal and vertical surfaces are combined with other climate elements to create a bio-climatic design guideline suitable for buildings in the hot and humid climate.
ECOLOGY – CREATIVITY:&#x0D; INNOVATIVE PATH OF YOUNG PEOPLE DEVELOPMENT

In the presented article, for the first time in the practice of domestic and foreign scientific research, two&#x0D; fundamental target settings were implemented:&#x0D; &#x0D; - an attempt was made to systematically analyze the fundamental factors of the formation and functioning&#x0D; of creative activity in the context of historical and modern trends in socio-ecological development;&#x0D; &#x0D; - attention is paid to the concretization of theoretical and methodological approaches in line with the formation&#x0D; of ecological and creative orientations of the individual, through the author's results of conducting specific sociological&#x0D; surveys of young people in senior classes of secondary schools in Minsk.
Fractal geometry and modern science

The essence of fractal geometry is disclosed, the subject of study of which is the point as the self-similarity of Genesis. The essence of the torsion form of movement was analyzed. A new concept of space and time has been developed. The ideological and theoretical proximity of the provisions of modern science and ancient Eastern philosophy has been determined. G. Kantors axiom on continuity (1872), as well as K. Gdels incompleteness theorem, has been interpreted. The possibility of science entering the sphere of the nomen world has been proven. The theory of pulsating Chaos or Supersymmetry is based. It has been revealed that events occur not only in space and time, but that space and time themselves affect events.
The influence of neural networks on the formation of media reality

With the development of information technology, there are both new opportunities and challenges. Media generated by a neural network is becoming one of the most relevant trends of the present time. As the capabilities of the neural network increase, the question of how the media reality is formed under these conditions becomes more and more relevant. The article deals with the problem of the influence of neural networks on reality.
Electronic justice as a mechanism for ensuring the right of access to justice in a pandemic: the experience of Ukraine and the EU

The article analyzes the measures implemented by countries in the field of access and administration of justice, focusing on the use of electronic justice as a comprehensive remote mechanism during the COVID-19 pandemic. The study examines the experiences of the European Union and Ukraine to understand the effectiveness of electronic justice in ensuring the right of access to justice. Various scientific methods such as legal-statistical, systematic, formal-legal, and cybernetic methods were employed in the study. The analysis reveals that the measures taken by countries to prevent restrictions on human rights in the judicial system are not perfect and require further development. The study identifies key issues in the practical implementation of electronic justice and provides specific recommendations for improvement. The research fills a gap in comprehensive scientific studies on continuous and effective consideration of court cases during the pandemic. The practical and scientific value of the article lies in its relevance to practitioners and scholars worldwide, who are interested in the realization of the right of access to justice and the functioning of electronic justice. The national experiences and recommendations presented in the article can also be applied by European countries to enhance the effectiveness of their electronic justice systems.
Protecting Medical Data on the Internet of Things with an Integrated Chaotic-GIFT Lightweight Encryption Algorithm

The convergence of blockchain technology and edge intelligence has emerged as a transformative force in the realm of smart cities, offering unprecedented opportunities to enhance sustainability. This paper presents a comprehensive review and prospective analysis of this convergence, shedding light on its potential to revolutionize urban environments. We commence with an exploration of the fundamental components of blockchain technology, emphasizing its core principles of decentralization, immutability, and consensus mechanisms. Simultaneously, we trace the historical evolution of blockchain from its origins with Bitcoin to its broad applications in diverse domains. In the context of edge intelligence, we examine how this paradigm shift decentralizes data processing, enabling real-time decision-making and enhancing data security. We elucidate its key components, such as edge devices and analytics algorithms, while highlighting its critical role in reshaping the urban landscape. The crux of this paper lies in the convergence of blockchain and edge intelligence, where we explore its profound implications for the sustainability of smart cities. From efficient energy management and waste reduction to improved transportation and green infrastructure, this convergence empowers smart cities to optimize resource usage, reduce environmental impact, and enhance the quality of life for their residents. Through a prospective analysis, we anticipate emerging trends and innovations that will shape the future of smart city sustainability, including AI integration, 5G connectivity, and circular economy initiatives. As smart cities continue to evolve, they stand at the forefront of addressing urbanization challenges while fostering sustainable, inclusive, and prosperous urban environments.
The Significance of Metadata and Video Compression for Investigating Video Files on Social Media Forensic

Digital forensics is an essential aspect of cyber security and the investigation of digital crimes. Digital recordings are routinely used as important evidence sources in the identification, analysis, presentation, and reporting of evidence. There has recently been concern that images and videos cannot be used as solid evidence since they may be altered very quickly due to the abundance of technologies available for the gathering and processing of multimedia data. The main goal of this endeavour is to comprehend advanced forensic video analysis methods to assist in criminal investigations. We first propose the acquisition extraction analysis in a forensic video analysis framework that employs efficient video and image enhancement techniques for low-quality video that would be transferred through social media applications and for CCTV footage analysis. The reliability of digital video recordings is essential in forensic science and other criminal investigation fields. Digital video forensic analysis is a technique that constantly faces new challenges. Currently, videos are authenticated using a variety of parameters, including pixel-based analysis, frame rate analysis, bit rate analysis, hash value analysis, and, most importantly, metadata analysis. It was believed that the development of technology required the development of a new method for the verification of digital video recordings. In this review study, we made a novel attempt by reviewing the media. Information and structural analysis of video containers in the MP4 file format have been used to distinguish between real and altered videos.
Multi-source Transfer Learning Based on the Power Set Framework

Abstract Transfer learning is a great technology that can leverage knowledge from label-rich domains to address problems in similar domains that lack labeled data. Most previous works focus on single-source transfer, assuming the source domain contains sufficient labeled data and is close to the target domain. However, in practical applications, this assumption is hardly met, and labeled data exist in different domains. To improve the adaptability of transfer learning models for multi-source scenarios, many existing methods utilize the commonality and specificity across source domains. They either map all source domains with the target domain into a common feature space for knowledge transfer or combine multiple classifiers trained on pairs of each source and target to form a target classifier. However, the correlations across multiple source domains that can bring significant impacts on learning performance are ignored. In light of this, we propose a novel multi-source transfer learning method based on the power set framework (PSF-MSTL). First, PSF-MSTL constructs a power set framework that enables different source domains to be interrelated. Second, PSF-MSTL makes the source-domain framework integral and able to provide complementary knowledge using a dual-promotion strategy. Additionally, PSF-MSTL is formulated as an optimization problem, and an iterative algorithm is presented to address it. Finally, we conduct extensive experiments to show that PSF-MSTL can outperform many advanced multi-source transfer learning methods.
Chatbot Development Through the Ages : A Survey

A chatbot is a computer program that aims to make a conversation between both humans and machines. The chatbot can be utilized in a variety of platforms, including messaging apps and virtual assistants. The chatbot has evolved a lot in decades starting from amusement usage to performing serious tasks. While designing a chatbot, design considerations like purpose, audience, channels, conversational flow, testing and iterations must be taken into account in order to ensure that it is accurate and user-friendly. Based on its domain, model, and conversation style, a chatbot can be categorized into customer-service, sales, informational, personal assistant, entertainment, health and educational chatbot. Chatbot technology continues to face a wide variety of challenges like contextual understanding, integration with backend systems, personalization, security and user acceptance. This paper explores and compares various recent chatbots from different domains that are being used. We have surveyed the entire development process and the different development techniques used to design chatbots and the audience they cater to. We also look at the various evaluation methodologies used in checking the efficiency and enforceability of the considered chatbots.
News category and location based approach for news recommendations

With the quick advancement of technology, we now have networks and fast connections that allow us to communicate with people right away. Most people currently have access to daily use devices like mobile phones, tablets, and laptops. We must keep up with all happenings and news in our quick and information-driven environment. Users of this Android news app can access the most recent news from 120+ newspapers in 50+ countries. This application's major goal is to connect news stories from all around the world and provide them to users as quickly as possible in the best visual manner.
Classification of Gujarati Articles using Bernoulli Naïve Bayes Classifier and Extra-trees Classifier

On the internet, information technology generated massive amounts of data. Because this data was initially primarily in English, the majority of data mining research was conducted on English text documents. As internet usage grew, so did data in other languages such as Gujarati, Marathi, Tamil, Telugu, and Punjabi, among others. We present a text categorization method based on artificial text summarization of Gujarati Articles in this paper. For the classification of text documents, various learning techniques such as Naïve Bayes, Support Vector Machines, and Decision Trees are available. We gathered articles from various e-newspaper editorials. This paper focuses on a brief review of the various techniques and methods for Gujarati Articles Classification, so that research in Text Classification can be further explored using various classifier algorithms. The dataset, which contains 1604 documents from 8 different categories, is used by the system. The result shows that Stacking Classifier with Bernoulli Naïve Bayes Classifier and Extra-trees Classifier is efficient for Gujarati Articles.
Host utilization prediction using Taylor Kernel Convolutional Neural Network (TKCNN) and workflow scheduling for smart irrigation cloud data centers

One of the most significant economic sectors in these nations is agriculture, which emphasises the significance of controlling the water resources at hand to maintain the survival of this industry. Sensor and cloud computing (CC) technology are widely used in many real-time applications, particularly in agriculture. To ensure Quality of Service in the CC environment, Virtual Machine (VM) Migration as part of VM Consolidation is very important (QoS). Predicting host utilization utilizing utilization history and lowering energy consumption while satisfying Service Level Agreements (SLAs) is a challenging task as a result of the large fluctuation in the consumption of cloud resources and the dynamic workloads.CC technologies are widely used in many real-time applications, particularly in agriculture for smart irrigation cloud data centers difficult task. To solve this issue proposed work introduced a Taylor Kernel Convolutional Neural Network (TKCNN) algorithm, which trains the past history for host usage prediction in smart irrigation cloud data centers. The Optimum Energy and Resource Aware Workflow Scheduling- Host Utilization Prediction (OERES-HUP) method, which attempts to schedule the task workflow to VMs engaging in computing, was influenced by the well-known Fuzzy Multi-Verse Optimizer (FMVO) algorithm. Additionally, it focuses on dynamically deploying and underplaying VMs in accordance with job requirements. The effectiveness of scheduling algorithms is evaluated using key metrics including Resource Utilization (RU), Energy Consumption or Task (ECT), Total Energy Consumption (TEC), Makespan, and Execution Time per Task (ETT).The outcomes are a good representation of how effective the suggested algorithm is compared to current techniques. To test the efficacy of the suggested model, the cloud environment is simulated using the Cloud Sim simulator.
Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

Abstract In this work, a day and night time vehicle detection system for traffic surveillance is proposed. Our system is composed of two main processes, day time and night time processes. In the night time, the vehicles are detected based on their taillights and headlights. First of all, the 2D-DWT (Two Dimensional Discrete Wavelet Transform) and the background subtraction are applied to the input image. Then, the connected component technique is used to extract the region of interest. If it is the daytime, the connected component candidates are taken as potential vehicles after applying a pre-processing algorithm to improve the result. If it is the night-time, a filtering operation is used to keep only the bright white and red connected component candidates (which represent potential headlights and taillights, respectively). Finally, potential lamp sets are formed by grouping the extracted components on the basis of their positions, sizes, and colours. The potential extracted vehicles are classified as a vehicle or non-vehicle by using a pre-trained CNN (Convolutional Neural Network) classifier. The proposed system was tested and evaluated using different works from the literature. The experiments show that our proposed system has reached a high accuracy in terms of vehicle detection process whether in day or night time. The experiments were performed using four different videos and were implemented using the C++ language, which facilitates mathematical computation, and its OpenCV library, which is used to run the image processing algorithms used, as well as the TensorFlow library, which facilitates transfer learning of pre-trained CNN models.
Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Abstract Just‐In‐Time (JIT) defect prediction aims to predict the defect proneness of software changes when they are initially submitted. It has become a hot topic in software defect prediction due to its timely manner and traceability. Researchers have proposed many JIT defect prediction approaches. However, these approaches cannot effectively utilise line labels representing added or removed lines and ignore the noise caused by defect‐irrelevant files. Therefore, a JIT defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT‐FF) is proposed. Firstly, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Secondly, to obtain semantics‐enhanced code representation, a cross‐attention‐based line label fusion method to perform complementary feature enhancement is proposed. Thirdly, to generate code changes containing fewer defect‐irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning‐based file filtering method. Finally, based on generated code changes, CodeBERT‐based commit representation and multi‐layer perceptron‐based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT‐FF can predict defective software changes more effectively.
Phishing Website Detection Based on Machine Learning Algorithm

Phishing websites utilise a variety of techniques to imitate the URL address and page content of a legitimate website in order to steal users' personal information. In this study, we analyse the structural properties of the phishing website URL, extract 12 different types of data, and train four machine learning algorithms. Then, in order to identify unknown URLs, utilise the method that performed the best as our model. The recommendation of the original regular web page of the phishing web page is implemented after a snapshot of the web page is extracted and compared with the regular web page snapshot.
Research on information lossless teleportation via the W states

Abstract In this article, a protocol for information lossless teleportation using W states is proposed. Firstly, the information lossless teleportation of an unknown state with a maximally entangled W‐state channel, which protects the original unknown state information even in case of teleportation failure is investigated. Next, we generalise our scheme to non‐maximally entangled W‐state channels. Finally, the principle of the proposed scheme is validated by performing experiments on the quantum circuit simulator Quirk. Our study shows that W states can be used to teleport any quantum state without information loss through single‐qubit measurements and local unitary operations.
Investigation of student reasoning skills about flow processes in logistics

We present preliminary results of a pilot study investigating the reasoning skills of students in logistics and mobility in the context of hydrodynamics. Particularly, we were interested in the students’ thinking of parcel flow in logistics and steady-state pipe flow. Our goal was to investigate if students of logistics would show more, less, the same, or different misconceptions commonly found in students confronted with hydrodynamics problems. We hypothesize that, due to their exposure to flow-like systems (transport chains, band conveyors, hub systems etc.), students develop a certain intuition about flow processes. Interviews were held with several students that attended “Transportation and Handling Technology” at Hamburg University of Technology (TUHH) in the two winter semesters of 2020/2021 and 2021/2022. We found that half of the students were unable to solve questions regarding the parcel flow on a band conveyor or the steady-state flow in a narrowed pipe, while the other half of the participants showed the correct understanding of flow processes to solve all the problems. We present evidence that misconceptions about hydrodynamics strongly affected participants’ ability to solve logistics problems. We also discuss the application of our findings for instruction in logistics.