Reviewerly/exHarmony huggingface dataset:
Complex Background Reconstruction for Novelty Detection

Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. This paper proposes a novel reconstruction network and mutual information Siamese network. The reconstructed network aims to make the distribution of reconstructed samples consistent with that of original samples, intending to reduce background interference in the reconstruction process. After this, we measure the distance between the original and generated images based on a mutual information Siamese network, which extracts more discriminative features to calculate the similarity between the original images and their reconstructed ones. This part of the network uses global context information to improve the detection accuracy. We conduct extreme experiments to evaluate the proposed solution on two challenging public datasets. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods.
Time-Sensitive and Resource-Aware Concurrent Workflow Scheduling for Edge Computing Platforms Based on Deep Reinforcement Learning

The workflow scheduling on edge computing platforms in industrial scenarios aims to efficiently utilize the computing resources of edge platforms to meet user service requirements. Compared to ordinary task scheduling, tasks in workflow scheduling come with predecessor and successor constraints. The solutions to scheduling problems typically include traditional heuristic methods and modern deep reinforcement learning approaches. For heuristic methods, an increase in constraints complicates the design of scheduling rules, making it challenging to devise suitable algorithms. Additionally, whenever the environment undergoes updates, it necessitates the redesign of the scheduling algorithms. For existing deep reinforcement learning-based scheduling methods, there are often challenges related to training difficulty and computation time. The addition of constraints makes it challenging for neural networks to make decisions while satisfying those constraints. Furthermore, previous methods mainly relied on RNN and its variants to construct neural network models, lacking a computation time advantage. In response to these issues, this paper introduces a novel workflow scheduling method based on reinforcement learning, which utilizes neural networks for direct decision-making. On the one hand, this approach leverages deep reinforcement learning, eliminating the need for researchers to define complex scheduling rules. On the other hand, it separates the parsing of the workflow and constraint handling from the scheduling decisions, allowing the neural network model to focus on learning how to schedule without the necessity of learning how to handle workflow definitions and constraints among sub-tasks. The method optimizes resource utilization and response time, as its objectives and the network are trained using the PPO algorithm combined with Self-Critic, and the parameter transfer strategy is utilized to find the balance point for multi-objective optimization. Leveraging the advantages of reinforcement learning, the network can be trained and tested using randomly generated datasets. The experimental results indicate that the proposed method can generate different scheduling outcomes to meet various scenario requirements without modifying the neural network. Furthermore, when compared to other deep reinforcement learning methods, the proposed approach demonstrates certain advantages in scheduling performance and computation time.
Quantum networks boosted by entanglement with a control system

Networks of quantum devices with coherent control over their configuration offer promising advantages in quantum information processing, including quantum communication, computation, and sensing. So far, the investigation of these advantages assumed that the control system was initially uncorrelated with the data processed by the network. Here, we explore the power of quantum correlations between data and control, showing two communication tasks that can be accomplished with information-erasing channels if and only if the sender shares prior entanglement with a third party (the ``controller'') controlling the network configuration. The first task is to transmit classical messages without leaking information to the controller. The second task is to establish bipartite entanglement with a receiver, or, more generally, to establish multipartite entanglement with a number of spatially separated receivers.
Intelligent Tutoring System and Learning: Complexity and Resilience

The paper aims to investigate, through a case study, the relationship between learning and Information and Communication Technologies (ICT), in a historical moment in which the rampant use of Artificial Intelligence (AI) is to forcefully intertwine with the teaching–learning process. The research aims to outline, in particular, one of the possible ways of using AI with students in the study of solfeggio and ear training, through a digital tutor who supports specific activities proposed by the teacher. The project idea stems from the desire to investigate more closely the relationship between students and new technologies, and to understand their educational potential and the expression of student needs. The results show that Artificial Intelligence is a useful tool in the didactic field taken into consideration, but at the same time that there is much to be done, since the road traveled up to here can be a good starting point for reaching long-term educational goals.
Review on Analyzing and Detecting Crimes

Crime is a term that means a disastrous act for the entire humanity and an obstacle to communities' safety and life. With the increasing use of technology in solving crimes, computer Scientists in addition to Analysts have begun working with law enforcement organizations to improve the speed of investigating crimes. Crime intelligence analysis helps to discover patterns of crimes and criminal behavior that may help in the discovery of where and why crimes are likely to occur, but this data should be authentic and obtained from real-world sources or online resources such as social media, news websites or any other resources. Thus, efficient data analysis techniques can be used for this purpose. This review paper will examine in detail some of the current literature on analyzing and detecting crimes to help in proposing new methods to extract information from real-world crime data to develop an intellectual framework for analyzing and detecting crimes which can be of significant help to support the judicial agencies and law enforcement specialists for detecting and preventing crimes.
Using Telepresence Robots for Remote Participation in Technical Subjects in Higher Education

Remote participation in higher education courses, especially in technical subjects, is challenging, as there is a lot of practical knowledge to be applied or directly instructed, and it has to happen at the university premises since the labs are set up there. In this study, we allowed three ICT lecturers teaching at the undergraduate level to experiment with telepresence robots, augment the teaching and learning environment, and understand their readiness to adopt this technology. The experiments were documented, and semi-structured interviews were conducted with participants in order to obtain additional details for the experiment documentation. We found that using telepresence robots in labs requires both a well-planned teaching process and appropriate teaching environment settings to overcome technical limitations. As for future studies, there is a need for studies with bigger samples of ICT lecturers to implement these proposed scenarios to understand better the possibilities of effectively using telepresence robots for hybrid teaching in ICT subjects.
Implications of Forensic Investigation in Dark Web

The dark web has become a critical area of concern for law enforcement agencies and cybersecurity experts due to its association with illegal activities such as drug trafficking, money laundering, and cybercrime. Dark web forensics involves the collection, analysis, and preservation of digital evidence from the dark web, which presents several challenges such as anonymity, encryption, and the use of cryptocurrencies. This paper presents a comprehensive review of the current state of research on dark web forensics, with an emphasis on the methods, tools, and challenges associated with this field. The continuous improvement of darknet forensics technology has important practical significance for law enforcement agencies to combat darknet crimes.
HoloLearn: Towards a Hologram Mediated Hybrid Education

Educational institutions worldwide were thrust into adopting an online educational models due to the COVID-19 pandemic exposing severe limitations of tools used in online education, much to chagrin of both teachers and students. However, despite these limitations, hybrid educational models garnered significant interest. Holograms have shown potential in addressing several of the limitations of online educational tools that are currently in use. This paper reports the findings of an exploratory pilot study which investigates the benefits and limitations of holograms in hybrid and online educational models. The study compares student’s holographic viewing experience of the teacher in several dimensions such as presence, zoom exhaustion, and fatigue. Results are drawn from the comparison across a holographic display, virtual reality glasses, a video conferencing tool, and a tele-presence robot. The paper concludes with takeaways for the future steps of the study.
Energy-efficient RGB camera based virtual keyboard system

With the development of computer technology and the increasing maturity of its application in various fields, it is difficult for people's usual mechanical keyboards to accompany them in different working situations, and the public increasingly needs a convenient and portable virtual keyboard. In this paper, a virtual keyboard system based on RGB camera is designed based on image analysis, infrared laser positioning and laser projection technology. Compared with the traditional keyboard, this system can project the keyboard image onto the desktop, and also use the camera to capture and record the movement of the fingers to determine whether there is a keystroke. The system can be used with mobile terminals to help people deal with daily office work or even emergency business anywhere, anytime.
Real time image processing and operation management of multicultural communication platforms based on deep learning

The promotion and utilization of modern technology has made intercultural exchanges more frequent and deep, and secure image encryption and processing are hot issues for multicultural IOT communication platforms in the context of globalization for smart cities. In this paper, the re-al-time image encryption test and resource scheduling optimization analysis of multicultural communication platform are carried out based on convolutional neural network. First, we encrypt the image based on the layered image encryption technology of the chaotic system and the Feistel network. Secondly, through different strength encryption algorithms and different convolutional network structures, the encrypted images are classified and analyzed, and the encryption effect of the images is tested by the classification accuracy. In addition, we construct a task-resource scheduling model and design scheduling optimization schemes for the model from the aspects of data flow and control flow, so as to realize the mapping of network model to the IOT cloud plat-form and the reasonable scheduling of task resources on the software and hardware side.
Enterprise ecological environment information management mechanism based on deep learning

Ecological civilization is characterized by harmonious coexistence between man and nature, and peaceful coexistence between society and ecology. In the current social and economic process, there are problems such as environmental management problems, imperfect environmental management mechanisms such as economic activities and production and sales, and ineffective implementation of pollution control. Based on this, in this paper, we combine deep learning technology to innovate the classification and scientific treatment mechanism of specific pollutants. In addition, this paper focuses on evaluating the strengths and feasibility of enterprises by introducing SWOT research methods. Simulation tests show that the introduction of deep learning and big data into the field of informatization and intelligence to establish an environmental monitoring system can effectively improve the emergency environmental accident management system, public environmental safety and emergency management level.
Performance evaluation of quantum secure edge data center architecture

Thanks to the development of 5G technology, the construction of edge data centers has been highly valued. In order to ensure the security of signal transmission in edge data centers, it is a promising measure to apply the quantum key distribution (QKD) technology to edge data centers. To solve the problem of noise interference in the process of applying QKD technology, this paper first proposes the edge-end quantum one-way edge data center architecture, that is, the edge data center that first distributes the data to the surrounding clusters, and then transmits the single data from the edge to the end. Directed quantum signals increase the safe communication distance to meet the requirements of information encryption. Secondly, we conduct research on the four-wave mixing (FWM)noise interference in the architecture and the secure key rate model of the QKD system and perform theoretical calculations of the noise power and the rate of the secure key. Finally, through simulation, we can evaluate that QKD has the characteristic of high degree of security and its performance is affected by channel location, classical signal power and QKD detection efficiency, etc. The FWM noise power increases first and then decreases with the increase of transmission distance, with the maximum distance of 15 km. When the distance between the classical channel and the quantum channel is more than 200 GHz, the effect of FWM noise on QKD is weak.
Mask YOLO: a fine-grid method for multi-scale targets detection

Multi-scale and multi-class target detection in complex scenes has always been a very difficult issue. In this paper, we propose a target detection network based on object detection and multi-scale context. The proposed method overcomes the interference caused by the wide dispersion of scale ranges and effectively counteracts the features extracted for targets of different sizes. It combines 3 scale information with Generalized Intersection over Union (GIoU) and FocalLoss to perform the object detection task more accurately. The experimental results show that the method we proposed can effectively improve the accessibility of detection for abnormal size and orientation targets. It successfully generates a mask that highlights the objects on the input image.
FOREIGN LANGUAGE LEARNING USING A COMMUNICATIVE APPROACH WITH NEW LITERACY THEORY

This study discusses a number of influences related to teachers' understanding of the application of new literature theory and communicative learning, such as teacher trust, teacher education, and teacher professional development. The research is literature-based, with documentary data collection and analysis methods using content analysis. This research focuses on important issues that can help in the development of foreign language teaching curriculum. The focus of discussion in this article includes the lack of understanding of new literacy theories and communicative language teaching methods, curriculum understanding, teacher professional development through new literacy theories, and ideological tensions arising from the foreign language teaching gap in Indonesia. Implementation of the contemporary curriculum may differ between cities and villages. The lack of language activities, especially listening and speaking skills, in the old curriculum is what causes apathy and boredom among students and prevents them from interacting with their communities.
A Methodological Problem of Choice for 4E Research

The 4E approaches have been frequently linked to the claim that they fundamentally change how we theoretically analyze and empirically investigate cognitive phenomena. Despite this aspiration, the methodology of 4E researchers received surprisingly little attention, although the envisaged change of mainstream cognitive studies presupposes elaborated methodical competencies. This chapter argues that a methodology is necessary for the 4Es. However, several preliminary considerations need to precede a methodological analysis of the branch. It is claimed that methodological analyses of 4E research first need to answer the problem of choice whether to support a version of explanatory pluralism or explanatory unification. The answer to this problem significantly influences how a methodology can be developed. The problem can only be properly met, however, if the different versions of pluralist and unificationist positions are related so that we can understand where exactly they diverge or overlap. Below, the spectrum of integration is introduced, which suggests a relation between pluralist and unificationist views. The spectrum is a provisional guide to a 4E methodology as it invites 4E researchers to place themselves within this spectrum and approach methodological and methodical questions from there.
Intelligent Chatbot Assistant in Agriculture Domain

Agriculture is known as the economic game changer of India. It is the primary driver of GDP growth because of India’s robust agricultural industry, and proper knowledge about agriculture techniques help increase crop yield. So, answering the different types of crop-related queries is essential. We proposed the intelligent chatbot application in the agriculture domain so that farmers can get the correct information about farming practices. Our system is farmer-friendly and capable enough to instantly answer farm-related queries from the knowledge base, such as plant protection, fertilizer uses, government schemes, and many others. We used the agriculture-related data in question-answer format and implemented the pre-trained model of the Sentence-Transformer approach to answer providing. We also deployed the TF-IDF and Bag-of-Words method but achieved a reasonable accuracy rate for the test data in the sentence transformer pre-trained model. With the help of API services, our system also shows the crop’s latest mandi (market) rate and current weather information. So, the proposed chatbot system will keep the contribution for farmer’s cost savings. Overall, our chatbot system is straightforward and more efficient for the farmer to make better decisions.
Automated Agriculture News Collection, Analysis, and Recommendation

A country like India mainly depends on the sector of agriculture. Most people’s economies are intensely engaged in the field of agriculture. So, developing the agriculture sector will be an excellent benefit for any country. Nowadays, People can immediately find any solution regarding agriculture through technology’s modernization. We can get any news from online articles anytime without any movement. Agriculture news should also be available in online news articles so that people who are intensely engaged with the agriculture field and economy can quickly get their valuable news. People must go through many online news sites to gather all the agriculture-related news. We have proposed an NLP-based solution so people can get all agriculture-related news in one place combining multiple features. In this process, we have collected many articles from multiple online newspapers and classified the agriculture news articles. For the classification process, we have applied several classification models. We have also added a machine learning-based model to check the duplication between news articles. Although, there will be multiple categories of agriculture news so that people can directly follow the news as they want. People will also be recommended articles based on content and times. So, Getting information about agriculture will be more straightforward for the farmer, and they can know about new technologies to apply in their work. Finally, in this proposed work, people can get all the essential agriculture news from various sources in one central point, including many exciting features.
Fast Rotated Bounding Box Annotations for Object Detection

Traditionally, object detection models use axis-aligned bounding boxes (AABBs), but these are often a poor fit for elongated object instances, such as wheat heads. Using rotated bounding box (RBB) annotations can improve object detection results, but RBBs are much more time-consuming and tedious to annotate than AABBs for large datasets. In this work, we propose a novel annotation tool for producing high-quality RBB annotations with low time and effort. The tool generates accurate RBB proposals for objects of interest as the annotator makes progress through the dataset. It can also adapt available AABBs to generate RBB proposals. Furthermore, a multipoint box drawing system is provided to reduce manual annotation time. Across three diverse datasets, we show that the proposal generation methods can achieve a maximum of 88.92% manual workload reduction. We also show that our proposed manual annotation method is twice as fast as the existing system with the same accuracy. Lastly, we publish the RBB annotations for two public datasets.
An Optimization Approach for Distribution Network Planning Strategy Based on Combined Improved Cloud Model and Evidence Theory

Abstract With the continuous promotion of China’s power reform, higher requirements and standards have been put forward for distribution network investment decisions. However, the current work of distribution network project optimization in China has the problems of high subjectivity of investment decisions, fuzzy evaluation method, and high uncertainty of project optimization. In response to the above problems, this paper proposes a distribution network planning scheme optimization method based on the improved cloud model and evidence theory. Firstly, the optimal index system of the planning scheme is constructed from the dimensions of load transfer capacity, grid structure level, load capacity, investment benefit, social benefit, and life-cycle cost. Secondly, for subjectivity and ambiguity problems, the traditional degree of the membership function is replaced by a cloud model optimized by cloud entropy. Thirdly, the problem of uncertainty is addressed through evidence theory, while the game theory approach is used to identify conflicting evidence for evidence revision and fusion in the case of conflicting evidence arising from traditional evidence theory. Moreover, by improving the TOPSIS method and comparing the distances between the basic probability assignments of each planning scheme and the positive and negative virtual optimal solutions, the final decision results can be determined. Finally, the effectiveness and superiority of the proposed method are verified by experiments.