Reviewerly/exHarmony huggingface dataset:
Innovative strategies for university students during COVID-19 pandemic

The COVID-19 pandemic has wreaked havoc on the educational landscape. Approaches of teachers have had to shift as a result of this transformation since they have been forced to use a variety of technological resources to improve their teaching. This pandemic compelled educators to devise the most effective plan for resolving the educational crisis. This study described innovative teaching practices and responses of students to adapt these strategies during the lockdown period. We adjusted courses to make them virtual comprehensible so that we could continue to deliver effective instruction. Hands-on training is essential for good learning of applied courses; thus we established a virtual lab where students were given online instructions via digital platforms such as Microsoft Teams and Blackboard. Later, students were asked to make videos of their lab performances. Furthermore, breakout rooms were created in which students explored various themes in small groups before having a representative from each group convey the main points of their conversation. Another essential tactic was to increase one-on-one interaction with students, giving them feedback on their assignments and other class duties. Importantly, using Microsoft Teams and Blackboard for live invigilation made conducting online exams a lot easier. Finally, these tactics were extremely effective in maintaining a competitive learning environment for diverse students during the COVID-19 crisis.
Multimedia Products in Library and Information Service: Domestic and Foreign Experience

User service by means of multimedia is an actual direction of library practice. Modern forms of interaction with the user have wide functionality, attract readers, form the image of an information institution and provide comfortable service conditions. The analysis of the experience of libraries in the creation and use of multimedia products allows the identification of main trends in the use of multimedia in library practice, studying the range of existing multimedia products, their features, advantages and typical errors. The purpose of the work is to study the foreign experience of creating and using multimedia products in servicing library users. In the course of the research, the following methods were used: analysis of the document flow based on the study of foreign professional periodicals, analysis of the websites of foreign national libraries, comparative analysis of foreign and domestic experience in presenting multimedia products on library websites. Foreign experience is considered on the basis of publications presented in foreign professional periodicals and analysis of multimedia sites of foreign national libraries. Multimedia products of domestic libraries are considered based on the analysis of the assortment presented on the websites of libraries of various levels and departmental affiliation. The description of the features of the implementation of multimedia products and their specific assortment is given. The analysis of publication activity on the problem under consideration and the thematic distribution of publications are presented, foreign journals with the largest number of articles on the research topic are identified. It is established that the issues of creating and using multimedia products are relevant not only for domestic, but also for foreign library practice. The interest of foreign authors in providing services in a remote format is noted. The creation and use of multimedia products in publications is mainly presented by the example of describing the experience of national libraries and libraries of educational institutions. The paper analyzes in detail the range and features of the implementation of multimedia products on the websites of foreign libraries in comparison with domestic library practice. The common types of multimedia products presented by foreign libraries and their difference from similar domestic ones are revealed. The forms of information presentation and additional features implemented in multimedia products are considered. It can be noted that the websites of foreign libraries do not reveal unique multimedia products that are not typical for domestic library practice, at the same time, a number of features in the presentation of information and the species assortment are noted.
ReptileJam 2021: Designing Enrichment for Reptiles in Captivity

This workshop is focused on the design of novel kinds of environmental enrichment for zoo-housed reptiles, using technology to support the development of interactive systems and devices for capturing data. Participants will work virtually in small groups to ideate, reflect on and develop concepts, using a ZooJam approach, which is similar to a game jam. Briefs for participants may include lizards, snakes, turtles and crocodilians.
Development of a computer platform for the enrichment of cultural environments using VR and AR

The implementation of technologies plays a very important role in the user experience within cultural spaces, allowing to add life to static objects in the real world with sounds, visual content and additional information. In this work we propose a web platform design for registration of works that allows and provides support to cultural sites, registering and digitizing their various works, sculptures or exhibition sites, regardless of the technique implemented in these. In addition, technological tools such as vuforia and unity are used to create a mobile application that turns a smartphone into a personal guide that can not only provide textual stories, but can also perform virtual tours of the installations. The functionality of this platform is evaluated by implementing it in a gallery located at the Don Bosco University, demonstrating its versatility.
MapView: Exploring Datasets via Unsupervised View Recommendation

Exploring large datasets in search for valuable insights requires time and sufficient technical knowledge. In order to alleviate this task, we propose and implemented a prototype of a data exploration tool. It is based on Self-Organizing Maps (SOM) and helps non-technical users with limited technical expertise and time. Our proposed approach employs SOM as a clustering mechanism to group and recommend exploratory data views to the user. This recommendation process can also be personalized to meet user’s intention in an interactive manner. Experimental results show that the reported prototype is effective in recommending valuable views, hence, being of aid in data exploration tasks.
A Product Recommendation System for e-Shopping

With the increase in number of ecommerce websites, it is difficult for a user to find a reasonable product he/she wants. In addition, users have no way to find the best product for him rather than searching product on different website and looking for the best product suitable to him. Product recommendation system is a web-based system that solves the problem of users by helping them to decide which product suits them according to the features and price they require. Our system is independent of the structure of websites as it uses set of rules and matches the regular expressions of the specifications with the text of the website. This system is visualizing results graphically for the ease of user decision. This product recommendation system provides results realistically.
A Propose of Genetic Operators for Quantum Algorithms with Real Representation

In the literature, there are many Quantum Inspired Evolutionary Algorithms (QIEA) that proved good performance and results when applied to solving a myriad of problems. However, the real quantum representation increases precision for numerical optimisation problems and reduces the memory storage need, alternatively to the binary representation. On the other hand, evolutionary algorithms' performance mainly depends on the crossover and mutation operators, reaching equilibrium between the explorative and exploitative features of evolutionary algorithms. This paper proposes new quantum genetic operators for real quantum representation, instead of the 1/5 rule present in AEIQ-R, to improve the performance of the quantum genetic algorithm with real representation. Three crossover and two mutation operators are proposed and tested to optimise four functions with different dimensions. Friedman's test indicated a significant difference between the combinations, and that combination composed of quantum arithmetic crossover and quantum creep mutation have the best performance.
Reducing the effort of Evolutionary Calibrator Using Opposite Information

Metaheuristics have been successfully applied to solve complex real-world problems in many application domains. Their performance strongly depends on the values of their parameters. Many tuning algorithms have already been proposed to find a set of suitable values. However, the amount of computational time required to obtain these values is usually high. Our goal is to propose a collaborative strategy to help to reduce the configuration effort during the tuning process. Here, we introduce a novel initialization strategy that learns from poor quality configurations in a pre-processing phase. We evaluate our collaboration using the well-known Evolutionary Calibrator (Evoca). Moreover, we tune two different algorithms: the Ant Knapsack algorithm, using hard instances of the Multidimensional Knapsack Problem, and a Genetic Algorithm for solving landscapes that follow the NK model (N components and degree K). Evoca obtains promising results using our novel strategy, consuming less computational resources.
Research on Target Distance Detection Technology of Vehicle Assisted driving Based on monocular Vision

ADAS has been applied in autonomous driving widely and is the active field of AI applications. This is a survey of distance measurement with monocular camera to detect objects in front of the vehicle and anti-collision warning to make driving more safe. We present a new distance measurement method that YOLOv5 algorithm is applied to detect things in front of the vehicle and the improve method based on pixel feature scale ratio is used to measure distance. In our approach, we use cubic Bezier curve fitting algorithm to optimize the measurement data to improve the distance measurement accuracy. Experimental results on real driving picture show that the proposed distance measurement is robust and accurate of vehicle distance estimation.
Multi-Rotor UAV Autonomous Tracking and Obstacle Avoidance Based on Improved DDPG

To solve the problem of multi-rotor UAV autonomous tracking dynamic ground targets in obstacles environment, we used Markov decision process (MDP) to establish an autonomous maneuvering model of multi-rotor. Considering the obstacle avoidance requirements of UAV during the tracking process, we integrated the Long Short-Term Memory (LSTM) neural network with memory unit and time series data processing characteristics into the Deep Deterministic Policy Gradient (DDPG) algorithm framework, so that the Actor network can fully refer to the prior state information when making decisions. Finally, the performance test was implemented on the UAV 3D simulation platform based on Robot Operating System (ROS). The results show that the method proposed in this paper can enable the UAV to complete the whole process of autonomous tracking of the ground dynamic target. Compared with the traditional DDPG algorithm, the DDPG algorithm combined with LSTM has stronger accuracy and real-time performance, and can better meet the tracking and obstacle avoidance mission requirements of the multi-rotor UAV.
Research on GEMD Image Hiding Technique Based on Chinese Remainder Theorem

In order to improve the efficiency of Generalized Exploiting Modification Direction (GEMD) image hiding and protect the private information from being leaked, an improved GEMD image hiding scheme based on the Chinese residual theorem is proposed. The main idea is to decompose the binary confidential information by the Chinese remainder theorem, and then combine the GEMD hiding algorithm to embed the decomposed confidential information into the carrier image to form a camouflaged image. The results of the experiments show that the improved GEMD hiding algorithm has significantly improved the embedding amount and PSNR value, and greatly enhanced its security performance.
An Implementation of Face Recognition System with Face Presentation Attack Detection on Raspberry Pi

With the popularity of unlocking and payment of using the face-swiping function, the era of face recognition has arrived. However, the current face recognition technology can identify the identity of the face image but cannot accurately distinguish the authenticity of the input face on embedded devices. Recently, face recognition with face presentation attack detection has become a research hotspot. In this paper, we develop an intelligent face recognition system on Raspberry Pi to handle this issue. Specifically, we first add a face presentation attack detection process to form a three-stage face recognition method to realize face recognition with face anti-spoofing. Second, we select a face detection model, a face presentation attack detection model and a face recognition model suitable for Raspberry Pi, and transplant these models previously trained in the Python environment to the C++ environment. Then we integrate and deploy these three models on Raspberry Pi and use the NCNN inference framework for efficient reasoning within limited memory and computing resources. Finally, we implement the face recognition system and conduct real-world tests to verify the performance. The experimental results demonstrate that our system can be applied in practice, and can achieve good effects.
News Recommendation Based On Multi-Feature Sequence Transformer

Personalized news recommendation system aims to screen out the news that users are interested in from explosion amount of news for display. In recent years, deep learning methods have been widely used in news recommendation system. However, whether in traditional news recommendation methods or advanced deep learning models, most of them are only modeled after feature extraction of news titles or modeled after adding user preferences. There are two problems: insufficient expression of news and insufficient exploration of the implicit meaning of users, continuous behavior. Therefore, in this paper, we propose a news recommendation model based on a multi-feature sequence transformer (MFST). It first extracts multiple attributes of news and merges them together for learning unified news representation. Secondly, a powerful Transformer component is applied to process the user's historical reading behavior seq uence information to express the news in more details by strengthening the learning ability of news representation and capturing the meaning behind the user's continuous historical reading behaviors. In addition, we also attached an attention network to calculate the closeness of the clicked news to the candidate news. Experimental results based on the real-world news dataset confirmed that our proposed MFST model is effective for personalized news recommendation compared the state-of-the-art deep learning models.
Panoramic Light Field - Oriented Image Stitching Method with Semantics and Projection

Existing light field (LF) stitching approaches mainly consider the geometric correspondence and ignore the high-level semantic information, so it is difficult to ensure the angular consistency of the stitched results while aligning the LF images. To address this problem, this paper proposes a panoramic LF - oriented image stitching method with semantics and projection transformation. Specifically, semantic information is first extracted from the LF images and applied to classify feature points. Hence, all the feature points are endowed corresponding semantic information based on the classification result. Afterwards, the geometric and semantic information of the feature points are combined to estimate the local homography matrix to align the input LF images. Finally, a weighted fusion strategy is employed to fuse the aligned results. Experimental results show that the proposed method can not only guarantee the spatial quality of the stitched LF image. But also effectively preserve accurate angular consistency. Moreover, the proposed method outperforms the existing stitching methods in both subjective and objective comparisons.
SAD: A novel method for ensemble outlier detection with dynamic prediction label

Majority voting outlier detection is a traditional method that has been widely used in many fields. It uses the strategy of majority vote to make a prediction, which makes it perform poorly in acc index sometimes. In this paper, a method called second anomaly detection (SAD) is proposed, to detect the connection of outlier scores between each other and decide the advantage strength of a sample when defining the outlierness, which is expressed as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> factor, then the prediction label of a sample is ascertained according to the a value. Finally, SAD is compared with several majority voting anomaly detection algorithms in accuracy performance, such as iForest, HBOS, AutoEncoder, it is shown that the proposed algorithm SAD is effective.
Model-based Customer-Relationship Management System and Strategic Board Game: Analogical Training

The pandemic has caused much mental, social and financial stress. Policies and standard operating procedures have helped to alleviate some stress and create systematicity, expectancy, and predictability. This paper aims to alleviate some of the stress through model-based social customer relationship management system, an Augmented Reality board game, and two mini tourism games. The first aims to increase collaboration among stakeholders and to increase more effective and efficient resource management. The second phases players through the preparation, recruitment, attack and observation phases at a slower pace than competitive gaming. The third and fourth project introduce places and trigger memories through mini games respectively. These provide analogical training in social customer relationship management and in developing more systematic thinking, expectations, fun and mindfulness. AI-assistance would enable personalization/intelligent context-aware recommendations.
New Pruning Method Based on DenseNet Network for Image Classification

Deep neural networks have made significant progress in the field of computer vision. Recent works have shown that depth, width and shortcut connections of the neural network architectures play a crucial role in their performance. As one of the most advanced neural network architectures, DenseNet, which achieves excellent convergence speed through dense connections. However, it still has obvious shortcomings in the use of memory. In this paper, we introduce two new pruning methods using threshold, which refers to the concept of threshold voltage in MOSFET. Now we have implemented one of the pruning methods. This work uses this method to connect blocks of different depths in different ways to reduce memory usage. We name the proposed network ThresholdNet, evaluate it and other different networks on two datasets (CIFAR-10 and STL-10). Experiments show that the proposed method is 60% faster than DenseNet, 20% faster and 10% lower error rate than HarDNet.
Convolutional Neural Networks for Image Classification

Computer Vision is a popular research area even though it is considered a simple concept. Over the past decades, a series of significant studies are conducted in this direction, for example, PCA, LDA, LBDA, etc. CNN model has been widely and effectively applied in various classification tasks such as image classification and text classification. It can be categorized into engineering and science since our goal is to make computer vision serve as a counter part of a human&#x0027;s vision system. Not only to make it look at the world like we do, but also to adapt to the environment. Image classification as a key concept in computer vision has plenty of applications that have a huge impact on human lives. Due to the fact that image classification is based on deep learning, it can process data without manual selections or designs.
Research on Application of Blockchains in Supply Chain Risk Management

These years have witnessed overcapacity of blockchains caused by low demand implementation and the lack of product usability. The excessive inventory increases the inventory cost, and reduces the value of the whole ecosystem of the supply chain. As the blockchain technology develops and matures, information systems of each link in the supply chain, including the supplier, the manufacturer, the retailer, and the logistics are able to log the information onto the chain to realize real-time tracking and collaboration. Besides, the supply chain built on the blockchain technology is more dynamic and flexible. In the present work, a three-layer supply chain model consists of a blockchain technology layer, a network layer, and an application layer to achieve real-time information sharing and collaboration of enterprises at different links of supply chain, and to increase the accuracy of demand forests and capacity of inventory replenishment, reduce supply chain risk, establishing a trust management mechanism, and unveil the principles underlying the efficiency of applying blockchain technology to control risks involved in supply chain.
Research on Fake Reviews Detection Based on Feature Construction and EasyEnsemble-RF

This article analyzes the entity attributes of the data set of the Yelp website and constructs a feature set that can effectively identify fake reviews. It solves the problem that users post fake reviews with few features and a lack of effective detection methods. Use random forest to replace EasyEnsemble's base classifier to combat the interference of abnormal data in minority classes. After processing the data using the under-sampling method, the result of the classification model is determined based on the random forest. The experimental results show that the constructed features and the proposed method can effectively improve the detection of fake reviews.
Collision Avoidance Path Planning of Nuclear Robot with Dual Manipulators

Aiming at the requirement of collision-free and precise synchronized kinematics autonomous coordinated control of the dual-manipulator heavy-duty robot in carrying, grasping and dismantling operations, this paper studied the method of collision avoidance path planning in the dual-manipulator operation. The dual mechanical arm is an important part of the system, and its working state is directly related to the stability and reliability of the entire system. In this paper, we used the improved DH method to mathematically model the position and posture of the dual robotic arms. Under the robot operating system, we adopted Moveit! to control the movement towards the robotic arms. Finally, under obstacles at different distances, we simulated the improved fusion algorithm and compared it with the existing artificial potential field method and RRT* algorithm. The experimental results show that the improved fusion algorithm has better path planning effect and can make the robot arm reach the goal smoothly along the path of the least cost without collision.