Reviewerly/exHarmony huggingface dataset:
Virtual Reality Based Moon And Space Station

This paper deals with the innovation to make kids get interested in the subject and learn things effectively and smartly using VR. Initially, the user will appear at the space station. The Space station consists of information about space and also the things/materials interactively used in a space station. The humanoid robot which is given to the physics of the object explains the information provided in the space station. The space station and moon were built using Blender and Unity3D along with C# scripts to bring a realistic surrounding to the player. The space station consists of a nuclear propulsion system that uplifts in space. Living rooms, a water production plant, and a laboratory are designed to replicate a real and futuristic environment. A secret room in the space station has a bot rocket that will take users to the moon. An animation appears in which the user can experience the entire solar system and then landed on the moon virtually. He can experience the immersive 360degree view on the moon surrounded by stars and other planets. Some interesting facts about the moon will be displayed for the kids to get better at the moon and space. The realistic gravity provided on the moon makes the user feel that he is walking on the moon using a Virtual Reality headset. The students can explore the immersive and attractive environment of the space station and the moon that will encourage fun learning.
Interpreting Neural Networks Prediction for a Single Instance via Random Forest Feature Contributions

In this paper, we are focusing on the problem of interpreting Neural Networks on the instance level. The proposed approach uses the Feature Contributions, numerical values that domain experts further interpret to reveal some phenomena about a particular instance or model behaviour. In our method, Feature Contributions are calculated from the Random Forest model trained to mimic the Artificial Neural Network’s classification as close as possible. We assume that we can trust the Feature Contributions results when both predictions are the same, i.e., Neural Network and Feature Contributions give the same results. The results show that this highly depends on the level the Neural Network is trained because the error is then propagated to the Random Forest model. For good trained ANNs, we can trust in interpretation based on Feature Contributions on average in 80%.
Artificial Error-Based Code Scrambling Scheme for Secure Communications in Wiretap Channels with Channel Reciprocity

In this letter, an artificial message error-based code scrambling scheme is proposed for secure communications in wiretap channels with channel reciprocity. In the proposed scheme, the artificial message bit errors agreed between the legitimate transmitter and receiver are added to the scrambled message bits at the transmitter prior to the channel encoding procedure, through which the artificial errors are generated by using the reciprocal channel between the legitimate transmitter and receiver. Because of the inaccessibility to the channel state information between the legitimate transmitter and receiver, an eavesdropper would fail to compensate for the artificial errors perfectly. Thus, in addition to decoding errors, the residual artificial errors will also be spread over the descrambled message of the eavesdropper by the error spreading effect of code scrambling. Therefore, unlike the conventional code scrambling scheme, the proposed scheme can provide strong message confidentiality for non-degraded eavesdropping channels, e.g., when the eavesdropper experiences no decoding errors. Furthermore, given that the artificial errors are introduced before the channel encoding procedure, the spread residual errors in the descrambled message remain undetected after the decoding procedures of the eavesdropper. Simulation results confirm that the proposed scheme outperforms the conventional scheme and provides strong message confidentiality in wiretap channels.
Zero-Shot Recognition based on Semantic Embeddings and Deep Clustering

This work introduces a model that can discover new objects in images even if no training data is available for the object class. Different with zero-shot learning models, which needs text corpora about the new objects, our model can operate on a mixture of seen and unseen classes with no extra information, simultaneously obtaining reasonable performance on unseen classes. This achieved by seeing the distributions of words in texts as a semantic space for understanding what objects look like. Our deep learning model can be adapted to the presence or absence of semantic or visual feature vectors annotated manually, and does not need text corpus other than the semantic space obtained by training set. Images are mapped to be close to semantic word vector corresponding to their classes in training set and to be clustered. The resulting image embeddings can be used to distinguish whether an image is of a seen or unseen class. We train a novelty neural network structure here to discover new objects, which consider two aspects, i.e., the accuracy of mapping in semantic space and the fitness for embeddings’ clustering.
The impact of self-organization course on the cognitive motivation of first-year students in Online Learning Environments

Lockdown has spawned a rapid and unprepared transition to online learning formats. Not all educators and students have been prepared for this transition, which requires the development of measures to adapt and intensify forced online learning. The study aims to track the integrated courses effectiveness to adapt first-year students to the new learning format through targeted self-organization training. The hypothesis of the study is that the educational activity effectiveness, which is largely based on academic motivation, is also significantly increased when students are purposefully taught self-organization and self-regulation skills. The study was attended by 60 people who studied a specially designed course "Self-organising Technology" for four months. Through input and output testing (Academic Motivation Scale (AMS), Self-management Test (Seiwert)), the level of their self-organization formation and academic motivation was measured. The results were validated by Wilcoxon signed-rank and a correlation analysis by calculating the Pearson linear correlation coefficient based on sample size. According to the results obtained, first-year students studying the course "Self-organization Technology" contributes to the development of self-organization and motivation for learning. Further research in this area needs to be carried out both in the field of adaptation of the course to the online educational space (and in terms of methodology and psychological support) and in the field of offline communication. According to the authors, such a course should be compulsory for all first-year students, regardless of their specialization.
Offensive Cyber Security Trainer for Platform Management Systems

To protect its platforms against cyber attacks, the Royal Canadian Navy (RCN) must train specialists in platform cyber security. These specialists will need to understand the offensive capabilities of their adversaries in order to defend these platforms and to develop more secure systems. As a result, these specialists will require an environment which can facilitate training in offensive cyber security techniques. Currently, no cyber security trainer exists for the RCN's Platform Management Systems (PMS), nor does one exist for any of the RCN's other platform systems. The aim of this research is to develop a PMS environment based on effective training techniques and capable of training RCN personnel in offensive cyber techniques. Effective training techniques in this context will reflect best practices from pedagogical literature. The training environment in this case is an offensive cyber security trainer which facilitates the training of personnel to execute cyber kill chains mapped from real attacker tactics, techniques, and procedures. Training cyber defenders to perform these kill chains will provide them with a greater understanding of how attacks can be executed against RCN platform systems. This in turn will enable the RCN to better defend against such kill chains. In order to accomplish this aim, an offensive cyber security trainer for a PMS is developed which utilizes a combination of simulation, emulation, and virtualization to provide an effective level of control and flexibility while also maintaining a high level of realism. This training also specifically leverages a Capture the Flag (CTF) framework to enhance personnel engagement within the environment. The functionality of this trainer is demonstrated by its ability to facilitate the training program and the execution of multiple kill chains against the PMS. The effectiveness of the trainer is validated on its application of current research methodology in effective gamified training environment design.
A Study Of Personal Growth In Inner Engineering: A Yogi’s Guide To Joy

This book focuses on the importance of spiritual growth in the life of human beings. The authоr Sаdhguru exрlоres the раth tо sрirituаl enlightenment in his bооk, Inner Engineering. In оrder tо exрerienсe jоy соnstаntly, рeорle must become the master of their body and mind. Tо make use of the intelligenсe оf their оwn life energy, рeорle should turn intо understanding оf yоgа аnd its trustworthy principles. Through yoga people can touch the fantastic and godly nature. Yoga gives chances for inner inception by using the procedures that are the refined spirit of yogic sciences and covey real intelligence to control mind, body, emotions and the basic life energy inside. The аuthоr has exрerienсed а higher level оf соnsсiоusness. He is trying tо teасh оthers the рrinсiрles thаt led him there. Reаders саn leаrn frоm his insights, but they shоuld аlsо try it fоr themselves аnd nоt just memоrize whаt he sаys. In оrder tо sоlve sосiety’s рrоblems, рeорle must first find their оwn рersоnаl jоy. If mоre рeорle саn dо this, they will be hаррier аnd helр сreаte а better wоrld.
Key Digital Competencies Among University Students to Ensure Sustainable Development of Economic Systems

The paper focuses on the problem of the formation of basic digital competencies among university students. It is a necessary condition for the formation of stability and sustainability of economic systems, through the organization of a new format of intensive project activities. The research relevance is due to the high rates of digitalization of the economy and the need to train qualified personnel capable of the digital transformation of economic structure as the basis for the sustainable development of socio-economic systems. A successful solution to this issue requires the emergence of more flexible and innovative forms of vocational training focused on building an individual trajectory of student development for the dynamic demands of the economy. One of such formats is the project and educational intensive, which, as a large-scale pedagogical experiment, was implemented on the basis of the Stavropol State Agrarian University under the auspices of the STI University 20.35. The authors reveal various approaches to determining the list of basic competencies of the digital economy among students and describe the algorithm for implementing the “SmartAgro” intensive for the formation of these competencies. The key stages of the design and educational intensive are digital input diagnostics on the platform of the University 20.35, selection to the intensive based on the results of diagnostic activities, recruiting interfaculty multiage project teams based on the roles identified using the diagnostic tools, and the project and educational activities immersed in a digital environment. The “SmartAgro” intensive became a successful experience of accelerated short-term preparation for the implementation of entrepreneurial project activities at a university, focused on the integrated formation of key competencies of the digital economy of future agribusiness specialists to increase their competitiveness.
Human Tracking Using Size Adaptation and Vector Position for Person Following Robot Using MS Kinect Xbox 360

This study involves a robot that follows human, tracked by using its position and direction by size adaptation and position vector. The Kinect sensor uses the human skeleton tracking method to track the position and distance of the target. Microsoft Kinect Xbox 360 generates a three-dimensional image of the objects in its field of view and recognizes human beings among those objects. Position and direction are used as a tracking algorithm. As the system identify the target, the position of it will be used to track the target itself. When occlusion occur between the distracter and the target, the direction of the target will be used for tracking. A laptop is used in this research to process the data from the Kinect sensor and transferred to the microcontroller. The person following robot is operated by a two-stepper motor that acts as its actuator for the wheels. The experimental results demonstrate that our robot can robustly follow the target.
Research on Second-language Teaching Aid System Based on Network Technology

Abstract The computer network-assisted language system has played a very important role in language teaching and learning. It makes up for the shortcomings in language teaching. It uses the computer network as the medium, which can implement foreign language teaching in a larger capacity and a wider range. This paper mainly analyses the developmental process of computer network-assisted language learning, and it also summarizes the teaching design of computer network-assisted second language learning.
Modified Method Of Diagnosis Of Blood Cancer Using MRI Classification Through Machine Learning

the formation of cancer can attack any organic system of the human body. And talking about the bird look mea that refers to the cancer that affects the blood cells. And as part of blood cancer it rapidly multiplies the cells and attacks the different aspects of the circulatory system. talking about the automatic detection we can detect the cancer that reduce the error and the results obtained from the microscopic images and even by the mistakes that have been occurred by the pathology pathologist that mainly defines the detection of leukemia and as a result. The detection Detection of Leukemia with the help of microscopic images with the help of microscope images as the images are pre processed and the noise is removed as the images are also segmented for the classification to form appropriate structures as it contains the components which is being classified as Eosinophil, Neutrophil, Lymphocyte, Basophil, and Monocytand we have used the back propagation algorithm for the training of the images and the accuracy is achieved is around 85%
Application of Breathing Method in Intelligent Physical Training System based on Face Image Fusion

This paper studies the application of breathing method in intelligent physical training system based on the face image fusion. In the research of monomodal emotion recognition, the amount of information is often insufficient and it is easily affected by various external factors, such as facial expressions or body occlusion, voice affected by noise, etc. To solve this, a fusion model is proposed to enhance the accuracy. To implement the system, the novel breathing monitoring method combined with the hardware implementations is also designed. The experiment conducted on the intelligent physical training system has proven its effectiveness.
Blockchain-Based Mobile Cryptocurrency Wallet

As the monetary value of cryptocurrency increases, the security measures for cryptocurrency becomes more important. A limitation of the existing cryptocurrency exchanges is their vulnerability to threats of hacking due to their centralized manner of management. In order to overcome such limitation, blockchain technology is increasingly adopted. The blockchain technology enables decentralization and Peer-to-Peer(P2P) transactions, in which blocks of information are linked in chain topology, and each node participating in the blockchain shares a distributed ledger. In this paper, we propose and implement a mobile electronic wallet that can safely store, send and receive cryptocurrencies. The proposed mobile cryptocurrency wallet connects to the network only when the wallet actively is used. Wallet owner manages his or her private key offline, which is advantageous in terms of security. JavaScript based wallet apps were implemented to respectively run on Android and iOS mobile phones. I demonstrate the process of transferring Ethereum cryptocurrency from an account to another account through Ropsten, a test net for Ethereum. Hardware wallets, such as Ledger Nano S, provide a slightly higher level of security, yet have the disadvantages of added burden of carrying additional physical devices and high costs (about 80$).
An item recommendation algorithm based on matrix decomposition and topic model

Traditional collaborative filtering models are often faced with sparse user data, and the relationship between users and items is not clear enough so that the accuracy of user-item rating prediction is still not high. According to the idea that LDA model can mine hidden information in user comments and Probability Matrix Decomposition model can alleviate user data sparse, this paper proposes IRCMT algorithm. Firstly, IRCMT algorithm using the LDA model for the user(item) - topic distribution, thus obtains the user (item) theme similarity. Secondly, the user(item) topic similarity can be incorporated into probability matrix decomposition, resulting in improved user (item) feature matrix, and predict refactoring score matrix, finally combining items-theme similarity to calculate for the final prediction score, Office Products dataset experiments show that the algorithm in terms of rating prediction accuracy is superior to most of the traditional recommendation algorithm and prove that users' comments have great research value.
Influence of Improved Apriori Algorithm on Predictive Public Management

The theory and practice of traditional public management cannot carry out predictive management due to the limitation of technology development. The intelligent era characterized by big data, algorithm and artificial intelligence provides strong technical support for predictive public management. However, intelligent management can not automatically derive from predictive management. The government must take the initiative to make efforts in data expansion, algorithm update, standard restriction and other aspects to ensure the correct value orientation of predictive management.
A Systematic Literature Review of E-Banking Frauds: Current Scenario and Security Techniques

The digitization of the banking industry has provoked a revolution in digital frauds. At present, e-banking frauds is an issue experienced globally and have turned into an industry in which cybercriminals are using advanced tools. The tools such as denial of service attack, malware, phishing, trojans, viruses and identity theft. This study conducted a systematic literature review and highlighted the technologies that banking institutions are currently using to secure e-banking system and techniques behind security vulnerabilities within the e-banking system. The study included articles from online databases such as Emerald insight, Google scholar, IEEE, JSTOR, Springer and Science Direct.  The detail assessment proved glimpse of current situation and highlight advance security techniques that can effectively overcome the adverse effect on e-banking success.
Adaptive coordinate sampling for stochastic primal–dual optimization

Abstract We consider the regularized empirical risk minimization (ERM) of linear predictors, which arises in a variety of problems in machine learning and statistics. After reformulating the original ERM as a bilinear saddle‐point problem, we can apply stochastic primal–dual methods to solve it. Sampling the primal or dual coordinates with a fixed nonuniform distribution is usually employed to accelerate the convergence of the algorithm, but such a strategy only exploits the global information of the objective function. To capture its local structures, we propose an adaptive importance sampling strategy that chooses the coordinates based on delicately designed nonuniform and nonstationary distributions. When our adaptive coordinate sampling strategy is applied to the Stochastic Primal‐Dual Coordinate (SPDC), we prove that the resulting algorithm enjoys linear convergence. Moreover, we show that the ideal form of our adaptive sampling exhibits strictly sharper convergence rate under certain conditions compared with the vanilla SPDC. We also extend our sampling strategy to other algorithms including Doubly Stochastic Primal‐Dual Coordinate (DSPDC) and Stochastic Primal‐Dual with O(1) per‐iteration cost and Variance Reduction (SPD1‐VR), where both primal and dual coordinates are randomly sampled. Our experiment results show that the proposed strategy significantly improves the convergence performance of the methods when compared with existing sampling strategies.
Simulation-Based Methodical Approach to Applying Managerial Decisions in Innovation

Research of innovation has recently come into spotlight. Innovative development involves not only cutting-edge R&D but also effective cooperation of those involved in the process, defining the priorities and tools to be used under the innovation policy, evaluating the performance of innovation. In this context, this research seeks to expand the capabilities of MATLAB-based mathematical modeling when used for assessing the innovation actors. Interim results of the study can be used to make projections for managerial decision making.
Low-Rank Transfer Learning for Multi-stream Data Classification

Leveraging the labeled data from source stream to handle unlabeled data from target stream is a promising method for multi-stream classification. However, there are still two main issues, concept drifts and concept revolution, needed to be addressed. A couple of methods is proposed to solve them, such as adding a change detection module, modifying sliding window size and setting concept confidence. However, the majority of these approaches did not consider shared data structures among multiple data streams, which can be employed to handle these two issues. At the same time, a lot of algorithms assume that the source data stream and the target data stream are all come from the same domain. In reality, however, most data streams are from different domains with different data distributions. In this paper, therefore, we propose a novel framework Low-Rank Transfer Learning (LRTL) to address cross-domain multi-stream classification. LRTL is mainly composed of three modules, i.e., domain adaptation module, change detection module and classification module. Different from these existing multi-stream classification frameworks, we design a novel domain adaptation module, which utilizes low-rank representation and graph embedding to preserve data structures that have many benefits in dealing with concept drifts and concept revolution. Extensive experiments are carried out on the domain adaptation module and the proposed framework LRTL on multiple benchmarks, which verifies that our domain adaptation methods and frameworks can achieve the state-of-the-art results with significant improvements.