Reviewerly/exHarmony huggingface dataset:
Technological Evaluation of Virtual and Augmented Reality to Impart Social Skills

The global spread of COVID-19 has disrupted education in recent times. Imparting social skills necessary to survive in professional life is becoming increasingly challenging in the post-COVID-19 new normal. The existing online learning platforms which have been primarily developed as a technology for content delivery need to be augmented with additional features required to impart social skills and provide better learning experiences. In this paper, we provide a technical evaluation of two emerging technologies called augmented and virtual reality from the point of view of imparting social skills such as working as a team member, group-based learning, participating in real-life physical events (e.g., seminars and conferences, group discussion), and carrying out physical experiments in the laboratories. This paper describes the approaches to impart social skills through AR and VR based platforms.
ADAPT: Adversarial Domain Adaptation with Purifier Training for Cross-Domain Credit Risk Forecasting

Recent research on transfer learning reveals that adversarial domain adaptation effectively narrows the difference between the source and the target domain distributions, and realizes better transfer of the source domain knowledge. However, how to overcome the intra/inter-domain imbalance problems in domain adaptation, e.g. observed in cross-domain credit risk forecasting, is under-explored. The intra-domain imbalance problem results from the extremely limited throngs, e.g., defaulters, in both source and target domain. Meanwhile, the disparity in sample size across different domains leads to suboptimal transferability, which is known as the inter-domain imbalance problem. In this paper, we propose an unsupervised purifier training based transfer learning approach named ADAPT (Adversarial Domain Adaptation with Purifier Training) to resolve the intra/inter-domain imbalance problems in domain adaptation. We also extend our ADAPT method to the multi-source domain adaptation via weighted source integration. We investigate the effectiveness of our method on a real-world industrial dataset on cross-domain credit risk forecasting containing 1.33 million users. Experimental results exhibit that the proposed method significantly outperforms the state-of-the-art methods. Visualization of the results further witnesses the interpretability of our method.
Global Standard Essential Patent Litigation: Anti-Suit and Anti-Anti-Suit Injunctions

The global litigation of standard essential patents (SEP) is taking a new turn with the jurisdictional battle between national courts. Some courts have started issuing anti-suit injunctions (ASI) to prohibit parallel litigation and consolidate the dispute at a single venue, while others have retaliated with anti-anti-suit injunctions (AASI), barring parties from seeking or enforcing foreign ASIs. The anti-suit injunction saga benefits no one: the parties in SEP licensing disputes are faced with legal uncertainty as to which court will hear their case; it incentivises a race to the court to secure the most favourable jurisdiction instead of focusing on licensing negotiations; it increases litigation costs of having to pursue multiple ASIs and AASIs; and parties face fines and imprisonment of officials for non-compliance. The article examines the general conditions for the grant of ASIs and AASIs and analyses their application in SEP disputes. The article then proposes three measures that courts can take to stop the global jurisdictional race. First, courts should exercise judicial restraint and return to the originally strict criteria for the granting of ASIs. In principle, ASIs in SEP cases should not be granted as they are incompatible with international comity, fundamental rights to property and access to a court. Only anti-enforcement injunctions may exceptionally be granted, and under strict conditions. Second, a court could hold a party seeking an ASI as ‘unwilling’ to license, acting as a strong deterrent from seeking this type of remedy. Third, courts could encourage parties to agree on the arbitration of FRAND licensing terms by holding that an unjustified refusal to enter into arbitration is a sign of ‘unwillingness’ to license. Taking these principles into account would ensure that each national court is respected and would incentivise parties to focus on the resolution of the key issue behind every SEP dispute – the appropriate FRAND licensing terms.
Turning path tracking control technology for fixed-wing UAV to realize video transmission relay station

Unmanned Aerial Vehicles (UAV) are currently used to provide many kinds of services. A video transmission relay station system using fixed-wing UAV that can fly for a long time are now investigated. In the system, the fixed-wing UAV needs to keep turning in the area accurately as a relay station. To realize that we propose a new turning path control system by both switching the roll angle commands to satisfy the balance of both forces and azimuth control to correct the turning radius deviation. The effectiveness was confirmed by computer simulations assuming an actual flight aircraft.
Application of Intelligent Pension System Based on Information Technology Analysis

In recent years, as the construction of "smart cities" continues to deepen, in response to the increasingly severe aging situation and the problems of smart elderly care services, we must continue to promote the construction of smart elderly care service systems, give full play to the role of information technology in improving quality and efficiency, and further optimize the allocation of social resources related to wisdom, and continuously improve the efficiency, quality and level of smart elderly care services, so that the elderly can "live happily, live healthily, and age gracefully". Therefore, this article starts from the analysis of the needs of elderly care services, embeds the intelligent elderly care supported by informatization and intelligence into the elderly care service system, and proposes the development ideas of the "smart elderly care service system” in terms of concept, function, technology, and conditions, so as to provide reference for the development of urban elderly care service industry in the future.
Research on the Attack Strategy to Reduce Network Effectiveness Based on Service Capacity

In network countermeasure, it is often necessary to use limited attack resources to reduce the network effectiveness to the greatest extent. This paper proposes a network attack strategy based on service capacity. Firstly, the service capacity distribution of the network is obtained through the topology of the network and the service capacity of each link. Secondly, considering the impact of link and topology connectivity on network service capacity, a network attack optimization model is established. And finally, under the constraint of attack resources, the optimal attack strategy is obtained by solving the optimization model. Theoretical analysis and simulation examples show that the optimal attack strategy can effectively reduce the communication effectiveness of the network.
Methods of Evaluation and Comparative Research of Cryptographic Conversions

The security of decentralized systems built on Blockchain technology is directly determined by the efficiency of the applied cryptographic transformations. Classical blockchain technology mainly uses several cryptographic primitives: hash function, electronic signature, encryption, etc. In particular, the hash function is used for several purposes as the generation of an address or user ID; hashing of transactions and blocks to confirm the indisputable block against network errors, as well as the hash value of the previous block is used as a reference in the formation of the next. The use of digital signatures in the blockchain system introduces services for the integrity and integrity of transactions. In turn, in any blockchain system there is a pool of transactions, each of which must be signed. This ensures that the transaction will not be altered, the signature of the transaction ensures that it is signed by a person who can then be identified, and, in fact, the signature can help determine “whether the user has the right to conduct the transaction.” Block ciphers are one of the most common cryptographic primitives, which is also used as a structural element of hash functions, message authentication codes, and so on. Thus, for the operation of monitoring and tracking programs using Blockchain technology, it is necessary to study various cryptographic primitives, in particular, hash functions, electronic signatures, encryption schemes, etc.
AZY-GCN: Multi-scale feature suppression attentional diagram convolutional network for human pose prediction

Due to the randomness and non-periodic nature of the future posture of the human body, the prediction of the posture of the human body has always been a very challenging task. In the latest research, graph convolution is proved to be an effective method to capture the dynamic relationship between the human body posture joints, which is helpful for the human body posture prediction. Moreover, graph convolution can abstract the pose of the human body to obtain a multi-scale pose set. As the level of abstraction increases, the posture movement will become more stable. Although the average prediction accuracy has improved significantly in recent years, there is still much room for exploration in the application of graph convolution in pose prediction. In this work, we propose a new multi-scale feature suppression attention map convolutional network (AZY-GCN) for end-to-end human pose prediction tasks. We use GCN to extract features from the fine-grained scale to the coarse-grained scale and then from the coarse-grained scale to the fine-grained scale. Then we combine and decode the extracted features at each scale to obtain the residual between the input and the target pose. We also performed intermediate supervision on all predicted poses so that the network can learn more representative features. In addition, we also propose a new feature suppression attention module (FISA-block), which can effectively extract relevant information from neighboring nodes while suppressing poor GCN learning noise. Our proposed method was evaluated on the public data sets of Human3.6M and CMU Mocap. After a large number of experiments, it is shown that our method has achieved relatively advanced performance.
Implementing Facial Recognition to Safeguard against Password Attacks at Universities

In order to secure information technology (IT) systems, authentication is a key tool that is employed to assist in mitigating the problem of password hacking into IT systems. A number of password-based authentication methods have been implemented in industry over the years to minimize password hacking incidents. Among the methods proposed is two-factor authentication. Two-factor authentication combines two single-factor authentication methods to provide enhanced security using two distinct forms of identification for user login credentials for IT applications, websites, databases among others. In large establishments such as universities' whose IT systems hold vital and sensitive information, the two-factor authentication is a crucial measure to be implemented in order to prevent information from being leaked or hacked. Two-factor authentication has been widely adopted and continues to evolve with the development of more sophisticated methods. This research studies and analyses existing two-factor authentication methods for the purpose of determining their limitations and towards the development of more efficient two-factor authentication methods. Furthermore, the research investigates the implementation of the Local Binary Pattern Histogram (LBPH) algorithm and the Haar cascades classifier to implement facial recognition as a second authentication factor for ensuring improved security of IT systems. In an attempt to provide an improved security system, a web application which employs two-factor authentication for enhancing the current security system is developed in order to protect sensitive information that is held by the universities Intelligent Transportation System (ITS). The application employs both a traditional password login method and a facial recognition frame to authenticate users. This application provides the combination of a normal single factor authentication method and a second factor authentication method which is simple to use and does not require additional effort (i.e., having to type in another password) from the user of the application.
Pengembangan Aplikasi Pengelolaan Tugas dan Fungsi Balai Monitor (Studi Kasus: Sarana dan Pelayanan)

Facilities and Services at the Balai Monitor Radio Kelas I Pekanbaru is one of the fields of service to the community.Currently, facilities and services have not implemented and utilized the existence of integrated information technology to support daily work activities so that most service operational activities are still carried out manually.Some activities are still carried out manually, automatically making it difficult and slow for officers to serve the community and make reports.The Management System for Duties and Functions of the Facilities and Services Section is expected to help overcome problems in providing services to the community and managing data services. The system was built using the prototyping method 3 times iteration. Testing of this application is done by UAT through questionnaires and Blackbox Testing to test functional suitability. Based on the results of the questionnaire found that the application meets the criteria of UAT where 100% of respondents agreed that the application display design was easy to remember.95% of respondents agree that the application is easy to reach. and 98.3% of respondents agreed to have an efficient function. Based on the results of Blackbox testing,the application can run smoothly according to existing needs
Remote Sensing Image Captioning Using Transformer

Image captioning generates a semantic description for the images, and with the development of deep learning, it usually combines computer vision and natural language processing. Image captioning needs not only recognize the important objects, attributes and the spatial relationships with the surrounding objects in the image, but also generate text descriptions that correspond to the language rules of people. In this paper, we proposed a image captioning model based on transformer. In the image understanding part, VGG16 was used to extract image information, and transformer encoder was used to extract relation from different image regions. The text generation extracts relations of word features in the description, and calculates the correlation between text and images from a variety of perspectives. The experimental results with indices BLEU4, METEOR, ROUGE, and CIDEr on the RSICD dataset are 0.29, 0.34, 0.61, and 2.53, respectively. These results are competitive and even better than the SOTA results. It is seen that show that transformer can alleviate overfitting on small datasets, accelerate the training process, and be generalized better.
A Generalized Net Model of the Pattern of Behavior in Patients with eGFR &lt; 20 mL/min (CKD Stage IV-V)

In the present study a mathematical model of behavior in patients with eGFR < 20 mL/min. (Chronic kidney disease (CKD) stage IV-V) based on generalized nets (GNs) is proposed. The developed reduced GN-model allows for optimization of clinical behavior in patients with CKD stage IV-V.
Controlling Resource Allocation using Blockchain-Based Delegation

Allocation of resources and their control over multiple organisations is challenging. This is especially true for a large-scale and dynamic system like the Internet of Things (IoT). One of the core issues in such a system is the provision of secure access control. In particular, transfer of access rights from one entity to another in a secure, flexible and fine-grained manner. In this paper, we present a multi-organisational delegation framework using blockchain. Our framework takes advantage of blockchain smart contracts to define the interactions and resource allocation between the consortium of organisations. We show the feasibility of our solution in a real-world scenario using the allocation of transportation credits in a multi-level organisational setting as a use-case. We provide proof of implementation of the proposed framework using the Hyperledger Fabric blockchain platform. Our results indicate that the proposed framework is efficient and can be used for city-wide transport, potentially even scale country-wide with a shared blockchain with complex access control rules. It also bestows better transparency to the delegation of access rights and control over the employees’ transportation access for the organisations.
Resource allocation in cloud data center using dynamic resource assignment frame work

Cloud data centers experience an important testing issue in which the segment should be considered in addition to the migration of reconfigurable virtual machines. Further, one should also consider the integrated characteristics of facilitating physical machines during the relocation of VMs. In this study, a Dynamic Resource Assignment Framework (DRAM) is presented for cloud data centers. In traditional stack balance arranging calculations, only one factor is considered, for instance CPU stack in physical servers. However, the proposed strategy takes multiple factors into account such as CPU, memory and framework information for both virtual machines as well as physical machines. The authors created a joint estimate on irregularity dimensions of a cloud data center. In addition to this, every server's ordinary disproportion dimension was also considered in this study. The results inferred that DRAM is excellent in terms of execution which can be understood via its abnormality level, heavy dimension of every server and its general running time.
Target Detection and Recognition System Based on PYNQ Platform

Target detection and recognition is a very important part in artificial intelligence field. In recent years, how to achieve target detection with low power consumption has become a research hotspot. In this paper, based on the PYNQ platform, the neural network single step multi-frame detection algorithm (SSD) is adopted, the overall function planning and system construction are carried out through software and hardware cooperation. Firstly, the convolutional neural network (CNN) training model is used for training and then weight parameter files is generated. After that we use HLS to design custom IP core and build system access. The last step is calling PC program. The test results under Jupyter Notebook environment show that the system can achieve effective target classification with low power consumption, which proves the feasibility of the whole system design.
Design of access control system using face recognition based on mathematical morphology

Face recognition technology has its unique convenience and versatility in information security. This technology is also the product of social development. A face recognition access control system based on mathematical morphology was proposed. By pre-processing the original image, the binary image is obtained. Then the mathematical morphology of the image is used to integrate it into a face image that can locate the face. Finally, the face location is realized by extracting the eyes. Through the research, it can be found that it was convenient to realize and could be applied to the actual scene, which provided a feasible scheme for the access control system and a research basis for deep face recognition.
Knowing What I Don’t Know: A Generation Assisted Rejection Framework in Knowledge Base Question Answering

Existing Knowledge Base Question Answering (KBQA) systems suffer from the sparsity issue of Knowledge Graphs (KG). To alleviate the issue of KG sparsity, some recent research works introduce external text for KBQA. However, such external information is not always readily available. We argue that it is critical for a KBQA system to know whether it lacks the knowledge to answer a given question. In this paper, we present a novel Generation Assisted Rejection (Gear) framework that identifies unanswerable questions well. Gear can be applied to almost all KBQA systems as an add-on component. Specifically, the backbone of Gear is a sequence-to-sequence model that generates candidate predicates to rerank the original results of a KBQA system. Furthermore, we devise a Probability Distribution Reranking algorithm to ensemble Gear and KBQA since the architectural distinctions between Gear and KBQA are vast. Empirical results and case study demonstrates the effectiveness of our framework in improving the performance of KBQA, particularly in identifying unanswerable questions.
Adaptive Margin Ranking for Supervised Cross-modal Retrieval

Cross-modal retrieval is to achieve flexible query between different modalities. Many approaches solve the problem by learning a common feature space under to separate the multimodal instances from different categories. But it is challenge to design an effective projecting function. In this paper, we propose a novel cross-modal retrieval method, called Adaptive Margin Ranking for Supervised Cross-modal Retrieval (AMRS). In the solution, we design a neural network as the nonlinear mapping function. To maximize the discrimination of multimodal feature in common representation space, we keep away the samples with different semantic by an adaptive margin, and jointly force the modality invariance to eliminate cross-modal discrepancy. Experimental results on widely used benchmark datasets show that the proposed method is effective in cross-modal learning.
Face Recognition in Databases of Images with Hidden Markov’s Models

In this paper, we present a new approach to Facial Recognition (FR) that uses Hidden Markov Models. The method we propose enhances the HMMs by integrating a clustering step for the partitioning of each face image before building the associated model. We then apply this algorithm to a publicly available image databases FERET, which allowed us to obtain 98.02% True Positive Rates (TPR) and 99.01% True Negative Rates (TNR) for our enhanced HMMs method, compared to 96.04% of TPR and 98.81% of TNR with Grid + HMMs, then 77.14% of TPR and 79.05% of TNR with Discrete Markov Models. These performances are obtained on the basis of their confusion matrix, then the sensitivity and specificity of each of these methods.
Versatility or Validity: A Comprehensive Review on Simulation of Datacenters Powered by Renewable Energy Mix

Nowadays, Datacenters powered by Renewable Energy mix (DCRE) appear worldwide. It brings a hot topic on studying how to fully utilize renewable energy and save brown energy. However, for the vast and complex infrastructure, researchers would perform their studies on a DCRE simulator rather than a real DCRE. It brings a question: do these simulators have the same functions and result for the same optimization approach? This paper focuses on the question and proposes the versatility and validity models and relationships for these simulators. We have reviewed the simulation works for DCRE in the last ten years. After that, we rebut the widespread practice that a simulation has functions for the particular study with an assumed validity. And we claim that uniform metrics for both versatility and validity lead to a better simulator. Finally, we back up this claim with comprehensive benchmarking. The experimental results show that the more versatile a simulator is, the better its validity.