Reviewerly/exHarmony huggingface dataset:
The Importance of Blockchain for Ecomobility in Smart Cities: A Systematic Literature Review

By 2050, cities will house at least 70% of the world's population. Already, metropolitan areas are responsible for 70% of all greenhouse gas emissions caused by energy usage. The expanding population of city dwellers has presented new challenges to contemporary urban areas. Congestion and other kinds of inefficient urban movement irritate city people and reduce their quality of life. Cities are reducing carbon dioxide emissions in an attempt to prevent global warming, which necessitates innovative new modes of transportation. Smart cities are innovative problem-solving projects for difficulties that have evolved as a consequence of increasing urbanization and changing environmental circumstances. Today's cities depend largely on people's capacity to travel freely and easily in order to minimize traffic congestion, provide a high quality of life for residents and tourists, and reduce carbon emissions and environmental deterioration. Everything is dependent on an effective and sustainable transportation infrastructure. Blockchain technology is being researched for smart city applications and is used in smart mobility for ridesharing, electric charging, platoon member interactions, and vehicle communication. Although exploratory research on this issue exists, it is spread over different use-cases and applications. This research conducts a thorough analysis of the literature on the implications of blockchain for smart city mobility and transportation, as well as its potential to boost efficiency in these sectors. The goal of this article is to synthesize and summarize existing information on the issue and contribute to closing the knowledge gap by researching how blockchain technology might improve ecomobility by reducing pollution, travel time, and congestion. The results of the literature review are divided into six use-case categories.
A Distributed Multi-key Generation Protocol with a New Complaint Management Strategy

Privacy protection is a main goal in the majority of the Blockchain studies. However some dishonest users may abuse from the benefits of this property and the fact of not being identified to do illegal crimes. That is why several researches focus on implementing identity tracing to avoid the flaws related to privacy protection in Blockchain applications. In this paper, we propose a Distributed Multi-Key Generation (DMKG) protocol without private channels built on the DMKG protocol of the Blockchain Traceable Scheme with Oversight Function (BTSOF) presented in [8]. Our protocol introduces a new strategy to manage complaints between participants that avoids them to publicly reveal the values of their shares of secrets. This new management of complaints and the use of public channels allow a precise identification of malicious participants. We prove that our solution satisfies the security requirements of the Verifiable Multi-Secret Sharing (VMSS) schemes and DMKG protocols.
Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction

In this paper, we propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method based on the sequential quadratic programming paradigm that employs variance reduction in the gradient approximations. Under reasonable assumptions, we prove that a measure of first-order stationarity evaluated at the iterates generated by our proposed algorithm converges to zero in expectation from arbitrary starting points, for both constant and adaptive step size strategies. Finally, we demonstrate the practical performance of our proposed algorithm on constrained binary classification problems that arise in machine learning.
The ultra-short-term photovoltaic power prediction based on multi-exposure high-resolution total sky images using deep learning

With the increasing installed capacity of renewable energy sources such as photovoltaic (PV) generation in the power system, ultra-short-term PV power prediction is more important for the stability. However, the prediction accuracy in the traditional methods cannot be guaranteed. Based on multi-exposure high-resolution total sky images (TSIs), this paper presents an advanced ultra-short-term PV prediction method using deep learning. To fully utilize the high-resolution images, an overlapping sliding window cutting and concatenating strategy are described to capture the global and local features of an image. The multi-exposure images are fused to provide more details about edge information and brightness. To better extract the image features and sequential features for PV prediction, a convolutional long–short-term memory model (CLSTM) with a multi-head self-attention mechanism is presented. The experiments use real-world datasets in the Zero Carbon Emission laboratory at Zhejiang University. The simulation results show that the proposed model can utilize TSIs to achieve the desired accuracy consistently. Under different weather conditions, the prediction accuracy of this model is improved by 49.1%–66% compared with that of other models.
Horizontal Fixed Attitude Flight of Quad Rotor Helicopter with Tilting Rotor

A quad rotor helicopter (QRH), a type of unmanned aerial vehicle (UAV), uses a tilted attitude to generate a horizontal thrust component in the flying direction. In the case of autonomous control, the attitude control system is used to tilt the airframe against disturbances such as crosswinds. Consequently, the flying attitude of a QRH is always inclined. In this study, a tilting mechanism for rotors (TMR) was mounted on a QRH to maintain a horizontal attitude. The TMRs were tilted to generate thrust against disturbances without inclining the airframe. The system was constructed using a QRH and TMRs tilted around only one axis and allocated every 90°. Because the airframe is always horizontal, this system can be used for the precise measurement of landforms and buildings. This paper reports the dynamic modeling of QRH equipped with TMRs and discusses the horizontal constant attitude flight using the proposed system.
Gesture Interface and Transfer Method for AMR by Using Recognition of Pointing Direction and Object Recognition

This paper describes a gesture interface for a factory transfer robot. Our proposed interface used gesture recognition to recognize the pointing direction, instead of estimating the point as in conventional pointing gesture estimation. When the autonomous mobile robot (AMR) recognized the pointing direction, it performed position control based on the object recognition. The AMR traveled along our unique path to ensure that its camera detected the object to be referenced for position control. The experimental results confirmed that the position and angular errors of the AMR controlled with our interface were 0.058 m and 4.7° averaged over five subjects and two conditions, which were sufficiently accurate for transportation. A questionnaire showed that our interface was user-friendly compared with manual operation with a commercially available controller.
A novel self-attention enriching mechanism for biomedical question answering

The task of biomedical question answering is a subtask of the more general question answering task, that is concerned only with biomedical questions. The current state-of-the-art models in this task like BioBERT, and BioM-ELECTRA are all based on the transformer architecture. The self-attention layer in the transformer plays a central role in the model predictions. Recent studies on the inner-workings of the transformer self-attention layer in the case of question answering hypothesize that context passage tokens with bigger attention scores have a bigger probability of being part of the predicted answer. Starting from this hypothesis, we experimented with a novel self-attention enriching mechanism for biomedical question answering targeting factoid and list type questions. In our approach, we enrich BioBERT’s self-attention layer with biomedical and named entity information previously extracted from the question and the context passage. The proposed enriching mechanism increases the attention scores for the biomedical and named entities. Which are in most cases the answer to the question. This increase in attention scores influences the model final prediction as hypothesized. Our proposed method achieves state-of-the-art results on several batches of the BioASQ’s 10b, 9b, 8b, and 7b datasets.
High Efficiency Secure Channels for a Secure Multiparty Computation Protocol Based on Signal

Secure multiparty computation (MPC) requires the messages transmitted in secure channels which can provide encryption and authorization to the messages. To implement a secure channel for MPC protocols, researchers have tried some communication protocols, such as TLS and Noise. However, these methods have some limitations. These protocols need a trusted certification authority to provide identity authorization which is difficult for an MPC protocol, and how participants manage the key of each party and how to use the key to establish communication is also a problem. A Signal protocol is an end-to-end encryption communication protocol, which is known as the most secure communication protocol in the world. Based on the Signal protocol, we implemented a signal-based secure multiparty computation protocol, which can run the MPC protocol and transmit messages through a signal-based secure channel. Compared with previous research on the MPC protocol over Signal secure channels, the new MPC client adds group communication of the Signal protocol to transmit messages, which significantly improves the communication efficiency of broadcast messages of MPC protocols. To test the communication efficiency of the new MPC client, we implemented a concrete BLS threshold signature protocol on the new client, comparing the elapsed time of key generation and signing on the new client to that on the client only using Signal end-to-end communication. According to our experiment result, we found that the new client run at least 37.48% faster than the old client on the BLS threshold signature whose number of parties ranges from 3 to 5, if the parties have sent Signal group messages to each other. The more parties in the MPC protocol, the higher the proportion of broadcast messages and the more obvious the performance improvement of the new client. Our work improves the performance of MPC secure channels based on the Signal protocol, especially for complex MPC protocols with many participants.
Model-based Player Experience Testing with Emotion Pattern Verification

Abstract Player eXperience (PX) testing has attracted attention in the game industry as video games become more complex and widespread. Understanding players’ desires and their experience are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents a framework for automated player experience testing by formulating emotion patterns’ requirements and utilizing a computational model of players’ emotions developed based on a psychological theory of emotions along with a model-based testing approach for test suite generation. We evaluate the strength of our framework by performing mutation test. The paper also evaluates the performance of a search-based generated test suite and LTL model checking-based test suite in revealing various variations of temporal and spatial emotion patterns. Results show the contribution of both algorithms in generating complementary test cases for revealing various emotions in different locations of a game level.
Yet Another Model! A Study on Model’s Similarities for Defect and Code Smells

Abstract Software defect and code smell prediction help developers identify problems in the code and fix them before they degrade the quality or the user experience. The prediction of software defects and code smells is challenging, since it involves many factors inherent to the development process. Many studies propose machine learning models for defects and code smells. However, we have not found studies that explore and compare these machine learning models, nor that focus on the explainability of the models. This analysis allows us to verify which features and quality attributes influence software defects and code smells. Hence, developers can use this information to predict if a class may be faulty or smelly through the evaluation of a few features and quality attributes. In this study, we fill this gap by comparing machine learning models for predicting defects and seven code smells. We trained in a dataset composed of 19,024 classes and 70 software features that range from different quality attributes extracted from 14 Java open-source projects. We then ensemble five machine learning models and employed explainability concepts to explore the redundancies in the models using the top-10 software features and quality attributes that are known to contribute to the defects and code smell predictions. Furthermore, we conclude that although the quality attributes vary among the models, the complexity, documentation, and size are the most relevant. More specifically, Nesting Level Else-If is the only software feature relevant to all models.
Artificial Intelligence based Cyber Security Threats Identification in Financial Institutions Using Machine Learning Approach

As digital assets become more interconnected, cyber threats are growing at an unprecedented rate. Financial institutions need to invest in artificial intelligence-based solutions for identifying these threats and protecting their assets. Machine learning is a powerful tool for investigating complex financial security threats that constantly evolve and can be difficult to predict. By leveraging AI technologies such as natural language processing, algorithms, and automated reasoning systems, banks can develop a better understanding of potential risks and create more efficient controls around their data. In this paper, an artificial intelligence based cyber security threats identification has proposed in financial institutions using machine learning approach. Machine learning algorithms are constantly being improved to identify anomalies in the data that might indicate a security threat. This approach enables financial firms to identify and defend against malicious attacks using custom-made models that provide actionable insights into both internal and external risks.
Deformable Part Region Learning and Feature Aggregation Tree Representation for Object Detection

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided promising detection results. However, the accuracy of the convolutional object detectors can be degraded often due to the low feature discriminability caused by geometric variation or transformation of an object. In this article, we propose a deformable part region (DPR) learning in order to allow decomposed part regions to be deformable according to the geometric transformation of an object. Because the ground truth of the part models is not available in many cases, we design part model losses for the detection and segmentation, and learn the geometric parameters by minimizing an integral loss including those part losses. As a result, we can train our DPR network without extra supervision, and make multi-part models deformable according to object geometric variation. Moreover, we propose a novel feature aggregation tree (FAT) so as to learn more discriminative region of interest (RoI) features via bottom-up tree construction. The FAT can learn the stronger semantic features by aggregating part RoI features along the bottom-up pathways of the tree. We also present a spatial and channel attention mechanism for the aggregation between different node features. Based on the proposed DPR and FAT networks, we design a new cascade architecture that can refine detection tasks iteratively. Without bells and whistles, we achieve impressive detection and segmentation results on MSCOCO and PASCAL VOC datasets. Our Cascade D-PRD achieves the 57.9 box AP with the Swin-L backbone. We also provide an extensive ablation study to prove the effectiveness and usefulness of the proposed methods for large-scale object detection.
Global Aligned Structured Sparsity Learning for Efficient Image Super-Resolution

Efficient image super-resolution (SR) has witnessed rapid progress thanks to novel lightweight architectures or model compression techniques (e.g., neural architecture search and knowledge distillation). Nevertheless, these methods consume considerable resources or/and neglect to squeeze out the network redundancy at a more fine-grained convolution filter level. Network pruning is a promising alternative to overcome these shortcomings. However, structured pruning is known to be tricky when applied to SR networks because the extensive residual blocks demand the pruned indices of different layers to be the same. Besides, the principled determination of proper layerwise sparsities remains challenging too. In this article, we present Global Aligned Structured Sparsity Learning (GASSL) to resolve these problems. GASSL has two major components: Hessian-Aided Regularization (HAIR) and Aligned Structured Sparsity Learning (ASSL). HAIR is a regularization-based sparsity auto-selection algorithm with Hessian considered implicitly. A proven proposition is introduced to justify its design. ASSL is for physically pruning SR networks. Particularly, a new penalty term Sparsity Structure Alignment (SSA) is proposed to align the pruned indices of different layers. With GASSL, we design two new efficient single image SR networks of different architecture genres, pushing the efficiency envelope of SR models one step forward. Extensive results demonstrate the merits of GASSL over other recent counterparts.
Deterministic joint remote state preparation via a non-maximally entangled channel

Ideal deterministic quantum communication tasks require maximally entangled channels. The reality is that the maximally entangled channel is inevitably degraded to a non-maximally entangled one because of various decoherence mechanisms, seriously deteriorating the performance of quantum communication. Instead of adopting traditional entanglement purification or distillation to rebuild maximally entangled channels, we have designed a novel deterministic joint remote state preparation scheme using the degenerated non-maximally entangled state directly. A protocol for deterministic joint remote preparation of a two-dimensional quantum state via a non-maximally hyperentangled quantum channel has been devised with the help of auxiliary qudits. Then we generalize it to prepare a high-dimensional quantum state faithfully. No matter how weak the shared entanglement is, the success probability of communication is maintained at 100% as soon as it exists. This investigation provides an ideology for the construction of practical quantum communication networks.
Mixed Reality collaboration environment improves the efficiency of human-centered industrial system: A case study in the mining industry

The emerging information and communication technologies (ICT) promote the intelligence of industrial systems. However, humans still play an important role in industrial systems, and they are required to collaborate in many scenarios to perform complex tasks. Mixed Reality (MR) technology allows the virtual-real fusion and real-time interaction, and has unparalleled advantages in building a collaborative environment. In this study, Theoretical and technical studies have been conducted to provide a universal solution for the industrial applications of MR collaboration. In terms of theory, a framework of the intelligent industrial system is first proposed by combining the human-centered idea and cloud-edge-terminal collaborative architecture. Based on this framework, the MR collaboration environment is then designed. Finally, the MR task scheduling model is developed to illustrate how the MR collaboration environment uses the cloud-edge-terminal collaborative architecture to improve the performance. The technical study is conducted based on the theoretical foundation. The key technologies involved in both co-located and remote collaboration modes, that can be achieved by the MR collaboration environment, are studied. In addition, the human–computer interaction (HCI) technology in MR collaboration is explored. Based on this study, the proposed MR collaboration environment is developed, tested, and evaluated by considering the mining industry as an example. Moreover, user surveys from a human-centered perspective are conducted. The results show that the MR collaboration environment has high applicability in multiple links in industrial scenarios represented by longwall mining. It can improve the quality, increase the efficiency, and reduce the costs. It can also provide a human-centered collaboration experience.
Practical Evaluation of Human–Computer Interaction and Artificial Intelligence Deep Learning Algorithm in Innovation and Entrepreneurship Teaching Evaluation

The rise of knowledge economy has drawn much attention to Innovation and Entrepreneurship Education (IEE). IEE is conducive to helping graduates relieve employment pressure, and also to cultivating students’ innovation ability and entrepreneurial willingness, which is also of great significance for promoting high-quality quantitative and sustainable development of social economy. However, the current IEE still has some limitations, such as the students’ insufficient entrepreneurial willingness, low innovation practice ability, and unreasonable evaluation methods of innovation and entrepreneurship teaching, which hinder the further development of IEE. Therefore, for the purpose of further promoting the development of IEE, this article studied the evaluation of innovation and entrepreneurship teaching, and proposed an innovation and entrepreneurship teaching evaluation system combining Human–Computer Interaction (HCI) and Deep Learning (DL). The evaluation system was used to evaluate the innovation and entrepreneurship teaching of Class A and Class B. The evaluation result showed that Class A’s innovation and entrepreneurship teaching evaluation score was 7; Class B’s innovation and entrepreneurship teaching evaluation score was 7.1. The evaluation of innovation and entrepreneurship teaching in Class A and Class B was still not high enough, and the teaching quality and effect still needed to be improved; the innovation and entrepreneurship teaching evaluation system combined with HCI and DL had strong operability.
The Efficacy of Artificial Intelligence in making Best Marketing Decisions

Internet of Things (IoT), Blockchain, Data Analytics, Artificial Intelligence, and Machine Learning have revolutionized the way businesses run. AI, the most recent of these disruptive technologies, has enormous promise at strategic levels of doing business and making better strategic decisions. The objective of this research is to learn how Artificial Intelligence may aid in strategic decision-making and to provide a thorough overview of AI in marketing. Because data collection is the most important aspect of marketing, it will be shown, how data can be collected using AI to better understand the target audience, customer profiles, and buying behaviour. Till the present time, there are minimal efforts made to assess the relation of AI with the strategic decision-making process, however, this study will bring value to the overall AI applications and how it will help make better marketing decisions. The research studies the implementation of Artificial Intelligence (AI) into marketing applications which improves business performance.