Reviewerly/exHarmony huggingface dataset:
The Effect Of Information Technology, Learning Environment , And Library Facilities On Reading Interest Of Students 92 Senior High School

This study aims to determine the positive relationship between Information Technology (X1), Learning Environment (X2), and Library Facilities (X3) on the Variable Reading Interest of Students at SMA Negeri 92 Jakarta (Y). The method used in this research is quantitative research. The type of research used in this study is ex post facto. The population of this research is 242 students of class XII SMA Negeri 92 Jakarta. The sample in this study was 115 students. The data was collected through an Online Questionnaire using the Google Form Platform and data processing was carried out using the SPSS (Statistical Product and Service Solutions) application. Based on the results of this study shows that: (1) Information Technology has a significant effect on Students' Reading Interest which shows a T Count value > 1.981 or 2.192 > 1.981 with a significance value of 0.000 < 0.05. (2) Most of the learning environment does not significantly affect students' reading interest. It is stated that the value of T Count < T table or 1.739 < 1.981 with a significance value of 0.085 > 0.05. (3) Library facilities have a significant effect on Students' Reading Interest which shows that the T Count value is > 1.981 or 2.192 > 1.981 with a significance value of 0.030 < 0.05. (2) Most of the learning environment does not significantly affect students' reading interest. It is stated that the value of T Count < T table or 1.739 < 1.981 with a significance value of 0.085 > 0.05. (3) Library facilities have a significant effect on Students' Reading Interest which shows that the T Count value is > 1.981 or 2.192 > 1.981 with a significance value of 0.030 < 0.05. (2) Most of the learning environment does not significantly affect students' reading interest. It is stated that the value of T Count < T table or 1.739 < 1.981 with a significance value of 0.085 > 0.05. (3) Library facilities have a significant effect on Students' Reading Interest which shows that the T Count value is > 1.981 or 2.192 > 1.981 with a significance value of 0.030 < 0.05.
MPANet: Multi-level Progressive Aggregation Network for Crowd Counting

Crowd counting has important applications in many fileds, but it is still a challenging task due to background occlusion, scale variation and uneven distribution of crowd. This paper proposes the Multi-level Progressive Aggregation Network (MPANet) to enhance the channel and spatial dependencies of feature maps and effectively integrate the multi-level features. Besides, the Aggregation Refinement (AR) module is designed to integrate low-level spatial information and high-level semantic information. The proposed AR module can effectively utilize the complementary properties between multi-level features to generate high-quality density maps. Moreover, the Multi-scale Aware (MA) module is constructed to capture rich contextual information through convolutional kernels of different sizes. Furthermore, the Semantic Attention (SA) module is designed to enhance spatial and channel response on feature maps, which can reduce false recognition of the background region. Extensive experiments on four challenging datasets demonstrate that our approach outperforms most state-of-the-art methods.
Application and Challenges of Blockchain in Heterogeneous Identity Trust

Blockchain is an innovative technology with cryptography as the core, and network identity management is an important part of cyberspace security protection. In response to the needs of heterogeneous identity management and mutual trust services, this paper designs a heterogeneous identity mutual trust model based on the alliance chain, proposes the alliance chain system architecture which uses network identity management systems as of the nodes, and designs a heterogeneous network identity mutual trust protocol. This solution is useful to integrate the existed variety of heterogeneous identity management systems, and create a cross-domain mutual trust identity management system.
Attribute-Based Facial Image Manipulation on Latent Space

Using machine learning to generate images has become more mature, especially the images produced using a Generative Adversarial Network. Unfortunately, the complicated architecture of those models makes it difficult for us to ensure the output images’ diversity and controllability without introducing little embarrassment in implementation. Therefore, some researchers try to edit the latent codes generated by a given learning model directly on the latent space for manipulating the output image by simply inputting the new latent codes into the original model without changing the model’s structure and learned parameters. However, the methods mentioned above faced the problems that the size of latent space cannot be too large or the trouble-some of features entanglement. In this work, we propose an approach to conquer the problems mentioned above, which is to compress the original latent space to better the applicability and usability of the methods limited by the size of the latent space. Compared with the existing methods, this method can be applied to more models and still reach the target of image manipulation.
Risk Assessment Method of Power Monitoring System Vulnerability Based on Attack Graph

In recent years, incidents threatening the network security of the power system industry have become more frequent, hackers’ attack methods have become more and more complex, and the difficulty of power system network security protection has continued to increase. Aiming at the problems of complex attack strategies and irrational attackers in the actual power system environment, a vulnerability risk assessment method for power monitoring systems based on attack graphs is proposed. First, according to the dependency between the vulnerabilities, based on the Common Vulnerability Scoring System (CVSS) framework of industrial control vulnerabilities, the attack path and attack graph model are established, and then the possibility of the vulnerability being exploited and the overall impact of the exploit are calculated. The damage degree of the power system network, and on this basis, the risk value of the vulnerability is calculated. The vulnerability risk calculated by this method can accurately reflect the impact of the vulnerability on the entire power system network. The experimental results show that the vulnerability risk assessment method of power monitoring system based on attack graph proposed in this paper can effectively quantitatively evaluate industrial control vulnerabilities and improve the level of industrial control security protection.
Study of Mobile App Development Industry

We are living in an era where a lot of disruptive technologies that change the entire perspective of the market are arriving very often. One such is the emergence of Mobile App Development, which completely changed the way users utilized software. Even though software development generally meant a stand-alone or Web application in the past, now it has enormous space given to Mobile App Development. Standalone technologies demanded the user to have a Personal Computer at least, with them to utilize their applications. However, since the platform on which an app can be deployed is just a mobile phone, the target audience for this type of application development becomes manifold. App development that targets handheld devices has now become a standard by which services reach the target audience faster since almost everyone has access to a mobile device. The App Development industry has only grown in leaps and bounds ever since its inception. This paper discusses in detail the Mobile App Development Industry and its predominance in India. Objectives: To analyse and understand the Mobile App Development Industry worldwide and in India, to understand the preference, usage, revenue, cost, and scope of App Development in India and to perform a SWOC Analysis for the Mobile App Development Industry. Design/Methodology/Approach: A detailed analysis of the Mobile App Development Industry is done by various secondary data sources such as websites and blogs. Some Scholarly research articles are also referred to understand the challenges in the industry. Findings/Result: Based on the SWOC Analysis of the Mobile App Development Industry, we can say that this industry is going to be one of the most sought-after industries especially in a pandemic-stricken world. Originality/Value: This paper, analyses various aspects of the Mobile App Development Industry. Based on the analysis, the current and future state of the industry is presented. Paper Type: A research case study paper on the Mobile App Development Industry.
DRPEC: An Evolutionary Clustering Algorithm Based on Dynamic Representative Points

This paper discusses an evolutionary clustering algorithm that uses dynamic representative points as the core of sample cluster (DRPEC). DRPEC algorithm calculates the similarity between samples and representative points by Gaussian function, and splits the winning representative points according to the principle of “Winner-take-all”, to construct a representative point spanning tree that can represent the cluster relation. DRPEC algorithm takes a representative point as the starting point, realizes the dynamic evolution of the representative points in an incremental way, and merges the sub-clusters represented by the nodes in the representative point spanning tree according to our designed measuring function, in order to effectively find the natural clusters existing in the data space. Finally, numerous experiments were conducted on UCI datasets, and compared with the current popular clustering algorithm, the results show that the DRPEC algorithm has excellent clustering performance and strong robustness.
A Reinforcement Learning Approach for Abductive Natural Language Generation

Teaching deep learning models commonsense knowledge is a crucial yet challenging step towards building human-level artificial intelligence. Abductive Commonsense Reasoning (\(\mathcal {ART}\)) is a benchmark that investigates model's ability on inferencing the most plausible explanation within the given context, which requires model using commonsense knowledge about the world. \(\mathcal {ART}\) consists of two datasets, \(\alpha \)NLG and \(\alpha \)NLI, that challenge models from generative and discriminative settings respectively. Despite the fact that both of the datasets investigate the same ability, existing work solves them independently. In this work, we address \(\alpha \)NLG in a teacher-student setting by getting help from another model with adequate commonsense knowledge fully-trained on \(\alpha \)NLI. We fulfill this intuition by representing the desired optimal generation model as an Energy-Based Model and training it using a reinforcement learning algorithm. Experiment results showed that our model achieve state-of-the-art results on both automatic and human evaluation metrics, which have demonstrated the effectiveness and feasibility of our model (Code available in https://github.com/Huanghongru/commonsense-generation).
Research on Security Vulnerability Detection of Smart Contract

In recent years, the second-generation blockchain platforms and applications represented by smart contracts have seen explosive growth, but frequent smart contract vulnerability incidents have seriously threatened the ecological security of blockchain. In view of the low efficiency of code audit based on expert experience, it is important to develop a general automation tool to mine smart contract vulnerabilities. In the beginning, the security threats of smart contracts should be investigated and analyzed, and many smart contract vulnerabilities and attack modes that occur frequently, such as code reentrant, access control, integer overflow, etc., were summarized. Then, the technology method of smart contract vulnerability detection conforming to The Times is obtained, and the current samples of smart contract vulnerability detection are summarized. The current investigation includes too few types of vulnerabilities, with a variety of inaccuracies and deviations. It is only carried out through manual audit. Through these methods, according to the state of including after put forward the general ideas of this kind of situation, and puts forward a kind of symbolic execution auxiliary fuzzy test framework, can reduce the symbolic execution channel congestion and fuzzy test code coverage degree is too little, so as to improve test efficiency, easy to dig holes of large and medium-sized intelligent contracts quality improvement.
A Neuroinformatics Theory for Cognitive Neurorehabilitation

Neurorehabilitation is an emerging and transdisciplinary field that is not only highly demanded in medical science and healthcare, but also a challenging problem in cognitive neurology, neurosignaling theory, neuroinformatics, and brain science. This paper presents findings in basic research on neuroinformatics towards neurorehabilitation by exploring its cognitive and mathematical foundations. It introduces novel theories for neurorehabilitation encompassing neural spike frequency modulation, basic neural circuits, and neurosignaling pathways. It develops diagnostic and assessment technologies for clinic neurorehabilitation such as consciousness status and cognitive memory recovery.
Detecting Hate Speech Contents Using Embedding Models

The rise of hate speech contents on social network platforms has recently become a topic of interest. There have been a lot of studies to develop systems that can automatically detect hate speech contents. In this paper, we propose a knowledge-rich solution to hate speech detection by incorporating hate speech embeddings to generate a more accurate representation of the given text. To obtain the hate speech embeddings, we construct a hate speech dictionary in a semi-supervised fashion. We conduct experiments on two popular datasets, which show that the combination of word embeddings and hate speech embeddings can produce promising results when compared with the methods that employ large-scale pre-trained language models.
Fully Three-Dimensional Radial Visualization

We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets. The classical two-dimensional (2D) RadViz visualizes multivariate data in the 2D plane by mapping every observation to a point inside the unit circle. Our tool, RadViz3D, distributes anchor points uniformly on the 3D unit sphere. We show that this uniform distribution provides the best visualization with minimal artificial visual correlation for data with uncorrelated variables. However, anchor points can be placed exactly equi-distant from each other only for the five Platonic solids, so we provide equi-distant anchor points for these five settings, and approximately equi-distant anchor points via a Fibonacci grid for the other cases. Our methodology, implemented in the R package $radviz3d$, makes fully 3D RadViz possible and is shown to improve the ability of this nonlinear technique in more faithfully displaying simulated data as well as the crabs, olive oils and wine datasets. Additionally, because radial visualization is naturally suited for compositional data, we use RadViz3D to illustrate (i) the chemical composition of Longquan celadon ceramics and their Jingdezhen imitation over centuries, and (ii) US regional SARS-Cov-2 variants' prevalence in the Covid-19 pandemic during the summer 2021 surge of the Delta variant.
Automated Laser Alignment and Image Processing based Robotic Carrom Player

With the advancement of robotic gaming technology, there are various approaches to playing the famous board games using robots. With Machine Learning and Computer Vision playing a vital role, the hardware approach is limited. The game of Carrom is one such game where the visualization aspect and the software approach is abundant. However, while integrating software approach to hardware, it is essential to consider the striking mechanism, alignment of the linear actuation system to target pouch and proper detection of coins. This work aims at implementing the software approach into hardware model. The hardware model represents a unique and novel implementation in the aspects of alignment of the striker to the pouch using laser, computer vision by altering the view of the camera and the development of striking mechanism using linear actuation. The hardware model successfully detects the coins and accordingly actuates the striker to push the coins to the target pouch.
An Experimental-Computational Investigation of Damaged UAV Wings – Part III: Detecting and Locating Damage Using Machine Learning

The use of drones for deliveries and Urban Air Mobility (UAM) are two of the most talked about emerging technologies that could potentially add a new dimension for low altitude airspace travel. With the exponential increase predicted in the use of drones over the next 10-20 years, it is essential that the drones are well equipped to handle worst case scenarios such as in the aftermath of a sudden in-flight damage. If the drone has the capability to detect and identify such damage, the drone can decide whether to continue its current mission or land in a safe and controlled manner and it could add another level of safety for flying in densely populated areas and using this ability. Having “Control” especially when passengers or time critical items are involved is critical in the drone not crashing into something or more importantly someone. Following our original design, we propose developing a tool that can detect and categorize damage during flight and in real time, which can potentially be integrated into the onboard flight controller of a UAV.
Preclinical Stage of Building a Machine Learning System for Sepsis Prediction: A Comparative Study of Four Algorithms

From the point of view of data engineering, the application of machine learning methods in the case of data represented by multivariate multimodal time series is a difficult task, but with a possible practical potential, which is insufficiently explored. From a medical point of view, the conditions described by this type of data are current problems in medicine, especially in anesthesia and intensive care and particularly in case of sepsis. The early diagnosis of life-threatening conditions can lead to increased treatment success, reduced mortality and reduced cost of care for this group of patients, especially in complex cases. This paper describes a research focused on identifying the most appropriate machine learning algorithm to be used for further investigations. It presents some results concerning the preclinical stage in developing of a machine learning system for early sepsis prediction with an anticipated potential of improving the clinical management of patients with sepsis.
Parallel Distributed Implementation of Neuroevolution of Augmenting Topologies in Continuous Control Tasks

This paper proposes a novel distributed implementation of neuroevolution of augmenting topologies method, which, considering the availability of sufficient computational resources, allows drastically speed up the process of optimal neural network configuration search. The proposed solution includes batch genome evaluation for the purpose of performance optimization, fair, and even computational resources usage. The benchmarking shows that the generated neural networks evaluation process can give orders of magnitude increase of efficiency on the demonstrated continuous control task and computational environment.
Development of knowledge-based resources in Ukraine

The article considers the concept of open science. The brief overview of different approaches to its interpretation is provided, the components and principles of implementation are given. One of the key elements of open science is an open scientific infrastructure, in particular — knowledge-based resources – scientific archives, platforms, repositories. Without proper attention from the scientific and educational community, as well as the state, it is impossible to ensure their proper development. After all, any infrastructure requires constant support aimed at ensuring the proper functioning and modernization in accordance with world best practices. In this regard, Ukraine needs political solutions that support the establishment of open science. They, in its turn, should be implemented through a project approach to individual infrastructure (including the National Repository of Academic Texts) and through the introduction of a common framework, standard guidelines, proven approaches, and established practices that can be scaled within the state, extended to institutions of scientific and educational sphere. The article examines in detail the project of creating a National Repository, as well as identifies promising areas for its development.
The Sun, Energetic option for Universidad Tecnológica de Salamanca

For the Universidad Tecnológica de Salamanca it has been necessary and very important its building become sustainable, therefore, the sun is an energy option to achieve it, this study shows the use of clean energy to produce electrical energy, the objective is to use the photovoltaic effect using technology and regional suppliers, so that when it is a sunny day, electricity is generated and when this resource is not available, the service of the electricity company, CFE in Mexico provides it. It begins by locating within the globe the point where the SFVI (interconnected photovoltaic system) will be installed, in this case latitude 20.577136 and longitude -101.232293, there are two requirements prior to meeting, which has to do with the selection of the location, the first It refers to technical aspects, such as solar resources, type of terrain and the shadows that could be generated in the place, according to the fulfillment of legal requirements and necessary permits for the selected place. The criteria used in the Interconnected photovoltaic system are shown, to achieve sustainability for Universidad Tecnológica de Salamanca.
An Efficient Public-Key Dual-Receiver Encryption Scheme

Public-key dual-receiver encryption (PK-DRE) is a kind of particular public-key encryption for enabling two independent recipients to obtain the same plaintext from the same ciphertext. Due to its dual-receiver property, PK-DRE is quite helpful in many scenarios, such as deniable authentication, global key escrow, security puzzle, and even blockchain. In this paper, we revisit the PK-DRE scheme <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt {CFZ}14$ </tex-math></inline-formula> proposed at CT-RSA 2014 and propose a variant. This variant is original from a new security proof which allows us to remove some steps in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt {CFZ}14$ </tex-math></inline-formula> . To the best of our knowledge, the obtained variant is more efficient than the existing PK-DRE schemes in terms of public verifiability and key size.