Reviewerly/exHarmony huggingface dataset:
Graphical Password Authentication System

Abstract: Graphical password is one of technic for authentication of computer security. Now days digital/computer security is most important things in computer science for protected user or customer data. And Shoulder-surfing is a one of the threats where a criminal can steal a password by direct observation or by recording the authentication session. There are several techniques available for this authentication, the most prevalent and simple of which is the Graphical password technique. So, we suggest a new technique to combat this problem. We have developed two concepts to combat shoulder surfing attacks. First, the user must register if the registration does not exist. Second, you must log in with a valid user ID and password. The password is a grouping of characters and numbers. Third, user has to cross image-based authentication where user can choose their password and this method have higher chances to offset each other. You should choose password according to the registration password, it must to match at login time. In colour base authentication, there should be several colour base passwords and depending on the colour, you need to remember the password sequence. And it’s like three-factor authentication. So, here is proposed a new graphical password authentication technique that is resilient to shoulder surfing and also to other types of probable attacks. Keyword: Computer Authentication, Graphical Password, Computer security
TEDdet: Temporal Feature Exchange and Difference Network for Online Real-Time Action Detection

Localizing and interpreting human actions in videos require understanding the spatial and temporal context of the scenes.Aside from accurate detection, vast sensing scenarios in the real-world also mandate incremental, instantaneous processing of scenes under restricted computational budgets.However, state-of-the-art detectors fail to meet the above criteria.The main challenge lies in their heavy architectural designs and detection pipelines to reason pertinent spatiotemporal information, such as incorporating 3D Convolutoinal Neural Networks (CNN) or extracting optical flow.With this insight, we propose a lightweight action tubelet detector coined TEDdet which unifies complementary feature aggregation and motion modeling modules.Specifically, our Temporal Feature Exchange module induces feature interaction by adaptively aggregating 2D CNN features over successive frames.To address actors' location shift in the sequence, our Temporal Feature Difference module accumulates approximated pair-wise motion among target frames as trajectory cues.These modules can be easily integrated with an existing anchor-free detector to cooperatively model action instances' categories, sizes, and movement for precise tubelet generation.TEDdet exploits larger temporal strides to efficiently infer actions in a coarse-to-fine and online manner.Without relying on 3D CNN or optical flow models, our detector demonstrates competitive accuracy at an unprecedented speed (89 FPS) that is more compliant with realistic applications.Codes will be available at https://github.com/alphadadajuju/TEDdet.
Novel view synthesis in embedded virtual reality devices

Virtual Reality and Free Viewpoint navigation require highquality rendered images to be realistic.Current hardware assisted raytracing methods cannot reach the expected quality in real-time and are also limited by the 3D mesh quality.An alternative is depth image-based rendering (DIBR) where the input only consists of images and their associated depth maps for synthesizing virtual views to the Head Mounted Display (HMD).The MPEG Immersive Video (MIV) standard uses such a DIBR algorithm called the Reference View Synthesizer (RVS).We have first implemented a GPU version, called the Real-time Accelerated View Synthesizer (RaViS), that synthesizes two virtual views in real-time for the HMD.In the present paper, we explore the differences between desktop and embedded GPU platforms, porting RaViS to an embedded HMD without the need for a separate, discrete desktop GPU.The proposed solution gives a first insight into DIBR View Synthesis techniques in embedded HMDs using OpenGL and Vulkan, a cross-platform 3D rendering library with support for embedded devices.
Towards Sustainable Careers for Saudi Employees in Tourism and Hospitality Sector: The Impact of Career Competencies on Turnover Intention

The aim of this research is to provide a model for a healthy work environment for Saudi employees in the tourism and hospitality sector to decrease their turnover. The current research examines the causal relationships of career competencies (reflective, communicative, and behavioral), job involvement (JI), and organizational commitment (OC), with turnover intention (TI) of Saudi employees in the tourism and hospitality sector. This study utilizes structural equation modeling (SEM) and analyzes data collected from a sample of 436 Saudi employees in the tourism and hospitality sector, positive associations were found between career competencies (reflective and behavioral) and job involvement. Positive associations were found between career competencies (reflective and communicative) and organizational commitment. Furthermore, positive associations were found between job involvement and organizational commitment. On the other hand, the research revealed the negative associations between career competencies (communicative and behavioral), job involvement, and organizational commitment with employees’ turnover intention. The organizational commitment had partial mediating effects on the relationship between career competencies and turnover intention. The implications of these findings for the industry and future research are deliberated as well.&#x0D; &#x0D; Received: 19 January 2022 / Accepted: 20 March 2022 / Published: 5 May 2022
Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment

In text-to-SQL tasks -as in much of NLPcompositional generalization is a major challenge: neural networks struggle with compositional generalization where training and test distributions differ.However, most recent attempts to improve this are based on word-level synthetic data or specific dataset splits to generate compositional biases.In this work, we propose a clause-level compositional example generation method.We first split the sentences in the Spider text-to-SQL dataset into subsentences, annotating each sub-sentence with its corresponding SQL clause, resulting in a new dataset Spider-SS.We then construct a further dataset, Spider-CG, by composing Spider-SS sub-sentences in different combinations, to test the ability of models to generalize compositionally.Experiments show that existing models suffer significant performance degradation when evaluated on Spider-CG, even though every sub-sentence is seen during training.To deal with this problem, we modify a number of state-of-the-art models to train on the segmented data of Spider-SS, and we show that this method improves the generalization performance. 1
The Supply of Rural Public Cultural Services Relying on Data Mining Technology under the Rural Revitalization Strategy

The rural revitalization policy can be said to be a major change in the party’s agricultural policy and rural history. Rural revitalization is the rebirth of rural civilization, which is intimately connected with the growth of soft power. Culture should become a productive force; combined with the driving force of the village, culture should improve the quality of life, improve development conditions, guide civilization, and benefit the villagers. This article takes advantage of the data mining technology under the platform of big data to gather data related to rural public cultural services and presents the existing problems and countermeasures. In the satisfaction survey of existing facilities and venues, the satisfaction rate of the library is 47.36%, the satisfaction rate of the cultural center is 50.12%, and the satisfaction rate of the cultural square and Dabazi is 67.25%.
The rubber hand illusion in microgravity and water immersion

Our body has evolved in terrestrial gravity and altered gravitational conditions may affect the sense of body ownership (SBO). By means of the rubber hand illusion (RHI), we investigated the SBO during water immersion and parabolic flights, where unconventional gravity is experienced. Our results show that unconventional gravity conditions remodulate the relative weights of visual, proprioceptive, and vestibular inputs favoring vision, thus inducing an increased RHI susceptibility.
Detection Anomaly in Video Based on Deep Support Vector Data Description

Video surveillance systems have been widely deployed in public places such as shopping malls, hospitals, banks, and streets to improve the safety of public life and assets. In most cases, how to detect video abnormal events in a timely and accurate manner is the main goal of social public safety risk prevention and control. Due to the ambiguity of anomaly definition, the scarcity of anomalous data, as well as the complex environmental background and human behavior, video anomaly detection is a major problem in the field of computer vision. Existing anomaly detection methods based on deep learning often use trained networks to extract features. These methods are based on existing network structures, instead of designing networks for the goal of anomaly detection. This paper proposed a method based on Deep Support Vector Data Description (DSVDD). By learning a deep neural network, the input normal sample space can be mapped to the smallest hypersphere. Through DSVDD, not only can the smallest size data hypersphere be found to establish SVDD but also useful data feature representations and normal models can be learned. In the test, the samples mapped inside the hypersphere are judged as normal, while the samples mapped outside the hypersphere are judged as abnormal. The proposed method achieves 86.84% and 73.2% frame-level AUC on the CUHK Avenue and ShanghaiTech Campus datasets, respectively. By comparison, the detection results achieved by the proposed method are better than those achieved by the existing state-of-the-art methods.
Identification of End-User Economical Relationship Graph Using Lightweight Blockchain-Based BERT Model

Current methods for extracting information from user resumes do not work well with unstructured user resumes in economic announcements, and they do not work well with documents that have the same users in them. Unstructured user information is turned into structured user information templates in this study. It also proposes a way to build person relationship graphs in the field of economics. First, the lightweight blockchain-based BERT model (B-BERT) is trained. The learned B-BERT pretraining model is then utilized to get the event instance vector, categorize it appropriately, and populate the hierarchical user information templates with accurate user characteristics. The aim of this research is that it has investigated the approach of creating character connection graphs in the Chinese financial system and suggests a framework for doing so in the economic sector. Furthermore, the relationship between users is found through the filled-in user information template, and a graph of user relationships is made. This is how it works: finally, the experiment is checked by filling in a manually annotated dataset. In tests, the method can be used to get text information from unstructured economic user resumes and build a relationship map of people in the financial field. The experimental results show that the proposed approach is capable of efficiently retrieving information from unstructured financial personnel resume text and generating a character relationship graph in the economic sphere.
Lexical Knowledge Internalization for Neural Dialog Generation

We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models.Instead of further conditioning the knowledge-grounded dialog (KGD) models on externally retrieved knowledge, we seek to integrate knowledge about each input token internally into the model's parameters.To tackle the challenge due to the large scale of lexical knowledge, we adopt the contrastive learning approach and create an effective token-level lexical knowledge retriever that requires only weak supervision mined from Wikipedia.We demonstrate the effectiveness and general applicability of our approach on various datasets and diversified model structures.
Analysis of the Influencing Factors of English Majors’ Cross-Cultural Communicative Competence under Mobile Internet

In order to study the influencing factors of cross-cultural communicative competence of English majors under mobile Internet, firstly, we made an empirical study on cross-cultural communication teaching for English majors. Then, we performed a comparative experiment between the mobile Internet-assisted high school English cross-cultural communication teaching model and the traditional teacher culture teaching-based teaching model. Finally, through questionnaires, informal interviews, and teachers’ observation of the teaching process, this paper aims to make an empirical study on the feasibility and effectiveness of mobile Internet assisted foreign language teachers in cultivating students’ awareness and ability of cross-cultural communication, so as to provide a basis for the follow-up research of cross-cultural communication teaching for English majors. The experimental results show that 93.4% of the students are satisfied with the mobile network-assisted English cross-cultural communication teaching model, and the integration of mobile network technology into English cross-cultural communication teaching has brought great changes to the teaching process. Compared with the traditional teaching mode of “chalk and blackboard,” multimedia organically integrates text, pictures, audio, video, and network, which greatly attracts the attention of teachers and students.
Compositional Task-Oriented Parsing as Abstractive Question Answering

Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm.A popular approach to TOP is to apply seq2seq models to generate linearized parse trees.A more recent line of work argues that pretrained seq2seq models are better at generating outputs that are themselves natural language, so they replace linearized parse trees with canonical natural-language paraphrases that can then be easily translated into parse trees, resulting in so-called naturalized parsers.In this work we continue to explore naturalized semantic parsing by presenting a general reduction of TOP to abstractive question answering that overcomes some limitations of canonical paraphrasing.Experimental results show that our QA-based technique outperforms stateof-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings.
The Use of Cooperative Learning in English Writing Teaching

Writing is one of the five major English language skills according to the new English curriculum standards, and writing also reflects the core competence of students in English. English writing ability is a basic skill that students should master in the process of English learning. However, English writing teaching is one of the most difficult and neglected parts of teaching. The current writing teaching model is generally teacher-centered, focusing on results and neglecting process. This teaching method is often time-consuming, and has little effect. Cooperative learning is student-centered, values the learning process of students, and encourages students to help each other, discuss together, and actively participate in writing, thereby inspiring students’ interest in writing. This article analyzes the current researches on cooperative learning abroad and at home, introduces the theory of cooperative learning and its significance in writing teaching, and puts forward suggestions on how to better use cooperative learning in writing teaching.
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem

Word embeddings are one of the most fundamental technologies used in natural language processing.Existing word embeddings are high-dimensional and consume considerable computational resources.In this study, we propose WORDTOUR, unsupervised onedimensional word embeddings.To achieve the challenging goal, we propose a decomposition of the desiderata of word embeddings into two parts, completeness and soundness, and focus on soundness in this paper.Owing to the single dimensionality, WORDTOUR is extremely efficient and provides a minimal means to handle word embeddings.We experimentally confirmed the effectiveness of the proposed method via user study and document classification.
A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on “API key” and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data.
Dangling-Aware Entity Alignment with Mixed High-Order Proximities

We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs.Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities.We propose a framework using mixed high-order proximities on dangling-aware entity alignment.Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment.Extensive experiments with two evaluation settings shows that our framework more precisely detects dangling entities, and better aligns matchable entities.Further investigations demonstrate that our framework can mitigate the hubness problem on danglingaware entity alignment.
Gamification in Non-Formal Adult Educational Practices

The presented article reveals and generalizes the experience of using gamification in the context of non-formal adult educational practices. From this perspective, the basic understanding of gamification is defined, priorities are set for the use of gamification components, the non-game context as a basis for gamification of training course programs is analysed. An empirical study of extrapolated game mechanics in non-formal adult educational practices of Adult Education Centres has been carried out. Definitions are given regarding consideration of social and psychological types of adult students for application of gamification of non-formal education. In the article the gamified educational non-game context is formulated as non-imitation with apreserved invariable content of educational activity at changingthe way of organization of this activity. Among the most common game mechanics found to meet the priority needs of adult students are those in which reward was offered for the task; balanced competition was stimulated; different types of interaction were envisaged; an opportunity was provided to share results and achievements outside of training; quick feedbackwas provided. Variability of extrapolation of game mechanics into non-formal educational practices of adults concerns the following main didactic categories of educational programs: learning objectives; the forms of training organization; teaching methods; learning motivation; control of learning outcomes.
Importance of a Teacher’s Personal Patterns in the Professional Development of Adepts

Personal patterns have a great influence on the formation of a young person, including a candidate for the teaching profession. This is effect to the developmental need to have a pattern in the formed system of values, attitudes, behaviours and personal characteristics. Having personal patterns is also important from the perspective of training for the teaching profession, developing professional competences and shaping identity. The article focuses on the personal models of teachers present in the personal experiences of candidates for this profession. The aim of the research was to determine the teacher’s pattern (categories and characteristics within life goals, motives for actions and behaviours and personal characteristics) present in the lives of future teachers of art schools. The conducted research shows that students of an art university still have the current personal patterns that determine their development in the form of teachers from art schools.
Assessment of Pollution and Identification of Sources of heavy Metals, and Radionuclides Contamination in Sand along the Southern part of the Cameroonian coast (South-West, Africa)

Sandy sediments collected along the southern part of the Cameroonian coast have been analyzed geochemically by ICP MS methods to investigate the distribution characteristics, contamination levels and related ecological risks. In these sediments, the concentration (mg/kg) of selected elements are in order Fe &gt; Mn &gt; Cr &gt; V &gt; Th &gt; Ni &gt; U &gt; Co. Indices of pollution such as index of geoaccumulation, where values of all elements in the sediments were &lt; 0, except Th in the sediments station of Yoyo II, and Cr in sediment of all stations were Igeo &gt; 0. Contamination factor shows that the station of Yoyo II has values of CF &lt; 1, such as Fe, V, Ni and Co. while Uranium, Th and Mn values vary from 1 to 3 and 3 to 6, and for Cr values of CF &gt; 6. However, Kribi and Campo stations show that all the elements have values of CF &lt; 1, except Cr which has values of CF &gt; 6. The degree of contamination values ranges between (9.48 – 37.13) for the station of Yoyo II, (8.84 – 17.62) for Kribi station, and (6.52 – 13.56) for Campo station. The pollution loading index values at all sampling stations are lower than 1. The potential ecological risk (Er and RI), indicates that this coastal area is a low risk region. Pearson correlation, cluster analysis and principal component analysis supported that heavy metals (Fe, Mn, Cr, V, Ni and Co) have common human influences while radionuclides (Th and U) have a natural source. The presence of human activities such as domestic waste, intensive farming and the processing of industrial products could be potential sources of anthropogenic environmental pollution, thereby threatening the environmental concerns of the entire study area.