Reviewerly/exHarmony huggingface dataset:
The Unified Framework of Deep Multiple Kernel Learning for Small Sample Sizes of Training Samples

Recently, deep learning technologies have rapidly developed. They have shown excellent performances in many fields. However, deep learning networks have weak adaptability to small sample sizes. In this paper, we proposed a novel depth-width-scaling multiple kernel learning unified framework. It has the ability to adjust the architecture according to the input data. We optimize the estimation of leave-one-out error by using the span bound instead of the dual objective function. Finally, we choose a large number of datasets from the UCI benchmark datasets for classification tasks. The experimental results show that different frameworks have different performances on the same datasets. Our DWS-MKL algorithm obtains better classification results than the state-of-the-art MKL algorithms, as determined through comparison. The encouraging results demonstrate that our method has the potential to improve the generalization performance of the model.
Image Forgery: Detection of Manipulated Images Using Neural Network

The emergence of new technology has resulted in mass usage of web applications which lets anyone crowd source anything. In the digitized world, using a wide range of online available tools, anyone can modify and edit images. So, it is necessary to ensure the authenticity of the images. Digital forensic techniques are needed to detect tampering and manipulation of images for illegal purposes [8]. This paper proposes a noble image manipulation detection system using neural networks. There are two techniques for identifying manipulated images in this project, the first one is metadata analysis and the second one is using Convolutional Neural Network. The metadata analysis checks the information contained within the image file and looks for tags that relate to faking. Error Level Analysis is used to detect the compression ratio of foreign content in a fake image.
The Internet of Things Readiness in Public Organization: Descriptive Analysis

The Internet of Things (IoT) is a system of interrelated computing devices. IoT involves mechanical and digital machines, objects, animals or people that are indicated with unique identifiers and the ability to transfer data over a network without requiring human-to-human as well as human-to-computer interaction. Government of Malaysia in 2015 has introduced the National Internet of Things Strategic Road Map as a way to provide the backbone of modern and smart cities as well to sustain the economic growth. However, the factors influencing IoT readiness in Malaysian organization needs to be further investigated. The purpose of this study is to investigate, identify and propose the readiness factors of IoT implementation. A survey involving a distribution of questionnaire to 30 respondents to get a descriptive indication coupled with interviews with 5 key expert employees from relevant agencies were executed. As a result, there are five factors being proposed and validated descriptively to influence the readiness of implementing IoT in Malaysia. These factors are Human Perspective, Technology Perspective, Governance and Management Perspective, Security Perspective and Policy and Law Perspective. For future work, this study suggest to include more participants using power statistics, hypothetical model testing and expand the questionnaire to other organization in order to develop a more comprehensive result ofIoT readiness in Malaysia.
Design and Implementation of Robust Digital Watermarking using Hybrid technique for Copyright Protection Digital data

In this research paper, we concerned with the creation of a comprehensive digital watermarking framework based on DWT. In order to improve imperceptibility and robustness, the watermark is inserted only in chosen frames. The picked frames are the frames in which a change of scene happens. The key objective, therefore, is to detect the correct transformation of the scene. The Scene Shift Detector identifies correct frames that have been modified using the successive histogram discrepancy process. Two schemes proposed using the same method of scene detection. Both suggested schemes achieve a good watermark rating with good (PSNR) valuesThere, Because the watermark integration is done exclusively on the scene with low and high frequency DWT subbands, the image processing assaults, geometric aggressions, JPEG compression, high normalised image attacks are immune,and low-bit error rates (BER). Comparative analysis of two algorithms is also carried out.
On the Cultivation of Students’ Innovative Ability in Higher Vocational Computer Teaching

With the rapid change of the world, the continuous development of science and technology, and the development of society at different stages, the requirements for talents are different.Now, comprehensive innovative talents are needed.In particular, the computer specialty in higher vocational colleges should pay more attention to the cultivation of innovative talents.However, due to the shortcomings of our education mechanism, the ability of colleges and universities to cultivate innovative talents is generally insufficient.This paper will analyze the teaching status of computer major in higher vocational colleges, elaborate the importance of cultivating students' innovation ability, analyze the effectiveness of the strategy of cultivating students' innovation ability in computer major of higher vocational colleges, and put forward suggestions on how to cultivate students' innovation ability.
Algorithmically Distinguishing Irreducible Characters of the Symmetric Group

Suppose that $\chi_\lambda$ and $\chi_\mu$ are distinct irreducible characters of the symmetric group $S_n$. We give an algorithm that, in time polynomial in $n$, constructs $\pi\in S_n$ such that $\chi_\lambda(\pi)$ is provably different from $\chi_\mu(\pi)$. In fact, we show a little more. Suppose $f = \chi_\lambda$ for some irreducible character $\chi_\lambda$ of $S_n$, but we do not know $\lambda$, and we are given only oracle access to $f$. We give an algorithm that determines $\lambda$, using a number of queries to $f$ that is polynomial in $n$. Each query can be computed in time polynomial in $n$ by someone who knows $\lambda$.
Improving Loanword Identification in Low-Resource Language with Data Augmentation and Multiple Feature Fusion

Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usually put efforts on high-resource languages (such as Chinese, English, and Russian); for low-resource languages, such as Uyghur and Mongolian, due to the limitation of resources and lack of annotated data, loanword identification on these languages tends to have lower performance. To overcome this problem, we first propose a lexical constraint-based data augmentation method to generate training data for low-resource language loanword identification; then, a loanword identification model based on a log-linear RNN is introduced to improve the performance of low-resource loanword identification by incorporating features such as word-level embeddings, character-level embeddings, pronunciation similarity, and part-of-speech (POS) into one model. Experimental results on loanword identification in Uyghur (in this study, we mainly focus on Arabic, Chinese, Russian, and Turkish loanwords in Uyghur) showed that our proposed method achieves best performance compared with several strong baseline systems.
embARC MLI based Design and Implementation of Real-time Keyword Spotting

Efficient implementation of inference is essential for neural network applications on edge devices. This paper presents a neural network based Keyword Spotting (KWS) system built with embARC MLI Library and ARC EM9D micro-processor. embARC MLI Library is a highly optimized machine learning inference library for IoT edge devices, and it is open source. With unique XY-architecture, EM9D processor achieves high efficiency when executing continuous MAC instructions. Performance of the combination is analyzed in detail and is compared with other processors. As edge devices generally have limited computing and memory resources, there are many optimization tasks need to be done and they are also presented in the paper. The paper shows that with highly optimized code based on particular hardware, AI applications can meet real-time requirements even on low-cost edge devices.
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents

One strategy for facilitating reading comprehension is to present information in a questionand-answer format.We demo a system that integrates the tasks of question answering (QA) and question generation (QG) in order to produce Q&A items that convey the content of multi-paragraph documents.We report some experiments for QA and QG that yield improvements on both tasks, and assess how they interact to produce a list of Q&A items for a text.The demo is accessible at qna.sdl.com.
Formación Flipped en un entorno virtual 3D para el desarrollo de las competencias docentes

The main objective of this study is the results’ evaluation of a training program on active methodologies, aimed at teachers, developed in a virtual 3D environment under a Flipped model. Two criteria are assessed, the first one is the level of acquisition of the program contents (teaching skills) using a one group pre-experimental design carrying out measurements before and after the educational intervention. The second is the opinion of the participants on the role that different aspects of the teaching process had in the acquisition of skills. Pretest-posttest changes of competencies are studied with the Student’s T-test for related groups, item-to-item changes are also checked using the Wilcoxon signed rank test, and Cohen’s D effect sizes are calculated. For the second criterion, the deviation of the participants’ opinion from what was expected in global terms was analyzed, the chi-square statistic for a sample he chi-square statistic was used to compare the observed and expected frequency distribution. The results show that students perceive an improvement in their teaching competence, the greatest change occurs in the digital area with a high effect size (d=0.84), followed by the social, didactic, and innovation and improvement areas. The learning process aspects that contribute most to achieving this goal are frequent teacher feedback, interaction with the teacher and Flipped experiences, all with more than 60 % of cases indicating the highest level of assessment.
Challenges in Designing Natural Language Interfaces for Complex Visual Models

Intuitive interaction with visual models becomes an increasingly important task in the field of Visualization (VIS) and verbal interaction represents a significant aspect of it. Vice versa, modeling verbal interaction in visual environments is a major trend in ongoing research in NLP. To date, research on Language & Vision, however, mostly happens at the intersection of NLP and Computer Vision (CV), and much less at the intersection of NLP and Visualization, which is an important area in Human-Computer Interaction (HCI). This paper presents a brief survey of recent work on interactive tasks and set-ups in NLP and Visualization. We discuss the respective methods, show interesting gaps, and conclude by suggesting neural, visually grounded dialogue modeling as a promising potential for NLIs for visual models.
An investigation into the influence of Corporate Social Responsibility disclosures on the satisfaction level of employees during COVID-19 times: Evidences from Multinational Corporations in UAE

The purpose of this research is to present empirical findings related to the impact of CSR on the job satisfaction level of employees during the Pandemic times, especially in reference to the case of multinational corporations operating within the boundaries of United Arab Emirates. In this context, the factor of CSR was further categorised into three essential constructs of philanthropic responsibilities, environmental responsibilities, and welfare of general public. In reference to the methodological choices, the study has undertaken quantitative research design, which has been supported through the use of positivist philosophy and deductive approach. In addition, the study has collected primary data from a total of 250 employees through questionnaire, which had a total of 5 sections including; demographics, philanthropic responsibility, environmental responsibility, welfare of general public, and employee satisfaction. The study found that employee satisfaction had strongly positive (uphill) linear relationship with the variables of philanthropic responsibilities and environmental responsibilities. The study further elaborated that the organizational philanthropy in the era of Covid-19 has significantly positive impact on the satisfaction levels of employees.&#x0D;
Counteraction to methods of social engineering as one of the areas of information protection of organizations with varying degrees of state participation

The relevance of this topic is due to the constant improvement of technical means of transmission and processing of information, as well as the creation of new technologies for its processing and storage. At the same time, the growth in the value of information as a resource in the modern world and the transition to electronic document management in the activities of organizations is accompanied by the emergence of new methods of illegal access. The purpose of this study is to analyze the most relevant methods of influence and create a training program for staff that would minimize the risks associated with illegal access to information of organizations. The paper presents and analyzes data characterizing the share of the impact of social engineering methods on individuals and legal entities. The study has confirmed that the most effective way to protect against social engineering is to train workers. It is advisable to organize the process periodically in a distance format, adapting the program to the position occupied by the employee. Training, in addition to theoretical lessons on the study of methods of protection, should contain active methods, discussions on the analysis of current information security incidents and solution of case studies.
A Conceptual Study of Proposing a Model to Use Robots in Hospitality Industry based on the Empirical Evidences

The aim of this paper is to analyze the development perspectives of robots in the hospitality industry and to draw a research model which may contribute in future reserch.More precisely, the current work examines the intentions of the consumers and employees to use robots on hotel services based on the findings of the related literature.The existing literature indicates that there is a gap due of the lack of academic research and of the fact that robots are used only in a small number of hotels.Recent publications indicate that there is a dichotomy over whether robots can benefit or not the hospitality sector.Based on the findings of the literature review, the authors have moved on with a research model which is going to be used for the case of Greek hotels.Hence, this paper aims on developing a conceptual framework for the intention to use robots from hotels and its impact on the expectations from the service quality of the hotels which use robots.However, this model has not been tested yet.Regarding the value of developing and using the proposed research model, by implementing this model, the academia and the practitioners would be able to understand on which type of hotel the robots would provide a better value.The use of robots on the hospitality industry is something new.This means that there is a lack of consensus over its value and usage along with a lack of a conceptual framework.For this reason, this study helps the academia and practitioners to better understand the contribution of robots in the hospitality industry.
MENADŽMENT KONTROLE KVALITETA SOFTVERA UPOTREBOM BLACK-BOX TESTIRANJA NA POSTOJEĆEM WEBSHOP-U TRINITISHOP

Creating the need for continuous growth and progress of all involved and connected business entities. They require compliance with certain web standards and support for multiple different browsers and devices. To be convinced of the version of the published code, we need a team that loves challenges, has the creativity and a desire for constant learning. Code, as a basis for a successful business, must be written appropriately with minimal deficiencies in logic and writing. The correctness and validity of the production code mostly depend on the program team itself and its responsibility for the written code. Vital code with errors can produce serious problems and unforeseen consequences. To achieve complete accuracy of all parts of the written code, it is necessary to use software testing and QA technique - quality assurance. The focus of the work is on the application and writing of the necessary programming code that uses QA/QC and the black-box method of testing the existing webshop. After analysis, the authors conclude that software quality control will be a constant challenge for many companies to survive in the short term of the adjustment process and that the crisis is an opportunity for courageous companies to invest ambitiously and timely in their internet business to become market leaders.
Applying Automatic Mapping Processing By GMT to Bathymetric and Geophysical Data: Cascadia Subduction Zone, Pacific Ocean

Abstract The Cascadia Trench is stretching along the convergent plate boundaries of Pacific Plate, North America Plate and Juan De Fuca Plate. It is an important geomorphological structural feature in the north-east Pacific Ocean. The aim of the paper is to analyse the geomorphology of the Cascadia Trench west of Vancouver Island (Canada and USA) using the GMT cartographic scripting toolset. The unique geomorphological feature of the Cascadia Trench is that the thick sediment layer completely obscures the subduction zone and abyssal hills. This results in the asymmetric profile in the cross-section of the trench. Bathymetric data were extracted from the GEBCO 2019 dataset (15 arc-second grid), sediment thickness by the GlobSed dataset. Due to the dominance of high sedimentary rate and complexity of the tectonic processes and geologic settings, Cascadia Trench develops very specific asymmetric geomorphic shape comparing to the typical V-form. The results of the geomorphic modelling show that eastern side of the trench has a gentle curvature (slope: 35.12°), partially stepped, due to the tectonic movements and faults. The opposite, oceanward side is almost completely leveled. The trench is narrow with maximal depth at the selected segment -3489 m and for the whole dataset -6201 m. The most repetitive depth is in a range -2500 to -2400 m (267 samples) and -2500 to -2600 m (261 samples). The bottom is mostly flat due to the high sedimentation rates indicating the accumulative leveling processes. Marine free-air gravity anomalies along the Cascadia Subduction Zone are characterized by weakly positive values (20 mGal) increasing rapidly in the zone of the continental slope (&gt;200 mGal), which is associated with a decrease in thickness of the Earth’s crust.
Binary Priority Outlier Classifier Based Outlier Elimination

Outliers are records that deviate from normal behavioral pattern. This causes a serious issue when it comes to analysing data. In the recent years there has been great research to identify these outliers. Identifying them not only helps improve analysis of data but also provides many applications. The paper presents a way of indenting these outliers based on priority assigned to the attributes. The priorities are then added for each record in the dataset and the pattern is analysed. A concept based on interquartile range is used to eliminate the outliers. Hence the classifier divides the dataset into two classes: outliers and normal data.
Dynamic Data Replication for Higher Availability and Security

The paradigm and domain of data security is the key point as per the current era in which the data is getting transmitted to multiple channels from multiple sources. The data leakage and security loopholes are enormous and there is need to enforce the higher levels of security, privacy and integrity. Such sections incorporate e-administration, long range interpersonal communication, internet business, transportation, coordinations, proficient correspondences and numerous others. The work on security and trustworthiness is very conspicuous in the systems based situations and the private based condition. This examination original copy is exhibiting the efficacious use of security based methodology towards the execution with blockchain programming utilizing robustness and different devices. The blockchain based mix is currently days utilized for e-administrations and military applications for the noticeable security based applications. To work with the high performance approaches and algorithms, the blockchain technology is quite prominent and used in huge performance aware patterns whereby the need to enforce the security is there. The work integrates the usage patterns of blockchain technologies so that the overall security and integrity can be improved in which there is immutability and strength based algorithms for enforce the security measures.
Causes of Cybercrime Victimization: A Systematic Literature Review

A systematic literature review on causes of cybercrime victimization has been done for this study to explore the severity of cybercrime. While 111 articles from Scopus and ASSIA databases were thematically analyzed to find trajectories of factors of cybercrime. Cyberbullying are prevalent among various forms of cybercrime. It is evident that adolescents are most targeted victims of cybercrime. It observed attitude, low self control, psychopathic behaviors, bystander behavior, social inequality, more use of cell phone and Internet, and school delinquency as the main causes of cyberbullying. Particularly, older member of the society is responsible for online fraud. The causes of online fraud found vulnerability, greed, trust, naivete, strong emotions, access to internet from home, lack of awareness, and chronic underreporting of cybercrime. In addition, software piracy, online harassment and computer hacking as cyber deviance caused due to availability of personal information in Social Networking Sites (SNS), socioeconomic, psychosocial, and geopolitical aspects, pornography, sexual promiscuity, minor daily stressors, living without parents and less active offline social life. Crypto market is a new form of cybercrime where criminals maintain a website to keep them anonymous for drugs dealing. Breakup of relationship and coercion to woman by male counterparts are the causal factors of cyber stalking and sexting respectively. However, follow up strategy, warning, sanction and educational programs were identified as prevention initiatives. Hence, this study is not beyond the limitation of empirical observations which will be the future research initiative to construct reporting mechanism of cybercrime.