Reviewerly/exHarmony huggingface dataset:
What’s in the Image? Explorable Decoding of Compressed Images

The ever-growing amounts of visual contents captured on a daily basis necessitate the use of lossy compression methods in order to save storage space and transmission bandwidth. While extensive research efforts are devoted to improving compression techniques, every method inevitably discards information. Especially at low bit rates, this information often corresponds to semantically meaningful visual cues, so that decompression involves significant ambiguity. In spite of this fact, existing decompression algorithms typically produce only a single output, and do not allow the viewer to explore the set of images that map to the given compressed code. In this work we propose the first image decompression method to facilitate user-exploration of the diverse set of natural images that could have given rise to the compressed input code, thus granting users the ability to determine what could and what could not have been there in the original scene. Specifically, we develop a novel deep-network based decoder architecture for the ubiquitous JPEG standard, which allows traversing the set of decompressed images that are consistent with the compressed JPEG file. To allow for simple user interaction, we develop a graphical user interface comprising several intuitive exploration tools, including an automatic tool for examining specific solutions of interest. We exemplify our framework on graphical, medical and forensic use cases, demonstrating its wide range of potential applications.
Web Science for Digital Empowerment

Digital Capabilities is an emerging term that relates the economic notion of capability with ICT, especially Web and Internet technologies as an enabler. This workshop aims to bring together a disparate and eclectic population of researchers, practitioners and policy-makers to help build this emerging discipline.
Unsupervised Feature Selection using Pseudo Label Approximation

Feature selection is a machine learning technique that selects a representative subset of all features available in order to reduce the time and space needed to process high-dimensional data. Traditional feature selection methods include filter, wrapper, and embedded approaches. However, many conventional methods’ performances are not suitable in many contexts. This paper proposes a new unsupervised feature selection model based on pseudo label approximation. The new derived model incorporates a projection error, a sparsity regularization, and a manifold regularization term that preserves the manifold structure of the original data. Finally, implementation of the new model onto five distinct datasets validates the effectiveness of the proposed model.
The Role of Cybersecurity in Addressing the Rise on Cybercrime Activities

Innovations and technological advances have resulted in several web-based products. Among them are social networking sites that are dominating internet usage among users. This article discusses many types of cybercrime that are prevalent today, namely, hacking, cyber fraud, spamming and cyber harassment. This article review was conducted as a systematic using computer based electronic search to access all journals related for cyber crime by using Google Scholar and Researchgate to collect the information for get the high impact on this article. The objective of this article is to discuss about CyberSecurity is a service created by the Ministry of Technology and Innovation of Malaysia that receives complaints from users who are victims of cybercrime and then the body will make statistics based on complaints received. Malaysia use the MyCERT service created by the Ministry of Science and Technology to receive any complaints related to this cybercrime activity. Based on the statistic in MyCERT type of cybercrime like fraud had the highest cases that have been recorded by MyCERT.  Recorded by BruCERT from 2011 to 2015, with 61% of Malicious Software activities recording the highest percentage compared to cyber harrasment, website, spam and scam. In addition, it was followed by cyber harassment of 31%, website 5% and scam 3%. In this case, shows in Brunei also experience similar criminal activity in Malaysia. The support and responsibility of certain parties and societies play an important role in providing awareness and exposure to cybercrime. IT education is also able to curb issues related to cybercrime as a precautionary measure for students and adolescents to avoid becoming victims of cybercrime.
Student-led development of a lightboard to enhance future student learning

Lightboards are emerging technologies, helping to optimize online and flipped classroom environments by enabling the instructor to face the audience while writing on a transparent board. Although both commercial and do-it-yourself designs of lightboards are currently available, we have conducted a student-led engineering research and design project to optimize a lightboard design for broader use on campus to enhance the learning of future students. Through systematic side-by-side comparison, the team determined that Starphire Ultra-Clear™ glass is more appropriate than standard window glass for larger lightboard designs, despite its higher cost. The fully built and validated solution is housed in the campus Library for broad implementation into classroom environments throughout all institution outlets. The effectiveness of the lightboard is evaluated through a student comparison of short online videos generated using three separate formats: narrated slides, whiteboard, and the new lightboard. Students in a hybrid in-person and online class favored having the instructor facing the camera (76%), hand-written content (64%), and the use of more advanced technology (67%). Furthermore, through a pairwise polling comparison, students consistently favored the lightboard over narrated slides or simple whiteboard presentation formats.
Supervısed Learnıng Algorıthm: A Survey

The supervised learning algorithm is exactly what in which we've input as well as output variable which is able to be A or maybe B which uses an algorithm to find out the mapping feature from the feedback to the output. supervised algorithms like SVM, decision tree, naive Bayes distinction, etc. The primary motive of this particular paper is actually discussing the supervised learning methods that can help to the distinction of information on real-life information sets. It also details the charts of references within the last second area in which we compared which algorithm is perfect in which situation and exactly why based on particular parameters. The potential range of this paper is to survey on supervised learning algorithms and the comparison between them so that a brand new individual who's wanting to master machine learning could comprehend in layman language.
Training framework based on multi model competition for deep reinforcement learning

Abstract In this paper, we propose a simple, effective and universal deep reinforcement learning training framework, inspired by A3C algorithm and genetic algorithm. The framework uses multi-process technology to realize multiple model competition and optimal evolution to optimize the deep neural network during the training process. The experimental results show that the proposed training framework can improve the training effect of reinforcement learning algorithms to a certain extent.
Speech Acts: Is Racial Vilification a Form of Racial Discrimination?

This article examines three issues concerning the relationship between racially offensive speech and laws prohibiting racial discrimination. First, it examines whether there is an overlap between the provisions of pt II of the Racial Discrimination Act 1975 (Cth) (‘RDA’) (which prohibits various forms of racial discrimination), and pt IIA (which prohibits racial vilification). It examines two decisions of the Federal Court of Australia, in which the Court held that racially offensive speech may, in certain circumstances, infringe pt II of the RDA. Second, it examines whether prohibitions on racial vilification are underpinned by the same values as laws prohibiting racial discrimination. The article determines that respect for individual autonomy and dignity underpins both sets of laws, and that racial vilification laws can be regarded as an aspect of the prohibition on racial discrimination. Third, the article argues that the distinction between conduct and speech is not tenable, and that racial vilification can simply be regarded as a form of harmful conduct. Therefore, courts should focus on the effects of such conduct, particularly on its targets, rather than the motives of respondents or the importance of disseminating ‘ideas’.
Fundamentals of materials (text readability evaluation)

Fundamentals of materials represent a core subject at engineering universities dealing with various construction materials. The study of the discipline and the acquiring of knowledge demand a properly designed manual. Apart from content, manual authors should pay attention to the way they compose the text. Different tools are applied to assess the quality of a manual (its difficulty, complexity, readability). Readability formulae play a significant role in the sphere. Dealing with quantitative parameters of a text (the length of sentences, the length of words and others), readability formulae are able to predict the level of text readability and relate the text to a potential reader in respect to their age and academic level. Flesch-Kincaid Readability Level formula is chosen to analyze two manuals: one comes in the English language, the other – in Russian. The idea is to check how readable the texts of both manuals are. 17 English and 17 Russian texts are taken for the analysis. Pearson correlation is applied to illustrate whether the manuals demonstrate decrease or increase of the readability level of texts throughout the books.
Improving Confidence in the Estimation of Values and Norms

Autonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior is aligned with our values and norms. Hence, an AA will need to estimate the values and norms of the humans it interacts with, which is not a straightforward task when solely observing an agent’s behavior. This paper analyses to what extent an AA is able to estimate the values and norms of a simulated human agent (SHA) based on its actions in the ultimatum game. We present two methods to reduce ambiguity in profiling the SHAs: one based on search space exploration and another based on counterfactual analysis. We found that both methods are able to increase the confidence in estimating human values and norms, but differ in their applicability, the latter being more efficient when the number of interactions with the agent is to be minimized. These insights are useful to improve the alignment of AAs with human values and norms.
On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets

Non-intrusive load monitoring (NILM) aims to infer information about the electric consumption of individual loads using the premises' aggregate consumption. In this work, we target supervised multi-label classification for non-intrusive load identification. We describe how we have created a new dataset from Moroccan households using a low sampling frequency. Then, we analyze the performance of three machine learning models for NILM, and investigate the impact of signal input length on performance.
Crowdsourced Mobile Service for Preventing Child Trafficking Using Image Analytics

Child trafficking are increasing in urban locations and the count is only increasing. Mostly the children are abused and exploited for begging, involuntary domestic servitude, forced child labor, commercial sex, and other illegal activities. In India, trafficking has grown over 14 times over the last decade according to National Crimes Records Bureau (NCRB) and the country has become a transit point for traffickers. The necessity to prevent child trafficking and engaging them in self-development, is the responsibility of every citizen towards nation building. Technological advancements have taken a huge leap in the domain of mobile analytics and social networking and therefore it should be used as a tool to mitigate this burning issue. Empowering every individual to report child trafficking using mobile devices and let the technology do the rest of analytics to identify vulnerable spots, missing child, type of trafficking and reporting to authorities can make a difference. Therefore, our proposed work is to develop an mobile application environment with the general public as the frontline workers, who can capture an image or video of the scene of trafficking along with the location information. The crowd sourced data is pulled from the cloud and segregated based on location. Further the date is pre-processed using image processing algorithms specifically Convolutional Neural Network (CNN) is used. As a case study, the work identifies a missing child, by applying the crowdsourced data to the CNN based training algorithm to match with the available sources. The algorithm also classifies the gender and age using cascade classifier and physical impairments using MAX pooling filter.
A Study on the Recognition of Hangeul Through Transitional Learning in Handwritten Application

Through the utilization and convergence of technologies, such as artificial intelligence, big data, and the Internet of Things, that can represent the fourth industrial revolution, today’s world is changing rapidly. However, public organizations in Korea are still manually entering reports written by hand in many fields into the information system by a government official in charge. This study studies the improvement method for optical character recognition (OCR) of hand-written General Educational Development (GED) application. Through learning using cursive fonts, separating images for items subject to information service, and managing whitelist by item, the recognition rate is to be improved. In the field of optical character recognition (OCR), the recognition rate of printed texts written in Hangeul font is over 90%, but the recognition rate of cursive writing is very low. Various tables and letters of different sizes in hand-written application form documents hinder the recognition rate. Although the recognition rate has improved a lot recently with various studies using Deep Learning, there is a problem that takes too much processing time. Of the 11,172 Hangeul characters, 2350 are used. On the other hand, English has 52 case-sensitive characters. The recognition rate of Hangeul, which consists of a combination of initial sound, medial vowels, and final consonant, is bound to drop significantly. As mentioned earlier about a performance issue, software with open source-based neural network learning engines was used. In addition, the status of Hangeul used in names and addresses was investigated for the application of whitelists. The cursive font learning, image separation and whitelist proposed in this study have been effective in improving recognition rates.
Cross-layer channel attention mechanism for convolutional neural networks

Recently channel attention mechanism has been widely used to improve the performance of convolutional neural networks. However, most channel attention mechanisms applied to the backbone convolutional neural networks of the computer vision use the global pooling features of the output from each block to obtain the channel attention weights of corresponding channels, ignore the spatial information of the corresponding original features and the potential relationship between adjacent layers. For insufficient utilization of space information of origin features and inability to adaptively learn the potential association of all features in a block before the process of producing channel attention weights, we propose a new Cross-layer Channel Attention Mechanism（CCAM）, in which a matrix with spatial information is used to replace the global pooling operation, uses the input and output features of each block as the inputs, and outputs the channel attention weights of corresponding features simultaneously. Compared with other attention mechanisms, the CCAM have the following three advantages: first, it makes full use of the spatial information of each layer of features; second, it encourages feature reuse and fusion; third, it is better at discovering the potential relationship between the features of different layers in a block. Our simulation results have demonstrated that CCAM can effectively extract the attention weights of diffident layers, and achieve better performance on CIFAR- 10, CIFAR-100, ImageNet-1K, MS COCO detection, and VOC detection with small additional computational cost compared with the corresponding convolutional neural network.
Real-Time Unsupervised Multi-View Depth Estimation Network For Virtual View Synthesis

The existing learning-based multi-view stereo (MVS) approaches achieve impressive results compared with traditional methods. However, most of them rely on ground-truth 3D data as supervision, and the acquisition of high-quality ground truth for various scenes is a challenging problem. In this paper, we propose a novel real-time unsupervised multi-view depth estimation network for virtual view synthesis tasks and take multi-view images as supervision. To improve the completeness and accuracy of the virtual viewpoint, we propose a novel shared occlusion mask to deal with the artifacts caused by occlusion in the reconstructed image, and filter out the unreliable points in the depth map. Besides, we also design a mask-based photometric loss to guide our network to generate more reasonable masks and high-quality depth maps. Experimental results on the IEEE1857.9 virtual viewpoint synthesis dataset demonstrate that our proposed method outperforms other recent MVS methods and achieves more excellent real-time performance.
The Effect of Perceived Usefulness and Perceived Ease to Use on Intention to Use Online Applications in The Learning Process in Pandemic Era

Pandemic conditions have brought changes to various human activities in this world. One of them is learning activities, which usually at school now have to be done at home. because these demands make educational institutions must be able to accommodate the online learning process by preparing an application platform. with the use of this new technology is it acceptable to use it. then the researcher uses the variable perceived of usefulness and perceived ease of use. This research was conducted at 6 private universities in Indonesia randomly and got 233 respondents. The data collective by questionnaire  and analysis technique used multiple regression analysis. The results showed that Perceived Usefulness (X1) and Perceived Ease to Use (X2) have a significant effect on Intention to Use (Y) the use of e-learning applications by 43.9%Keywords: Adoption Technology, Online Learning, Pandemic
Phase Selective Convolution

This paper introduces Phase Selective Convolution (PSC), an enhanced convolution for more deliberate utilization of activations in convolutional networks. Unlike conventional use of convolutions with activation functions, PSC preserves the full space of activations while supporting desirable model nonlinearity. Similar to several other network operations, e.g., the ReLU operation, at the time of their introduction, PSC may not execute as efficiently on platforms without hardware specialization support. As a first step in addressing the need for optimization, we propose a hardware acceleration scheme to enable the intended efficiency for PSC execution. Moreover, we propose a PSC deployment strategy, with which PSC is applied only to selected layers of the networks, to avoid excessive increase in the total model size. To evaluate the results, we apply PSC as a drop-in replacement for selected convolution layers in several networks without affecting their macro network architectures. In particular, PSC-enhanced ResNets achieve higher accuracies by 1.0-2.0% and 0.7-1.0% on CIFAR-100 and ImageNet, respectively, in Pareto efficiency. PSC-enhanced MobileNets (V2 and V3 Large) and MobileNetV3 (Small) achieve 0.9-1.0% and 1.8% accuracy gains, respectively, on ImageNet at little (0.2-0.7%) total model size increase.
THE EFFECT OF INCLUSIVE LEADERSHIP IN OPEN INNOVATION STRATEGIES

The current research aims through its chapters to test the effect of inclusive leadership as an independent variable in open innovation strategies as a responsive variable in the office of the Ministry of Higher Education and Scientific Research, as well as to identify the levels of its availability and employ them to be more appropriate to the reality of the current conditions of the ministry in the field of research and what is required of it in light of the turbulent regulatory environment In Iraq, and based on the importance of the research topic and the great importance in which these variables were given, especially for public organizations and the interpretation of the relationships and ties between them, and the analytical approach was adopted in the completion of this research, and data were collected from (150) respondents represented by an intentional sample of (general director, assistant general manager, department director and division official) working in the ministry, and data was collected using the questionnaire and with the help of field visits and personal interviews with a number of the sample researched, and the data was analyzed through a set of statistical programs (Amos, Spss, Excel), and the results showed the validity of the assumptions of correlation relationships. And the impact on the level of the main variables (inclusive leadership, open innovation strategies) and sub-dimensions were significant, and the inclusive leadership has an active and essential role in adopting open innovation strategies.
Changing Children’s Behavior Based on Persuasive Game: Design for Children’s Safety Education

This study uses persuasive game design to improve the safety education of left-behind children in rural China. Persuasive game is a design method that is used to change or shape the user's behavior or attitude in recent years. It has been practiced and applied in many fields such as medical health [1–4], social interaction [5], advertising [6]. By exploring how the Persuasive Technology and Behavior Change Model affect user behavior change, this paper optimizes the design model of persuasive games in the existing research and clarifies the four stages of the persuasive game on user behavior change. Finally, we verify the feasibility of persuasive games in the field of children's safety education through a design practice case named “Class is Over”.