Reviewerly/exHarmony huggingface dataset:
Selective Knowledge Transfer for Cross-Domain Collaborative Recommendation

Data sparsity is a major challenge for collaborative filtering recommender systems. A promising solution is to utilize feedback or ratings from multiple domains to improve the performance of recommendations in a collective way, known as the cross-domain recommendation. Cross-domain recommendation using heterogeneous feedback is a popular solution, which transfers knowledge from the more easily available auxiliary binary feedback to improve the prediction performance of the target domain. Most of the existing work focuses on the transfer of knowledge between different domains from the same website, where user behavior data in different domains can be fully shared. The existing work mainly assumes that data from different domains can be fully shared. However, due to the constraints of business privacy policies, it is difficult to directly share exactly the same user behavior data between different e-commerce websites. It results in that the user’s latent factors learned in the auxiliary domain cannot be directly transferred to the target domain, otherwise, it will cause a negative transfer issue. In this article, we consider that the auxiliary domain with numerical ratings and target domains with binary feedbacks only share overlapping items rather than users. We propose a Selective Knowledge Transfer for Cross-domain Collaborative Recommendation, called SKT. The proposed SKT framework not only transfers the item’s latent factors learned from the auxiliary domain to the target domain, but also selectively transfers the user’s latent factors learned from the auxiliary domain to the target domain. In addition, due to the introduction of co-graph regularization of user graphs and item graphs, SKT can maintain respective intrinsic geometric structure within each domain and thus avoid negative transfer issue. Extensive experiments conducted on two real-world datasets show that our SKT method is significantly better than all baseline methods at various density levels.
SEEDDUP: A Three-Tier SEcurE Data DedUPlication Architecture-Based Storage and Retrieval for Cross-Domains Over Cloud

Data deduplication has become a promising technique that significantly reduces space utilization of cloud storage servers and optimizes the network bandwidth. A variety of deduplication techniques have been proposed to enhance the cloud server's storage efficiency. Most of the deduplication schemes do not address global indexing and user privacy issues. This paper elucidates a novel three-tier secure data deduplication architecture (referred to as SEEDDUP) for data storage and retrieval in the cloud. SEEDDUP provides data deduplication with proper indexing and user privacy. In this work, the optimal chunk size, for a given file size and format, is calculated using Cuckoo Search Algorithm. The chunk existence in the cloud is verified by the hash value generated via SHA3-512. To improve data deduplication speed, Merkle Hash Tree based MapReduce (MHTMR) is used for fast index lookup, which improves the performance at data storage and retrieval. In addition, data are encrypted based on its sensitivity level (low and high) using Niederreiter Public Key Cryptosystem. With this new SEEDDUP architecture, only legitimate users can request data successfully for limited times (k) and spurious data requests cannot be processed or handled in the cloud. Experiment results show that the proposed SEEDDUP can reduce latency, achieved good throughput and deduplication efficiency (chunk size variance and security measures) in data deduplication.
Bioinspired Scene Classification by Deep Active Learning With Remote Sensing Applications

Accurately classifying sceneries with different spatial configurations is an indispensable technique in computer vision and intelligent systems, for example, scene parsing, robot motion planning, and autonomous driving. Remarkable performance has been achieved by the deep recognition models in the past decade. As far as we know, however, these deep architectures are incapable of explicitly encoding the human visual perception, that is, the sequence of gaze movements and the subsequent cognitive processes. In this article, a biologically inspired deep model is proposed for scene classification, where the human gaze behaviors are robustly discovered and represented by a unified deep active learning (UDAL) framework. More specifically, to characterize objects' components with varied sizes, an objectness measure is employed to decompose each scenery into a set of semantically aware object patches. To represent each region at a low level, a local-global feature fusion scheme is developed which optimally integrates multimodal features by automatically calculating each feature's weight. To mimic the human visual perception of various sceneries, we develop the UDAL that hierarchically represents the human gaze behavior by recognizing semantically important regions within the scenery. Importantly, UDAL combines the semantically salient region detection and the deep gaze shifting path (GSP) representation learning into a principled framework, where only the partial semantic tags are required. Meanwhile, by incorporating the sparsity penalty, the contaminated/redundant low-level regional features can be intelligently avoided. Finally, the learned deep GSP features from the entire scene images are integrated to form an image kernel machine, which is subsequently fed into a kernel SVM to classify different sceneries. Experimental evaluations on six well-known scenery sets (including remote sensing images) have shown the competitiveness of our approach.
Histori Bencana dan Penanggulangannya di Indonesia Ditinjau Dari Perspektif Keamanan Nasional

[ History Of Disasters And The Prevention In Indonesia From A National Security Perspective ] The development of disaster studies is currently very fast . Disaster events from year to year are a record as well as data on how disasters are very detrimental to human survival as a history. We need a disaster preparedness in anticipation of disaster management by paying attention to the history of disasters and their countermeasures . T he authors are interested in analyzing the data from a national security perspective. The research approach used was a descriptive quantitative approach which aims to analyze and describe the history of disasters and disaster management in Indonesia. Sources of data in this study were secondary data in the form of DesInvertar Indonesia and Indonesian Disaster Risk Index data ( IRBI ) . The data analysis technique used was the quantitative method through univariate analysis of the research variables. This research contributes to the latest knowledge regarding the history of disasters and their countermeasures in Indonesia from a national security perspective. The results showed that the incidence of disasters was increasingly varied which caused losses in society. Disasters that occurred in all regions of Indonesia in the period 1815 to 2019 were dominated by climate-induced disasters such as floods with a total of 10,438 events, landslides totaling 6,050 incidents, 2,124 drought events, and forest and land fires totaling 1,914 events. There is an increasing trend of disaster incidents every year, where the total number of disasters in 1815 amounted to 1 increased to 3,885 incidents in 2019. Maintaining national security through disaster risk management is absolutely necessary for the sovereignty of a country, so that good disaster management is a form of protection for the entire nation from all threats, especially national security threats originating from non-military threats in the disaster aspect.
Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.
Multi-Modal Anomaly Detection by Using Audio and Visual Cues

This paper considers the problem of anomaly detection in an outdoor environment where surveillance cameras are usually installed to monitor activities of general public. A novel solution is proposed which combines audio and visual data to automatically detect abnormal activities. The proposed anomaly detection algorithm makes use of both visual and audio features to automatically detect anomalous activities in scenes. Visual features such as optical flow technique combined with particle swam optimization and social force model are used, whereas, acoustic features such as, energy, zero crossing rate, volume, spectral-centroid, spectral spread, spectral roll-off, spectral flux, cross correlation and the mel-frequency cepstral coefficients (MFCCs) are used. An anomaly inference is developed which is based on both visual and audio features. The performance of the proposed algorithm is evaluated by testing it on the publicly available UMN datasets combined with the audio recordings. The proposed algorithm is compared with state-of-the-art techniques and is shown to achieve improved performance in terms of accuracy.
The Impacts of Visual Effects on User Perception With a Virtual Human in Augmented Reality Conflict Situations

Virtual humans (VHs) in augmented reality (AR) can provide users an illusory sense of being together in the real space. However, such an illusion can easily break when the augmented VH conflicts (or is overlaid) with the real objects. Recent spatial understanding technology is starting to make physically plausible VHs in response to collisions, but there are still limitations (e.g., resolution, accuracy) and inevitable conflict situations (e.g., unexpected passer-by), especially in daily life. Moreover, depending on the situation, VH's plausible behavior to avoid collision may rather interfere with the original interaction with the users. In this paper, we investigate three such situations: (1) when VH appears in a room through a closed door, (2) when the VH's body overlaps with static real objects, and (3) when a real moving object passes through the VH. While we considered (2) as an avoidable situation where physically plausible behaviors of VHs might be required, (1) and (3) were considered as inevitable situations (e.g., VH appearing out of nowhere, or passer-by cannot be aware of a virtual being), and we may not present VH's plausible behaviors, so alternatives might be required. Thus, for each of these notable situations in AR, we tested different visual effects as presentation methods for physical conflicts between a VH and real objects. Our findings indicate that visual effects improve VH's social/co-presence and physicality depending on the situations and effect types as well as influence users' attention/social behaviors. We discuss the implications of our findings and future research directions.
LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots

This paper presents the application of different approaches of intelligent controllers based on LAMDA (Learning Algorithm for Multivariate Data Analysis) to perform the trajectory tracking task of a mobile robot Pioneer 3-DX simulated in the Virtual Robot Experimentation Platform V-REP environment. The paper proposes the design of a cascade control strategy composed of two controllers, the external controller based on a feedback linearization for the robot kinematics and the internal controller applied to the robot dynamics where the LAMDA approaches are applied. In this work, it is considered an uncertain dynamic model. Thus, its identification and control are based on a reduced-order model for designing purposes. Finally, the dynamic controller is designed using different controllers based on LAMDA, such as LAMDA-PID, LAMDA-Sliding Mode Control, and Adaptive LAMDA. To perform a comparative analysis between them that includes a conventional PID, SMC, and Fuzzy-PID, different trajectories are examined to evaluate the methods. The obtained results are compared qualitatively and quantitatively to determine which proposal performs the most accurate control and presents the best results in terms of performance.
Web Vulnerability Through Cross Site Scripting (XSS) Detection with OWASP Security Shepherd

Web applications are needed as a solution to the use of internet technology that can be accessed globally, capable of displaying information that is rich in content, cost effective, easy to use and can also be accessed by anyone, anytime and anywhere. In the second quarter of 2020, Wearesocial released information related to internet users in the world around 4.54 billion with 59% penetration. People become very dependent on the internet and also technology. This condition was also triggered due to the Covid-19 pandemic.One thing that becomes an issue on website application security is internet attacks on website platforms and we never expected the vulnerability. One type of attack or security threat that often arises and often occurs is Cross Site Scripting (XSS). XSS is one of Top 10 Open Web Application Security Projects (OWASP) lists.There are several alternatives that we can use to prevent cyber-attack. OWASP Security Shepherd can be used as a way to prevent XSS attacks. The OWASP Security Shepherd project allows users to learn or develop their manual penetration testing skills. In this research, there are several case examples or challenges that we can use as a simulation of the role of OWASP Security Shepherd to detect this XSS. The purpose of this paper is to conduct a brief and clear review of technology on OWASP Security Shepherd. This technology was chosen as an appropriate and inexpensive alternative for users to ward off XSS attacks.
Impact of Covariates in Compositional Models and Simplicial Derivatives

In the framework of Compositional Data Analysis, vectors carrying relative information, also called compositional vectors, can appear in regression models either as dependent or as explanatory variables. In some situations, they can be on both sides of the regression equation. Measuring the marginal impacts of covariates in these types of models is not straightforward since a change in one component of a closed composition automatically affects the rest of the composition. Previous work by the authors has shown how to measure, compute and interpret these marginal impacts in the case of linear regression models with compositions on both sides of the equation. The resulting natural interpretation is in terms of an elasticity, a quantity commonly used in econometrics and marketing applications. They also demonstrate the link between these elasticities and simplicial derivatives. The aim of this contribution is to extend these results to other situations, namely when the compositional vector is on a single side of the regression equation. In these cases, the marginal impact is related to a semi-elasticity and also linked to some simplicial derivative. Moreover we consider the possibility that a total variable is used as an explanatory variable, with several possible interpretations of this total and we derive the elasticity formulas in that case.
Zero Trust Validation: From Practical Approaches to Theory

How can high-level directives concerning risk, cybersecurity and compliance be operationalized in the central nervous system of any organization above a certain complexity? How can the effectiveness of technological solutions for security be proven and measured, and how can this technology be aligned with the governance and financial goals at the board level? These are the essential questions for any CEO, CIO or CISO that is concerned with the wellbeing of the firm. The concept of Zero Trust (ZT) approaches information and cybersecurity from the perspective of the asset to be protected, and from the value that asset represents. Zero Trust has been around for quite some time. Most professionals associate Zero Trust with a particular architectural approach to cybersecurity, involving concepts such as segments, resources that are accessed in a secure manner and the maxim "always verify never trust". This paper describes the current state of the art in Zero Trust usage. We investigate the limitations of current approaches and how these are addressed in the form of Critical Success Factors in the Zero Trust Framework developed by ON2IT 'Zero Trust Innovators' (1).
Towards adopting AI techniques for monitoring social media activities

Social media provide opportunities for organizations to reach the largest amount of people by measuring the general perception of the consumer and knowing his/her feelings and some of his reactions towards brands and products. On the other hand, these organizations as well as people are keen to preserve the privacy of their data, which will only be achieved by using applications and techniques of artificial intelligence. This scientific paper aims to analyze and discuss the impact of using artificial intelligence applications with their various technologies on social media through the method of critical analysis and evaluation that was applied to previous recent studies that dealt with the impact of artificial intelligence on social media. Then, this paper conducted a critical review of these studies coming up with findings, conclusions, and special recommendations on the subject. This resulted in the fact that studies agree on the great role that artificial intelligence plays in social media platforms in terms of preserving the privacy of the user and organizations and in terms of marketing and increasing the profits of organizations. Moreover, companies that do marketing through social media using artificial intelligence have benefited three times more than other companies that do not. In addition, artificial intelligence contributes to the preservation and security of privacy and data of users and digital owners in social networking sites, and contributes to increasing the profits of companies that use marketing through social media platforms based on artificial intelligence techniques, as their revenues increased by 10%, costs decreased, and productivity and logistic networks improved. This study recommended working to develop the mechanism of artificial intelligence in social media platforms, conducting more studies on the contribution of artificial intelligence in increasing the revenues earned by social media platforms, reducing the costs of social media creation and management, and finally the necessity for the commitment of social media companies to apply artificial intelligence techniques to maintain user privacy.
Manipulating phonons of a trapped-ion system using optical tweezers

We propose an experimental architecture where an array of optical tweezers affords site-dependent control over the confining potential of a conventional radio-frequency ion trap. The site-dependent control enables programmable manipulation of phonon modes of ions, with many potential applications in quantum information processing (QIP) and thermodynamics. We describe protocols for programming the array of optical tweezers to attain a set of target phonon modes with high accuracy. We propose applications of such controls in simulating quantum thermodynamics of a particle of programmable effective mass via Jarzynski's equality and improving the efficiency of sympathetic cooling and quantum logic gates in a multispecies ion system of disparate masses. We discuss the required optical parameters in a realistic ion trap system and potential adverse effects of optical tweezers in QIP. Our scheme extends the utility of trapped ions as a platform for quantum computation and simulation.
Traffic Police Gesture Recognition Based on Gesture Skeleton Extractor and Multichannel Dilated Graph Convolution Network

Traffic police gesture recognition is important in automatic driving. Most existing traffic police gesture recognition methods extract pixel-level features from RGB images which are uninterpretable because of a lack of gesture skeleton features and may result in inaccurate recognition due to background noise. Existing deep learning methods are not suitable for handling gesture skeleton features because they ignore the inevitable connection between skeleton joint coordinate information and gestures. To alleviate the aforementioned issues, a traffic police gesture recognition method based on a gesture skeleton extractor (GSE) and a multichannel dilated graph convolution network (MD-GCN) is proposed. To extract discriminative and interpretable gesture skeleton coordinate information, a GSE is proposed to extract skeleton coordinate information and remove redundant skeleton joints and bones. In the gesture discrimination stage, GSE-based features are introduced into the proposed MD-GCN. The MD-GCN constructs a graph convolution with a multichannel dilated to enlarge the receptive field, which extracts body topological and spatiotemporal action features from skeleton coordinates. Comparison experiments with state-of-the-art methods were conducted on a public dataset. The results show that the proposed method achieves an accuracy rate of 98.95%, which is the best and at least 6% higher than that of the other methods.
Combining Chatbot and Social Media: Enhancing Personal Learning Environment (PLE) in Language Learning

Transformation of the global learning landscape in twenty-first century is shaped by the uptake of digital technology and social network applications, along with students’ alteration of characteristics, needs, and demands. As an attempt to integrate digital technology and social network application, this study aimed to develop a chatbot-based application integrated with social media LINE to enhance language learning, specifically for learning Japanese grammar. The application, namely Gengobot, is a chatbot-based grammar application, consisting of Japanese Language Proficiency Test Level 5 and Level 4 (N5 and N4) grammar materials in three language: Indonesian, English, and Japanese. This study applied design-based research method with Waterfall application development procedure, and a questionnaire to gather feedbacks from fifty-three students regarding Gengobot features and contents. Gengobot application was successfully developed using code igniter framework, MySQL database, and webhook to integrate Gengobot application with LINE messaging API. Application testing confirmed that Gengobot is successfully developed and operated properly. The students agreed that Gengobot materials and features considered to be adequate, useful, user friendly, and suitable to support language learning. Gengobot is also highly accessible since it is integrated to social media LINE, allowing students to adjust its use to their own learning preference and needs, which is suitable to enhance students’ personal learning environment.