Reviewerly/exHarmony huggingface dataset:
Construction of feature analysis model for demeanor evidence investigation based on data mining algorithm

Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice.
Study on reaction time depending on display parameters of gaming displays

Abstract Display technology plays a significant role in gaming and, thus, display parameters are an important consideration when choosing a gaming display. In general, interest in display parameters such as contrast ratio, response time, refresh rate, and input lag in gaming displays is very high. In this article, we studied the reaction time of gaming displays, as a measure of game performance, with respect to different contrast ratios, response times, and refresh rates. Contrast ratio and response time turned out to be the key factors influencing reaction time. The higher the contrast ratio is, the shorter the reaction time tends to be. In addition, the faster the response time is, the shorter the reaction time is. Displays with high contrast ratio and fast response time are advantageous in terms of reaction time, which is also expected to be more advantageous in a fast‐paced real gaming environment.
A Systematic Review of Blockchain-based Identity Management Solutions

The involvement of digital identity in almost all online services contributes to the growing reliance on Identity Management Systems (IDMS) that establish, verify, and manage digital identities. However, digital identities are still kept in central repositories. Which are controlled by a single authority that may have many vulnerabilities due to low security, leading attackers to exploit these vulnerabilities and causing various security breaches such as identity theft or disclosure of sensitive information. Additionally, powerful entities who have access to these repositories, could gather and abuse users' information without their knowledge or consent. The concept of Self-sovereign Identity (SSI) allows users to exert ownership of their identity and gain insight into how their data is being used. The development of Blockchain technology has made a breakthrough in achieving SSI by giving individuals the ability to be the final arbiter of who can access and use their own identity. This paper overviews the traditional identity management (IdM) models and presents the next generation of distributed IDMS using Blockchain technology that targets user-centricity and eliminates the identity provider as a trusted third party. Furthermore, It gives an analysis of the recent Blockchain-based IdM solutions, discussing their architecture, components, and features. It also, reveals their weaknesses to identify the gaps between these solutions for future secure IDMS.
Low tensor train and low multilinear rank approximations of 3D tensors for compression and de-speckling of optical coherence tomography images

Objective. Many methods for compression and/or de-speckling of 3D optical coherence tomography (OCT) images operate on a slice-by-slice basis and, consequently, ignore spatial relations between the B-scans. Thus, we develop compression ratio (CR)-constrained low tensor train (TT)-and low multilinear (ML) rank approximations of 3D tensors for compression and de-speckling of 3D OCT images. Due to inherent denoising mechanism of low-rank approximation, compressed image is often even of better quality than the raw image it is based on.Approach. We formulate CR-constrained low rank approximations of 3D tensor as parallel non-convex non-smooth optimization problems implemented by alternating direction method of multipliers of unfolded tensors. In contrast to patch- and sparsity-based OCT image compression methods, proposed approach does not require clean images for dictionary learning, enables CR as high as 60:1, and it is fast. In contrast to deep networks based OCT image compression, proposed approach is training free and does not require any supervised data pre-processing.Main results. Proposed methodology is evaluated on twenty four images of a retina acquired on Topcon 3D OCT-1000 scanner, and twenty images of a retina acquired on Big Vision BV1000 3D OCT scanner. For the first dataset, statistical significance analysis shows that for CR ≤ 35, all low ML rank approximations and Schatten-0 (S0) norm constrained low TT rank approximation can be useful for machine learning-based diagnostics by using segmented retina layers. Also for CR ≤ 35,S0-constrained ML rank approximation andS0-constrained low TT rank approximation can be useful for visual inspection-based diagnostics. For the second dataset, statistical significance analysis shows that for CR ≤ 60 all low ML rank approximations as well asS0andS1/2low TT ranks approximations can be useful for machine learning-based diagnostics by using segmented retina layers. Also, for CR ≤ 60, low ML rank approximations constrained withSp,p∊ {0, 1/2, 2/3} and one surrogate ofS0can be useful for visual inspection-based diagnostics. That is also true for low TT rank approximations constrained withSp,p∊ {0, 1/2, 2/3} for CR ≤ 20.Significance. Studies conducted on datasets acquired by two different types of scanners confirmed capabilities of proposed framework that, for a wide range of CRs, yields de-speckled 3D OCT images suitable for clinical data archiving and remote consultation, for visual inspection-based diagnosis and for machine learning-based diagnosis by using segmented retina layers.
G-DAIC: A Gaze Initialized Framework for Description and Aesthetic-Based Image Cropping

We propose a new gaze-initialised optimisation framework to generate aesthetically pleasing image crops based on user description. We extended the existing description-based image cropping dataset by collecting user eye movements corresponding to the image captions. To best leverage the contextual information to initialise the optimisation framework using the collected gaze data, this work proposes two gaze-based initialisation strategies, Fixed Grid and Region Proposal. In addition, we propose the adaptive Mixed scaling method to find the optimal output despite the size of the generated initialisation region and the described part of the image. We address the runtime limitation of the state-of-the-art method by implementing the Early termination strategy to reduce the number of iterations required to produce the output. Our experiments show that G-DAIC reduced the runtime by 92.11%, and the quantitative and qualitative experiments demonstrated that the proposed framework produces higher quality and more accurate image crops w.r.t. user intention.
Penggunaan Facebook Dalam Pembelajaran IPS di SMP Negeri 2 Sambas Kabupaten Sambas

The existence of the Covid-19 virus has had an extraordinary
Are Female Chatbots More Empathic? - Discussing Gendered Conversational Agent through Empathic Design

Studies have shown that feminine chatbots are perceived as warmer (e.g., likable, friendly) and imbue more humanness to a machine than masculine or gender-androgynous chatbots. As chatbots are being widely deployed in various empathic contexts (e.g., revealing sensitive personal information or facilitating charity donations), how to design the gender of chatbots remains a critical question in the empathic design community. Should designers assign feminine identities to chatbots to improve empathic reactions? In this position paper, we explore the tension between designing empathic agents and the gender assignment of chatbots and how they can relate to the design of the metaphor of the chatbots. After analyzing the problem, we discuss the possible design strategy and their trade-offs. We conclude with possible future work directions that could inform CA gender design that elicits user empathy.
Towards Empathic Buildings: Exploring How Smart Buildings May Be Designed to Address Occupants’ Subjective Needs

Given that people spend a significant amount of time within buildings, designing spaces while taking into consideration the impact that they may have on occupants’ well-being is a challenge. While architecture relies on design practices to elicit positive experiences among occupants, they may fall short in terms of sensing occupants and reciprocating their needs in an empathic manner. Therefore, in this position paper, we argue for empathic buildings that understand and respond to human emotions. We believe that buildings that can empathically interact with occupants will enhance occupants’ experiences of well-being in indoor spaces. To that end, we describe one such smart building that may be made human-centric through the incorporation of empathic technologies for various architectural attributes. We thereafter talk of an ongoing case study for identifying the novel, subjective human experiences that may be afforded by empathic buildings of the digital age.
A Critical Analysis of EmpatheticDialogues as a Corpus for Empathetic Engagement

This paper aims to analyze the content and relevance of one of the most popular contemporary training corpora for empathetic conversational agents: EmpatheticDialogues [23]. We provide a detailed qualitative breakdown of the corpus including the corpus creation methodology and point out some critical shortcomings of the corpus. Given the significance of the corpus as the only one of its kind at the moment, we also provide a quantitative comparison of EmpatheticDialogues to other contemporary small-talk corpora including DailyDialog and Persona-Chat, including conversation length, the ratio of conversant interaction, lexical choice, etc. With this analysis, we discuss the merit and implications of indicating a specific small-talk dialogue corpus is more empathetic than other small-talk corpora. Finally, we provide a new lens for developing conversational agents with empathetic engagement capabilities by augmenting existing dialogue datasets.
Using Empathy-Centric Design in Industry: Reflections from the UX Researcher, the Client, and the Method Expert

Empathic design provides tools and frameworks supporting designers to understand users’ experiences with products or services. However, how does one hand over this empathic understanding of users to other internal stakeholders shaping the service experience? In this contribution, we reflect on a three-year implementation of an empathy-centric design approach in an industrial context with a low user experience maturity from three different professional viewpoints: ours as UX researchers, the one of a company manager, and an expert researcher on empathy in design. These narrative introspective accounts unveil some of the main benefits, opportunities, and challenges of implementing an empathy-centric design approach in the industry. We discuss and confront them to prior work. We contribute to the field of empathic design with rich in-situ research insights and principles for a successful empathic approach.
Detecting Arabic sexual harassment using bidirectional long-short-term memory and a temporal convolutional network

Due to advances in technology, social media has become the most popular medium for spreading news. Many messages are published on social media sites such as Facebook, Twitter, Instagram, etc. Social media platforms also provide opportunities to express opinions and social phenomena such as hate, offensive language, racism, sexual content, and all forms of verbal violence, which have amazingly increased. These behaviors do not only affect specific countries, groups, or societies but extend beyond these areas into people's daily lives. This study examines sexual content and harassment discourse in Arabic social media to build an accurate system for detecting sexual harassment expressions. The dataset was collected from Twitter posts to make the classification. A deep learning model was developed as a classification system to identify sexual speech using Bidirectional Long-Short-Term Memory (BiLSTM), Temporal Convolutional Network (TCN) with word embedding and the FastText previously trained on the Arabic language model. The proposed (TCN-BiLSTM) model was compared with Extreme Gradient Boosting (XGBoost). The CASH dataset implemented with the (TCN -Bi-LSTM) model gate obtained an accuracy rate of 96.65% and an F0.5 value of 0.969. The implementation of XGBoost using word embeddings resulted in an accuracy rate of 92.56% and an F0.5 value of 0.925. Findings and manual interpretation showed that different text representation methods with various deep learning algorithms obtain higher classification performance easily in complex sentences. This strategy is helpful with languages that are difficult to study morphologically, like Arabic, Turkish, and Lithuanian.
Closed-loop Control of Biaxial Fatigue Loading of Wind Turbine Blades Based on Visual Inspection

Abstract Based on the inertial vibration excitation device, a biaxial fatigue loading system for wind turbine blades is established by using visual inspection technology and virtual spindle decoupling to control the vibration excitation motion of wind turbine blades. The image feature point tracking method is used to detect the blade vibration state. Considering the system vibration frequency characteristics, the phase difference between the exciting force and the vibration displacement is detected based on the phase detector, and the system resonance frequency is adaptively tracked to achieve accurate control of the double-free amplitude of the blade. The fatigue test shows that the control system can maintain the amplitude error of the blade, the adaptive tracking control method is reliable and effective, and the accuracy and reliability of the fatigue test are improved.
Spectral And Fractal Analysis of Volcanic Tremor at Ijen Volcano, Indonesia

Abstract A volcanic tremor is one of the seismic events that is usually recorded at the Ijen volcano. In 2014 the status of the Ijen volcano had yet to be declared normal after being critical in 2012. Many volcanic tremors are still recorded since then. This study aims at analyzing the volcanic tremors of Ijen volcano using the January to March 2014 seismic recording data obtained from the Center for Volcanology and Geological Disaster Mitigation, Bandung, Indonesia. We analyzed the spectra and fractal of volcanic tremors. Fractal and spectral analysis was conducted to investigate the dynamical system regarding the sources of the mechanism. Spectral analysis was estimated using the Fast Fourier Transform method, and fractal analysis was estimated using the regularization method. The spectra of a single volcanic tremor had frequencies of 1.1-1.5 Hz. Fractal analyzes were performed to identify dynamic systems. The fractal of a single volcanic tremor had a dimension of 2-2.3. Based on the frequency and fractal dimension values obtained, we suspect that the trigger of the volcanic tremors of the Ijen volcano is a fluid-filled crack mechanism.
The comparison of indoor and outdoor pyranometer calibration method in Jakarta

Abstract The increasing energy crisis motivated people to develop renewable energy, one of the promising renewable energies is solar energy. Considering that Indonesia has the potential for sunlight throughout the year, this country should be able to take full advantage of this solar radiation energy. To discover potential area in Indonesia, good and calibrated equipment are needed such as pyranometer, pyrheliometer etc. The objective of this study is to compare developed method of outdoor and indoor pyranometer calibration responsivity. There are 4 pyranometer calibration methods, one of them is called Continuous Sun and Shade Method (CoSSM). However, this method has never been carried out before, so it is going to be developed by the Agency for Meteorology, Climatology, and Geophysics (BMKG) Indonesia. The CoSSM method utilizes two standard components consist of direct and diffuse irradiance to obtain new responsivity values from calibrated global radiation sensor or so called Unit Under Test (UUT). This experiment used 4 UUT and carried out for 12 days. The new responsivity values then compared with the responsivity value that had been obtained from BMKG indoor calibration accredited by National Accreditation Committee (KAN). The results obtained from calibration process provided the highest percentage difference of 6.2% and the smallest 0.2%. This shows that outdoor calibration in cloudy and polluted conditions in Jakarta still give quite good result.
A comprehensive review of automatic programming methods

Automatic programming (AP) is one of the most attractive branches of artificial intelligence because it provides effective solutions to problems with limited knowledge in many different application areas. AP methods can be used to determine the effects of a system’s inputs on its outputs. Although there is increasing interest in solving many problems using these methods for a variety of applications, there is a lack of reviews that address the methods. Therefore, the goal of this paper is to provide a comprehensive literature review of AP methods. At the same time, we mention the main characteristics of the methods by grouping them according to how they represent solutions. We also try to give an outlook on the future of the field by highlighting possible bottlenecks and perspectives for the benefit of the researchers involved.
Piecewise: A Non-Isomorphic 3D Manipulation Technique That Factors Upper-Limb Ergonomics

Virtual reality (VR) is gaining popularity as an educational, training, and healthcare tool due to its decreasing cost. Because of the high user variability in terms of ergonomics, 3D manipulation techniques (3DMTs) for 3D user interfaces (3DUIs) must be adjustable for comfort and usability, hence avoiding interactions that only function for the typical user. Given the role of the upper limb (i.e., arm, forearm, and hands) in interacting with virtual objects, research has led to the development of 3DMTs for facilitating isomorphic (i.e., an equal translation of controller movement) and non-isomorphic (i.e., adjusted controller visuals in VR) interactions. Although advances in 3DMTs have been proven to facilitate VR interactions, user variability has not been addressed in terms of ergonomics. This work introduces Piecewise, an upper-limb-customized non-isomorphic 3DMT for 3DUIs that accounts for user variability by incorporating upper-limb ergonomics and comfort range of motion. Our research investigates the effects of upper-limb ergonomics on time completion, skipped objects, percentage of reach, upper-body lean, engagement, and presence levels in comparison to common 3DMTs, such as normal (physical reach), object translation, and reach-bounded non-linear input amplification (RBNLIA). A 20-person within-subjects study revealed that upper-limb ergonomics influence the execution and perception of tasks in virtual reality. The proposed Piecewise approach ranked second behind the RBNLIA method, although all 3DMTs were evaluated as usable, engaging, and favorable in general. The implications of our research are significant because upper-limb ergonomics can affect VR performance for a broader range of users as the technology becomes widely available and adopted for accessibility and inclusive design, providing opportunities to provide additional customizations that can affect the VR user experience.
Smart Healthcare Application Implementation of AI and Blockchain Technology

Patients can control, share, and manage their health records with family, friends, and healthcare professionals utilizing electronic health records (EHRs), which use an open channel, or the Internet. When a lot of data is available, DL methods show promise in these health applications. A distributed blockchain-based IoT system would benefit greatly from these ideas. This research proposes novel technique in Healthcare Data Based Feature Selection and Classification Using Blockchain and Machine Learning Architectures. The network has been secured using centralized blockchain sensor network. Here the input sensor-based healthcare data has been collected and processed for noise removal and smoothening. Then the processed data feature has been selected using Greedy Mixed Forward Colony Optimization feature selection. The suggested framework's superiority is supported by security research and experimental findings using the IoT-Botnet and ToN-IoT datasets. Proposed technique attained acc of 95%, precision of 85%, recall of 76%, F1 score of 63%, sec rate of 95%, DT Rate of 85%.
Geometric SMOTE-Based Approach to Improve the Prediction of Alzheimer's and Parkinson's Diseases for Highly Class-Imbalanced Data

In many applications where classification is needed, class imbalance poses a serious problem. Class imbalance refers to having very few instances under one or more classes while the other classes contain sufficient amount of data. This makes the results of the classification to be biased towards the classes containing many numbers of samples comparatively. One approach to handle this problem is by generating synthetic instances from the minority classes. Geometric synthetic minority oversampling technique (G-SMOTE) is used to generate artificial samples. G-SMOTE generates synthetic samples in the geometric region of the input space, around each selected minority instance. The performance of the classifier is compared after oversampling using G-SMOTE, synthetic minority oversampling technique and without oversampling the minority classes. The work presents empirical results that show around 10% increase in the accuracy of the classifier model when G-SMOTE is used as an oversampling algorithm compared to SMOTE, and around 30% increase in performance over a class imbalanced data.
Application of Machine Learning and Blockchain Technology for Smart Healthcare Applications

Smart health care depends on emerging technologies like electronic health records, along with internet of things, artificial intelligence, mobile communications, cloud computing, etc. This helps to create various systems like health management systems and medical service systems. This chapter includes: (1) development of smart health care using machine learning and blockchain technology; (2) uses of machine learning and blockchain technology with smart healthcare applications; (3) impact of smart healthcare applications on human health; (4) features of smart healthcare applications; and (5) limitations.