Reviewerly/exHarmony huggingface dataset:
Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes

In recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more accurately and quickly to maximize the reward. However, the rewards calculated by the system and via human intervention (that make up the learning environment) differ and must be used accordingly. In this paper, we propose a framework for learning the problems of competitive network topologies, wherein the environment dynamically changes agent, by computing the rewards via the system and via human evaluation. The proposed method is adaptively updated with the rewards calculated via human evaluation, making it more stable and reducing the penalty incurred while learning. It also ensures learning accuracy, including rewards generated from complex network topology consisting of multiple agents. The proposed framework contributes to fast training process using multi-agent cooperation. By implementing these methods as software programs, this study performs numerical analysis to demonstrate the effectiveness of the adaptive evaluation framework applied to the competitive network problem depicting the dynamic environmental topology changes proposed herein. As per the numerical experiments, the greater is the human intervention, the better is the learning performance with the proposed framework.
On Sufficient and Necessary Conditions in Bounded CTL: A Forgetting Approach

Computation Tree Logic (CTL) is one of the central formalisms in formal verification. As a specification language, it is used to express a property that the system at hand is expected to satisfy. From both the verification and the system design points of view, some information content of such property might become irrelevant for the system due to various reasons, e.g., it might become obsolete by time, or perhaps infeasible due to practical difficulties. Then, the problem arises on how to subtract such piece of information without altering the relevant system behaviour or violating the existing specifications over a given signature. Moreover, in such a scenario, two crucial notions are informative: the strongest necessary condition (SNC) and the weakest sufficient condition (WSC) of a given property. To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model and over a given signature. We study its theoretical properties and also show that our notion of forgetting satisfies existing essential postulates of knowledge forgetting. Furthermore, we analyse the computational complexity of some basic reasoning tasks for the fragment CTLAF in particular.
Using Intel SGX to improve private neural network training and inference

The importance of leveraging machine learning (ML) algorithms to make critical business and government decisions continues to grow. To improve performance, such algorithms are often outsourced to the cloud, but within privacy sensitive domains this presents several challenges for ensuring data is protected from malicious parties. One practical solution to these problems comes from Trusted Execution Environments (TEEs), which utilize hardware technologies to isolate sensitive computations from untrusted software. This paper investigates a new technique utilizing a TEE to allow for the high performance training and execution of Deep Neural Networks (DNNs), an ML algorithm that has recently been used with great success in a variety of challenging tasks, including speech and face recognition.
Cognitive behavioural therapy in virtual reality treatments across mental health conditions: a systematic review

Background. Virtual reality (VR) has been effectively used in the treatment of many mental health disorders.However, significant gaps exist in the literature. There is no treatment framework for researchers to use whendeveloping new VR treatments. One recommended treatment across a range of diagnoses, which may be suitablefor use in VR treatments, is Cognitive Behavioural Therapy (CBT). The aim of this systematic review is to investigateCBT treatment methods that utilize VR to treat mental health disorders.&#x0D; Objectives. To investigate how CBT has been used in VR to treat mental health disorders and to report onthe treatment characteristics (number of sessions, duration, and frequency) that are linked to effective andineffective trials.&#x0D; Methods. Studies were included if patients had a mental health diagnosis and their treatment included immersiveVR technology and CBT principles. Data were extracted in relation to treatment characteristics and outcomes,and analysed using narrative synthesis.&#x0D; Results. Ninety-three studies were analysed. Exposure-based VR treatments were mainly used to treat anxietyrelated disorders. Treatments generally consisted of eight sessions, once a week for approximately one hour.VR treatments were commonly equal to or more effective than traditional face-to-face methods. No specifictreatment characteristics were linked to this effectiveness.&#x0D; Conclusion. The number, frequency and duration of the VR treatment sessions identified in this review, couldbe used as a treatment framework by researchers and clinicians. This could potentially save researchers timeand money when developing new interventions.
Dehazing with Offset Correction and a Weighted Residual Map

In hazy environments, image quality is degraded by haze and the degraded photos have reduced visibility, making the less vivid and visually attractive. This paper proposes a method for recovering image information from a single hazy image. The dark channel prior algorithm tends to underestimate the transmission of bright areas. To address this problem, an improved dehazing algorithm is proposed in this paper. Assuming that intensity in a dark channel affected by haze produces the same offset, the expected value of the dark channel of a hazy image is used as an approximation of this offset to correct the transmission. However, this correction may neglect scene difference and affect the clarity of the recovered images. Therefore, a weighted residual map is used to enhance contrast and recover more information. Experimental results demonstrate that our algorithm can effectively lessen color oversaturation and restore images with enhanced details. This algorithm provides a more accurate transmission estimation method that can be used with a weighted residual map to eliminate haze and improve contrast.
A Comparison of Artificial Neural Network and Decision Trees with Logistic Regression as Classification Models for Breast Cancer Survival

In the field of medicine, several recent studies have shown the value of Artificial Neural Networks, decision trees, logistic regression are playing a major role as the predictor, and classification methods. The research has been expanded to estimate the incidence of breast, lung, liver, ovarian, cervical, bladder and skin cancer. The main aim of this paper is to develop models of logistic regression, Artificial Neural Networks, and Decision trees using the same input and output variables and to compare their success in predicting breast cancer survival in woman. To find the best model for breast cancer survival, the sensitivity and specificity of all these models are measured and evaluated with their respective confidence intervals and the ROC values.
A corpus study of verbs in opinion articles of The Jakarta Post and the relation with text characteristics

As one of the text categories, opinion texts have distinctive characteristics compared to any other texts in newspapers, including the choice of verb usage. This study then aims at preliminarily examining the verbs used in opinion articles in The Jakarta Post to find out the relation between frequency and text characteristics. This study collected the opinion articles of The Jakarta Post comprising 47.143 words. This study was assisted by Lancsbox to store the corpus of opinion section texts, to identify the verb lemmas, and to count the frequency of verbs. The verbs found in this study were then classified based on Scheibman’s main verb classification (which is based on Halliday’s and Dixon’s verb types). The results of the study show that there are three most frequent verb types used in opinion texts in The Jakarta Post; they are material, verbal, and feeling verb types. Meanwhile, the lesser frequent ones are perception, possessive/relational, relational, and cognition verbs types. Meanwhile, the least frequent verb types are existential, corporeal and perception/relational verbs types. As opinion text conveys the argument of the writer, it is plausible to find feeling verb type belongs to the third most frequent types, along with material type to show concrete actions and verbal type to report the information. These frequencies exhibit that there is a firm relationship between text characteristics and the tendency of verb choice.
Developing of Intelligence Walking Stick and Mobile Application for Elderly Health Care using the Internet of Things

Nowadays, Thailand is stepping into an aging society. This research purposes developing the intelligence walking stick for the elderly in terms of the health care system by applied the internet of things devices and biometric sensors in a real-time system. The heart rate, blood pressure, oxygen saturation, and temperature were measured at the finger of the elderly that holding the intelligence walking stick. All data can monitor and display on mobile devices. The intelligence walking stick system was evaluated by twenty users who are five experts and fifteen elderly in Ratchaburi province. As a result of the mean value at 4.15 and 4.61 by experts and elderly, respectively. Moreover, the acceptance of the intelligence walking stick for the elderly that could be detected by elderly people while being used has high consensus. This study indicates that the developed intelligence walking stick by using IoT can help and improve the daily living of the elderly at high level.
Learning about phraseology from corpora: A linguistically motivated approach for Multiword Expression identification

Multiword Expressions (MWEs) are idiosyncratic combinations of words which pose important challenges to Natural Language Processing. Some kinds of MWEs, such as verbal ones, are particularly hard to identify in corpora, due to their high degree of morphosyntactic flexibility. This paper describes a linguistically motivated method to gather detailed information about verb+noun MWEs (VNMWEs) from corpora. Although the main focus of this study is Spanish, the method is easily adaptable to other languages. Monolingual and parallel corpora are used as input, and data about the morphosyntactic variability of VNMWEs is extracted. This information is then tested in an identification task, obtaining an F score of 0.52, which is considerably higher than related work.
Hiding Encrypted Text in Image using Least Significant Bit image Steganography Technique

Today, in this new era of the internet, Information Security is becoming the biggest challenge for the world due to the rapid growth of internet users day by day.Unauthorized access to secret data can have serious repercussions like a financial loss.One of the best techniques for secure communication is Steganography-or covert writing.It is an art of hiding the very existence of communicating the message itself.This research presents a state of dual stenographic technique.Dual Steganography combines two security mechanisms, steganography and cryptography both together.This mechanism has the advantage of providing high security and low time complexity.This research proposes a dual steganography technique with an additional level of security.The proposed algorithm embeds secret text messages in cover image in two phases.The two phases include a chaotic map cipher technique for encrypting text and LSB (Least Significant Bit) image steganography technique for hiding encrypted text in an image.The text containing encrypted data is hidden in an RGB image.
Editorial for accountability and privacy issues in blockchain and cryptocurrency

The increasing popularity of blockchain and cryptocurrency reinforces the importance of accountability and privacy. Privacy is a fundamental human right, yet, malevolent or criminal should be held accountable, which mitigates the severity of malicious and criminal exploitation even with privacy protections in place. This editorial reports on the findings from seven accepted papers (acceptance rate of 30.44%).
MLGrafViz: multilingual ontology visualization plug-in for protégé

Natural Language Processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge based NLP systems to provide better results. Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically. The concepts that are visualized graphically are more understandable than in the text form. In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.
Research on a Shared Bicycle Deposit Management System Based on Blockchain Technology

As a green travel mode, bike sharing is developing rapidly across China. At present, charging deposits from users is the common operation mode adopted by shared bicycle enterprises. The large number of shared bicycle enterprises generates fierce market competition, and the eliminated enterprises always refuse to return user deposits. Even regular running enterprises still have trouble with the immediate return of deposits. This situation severely affects the reputation of shared bicycle enterprises, and concerns have been shared widely across the society. Meanwhile, there is a general expectation among users that their deposits could be refunded timely and a broad appeal for technical management to resolve this problem. This article uses blockchain technology to reform the current management mode for shared bicycle deposits and constructs a decentralized, user information and deposit visualized, and multidimensional supervised management system. The proposed management system makes the real-time flow direction supervision of user deposits to be realized. Furthermore, a smart contract of shared bicycle deposits with punishment mechanism is also designed. Finally, the differences between the proposed deposit management mode and the current deposit management mode are analyzed, and a simulation experiment is conducted. In the simulation experiment, the deposit theft rate of our deposit management system is 0%, which is far better than the two existing bike deposit management systems. The results show that the outstanding advantages of the proposed deposit management mode, which include improving deposit supervision and guaranteeing user deposit security, are also conducted. This article has made effective technical management exploration to reduce deposit management risks and improve deposit management institutions for shared bicycles. It has important practical reference value for accelerating the sustainable development of shared bicycle enterprises.
Real-Time 3D Facial Tracking via Cascaded Compositional Learning

We propose to learn a cascade of globally-optimized modular boosted ferns (GoMBF) to solve multi-modal facial motion regression for real-time 3D facial tracking from a monocular RGB camera. GoMBF is a deep composition of multiple regression models with each is a boosted ferns initially trained to predict partial motion parameters of the same modality, and then concatenated together via a global optimization step to form a singular strong boosted ferns that can effectively handle the whole regression target. It can explicitly cope with the modality variety in output variables, while manifesting increased fitting power and a faster learning speed comparing against the conventional boosted ferns. By further cascading a sequence of GoMBFs (GoMBF-Cascade) to regress facial motion parameters, we achieve competitive tracking performance on a variety of in-the-wild videos comparing to the state-of-the-art methods which either have higher computational complexity or require much more training data. It provides a robust and highly elegant solution to real-time 3D facial tracking using a small set of training data and hence makes it more practical in real-world applications. We further deeply investigate the effect of synthesized facial images on training non-deep learning methods such as GoMBF-Cascade for 3D facial tracking. We apply three types synthetic images with various naturalness levels for training two different tracking methods, and compare the performance of the tracking models trained on real data, on synthetic data and on a mixture of data. The experimental results indicate that, i) the model trained purely on synthetic facial imageries can hardly generalize well to unconstrained real-world data, ii) involving synthetic faces into training benefits tracking in some certain scenarios but degrades the tracking model's generalization ability. These two insights could benefit a range of non-deep learning facial image analysis tasks where the labelled real data is difficult to acquire.
GeoGebra-Assisted Problem Based Learning to Improve Mathematical Problem Solving Ability

The role of computers becomes an inseparable part of daily life, including as a media for learning mathematics, including making it easier to visualize 3D objects (building three-dimensional space).GeoGebra is software that can be used for mathematics education by combining geometry, algebra, and calculus.This research is an experimental research and the design used is the nonequivalent control group design.Sampling and sampling in this study using purposive sampling techniques.Based on the different test of the two average gain indexes, it was concluded that improving the problem-solving ability of students who studied using the GeoGebra-assisted problem-based learning approach was better than students who studied with conventional learning.The results of the attitude scale, it was found that in general students are positive towards mathematics learning by using the GeoGebra-assisted problembased learning approach.The results of the bivariate correlation calculation concluded that there is a positive correlation between students 'attitudes towards learning using the application of GeoGebra-assisted problem-based learning and improving students' mathematical problem-solving abilities.
Mediation of employee job satisfaction on the relationship between internal corporate social responsibility and affective commitment

This paper proposes a quantitative approach with statistical techniques for identifying the relationship between Internal Corporate Social Responsibility (ICSR) and Affective Commitment (AC) and clarifying the mediating effects of job satisfaction of employee.Five factors of ICSR including human rights, training and education, health and safety, work-life balance, and workplace diversity were examined.The major method in this paper is based on the quantitative approach, in which statistical techniques including Factor Analysis, Multiple Regression, and Path Analyses were performed.The unit of analysis was at individual level with the sample size of 233 employees who are working in different departments.The findings of this study showed that the higher levels of human rights, health and safety, job satisfaction and work-life balance employees were perceived at the higher level of AC.Based on the results of the path analysis, this study argued that to achieve high employee AC, employers should reach higher level of employee job satisfaction and improve human rights, health and safety and work-life balance.Although there were limitations, this study is expected to highlight several implications for small and medium companies in building ICSR.
Embodying an invisible face shrinks the cone of gaze.

The possibility of being invisible has long fascinated people. Recent research showed that multisensory illusions can induce experiences of bodily invisibility, allowing the psychological consequences of invisibility to be explored. Here, we demonstrate an illusion of embodying an invisible face. Participants received touches on their face and simultaneously saw a paintbrush moving synchronously in empty space and defining the shape of an invisible face. Using both explicit questionnaire measures (Experiment 1) and implicit physiological measures (Experiment 2), we show that such invisible enfacement induces a sense of ownership. We further demonstrate that embodying an invisible face shrinks the width of the cone of gaze (i.e., the range of eye deviations people judge as directed toward themselves; Experiments 3 and 4). These results suggest that the experience of invisibility affects the way in which we process the attention of others toward the self, starting from the perception of gaze direction. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Evaluating paper-based TOEFL preparation program using the Context, Input, Process, and Product (CIPP) model

An evaluation for the development of a paper-based test of English as a Foreign Language (TOEFL) for English Education Department students at one of the colleges in Langsa, Aceh, Indonesia, is necessary, considering their unsatisfactory scores even though the preparation program has been conducted. This research employed context, input, process, and product (CIPP) model developed by Stufflebeam upon the program. Methodologically, interviews, questionnaires, and observation were executed to five TOEFL lecturers and 34 students, while the data obtained were analyzed with the qualitative analysis method of Miles and Huberman. Based on the evaluation of context, the program had an appropriate background, goals, and objectives. Regarding the input, it still had problems related to the availability of resources and facilities. There were no standardized learning materials available, including the broken language laboratory. It affected the process since the lecturers could not maximize the facilities to create an effective learning environment. Eventually, it affected the product since most students could not reach an expected score. Most of the participants agreed that the program needed to be improved. Several improvements can be made to overcome these problems by providing required learning facilities, designing standardized learning materials, and clustering classes in accordance with students’ abilities.