Reviewerly/exHarmony huggingface dataset:
Adversarial robustness in deep neural networks based on variable attributes of the stochastic ensemble model

Deep neural networks (DNNs) have been shown to be susceptible to critical vulnerabilities when attacked by adversarial samples. This has prompted the development of attack and defense strategies similar to those used in cyberspace security. The dependence of such strategies on attack and defense mechanisms makes the associated algorithms on both sides appear as closely processes, with the defense method being particularly passive in these processes. Inspired by the dynamic defense approach proposed in cyberspace to address endless arm races, this article defines ensemble quantity, network structure, and smoothing parameters as variable ensemble attributes and proposes a stochastic ensemble strategy based on heterogeneous and redundant sub-models. The proposed method introduces the diversity and randomness characteristic of deep neural networks to alter the fixed correspondence gradient between input and output. The unpredictability and diversity of the gradients make it more difficult for attackers to directly implement white-box attacks, helping to address the extreme transferability and vulnerability of ensemble models under white-box attacks. Experimental comparison of ASR-vs.-distortion curves with different attack scenarios under CIFAR10 preliminarily demonstrates the effectiveness of the proposed method that even the highest-capacity attacker cannot easily outperform the attack success rate associated with the ensemble smoothed model, especially for untargeted attacks.
The vision of biostatistics and biometrics on the analysis of biometric data: the Framingham risk score as an example

The fields of Biostatistics and Biometrics play a crucial role in unraveling the mysteries of human health and empowering healthcare professionals to make informed decisions. By mining health data for meaningful patterns, they enable medical practitioners to identify emerging trends and develop targeted, effective policies and practices. Meanwhile, the analysis of biological data unleashes a wealth of insights, enabling accurate diagnoses and unleashing innovative therapies that transform lives. Together, these fields are a powerful force for good, fueling breakthroughs that enable us to better understand and heal the human body (Clayton and Hills 2019).
IAG Membership Information

Geostandards and Geoanalytical ResearchVolume 47, Issue 3 p. 713-713 Membership InformationFree Access IAG Membership Information First published: 08 August 2023 https://doi.org/10.1111/ggr.12436AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue3September 2023Pages 713-713 RelatedInformation
PERAN MANAJEMEN PERPUSTAKAAN DALAM MEMBERIKAN PELAYANAN KEPADA MASYARAKAT DIKABUPATEN BULELENG

This article is the result of an exploration of the literature that supports the role of library management in improving quality with various cultural backgrounds. The method of writing analysis from the literature found by the author with a qualitative approach. The exploration results show that one of the supporting facilities for the Regional Archives and Libraries Service of Buleleng Regency is the role that exists in the library. The provision of a library also supports the implementation of development in Buleleng Regency. The library is a dynamic space that can build works of human civilization. The progress of culture and civilization of a nation cannot be separated from the progress of the available libraries. The progress and decline of a nation lies in the library it has. Like the life of the library is the heart of a life.
Mirror exposure therapies: Effect of the distance to the mirror on the attentional pattern in a Virtual Reality immersive environment

Introduction Mirror exposure therapies (MET) have been proposed to reduce symptomatology in patients with Anorexia Nervosa. However, most MET protocols or related studies do not specify the patients’ distance to the mirror, or when they do so, such a distance may differ significantly (from 0,5 to 3 meters). Such modifications of mirror positioning could imply variations in patients’ fixation patterns on different body parts (i.e., attentional bias between weight-related and non-weight related body parts), since previous studies shown that dissociated neural systems (either in left or right cerebral hemispheres) are involved in the attentional patterns and scanning strategies depending on the distance (i.e., in near and far space). Furthermore, as the body-related attentional bias (AB) has been shown to be a part of the maintenance mechanism of AN symptomatology, any modification of attentional patterns due to mirror’s distance variations may influence the efficacy of MET. Objectives This study aims to use Virtual Reality (VR) and Eye-Tracking (ET) technologies to precisely analyse the effect of the distance to the mirror on the attentional patterns. Methods 137 female college students were immersed in a VR environment in which they could look in the mirror at their respective avatars created from the measurements and photos of their real bodies. The mirror was positioned at 3.30m in front of the participants in “group 1” (n 1 = 54), and at 1.54m in front of the participants in “group 2” (n 2 = 83). Eye-Tracking feature and OGAMA software (Freie Universität, Berlin, Germany) were used to record and process the visual attentional pattern of each participant, during a 30-second free viewing task at her avatar. Complete Fixation Time (CFT) was assessed as the fixation time difference between weight- and non-weight- related body parts, defined from the weight scale of the PASTAS questionnaire. Independent Sample t-Test was conducted to analyse CFT mean difference between both groups. Results Independent Samples t-Test shows statistically significant CFT mean difference (F (1, 135) = 1.571, p &lt; 0.001, 95% IC [1717; 5581]) between both groups. While fixation pattern of the group positioned further to the mirror (group 1) was more focused on weight-related body parts (CFT mean = 2282ms, SD = 809), the fixation pattern of the group positioned closer to the mirror (group 2) was more focused on non-weight-related body parts (CFT mean = -1367ms, SD = 587). Conclusions This study shows new opportunities to use VR and ET technologies to precisely analyse the variations of fixation patterns as a function of mirror position in MET. Such information may contribute to adapt and develop new MET’s protocols for AN patients, optimizing the distance to the mirror. It also underscores the importance of specifying the distance to the mirror in MET-related studies to improve replicability. Disclosure of Interest None Declared
An empirical staging model for schizophrenia using machine learning

Introduction One of the great challenges still to be achieved in schizophrenia is the development of a staging model that reflects the progression of the disorder. The previous models suggested have been developed from a theoretical point of view and do not include objective variables such as biomarkers, physical comorbidities, or self-reported subjective variables (Martinez-Cao et al. Transl Psychiatry 2022; 12(1) 1-11). Objectives Develop a multidimensional staging model for schizophrenia based on empirical data. Methods Naturalistic, cross-sectional study. Sample: 212 stable patients with Schizophrenia (F20). Assessments: ad hoc questionnaire (demographic and clinical information); psychopathology: PANSS, CDS, OSQ, CGI-S; functioning: PSP; cognition: MATRICS; laboratory tests: C-Reactive Protein (CRP), IL-1RA, IL-6, Platelets/Lymphocytes (PLR), Neutrophils/Lymphocytes (NLR), and Monocytes/Lymphocytes (MLR) ratios. Statistical analysis: Variables selection was performed with an ad hoc algorithm developed for this research. The referred algorithm makes use of genetic algorithms (GA) to select those variables that show the best performance for the patients classification according to their global CGI-S. The objective function of the GA maximizes the individuals correct classification of a support vector machines (SVM) model that employs as input variables those given by the GA (Díez-Díaz et al. Mathematics 2021; 9(6) 654). Models performance was assessed with the help of 3-fold cross-validation and these process was repeated 10,000 times for each one of the models assessed. Results Mean age(SD): 39.5(13.54); men: 63.5%; secondary education: 59.50%. Most patients in our sample had never been married (74.10%), and more than a third received disability benefits due to schizophrenia (37.70%). The mean length of the disease was 11.98(12.02) years. The best SVM model included the following variables: 1)Clinical: number of hospitalizations, positive, negative, depressive symptoms and general psychopathology; 2)Cognition: speed of processing, visual learning and social cognition; 3)Functioning: PSP total score; 4)Biomarkers: PLR, NLR and MLR. This model was executed again 100,000 times applying again 3-fold cross-validation. In 95% of the algorithm executions more than a 53.52% of the patients were classfied in the right CGI-S category. On average the right classification was of 61.93%. About specificity and sensitivity the average values obtained were of 0.85 and 0.64 respectively. Conclusions Our staging model is a robust method that appropriately distributes patients according to the severity of the disorder. Highlights the importance of clinical, functional and cognitive factors to classify patients. Finally, the inflammatory parameters PLR, NLR and MLR have also emerged as potential biomarkers for staging schizophrenia. Disclosure of Interest None Declared
Neural Networks, Fuzzy Systems and Pattern Recognition: A Comparative Study

Artificial Intelligence (AI) and Machine Learning (ML) have been rapidly advancing in recent years, with many new techniques and models being developed. One area of AI and ML that has more focuses on Pattern Recognition (PR). PR is a subfield of ML that deals with the identification and classification of patterns in data. This field is closely related to other subfields of AI and ML, such as Neural Networks (NNs) and Neuro-Fuzzy Systems (NFS). NNs are a kind of artificial intelligence inspired by the way our brains work. This paper will provide a comparative research of three fields: Neural Networks (NNs), Neuro-Fuzzy Systems (NFS) and Pattern Recognition (PR), highlighting their similarities and differences. NNs, NFS, and PR are three closely related fields of research in the field of AI and ML. The paper begins with a brief introduction to each of these fields, followed by a discussion of their similarities and differences. NNs are a type of AI that are modeled after the function and structure of the human brain system. They integrate a wide-range of interlinked processing nodes, known as neurons that are used to perform various tasks such as PR and control. NNs are particularly useful for tasks that involve large amounts of data, such as image and speech recognition.
Gestão do conhecimento em unidades de informação um estudo sobre a Rede Sesc de Bibliotecas

The library networks are organizations with affinities and common goals, but many of them do not have their administrative procedures established in institutional documents, nor a centralized management. The Sesc Network of Libraries (RSB), for example, object of this study, is composed by different information units, located in all regions of Brazil, which adopts a decentralized management model. This research aims to investigate managerial and librarianship information sharing practices, from the perspective of knowledge management (KM). Methodologically, this is an explanatory, descriptive, documentary research, which uses the case study. It presents as a resultthe discrete existence of knowledge management and librarianship practices at RSB, and suggests the implementation of such practices by all librarians and libraries at Sesc
Informação e memória, biblioteca e memória o caso EMATER/MG

The treatment and preparation of a collection for a new destination after the closure of a library is a subject under explored in research and an activity for which there are no prepared instruments. This was the challenge of an action at the Nilza TavaresDias library of the Technical Assistance and Rural Extension Company of the State of Minas Gerais (EMATER-MG), in Brazil, deactivated in 2018. Since 2020, the inventory work for the proper disposal of the collection was started. For this purpose, it was necessary to develop the work methodology, as its base for recording and physical restructuring of the library for the activities’development. During the process, a large quantity/quality of EMATER publications produced since its creation was found, thus arising the need for a more depth study of these materials as an information, documentary and institutional memory source.
A percepção do bibliotecário universitário sobre a ética no tratamento temático da informação

This study is focused on the subject representation based on a reflective perspective in terms of ethics in the field. It aims to analyze the professional perception of Brazilian university librarians about ethics in subject representation. It is a descriptive study of a quantitative nature that usesthe application of online survey, which was applied to professionals in charge of managing the technical processing section of university libraries located in federal public universities in Brazil. The results demonstrate that ethics is well assimilated by technical processingmanagers, even though they face interpretive dilemmas as well as a major concern about indexing process and its embodiment via term. Despite every care in the equal treatment of the different issues, reported by the respondents, inconsistent substitutes are still echoing in the information retrieval system, making it necessary to seek training and unrestricted use of documentary instruments in order to avoid biased interpretations and representations. Simultaneously, qualification, collaboration and participation of the user are understood as a way of supporting the ethical instance of the subject representation, as well as the need for further evolution of the library literature within thisperspective. It is concluded that the ethical dimension in the subject representation is understood by Brazilian university librarians who can identify impasses, difficulties, problems, solutions and needs for greater assertiveness in this context.
The Role of Supervisory Agency in the Circulation of Cosmetics With Fake Distribution Permission

This study aims to determine the role of the Food and Drug Supervisory Agency which (will be referred to as BPOM) in the distribution of cosmetics that include a fictitious distribution permit. The method used in this study is an empirical research methodology. This study uses a qualitative approach and sampling using purposive sampling and using descriptive analysis techniques. The results of this study show that the role of BPOM in the distribution of cosmetics that includes a fictitious distribution permit continues to carry out Supervision, Guidance, and Prosecution of every producer who violates government regulations related to the circulation of counterfeit cosmetics that include a fictitious distribution permit number and several programs are run to deceive consumers. get the right. In addition, it continues to protect consumers who experience losses by providing administrative sanctions to the perpetrators to be able to pay for the losses suffered by the consumers themselves. Protecting Consumers from the Circulation of Dangerous Cosmetics BPOM has several programs, namely the National Movement to Beware of Illegal Drugs and Food (GNWOMI), GENPOPA, BPOM Checks, Public Warnings, Educational Videos, Community Awareness Index Surveys, Implementing Teams for Quality Improvement of Community Empowerment and Consumer Protection.
The use of ICT in the education of students with dyslexia

With the use of ICT, in general in special education and in particular in dyslexia, there are tangible results that allow the development of the student. New technologies, therefore, give the possibility to create a digital environment that will enhance the use of the senses on the part of the student, which is not always easy in the case of conventional education for the student diagnosed with dyslexia.
Human Identification and Obstacle Detection System for Blind

Abstract: According to the World Health Organization (WHO), there are millions of visually impaired individuals worldwide in detecting obstacles and identifying people. It emphasizes the advancements in information technology and spatial cognition theory for visually impaired individuals as a new opportunity. The prototype proposes a simple and cost-effective solution using artificial vision through an AI-based intelligent system. The system utilizes the Faster Region Convolutional Neural Network (FRCNN) architecture to recognize human and scene objects or obstacles in real-time, even in complex environments. It provides users with comprehensive information about the presence, position, and nature of targets, and uses voice messages to alert blind individuals about obstacles or people nearby. The goal is to create a user-friendly technology that facilitates communication and independence for visually impaired individuals, enabling them to navigate both indoor and outdoor locations effectively.
Blockchain and its applications in Intellectual Property Rights Management

The decentralized, unbiased and immutable property of the blockchain systems provide security and transparency and due to this very fact, these systems are highly feasible to get attention from the legal authorities to get a substantial legal status where in these systems would be put to use in different aspect where blockchain might produce a substantial result. The judicial system might take a few years for the official procedures to accomplish, but the future withholds blockchain technology for a new generation. The counterfeit transaction detection mechanism in blockchain based systems is the key highlight that proves the existence and uniqueness of the copyrighted work. The EUIPO (European Union Intellectual Property Office) has also recognized the potential of the systems to devalue the counterfeiting mechanism to bypass the system. In order to increase its popularity, the organization has also decided to organize a blockchain hackathon aimed at proof of concepts wherein next level infrastructure is being built to combat the counterfeit transactions.
Joint Computation Offloading and Energy Trading in Electric Vehicular Networks

With the rising number of electric vehicles (EVs), the high computational task and energy management of vehicles bring great challenges to the intelligent transportation system. In this work, we investigate the joint offloading and energy trading strategy in vehicular edge computing (VEC) network. We propose an offloading-trading framework, in which EVs can offload tasks to road side unit (RSU) equipped with edge servers or Energy Fog Center (EFC), i.e, edge nodes and fog nodes, and sell excess power to EFC through Vehicle-to-grid (V2G) technology to improve energy efficiency. We aim to maximize the system utility while satisfying the offloading-trading requirements. Since the original problem is non-convex, we decompose it into two subproblems, i.e., trading energy subproblem and trading-offloading subproblem, and proposed the Farthest and Nearest Comparison Searching (FNC-S) algorithm. Specifically, we derive the closed-form expressions of trading electric energy in the trading energy subproblem. Besides, trading-offloading strategy is obtained at two boundaries of distance based on optimal moving distance searching in the trading-offloading subproblem. Simulation results show that the proposed FNC-S algorithm can significantly improve the utility compared with other baseline schemes.
Retracted: Research on the Relationship between Employee Mental Health and Enterprise Work Performance

[This retracts the article DOI: 10.1155/2022/3008947.].
YOLO-H: a lightweight object detection framework for helmet wearing detection

In construction, coal mining, tobacco manufacturing and other industries, wearing helmets is crucial safety measure for workers, and the monitoring of helmet wearing plays a significant role in maintaining production safety. However, manual monitoring demands substantial human, material and financial resources, and will suffer from low efficiency and are error prone. Therefore, we proposed a lightweight real-time deep learning-based detection framework called YOLO-H, for automatic helmet wearing detection. Our YOLO-H model was developed on the foundation of YOLOv5-n by introducing the state-of-the-art techniques such as re-parameterization, decoupled head, label assignment strategy and loss function. Our proposed YOLO-H performed more efficiently and effectively. On a private dataset, our proposed framework achieved 94.5% mAP@0.5 and 65.2% mAP@0.5:0.95 with 82 FPS (Frames Per Second), which surpassed YOLOv5 by a large margin. Compared to other methods, our framework also showed overwhelming performance in terms of speed and accuracy. More importantly, the developed framework can be applied to other object detection scenarios.
A Practical Attack on the TLSH Similarity Digest Scheme

Similarity digest schemes are used in various applications (e.g., digital forensics, spam filtering, malware clustering, and malware detection), which require them to be resistant to attacks aiming at generating semantically similar inputs that have very different similarity digest values. In this paper, we show that TLSH, a widely used similarity digest function, is not sufficiently robust against such attacks. More specifically, we propose an automated method for modifying executable files (binaries), such that the modified binary has the exact same functionality as the original one, it also remains syntactically similar to the original one, yet, the TLSH difference score between the original and the modified binaries becomes high. We evaluate our method on a large data set containing malware binaries, and we also show that it can be used effectively to generate adversarial samples that evade detection by SIMBIoTA, a recently proposed similarity-based malware detection approach.