Reviewerly/exHarmony huggingface dataset:
Boosting Relationship Detection in Images with Multi-Granular Self-Supervised Learning

Visual and spatial relationship detection in images has been a fast-developing research topic in the multimedia field, which learns to recognize the semantic/spatial interactions between objects in an image, aiming to compose a structured semantic understanding of the scene. Most of the existing techniques directly encapsulate the holistic image feature plus the semantic and spatial features of the given two objects for predicting the relationship, but leave the inherent supervision derived from such structured and thorough image understanding under-exploited. Specifically, the inherent supervision among objects or relations within an image can span different granularities in this hierarchy including, from simple to comprehensive, (1) the object-based supervision that captures the interaction between the semantic and spatial features of each individual object, (2) the inter-object supervision that characterizes the dependency within the relationship triplet ( &lt;subject-predicate-object&gt; ), and (3) the inter-relation supervision that exploits contextual information among all relationship triplets in an image. These inherent multi-granular supervisions offer a fertile ground for building self-supervised proxy tasks. In this article, we compose a trilogy of exploring the multi-granular supervision in the sequence from object-based, inter-object, and inter-relation perspectives. We integrate the standard relationship detection objective with a series of proposed self-supervised proxy tasks, which is named as Multi-Granular Self-Supervised learning (MGS). Our MGS is appealing in view that it is pluggable to any neural relationship detection models by simply including the proxy tasks during training, without increasing the computational cost at inference. Through extensive experiments conducted on the SpatialSense and VRD datasets, we demonstrate the superiority of MGS for both spatial and visual relationship detection tasks.
Lessons Learned from the Development of an Immersive Virtual Reality (IVR) Game for Construction Safety

The use of Immersive Virtual Reality (IVR) games as a learning tool has gained researchers' attention because of their ability to deliver an immersive environment for learners. As it is a growing area of research, the efficacy of IVR applications for learning in various fields needs to be investigated before their full-scale implementation. The authors developed an immersive environment game for construction hazard identification. The game allows players to explore a virtual construction site setting to identify potential safety hazards and associated controls. To create such games, the developer should have expertise in using game engines, coding in computer languages, creating game assets, and designing user interfaces. These requirements can make game development difficult for many instructors who want to build a game-based teaching tool but have no game development experience. Without compelling evidence that learning games are effective in a particular area, training institutions and business organizations may not be willing to invest resources to hire game developers. Therefore, the authors present lessons learned from their game development experience to help those interested in creating immersive Virtual Reality content but lack game development experience.
Advances and Challenges of Deep Learning

Abstract: This editorial presents the recent advances and challenges of deep learning. We reviewed four main challenges: heterogeneity, copious size, reproducibility crisis, and explainability. Finally, we present the prospect of deep learning in industrial applications.
Empirically Evaluating the Effect of Security Precautions on Cyber Incidents

Empirical evidence that connects firm investment in cybersecurity defenses to the likelihood of being attacked has been very hard to come by. The Israel National Cyber Directorate (INCD) and the Israeli Central Bureau of Statistics (CBS) recently surveyed Israeli firms about their ICT operations including cyber defenses and attacks. Using the survey, in this paper, we empirically examine whether security precautions adopted by firms do in fact reduce the chances of being attacked. We find that for ‘large” firms with significant revenues using ecommerce and cloud services (the riskiest firms), employing 18 out of 20 security precautions (covered by the survey) rather than 13 security precautions reduces the probability of experiencing a cyber incident from 81% to 58%. Using an alternative measure for security, we find that for ‘large” firms with significant revenues using ecommerce and cloud services, using all six basic security precautions reduces the probability of experiencing a cyber incident from 80% to 42%.
A hybrid artificial neural network for the generation of critical fluctuations and inter-spike intervals

The recently introduced [Contoyiannis et al., 2021] hybrid artificial neural network can simulate the dynamics of membrane potential fluctuations of real neurons based on fundamental principles of Physics. Here, we propose a temporal description of the membrane potential fluctuations, which resembles the soliton solutions in φ4 field theory. Within this framework, kink-antikink dynamics are associated with spike generation. Furthermore, we show that the simulation can also reproduce the distribution of inter-spike intervals of biological neurons in their critical state [Kosmidis et al., 2018]. A proposal for the intermittency origin of these fluctuations is discussed.
Gated Homogeneous Fusion Networks With Jointed Feature Extraction for Defect Prediction

Software defect prediction is aimed at helping developers to quickly locate defective components in the code repository and thus better allocate resources. However, most of the current traditional defect prediction methods mainly depend on the design of static metrics, but these methods ignore the semantic and structural information of the code. As a result, researchers have turned to building models by extracting semantic features from code through abstract syntax trees. In this article, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gated homogeneous fusion network</i> for defect prediction namely GHFNet, jointing high-level semantic feature extraction and weighted static feature extraction. Through the mechanism of homogeneous gating fusion, weights are adaptively assigned to the two types of features based on the correlation of these features to form fused features for defect prediction in the code. Experimental results show that the proposed approach is a significant improvement. Specifically, for the reference method we present GHFNet improved from 9.4 to 15.2 percentage points in effort-unaware scenarios (F-measure) and from 3.7 to 8 percentage points in effort-aware scenarios (Popt) for defect prediction.
Utility-Prioritized Differential Privacy for Quantitative Biomedical Data

In the context of heavy investments in life sciences, one consequence of rapid biotechnical developments is the generation of very large amounts of biological data that not only support bioinformatics research used in medical and health services by data analysis but also cause genetic privacy breaches for the data originators, limiting biomedical data sharing. However, the original intention of medical informatization is to improve medical efficiency and break the information barriers between medical institutions, which is also limited by genetic privacy risks from growing bioinformatics data. To share biomedical data correlated with genes usefully under the premise of ensuring genetic privacy, with the concept of medical reference interval, we design a query strategy based on a flexible differential privacy algorithm that setting weights for noise added according to the purpose of the inquirer. We generate a series of biomedical data to test the effectiveness of our query system, the test results show that our algorithm ensures data’s utility with acceptable privacy breaches that addresses the problem of biomedical data sharing to a certain extent.
The prosecution of cybercrime – why transnational and extraterritorial jurisdiction should be resisted

Cybercrime is a scourge that blights the lives of many around the globe.It has a significant transnational component.Despite established international and national regulation, its growth in scale and breadth persists.One result of which has been increased recourse to transnational and extraterritorial jurisdiction.This is misplaced.There are a number of factors militating against it.The foundations of international law, human rights, the interests of justice, complexity and cost and the underlying purposes of criminalisation conspire to demand a reconsideration of the use of transnational and extraterritorial jurisdiction in the fight against cybercrime.While there are undoubted difficulties attendant to the alternative, enhanced subjective territorial regulation and enforcement, it is undoubtedly the most effective long-term means of fighting cybercrime.The normalisation of transnational and extraterritorial cybercrime jurisdiction should be resisted.
Effectiveness of Interactive Reading Mode Based on Multisensor Information Fusion in English Teaching

With the development of the data era, information is available in complex and diverse forms, and the information obtained by a single sensor is always incomplete and cannot grasp the global content. Multisensors, on the other hand, can obtain more dimensional information about the target and fuse the multidimensional information, thus changing the shortcomings of a single sensor. For students, reading is an essential means of communicating ideas and acquiring information, yet many teachers focus too much on decoding language and teaching literal meaning. In general, students only passively acquire knowledge, which does not effectively improve their analytical and reasoning skills. Under the guidance of an efficient reading mode, it is crucial to develop good reading habits and use correct reading strategies. To enhance the effectiveness of interaction between students and teachers, this paper proposes an interactive reading model based on multisensor information fusion to help students’ acquisition of background knowledge and language skills, including teacher-student interaction before reading, during reading, and after reading, which ensures the completeness and accuracy of the interactive information and helps decision-making judgments. In this paper, by introducing the theoretical knowledge of multisensor information fusion and interactive reading mode, and by evaluating the English teaching in two parallel classes with the traditional English teaching mode and using the interactive reading mode, we found that the students in the experimental class B believed that their English level in all aspects had improved significantly, and the number of students who performed the 30–60 minutes English reading time period had increased substantially compared with that before the experiment, which could improve the students’ reading level of interest in reading, so English teaching using the interactive reading model is effective.
Design and Implementation of Software-Defined Data Center (SDDC) for Medical Colleges and Universities

The current medical universities culture can reflect their professional nature, showing a strong development trend. The current socialist economy of China is rapidly developing along with cloud computing for data centers. For cultural and educational activities, virtualization and software-defined data center (SDDC) technologies are being used. The most widely used open-source virtualization technologies are SDDC and virtualization. Users may utilize OpenStack to establish private cloud computing environments. The separate control and forwarding architecture of SDDC also make it naturally suitable for the data center’s network environment. Ryu controller has become one of the most widely used SDDC controllers because of its lightweight, high efficiency, and modularity features. Due to the large variety of network operations required by the SDDC, appropriate management and control function modules must be developed on Ryu when it is used as the controller. This study investigates the construction of campus culture at medical colleges and universities using SDDC. Our main objective is to improve the efficiency of data construction in medical colleges and universities and creating a positive cultural environment. On the one hand, the addition of functional modules makes the management of the controller itself easier. Ryu lacks an intuitive interactive platform. Although OpenStack provides an interactive interface, it cannot meet the integration requirements of Ryu. The unified control and school scheduling system have some research and application potential. The experimental results show that our proposed approach of designing an SDDC for medical colleges and universities has a significant impact and has vast potential for future studies.
Application of Speech Interaction System Model Based on Semantic Search in English MOOC Teaching System

In order to improve the effect of English MOOC teaching, this study combines the semantic search algorithm to construct an English MOOC teaching system to promote the interactive effect of English teaching. Moreover, this study analyzes a DOA estimation method based on sparse iterative covariance and an improved method, and no prior knowledge of noise is required to use this method in noisy data scenarios. In addition, this study constructs an English interactive teaching system based on semantic search. Finally, this study uses the course resources of the MOOC platform to teach students, perform organizational collaboration, and answer the questions. The research shows that the speech interaction system model based on semantic search proposed in this study has a better application effect in the English MOOC teaching system.
Peer reviewer topic choice and its impact on interrater reliability: A mixed-method study

Abstract One of the main critiques of academic peer review is that interrater reliability (IRR) among reviewers is low. We examine an underinvestigated factor possibly contributing to low IRR: reviewers’ diversity in their topic-criteria mapping (“TC-mapping”). It refers to differences among reviewers pertaining to which topics they choose to emphasize in their evaluations, and how they map those topics onto various evaluation criteria. In this paper we look at the review process of grant proposals in one funding agency to ask: How much do reviewers differ in TC-mapping, and do their differences contribute to low IRR? Through a content analysis of review forms submitted to a national funding agency (Science Foundation Ireland) and a survey of its reviewers, we find evidence of interreviewer differences in their TC-mapping. Using a simulation experiment we show that, under a wide range of conditions, even strong differences in TC-mapping have only a negligible impact on IRR. Although further empirical work is needed to corroborate simulation results, these tentatively suggest that reviewers’ heterogeneous TC-mappings might not be of concern for designers of peer review panels to safeguard IRR.
Principal microbial groups: compositional alternative to phylogenetic grouping of microbiome data

Statistical and machine learning techniques based on relative abundances have been used to predict health conditions and to identify microbial biomarkers. However, high dimensionality, sparsity and the compositional nature of microbiome data represent statistical challenges. On the other hand, the taxon grouping allows summarizing microbiome abundance with a coarser resolution in a lower dimension, but it presents new challenges when correlating taxa with a disease. In this work, we present a novel approach that groups Operational Taxonomical Units (OTUs) based only on relative abundances as an alternative to taxon grouping. The proposed procedure acknowledges the compositional data making use of principal balances. The identified groups are called Principal Microbial Groups (PMGs). The procedure reduces the need for user-defined aggregation of $\textrm{OTU}$s and offers the possibility of working with coarse group of $\textrm{OTU}$s, which are not present in a phylogenetic tree. PMGs can be used for two different goals: (1) as a dimensionality reduction method for compositional data, (2) as an aggregation procedure that provides an alternative to taxon grouping for construction of microbial balances afterward used for disease prediction. We illustrate the procedure with a cirrhosis study data. PMGs provide a coherent data analysis for the search of biomarkers in human microbiota. The source code and demo data for PMGs are available at: https://github.com/asliboyraz/PMGs.
Towards an Efficient and Robust Adversarial Attack Against Neural Text Classifier

Adversarial attack is a serious threat to neural network-based natural language processing applications. Adversarial attack uses tiny well-crafted perturbations to mislead neural networks. While existing adversarial text attacks can achieve good attack effects, they still do not guarantee efficiency and robustness. The adversarial text attacks are more efficient if they use less perturbation to achieve a higher attack success rate. The attacks are more robust if they can achieve a higher success rate when defense strategies are applied. To improve the efficiency and robustness of the adversarial attack, we propose SMAL: Saliency Map Attack with Levenshtein-similarity. The proposed attack consists of two parts: (1) The saliency map measures the perturbation priority of each word. It considers not only the influence of each word on the classification result but also how to maintain the misled classification result to improve the robustness of the attack. (2) Levenshtein-similarity network embeds words into edit distance space. When perturbing sentences, some words are replaced by substitutions with less edit distance. This can reduce the amount of modification, which improves the efficiency of the attack. Since the words are embedded in edit distance space rather than semantic space, the semantic-based defense is not effective for this attack, which improves the robustness. The experiments show that SMAL achieves a higher attack success rate with fewer perturbations. Also, the proposed attack is better when attacking a classifier defended by adversarial training.
Large-Scale Evolutionary Optimization Approach Based on Decision Space Decomposition

The identification of decision variable interactions has a crucial role in the final outcome of the algorithm in the large-scale optimization domain. It is a prerequisite for decomposition-based algorithms to achieve grouping. In this paper, we design a recognition method with higher efficiency and grouping accuracy. It is based on the decomposition strategy of min hash to solve large-scale global optimization (LSGO) problems, called MHD. Our proposed method focuses on discovering the interactions of decision variables through min hash and forming subcomponents with a principle that the interdependencies between these subcomponents are maintained at a minimal level. This is described as follows: first, the min hash performs several permutations of the vector composed of decision variables. Second, the index value of the first non-zero row of the vector after rearrangement is found to obtain the new feature vector. Third, the probability of identical data at each position is calculated based on the new feature vector to decide whether there are some certain interactions between the decision variables. The advantages of min hash are: simpler computation and greater efficiency improvement than comparison between two or two decision variables; ability to find similar decision variables very quickly; and ability to cluster decision variables in a simple way. Therefore, the efficiency as well as the reliability of MHD is guaranteed. On the accuracy aspect, the proposed algorithm performs well in various types of the large-scale global optimization benchmark test function. Finally, the experimental results analysis and summarize the performance competitiveness of our proposed MHD algorithm from several aspects when it is used within a co-evolutionary framework.
Preventing social engineering: a phenomenological inquiry

Purpose The purpose of this transcendental phenomenological qualitative research study is to understand the essence of what it is like to be an information systems professional working in the USA while managing and defending against social engineering attacks on an organization. The findings add to the information system (IS) body of literature by uncovering commonly shared attitudes, motivations, experiences and beliefs held by IS professionals who are responsible for protecting their company from social engineering attacks. Design/methodology/approach This is a qualitative, transcendental phenomenological study that was developed to gain a deeper understanding about the essence of what it is like to be an IS professional defending a US business against social engineering attacks. This research design is used when sharing the experiences of study participants is more important than presenting the interpretations of the researcher. To target participants from the industries identified as regularly targeted by social engineers, purposive sampling was used in conjunction with the snowball sampling technique to find additional participants until saturation was reached. Findings Ten themes emerged from the data analysis: (1) foster a security culture, (2) prevention means education, (3) layered security means better protection, (4) prepare, defend and move on, (5) wide-ranging responsibilities, (6) laying the pipes, (7) all hands on deck, (8) continuous improvement, (9) attacks will never be eliminated and (10) moving pieces makes it harder. The ten themes, together, reveal the essence of the shared experiences of the participants with the phenomenon. Originality/value Understanding how to defend an enterprise from social engineering attacks is an international issue with implications for businesses and IS professionals across the world. The findings revealed that to prevent social engineer attacks, all employees – IS and non-IS professionals alike – must be unified in their desire to protect the organization. This means IS professionals and organizational leadership must establish a strong security culture, not only through layered technology and electronic controls but also through open communication between all departments and continuously engaging, training and reinforcing social engineering education, policies, procedures and practices with all employees.
LMS Moodle interactive modules in student motivation management

The article describes the experience of using the LMS Moodle interactive “Seminar” module to activate students’ independent activities, based on the example of teaching chemical disciplines. Special focus is placed upon student motivation to participate in discussion projects related to the role of chemical processes in their future professional activities. In the context of this type of activities, the students were asked to write a paper on one of the suggested topics in compliance with previously specified criteria. They were also asked to make a critical assessment of other students’ papers on the selected seminar topic. The article reviews the reports on various types of students’ activities and reviews the quality of the online course and e-learning system based on student feedback. For a numerical assessment of the quality of teaching and learning materials, it is proposed to use systems of equations of fuzzy sets. The technologies described in the article expand the set of criteria to assess the students’ proficiency and promote emergence effect.