Reviewerly/exHarmony huggingface dataset:
Accomplishing Reality Media: The Affective Lure of Online Crime Discussions

Drawing from interviews with posters and an analysis of a dozen discussion threads on the Swedish online discussion forum Flashback, this article sets out to investigate the dramatization of crime news from the point of view of the participants themselves. Analyzing both the online discussions and the articulated motivations and activities of the posters, this article focuses on how participants in these crime discussion threads come together around an epistemic quest for the truth, but also how discussions are ritualized so as to give rise to a collective effervescence and unity when the epistemic drama is perceived to have been resolved, and the truth is revealed to the wider public. Accordingly, this article seeks to remedy a gap in the previous research on online crime discussions by focusing less on the investigative aspects of such work – for example, how participants collaborate to solve crimes – and more on the symbolic and affective aspects of the dramatization of these discussions of crime. What is at the forefront is thus how participants make sense of their engagement and experience of these online discussions, rather than the actual criminal case. To refer to this as an epistemic drama is to highlight how activities, ideals and identities are ordered and sequenced through a ritualization of collective online participation, but also how it involves the establishment of (1) a particular predicament, (2) a collective objective, and (3) ultimately some sort of perceived emotional climax related to solving this predicament through the collective objective.
Approaches, Methods, and Resources for Assessing the Readability of Arabic Texts

Text readability assessment is a well-known problem that has acquired even more importance in today’s information-rich world. In this article, we survey various approaches to measuring and assessing the readability of texts. Our specific goal is to provide a perspective on the state-of-the-art in readability assessment research for Arabic, which differs significantly from other languages on which readability studies have tended to focus. We provide background on readability assessment research and tools for English, for which readability studies are the most advanced. We then survey approaches adopted for Arabic, both classical formula-based approaches and studies that combine Machine Learning (ML) with Natural Language Processing (NLP) techniques. The works we cover target text corpora for different audiences: school-age first language readers (L1), foreign language learners (L2), and adult readers in non-academic contexts. Therefore, we explore differences between reading in L1 and L2 and consider how they play out specifically in Arabic after describing language characteristics that may impact readability. Finally, we highlight challenges for Arabic readability research and propose multiple future directions to improve readability assessment and related applications that would benefit from more attention.
Assessing an Image-to-Image Approach to Global Path Planning for a Planetary Exploration

This work considers global path planning enabled by generative adversarial networks (GANs) on a 2D grid world. These networks can learn statistical relationships between obstacles, goals, states, and paths. Given a previously unseen combination of obstacles, goals, and an initial state, they can be asked to guess what a new path would look like. We performed experiments on a 64 × 64 pixel grid that generated a training set by using randomly positioned obstacles and goals. The heuristic search algorithm A* was used to create training paths due to its significant presence in the literature and ease of implementation. We experimented with architectural elements and hyperparameters, converging to a pix2pix-based architecture in which the generator was trained to generate plausible paths given obstacles and two points. A discriminator tried to determine whether these maps were real or fake. Additionally, we defined a qualitative path-generation “success rate” metric derived from the Fréchet inception distance (FID) and optimized our architecture’s parameters, ultimately reaching a 74% success rate on the validation set. Furthermore, we discuss the applicability of this approach to safety-critical settings, concluding that this architecture’s performance and reliability are insufficient to offset the downsides of a black-box approach to path generation.
Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks

Artificial Intelligence (AI) has demonstrated superhuman capabilities in solving a significant number of tasks, leading to widespread industrial adoption. For in-field network-management application, AI-based solutions, however, have often risen skepticism among practitioners as their internal reasoning is not exposed and their decisions cannot be easily explained, preventing humans from trusting and even understanding them. To address this shortcoming, a new area in AI, called Explainable AI (XAI), is attracting the attention of both academic and industrial researchers. XAI is concerned with explaining and interpreting the internal reasoning and the outcome of AI-based models to achieve more trustable and practical deployment. In this work, we investigate the application of XAI for network management, focusing on the problem of automated failure-cause identification in microwave networks. We first introduce the concept of XAI, highlighting its advantages in the context of network management, and we discuss in detail the concept behind Shapley Additive Explanations (SHAP), the XAI framework considered in our analysis. Then, we propose a framework for a XAI-assisted ML-based automated failure-cause identification in microwave networks, spanning model's development and deployment phases. For the development phase, we show how to exploit SHAP for feature selection and how to leverage SHAP to inspect misclassified instances during model's development process, and how to describe model's global behavior based on SHAP's global explanations. For the deployment phase, we propose a framework based on predictions uncertainty to detect possibly wrong predictions that will be inspected through XAI.
Best Lecturer Decision Support System Using Method Analytical Hierarchy Process (AHP) and Simple Adaptive Weighting (SAW)

Data processing as one of information technology area has wide range of applications including the selection of best lecturer in universities. Best Lecturer Assessment is required by the university to identify qualified human resources and in the future this process will give motivations to the lectures to be more productive and increase the quality of doing their jobs. In Buana Perjuangan University, Karawang, the selection of best lecturer is still conducted manually, where giving result to be subjective and questionable. Additionally, decision support system method has not been applied in determining the best lecturer. In this study, we proposed hybrid method which combines Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) to determine the best lecturer by using five criteria, namely attendance at lecture meetings, academic rank, number of publications, additional assignments, and education levels. The data sample used were obtained using a random sampling technique on lecturer data, provided by the University Data and Information Center. The study involves two phases. Phase 1 involves in developing a questionnaire that contains a list of criteria that a best lecturer must have. In Phase 2, an AHP decision matrix is developed, and the candidates will be ranked based on the selected criteria obtained from Phase 1 and their weight values. The result will then be ranked by using SAW to obtain even more accurate decision of best lecturer. The results obtained indicate that the value of the Consistency Index (CI) for all respondents is consistent, 0.8 in average. Then, the CI result is used to determine the Consistency Ratio (CR), where the result is also consistent for all of the respondents, -0.7 in average. These result shows that the proposed method has the same output with the expert (respondents) decisions.
sEMG-based technology for silent voice recognition

Silent speech recognition (SSR) is a system that implements speech communication when a sound signal is not available using surface electromyography (sEMG)-based speech recognition. Researchers have used surface electrodes to record the electrically-activated potential of human articulation muscles to recognize speech content. SSR can be used for pilot-assisted speech recognition, communication of individuals with speech impairment, private communication, and other fields. In this feasibility study, we collected sEMG data for ten single Mandarin numeric words. After reducing power frequency interference and power supply noise from the sEMG signal, short-term energy (STE) was used for voice activity detection (VAD). The power spectrum features were extracted and fed into the classifier for final identification results. We used the Hold-out method to divide the data into training and test sets on a 7-3 scale, with an average accuracy of 92.3% and a maximum of 100% using a support vector machine (SVM) classifier. Experimental results showed that the proposed method has development potential, and is effective in identifying isolated words from the sEMG signal of the articulation muscles.
Potentially toxic elements in the soils of Campi Flegrei (south Italy) and the immediate surroundings: Spatial distribution, origin and probabilistic human health risk

Campi Flegrei is an active volcanic field in south Italy where the potentially toxic elements (PTEs) are of growing concern because the intensive anthropogenic and volcanic activities might pose adverse human health effects. In this article, 394 topsoils (0-15 cm) are collected for instrumental analysis of the <2 mm fraction. The geochemical maps indicate that higher concentrations of Pb, Zn, Cd, Cr, Hg, Ni and Sb are related to the urban area, but greater levels of As, Tl, Co, Cu, Se and V are observed in the other parts. A robust principal component analysis detected: (1) the Pb-Zn-Hg-Cd-Sb-Cr-Ni association that probably highlights anthropogenic activities such as heavy traffic load and fossil fuel combustion in the urbanized area; (2) the Al-Fe-Mn-Ti-Tl-V-Co-As-U-Th association that mostly reveals the contribution of pyroclastic deposits; and (3) the Na-K-B association that feasibly indicates the weathering degree. The probabilistic health risk modeling for the children under 6 years old shows negligible Pb and Zn non-carcinogenic risk and unexpected Pb carcinogenic risk for exposure through soil ingestion. However, for the inhalation pathway, the children aged <1 year old have the highest chance (90%) of acceptable (i.e. between 1E-6 and 1E-4) Pb carcinogenic health risk. This should not be overlooked because Naples is under high environmental pressure and previous studies reported the increased Pb and Zn quantities in soil over a 26-year timespan. Overall, the results of geostatistical interpolation, compositional data analysis and probabilistic health risk modeling potentially uncover the link between soil geochemistry and human health.
IT-based smart-university driven management of higher education training activities

The IT-based management of training activities at universities has many inadequacies. According to the facilitation of comprehensive IT system application under digital transformation in education sector with the aim of smart-university driven model application, this paper provides a systematic view of a smart university, and analyses some issues related to IT-based training management, which supports Vietnamese universities to integrate smart-university driven management systems.
Dense Vehicle Counting Estimation via a Synergism Attention Network

Along with rising traffic jams, accurate counting of vehicles in surveillance images is becoming increasingly difficult. Current counting methods based on density maps have achieved tremendous improvement due to the prosperity of convolution neural networks. However, as highly overlapping and sophisticated large-scale variation phenomena often appear within dense images, neither traditional CNN methods nor fixed-size self-attention transformer methods can implement exquisite counting. To relieve these issues, in this paper, we propose a novel vehicle counting approach, namely the synergism attention network (SAN), by unifying the benefits of transformers and convolutions to perform dense counting assignments effectively. Specifically, a pyramid framework is designed to adaptively utilize the multi-level features for better fitting in counting tasks. In addition, a synergism transformer (SyT) block is customized, where a dual-transformer structure is equipped to capture global attention and location-aware information. Finally, a Location Attention Cumulation (LAC) module is also presented to explore the more efficient and meaningful weighting regions. Extensive experiments demonstrate that our model is very competitive and reached new state-of-the-art performance on TRANCOS datasets.
An Overview of Security Breach Probability Models

Cybersecurity breach probability functions describe how cybersecurity investments impact the actual vulnerability to cyberattacks through the probability of success of the attack. They essentially use mathematical models to make cyber-risk management choices. This paper provides an overview of the breach probability models that appear in the literature. For each of them, the form of the mathematical functions and their properties are described. The models exhibit a wide variety of functional relationships between breach probability and investments, including linear, concave, convex, and a mixture of the latter two. Each model describes a parametric family, with some models have a single parameter, and others have two. A sensitivity analysis completes the overview to identify the impact of the model parameters: the estimation of the parameters which have a larger influence on the breach probability is more critical and deserves greater attention.
A Retail Object Classification Method Using Multiple Cameras for Vision-Based Unmanned Kiosks

Several unmanned retail stores have been introduced with the development of sensors, wireless communication, and computer vision technologies. A vision-based kiosk that is only equipped with a vision sensor has significant advantages such as compactness and low implementation cost. Using convolutional neural network (CNN)-based object detectors, the kiosk recognizes an object when a customer picks up a product. In retail object recognition, the key challenge is the limited number of detections and high interclass similarity. In this study, these challenges are addressed by utilizing the “view-specific” feature of an object; specifically, an object class is divided into multiple “view-based” subclasses, and the object detectors are trained using these data. Further, the “view-aware feature” is defined by aggregating subclass detection results from multiple cameras. A superclass classifier predicts a superclass by utilizing an informative subclass detection result that distinguishes the target object from other similar-looking objects. To verify the effectiveness of the proposed approach, a prototype of the vision-based unmanned kiosk system is implemented. Experimental results indicate that the proposed method outperforms the conventional method, even on a state-of-the-art detection network. The dataset used in this study has been subsequently provided in the IEEE DataPort for reproducibility.
On training non-uniform fuzzy partitions for function approximation using differential evolution: A study on fuzzy transform and fuzzy projection

This paper focuses on the use of differential evolution to improve the approximation properties of function approximation models based on fuzzy partitions. Two cases are considered: Fuzzy transform and Fuzzy projection, and the design of hybrid evolutionary fuzzy systems, is studied. Even though function approximation techniques based on fuzzy partitions have been well studied, few papers consider the problem of centroid selection of the basis functions. Thus, in most cases uniform fuzzy partitions are considered. By using an evolutionary algorithm a systematic approach on the selection of the partition, is provided. The optimisation problem involves the determination of the model parameters, which in our case are the fuzzy partition’s membership functions’ locations. The proposed method is tested on the scattered data approximation problem, in a regression sense, that is given a set of sparse data the latent function is approximated. Numerical studies on one and two-dimensional test functions demonstrate that the evolutionary algorithm based fuzzy projection displays high performance in terms of approximation error. Moreover, the proposed approach shows high approximation capabilities with a small number of basis functions. Comparison results, with uniform fuzzy partition models, neural networks and support vector machines, are provided.
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution

In this paper, we consider the task of space-time video super-resolution (ST-VSR), which can increase the spatial resolution and frame rate for a given video simultaneously. Despite the remarkable progress of recent methods, most of them still suffer from high computational costs and inefficient long-range information usage. To alleviate these problems, we propose a Bidirectional Recurrence Network (BRN) with the optical-flow-reuse strategy to better use temporal knowledge from long-range neighboring frames for high-efficiency reconstruction. Specifically, an efficient and memory-saving multi-frame motion utilization strategy is proposed by reusing the intermediate flow of adjacent frames, which considerably reduces the computation burden of frame alignment compared with traditional LSTM-based designs. In addition, the proposed hidden state in BRN is updated by the reused optical flow and refined by the Feature Refinement Module (FRM) for further optimization. Moreover, by utilizing intermediate flow estimation, the proposed method can inference non-linear motion and restore details better. Extensive experiments demonstrate that our optical-flow-reuse-based bidirectional recurrent network (OFR-BRN) is superior to state-of-the-art methods in accuracy and efficiency.
PCA and other dimensionality-reduction techniques

The article provides an insight into three most popular methods for data dimension reduction: principal component analysis (PCA), exploratory factor analysis (EFA) and linear discriminant analysis (LDA). First, the basic definitions and notations of the data matrices are presented and illustrated with a small real data set. Next, some essential facts from the matrix theory are summarized. The two main tools for dimension reduction, eigenvalue and singular value decomposition, help to explain how PCA and EFA work with a correlation and a row data matrix. To see them in action, PCA and EFA are applied to the small data set, and the results are interpreted and compared. Finally, LDA achieves dimension reduction of data with observations divided into several groups is considered. As PCA and EFA, the method performs either with between- and within-groups covariance matrices, or, more economically, with the row data.
Educational assessment of 21st century skills—novel initiatives, yet a lack of systemic transformation

Our aim is to discuss critical issues related to educational assessment in contemporary societies, especially of so-called 21st century skills. We introduce some key definitions of 21st century skills, outline international initiatives dedicated to advancing them, and present two examples of national curricular developments emphasizing and integrating these. Next, we present some examples of projects illustrating the interrelationship between assessment, education and technological development. Last, we introduce some perspectives on the assessment and transformation of education that point toward opportunities and pitfalls for the future. Our analysis shows that while there are a number of ongoing initiatives dedicated to developing instruments and curricula for assessment of 21st century skills, there are also fundamental challenges for the development of relevant assessments on a systemic level and for transforming practices related to 21st century skills more broadly. The lack of representation from several parts of the world, in international assessment initiatives on 21st century skills, is also striking. By taking a meta-perspective, we identify evolving themes within this field and provide a critical perspective on these developments. We conclude there are a number of opportunities and pitfalls for creating good synergies between 21st century skills in curricula and practices, as well as for developing assessment strategies.
Flexible, highly scalable and cost-effective network structures for data centers

The ever-increasing number of online services and tremendous data volume make the data center networks expand rapidly. The data center network must be flexible in scalability and cost-effective in order to respond quickly to the ever changing service requirements. However, the costs of switch-centric topologies are high, while the scalabilities of server-centric topologies are not good. How to build a flexible, highly scalable and cost-effective data center network structure is becoming a very challenging problem. Based on the Cartesian product graph, this paper proposes a new type of data center network structure named SDCCP (Scalable Data Center network structure based on Cartesian Product graphs). It is constructed by using commodity m-port switches and 2-port servers. Based on the m-degree Cartesian product graphs, we can construct different kinds of SDCCP structures which can be expanded in different scales by using the same type of m-port switches. In order to achieve incremental scalability, we further propose an incomplete SDCCP structure. Servers can be gradually added into the incomplete SDCCP structure while maintaining their topological properties unchanged. Compared with Fat-Tree and BCube, the SDCCP reduces the cost by about 30% and 60%, respectively; and reduces the energy consumption by about 27% and 35%, respectively. The performance analyzes and experimental results demonstrate that SDCCP strikes a good balance in terms of flexibility, scalability, cost-effectiveness, and low energy consumption in contrast to the typical data center network structures.