Reviewerly/exHarmony huggingface dataset:
Information items used by online fraudsters and its relationship to Indonesian digital literacy

Introduction. This research was conducted to examine the information items used by online fraudsters related to the level of digital literacy of the society. This research was done on the pillars of data security in digital literacy.&#x0D; Data Collection Methods. Data was collected by watching videos on Youtube, online interviewing people who complained about the fraud they experienced on Facebook and Whatsapp in the period of 2019-2022.&#x0D; Data Analysis. The data was descriptively analyzed and classified to describe information items used by the fraudsters.&#x0D; Results and Discussion. The items used by the fraudsters are banking service terms, real but fake URLs, police standard operating procedures, and English terms used by market place. Therefore, continuous socialization from institutions related to these items of information is needed to improve digital literacy of society which has the potential to cause financial losses. In terms of socialization, libraries can play a proactive role.&#x0D; Conclusion. Information items that often used by online fraudsters are information related to banking services, police, marketplace, and fake social media accounts. This research may be continued to understand how low the victims' ignorance about the information items.
The Republican Scientific and Technical Library – 45 years in the service to science and industries. Information resources supporting science and research activities

The information resources of the Republican Scientific and Technical Library are reviewed. The Library actualizes the most important mission of information support of science and technologies, as well as information and library support of state authorities in science and technology, industrial enterprises, scientific research institutions, research and development organizations in the Republic of Belarus independently of their departmental subordination, as well as provision of library, bibliographic and reference and information services to sci-tech professionals. The main vectors are characterized; the structure of the Library’s collection is discussed. The most popular information resources meet the demands of the applied sciences and real economy for information supporting efficiency of research, sci-tech, and innovative activities in compliance with the global trends. The resources are structured in three blocks: those supporting science, research, innovative activities, as well as invention and efficiency drive; marketing information, market research materials, information on business conditions, address, reference and factual information: for process engineering, and development of controlling documents and specifications.
Self-aware cycle curriculum learning for multiple-choice reading comprehension

Multiple-choice reading comprehension task has recently attracted significant interest. The task provides several options for each question and requires the machine to select one of them as the correct answer. Current approaches normally leverage a pre-training and then fine-tuning procedure that treats data equally, ignoring the difficulty of training examples. To solve this issue, curriculum learning (CL) has shown its effectiveness in improving the performance of models. However, previous methods have two problems with curriculum learning. First, most methods are rule-based, not flexible enough, and usually suitable for specific tasks, such as machine translation. Second, these methods arrange data from easy to hard or from hard to easy and overlook the fact that human beings usually learn from easy to difficult, and from difficult to easy when they make comprehension reading tasks. In this article, we propose a novel Self-Aware Cycle Curriculum Learning (SACCL) approach which can evaluate data difficulty from the model’s perspective and train the model with cycle training strategy. The experiments show that the proposed approach achieves better performance on the C 3 dataset than the baseline, which verifies the effectiveness of SACCL.
Trade-off between uncertainty reduction and reward collection reveals intrinsic cost of gaze switches

In a dynamic and uncertain visual environment, the location with the highest task reward is not always the location with the highest information gain. In such situations, the visual system needs to trade off collecting reward against the risk of missing important events. Here we use a gaze-contingent paradigm in a visual detection task to investigate the learning and planning of temporal eye movement strategies by spatially separating the locations where the uncertainty of obtaining a task reward can be reduced and where the reward can be collected. In three different conditions within the experiment we vary the reward rate to measure subjects’ adaptive behavior in response to these altered task demands. In addition to changing locations using their gaze, subjects completed the same three task conditions by bringing the two separate locations into fixation through a button press instead of a gaze switch. This design allowed comparing the strategies in switching between task reward collection and uncertainty reduction either through gaze switches or through button presses. We find significant differences in switching behavior as function of the three reward rates but also for the two different switching conditions, indicating that humans can adapt their temporal strategies both in response to the obtainable reward rates as well as depending on the switching modality. In order to quantitatively understand the switching behavior, we develop a probabilistic planning model using Partially Observable Markov Decision Processes, which allows inferring participants’ individual behavioral switching costs and perceptual uncertainties. This model reveals that, contrary to a common belief, the subjective internal cost of a gaze switch is quite high, e.g. compared to a manual key press. Our model is able to predict key aspects of subjects’ behavioral data and we conclude that temporal eye movement strategies agree with probabilistic planning under uncertainty.
Aging attenuates the memory advantage for schema incongruent objects embedded in real-world scenes

Prior knowledge is essential to make predictions about the world and to guide our gaze. Previously, it has been suggested that older adults tend to rely more on prior knowledge, which, in turn, can affect active vision and memory performance in case of inaccurate predictions. While younger adults’ memory performance benefits from schema violations, the effects on older adults’ memory remain controversial. Here, we studied age effects on this memory advantage in a sample of 23 younger (mean age: 26.8 years) and 23 older adults (mean age: 69.3 years). In an encoding phase, participants viewed 60 images of real-world scenes taken from the SCEGRAM database. These scenes contained target objects that were either semantically congruent or incongruent with the scene context (e.g. shampoo in the shower versus in the fridge). After a delay, participants indicated for 60 target and 60 additional distractor objects whether they had been presented during the encoding phase. Subsequently, they provided a confidence judgment. If they responded that they recognized the object, they were asked to select one out of three scenes in which it was previously presented. Using a signal detection approach, we analyzed participants’ memory performance for congruent and incongruent target objects. Recognition of congruent targets was not affected by age. However, while schema violations were overall beneficial for memory performance in both age groups, this advantage was significantly less pronounced in older adults. Preliminary analyses of eye movements suggest a relationship between fixation duration on target objects during encoding and memory performance. In addition, analyses of scene memory revealed that older adults’ memory representations were biased more towards congruent information. We suggest that encoding of incongruent objects is strengthened in both age groups, but aging compromises decoding for recognition due to down-weighting of schema violations.
Spatial Coding of Visual Targets in the Frontal and Supplementary Eye Fields

Previous neurophysiological studies have suggested that visual response fields in the gaze control system primarily utilize an eye-centered frame of reference at both the single unit and population level (Sajad et al. 2015; Sadeh et al. 2016). We recently confirmed that visual responses to saccade targets in the monkey frontal (FEF) and supplementary eye fields (SEF) showed a preference for eye-centered coding at the population level, even in the presence of a large visual landmark (Bharmauria et al. 2020, 2021). Specifically, visual activity showed less variance when plotted relative to target-in-eye coordinates (Te) as opposed to Th (head) or Ts (space) coordinates. Here, we re-examined this database at the single unit level, and used a more sensitive spatial continuum analysis in the spatially tuned visual responses of 102 FEF neurons and 43 SEF neurons. We found that, consistent with previous reports, approximately 95 % of tuned FEF neurons preferentially coded for Te. However, in the case of SEF, neurons almost equally preferred Te, Th and Ts. Our continuum analysis (of fits between these three models, i.e., Te-Ts, Te-Th and Ts-Th) showed distribution peaks near Te, Th and Ts with a sparse scattering of best fits in between these continua. These data suggest that whereas FEF retains a fairly simple egocentric visual code, SEF visual responses can show more complex levels of spatial preprocessing (at least in the presence of a visual landmark). This result seems to complement the finding where it was shown that SEF stimulation is capable of producing gaze shifts toward eye, head, or space-fixed goals (Martinez-Trujillo et al. 2004).
Insane in the vembrane: filtering and transforming VCF/BCF files

We present vembrane as a command line variant call format (VCF)/binary call format (BCF) filtering tool that consolidates and extends the filtering functionality of previous software to meet any imaginable filtering use case. Vembrane exposes the VCF/BCF file type specification and its inofficial extensions by the annotation tools VEP and SnpEff as Python data structures. vembrane filter enables filtration by Python expressions, requiring only basic knowledge of the Python programming language. vembrane table allows users to generate tables from subsets of annotations or functions thereof. Finally, it is fast, by using pysam and relying on lazy evaluation.Source code and installation instructions are available at github.com/vembrane/vembrane (doi: 10.5281/zenodo.7003981).Supplementary data are available at Bioinformatics online.
Research for building the smart university model at Thu Dau Mot University

In recent years, society has been evolving rapidly, and contemporary education is being adapted to satisfy people's requirements in accordance with 4.0 trends and the industrial revolution.This gives birth to the definition of Smart University, the study of which seeks to identify the required components for constructing a smart learning environment. The research group analysed nationally and internationally advanced models, proposing a smart university model for Thu Dau Mot University (T-SMARTHS) with smart levels matching the phases established and developed at Thu Dau Mot University.
Enterprise Message Queues

A message queue is a special service that handles message passing between services in your cluster. Using message queueing in your cloud architecture enables a whole host of benefits, including the following:
THE USE OF RFID TECHNOLOGY IN AUTOMATIC VEHICLE IDENTIFICATION – PROPOSAL FOR SINIAV

This work presents the Brazilian experience in the development of the National System for Automatic Vehicle Identification -SINIAV, which consists of the use of radiofrequency identification technology.(RFID) for the entire vehicle fleet in the country.It also highlights aspects related to the use of technology in the regulatory and institutional context, aiming at its functionalities and applications in solutions for smart cities (Smart Cities) and intelligent transport systems (Intelligent Transport Systems -ITS).It then presents a proposal for a technological update of the model, with the reduction of costs and joint implementation with the vehicle identification plate with digital security, in the process of implementation in Brazil.Finally, it presents SINIAV as a proposed model for the integration and exchange of information between Latin American countries, through the standardization of protocols and systems, guaranteeing the interoperability and the safe flow of information, with a focus on reducing logistical costs, as a tool for preventing and combating theft and theft of vehicles and cargo, for controlling tax evasion, for automated data collection for planning and operational traffic control and transport, as well as the provision of numerous services to society.
Ultra-fast water-based switch could enable sci-fi technology

Salty water might be used to make next-generation ultra-high speed computing technologies.
Neural-signature methods for structured EHR prediction

Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain.
RDF Graph Summarization Based on Node Characteristic and Centrality

The explosive growth of RDF data makes it difficult to be efficiently queried, understood and used. RDF graph (RDFG) summarization aims to extract the most relevant and crucial data as summaries according to different criteria. Current summarization approaches mainly apply single strategies such as graph structure, pattern mining or relevance metrics to calculate RDFG summaries. Different to the existing approaches, this paper proposes a summarization approach to automatically generating RDFG summary, which can capture both structure and centrality information. Specifically, we present three algorithms, SumW (merging nodes based on node characteristics or similar types), SumS (merging nodes based on typed node characteristics) and SummaryFL (retrieving central nodes by combining node frequency and bridging coefficient). The three algorithms can be used by two summarization strategies: SumS or SumW only, and SumS+SummaryFL or SumW+SummaryFL. We conducted experiments over large and real-world RDF datasets to verify the effectiveness of our method with respect to time complexity, compression capability and coverage of the summary. The experiment results demonstrate that our approach outperformed the comparative algorithms.
Introduction

Quantum machine learning (QML) is a rapidly emerging research area where the power of quantum computing is applied to machine learning tasks in order to pursue computational advantages in view of the present context of ever-growing amounts of data to manage. More generally, QML turns out be a promising path to explore new opportunities offered by quantum machines, both the existing ones and those not yet built. It is currently a hot topic within the scientific communities working on quantum technologies and attracts interests also from industries and non-academic institutions which are excited by the idea of dealing with big data running machine learning algorithms on quantum computers. This volume essentially provides an introduction to the possibilities offered by quantum computers to machine learning discussing the fundamental aspects and presenting an overview on the most recent and remarkable developments in this scientific and technological area.
SPAM (An)Archive: Performing under Surveillance

Inspired by the attempt to disrupt data gathering and asking ourselves about “spam” digital practices as forms of resistance, the project SPAM (An)Archive brought together the work of the (post)composer and media artist Óscar Escudero and the collective SoundTrieb, whose collaboration took place digitally via Zoom and physically in Switzerland in 2021. Escudero’s collaborative work in the last few years with the (post)composer and musicologist Belenish Moreno-Gil has been fruitful at a sociological and cultural level of analysis, beyond music aesthetics, as they both deal with social networks and digital behavior in their music theater compositions. Using Facebook as a digital stage, SoundTrieb’s version of [Custom #2] (a digital melodrama) allowed performers to develop a double personalization of the piece, intervening their own social media profiles. This paper does not intend to discuss a theoretical approach to these artworks, but their subversive capability. Through a cultural analysis approach, this article recognizes the critical potential against the architecture of digital surveillance that they can achieve, while drawing attention to our complacency to ubiquitous technologies. For this purpose, I introduce notions such as DIY music and coded representation, but I also borrow concepts such as Brigitta Muntendorf’s social composing and Escudero’s spam art. This paper explores, through work examples by all the above-mentioned artists, recent models of compositional work that blur the line between the digital and the physical, the virtual and the real, the private and the public.
A new encryption system for IoT devices using embedded key cryptosystem

IoT constrained devices have special phenomena of constrained resources such as power source, memory and processing power. Besides, it is vital to achieve an acceptable level of security and privacy while preserving the IoT device resources. In this paper, a new cryptographic algorithm is developed that would be suitable for securing IoT devices using embedded key cryptosystem. The encryption/decryption processes are achieved by segmenting the message into blocks of certain length. Each block is encrypted/decrypted using a key that is generated according to the segment itself, i.e., the keys are embedded into the blocks. The algorithm adopts two tables: one for the intended character set and the other for the key elements generation. The secrecy of these tables is responsible for securing the key strength. Experimental implementation proves the algorithm feasibility and strength against hackers and intruders.
Physiologically-inspired neural model for social interaction recognition from abstract and naturalistic videos

INTRODUCTION: Humans recognize social interactions easily from visual stimuli showing interacting bodies, as well as from abstract stimuli such as moving geometric figures (Heider & Simmel, 1944). Whether social interaction perception from such different stimuli is accomplished by the same neural machinery remains unclear. We present a physiologically plausible neural model that achieves interaction recognition from abstract stimuli as well as from fully articulating bodies of animals in natural environments. METHODS: Exploiting a novel algorithm for the generation of interaction behavior that is based on dynamical navigation models for humans (Hovaidi-Ardestani, et al., 2018, Warren, 2006), we created matched pairs of abstract and naturalistic interaction stimuli (geometric shapes vs. articulating animals), balancing interaction-relevant visual features. Our neural recognition model combines a Deep Neural Network in shape-recognition pathway (VGG16) with top-level neural networks (Radial Basis Function network followed by recurrent Dynamic Neural Fields and Motion Energy detectors), which implement robust tracking of the interacting agents and the extraction of interaction-specific visual features (relative instantaneous position, speed, acceleration and orientation by means of a gain-field mechanism). RESULTS: The combination of these features by a simple neural classifier permits to reproduce psychophysical results on interaction recognition from abstract stimuli (Salatiello, et al. 2021) as well as recognition from interactions in naturalistic videos. CONCLUSION: The model predicts that recognition of social interactions can be achieved by simple physiologically plausible neural mechanisms. It makes concrete predictions about the behavior of neurons in relevant brain areas for the two stimulus classes, at the single cell as well as at the population level.
A Foveated Vision-Transformer Model for Scene Classification

Introduction: Humans can rapidly categorize scenes (R. VanRullen & S. Thorpe, 2001), even using peripheral vision (Larson & Loschky, 2009). Various computational models have been proposed for rapid scene categorization in terms of low-level properties such as spatial envelopes (Oliva & Torralba, 2001) and texture summary statistics (TTM, Rosenholtz et al., 2012). Yet, these models do not explicitly model the foveated properties of the visual system nor the interaction between eye movements and the scene category task. We propose a model with a foveated visual system and eye movements that can predict the dependence of human categorization performance across fixations. The model combines square pooling regions with the computer vision-transformer architecture (Dosovitskiy et al., 2020, Touvron et al., 2020) and makes multiple fixations to maximize classification using the technique of self-attention (Parikh et al., 2016, Bahdanau et al., 2015). Methods: Twenty-two participants classified 360 images (Places365 database, places2.csail.mit.edu) into 30 classes. Images subtended a viewing angle of 22.7 degrees. A gaze-contingent display was used to randomly interrupt the display after 1, 2, 3, or 4 fixations with initial forced-fixation at bottom-center or top-center. Results: We show that there is no significant improvement in performance after the 2nd fixation (Δ correct categorization=0.015; p=0.4729), unlike performance for object search (Koehler and Eckstein, 2017). The model correctly predicts modest classification improvements for free-viewing fixations (Δ=0.016). The model-human correlation in classification choices was not significantly lower than human-human correlations. Our findings suggest that human categorization of scenes within a single fixation can be explained by the spatially global distribution of the visual information in the scene and their availability even through the bottlenecks of the visual periphery. The newly proposed hybrid approach using biologically based modeling and Transformers can flexibly be applied to various naturalistic tasks and stimuli.