Reviewerly/exHarmony huggingface dataset:
An Arabic Manuscript Regions Detection, Recognition and Its Applications for OCRing

The problem of Region of Interest (RoI) in document layout analysis and document recognition has recently become an essential topic in OCRing systems. Arabic manuscript layout analysis and OCRing recognition using language detection, document category, and RoI with Keras and TensorFlow are terms of the state-of-the-art that should be investigated. This article investigates the problem of Arabic manuscript recognition problems with respect to in OCRing-based recognition. A new framework architecture, which integrates Fast Gradient Sign Method (FGSM) using Keras and TensorFlow with adversarial image generation during training procedure is proposed. Also, the article tries to improve the OCRing accuracy of the image enhancement, alignment, layout analysis, and recognition using deep learning in multilingual system. RoIs detections will be performed using a custom trained deep learning model using bounding box regression with Keras and TensorFlow. This topic investigates an extension of Page Segmentation Method (PSM) to enhance OCRing parameter modes and enhances Arabic OCRing system accuracy from reinforcement strategy. Therefore, the article achieves a significant improvement of OCRing results due to the three parameters: language identification, document category, and RoI types (Table, Title, Paragraph, figure, and list). This model is based on “region proposal algorithm” as a basis of CNN object detectors to find the number of the RoIs. Therefore, the proposed framework performs three distinctive tasks: (1) CNN architecture for adversarial training, (2) an implementation of the FGSM with Keras and TensorFlow, and (3) an adversarial training script implementation with the CNN and the FGSM method. The experiments on Arabic manuscript dataset including Arabic text, English/Arabic documents, and Latin digits’ datasets, demonstrate the accuracy of the proposed method. Moreover, the proposed framework performs well and succeeded in defending against adversarial attacks or adversarial images. The experimental results on our collected dataset illustrate the novelty of our proposed framework over the other existing PSM methods to be extended and updated to improve the quality of the OCRing system. The results show that the influence of PSM after expanding using the RoI types, language ID, and document/manuscript category can improve the OCRing accuracy. Also, the experimental results show significant performance by the new framework model with accuracy reached to 99% compared to relative methods.
Should a robot wear a mask during the pandemic?

• Tourists' responses to service robots' mask-wearing depend on political ideology. • Liberals prefer to interact with robots wearing masks. • Conservatives prefer to interact with robots without masks. • I-sharing may enhance tourists' acceptance of service robots.
Temporal-aware and multifaceted social contexts modeling for social recommendation

The social recommendation has utilized ego network in a static manner, assuming that the way of social influence impacts users’ preferences is simplex and constant over time. Such assumptions hinder the effectiveness of social recommendation since the online social contexts are complicated and friends’ tastes may change across items consumption. To address this research gap, we propose a novel social recommendation model that focuses on the effect of different social contexts and the dynamics of social influence. Specifically, we first leverage a community detection algorithm to divide online users into various communities, named community-based social contexts. Based on the community structure, we introduce a hierarchical attention mechanism to evaluate the influence of multiple community-based social contexts. Secondly, we choose the directly connected neighbors, namely the ego-network based social contexts. In such social contexts, the model can capture the user’s preferences that are influenced by their friends’ dynamic behaviors. Then, the model could have a comprehensive representation of users’ current preferences by merging the impact of two types of social contexts. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art social recommendation models. The implementation of our model and the datasets are available at https://github.com/socialsnail/TSC.
Support for cognition in decision support systems: an exploratory historical review

Decision support systems (DSS) have been traditionally developed to assist with unstructured and semi-structured problems. Early DSS researchers explored a broad range of techniques for supporting human cognition as part of decision making. Cognition during decision making was viewed in terms of two competing, and sometimes cooperating, systems: one that was automatic and fast, and one that was deliberative and slow. The aim of this research is to trace historical studies on cognitive aspects of decision support and determine the theoretical underpinnings of DSS support for cognition. We analysed articles drawing on the seminal literature to derive the relevant dimensions, including the classical Gorry & Scott Morton (1989) framework. This analysis identified opportunities for future research relevant to providing better support for cognition by highlighting some design parameters for information systems.
Sign Gesture Classification and Recognition Using Machine Learning

Gesture classification is one of the most emerging domains of today’s era. Among the major applications of gesture classification, sign gesture recognition is considered the core class of this domain. Keeping in mind the specifications of the region and language-based variations, the development of a standard sign gesture classifier is the main objective of today’s research. So far many language translators have been utilized to capture this communication deficiency. Software simulations and hardware prototype-based different models have been implemented using vision-based, sensor-based, and hybrid classifiers. This article focuses on the flex, accelerometer, and gyroscope sensors-based smart prototype which is designed to capture sign gestures. These sensors are placed on a glove to capture and compile digits i.e. 0 to 10, alphabets i.e. A–Z, and alphanumeric i.e. 0–10, A–Z datasets. The main objective of the proposed model is to classify sign gestures, made by deaf–mute individuals and recognize the actual meaning of performed gestures. K-nearest neighbor, discriminant analysis, and support vector machine-based machine learning algorithms are trained on our manually compiled datasets that are succeeded in achieving an average accuracy of 99.18%, 99.03%, and 99.82%, respectively.
Evaluating Soft Organ-Shaped Tangibles for Medical Virtual Reality

Connecting digital information with the physical is one of the essential ideas of tangible user interfaces. The design of the physical representation is important especially for specialised domains like surgery planning, because surgeons rely heavily on their tactile senses. Therefore, this research work investigates the effect of a soft and a hard 3D model as an interaction device for virtual reality surgical planning. A user study with 13 surgeons reveals a clear preference for the softer, more realistic material and a significantly higher haptic user experience for the soft model compared to the hard one. These results advocate for stressing material aspects along with the interaction design in domains with an inherently high focus on tactile aspects.
A Case Study on Supporting the Preservation, Valorization and Sustainability of Natural Heritage

Caves can be considered as features of the landscape that have been formatted through a long period and are signs of the past climatological conditions, of prehistoric animal and human inhabitation, and provide habitat for species. In the near past, caves have also gained tourism interest since they offer unique sightseeing experiences. The sustainability and protection of cave heritage have been recently discussed mainly because over-tourism has been proven to have negative side effects on the preservation of the heritage site. Technology today has provided widely adopted inexpensive technical means to support immersive visiting experiences to sites of heritage interest that could support their valorization and sustainability in forms more friendly to the site. In this work, such an alternative visiting approach is explored through a use case applied to the Alistrati cave near Serres, Greece, where a VR tour guide can support immersive visiting experiences to the heritage site. By employing means of digital preservation of heritage sites the VR solution aspires to offer immersive close to reality engaging visiting experiences.
Identification of soil heavy metal pollution by constructing 2D plane using hyperspectral index

This study proposes a method for the rapid identification of elements in soil heavy metal pollution using spectral indices. Set up a simulation experiment of planting crops in soil polluted by multi-gradient Cu and Pb. The obtained polluted soil spectral data was initially pre-processed to obtain the original spectrum (OR), the continuum removed spectrum (CR), and the first-order differential spectrum (FOD). Then the preliminary model of soil heavy metal pollution index (SHMPI) was constructed. Using the correlation optimal algorithm, the maximum median distance algorithm, and the maximum average distance algorithm to select the optimal bands corresponding to the OR, CR, and FOD. The optimal bands selected by each algorithm were substituted into the SHMPI. Each algorithm obtains three indices, and two of them were selected as the x-axis and y-axis to form a two-dimensional pollution identification plane. Nine two-dimensional planes can be obtained by three algorithms and three combinations of OR, CR, and FOD. Support vector machine classifier was used to classify the Cu and Pb polluted samples in the planes, and nine classification models to distinguish Cu and Pb pollution in soil were constructed. The results show that using the correlation optimal algorithm to extract the optimal bands, and using OR and CR to construct SHMPI, the accuracy of the classification line model of Cu and Pb pollution obtained was 93% in the training group and 86% in the validation group. This method can stably and effectively identify the types of heavy metal pollution in soil, and can also effectively identify whether the soil is polluted by heavy metals, which is expected to guide the rapid and non-destructive identification of heavy metal pollution in polluted areas, and provide new ideas for the identification of other types of heavy metals in soil.
Exploring the Design Space of Headphones as Wearable Public Displays

The need for online meetings increased drastically during the COVID-19 pandemic. Wearing headphones for this purpose makes it difficult to know when a headphone wearing person is available or in a meeting. In this work, we explore the design possibilities of headphones as wearable public displays to show the current status or additional information of the wearer to people nearby. After two brainstorming sessions and specifying the design considerations, we conducted an online survey with 63 participants to collect opinions of potential users. Besides the preference of the colors red and green as well as using text to indicate availability, we found that only 54 % of our participants would actually wear headphones with public displays attached. The benefit of seeing the current availability status of a headphone-wearing person in an online meeting or phone call scenario were nonetheless mentioned even by participants that would not use such headphones.
Differential Human Learning Optimization Algorithm

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.
Single Neuron for Solving XOR like Nonlinear Problems

XOR is a special nonlinear problem in artificial intelligence (AI) that resembles multiple real-world nonlinear data distributions. A multiplicative neuron model can solve these problems. However, the multiplicative model has the indigenous problem of backpropagation for densely distributed XOR problems and higher dimensional parity problems. To overcome this issue, we have proposed an enhanced translated multiplicative single neuron model. It can provide desired tessellation surface. We have considered an adaptable scaling factor associated with each input in our proposed model. It helps in achieving optimal scaling factor value for higher dimensional input. The efficacy of the proposed model has been tested by randomly increasing input dimensions for XOR-type data distribution. The proposed model has crisply classified even higher dimensional input in their respective class. Also, the computational complexity is the same as that of the previous multiplicative neuron model. It has shown more than an 80% reduction in absolute loss as compared to the previous neuron model in similar experimental conditions. Therefore, it can be considered as a generalized artificial model (single neuron) with the capability of solving XOR-like real problems.
Building Self-Healing Feature Based on Faster R-CNN Deep Learning Technique in Web Data Extraction Systems

Web data extraction has evolved over the years with extracting data from documents to today’s World Wide Web (WWW). The WWW growth has placed data at the centre of this ecosystem and benefited society at large, businesses and consumers. The proposed system uses deep learning technique, Faster region convolutional neural network (R-CNN) for automated navigation, extraction of data and self-healing of data extraction engine to adapt to dynamic changes in website layout. The proposed system trains the Faster R-CNN model for detection of product in the web page using bounding box image detection technique and extracts product details with high extraction accuracy. Deep learning technique has advanced rapidly in the different fields for image detection, but its application in data extraction makes this paper unique. An ecommerce retail website is used as real-world example to prove the self-healing capability of the proposed automated web data extraction system.
Transparent Practices for Quantitative Empirical Research

Transparent research practices enable the research design, materials, analytic methods, and data to be thoroughly evaluated and potentially reproduced. The HCI community has recognized research transparency as one quality aspect of paper submission and review since CHI 2021. This course addresses HCI researchers and students who are already knowledgeable about experiment research design and statistical analysis. Building upon this knowledge, we will present current best practices and tools for increasing research transparency. We will cover relevant concepts and skills in Open Science, frequentist statistics, and Bayesian statistics, and uncertainty visualization. In addition to lectures, there will be hands-on exercises: The course participants will assess transparency practices in excerpts of quantitative reports, interactively explore implications of analytical choices using RStudio Cloud, and discuss their findings in small groups. In the final session, each participant will choose a case study based on their interest and assess its research transparency together with their classmates and instructors.
SpiderClip: Towards an Open Source System for Wearable Device Simulation in Virtual Reality

Smartwatches and fitness trackers integrate different sensors from inertial measurement units to heart rate sensors in a very compact and affordable form factor. This makes them interesting and relevant research tools. One potential application domain is virtual reality, e.g., for health related applications such as exergames or simulation approaches. However, commercial devices complicate and limit the collection of raw and real-time data, suffer from privacy issues and are not tailored to using them with VR tracking systems. We address these issues with an open source design to facilitate the construction of VR-enabled wearables for conducting scientific experiments. Our work is motivated by research in mixed realities in pervasive computing environments. We introduce our system and present a proof-of-concept study with 17 participants. Our results show that the wearable reliably measures high-quality data comparable to commercially available fitness trackers and that it does not impede movements or interfere with VR tracking.
Online Anomaly Detection of Industrial IoT Based on Hybrid Machine Learning Architecture

Industrial IoT (IIoT) in Industry 4.0 integrates everything at the level of information technology with the level of technology of operation and aims to improve Business to Business (B2B) services (from production to public services). It includes Machine to Machine (M2M) interaction either for process control (e.g., factory processes, fleet tracking) or as part of self-organizing cyber-physical distributed control systems without human intervention. A critical factor in completing the abovementioned actions is the development of intelligent software systems in the context of automatic control of the business environment, with the ability to analyze in real-time the existing equipment through the available interfaces (hardware-in-the-loop). In this spirit, this paper presents an advanced intelligent approach to real-time monitoring of the operation of industrial equipment. A hybrid novel methodology that combines memory neural networks is used, and Bayesian methods that examine a variety of characteristic quantities of vibration signals that are exported in the field of time, with the aim of real-time detection of abnormalities in active IIoT equipment are also used.
SSR-GNNs: Stroke-based Sketch Representation with Graph Neural Networks

This paper follows cognitive studies to investigate a graph representation for sketches, where the information of strokes, i.e., parts of a sketch, are encoded on vertices and information of inter-stroke on edges. The resultant graph representation facilitates the training of a Graph Neural Networks for classification tasks, and achieves accuracy and robustness comparable to the state-of-the-art against translation and rotation attacks, as well as stronger attacks on graph vertices and topologies, i.e., modifications and addition of strokes, all without resorting to adversarial training. Prior studies on sketches, e.g., graph transformers, encode control points of stroke on vertices, which are not invariant to spatial transformations. In contrary, we encode vertices and edges using pairwise distances among control points to achieve invariance. Compared with existing generative sketch model for one-shot classification, our method does not rely on run-time statistical inference. Lastly, the proposed representation enables generation of novel sketches that are structurally similar to while separable from the existing dataset.
Analysis of Behavioral and Information Security Awareness among Users of Zoom Application in COVID-19 Era

Behavior in using inappropriate technology and low level of individual awareness causes a high risk of cybercrime. This study aims to see the behavior and awareness of information security among users of the Zoom application, especially in the COVID-19 era in Indonesia. The measurement scales used in this study were RBS (Risky Behavior Scale), CBS (Conservative Behavior Scale), EOS (Exposure Offence Scale), and RPS (Risk Perception Scale). This research used an online questionnaire to collect data with 400 respondents. Data analysis techniques used the Independent Sample T-Test, Mann Whitney, One Way Anova, Krusskal Wallis, Tukey, Pairwise Comparison, and Spearman Rank, which were processed using the SPSS program. The results showed significant differences between demographics on behavior and information security awareness. Daily Internet usage time influenced behavior and awareness of information security. Security behavior and awareness of information security are essential to determine the risks due to behavior and lack of awareness of information security. The most important findings in this study can be used as a reference in designing information security training. Further studies are expected to add other variables such as psychography, geography, or other users of different video conferencing applications such as Google Meet, Microsoft Team, and Webex.
X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism

Machine translation has received significant attention in the field of natural language processing not only because of its challenges but also due to the translation needs that arise in the daily life of modern people. In this study, we design a new machine translation model named X-Transformer, which refines the original Transformer model regarding three aspects. First, the model parameter of the encoder is compressed. Second, the encoder structure is modified by adopting two layers of the self-attention mechanism consecutively and reducing the point-wise feed forward layer to help the model understand the semantic structure of sentences precisely. Third, we streamline the decoder model size, while maintaining the accuracy. Through experiments, we demonstrate that having a large number of decoder layers not only affects the performance of the translation model but also increases the inference time. The X-Transformer reaches the state-of-the-art result of 46.63 and 55.63 points in the BiLingual Evaluation Understudy (BLEU) metric of the World Machine Translation (WMT), from 2014, using the English–German and English–French translation corpora, thus outperforming the Transformer model with 19 and 18 BLEU points, respectively. The X-Transformer significantly reduces the training time to only 1/3 times that of the Transformer. In addition, the heat maps of the X-Transformer reach token-level precision (i.e., token-to-token attention), while the Transformer model remains at the sentence level (i.e., token-to-sentence attention).