Reviewerly/exHarmony huggingface dataset:
Development of Smart Campus Model

The challenges of higher education in the era of industrial revolution 4.0 are global competition and technological progress. Universities must be able to adapt to the fourth industrial revolution to be competitive and recruit new students in the face of such fierce competition. Universities in Indonesia must build a smart campus if they want to become more competitive. Before implementing a smart campus, it is necessary to build a smart campus model that becomes a reference for universities to implement smart campuses. The Smart Campus model created will refer to the Garuda Smart Campus Model and be integrated with the Higher Education model and the Smart System model can be a reference for universities to simulate the Smart Campus that will be implemented and can be the foundation for measuring the intelligence level of campus so that it can be a reference for colleges to simulate the Smart Campus that will be implemented.
Tactile stimulus is essential to increase motivation for touch interaction in virtual environment

This paper reports the effectiveness of a tactile stimulus in a virtual environment to increase people's motivations during a monotonous task by comparing a touch with only visual stimuli and another with both visual and tactile stimuli. Although touch interaction with robots showed various positive effects such as improved motivation, visual and haptic stimuli were not separated due to the form of the touch in physical environments. Virtual environments enable us to investigate such effects by separating the modality of touch: a visual-only-touch and a visual-tactile touch. We experimented in a virtual environment where participants did a monotonous task after experiencing either a visual-only-touch or a visual-tactile-touch by an agent to compare them in the context of motivation improvements and the perceived impressions. The experimental results showed that a visual-tactile touch significantly increases the motivations of the participants for monotonous tasks. On the other hand, their likability of the agents is not significantly changed by the touch modalities.
English learning enhanced by collaborative contextual drama in an authentic context

Under the traditional English learning context, English as a Foreign Language (EFL) learners have few opportunities to apply their knowledge to a real-life setting. According to situated learning theory, different contexts hold different concepts related to learners’ real experiences. The researchers, therefore, integrated drama activity into EFL learning with the support of mobile devices to create an authentic English learning environment. In this study, the researchers developed a mobile-based system named Collaborative Contextual Drama (CCD) which provides the learners with text/picture annotation and drama-making for the purpose of fostering their English learning in authentic contexts. A goal of this study is to examine how CCD, with contextual English drama activities, influences learners’ learning behaviors as well as learning achievements. The post-test revealed that the learners who learned with CCD in familiar authentic contexts could significantly improve their English compared to the learners who learned without CCD. Furthermore, delayed tests revealed that the learners who learned with CCD could retain their knowledge longer. In addition, regarding learners’ behaviors and drama performances, it was revealed that intonation, quantity of annotations they made, quantity of interactions with environment, and quantity of using physical movement significantly affected their EFL learning achievement.
Role of Blockchain in Tackling and Boosting the Supply Chain Management Economy Post COVID-19

The year 2020 has witnessed a deadly outbreak caused by a novel coronavirus. The unexpected and rapid spread of COVID-19 globally has increased the death rate to a very high rate, and transmission rate found in rich countries is much higher than other countries. With the limited available hospitals and diagnostic centers and deprived disease surveillance systems, it is very tedious to tackle the COVID-19 outbreak. In such situation, latest emerging technologies such as blockchain have capable solution for combating the coronavirus pandemic by enabling early detection of outbreaks and ensuring reliable medical supply chain during the outbreak tracking. This chapter presents the blockchain methods being used to fight against the deadly COVID-19 outbreak. First, we propose the supply chain management aspects that the blockchain can provide for curbing COVID-19 and further use cases, and finally, we present blockchain start-up, pilot, and deployment toolkit, an approach to boost up the economy post COVID-19 outburst.
Automatic Detection of Gaze and Body Orientation in Elementary School Classrooms

Detecting the direction of the gaze and orientation of the body of both teacher and students is essential to estimate who is paying attention to whom. It also provides vital clues for understanding their unconscious, non-verbal behavior. These are called “honest signals” since they are unconscious subtle patterns in our interaction with other people that help reveal the focus of our attention. Inside the classroom, they provide important clues about teaching practices and students' responses to different conscious and unconscious teaching strategies. Scanning this non-verbal behavior in the classroom can provide important feedback to the teacher in order for them to improve their teaching practices. This type of analysis usually requires sophisticated eye-tracking equipment, motion sensors, or multiple cameras. However, for this to be a useful tool in the teacher's daily practice, an alternative must be found using only a smartphone. A smartphone is the only instrument that a teacher always has at their disposal and is nowadays considered truly ubiquitous. Our study looks at data from a group of first-grade classrooms. We show how video recordings on a teacher's smartphone can be used in order to estimate the direction of the teacher and students’ gaze, as well as their body orientation. Using the output from the OpenPose software, we run Machine Learning (ML) algorithms to train an estimator to recognize the direction of the students’ gaze and body orientation. We found that the level of accuracy achieved is comparable to that of human observers watching frames from the videos. The mean square errors (RMSE) of the predicted pitch and yaw angles for head and body directions are on average 11% lower than the RMSE between human annotators. However, our solution is much faster, avoids the tedium of doing it manually, and makes it possible to design solutions that give the teacher feedback as soon as they finish the class.
Generalizing the Presuppositional Approach to the Binding Conditions

Abstract Almost all current approaches to the binding theory (the conditions that regulate covaluation between NPs within a sentence) have accepted the view that the binding theory should regulate only syntactic binding and not coreference. In this article, I argue that this is incorrect and that we need a binding theory that regulates both binding and coreference, as the classical binding theory had it. I also show some problems with the idea that the binding conditions somehow involve or should reduce to syntactic movement or syntactic agreement (Agree), as many recent works argue. I suggest instead that we should pursue a presuppositional approach to the binding conditions, as has been proposed for Binding Condition A. I spell out such an analysis and illustrate some benefits of pursuing it.
Adaptive Scaling for Archival Table Structure Recognition

Table detection and structure recognition from archival document images remain challenging due to diverse table structures, complex document layouts, degraded image qualities and inconsistent table scales. In this paper, we propose an instance segmentation based approach for archival table structure recognition which utilizes both foreground cell content and background ruling line information. To overcome the influence from inconsistent table scales, we design an adaptive image scaling method based on average cell size and density of ruling lines inside each document image. Different from previous multi-scale training and testing approaches which usually slow down the speed of the whole system, our adaptive scaling resizes each image to a single optimal size which can not only improve overall model performance but also reduce memory and computing overhead on average. Extensive experiments on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method.
Automatic Generation of Semi-structured Documents

In this paper, we present a generator of semi structured documents (SSDs). This generator can provide samples of administrative documents that are useful for learning information extraction systems. It can also take care of the document annotation operation which is generally difficult to do and time consuming. We propose a general structure for SSDs and we prove that it perfectly works on three SSD types: invoices, payslips and receipts. Both the content and the layout are managed by random variables allowing them to be varied and to obtain different samples. These documents have some sort of similarity that gives them a common global model with particularities for each of them. The generator outputs the documents on three formats: pdf, xml and tiff image. We add an evaluation step to choose an adequate dataset for the learning process and avoid the overfitting. We can easily extend the actual implementation (https://github.com/fairandsmart/facogen) to other SSD types. We use this generator results to experiment an information extraction system from SSDs.
Enhanced IPCGAN-Alexnet model for new face image generating on age target

Cross aging face recognition ability will decrease to recognize someone's face after a certain time. Adding synthetics face images at a certain age generated from face aging architecture is one way to increase the performance of cross aging face recognition. A synthetics face image can create use the Generative Adversarial Network-based architecture. The current Generative Adversarial Network-based in face aging still needs high computation to create a model. Based on that reason we proposed a new optimal variant of Identity Preserving Conditional Generative Adversarial Network (IPCGAN), to generate a synthetic face image at certain age groups. In the proposed architecture, change made at the structure in the generator module, age classification module, and change the objective function to increasing the accuracy performance when generates a realistic synthetic face image in certain age groups and also speed up the training time. Modification in the age classifier at the proposed network forces our architecture to generate better synthetics face in certain age groups. Evaluation using Facenet, and Age prediction shows our method accuracy has 4.2% better results in k-NN classification, 3.6% better accuracy results in SVM classification, 8.6% better accuracy result in age verification, and 4.5% fewer accuracy results in age prediction.
Reliable and Privacy-Preserving Top-<i>k</i> Disease Matching Schemes for E-Healthcare Systems

The integration of body sensors, cloud computing, and mobile communication technologies has significantly improved the development and availability of e-healthcare systems. In an e-healthcare system, health service providers upload real patients’ clinical data and diagnostic treatments to the cloud server. Afterward, the users can submit queries with specific body sensor parameters, to obtaining pertinent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> diagnostic files. The results are ranked based on ranking algorithms that match the query parameters to the ones in diagnostic files. However, privacy concerns arise while matching disease, since the clinical data and diagnostic files contain sensitive information. In this work, we propose two reliable and privacy-preserving Top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> disease matching schemes. The first scheme is constructed based on our proposed weighted Euclidean distance comparison algorithm under secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor technique to get <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> diagnostic files. It allows users to set different weights for each body indicator as per their needs. The second scheme is designed by comparing Euclidean distances under the modified Paillier homomorphic encryption algorithm where a superlinear sequence is used to reduce the computational and communication overhead. The user side incurs slightly higher computational costs, but the trusted party does not need to execute encryption operations. Hence, the proposed two schemes can be applied in different application scenarios. Simulations on synthetic and real data prove the efficiency of the schemes, and security analysis establishes the privacy-preservation properties.
Malicious accounts detection from online social networks: a systematic review of literature

The bourgeoning of Online Social Networks has triggered an increase in undesirable acts caused by some disruptive entities, e.g. fake accounts, bots, and cyber-extremists. Thence, detection systems for unveiling malicious accounts and mitigating their harmful behavior were taken by a storm. This paper presents a systematic review of the literature on malicious account detection and comprehensive analysis from a social network perspective. We critically explore the detection approaches to identify the unsolved problems in the domain. We scrutinized 147 articles to come out with the following findings: the targeted malicious accounts category, the list of features selected for the detection task, the social platform which offered features information, the application area that requires detection of malicious accounts, a comparison between detection methods, a comparison between available datasets, and the performance metrics used for validation. We also discuss the forthcoming challenges in terms of detection methods, annotation techniques, and validation protocols.
STARS: Spatial Temporal Graph Convolution Network for Action Recognition System on FPGAs

Graph convolution neural network is one of the hot demanding research areas in the last few years. Due to the issues of data irregularity and computation complexity driven by typical GNN networks, we propose a spatial temporal graph convolution network for action recognition system on FPGA(STARS). STARS has redesigned several computing kernels based on the original layers of ST-GCN and adopted specific algorithm with optimization strategies for different kernels. To optimize the performance of accelerator, the ping-pong buffers for data transmission and dynamic quantification for model inference are implemented. The effectiveness of STARS driven accelerator is verified on Xilinx Pynq-Z1 prototyping board.
The Algebra of Non-deterministic Programs: Demonic Operations, Orders and Axioms

Abstract Demonic composition, demonic refinement and demonic union are alternatives to the usual ‘angelic’ composition, angelic refinement (inclusion) and angelic (usual) union defined on binary relations. We first motivate both the angelic and the demonic via an analysis of the behaviour of non-deterministic programs, with the angelic associated with partial correctness and demonic with total correctness, both cases emerging from a richer algebraic model of non-deterministic programs incorporating both aspects. Zareckiĭ has shown that the isomorphism class of algebras of binary relations under angelic composition and inclusion is finitely axiomatized as the class of ordered semigroups. The proof can be used to establish that the same axiomatization applies to binary relations under demonic composition and refinement, and a further modification of the proof can be used to incorporate a zero element representing the empty relation in the angelic case and the full relation in the demonic case. For the signature of angelic composition and union, it is known that no finite axiomatization exists, and we show the analogous result for demonic composition and demonic union by showing that the same axiomatization holds for both. We show that the isomorphism class of algebras of binary relations with the ‘mixed’ signature of demonic composition and angelic inclusion has no finite axiomatization. As a contrast, we show that the isomorphism class of partial algebras of binary relations with the partial operation of constellation product and inclusion (also a ‘mixed’ signature) is finitely axiomatizable.
Latexify Math: Mathematical Formula Markup Revision to Assist Collaborative Editing in Math Q&amp;A Sites

Collaborative editing questions and answers plays an important role in quality control of Mathematics StackExchange which is a math Q&amp;A Site. Our study of post edits in Mathematics Stack Exchange shows that there is a large number of math-related edits about latexifying formulas, revising LaTeX and converting the blurred math formula screenshots to LaTeX sequence. Despite its importance, manually editing one math-related post especially those with complex mathematical formulas is time-consuming and error-prone even for experienced users. To assist post owners and editors to do this editing, we have developed an edit-assistance tool, MathLatexEdit for formula latexification, LaTeX revision and screenshot transcription. We formulate this formula editing task as a translation problem, in which an original post is translated to a revised post. MathLatexEdit implements a deep learning based approach including two encoder-decoder models for textual and visual LaTeX edit recommendation with math-specific inference. The two models are trained on large-scale historical original-edited post pairs and synthesized screenshot-formula pairs. Our evaluation of MathLatexEdit not only demonstrates the accuracy of our model, but also the usefulness of MathLatexEdit in editing real-world posts which are accepted in Mathematics Stack Exchange.
Stereo Matching Based on Visual Sensitive Information

The area of computer vision is one of the most discussed topics amongst many scholars, and stereo matching is its most important sub fields. After the parallax map is transformed into a depth map, it can be applied to many intelligent fields. In this paper, a stereo matching algorithm based on visual sensitive information is proposed by using standard images from Middlebury dataset. Aiming at the limitation of traditional stereo matching algorithms regarding the cost window, a cost aggregation algorithm based on the dynamic window is proposed, and the disparity image is optimized by using left and right consistency detection to further reduce the error matching rate. The experimental results show that the proposed algorithm can effectively enhance the stereo matching effect of the image providing significant improvement in accuracy as compared with the classical census algorithm. The proposed model code, dataset, and experimental results are available at https://github.com/WangHewei16/Stereo-Matching.
Digital Literacy Approach in Food Delivery Service Application

Purpose digital platforms have been so useful through pandemics. Many new applications are popping up in support of people running their daily lives. Specially to meet the needs of food and beverages. Thus, existing applications must be able to meet the needs of the community, and easy to understands as the literacy contained in the food delivery service application. The goal of this study is to learn more about the literacy approach utilized in Food Delivery service applications as well as its influence on intention to use. Design/Methodology/Approach. This research is quantitative. The population taken is the entire community of Go-Food application users in the Jakarta area, and sampling techniques using purposive sampling. Analysis techniques used are Structure Equation Modelling-Partial Least Square. Findings. The results of this study stated that digital literacy in the community in using food delivery service applications will affects customer intention to use this application. Originality. The impact of digital literacy on food delivery application to intention to buy from the customer.
VIRSA: Vectorized In-Register RSA Computation with Memory Disclosure Resistance

Memory disclosure attacks give adversaries access to sensitive data in memory, posing a serious threat to the security of cryptographic systems. For example, the plain private key in RAM is exposed to the attacker during RSA operation. In this paper, we propose a register-based RSA system with high efficiency, called VIRSA, so that CRT-enabled 2048-bit RSA is entirely carried out on CPU registers. The private key and the intermediate results during the calculation process are all stored in registers, and will not appear in memory, which effectively prevents memory disclosure attacks. The input RSA parameters are encrypted by an AES key. The AES key is stored in the privileged debug registers. For performance, we use AVX-512F instruction set to accelerate the RSA calculation. We adopt vector instructions to implement 1024-bit Montgomery multiplication and make use of redundant representation to solve the carry propagation problem. Experiments on Intel Xeon Silver 4208 CPU shows that VIRSA achieves a performance factor of 0.8 compared to the OpenSSL RSA implementation, which outperforms existing approaches such as PRIME. Furthermore, we make use of the windowing method to improve the RSA performance. The precomputed table is encrypted by the AES key to ensure security. The performance of VIRSA using the fixed windowing method slightly exceeds OpenSSL, achieving a performance factor of 1.02.
Source printer identification using printer specific pooling of letter descriptors

The digital revolution has replaced the use of printed documents with their digital counterparts. However, many applications require the use of both due to several factors, including challenges of digital security, installation costs, ease of use, and lack of digital expertise. Technological developments in the digital domain have also resulted in the easy availability of high-quality scanners, printers, and image editing software at lower prices. Miscreants leverage such technology to develop forged documents that may go undetected in vast volumes of printed documents. These developments mandate the research on creating fast and accurate digital systems for source printer identification of printed documents. We extensively analyze and propose a printer-specific pooling that improves the performance of printer-specific local texture descriptor on two datasets. The proposed pooling performs well using a simple correlation-based prediction instead of a complex machine learning-based classifier achieving improved performance under cross-font scenarios. The proposed system achieves an average classification accuracy of 93.5%, 94.3%, and 60.3% on documents printed in Arial, Times New Roman, and Comic Sans font types respectively, when documents printed in only Cambria font are available for training.