Reviewerly/exHarmony huggingface dataset:
The role of governance in quality education in sub‐Saharan Africa

Abstract This paper examines the nexus between governance and education quality in a panel of 49 sub‐Saharan African countries over the 2000–2012 period. Ordinary least squares (OLS) and quantile regression (QR) are employed as estimation strategies. The following findings are established. First, from the OLS, governance variables are negatively correlated with poor education quality. Second, with regards to QR, about half of the governance dynamics are not significantly correlated with poor education quality in the lowest quantile of poor education quality. With the exception of corruption control, the other governance dynamics are negatively correlated with poor education quality in a non‐monotonic pattern.
Geometry-based Completed Local Binary Pattern for Texture Image Classification

In view of the fact that the accuracy of texture image classification is easily affected by changes in illumination and rotation, based on the analysis of geometric curvatures information of the image microscopic geometric surface and the completed local binary pattern (CLBP), this paper proposed a new descriptor, named as Geometry-based Completed Local Binary Pattern (GCLBP). Inspired by the continuous rotation invariance and illumination robustness of the geometric curvature information, principal curvatures (PCs) of all pixels are first calculated and then used to represent the gradient magnitude information of the image, which are further exploited to replace the original gradient magnitude information in CLBP. To further improve the accuracy of texture classification, a cross-scale joint coding strategy is exploited to form the final GCLBP. The experimental results on two standard texture databases demonstrate that the GCLBP algorithm proposed in this paper is not only far superior to the original CLBP in terms of classification recognition accuracy and dimensionality of feature vector, but also better than most existing advanced texture classification methods.
Enhancing Affect Detection in Game-Based Learning Environments with Multimodal Conditional Generative Modeling

Accurately detecting and responding to student affect is a critical capability for adaptive learning environments. Recent years have seen growing interest in modeling student affect with multimodal sensor data. A key challenge in multimodal affect detection is dealing with data loss due to noisy, missing, or invalid multimodal features. Because multimodal affect detection often requires large quantities of data, data loss can have a strong, adverse impact on affect detector performance. To address this issue, we present a multimodal data imputation framework that utilizes conditional generative models to automatically impute posture and interaction log data from student interactions with a game-based learning environment for emergency medical training. We investigate two generative models, a Conditional Generative Adversarial Network (C-GAN) and a Conditional Variational Autoencoder (C-VAE), that are trained using a modality that has undergone varying levels of artificial data masking. The generative models are conditioned on the corresponding intact modality, enabling the data imputation process to capture the interaction between the concurrent modalities. We examine the effectiveness of the conditional generative models on imputation accuracy and its impact on the performance of affect detection. Each imputation model is evaluated using varying amounts of artificial data masking to determine how the data missingness impacts the performance of each imputation method. Results based on the modalities captured from students? interactions with the game-based learning environment indicate that deep conditional generative models within a multimodal data imputation framework yield significant benefits compared to baseline imputation techniques in terms of both imputation accuracy and affective detector performance.
QQIF: Quantum Quantitative Information Flow (invited paper)

The field of Quantitative Information Flow (QIF) is concerned with quantifying information leakage in systems. This work generalises the QIF framework to the quantum setting, having as foundations the recently developed g-leakage framework. Next, some recent results of the field of quantum statistical decision are presented and translated in terms of the "quantum" g-leakage framework, including a recently developed quantum generalisation of the Coriasceous Theorem, an important result for QIF.
Application of geostatistics to complete uranium resources estimation of Rabau Hulu Sector, Kalan, West Kalimantan

Kalan is one of the focus areas for uranium exploration in West Borneo that conducted by BATAN. Situated in the central part of Kalan, previous works in Rabau Hulu Sector consisted of surface geology and radiometric anomaly mapping, trenching, drilling, logging, and conventional uranium resource estimation. Nevertheless, the complete resource estimation of the previous work was still using 2D modeling, and the latest one using 3D modeling is a method-application case study in one orebody. To increase the confidence level and completing the uranium resource estimation of all orebodies in this sector, a geostatistical estimation with 3D orebody modeling using SURPAC mine planning software was conducted in this paper. Gamma-ray log data from 32 drill holes were collected and then interpreted to obtain uranium grade-thickness data. Based on the correlation of grade-thickness data according to surface orebody orientation, the orebody 3D modeling was done. It resulted in 26 orebodies with one control system of lithology as the mineralization only taken place in the quartzite unit. This 3D model then used as a constraint for block model with 4x4x2 m block size and 0.25x0.25x0.125 m minimum block size. Block model calculation was performed using ordinary kriging which generated the kriging efficiency attribute for the determination of the resource category. Within 25 meters searching radius, the calculation resulted in 408, 480 tons of ore, while total uranium resource was 268 tons of uranium with 677 ppm average grade. There were 214 tons of uranium (79%) categorized as measured while the other 54 tons of uranium (21%) categorized as indicated.
Efficient Comparison and Addition for FHE With Weighted Computational Complexity Model

Homomorphic encryption (HE) has broad application prospects in the cloud computing security field. Efficient homomorphic computations of primitive circuits are critical for the applications of HE. However, existing implementation methods over plaintext do not fit well with those over ciphertext. To address this issue, a concise evaluation model is proposed to compare different implementation methods, using weighted computational complexity (WCC). The number, depth, and distribution of homomorphic multiplications are considered together in the model for the first time. In addition, two primitive binary circuits on homomorphically encrypted data are optimized by using a unit called dot multiplication (DotMC). A novel comparison circuit based on DotMC is presented, and the number of homomorphic multiplications is reduced from O(n log n) to O(n) without increasing the multiplicative depth compared with the logarithm comparison, where n is the bit length of the operand. The WCC of comparison is reduced from O(n( log n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(n( log n)). The carry-lookahead adder is optimized by moving some DotMCs to levels with smaller weight, which reflects the effect of the distribution of homomorphic multiplications on performance. Finally, the proposed DotMC is accelerated with a single-instruction-multiple-data approach for even one operation; the number of homomorphic multiplication is reduced from O(n) to O( log n) compared with other comparison with the same strategy. Various circuits with the size from 4 to 2048 b are implemented with HElib to prove the optimization for comparison and addition, as well as the effectiveness of the proposed model.
A Neural Network-Aided Viterbi Receiver for Joint Equalization and Decoding

In recent years, many works have been focusing on applying machine learning techniques to assist with communication system design. Instead of replacing the functional blocks of communication systems with neural networks, a hybrid manner of ViterbiNet symbol detection was proposed to combine the advantages of Viterbi algorithm and neural networks, which achieves guaranteed performance with reasonable complexity. However, this block-based design not only degrades the system performance but also increases hardware complexity. In this work, we propose a ViterbiNet receiver for joint equalization and channel decoding, which simultaneously considers both the code structure and channel effects, thus achieving global optimum with 3 dB gain. Furthermore, a dedicated neural network model is proposed to avoid the need for perfect channel state information (CSI). It is shown to be more robust under CSI uncertainty with 1.7 dB gain.
Building Best Predictive Models Using ML and DL Approaches to Categorize Fashion Clothes

Today Deep learning approach DL becomes the new tendency of machine learning approach ML which is used since it gives much more sophisticated pattern recognition and image classification than classic machine learning approach. Among the most used methods in DL, CNNs are for a special interest. In this work, we have developed an automatic classifier that permits to classify a large number of fashion clothing articles based on ML and DL approaches. Initially, we proceeded to the classification task using many ML algorithms, then we proposed a new CNN model composed of many convolutional layers, one maxpooling layer, and one full connected layer. Finally, we established a comparison between different algorithms. As programming tools, we have used Python, Tensoflow, and Keras which are the most used in the field.
Generating collective online information sharing: The framing effect of questions on response network

Abstract Community Question‐Answering (CQA) sites are virtual communities where users participate in collective online information sharing. Questions in CQA sites serve as starting points for information sharing, eliciting a response network (RN), where nodes are the postings, while edges represent the responses between postings. This research employed framing theory to investigate how question frames affected their resulting RNs. Question frames were operationalized as conversational and informational. Social network analysis was conducted to explore the RNs of different question frames for both Science, Technology, Engineering and Mathematics (STEM) and non‐STEM communities. Results revealed that most RNs of STEM conversational questions were larger in size and had higher centrality, whereas RNs of informational questions comprised closer relationships between postings. However, no significant differences were found in the non‐STEM community. Our research suggests that community moderators and askers should appropriately utilize question frames to organize information sharing inside CQA communities.
A practical hybrid quantum-safe cryptographic scheme between data centers

The next-generation information security is facing quantum threats from the recent progress in quantum technologies. Quantum cryptography can provide true randomness and secure distribution of keys, but is prevented from wide applications due to challenges in real implementation. We propose and demonstrate a practical hybrid scheme with various quantum-safe technologies for encrypted communications between data centers. Quantum random number generators, quantum key distribution, post-quantum and classical cryptography algorithms are integrated in the hybrid quantum-safe scheme to enhance data transfer security in Alibaba platform. Compared with quantum cryptography and post quantum cryptography solutions, the proposed triple-level security cryptographic scheme provides a layered cryptographic solution for different applications, and is compatible with existing solutions. The demonstration shows a first step of quantum-safe secure data transfer solution in real data center environment, with the advantages of low cost, high stability, and easy operation.
ML-EPIC

Data-intensive systems are powerful platforms for collecting and exploring big data. Project EPIC (Empowering the Public with Information in Crisis) has been designing and developing data-intensive systems in support of crisis informatics research since Fall 2009. However, Project EPIC (https://epic.cs.colorado.edu/) has always focused on collecting and analyzing tweets written in English, which raises an interesting question: how do we collect and get insight into tweets from multiple languages during crisis events? We report on our efforts to design and develop a multilingual cloud-based analytical platform---ML-EPIC---which enhances Project EPIC's ability to perform crisis informatics research on global events. More generally, our research provides insight into tackling the challenges of big data in a multilingual context.
Smart Health Services for Epidemic Control

At the time of an epidemic, it is necessary to have information about the epidemiological situation from verified sources. The lack of such information can lead to the spread of news from non-objective sources, irresponsible behavior of citizens, but also the spread of panic. Epidemic control is one of the topics in the field of smart health within smart cities. Based on electronic health records (EHRs), various information on the epidemiological situation in healthcare institutions can be extracted. This paper proposes a framework that enables several smart health services intended for epidemic control and ways to implement them. Examples of the realization of these services are given, based on EHRs from Health Center Nis.
On the Influence of UML Class Diagrams Refactoring on Code Debt: A Family of Replicated Empirical Studies

Context: System modeling usually precedes coding activities during software development. Addressing model smells in the upfront can avoid their propagation to the source code. Technical debt (TD) affects several software development phases, including design, but little is still known about it at the modeling level. Goal: Investigate whether applying refactoring procedures in UML class diagrams improves the quality of the automatically generated code in terms of TD (code debt) reduction. Method: We perform three replications of an empirical study following the same protocol used in the original study, but with variations on the: (1) round- trip engineering tool, (2) code issue identification tool, and (3) analyzed class diagram. Each study considered two sets of refactoring tasks. The first applied successive model refactoring sessions in a class diagram and analyzed their resulting automatically generated code. The second applied successive code refactoring sessions and analyzed their resulting automatically generated model. Results: There is a weak relationship between the analyzed model smells and code issues. Round-trip engineering tools influence the presence of code issues. Lastly, code issues identification tools mostly consider code formatting problems, in detriment of design issues smells. Conclusion: Results confirm the findings of the original study and motivate further investigation on the correspondence between model smells and code issues to prevent code debt at the model level.
A Google Colab Based Online Platform for Rapid Estimation of Real Blur in Single-Image Blind Deblurring

Referenceless or Blind Image Deblurring (BID) is a challenging task in image restoration, because of the absence of previous information about the blurring process. In BID, the Point Spread Function (PSF) that causes blur is unknown and finding it is a challenging task to do. In contrast, reference-based image deblurring techniques are better in terms of achieving deblurred images, as in such cases we know the cause of blur and many filters and methods get the job done easily. In real life imaging, the presence of blur is caused due to many reasons including motion, defocus, atmospheric turbulence and noise in capturing device. Removing such blur to produce sharp images is very important in many cases as of today's technological driven era demands for images of unspoiled quality. This paper presents a method to eliminate blur in the fastest way. The scheme is devised to use a Genetic Algorithm (GA) on Google Colab to find PSF of the blur kernel. This work tries to estimate the parameters of blurring using quality score minimizing. Once the PSF is estimated, the Wiener filter is used for deblurring in Google Colab. This scheme presents the implementation of the algorithm on Google Colab to use GPU for achieving parallelism and reduce implementation time. Validation of the work has been carried out on different images and the system achieved marked improvements over existing approaches. The work presented is simple and efficient which requires no former information about the original sharp image or deblurring process.
Application of Deep Learning Based Object Detection on Unmanned Aerial Vehicle

Object recognition can be seen in every field thanks to today's high computation power. It has become possible to extract information from the images with high accuracy in different areas such as human, animal, vehicle, traffic signs and MR images. In this study, plastic water bottle detection was applied by using Inception v2, MobileNet v2 and ResNet50 architectures to be used in the Scientific and Technological Research Council of Turkey (TUBITAK) International Unmanned Aerial Vehicle Competition. Cloud based training applied to locate very small objects from up to 5 meters high. In the present study, the Single Shot Detector (SSD) ResNet50 model yielded the best results for mAP@0.75 and final loss. It was also determined that SSD Inception v2 model gave better results than SSD MobileNet v2 model. When the processing times were considered, it was determined that models could not work at Raspberry Pi 3 Model B+ equipment at sufficient speed. However, these results have shown that the Jetson Nano card can be operated correctly on a drone. In the next stage of this study, the aim will be determining environmental pollution level by detecting objects such as plastic bags, bottles and fruits on the streets.
Rational Delegation of Computation Based on Reputation and Contract Theory in the UC Framework

The previous schemes for rational delegation of computation resorting to game theory focus on what is the possible equilibrium results. However, they seem to ignore how to design an optimal incentive mechanism scientifically and effectively so that the final result meets designer’s expectations when a information structure is given. More importantly, there is an open question that whether the designed rational delegation of computation protocol is still secure in the UC framework. To address these challenges, in this paper, we first construct a reputation model leveraging Gompertz model to ensure that client can choose high-quality computing parties. Secondly, we design an optimal mechanism resorting to contract theory in the case of information asymmetry. Then we construct an ideal functionality for rational delegation of computation based on reputation and contract theory. In addition, we design a protocol for rational delegation of computation based on reputation and contract theory to securely realize the functionality. In the end, we prove that the proposed protocol is still secure in the UC framework.
Homomorphic Encryption versus Searchable Encryption for Data Retrieval on Cloud

Securing data retrieval on cloud has been a significant challenge for many years. Additionally, there are trade off between adequate security and performance of existing data retrieval systems. Searchable encryption and homomorphic encryption are used to secure data retrieval systems on cloud with specific architectures, which are inefficient. In this paper, we have proposed a novel architecture with searchable encryption for data retrieval on cloud, namely SDCA-SSE, which provide better performance results. We evaluated SDCA-SSE empirically with simulations. Performance evaluation results show that our architecture for secure data retrieval on cloud provides better performance.
Backfiring and favouring: how design processes in HCI lead to anti-patterns and repentant designers

Design is typically envisioned as aiming to improve situations for users, but this can fail. Failure can be the result of flawed design solutions, i.e. anti-patterns. Prior work in anti-patterns has largely focused on their characteristics. We instead concentrate on why they occur by outlining two processes that result in anti-patterns: 1) backfiring, and 2) favouring. The purpose of the paper is to help designers and researchers better understand how design processes can lead to negative impacts and to repentant designers by introducing a richer vocabulary for discussing such processes. We explore how anti-patterns evolve in HCI by specifically applying the vocabulary to examples of social media design. We believe that highlighting these processes will help the HCI community reflect on their own work and also raise awareness of the opportunities for avoiding anti-patterns. Our hope is that this will result in fewer negative experiences for designers and users alike.
Industry 4.0 in Educational Process

The presented paper describes the individual elements of industry 4.0 already introduced into the educational process of students of bachelor and master degree studies at the workplace of the Faculty of Mechanical Engineering, Brno University of Technology. For the purposes of education, approaches were taken into account with the requirements of Industry 4.0 in the field of sensor systems, OPC UA data collection, data processing, metrology, simulation, automation, robotics, M2M communication, Virtual Reality, and digital commissioning. Individual technologies are presented to students in the form of partial tasks that are mutually related. The result is an overall overview of the elements of industry 4.0 and their implementation into production systems.
A Comprehensive Study on the Recognition of Gurmukhi Script

Nowadays, we process all the important information of our lives electronically. Due to the involvement of computers in every sphere there may be a need to develop some efficient and fast techniques so that records can be easily transferred between people and computer systems. Offline text recognition provides an interface between humans and computers. Many researchers are working to recognize the text of Indian scripts like Bangla, Devanagari, Gurmukhi etc. but it is still a challenge to exchange data between people and computers due to the different writing style of the people and very little work has been done for Gurmukhi. In this article different accuracy results are reviewed which are achieved by different researchers using different classification techniques. Various classifiers for the recognition of characters like Support Vector Machine (SVM) based classifier (Upper zone classifier and Lower zone classifier), Hidden Markov Model (HMM) by using a set of features of the normalized x–y traces of the stroke, DCT2 feature set using Linear SVM classifier, Polynomial SVM with iDCT2 features, Multi layered perceptron (MLP) neural network and K nearest neighbor ( K NN) etc. classifiers have been used.