Reviewerly/exHarmony huggingface dataset:
A Moroccan News Articles Dataset (MNAD) For Arabic Text Categorization

In recent years Natural language processing is one of the most active areas of research especially with the emergence of deep learning algorithms. More attention has been given to Latin descendent languages e.g English, French, and Spanish given the availability of high-quality datasets and compute resources. In this paper, we present a moroccan News Articles Corpus collected from four of the major moroccan news websites. The corpus contains more than 418k news articles corresponding to 19 different categories, thus considered to be one of the largest Arabic news articles corpora. A description of the collection and processing steps were presented and exploration analysis was performed. To prove the utility of the dataset. An evaluation step was conducted in the context of text classification using four different Machine Learning baselines: Random Forest (RF), Multinomial Naive Bayes (MNB), Support Vector Machine (SVC), and Gradient Boosting (GradBoost) Classifiers. The experimental results are presented in terms of accuracy, F1-score, and confusion matrix.
Attack and protection schemes on fabric isomorphic crosschain systems

Crosschain solves the problem of value transfer and asset interaction between different blockchains with different consensus mechanisms, or different infrastructures. It not only realizes the mutual communication of multiple independent blockchains, but also ensures the data consistency. However, existing crosschain technologies like notary mechanism, hash locking, distributed private key control, and sidechain/relaychain have potential vulnerabilities, resulting in different kinds of attacks. For example, some vulnerabilities that only exist in a blockchain like DDOS, overflow, double-spending, and so on, may have an impact on another secure blockchain due to the openness of the crosschain system. Even if all blockchains are safe enough, the security of the crosschain system cannot be guaranteed, and there are still many loopholes in the process of crosschain, which affect all blockchains. The security of crosschain has emerged as an important issue. In this work, based on BitXHub, an open-source project of crosschain, we are motivated to study the security of Fabric isomorphic crosschain system. First, we analyze the vulnerabilities in different layers of Fabric isomorphic crosschain systems, and discuss the principles of different kinds of attacks. Based on the principles, we discover and define five new crosschain attacks, including crosschain integer overflow attack, crosschain transaction forgery attack, crosschain transaction replay attack, crosschain transaction sequence attack, and crosschain routing attack. Second, we implement all the five attacks, and indicate the applicable scenarios and the boundary conditions in which each attack may occur. Third, we propose five schemes to prevent the corresponding attacks and evaluate the effectiveness of the protection schemes. We comprehensively discuss and analyze the schemes of attacks and their corresponding prevention toward Fabric isomorphic crosschain systems.
A Study in Overlapping Factor Decomposition for Cooperative Co-Evolution

Large scale global optimization is where we seek to optimize a function with a high number of decision variables. Cooperative co-evolutionary algorithms (CCEA) improve optimization performance on these large scale problems through a divide and conquer approach. How the problem is divided can have a large impact on optimization performance. We provide two new decomposition methods that are capable of generating overlapping groups of variables. We apply a generalized CCEA called factored evolutionary algorithm (FEA) that is capable of optimizing and combining overlapping sub-problems. We compare results to existing methods to analyze the effect of introducing overlap in the sub-problems. We use five functions from the CEC‘2010 benchmark suite as a base of comparison for all algorithms. We show that overlap can be beneficial for optimizing problems that are not fully separable.
Establishing a Pattern Language for the Organization of Distributed Software Development

Despite considerable efforts to address organizational problems of distributed software development, currently available solutions do not seem to be sufficient. They are fragmented into individual patterns either not forming coherent pattern languages to address organizational distributed software development or being incorporated into extensive pattern languages for organizing software development in general. Another problem is their disconnection from the current technological support for collaboration. We attempt at overcoming these problems by providing a set of six organizational patterns for distributed software development. We relate them to each other and to other known patterns and practices practically establishing a pattern language for the organization of distributed software development. The overall idea of how this pattern language can be used is presented using a pattern story of a real company.
Simple Multi-resolution Gated GNN

Most Graph Neural Networks (GNNs) proposed in literature tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a simple linear multi-resolution architecture that implements a multi-head gating mechanism. We assessed the performances of the proposed architecture on node classification tasks. To perform a fair comparison and present significant results, we re-implemented the competing methods from the literature and ran the experimental evaluation considering two different experimental settings with different model selection procedures. The proposed convolution, dubbed Simple Multi-resolution Gated GNN, exhibits state-of-the-art predictive performance on the considered benchmark datasets in terms of accuracy. In addition, it is way more efficient to compute than GAT, a well-known multihead GNN proposed in literature.
Anomaly Detection on the Rail Lines Using Semantic Segmentation and Self-supervised Learning

This paper introduces a novel application of anomaly detection on the rail lines using deep learning methods on camera data. We propose a two-fold approach for identifying irregularities like coal, dirt, and obstacles on the rail tracks. In the first stage, a binary semantic segmentation is performed to extract only the rails from the background. In the second stage, we deploy our proposed autoencoder utilizing the self-supervised learning techniques to address the unavailability of labelled anomalies. The extracted rails from stage one are divided into multiple patches and are fed to the autoencoder, which is trained to reconstruct the non-anomalous data only. Hence, during the inference, the regeneration of images with any abnormalities produces a larger reconstruction error. Applying a predefined threshold to the reconstruction errors can detect an anomaly on a rail track. Stage one, rail extracting network achieves a high value of 52.78% mean Intersection over Union (mIoU). The second stage autoencoder network converges well on the training data. Finally, we evaluate our two-fold approach on real scenario test images, no false positives or false negatives were found in the the detected anomalies on the rail tracks.
Effects of Parameter Selections on Fitting Vertical Curves to Data

Vertical curve fitting is important both for road safety management and railway maintenance. Different parameterizations were proposed to specify a vertical curve satisfying the constraint of continuity between its components. The vertical curve-fitting model was developed to explore the effects of different parameter sets on the fitting performance for a vertical curve. The steepest descent (SD), Gauss-Newton (GN), and Levenberg-Marquardt (LM) algorithms were used to search for the optimum solution. Experiments showed that the different parameterizations have different effects on the performance of the three algorithms. For one parameter set, the three algorithms converged to different solutions. For the other, the three algorithms converged to the same optimal solution. The two parameterizations responded differently to initial values, which were illustrated visually. From six different initial values, the LM algorithm converged to different solutions for one parameterization, but to the same optimum for the other. The condition number (CN), an index for evaluating the extent of ill-posed problems as well as correlations between parameter pairs, was examined to interpret the reasons of one parameterization outperformed the other in both robustness and effectiveness. Results also showed that the LM algorithm outperformed the GN algorithm in robustness and outperformed the SD algorithm in efficiency.
Improved FunkSVD Algorithm Based on RMSProp

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms.
Decision Support for Infection Outbreak Analysis: the case of the Diamond Princess cruise ship

This paper focuses on designing a CI decision support to address rare events such as disease outbreaks in a ‘closed’ environment such as a cruise ship. We focus on a case study of the COVID-19 outbreak that happened on board the Diamond Princess cruise ship in 2020. It considers a graphical probabilistic model such as Bayesian Network. We consider this causal model to be a core of an intelligent decision support tool to help in emergency management. To prove this hypothesis, the prototype of a decision support tool was implemented and used to evaluate different scenarios. The results show that such system equipped with a reasoning engine is capable of evaluating the pandemic scenario risks, thus helping assess the impacts of certain preventive measures, and damages.
SOC Critical Path: A Defensive Kill Chain Model

Different kill chain models have been defined and analyzed to provide a common sequence of actions followed in offensive cyber operations. These models allow analysts to identify these operations and to understand how they are executed. However, there is a lack of an equivalent model from a defensive point of view: this is, there is no common sequence of actions for the detection of threats and their accurate response. This lack causes not only problems such as unstructured approaches and conceptual errors but, what is most important, inefficiency in the detection and response to threats, as defensive tactics are not well identified. For this reason, in this work we present a defensive kill chain approach where tactics for teams in charge of cyber defense activities are structured and arranged. We introduce the concept of SOC Critical Path (SCP), a novel kill chain model to detect and neutralize threats. SCP is a technology–independent model that provides an arrangement of mandatory steps, in the form of tactics, to be executed by Computer Network Defense teams to detect hostile cyber operations. By adopting this novel model, these teams increase the performance and the effectiveness of their capabilities through a common framework that formalizes the steps to follow for the detection and neutralization of threats. In this way, our work can be used not only to identify detection and response gaps, but also to implement a continuous improvement cycle over time.
Binary Pooling Circuits for Quantum Computing

Despite the successful results they have provided, the need of high computational power is the biggest obstacle in front of deep learning. In this study, the pooling operation that is used many times in a deep learning network is carried out with quantum circuits. In order to make able testing of the proposed circuits on today's quantum computers, binary inputs are used. In today's technology, it is not able to run whole deep learning process on reliable quantum computers. But, when it is possible to perform all deep learning steps much faster on quantum computers, it will eliminate the need to use pre-trained networks. With training the deep learning networks from scratch in much shorter time, a new era will start for deep learning. In this study, quantum binary pooling circuits are proposed for this purpose. Quantum circuits are proposed for 3, 4, 5, and 9-dimensional windows. In today's quantum computers that do not work iteratively, the proposed circuit only works for 1 step. In order to apply pooling to the whole image, the circuit must be looped. With the development of the relevant quantum computer architecture, the method will become applicable to the whole image.
Anomaly Detection in Flight Data Using the Naïve Bayes Classifier

Safety is the key to reliable civil aviation. In the airline industry, there is a growing emphasis on proactive safety management systems in order to improve the safety of current aviation operations. These systems utilize anomaly detection techniques to recognize and reduce the risk of accidents occurring. This work develops a new anomaly detection approach for commercial flight operations using routine operational data to enhance proactive safety management systems and utilizes data mining techniques to identify abnormal situations instantaneously during flights using real-life FDR (Flight Data Recorder) data. The Naïve Bayes classifier was used to detect normal and abnormal situations. This classifier was applied to a dataset of 100 flights and new abnormal situations could be recognized with a high probability of detection and a low probability of false alarm. The results strongly suggest that anomalies detected in a variety of flights can be recognized, which can help airlines with many different approaches, such as the deployment of predictive maintenance, the detection of early signs of performance divergence, safety support, and the training of staff accordingly.
Parallel Block Execution in SoCC Blockchains through Optimistic Concurrency Control

The blockchain ecosystem is growing rapidly with new decentralized applications being released constantly, boasting significant growth in functionality and adoption. With accelerating adoption rate, the load on all the existing blockchain networks grows as well, causing increased transaction delays. We seek to improve the performance of blockchain solutions by increasing transaction throughput. This paper presents a novel approach for executing blocks in parallel using optimistic concurrency control and conflict rescheduling. Specifically, we show that our approach improves the performance of Separation of Consensus and Compute (SoCC) blockchains with the potential to reduce smart contract transaction wait times.
Rosser Provability and the Second Incompleteness Theorem

This paper is a continuation of Arai's paper on derivability conditions for Rosser provability predicates. We investigate the limitations of the second incompleteness theorem by constructing three different Rosser provability predicates satisfying several derivability conditions.
Photrace: A Blockchain-Based Traceability System for Photographs on the Internet

This paper proposes a blockchain-based traceability system of photos. Thanks to a novel scheme that securely chains certificates of image data originating from a trusted origin certificate, the system authenticates the provenance of original and converted images, e.g., which digital camera created it. The system requires a Public Key Infrastructure (PKI) of digital cameras to authenticate the trusted origin certificate signed by the certified digital camera. It does not require a PKI of image conversion software. The proposed solution is suitable for the traceability system of photos on the Internet as the scheme of chaining certificates applies to any image conversion software, including open source-based photo editing applications and cloud server software that converts image data of photos. We have implemented a prototype of the traceability system using Ethereum as a permissioned blockchain and evaluated the system's feasibility.
Anti-Section Transitive Closure

The transitive closure of a graph is a new graph where every vertex is directly connected to all vertices to which it had a path in the original graph. Transitive closures are useful for reachability and relationship querying. Finding the transitive closure can be computationally expensive and requires a large memory footprint as the output is typically larger than the input. Some of the original research on transitive closures assumed that graphs were dense and used dense adjacency matrices. We have since learned that many real-world networks are extremely sparse, and the existing methods do not scale. In this work, we introduce a new algorithm called Anti-section Transitive Closure (ATC) for finding the transitive closure of a graph. We present a new parallel edges operation - anti-sections - for finding new edges to reachable vertices. ATC scales to massively multi-threaded systems such as NVIDIA's GPU with tens of thousands of threads. We show that the anti-section operation shares some traits with the triangle counting intersection operation in graph analysis. Lastly, we view the transitive closure problem as a dynamic graph problem requiring edge insertions. By doing this, our memory footprint is smaller. We also show a method for creating the batches in parallel using two different techniques: dual-round and hash. Using these techniques and the Hornet dynamic graph data structure, we show our new algorithm on an NVIDIA Titan V GPU. We compare with other packages such as NetworkX, SEI-GBTL, SuiteSparse, and cuSparse.
Knowledge-Based Conversational Recommender Systems Enhanced by Dialogue Policy Learning

The conversational recommender system (CRS) provides personalized recommendations for users through dialogues. Knowledge-based CRS, which applies external knowledge graphs into the CRS, can provide knowledge-aware recommendations, and has proved successful in many fields. However, existing models suffer from two limitations. First, previous knowledge-based CRSs ignore the transfer of topics in the conversation and cannot handle multi-task recommendation dialogues. Second, it takes many inquiries for traditional models to obtain knowledge-based user profiles, which affect the user's interactive experience. In this work, we use the dialogue policy learning to tackle these issues, and propose a model called CRSDP, standing for knowledge-based Conversational Recommender Systems enhanced by Dialogue Policy learning. We leverage the actor-critic framework to learn a dialogue policy in the reinforcement learning paradigm. The optimized dialogue policy leads the conversation strategically in multi-task dialogue scenarios, determines the user preference in fewer turns, and makes effective recommendations. We evaluate the performance of the model in the recommendation task and the conversation task. Experimental results validate the effectiveness of our model.
Patterns for ad-hoc online collaboration

This collection is focused on the design of ad-hoc online collaboration software, software that allows for an immediate co-creation of content, instantaneous discussions, brainstorming or other collaboration tasks, presenting none or only minimal barriers to do so. It is a special kind of software that incorporates knowledge about technical and design focused peculiarities, social dynamics, domain specific needs, time management, didactics, user characteristics and many more factors.
Improving Deep Learning Projections by Neighborhood Analysis

Visualization of multidimensional data is a difficult task, for which there are many tools. Among these tools, dimensionality reduction methods were shown to be particularly helpful to explore data visually. Techniques with good visual separation are very popular, such as those from the SNE-class, but those often are computationally expensive and non-parametric. An approach based on neural networks was recently proposed to address those shortcomings, but it introduces some fuzziness in the generated projection, which is not desired. In this paper we thoroughly explain the parameter space of this neural network approach and propose a new neighborhood-based learning paradigm, which further improves the quality of the projections learned by the neural networks, and we illustrate our approach on large real-world datasets.
ShapeEditor: A StyleGAN Encoder for Stable and High Fidelity Face Swapping

With the continuous development of deep-learning technology, ever more advanced face-swapping methods are being proposed. Recently, face-swapping methods based on generative adversarial networks (GANs) have realized many-to-many face exchanges with few samples, which advances the development of this field. However, the images generated by previous GAN-based methods often show instability. The fundamental reason is that the GAN in these frameworks is difficult to converge to the distribution of face space in training completely. To solve this problem, we propose a novel face-swapping method based on pretrained StyleGAN generator with a stronger ability of high-quality face image generation. The critical issue is how to control StyleGAN to generate swapped images accurately. We design the control strategy of the generator based on the idea of encoding and decoding and propose an encoder called ShapeEditor to complete this task. ShapeEditor is a two-step encoder used to generate a set of coding vectors that integrate the identity and attribute of the input faces. In the first step, we extract the identity vector of the source image and the attribute vector of the target image; in the second step, we map the concatenation of the identity vector and attribute vector onto the potential internal space of StyleGAN. Extensive experiments on the test dataset show that the results of the proposed method are not only superior in clarity and authenticity than other state-of-the-art methods but also sufficiently integrate identity and attribute.
Hands On vs. VR Lectures in Times of the Pandemic

The implementation of physical and optical experiments in a VR environment will be presented. A comparison between the hands on and the digital VR experiments will be analyzed and presented.