Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Model for Fake News Identification in Arabic Tweets

Over the last few years, the exponential rise of social media, particularly Twitter, is becoming a major source of news sharing and consumption among users. These platforms allow users to publish, author and distribute content. These environments may be used to report and spread gossip and false news, whether accidentally or maliciously. Fake news and inaccurate machine-generated text are serious issues affecting societies worldwide, particularly the Arab world. This motivates efforts to identify fake and distorted news. This paper aims to introduce a robust prediction model to identify fake news in Arabic Tweets. Several Natural Language Processing (NLP), feature selection, and advanced ML algorithms were exploited to achieve this purpose. NLP techniques were used to process and transform the given tweets into structured form. The recursive feature elimination (RFE) technique was employed to eliminate uninformative features. ML methods were used to build the prediction model. Experimental results revealed the superiority of the Logistic Regression (LR) classifier among other algorithms. Moreover, RFE proved its ability to enhance the overall performance of the LR classifier. Overall, the proposed model provided an acceptable prediction accuracy of 82%.
Research on the Teaching of Three-dimensional Graphic in Primary Schools Based on Augmented Reality Technology

At present, augmented reality technology is widely used in various disciplines of education. However, its application in elementary school mathematics three-dimensional graphics is still relatively small. The purpose of this research is to study how to apply augmented reality technology to the teaching of three-dimensional graphics in primary schools to help students better understand the connection between concrete three-dimensional models and abstract plane graphics. This research designs and develops an augmented reality teaching software for three-dimensional graphics teaching to support classroom teaching and self-learning in elementary schools. In addition, it designed and implemented a classroom-controlled experiment involving 97 fourth grade students in elementary school, with a view to investigating the impact of the software on students' learning effects and emotional attitudes. This augmented reality software can meet the actual needs of three-dimensional graphics teaching in primary schools and is easy to operate. The results show that after teacher use the software in class, the learning effect has been significantly improved, and it can promote positive changes in students' attitudes. Besides, it can enhance teacher-student exchanges.
A Hierarchical Feature Extraction Network for Fast Scene Segmentation

Semantic segmentation is one of the most active research topics in computer vision with the goal to assign dense semantic labels for all pixels in a given image. In this paper, we introduce HFEN (Hierarchical Feature Extraction Network), a lightweight network to reach a balance between inference speed and segmentation accuracy. Our architecture is based on an encoder-decoder framework. The input images are down-sampled through an efficient encoder to extract multi-layer features. Then the extracted features are fused via a decoder, where the global contextual information and spatial information are aggregated for final segmentations with real-time performance. Extensive experiments have been conducted on two standard benchmarks, Cityscapes and Camvid, where our network achieved superior performance on NVIDIA 2080Ti.
On the impossibility of an invariant attack on Kuznyechik

Currently numerous cryptographic systems are based on SP-networks. These primitives are supposed to be secure but recent investigations show that some attacks are possible. The aim of this work is to study how secure the Russian standardized block cipher Kuznyechik over invariant attacks. We study the already known decompositions of its permutation and show the ways of constructing invariant subsets. A new approach to invariant attacks is presented and it proves that there are no subsets based on S-Box properties that are invariant under round functions of Kuznyechik.
Combining Central Control with Collective Adaptive Systems

Collective Adaptive Systems (CAS) achieve high resilience by providing distributed self-* properties through autonomous elements. While they are very beneficial for achieving high reliability, they are more difficult for dealing with targeted external inputs or ensuring globally optimal results. It is easier to achieve these properties with centralized approaches. Ideally, one could combine the benefits of collective adaptive systems with aspects of central control to achieve the best of both paradigms. While such combinations have already been shown in previous work, here, we aim at a systematic discussion of the range of approaches to integrate both control paradigms. As a result, we present a taxonomy of control action types, which describes how a central control unit can interact with a CAS to achieve the desired overall behavior, providing a first step towards CAS control patterns and identifying the involved trade-offs. Ideally, one could combine the benefits of collective adaptive systems with aspects of central control to achieve the best of both paradigms. While such combinations have already been shown in previous work, here, we aim at a systematic discussion of the range of approaches to integrate both control paradigms. As a result, we present a taxonomy of control action types, which describes how a central control unit can interact with a CAS to achieve the desired overall behavior, providing a first step towards CAS control patterns and identifying the involved trade-offs.
Post-quantum Simulatable Extraction with Minimal Assumptions: Black-Box and Constant-Round

From the minimal assumption of post-quantum semi-honest oblivious transfers, we build the first $$\varepsilon $$ -simulatable two-party computation (2PC) against quantum polynomial-time (QPT) adversaries that is both constant-round and black-box (for both the construction and security reduction). A recent work by Chia, Chung, Liu, and Yamakawa (FOCS'21) shows that post-quantum 2PC with standard simulation-based security is impossible in constant rounds, unless either $${\textbf{NP}}\subseteq \textbf{BQP}$$ or relying on non-black-box simulation. The $$\varepsilon $$ -simulatability we target is a relaxation of the standard simulation-based security that allows for an arbitrarily small noticeable simulation error $$\varepsilon $$ . Moreover, when quantum communication is allowed, we can further weaken the assumption to post-quantum secure one-way functions (PQ-OWFs), while maintaining the constant-round and black-box property. Our techniques also yield the following set of constant-round and black-box two-party protocols secure against QPT adversaries, only assuming black-box access to PQ-OWFs: At the heart of the above results is a black-box extraction lemma showing how to efficiently extract secrets from QPT adversaries while disturbing their quantum states in a controllable manner, i.e., achieving $$\varepsilon $$ -simulatability of the after-extraction state of the adversary.
Interpretable Models for the Potentially Harmful Content in Video Games Based on Game Rating Predictions

Studies reported that playing video games with harmful content can lead to adverse effects on players. Therefore, understanding the harmful content can help reduce these adverse effects. This study is the first to examine the potential of interpretable machine learning (ML) models for explaining the harmful content in video games that may potentially cause adverse effects on players based on game rating predictions. First, the study presents a performance analysis of the supervised ML models for game rating predictions. Secondly, using an interpretability analysis, this study explains the potentially harmful content. The results show that the ensemble Random Forest model robustly predicted game ratings. Then, the interpretable ML model successfully exposed and explained several harmful contents, including Blood, Fantasy Violence, Strong Language, and Blood and Gore. This revealed that the depiction of blood, the depiction of the mutilation of body parts, violent actions of human or non-human characters, and the frequent use of profanity might potentially be associated with adverse effects on players. The findings suggest the strength of interpretable ML models in explaining harmful content. The knowledge gained can be used to develop effective regulations for controlling identified video game content and potential adverse effects.
Reinforcement Learning Combined with Heuristic Search for Solving Discrete Space Path Planning Problems

Reinforcement learning (RL) has been successfully applied to solve path planning problems, but learning is generally slow. The main reason is not making full use of information collected during interaction with the environment. This paper proposes a novel method to solve the discrete space path planning problem in an environment without prior knowledge with intensive obstacles based on RL and heuristic search. Firstly, we apply Dyna-Q algorithm of RL to explore the map and search for the target point and optimize its policy with upper confidence bound (UCB). Then, when the target point is found, we use heuristic search to plan the path from the starting point to the target point and narrow the path to a small range. Finally, we combine Dyna-Q algorithm with the heuristic search recommended path for path planning. We evaluate our algorithm using maze navigation problem. The results verify that heuristic search accelerates Dyan-Q convergence.
Threat Modelling of IoT Systems Using Distributed Ledger Technologies and IOTA

Internet of Things has emerged as a key techno-logical enabler for broader socio-technical and socio-economic paradigms, such as smart cities and Circular Economy. However, IoT systems are characterised by constraints and limitations which in order to be overcome they need to be deployed in conjunction and in synergy with other emerging ICT. Distributed Ledger Technologies (DLT) can help overcome challenges pertaining to data immutability, timeliness and security. However, the use of DLT does not satisfactorily mitigate security risks and vulnerabilities per se and currently cybersecurity aspects of IoT systems are addressed in a fragmented way. Furthermore, the conflict between the resource demanding Blockchains and the highly constrained nature of IoT devices hinders implementation efforts of corresponding systems. We consider networked systems that comprise both IoT and DLT technologies via the prism of Intelligent Transportation Systems (ITS). We elicit a three-tier threat model identifying attack vectors at the Device, the Network and the DLT layers. The identified attacks are then ranked by using the DREAD ranking scheme. The use of the threat model is demonstrated on a novel proof-of-concept IoT networked system implemented using the IOTA Tangle distributed ledger, where it helps to critically appraise the design of the system against the most critical attacks. Furthermore, the developed system is among the first in the literature to demonstrate the synergy of IoT and DLT on actual constrained embedded devices. The performance evaluation provides insights showing that such systems can be efficient and suitable for real-life deployment.
Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation

Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving. This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively. Using 2D region proposals in an RGB image, this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network (FPS-Net) and feature extraction network (FE-Net). Subsequently, the encoder-decoder network (ED-Net) implements 3D-oriented bounding box (OBB) regression. Meanwhile, the adaptive least square regression (ALSR) method is proposed to split 3D OBB. Finally, the reduced OBB intersection test is carried out to detect traffic accidents via separating surface theorem (SST). In the experiments of KITTI benchmark, our proposed 3D object detector outperforms other state-of-the-art methods. Meanwhile, collision detection algorithm achieves the satisfactory performance of 91.8% accuracy on our SHTA dataset.
Predicting SPARQL Query Dynamics

Given historical versions of an RDF graph, we propose and compare several methods to predict whether or not the results of a SPARQL query will change for the next version. Unsurprisingly, we find that the best results for this task are achievable by considering the full history of results for the query over previous versions of the graph. However, given a previously unseen query, producing historical results requires costly offline maintenance of previous versions of the data, and costly online computation of the query results over these previous versions. This prompts us to explore more lightweight alternatives that rely on features computed from the query and statistical summaries of historical versions of the graph. We evaluate the quality of the predictions produced over weekly snapshots of Wikidata and daily snapshots of DBpedia. Our results provide insights into the trade-offs for predicting SPARQL query dynamics, where we find that a detailed history of changes for a query's results enables much more accurate predictions, but has higher overhead versus more lightweight alternatives.
Modeling Collaborative AI for Dynamic Systems of Blockchain-ed Autonomous Agents

Artificial Intelligence has been strongly evolving disrupting almost every research and application domain. One of the key attributes - and at the same time an enabler - of today's innovations is the massive connectivity resulted in the opportunity to exploit Artificial Intelligence across distributed network of self-contained smart agents those could range from software bots to complex devices like autonomous vehicles, IoT collations, UAVs and Robot Swarms. Such heterogenous networks of Autonomous Agents could differ in size, networking topology, protocols, computing profiles, algorithms, interaction strategy among other attributes thus require different factors to achieve desired combined intelligent goals. While Blockchain technology has been evolving with the birth and swift breakthrough of cryptocurrencies and cryptocontracts, alternate types have emerged to serve as a layer of trust among independent peer-to-peer computing nodes lend itself to be a foundation for distributed systems of edge inference agents. In this paper we discuss adopting a Collaborative AI edge model for dynamic systems of Blockchain-ed Autonomous Agents.
Metamorphic Testing for Convolutional Neural Networks: Relations over Image Classification

Testing deep learning systems requires expensive labeled data. In recent years, researchers began to leverage metamorphic testing to address this issue. However, metamorphic relations on image data remain poorly understood. To gain a deeper understanding of these metamorphic relations, we survey common image operations modeling covariate shift, manually classify and categorize the underlying metamorphic relations, and conduct experiments to validate our classifications. In our experiments, we train three popular convolutional neural network architectures on an image classification task. Next, we apply metamorphic operations on input test images and measure the change in classification accuracy and cross-entropy loss. A hierarchical clustering algorithm clusters these results and plots a dendrogram. We compare the groups from manual classification and the clusters from the algorithm to provide key insights. We find that Affine and Noise relations are consistent. Furthermore, we recommend metamorphic relationships to save time and better test deep learning systems in the future.
Conceptualizing Visual Analytic Interventions for Content Moderation

Modern social media platforms like Twitch, YouTube, etc., embody an open space for content creation and consumption. However, an unintended consequence of such content democratization is the proliferation of toxicity and abuse that content creators get subjected to. Commercial and volunteer content moderators play an indispensable role in identifying bad actors and minimizing the scale and degree of harmful content. Moderation tasks are often laborious, complex, and even if semi-automated, they involve high-consequence human decisions that affect the safety and popular perception of the platforms. In this paper, through an interdisciplinary collaboration among researchers from social science, human-computer interaction, and visualization, we present a systematic understanding of how visual analytics can help in human-in-the-loop content moderation. We contribute a characterization of the data-driven problems and needs for proactive moderation and present a mapping between the needs and visual analytic tasks through a task abstraction framework. We discuss how the task abstraction framework can be used for transparent moderation, design interventions for moderators’ well-being, and ultimately, for creating futuristic human-machine interfaces for data-driven content moderation.
Magnetic Anomaly Detection Utilizing Generalized Morse Wavelet and Morphological Filter

Magnetic anomaly detection (MAD) methods have been used for decades to detect and localize visually obscured ferromagnetic targets. In this work, an alternative MAD scheme based on the generalized Morse wavelet and morphological filter is proposed. First, the measured raw time sequence is converted into a time-scale representation, denoted as scalogram. Then an appropriate threshold is defined based on the histogram of the scalogram magnitude to filter out most geomagnetic noise and digitalize the scalogram as a binary image. Last, a proper two-dimensional structuring element is adopted for the morphological filter to reduce the residual noise. Experiments show that the proposed method achieves better performance than the traditional orthogonal basis functions method.
Optimal Searching of Prefetched DASH Segments in Fog Nodes

Data prefetching at fog nodes that significantly reduces latency is critical for video streaming applications. In the framework of dynamic video streaming over fog, some segments (parts of video) can be prefetched by the network provider. Sharing the dynamic information about prefetched node locations to all of the users is not a scalable approach, due to signalling and performance overheads. However, at the time of downloading, the client devices can search for the availability of DASH video segments in the fog nodes present in their vicinity. There is, however, a dilemma of how many fog nodes can be queried, without affecting performance such as continuous playback of the video. \par In this paper, we propose an efficient mechanism for searching video segments over different fog nodes. This search mechanism is formulated as a sequential stochastic modeling framework known as Multi-Armed Bandit~(MAB). While the state space of this model is a countably infinite set, we propose an algorithmic approach to transform to a finite state space model without loss of optimality. With extensive simulation results, the analytical results are validated. We study the different parameters influencing the improved optimal DASH performance, in terms of segment prefetch probability, and number of search attempts made by the client.
Risk Exposure and Management in Software Development – A Survey of Multiple Software Startups

Context: Software startups perform many high-risk activities regarding both business and product development. Being aware of important risks and effectively managing them is important for startups, particularly in an era of a global pandemic such as this. Even though there are many studies about the failure and success factors of software companies, not much is understood about risk management for startups. Aim: Our aim is to characterize how startups identify, manage, and control different kinds of risks during their software product development. We also intend to investigate whether there is a mismatch between startups’ risk exposure and their risk management approaches. Method: We designed an online questionnaire with 72 questions and collected opinions from 89 software startups in different stages. Results: we preliminarily revealed the relevant team composition, methodology, and product risks in each stage of a startup. Our findings suggest that perceived risk exposures are reduced as startups progress from early to later stages. There was no observable difference in the ways risks are managed among startups in different stages. Startups rely on team-based, informal, and unstructured methods to identify, analyze, and control their risks. Contribution: Our results have direct implications for startup founders and project managers to become effective in managing software startup risks.
A super-resolution reconstruction method of underwater target detection image by side scan sonar

However, the scope and distance of optical imaging were limited, especially in the case of muddy water, the propagation of optical information was seriously interfered, and imaging became more difficult. Due to the complex and changeable underwater environment and the nature of acoustic imaging, sonar image has noise, low resolution and fuzzy details, which has a great impact on the recognition and interpretation of sonar image. On the basis of the original SRGAN network, this paper improves and optimates its network structure and loss function. Replace the ordinary convolution layer with the void convolution layer in the residual block structure of the generated network, delete the batch normalization layer (BN layer), reduce the resource consumption and expand the receiver field, so as to improve the training efficiency of the network; A gradient penalty term is added to the improved discriminant network loss function to accelerate the convergence of the network and improve the stability of training. Four classical image super resolution algorithms are compared with the improved SRGAN algorithm under the verification of sonar dataset. The experimental results show that the improved SRGAN network is superior to the traditional super resolution method in the reconstruction of sonar image in terms of rich texture and details, and improves the quality of sonar image super resolution reconstruction.
MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm

Population-based metaheuristic algorithms have received significant attention in global optimisation. Human Mental Search (HMS) is a relatively recent population-based metaheuristic that has been shown to work well in comparison to other algorithms. However, HMS is time-consuming and suffers from relatively poor exploration. Having clustered the candidate solutions, HMS selects a winner cluster with the best mean objective function. This is not necessarily the best criterion to choose the winner group and limits the exploration ability of the algorithm. In this paper, we propose an improvement to the HMS algorithm in which the best bids from multiple clusters are used to benefit from enhanced exploration. We also use a one-step k-means algorithm in the clustering phase to improve the speed of the algorithm. Our experimental results show that MCS-HMS outperforms HMS as well as other population-based metaheuristic algorithms.