Reviewerly/exHarmony huggingface dataset:
Social media hate speech in the walk of Ethiopian political reform: analysis of hate speech prevalence, severity, and natures

It is evident in this study that the social media sphere which has been highly controlled by the Ethiopian government for a long seems to untie right after the advent of the new political reform commenced in 2018. Following the transition, it is apparent that people are relatively enjoying the freedom of expression. On the contrary, the new digital platform is deemed to be subdued by the emergence of hate speech which is attributed to political, ethnic, and religious underpinnings. The study employs a quantitative approach to analyze users' comments collected from rivalry ethnic-based Television channels' Facebook and YouTube. Using a binary analysis, a substantial prevalence of social media hate speech is found. Mainly ethnic, religious, and political-based hate natures are also found. Most of the hate comments are offensive; however, a few comments are laid on incitement to violence, and genocide severity levels. Contesting memory of the past, associating ethnicity with religion, culture, and language issues are the main triggering factors of hate speech in Ethiopia. The online commenters imitate the offline ethnic tension in the country that upsurge during the political reform. Thus, identity-driven hate speech in tandem with the reform incidents has suffocated the Ethiopian social media.
Continuous Face Aging via Self-estimated Residual Age Embedding

Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial networks (GANs). Most existing face aging approaches divide the dataset into several age groups and leverage group-based training strategies, which lacks the ability to provide fine-controlled continuous aging synthesis in nature. In this work, we propose a unified network structure that embeds a linear age estimator into a GAN-based model, where the embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image and provide a personalized target age embedding for age progression/regression. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age, where all preceding aging bases are derived from the learned weights of the linear age estimator. This formulation brings the unified perspective of estimating the age and generating personalized aged face, where self-estimated age embeddings can be learned for every single age. The qualitative and quantitative evaluations on different datasets further demonstrate the significant improvement in the continuous face aging aspect over the state-of-the-art.
Pedestrian Traffic Lights Classification Using Transfer Learning in Smart City Application

Traffic accidents have become a serious issue in cities. Millions of people die in traffic accidents annually and among them the major cause is the pedestrian jaywalking. To solve this traffic issue and ensure efficient traffic monitoring, we introduced the surveillance system using AI powered UAVs in Internet of flying things based smart city scenario. To accurately classify the pedestrian traffic lights, we use the computer vision technology. We have created our own local dataset containing 809 images where 441 images belong to red signal class while 368 images belong to green signal class. We explore the power of transfer learning based on DNNs to overcome the limitation of dataset for pedestrian traffic lights classification. In this approach, we use the pre-trained MobileNetV2 model and freeze the weights. By leveraging the pre-trained convolutional base, we add our own fully connected layers on top of the model for classification. To handle the problem of limited data, we also perform the data augmentation. The task is formulated as binary classification problem. By using the MobileNetV2 on challenging and very diverse dataset, we achieve the accuracy of 94.92%, 91.84% specificity and 97.10% sensitivity.
Assessing Smart Contracts Security Technical Debts

Smart contracts are self-enforcing agreements that are employed to exchange assets without the approval of trusted third parties. This feature has encouraged various sectors to make use of smart contracts when transacting. Experience shows that many deployed contracts are vulnerable to exploitation due to their poor design, which allows attackers to steal valuable assets from the involved parties. Therefore, an assessment approach that allows developers to recognise the consequences of deploying vulnerable contracts is needed. In this paper, we propose a debt-aware approach for assessing security design vulnerabilities in smart contracts. Our assessment approach involves two main steps: (i) identification of design vulnerabilities using security analysis techniques and (ii) an estimation of the ramifications of the identified vulnerabilities leveraging the technical debt metaphor, its principal and interest. We use examples of vulnerable contracts to demonstrate the applicability of our approach. The results show that our assessment approach increases the visibility of security design issues. It also allows developers to concentrate on resolving smart contract vulnerabilities through technical debt impact analysis and prioritisation. Developers can use our approach to inform the design of more secure contracts and for reducing unintentional debts caused by a lack of awareness of security issues.
HCODF: Hybrid Cognitive Ontology Driven Framework for Socially Relevant News Validation

Nowadays everyone gets news from social media and most often it is very much difficult to validate its credibility. Social media platforms like twitter and WhatsApp are working on this for a long time, lots of research work has been done by many people but still an effective technique hasn’t been found out. This paper proposes a method to address this problem, with the use of a hybrid NLP model that uses an Event Ontology and LSTM to validate whether a news is authentic or not. The news dataset that has been used here are the tweets crawled and the news headlines, where the semantic similarity between the news and tweets on specific events are found out, which is later used to classify the tweets whether it is fake or not. This experiment’s performance is evaluated and it is compared with baseline models and it was found that the proposed approach is superior in terms of performance which recorded an F-Measure and False Negative Rate of 96.14% and 0.03 respectively.
Optimizing Network Transfers for Data Analytic Jobs Across Geo-Distributed Datacenters

It has become a recent trend that large volumes of data are generated, stored, and processed across geographically distributed datacenters. When popular data parallel frameworks, such as MapReduce and Spark, are employed to process such geo-distributed data, optimizing the network transfer in communication stages becomes increasingly crucial to application performance, as the inter-datacenter links have much lower bandwidth than intra-datacenter links. In this article, we focus on exploiting the flexibility of multi-path routing for inter-datacenter flows of data analytic jobs, with the hope of better utilizing inter-datacenter links and thus improve job performance. We design an optimal multi-path routing and scheduling strategy to achieve the best possible network performance for all concurrent jobs, based on our formulation of an optimization problem that can be transformed into an equivalent linear programming (LP) problem to be efficiently solved. As a highlight of this article, we have implemented our proposed algorithm in the controller of an application-layer software-defined inter-datacenter overlay testbed, designed to provide transfer optimization service for Spark jobs. With extensive evaluations of our real-world implementation on Google Cloud, we have shown convincing evidence that our optimal multi-path routing and scheduling strategies have achieved significant improvements in terms of job performance.
A Fuzzy Ontology Driven Integrated IoT Approach for Home Automation

Major advancements and development, in the field of Internet and other technologies is now prevalent in many modern areas and dominating the way of life. This in turn has allowed us to connect everyday objects, such as sensing and actuating devices, to the digital world. These intelligent devices empower the dawn of state-of-the-art services and applications for a multitude of various domains and help in improving the complete utilization of the given resources. In this paper, an interoperable, low cost, energy efficient Internet of Things platform using ontologies for a smart home is being implemented. The said system promises to controls the home appliances from anywhere i.e. offers remote operability and also provides the collected data to Third party programs if need be, so that the Third parties can creates tailor- made applications and services that cater to the need of the consumer. The system promises to reduce human intervention, enable automation and provide access to devices remotely from any place. Also, it allows for the collected data to be sent to third parties for generation of third- party services, and also for the analysis, and overall improvement of the system.
Design Space of Origin‐Destination Data Visualization

Abstract Visualization is an essential tool for observing and analyzing origin‐destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin‐destination data visualization (ODDV). We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.
Generalized Domain Adaptation

Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often ineffective for or not even applicable to another, which has prevented practical applications. In this paper, we give a general representation of UDA problems, named Generalized Domain Adaptation (GDA). GDA covers the major variants as special cases, which allows us to organize them in a comprehensive framework. Moreover, this generalization leads to a new challenging setting where existing methods fail, such as when domain labels are unknown, and class labels are only partially given to each domain. We propose a novel approach to the new setting. The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels. Extensive experiments using three benchmark datasets demonstrate that our method outperforms the state-of-the-art UDA methods in the new setting and that it is competitive in existing UDA variations as well.
Layer Importance Estimation with Imprinting for Neural Network Quantization

Neural network quantization has achieved a high compression rate using fixed low bit-width representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed-precision quantization. We propose an accuracy-aware criterion to quantify the layer’s importance rank. Our method applies imprinting per layer which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize first those with less accuracy gain. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration.
“It’s like a GPS community tool”: Tactics to foster Digital Commons through Artifact Ecology

Digital Commons have been widely studied in HCI and co-design literature as an alternative to technologies produced by capitalistic corporations. In this paper, we present an action-research process that facilitates the use of digital commons among a group of participants. The process core aspect is understanding our participants’ interests and values to present multiple off-the-shelf tools fitting their needs. In doing so, we leveraged on Artifact Ecologies, using them as an action-research tool. The process is articulated in three main phases: 1) we engaged participants in a reflection on their Artifact Ecologies, connecting the artifacts in use to their values; 2) we used these results to selected valuable digital commons; 3) we presented these tools to the group, connecting them to participants’ ecologies. At the end of the process, we performed a collective interview to evaluate the process alongside assessing the adoption of the proposed technologies. Based on our findings, we developed five tactics to foster digital commoning.
On the Copying Behaviors of Pre-Training for Neural Machine Translation

Previous studies have shown that initializing neural machine translation (NMT) models with the pre-trained language models (LM) can speed up the model training and boost the model performance.In this work, we identify a critical side-effect of pre-training for NMT, which is due to the discrepancy between the training objectives of LM-based pre-training and NMT.Since the LM objective learns to reconstruct a few source tokens and copy most of them, the pre-training initialization would affect the copying behaviors of NMT models.We provide a quantitative analysis of copying behaviors by introducing a metric called copying ratio, which empirically shows that pre-training based NMT models have a larger copying ratio than the standard one.In response to this problem, we propose a simple and effective method named copying penalty to control the copying behaviors in decoding.Extensive experiments on both indomain and out-of-domain benchmarks show that the copying penalty method consistently improves translation performance by controlling copying behaviors for pre-training based NMT models.
I Find Your Lack of Uncertainty in Computer Vision Disturbing

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.
Finding a Summary for All Maximal Cliques

The number of maximal cliques could be exponentially large with respect to the number of vertices. A clique summary is a subset of all the maximal cliques and can somehow represent all the maximal cliques. Finding such a summary is deemed important in information distribution, influence estimation, cost-effective marketing, etc. The existing approach that finds a maximal clique summary suffers from long running time due to the excessive number of costly bound calculations that are used to estimate the size of to-be-found cliques during the enumeration process. Furthermore, we found that, sometimes, the bound calculation is not necessary at all. As a result, in order to provide the best study of the problem, we propose four strategies in two directions to speed up the process of finding a maximal clique summary by (1) restricting the bound calculation operation to a particular subset of all search branches and (2) making the best use of the bounds that have been previously calculated. Extensive experiments are conducted on eight real-world datasets to validate our strategies. Results demonstrate that the proposed method can reduce the number of bound calculations by 3 ~ 5 orders of magnitude, and each run of our algorithm can be up to 2.x times faster than the state-of-the-art algorithm while still keeping the summary concise. Our method can potentially benefit other large-output enumeration based problems, such as frequent itemset mining, when a summary of results is needed.
Adversarial interference mitigation for automotive radar

With the massive application of radars in autonomous driving, mutual interference between radars has be-come a key issue. Over the past decades, many approaches have been proposed to solve this problem in time domain, frequency domain and space domain. These methods are mostly model-based, whose performance may suffer from inaccurate modeling. In some works, the convolutional neural networks are applied to address this problem. In this paper, we propose to mitigate automotive radar interferences using convolutional neural net-works with an adversarial framework. The performance of the model-based methods and the proposed learning-based method will be compared.
Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations

Audio-visual quality assessment remains as a complex research field. A great effort is being made to understand how visual and auditory domains are integrated and processed by humans. In this work, we analyzed and compared the results of three psychophisical experiments that collected quality and content scores given by a pool of subjects. The experiments include diverse content audio-visual material, e.g., Sports, TV Commercials, Interviews, Music, Documentaries and Cartoons, impaired with several visual (bitrate compression, packet-loss, and frame-freezing) and auditory (background noise, echo, clip, chop) distortions. Each experiment explores a particular domain. In Experiment 1, the video component was degraded with visual artifacts, meanwhile, the audio component did not suffer any type of degradation. In Experiment 2, the audio component was degraded while the video component remained untouched. Finally, in Experiment 3 both audio and video components were degraded. As expected, results confirmed a dominance of the visual component in the overall audio-visual quality. However, a detailed analysis showed that, for certain types of audio distortions, the audio component played a more important role in the construction of the overall perceived quality.
Analysis of Blockchain Induced Cryptocurrency: Regulations and Challenges of Cryptocurrencies

This paper focus on the different aspect of cryptocurrency, from their market to their vulnerabilities. As interest for cryptographic money develops, worldwide controllers are partitioned on the most proficient method to keep up. There is a significant amount of increase in online users which resulted into new market and new exchange currencies system. Cryptocurrency represent valuable and intangible objects which are used electronically in different applications and networks such as online social networks, online social games, virtual worlds, and peer to peer networks. Cryptocurrency is an internet-based medium of exchange which uses cryptographic functions to conduct financial transactions. With the huge potential, comes a great risk which should be regulated for fair practice. This paper is aimed to analyze the course of cryptocurrency and further we will discuss about the effect of cryptocurrency on market, use of blockchain in management and processing of cryptocurrency, threats to cryptocurrency and its capabilities.
Interactive AR App for Real-Time Analysis of Resistive Circuits

An augmented reality app for real-time analysis of direct current in resistive circuits is presented. The app allows the manipulation of circuit elements and computes the values of voltage and current intensity using the loop method and applying the Kirchhoff's voltage law. The app can be used in theoretical classes and laboratories. The contributions of this paper are two-fold. First, the app has higher levels of interactivity than other apps in the same domain since it allows defining the configuration and the parameters of the circuit. Second, the app performs more complex computations than similar apps in real-time.
Integrating business simulations software into learning environment of technical university

Abstract The development of active learning approaches and the need in raising economic competency of the people within all the age groups brings business simulations software to the front of the appropriate tooling lists. The aim of the study considers the ways and approaches to introduce and effectively integrate business simulations software into learning environments of technical university. The object of the study is the learning environment of Technical University. The subject of the study is using business simulations software in the learning environments of technical university. The result of the study: several approaches of integrating business simulations software into a learning environment of technical university were analyzed and discussed. The paper is dedicated to generalizing practical experience of the authors in technical and logical implementation in the pedagogical practice, usage of business simulations software as students and users with theoretical research of broader set of approaches used to integrate business simulations software into educational software-based and organizational environments. Conclusions and future plans based on the results of the experiments were made. Main conclusions and recommendations: the range of approaches between deep integration with the learning management tools used in the learning environments and independent usage of business simulations software is possible to implement. Choice of approach depends on the goals, ICT-related maturity of the learning infrastructure and the percentage of active learning cases in the syllabus of the class. Future experimentation is needed to collect extensive data on integration possibilities, efficiency and effectiveness of the level of integration of business simulations software into learning environment of technical university.