Reviewerly/exHarmony huggingface dataset:
TORCH: a TOSCA-Based Orchestrator of Multi-Cloud Containerised Applications

Abstract The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called “cloud orchestration tools” (orchestrators) are required to take care of the customer application’s life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypal implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORCH.
Determinants of Blockchain Technology Introduction in Organizations: an Empirical Study among Experienced Practitioners

Blockchain is expected to enable new types of interorganizational relationships, new approaches to governance and new approaches to settlement and clearing processes. Neverthless, although the interest on blockchain is on the rise, there are not many blockchain implementations in organizations and there is limited empirical research investigating the reasons for this. This paper contributes to filling this gap by investigating the following research question: what are the impeding and motivating factors for organizational blockchain adoption? Data were collected through a survey based on pairwise comparisons of key factors identified in the literature. The data collected were analyzed using the Analytical Hierarchy Process (AHP) which is a structured approach for deriving priorities among diverse elements. The results provide insights about the issues that matter the most for practitioners and show that infrastructural qualities (reliability, transparency, immutability) matter more than characteristics related to blockchain´s transformative potential (automation of transactions, decentralization). Furthermore, the results for impeding factors indicate that the most prominent concerns relate to maturity and scalability.
Motion Control of a Flock of 1-Trailer Robots with Swarm Avoidance

SUMMARY This paper addresses the motion planning and control problem of a system of 1-trailer robots navigating a dynamic environment cluttered with obstacles including a swarm of boids. A set of nonlinear continuous control laws is proposed via the Lyapunov-based Control Scheme for collision, obstacle, and swarm avoidances. Additionally, a leader–follower strategy is utilized to allow the flock to split and rejoin when approaching obstacles. The effectiveness of the control laws is demonstrated through numerical simulations, which show the split and rejoin maneuvers by the flock when avoiding obstacles while the swarm exhibits emergent behaviors.
Blind image quality assessment based on the multiscale and dual‐domains features fusion

Abstract Image quality assessment is to simulate subjective human visual perception and realize image quality inference automatically. Although deep neural networks have achieved great success, the majority of them do not fully consider perception characteristics. Therefore, according to the human visual scale characteristics, we proposed an image quality assessment algorithm based on multiscale and dual domains fusion. Firstly, the original image and its phase congruency respectively input into two branches, feature pyramid and channel attention mechanism are adopted to extract multiscale features. After that, bilinear pool is used to aggregate the spatial and frequency domain characteristics of the corresponding scales, and allows arbitrary scale input to ensure that the features are extracted from the inherent quality images. Finally, the single quality score is obtained through learned weights of each scale. Comparative experiments between our approach and state‐of‐the‐art are conducted on five public databases, the results demonstrate that the proposed algorithm is not only robust to different types and across database, but also sensitive to scale.
Revisiting Multi-Domain Machine Translation

When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.
PufferBot: Actuated Expandable Structures for Aerial Robots

We present PufferBot, an aerial robot with an expandable structure that may expand to protect a drone's propellers when the robot is close to obstacles or collocated humans. PufferBot is made of a custom 3D-printed expandable scissor structure, which utilizes a one degree of freedom actuator with rack and pinion mechanism. We propose four designs for the expandable structure, each with unique characterizations for different situations. Finally, we present three motivating scenarios in which PufferBot may extend the utility of existing static propeller guard structures.
Online Fraud Victimization in China: A Case Study of Baidu Tieba

When it comes to online populations and markets, China has some of the largest in the world. As a result, Chinese cybercriminals have more opportunities to target and access victims. While extant research in Western countries has examined online fraud victimization and offenses in virtual communities, a relatively small body of research on these phenomena has been conducted in non-Western societies. This study attempts to address this gap by analyzing online fraud victimization in Chinese online communities. Routine activity theory is applied to understand the patterns and dynamics of victimization. Data were collected from Baidu Tieba (a Chinese version of Craigslist), a prominent Chinese online platform for reporting victimization. This study highlights the range of services, types, and methods along which victimization occurs. The results, which reflect China’s rapid pace of technological development, show that different types of fraud are perpetrated online and that victimization methods are associated with particular types of media. This study also identifies implications for China and other countries where similar crimes and instances in cyberspace occur.
A υ-Constrained Matrix Adaptation Evolution Strategy With Broyden-Based Mutation for Constrained Optimization

To solve the nonconvex constrained optimization problems (COPs) over continuous search spaces by using a population-based optimization algorithm, balancing between the feasible and infeasible solutions in the population plays an important role over different stages of the optimization process. To keep this balance, we propose a constraint handling technique, called the υ -level penalty function, which works by transforming a COP into an unconstrained one. Also, to improve the ability of the algorithm in handling several complex constraints, especially nonlinear inequality and equality constraints, we suggest a Broyden-based mutation that finds a feasible solution to replace an infeasible solution. By incorporating these techniques with the matrix adaptation evolution strategy (MA-ES), we develop a new constrained optimization algorithm. An extensive comparative analysis undertaken using a broad range of benchmark problems indicates that the proposed algorithm can outperform several state-of-the-art constrained evolutionary optimizers.
Implementation and evaluation of blockchain based e-voting system with Ethereum and Metamask

The implementation of an electronic voting (e-voting) system which has begun to be widely applied in place of traditional electoral systems, still has a major problem in the level of trust results. E-voting systems are very vulnerable to manipulation issues, such as changes in election results due to hacking or by the electoral system maker. Centralized systems in a network result in data sources coming from one party having the right to store and to manage data sources. The issue of trust caused by a centralized data distribution system can be overcome by spreading data in a system network. Blockchain is a distributed ledger, where every party in the network has the same data source, and it has a powerful characteristic known as immutability which is very suitable for e-voting system. This research proposed a blockchain based e-voting system with Ethereum and metamask. We show that the proposed e-voting system fulfills six basic principles of an election system, namely secret ballot, one-man one-vote, voter eligibility, transparency, votes accurately, recorded and counted, and reliability. Furthermore, the performance evaluation of the e-voting system shows that the slow gas price option gives the lowest gas price per second result, i.e. the best trade-off.
Enterprise Systems and Blockchain Technology: The Dormant Potentials

This paper surveys the current status of blockchain technologies integration and its potentials with enterprise systems’ (ES). The blockchain technology has received substantial attention since the recent cryptocurrency-boom. The corporate world seeks to stay on the blockchain-train by exploring how they can benefit from the evolving technology and platforms. Hence, this research explores the capabilities and potentials of blockchain technology illustrated in the extant literature and investigates how it can reinforce, and be integrated with enterprise systems through semi-structured interviews with subject matter experts. The literature and collected data were classified into four components of blockchain technologies; identity, assets, logistics, and transactions. Our main findings suggest that there is scarce literature on blockchain integration with ES. Nevertheless, there is a huge potential for this integration, specifically on the transactions, identity, and logistics dimensions. In addition, our interview results suggest that blockchains can reinforce ES by accomplishing a single source of truth, and a common environment for shared information amongst a larger scope of actors and organizations.
TA-SPESC: Toward Asset-Driven Smart Contract Language Supporting Ownership Transaction and Rule-Based Generation on Blockchain

Aiming at insufficient situation to express and operate assets in smart contracts, in this article we attempt to add a new asset model into smart contract language (such as SPESC) through combing method of asset's expressions and transactions in real-world contracts. Moreover, a translation mechanism can be set up to accomplish a conversion from the asset model to an executable contract program. On this basis, we propose a new language design toward asset-driven specific smart contracts, called TA-SPESC. This language complies with the structure of real-world contracts and supports a formal definition composed of four modules: Party, asset, term, and contract attribute. This asset model on it can be used to define various types of rights (including the right of ownership, use, possession, usufruct, and disposition of assets), as well as five asset operations (including asset registration, deposit, withdrawal, transfer, and cancellation) to effectively support asset transaction. More important, a series of generation rules are proposed to translate the TA-SPESC contract to an executable contract program. Moreover, taking house rental contract as an example, we provide a TA-SPESC instance and its specific description of translation process according to the generation rules, which supports a semiautomatic generation to executable programs. Finally, the Solidity codes derived from TA-SPESC contracts are run and tested, and the experiment and comparison results indicate that TA-SPESC contracts have high abstraction and low complexity, as well as versatility and convenience of asset transaction, which lead to more reliable software with less errors and fewer misunderstanding.
Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos' parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria.
Radar emitter multi-label recognition based on residual network

In low signal-to-noise ratio (SNR) environments, the traditional radar emitter recognition (RER) method struggles to recognize multiple radar emitter signals in parallel. This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network. This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs. First, we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform (STFT). The time-frequency distribution image is then denoised using a deep normalized convolutional neural network (DNCNN). Secondly, the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established, and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model. Finally, time-frequency image is recognized and classified through the model, thus completing the automatic classification and recognition of the time-domain aliasing signal. Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.
Traffic Classification of User Behaviors in Tor, I2P, ZeroNet, Freenet

In recent years, more and more anonymous network have been developed. Since user's identity is difficult to trace in anonymous networks, many illegal activities are carried out in darknet. In this paper, we propose a hierarchical classifier of darknet traffic which can distinguish four types of darknet(Tor, I2P, ZeroNet, Freenet) and 25 darknet users' behavior. Due to the lack of public datasets, we deployed a darknet data probe that can capture real darknet traffic in Tor, I2P, ZeroNet, Freenet. After collecting and labeling darknet traffic, we extract 26 time-based flow features that can represent the characteristics of darknet traffic and train a hierarchical classifier constructed by 6 local classifiers. Results show that the classifier can easily distinguish Tor, I2P, ZeroNet, Freenet four kinds of darknet clients with an accuracy of 96.9% and identify 8 kinds of user behaviors for each type of darknet with an accuracy of 91.6% on average. With the help of this hierarchical classification method, darknet user behaviors can be accurately distinguished at the traffic exit.
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance

Anomaly detection is a key goal of autonomous surveillance systems that should be able to alert unusual observations. In this paper, we propose a holistic anomaly detection system using deep neural networks for surveillance of critical infrastructures (e.g., airports, harbors, warehouses) using an unmanned aerial vehicle (UAV). First, we present a heuristic method for the explicit representation of spatial layouts of objects in bird-view images. Then, we propose a deep neural network architecture for unsupervised anomaly detection (UAV-AdNet), which is trained on environment representations and GPS labels of bird-view images jointly. Unlike studies in the literature, we combine GPS and image data to predict abnormal observations. We evaluate our model against several baselines on our aerial surveillance dataset and show that it performs better in scene reconstruction and several anomaly detection tasks. The codes, trained models, dataset, and video will be available at https://bozcani.github.io/uavadnet.
The simulation manifesto: The limits of brute‐force empiricism in geopolitical forecasting

Abstract Intelligence analysis has traditionally relied on inside‐view, case‐specific modes of thinking: why did this actor—say, the USSR—do that and what might it do next? After 9/11, however, analysts faced a vastly wider range of threats that necessitated outside‐view, statistical modes of reasoning: how likely are threats to emerge from actors of diverse types operating in situations of diverse types? Area‐study specialists (who staffed most geopolitical desks) were ill‐equipped for answering these questions. Thanks to advances in long‐range sensing, digitization, and computing, the intelligence community was flooded with data, but lacked clear ideas about how to render it relevant. Empiricism, whether grounded in deep inside‐view knowledge of particular places or broad outside‐view knowledge of statistical patterns across the globe, could not and cannot solve the problem of anticipating high‐impact, rare events, like sneak attacks and pandemics. Contingency planning for these threats requires well‐calibrated conditional forecasts of the impact of policy interventions that in turn require synthesizing inside‐ and outside‐view analytics. Such syntheses will be best achieved by refining computer simulations that permit replays of history based on the interplay among initial conditions, chance, and social‐science models of causation. We offer suggestions for accelerating the development and application of theory‐guided simulation techniques.
Image Segmentation Using Encoder-Decoder with Deformable Convolutions

Image segmentation is an essential step in image analysis that brings meaning to the pixels in the image. Nevertheless, it is also a difficult task due to the lack of a general suited approach to this problem and the use of real-life pictures that can suffer from noise or object obstruction. This paper proposes an architecture for semantic segmentation using a convolutional neural network based on the Xception model, which was previously used for classification. Different experiments were made in order to find the best performances of the model (e.g., different resolution and depth of the network and data augmentation techniques were applied). Additionally, the network was improved by adding a deformable convolution module. The proposed architecture obtained a 76.8 mean IoU on the Pascal VOC 2012 dataset and 58.1 on the Cityscapes dataset. It outperforms SegNet and U-Net networks, both networks having considerably more parameters and also a higher inference time.
Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

Social distancing has been regarded as a key method by the authorities worldwide to manage the pandemic of COVID-19. Digital technologies play a crucial role to support the social, professional and economic activities when people are forced to stay locked-down in their homes. Internet of things (IoT) technologies have a track of providing high quality remote health care and automation services which could guarantee social distancing while maintaining health and well-being of populations. In this paper, we propose an end-to-end IoT architecture to support the social distancing in the event of pandemic. The architecture comprises of the major use cases of IoT in relevance with the COVID-19. Furthermore, we also present a short-term and long-term strategy to mange the social distancing methodology using the proposed IoT architecture. The challenges associated with each layer of architecture have been highlighted and design guidelines have been presented to deal with them.
Text Mining Techniques for Sentiment Analysis of Arabic Dialects: Literature Review

Social media attracts a lot of users around the world.Many reasons drive people to use social media sites such as expressing opinions and ideas, displaying their diaries and sharing them with others, social communication with family and friends and building new social relationships, learning and sharing knowledge.Written text is one of the most common forms used for communication while using social media sites.People use written texts in different languages, and due to the increased usage of social networking sites around the world, the amount of texts and data resulting from this use is large.These generated data considered as a valuable source of information that attracted business owners, companies, government institutions, and of course, it attracts researchers and data scientists as well.Researchers and data scientists increasingly presented great efforts in investigating and analyzing Arabic Language texts.Most of these efforts targeted the Modern Standard form of Arabic Language.While exploring the social media sites, most of the Arab users tend to use their dialects while utilizing Social Media sites, which results in generating a massive amount of Arabic Dialects texts.The number of researches and analysis of Dialects' form of the Arabic language are limited, however, it is increasing recently.This literature review aims to explore approaches and methods used for Sentiment Analysis of Arabic Dialects text.