Reviewerly/exHarmony huggingface dataset:
Energy Efficient Load Balancing in Cloud Data Center Using Clustering Technique

Cloud computing has seen tremendous growth in recent days. As a result of this, there has been a great increase in the growth of data centers all over the world. These data centers consume a lot of energy, resulting in high operating costs. The imbalance in load distribution among the servers in the data center results in increased energy consumption. Server consolidation can be handled by migrating all virtual machines in those underutilized servers. Migration causes performance degradation of the job, based on the migration time and number of migrations. Considering these aspects, the proposed clustering agent-based model improves energy saving by efficient allocation of the VMs to the hosting servers, which reduces the response time for initial allocation. Middle VM migration (MVM) strategy for server consolidation minimizes the number of VM migrations. Further, randomization of extra resource requirement done to cater to real-time scenarios needs more resource requirements than the initial requirement. Simulation results show that the proposed approach reduces the number of migrations and response time for user request and improves energy saving in the cloud environment.
Method towards discovering potential opportunity information during cross-organisational business processes using role identification analysis within complex social network

The key to discover potential opportunity information in cross-organisation business processes (COBPs) is to identify the primary roles and actors, i.e. how to obtain their associations according to the interactive behaviours within the complex social networks. The information of roles in COBPs is commonly considered important and explicitly related with activities contained in COBPs. In this paper, we define a role as a configurable resource model integrating the capabilities and knowledge required to the qualified actors. Furthermore, we introduce two networks named as role-based interactive behaviour network and handover of work social network to investigate the information on roles. How to build the complex social network mapped on roles from COBPs is also discussed, and an approach to obtain the potential opportunity information is proposed by combining with the significance of roles and actors. The simulation result shows that the primary roles may not completely correspond to the central position in networks, but they are closely associated with more reliable actors.
Handling negative mentions on social media channels using deep learning

Social media channels such as social networks, forum or online blogs have been emerging as major sources from which brands can gather user opinions about their products, especially the negative mentions. This kind of task, popular known as sentiment analysis, has been addressed recently by many deep learning approaches. However, negative mentions on social media have their own language characteristics which require certain adaptation and improvement from existing works for better performance. In this paper, we propose a new architecture for handling negative mentions on social media channels. As compared to the architecture published in our previous work, we expose substantial change in the combination manner of deep neural network layers for better training and classification performance on social-oriented messages. We also propose the way to re-train the pre-trained embedded words for better reflect sentiment terms, introducing the resultant sentimentally-embedded word vectors. Finally, we introduce the concept of a penalty matrix which incurs more reasonable loss function when handling negative mentions. Our experiments on real datasets demonstrated significant improvement.
Deep feature representation for anti-fraud system

Online payment is becoming popular due to the development of e-commerce. So payment safety is more important. Since there is a fraudulent situation, an anti-fraud system is indispensable. GMM was leveraged in many anti-fraud applications, but it only takes positive sample into account. Convolutional neural network is a strong strategy for learning deep representation of samples. So in this paper, we propose a CNNs architecture to deal with this problem. And the distance metric method can effectively identify whether candidates are the same person. Experimental results show the effectiveness of our method.
HAMPER

Recently, the machine learning technologies have been widely used in cognitive applications such as Automatic Speech Recognition (ASR) and Image Recognition (IR). Unfortunately, these techniques have been massively used in unauthorized audio/image data analysis, causing serious privacy leakage. To address this issue, we propose HAMPER in this work, which is a data encryption framework that protects the audio/image data from unauthorized ASR/IR analysis. Leveraging machine learning models' vulnerability to adversarial examples, HAMPER encrypt the audio/image data with adversarial noises to perturb the recognition results of ASR/IR systems. To deploy the proposed framework in extensive platforms (e.g. mobile devices), HAMPER also take into consideration of computation efficiency, perturbation transferability, as well as data attribute configuration. Therefore, rather than focusing on the high-level machine learning models, HAMPER generates adversarial examples from the low-level features. Taking advantage of the light computation load, fundamental impact, and direct configurability of the low-level features, the generated adversarial examples can efficiently and effectively affect the whole ASR/IR systems. Experiment results show that, HAMPER can effectively perturb the unauthorized ASR/IR analysis with 85% Word-Error-Rate (WER) and 83% Image-Error-Rate (IER) respectively. Also, HAMPER achieves faster processing speed with 1.5X speedup for image encryption and even 26X in audio, comparing to the state-of-the-art methods. Moreover, HAMPER achieves strong transferability and configures adversarial examples with desired attributes for better scenario adaptation.
Secure Dynamic Big Graph Data: Scalable, Low-Cost Remote Data Integrity Checking

As a special type of big data, the big graph has wide applications. The remote data integrity checking (RDIC) scheme enables public clouds to efficiently convince the clients that their graph data are stored properly, without the need of retrieving the actual data contents. The existing schemes support verifiable update often by adopting the authentication data structures (ADSs), e.g., Merkel hash tree (MHT). The main obstacle for applying the existing RDIC schemes to big graphs is due to the lack of support for verifiable sub-graph operations with high efficiency. In this paper, we propose a new RDIC scheme for big graphs, called GVD-RDIC, to support public auditing and verifiable dynamic graph updates with high efficiency. We have designed novel ADS based on the graph Voronoi diagram (GVD) and enhanced MHT to address the integrity of graph structure and verifiable sub-graph updates. In addition, our ADS can be applied to any type of graphs. Moreover, our GVD-RDIC scheme adopts a new construction for the homomorphic authenticator to enable index verifications for public auditors. A server response constructed with unchallenged block will be rejected. The proposed scheme is proven secure under a random oracle model. Both the theoretical analysis and the simulation results show that our scheme is practicable for real-world big graphs.
Developing a bangla currency recognizer for visually impaired people

Deep learning based assistive technologies for the visually impaired and blind people have gained increasing attention from various research communities in recent years. In this paper, we have developed a camera-based automatic currency recognizer for Bangladeshi bank notes that assists visually impaired people in Bangladesh. We have exploited the deep learning architecture MobileNet for classification of bank notes. We have evaluated the performance of our model using a novel dataset consisting of nearly 8000 images of Bangladeshi bank notes. To verify the effectiveness and efficacy of the proposed solution, we have developed a mobile Android application, and evaluated and validated the application with the users from a blind community. The validation shows that our proposed system is robust and highly effective with heterogeneous environment.
Blockchain for Security of Cloud-Based Online Auction

Online auction is one of the most successful internet business models. However, auction fraud has become the highest threat and hazard to the future of this business model. The blockchain provides a new perspective to resolve this problem. It can be used for current financial services, certificates, remittances, and online payments; meanwhile it also provides several crucial services such as smart contract, smart property, trust system, and security services. This chapter discusses how to apply blockchain to a cloud-based online auction and the principle of operation. The purpose is to fundamentally solve the problem of online fraud caused by information asymmetry of electronic transactions. To the best of the authors' knowledge, this is the first time that the blockchain has been applied to authentication of online auction. The preliminary contribution is for preventing auction fraud from the aspects of smart properties and smart contract.
An axiomatic characterization of temporalised belief revision in the law

This paper presents a belief revision operator that considers time intervals for modelling norm change in the law. This approach relates techniques from belief revision formalisms and time intervals with temporalised rules for legal systems. Our goal is to formalise a temporalised belief base and corresponding timed derivation, together with a proper revision operator. This operator may remove rules when needed or adapt intervals of time when contradictory norms are added in the system. For the operator, both constructive definition and an axiomatic characterisation by representation theorems are given.
Motion Planning of Non-Holonomic Wheeled Robots Using Modified Bat Algorithm

The chapter proposes a novel optimization framework to solve the motion planning problem of non-holonomic wheeled mobile robots using swarm algorithms. The specific robotic system considered is a vehicle with approximate kinematics of a car. The configuration of this robot is represented by position and orientation of its main body in the plane and by angles of the steering wheels. Two control inputs are available for motion control including the velocity and the steering angle command. Moreover, the car-like robot is one of the simplest non-holonomic vehicles that displays the general characteristics and constrained maneuverability of systems with non-holonomicity. The control methods proposed in this chapter do not require precise mathematical modeling of every aspect a car-like system. The swarm algorithm-based motion planner determines the optimal trajectory of multiple car-like non-holonomic robots in a given arena avoiding collision with obstacles and teammates. The motion planning task has been taken care of by an adaptive bio-inspired strategy commonly known as Bat Algorithm.
Extended Kalman Filter with Input Detection and Estimation for Tracking Manoeuvring Satellites

The technique of tracking a non-cooperative manoeuvring satellite is important for Space Situation Awareness (SSA). However, the classical extended Kalman filter cannot work successfully in this situation. Motivated by this problem, a novel Extended Kalman Filter with Input Detection and Estimation (EKF/IDE) method is proposed in this paper for tracking a non-cooperative satellite with impulsive manoeuvres. The impulsive manoeuvre is modelled as an unknown acceleration without any prior information. An unbiased minimum-variance input and state estimation method is introduced to estimate the manoeuvre acceleration. An approach based on the Mahalanobis distance of the manoeuvre estimate error is proposed for manoeuvre detection. With the impulsive manoeuvre being detected and estimated accurately, an adaptive extended Kalman filter is proposed to estimate the state of the target. An approach of covariance inflation is proposed to deal with the manoeuvre during the unobserved period. Simulations and Monte Carlo experiments are implemented to demonstrate the feasibility and validity of the proposed method. The results of simulations show that the proposed method can accurately detect and estimate unknown impulsive manoeuvres of a non-cooperative satellite. Through the compensation of the estimated manoeuvre, the estimation of position and velocity after the manoeuvre maintains the accuracy of the pre-manoeuvre period, demonstrating the robustness of the proposed method against unknown manoeuvres.
Dynamic credit allocation for researchers

Credit allocation of each author of a multiauthor paper has been a long standing concern. Regarding to the fact that the credit of each citing paper is not equal and the total credit of paper fades with time, in this paper, we introduce the PageRank algorithm and time parameter, and present an improved dynamic credit allocation method, namely IDCA method. By investigating the effect of the damping factor d of PageRank algorithm and the time parameter γ on the accuracy α of the IDCA method, finally, the two parameter (d,γ) are set (0.85,-3). Then, we validate the method by distinguishing the laureates of the Nobel Prize in Physics from the authors of prize-winning papers in American Physical Society(APS) dataset. Result shows that the IDCA method outperforms the state-of-the-art methods, and the accuracy of identifying the Nobel Prize laureates in Physics is 80.77% for 26 multiauthor prize-winning papers in APS dataset. Furthermore, we test on the real dataset with noise data generated from randomly adding and rewiring edges, and results indicate that the IDCA method still has higher accuracy compared with the state-of-the-art methods. Finally, by studying the temporal evolution of credit among coauthors in the Nobel-winning paper, we find that the effect of the Nobel Prize on credit share is remarkable. This method is not just valid for physics, also valid for any other branch of sciences.
Towards a Deeper Understanding of the Behavioural Implications of Bidirectional Activity-Based Ambient Displays in Ambient Assisted Living Environments

In this chapter, we investigate the extent to which the real-time bidirectional exchange of activity information can influence context-awareness, social presence, social connectedness, and importantly interpersonal activity synchrony in mediated ambient assisted living (AAL) environments. Additionally, we describe the design, development, and assessment of a bidirectional ambient display platform to support real-time activity awareness and social connectedness in mediated AAL contexts. In a semi-controlled study, we evaluate a conglomerate of activity-based lighting displays, to determine the effects of real-time bidirectional deployment on behaviour and social connectedness. Exploiting everyday objects, human activity levels are projected with a Philips Hue lamp, LED wallet, and LED walking cane, which render this information based on predefined patterns of light. Results from the current study show tendencies toward (1) an increase in implicit social interactions (e.g., the sense of experienced social presence and connectedness), (2) more positive social behaviours between the elderly and their caregivers in mediated AAL contexts, and (3) sporadic moments of interpersonal activity synchrony however, further investigation is necessary to determine the extent of this variable in mediated AAL contexts.
Geochemical signatures of waste rocks around Sarcheshmeh porphyry copper mine dumps, southeastern Iran: Implications for exploration, economic by-products and the environment

The Sarcheshmeh porphyry copper deposit (1.7 Gt @ 0.65% Cu, 0.02% Mo, 1.22 ppm Ag, 0.06 ppm Au) is one of the giant Miocene continental arc porphyry ore systems in the Tethyan metallogenic copper belt. The supergene sulfide ore zone (6 Mt. @ 2.43% Cu, 0.022% Mo, 1.55 ppm Ag and 0.114 ppm Au) is mainly capped by a hematitic gossan. This hematitic gossan is a possible surficial exploration target for unexposed porphyry copper ores. The total tonnage of waste rocks is about 2.6 G tonnes of gossanized as well as sulfide-bearing waste rocks with an average modal content of 10% pyrite-chalcopyrite. The gossanized waste rocks show boxwork texture and are composed of hematite, goethite, jarosite, malachite, chalcanthite, brochantite and delafossite. In order to assess the exploration, economic and environmental aspects of chalcophile and precious elements in the waste rocks, a total of 86 waste rocks and 10 residual gossan samples were collected by a systematic random sampling method, analysed by XRF and ICP-MS methods for major oxides and 50 minor and trace elements at ALS Minerals Lab, Ireland. The highly anomalous elements in immature residual gossans are Au (0.775 ppm), Ag (13.4 ppm) and Mo (122 ppm). The gossanized waste rocks are also enriched in Au (0.608 ppm), Ag (87 ppm) and Mo (2960 ppm). These geochemical signatures could be used as possible exploration guides for concealed porphyry copper deposits. The enrichment of Au, Ag and Mo is caused by the lower mobility of these elements in the immature gossans and gossanized waste rocks. The elemental enrichment factors normalized to the Sarcheshmeh ore zone are Au (2.50), Pd (1.05), Ag (4.67), Pb (4.6), Zn (17.34), Sn (2.64), W (1.47), Re (22.22), Se (5.38), Te (5.75), Tl (4.1), Bi (3.29), Ga (1.15), In (11.5), La (1.2), Ce (1.2) and Y (4.1), respectively. The mean contents of the most valuable by-product elements in the gossanized waste rocks include Cu (3168.2 ppm), Mo (180.22 ppm), Au (0.085 ppm), Ag (5.7 ppm), W (42.76 ppm), Re (0.01 ppm), Bi (10.87 ppm) and Se (13.45 ppm). The S-NNP diagram highlights that the sulfide-bearing waste rocks have also a negative impact on the environment, are prone to generate acid mine drainage (AMD) and release the potentially harmful metal (loids): Pb (107 ppm), Zn (365.3 ppm), As (83.53 ppm), Sb (10.24 ppm), In (0.44 ppm), and Se (13.45 ppm) into the surrounding area. These elemental values occurred at concentrations about 4–269 times greater than the crustal abundances and natural background of fresh granodiorite. As such, the heavy metal-rich gossans and sulfide-bearing waste rocks around the Sarcheshmeh mining areas may be considered as an exploration guide for valuable by-product elements as well as environmental concern.
On approximate enhanced covers under Hamming distance

A border p of a string x is an enhanced cover of x if the number of positions of x that lie within some occurrence of p is the maximum among all borders of x. (String p is a border of x if p is both a proper prefix and a suffix of x.) In this paper, two more general notions based on enhanced covers are introduced: a k-approximate enhanced cover and a relaxed k-approximate enhanced cover, where a fixed maximum number of errors k under the Hamming distance is considered. The k-approximate enhanced cover of x is its border, and its k-approximate occurrences are also considered in the covered number of positions of x. The relaxed k-approximate enhanced cover of x is a factor of x and a k-approximate border of x. Algorithms that compute all the variations of k-approximate enhanced covers mentioned above are presented in this paper.
The Lawyer of the Future as “Transaction Engineer”: Digital Technologies and the Disruption of the Legal Profession

This chapter introduces two connected arguments about the future of the legal professionLegal profession . First, the ongoing “digital revolutionDigital revolution ” will continue to disrupt legal work as it has traditionally operated. Various aspects of this disruption are outlined. Second, in contrast to previous technological revolutions, the “deployment” of disruptive innovationDisruptive Innovation in the context of the digital revolutionDigital revolution seems unlikely to be primarily “state-led.” Instead, new technologies will be deployed by a coalition of diverse private actors (entrepreneurs, technologists, consultants, and other professionals) working in collaboration. Crucial amongst these actors will be the lawyer of the future operating as “transaction engineerTransaction engineer .” The chapter outlines this transaction engineerTransaction engineer function and its importance in the deployment of emerging digital technologies.
Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Organizing multivariate data spaces by their dimensions or attributes can be a rather difficult task. Most of the work in this area focuses on the statistical aspects such as correlation clustering, dimension reduction, and the like. These methods typically produce hierarchies in which the leaf nodes are labeled by the attribute names while the inner nodes are often represented by just a statistical measure and criterion, such as a threshold. This makes them difficult to understand for mainstream users. Taxonomies in science, biology, engineering, etc. on the other hand, are easy to comprehend since they provide meaningful labels at the inner nodes as well. Labeling inner nodes of taxonomies automatically requires the identification of hypernyms. Our proposed framework, called Taxonomizer, takes a visual analytics approach to meet this challenge. It appeals to the wisdom of humans to liaise with state of the art data analytics, neural word embeddings, and lexical databases. It consists of a set of visual tools that starts out with an automatically computed hierarchy where the leaf nodes are the original data attributes, and it then allows users to sculpt high-quality taxonomies for any multivariate dataset.
Building a Telemedicine System for Monitoring the Health Status and Supporting the Social Adaptation of Children with Autism Spectrum Disorders

In the Russian Federation, a large number of children are born with autism spectrum disorders, and the sooner parents notice such disorders in a child, and the earlier the rehabilitation and treatment program begins, the higher the likelihood of his social adaptation. The difficulties in raising such a child lie in the complexity of his learning outside of children's groups and the complexity of his medical care. In this regard, the development of digital applications that facilitate medical care and education of such children at home is important and relevant. This paper formulates the requirements for building a telemedicine system that monitors the health status and supports the social adaptation of children with autism spectrum disorders. It is determined that the system will be an information resource on the Internet, including a hardware and software complex for remote monitoring of children's health at home, the workplace of a doctor monitoring a child, dynamic video surveillance system of a child's behavior using deep learning methods artificial neural networks with early diagnostics of reactive reactions, complex systems with elements of virtual reality and augmented reality for distance training and adaptation of the children, set of interactive tools and test definition of autism spectrum disorders in different age of the child. The methods for implementing such a telemedicine system are given. This work was supported by the RFBR grant 18-07-00987.