Reviewerly/exHarmony huggingface dataset:
Privacy-by-Design Distributed Offloading for Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a distributed computing paradigm that utilizes smart vehicles (SVs) as computational cloudlets (edge nodes) by virtue of their inherent attributes such as mobility, low operating costs, flexible deployment, and wireless communication ability. VEC extends edge computing services by expanding computing coverage and further improving quality-of-services (QoS) for devices. Due to limited onboard energy and computation capabilities of SV-mounted cloudlets, a single vehicle might not be able to execute a large number of tasks and guarantee their desired QoS. To address this problem, the overloaded vehicle can fulfill its overwhelming workload by offloading its tasks to other available connected vehicles. However, data privacy and accessibility are of critical importance that need to be considered for offloading. In this paper, we propose privacy-by-design offloading solutions for VEC to facilitate latency requirements of user demands and reduce energy consumption of vehicles.We formulate the Data pRotection Offloading Problem (DROP) as an Integer Program and prove its NP-hardness. To provide computationally tractable solutions, we propose three distributed algorithms by leveraging graph theory to solve this problem. We evaluate the performance of our proposed algorithms by extensive experiments and compare them to the optimal results obtained by IBM ILOG CPLEX. The results demonstrate the flexibility, scalability, and cost efficiency of our proposed algorithms in providing practical privacy-by-design offloading solutions enabling edge services along the cloud-to-thing continuum.
Automated Counting of Platelets and White Blood Cells from Blood Smear Images

Platelet Detection and Count are one of the major analysis of the pathological test of the blood. Conventional methods of analysis involve observation of blood smear samples under the microscope and manually identifying and counting the numbers. This process is slow and tedious. This work presents a method to automatically detect and count the number of platelets. A sample size of 270 images collected indigenously is used for carrying out the experiments with the proposed methodology, which result in an accuracy of 95.59% for platelets and 100% for WBCs respectively.
Underpinning Knowledge and Skills for Educators to Enhance Cyber Safety Awareness in South African Schools

It is important to start from an early age to educate cyber users about cyber safety so that the cyber users can be cyber safety aware. It would then be the right step to take in educating cyber users already at school level. Out of research done it was clear that the educators in South African schools does not have the right resources, knowledge and skills to enhance schools cyber users cyber safety awareness. A scoping review was performed to identify resources to establish the knowledge areas and skills that the educators need to enhance the cyber safety awareness within the South African schools. This research recommends a number of knowledge areas and skills through a proposed theoretical framework that can be used to create and enhance cyber safety awareness.
Suitability of Blockchains to Enable and Support Networking Functions

The underlying network infrastructure faces challenges from addressing maintenance, security, performance, and scalability to make the network more reliable and stable. Software-defined networking, blockchain, and network function virtualization were proposed and realized to address such issues in both academic and industry wise. This paper analyzes and summarizes works from implementing different categories of blockchains as an element or enabler of network functions to resolve the limitation. Blockchain as a network function has been proposed to give support to the underlying network infrastructure to provide services that have less lag, are more cost-effective, have better performance, guarantee security between participating parties, and protect the privacy of the users. This paper provides a review of recent work that makes use of blockchain to address such networking related challenges and the possible setbacks in the proposal.
Augmented Reality for Improved User Experience: Himalayan Wildlife Tour Book

Education with augmented reality (AR) is bringing a significant impact to the next generation education method, where differences between the education system before and after the existence of computers becomes highly apparent. Augmented reality can literally change the way we look at books. Usually, when we look at book pages, we can only see its 2D physical design such as colors, images and words. However, if we look at the pages of a book which features augmented reality with our smart phone, its content display becomes more detailed with 3D models becoming a part of readers’ practical learning experience. Humans is becoming less dependent on physical items for practical learning with this advancement of technology. Augmented reality can provide practical learning without spending more on practical lesson that a topic would need. What humans need these days is just a smart phone.
The Overhead of Confidentiality and Client-side Encryption in Cloud Storage Systems

Client-side encryption (CSE) is important to ensure that only the intended users have access to information stored in public cloud services. However, CSE complicates file synchronization methods such as deduplication and delta encoding, important to reduce the large network bandwidth overheads associated with cloud storage services. To investigate the overhead penalty associated with CSE, in this paper, we present a comprehensive overhead analysis that includes empirical experiments using four popular CSE services (CSEs) and four popular non-CSEs. Our results show that existing CSEs are able to implement CSE together with bandwidth saving features such as compression and deduplication with low additional overhead compared to the non-CSEs. The most noticeable differences between CSEs and non-CSEs are instead related to whether they implement delta encoding and how effectively such solutions are implemented. In particular, fewer CSEs than non-CSEs implement delta encoding, and the bandwidth saving differences between the applications that implement delta encoding can be substantial.
Blind Packet Forwarding in a Hierarchical Level-based Locator/Identifier Split

Network Address Confidentiality (NAC) classifies all network nodes as adversaries and limits access to the network packet addresses in cleartext exclusively to the communicating endpoints. In our model, an adversary can control a single network node as well as a part or all of the network nodes on the route of a packet. Moreover, an adversary can observe and exploit network packet addresses. NAC implies certain anonymity properties, namely sender/recipient and relationship unlinkabilities. In contrast to the existing approaches related to NAC and its unlinkability properties with regard to our strong adversary model, Blind Packet Forwarding (BPF) tackles this issue in a clean-slate manner by redesigning the packet forwarding and its associated services to blind ones transferring and processing packet addresses in end-to-end encrypted form. This paper proposes a BPF architecture combining two approaches being based on the Locator/Identifier Split principle for a future network architecture. This BPF design introduces a fine-grained, flexible and dynamic blindness providing multiple NAC and unlinkability levels classified into two blindness taxonomies. In the first taxonomy, the higher the masking rank being applied to an address, the bigger is the radius of network domains within which NAC applies to the address, beginning with the top-level network domain. By applying higher masking ranks in the second blindness taxonomy, an endpoint can mask its address within higher network domains in direction of the top-level network domain. This paper also adapts OpenFlow in order to achieve a BPF implementation which provides high performance and can thus support multiple real-time media communications each with a high sending rate.
Effective Utilization of Image Information Using Data Mining Technique

In recent, video databases data mining is widely used for various applications such as crime prevention, web searching, cultural heritage, advertising, news broadcasting, video, education and training and military. The advancement of databases specially the multimedia dates are in need to efficiently handle due to the growing amount of multimedia data include audio video, sound, animation, image etc. Revolution in the extensive database of computerized medias gives rise to the study of useful information from database. The study such as multimedia information retrieval, productive storage and organization of available information are in focus. This paper discuss how effectively handle the image data’s.
Video lectures intellectual analysis algorithms and software system development

The paper is devoted to solving the problem of preparing educational video content without an operator. The paper describes the development of algorithms and scenarios for identification, localization, recognition and classification elements in a frame, including using neural network technologies. The developed complex of algorithms and scenarios is implemented in the software system of intellectual analysis of educational video lectures, real-time temporal and sense synchronization of video lectures and presentation materials. It is assumed that the use of the developed system will lead to a significant acceleration and reduction in the cost of the preparing educational video lectures process.
Towards neural abstractive clinical trial text summarization with sequence to sequence models

The recruitment stage in clinical trials is key in ensuring enrollment of a large and diverse number of participants. Recent trends in clinical trials recruitment strategies have leveraged social media, mobile, and web-based platforms to advertise trials to a broader and more diverse set of potential participants. We develop a method to improve clinical trials enrollment rates through novel models of communication that provide accurate and unbiased information about the clinical trials and provide awareness to target participants. The contributions of this paper are two-fold. First we propose a model to generate abstractive summaries for clinical trials based on sequence to sequence networks with attention policies. Second, we present a preliminary evaluation of the model in terms of learning, vocabulary development, choices of attention policies, and summarization outputs. Finally, we generate a dataset consisting of multi-sentence clinical trials summaries to be used for bench-marking and in future work.
Analysis and Implementation of Ontology Based Text Classification on Criminality Digital News

Abstract This research, we search the trend by utillize all information about the criminality type. When the trend has found, the possibility of crime in West Java will decrease. We need to analyse the data to get the information about the criminality trend by using Ontology based Text Classification Method. A news will be grouped into some criminality classes based on the relation around that groups so the process of classification can be done simply and specifically. The criminality trend of West Java is Property Crime with 47,5% of occurrences. We used several testing method such as F1 Score, Precision, Recall, and Accuracy. Based on the methods, the performance of system is running well or not in different point of view. The researcher obtained satisfactory result with F1 score 87,05%, accuracy 86,74%, recall 100% and precision 77,08% with using comparison of composition crime dataset and non-crime dataset is 40 : 40. It happens because testing needs to be done by considering the number of both datasets. The more balance the comparison while do preprocess, the higher accuracy that will get. In order to know whether the system is able to clarify the information accurately and can separate the non-criminal news dataset.
Design of Student Attendance Information System with Fingerprints

Abstract Student attendance is one of the administrative activities in each semester and the lecturer records the attendance of students to determine whether students can take midterm or final semester examinations. The aim of this study is to design a model of student attendance information system with fingerprints for exam needs. The structured approach method is used to develop this model. Context diagram and data flow diagram used to describe the system. The result of the study is a design of student attendance information system with fingerprints. The attendance records of students that have not been automated are prone to errors in calculating the number of student attendance. Therefore, designing this information system can help the lecturer by storing student attendance data using fingerprints. Attendance data will be automatically saved to the server.
Joint Fine-Grained Components Continuously Enhance Chinese Word Embeddings

The most common method of word embedding is to learn word vector representations from context information of large-scale text. However, Chinese words usually consist of characters, subcharacters, and strokes, and each part contains rich semantic information. The quality of Chinese word vectors is related to the accuracy of prediction. Therefore, to obtain high-quality Chinese character embedding, we propose a continuously enhanced word embedding model. The model starts with fine-grained strokes and adjacent stroke information and enhances subcharacter embedding by combining the relationship vector representation between strokes. Similarly, we combine the subcharacter relationship vector and the character relationship vector to learn Chinese word embedding based on the enhanced subcharacter embedding. We construct the underlying stroke n-grams and adjacent stroke n-grams and extract the relationship vector used to enhance the relationship between the components, which can be used to learn Chinese word embedding and improve the accuracy. Finally, we evaluate our model on the word similarity calculations and word reasoning tasks.
An Explainable Intelligence Model for Security Event Analysis

Huge volume of events is logged by monitoring systems. Analysts do not audit or trace the log files, which record the most significant events, until an incident occurs. Human analysis is a tedious and inaccurate task given the vast volume of log files that are stored in a “machine-friendly” format. The analysts have to derive the context for an incident using the prior knowledge to find relevant events to the incident to recognise why it has happened. Although the security tools by providing visualization techniques and minimizing human interactions have been developed to make the process of analysis easier, far too little attention has been paid to interpret security incident in a “human-friendly” format. Besides, the current detection patterns and rules are not mature enough to recognize early breaches, which have not caused any damage. In this paper, we presented an Explainable AI model that assist the analysts’ judgement to infer what is happened from the security event logs. The proposed Explainable AI model includes storytelling as a novel knowledge representation model to present the sequence of the events which automatically are discovered from the log file. For automated discovering sequential events, an apriority-like algorithm by mining temporal patterns is utilized. This effort focused on security events to convey both short-life and long-life activities. The experimental results demonstrate the potential and advantages of the proposed Explainable AI model from the security logs that validated on the activities during the security configuration compliance on Windows system.
Integrating Multiple Sources of Knowledge for the Intelligent Detection of Anomalous Sensory Data in a Mobile Robot

For service robots to expand in everyday scenarios they must be able to identify and manage abnormal situations intelligently. In this paper we work at a basic sensor level, by dealing with raw data produced by diverse devices subjected to some negative circumstances such as adverse environmental conditions or difficult to perceive objects. We have implemented a probabilistic Bayesian inference process for deducing whether the sensors are working nominally or not, which abnormal situation occurs, and even to correct their data. Our inference system works by integrating in a rigorous and homogeneous mathematical framework multiple sources and modalities of knowledge: human expert, external information systems, application-specific and temporal. The results on a real service robot navigating in a structured mixed indoor-outdoor environment demonstrate good detection capabilities and set a promising basis for improving robustness and safety in many common service tasks.
Improving the performance of physics applications in atom-based clusters with rCUDA

Traditionally, High-Performance Computing (HPC) has been associated with large power requirements. The reason was that chip makers of the processors typically employed in HPC deployments have always focused on getting the highest performance from their designs, regardless of the energy their processors may consume. Actually, for many years only heat dissipation was the real barrier for achieving higher performance, at the cost of higher energy consumption. However, a new trend has recently appeared consisting on the use of low-power processors for HPC purposes. The MontBlanc and Isambard projects are good examples of this trend. These proposals, however, do not consider the use of GPUs. In this paper we propose to use GPUs in this kind of low-power processor based HPC deployments by making use of the remote GPU virtualization mechanism. To that end, we leverage the rCUDA middleware in a hybrid cluster composed of low-power Atom-based nodes and regular Xeon-based nodes equipped with GPUs. Our experiments show that, by making use of rCUDA, the execution time of applications belonging to the physics domain is noticeably reduced, achieving a speed up of up to 140x with just one remote NVIDIA V100 GPU with respect to the execution of the same applications using 8 Atom-based nodes. Additionally, a rough energy consumption estimation reports improvements in energy demands of up to 37x.
The Video Assistant Referee (VAR) Protocol

Throughout its history, the game of football has, in general, been reluctant to use technologies that have been adopted more readily by other sports. The way the game is played is, basically, very similar to how it was played in its early days, with very few modifications having been made to the Laws of the Game. In the same way, refereeing looks very similar to that carried out by the first match officials in charge of games that took place on rudimentary pitches. Technology, however, has, little by little, been creeping into a game that moves enormous amounts of money and is followed by millions of people around the world. Regarding help given to refereeing in football, a few years ago Goal-Line Technology (GLT) was introduced, as were devices for communication between the officials, initially a gadget that emitted a signal by means of vibration, and more recently headsets that allow for multiple and simultaneous dialogue on an open channel. Much more recently, however, we have seen a great step forward with the introduction of the VAR system in football, a complex, difficult matter, one that has provoked a great many opinions both in favour and against, and which is the subject of this chapter.
Gesture-Timbre Space: Multidimensional Feature Mapping Using Machine Learning and Concatenative Synthesis

This chapter explores three systems for mapping embodied gesture, acquired with electromyography and motion sensing, to sound synthesis. A pilot study using granular synthesis is presented, followed by studies employing corpus-based concatenative synthesis, where small sound units are organized by derived timbral features. We use interactive machine learning in a mapping-by-demonstration paradigm to create regression models that map high-dimensional gestural data to timbral data without dimensionality reduction in three distinct workflows. First, by directly associating individual sound units and static poses (anchor points) in static regression. Second, in whole regression a sound tracing method leverages our intuitive associations between time-varying sound and embodied movement. Third, we extend interactive machine learning through the use of artificial agents and reinforcement learning in an assisted interactive machine learning workflow. We discuss the benefits of organizing the sound corpus using self-organizing maps to address corpus sparseness, and the potential of regression-based mapping at different points in a musical workflow: gesture design, sound design, and mapping design. These systems support expressive performance by creating gesture-timbre spaces that maximize sonic diversity while maintaining coherence, enabling reliable reproduction of target sounds as well as improvisatory exploration of a sonic corpus. They have been made available to the research community, and have been used by the authors in concert performance.
On LSB Data Hiding in High-Definition Images Using Morphological Operations

The aim of steganography is to conceal the presence of communication by way of hiding secret data in perceptually irrelevant parts of a cover object. In this paper, we propose a method for hiding secret images in edge regions of high-definition (HD) images because the human visual system is less sensitive to intensity changes in these regions. In particular, least significant bit substitution is used to embed a secret image in the edge regions of a HD cover image. The edge regions are obtained using a Canny edge detector followed by morphological operations which are used to control the hiding capacity. A performance assessment of the proposed method reveals the trade-off between capacity, detectability, and perceptibility of the hidden data.
Laplacian Eigenmaps Regularized Feature Mapping for Image Annotation

In the past two decades, researchers have shown great interest in automatic image annotation. However, most existing research methods do not consider similarities among samples or do not obtain the suitable manifold information. Those methods also require the adequate and precise label sets. Considering above mentioned challenges, we propose a method, called laplacian eigenmaps regularized feature mapping for image annotation, which construct a laplacian matrix with all data in the training set (include labeled data and unlabeled data) and embed the laplacian matrix into feature mapping. Experimental results conducted on several benchmark image annotation datasets, such as Corel5K and ESP Game, demon-strate the effectiveness of the proposed method.