Reviewerly/exHarmony huggingface dataset:
A Multi-strategy Artificial Bee Colony Algorithm with Neighborhood Search

As an effective swarm intelligence based optimization technique, artificial bee colony (ABC) algorithm has become popular in recent years. However, its performance is still not satisfied in solving some complex optimization problems. The main reason is that both of the employed bee phase and onlooker bee phase use the same solution search equation to generate new candidate solutions, and the solution search equation is good at exploration but poor at exploitation. To solve this problem, in this paper, we propose a multi-strategy artificial bee colony algorithm with neighborhood search (MSABC-NS). In MSABC-NS, a multi-strategy mechanism is designed to use two different solution search equations, and a neighborhood search mechanism is introduced to make full use of good solutions. Experiments are conducted on 22 widely used benchmark functions, and three different ABC variants are included in the comparison. The results show that our approach can achieve better performance on most of the benchmark functions.
Improving Youtube video retrieval by integrating crowdsourced timed metadata

The development of efficient methods for searching and browsing large assets of video content has been considered by the academia and content owners for long. Different approaches that range from manual structured annotations, to unstructured metadata collected from several sources, as well as mult imedia processing for automatic description of the content, can be identified. The growth on the number of hours of video content put online in video sharing platforms has however shown that video retrieval is still quite inefficient as rich contextual data that describes the content is most of the times still not available. Additionally, metadata is usually not linked to timed moments of content, making direct access to the most relevant moments not possible. In this paper, an approach for making web videos available in the YouTube platform more accessible is presented. The solution is based on a collaborative process presented as a game that enables collecting metadata from the crowd while implementing mechanisms that remove erroneous information usually encountered in this type of information. Metadata, exported to YouTube in the form of captions and descriptions, contributes to enhance video retrieval, guaranteeing a better user experience and exposure of the content.
Edge Preserved Satellite Image Denoising Using Median and Bilateral Filtering

The satellite images acquired from long distances are affected by different atmospheric disturbances such as noise and the image quality is degraded. The images thus require pre-processing to preserve the image quality for use in classification, fusion, segmentation etc. In the domain of image processing, analyzing the different noise types which affect the satellite images and also design the filter according to the affected noise is important. The existing filtering methods are capable of removing the noise in the image but is not much effective in preserving the image information such as edges, lines etc. This paper proposes a hybrid filtering technique for impulse noise removal. The hybrid filter comprises of a median filter which removes the impulse noise followed by a bilateral filter for edge preservation. The performance is studied based on the Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Feature Simillarity Index (FSIM), Structural Similarity Index (SSIM), Entropy and CPU time by comparing the results with existing denoising filters.
Reducing global reductions in large-scale distributed training

Current large-scale training of deep neural networks typically employs synchronous stochastic gradient descent that incurs large communication overhead. Instead of optimizing reduction routines as done in recent studies, we propose algorithms that do not require frequent global reductions. We first show that reducing the global reduction frequency works as an effective regularization technique that improves generalization of adaptive optimizers. We then propose an algorithm that reduces the global reduction frequency by employing local reductions on a subset of learners. In addition, to maximize the effect of reduction on convergence, we introduce reduction momentum that further accelerates convergence.
Iterative Look-up Table Based Track Initialization of Doppler-only Passive Multi-static Radar

The ultimate goal of the research is to develop an alternative system for tracking non-cooperative aircraft based on the VHF Doppler phenomenon by exploiting transmitters of opportunity and radio receivers. Doppler-only information is used to track aircraft, which as a configuration is a challenging way of tracking aircraft. A particularly challenging sub-task is track initialization. The current paper presents an algorithm for that, based on a look-up table. After the initialization, tracking is done with the Extended Kalman filter from the Doppler-only measurements. The validity of the tracks is verified with reference data based on Automatic Dependent Surveillance - Broadcast (ADS-B) information.
Crowd Sourced Semantic Enrichment (CroSSE) for knowledge driven querying of digital resources

Today, most information sources provide factual, objective knowledge, but they fail to capture personalized contextual knowledge which could be used to enrich the available factual data and contribute to their interpretation, in the context of the knowledge of the user who queries the system. This would require a knowledge framework which can accommodate both objective data and semantic enrichments that capture user provided knowledge associated to the factual data in the database. Unfortunately, most conventional DBMSs lack the flexibilities necessary (a) to prevent the data and metadata, evolve quickly with changing application requirements and (b) to capture user-provided and/or crowdsourced data and knowledge for more effective decision support. In this paper, we present CrowdSourced Semantic Enrichment (CroSSE) knowledge framework which allows traditional databases and semantic enrichment modules to coexist. CroSSE provides a novel Semantically Enriched SQL (SESQL) language to enrich SQL queries with information from a knowledge base containing semantic annotations. We describe CroSSE and SESQL with examples taken from our SmartGround EU project.
Low-Power FPGA-Based Hardware Implementation of Reversible Watermarking Scheme for Medical Image

The data security and authenticity with good image quality are very much important in medical imaging applications. The goal of hardware-based watermarking techniques is to achieve low power consumption, real-time performance, reliability, and ease of integration with the existing consumer electronic devices. In this paper, the hardware architecture of reversible watermarking for medical images and its implementation in field-programmable gate array (FPGA) is proposed. The difference expansion (DE) method for watermarking in lifting-based DWT domain and channel coding is implemented in hardware to achieve the goal. The simulation result shows that the designed architecture consumes 44.66% less power and 44.67% higher operating frequency (operating speed) than the similar implementation.
Speculate-correct error bounds for k-nearest neighbor classifiers

We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that k-nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with $$\hbox {O} \left( \sqrt{(k + \ln n)/n} \right) $$ range around an estimate of generalization error for n in-sample examples.
Forecasting Hands and Objects in Future Frames

This paper presents an approach to forecast future presence and location of human hands and objects. Given an image frame, the goal is to predict what objects will appear in the future frame (e.g., 5 s later) and where they will be located at, even when they are not visible in the current frame. The key idea is that (1) an intermediate representation of a convolutional object recognition model abstracts scene information in its frame and that (2) we can predict (i.e., regress) such representations corresponding to the future frames based on that of the current frame. We present a new two-stream fully convolutional neural network (CNN) architecture designed for forecasting future objects given a video. The experiments confirm that our approach allows reliable estimation of future objects in videos, obtaining much higher accuracy compared to the state-of-the-art future object presence forecast method on public datasets.
Detecting review spammer groups in dynamic review networks

Online product reviews are becoming the second most trusted source of product information, second only to recommendations from family and friends, because consumers think that online product reviews reflect recommendations of "real" people. However, in order to maximize the impact, some merchants organize a group of fraudulent reviewers to post a lot of fraudulent reviews that mislead consumers, which is called review spammer group. Solutions for review spammer group detection are very limited, and most solutions focus on static review networks. In this paper, we propose an online two-step framework, called OGSpam, detecting review spammer groups in dynamic review networks. By model a dynamic review network as an initial static review network with an infinite change review stream, our framework first detects reviewer groups on the initial static review network (first snapshot) based on classical Clique Percolation Method (CPM). Then, it detects reviewer groups on snapshot T+1 using reviewer network at T+1 and reviewer groups at T. The experimental results on two real-world review datasets illustrate the efficiency and effectiveness of our framework. To the best of our knowledge, this is the first time to detect review spammer group in dynamic review network.
CuLDA_CGS

GPUs have benefited many ML algorithms. However, we observe that the performance of existing Latent Dirichlet Allocation(LDA) solutions on GPUs are not satisfying. We present CuLDA_CGS, an efficient approach to accelerate large-scale LDA problems. We delicately design workload partition and synchronization mechanism to exploit multiple GPUs. We also optimize the algorithm from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA_CGS outperforms them by a large margin (up to 7.3X) on a single GPU.
An Assessment of BYOD Control in Higher Learning Institutions: A Namibian Perspective

With the increasing demand for mobility at workplaces, the trend for Bring Your Own Device (BYOD) is spreading very quickly in business organisations. In education BYOD has become a centrepiece for the success of modern universities. Higher Learning Institutions (HLIs) embrace BYOD trend as it helps to reduce the cost of acquiring devices, providing maintenance and support for both students and staff. However, the adoption of BYOD presents both opportunities and security implications. The main objective of this study was to assess the extent of adoption of BYOD and effectiveness of controlling these devices and their secure use in Namibian institutions of higher learning. Case studies were carried out at two Namibian tertiary institutions. Data was collected using interviews and vulnerability assessments. The results show that the use of personal mobile devices is prevalent at these institutions as students and lecturers use them for educational purposes. It was also discovered that the institutions suffer from network access control challenges among other security issues discovered. Control of mobile end user devices was found very minimal due to the absence of BYOD security policies. The validation of findings confirmed that technical security solutions combined with explicit policies and procedures can help to improve BYOD control.
Comparison of LZ77-type parsings

We investigate the relations between different variants of the LZ77 parsing existing in the literature. All of them are defined as greedily constructed parsings encoding each phrase by reference to a string occurring earlier in the input. They differ by the phrase encodings: encoded by pairs (length + position of an earlier occurrence) or by triples (length + position of an earlier occurrence + the letter following the earlier occurring part); and they differ by allowing or not allowing overlaps between the phrase and its earlier occurrence. For a given string of length n over an alphabet of size σ, denote the numbers of phrases in the parsings allowing (resp., not allowing) overlaps by z (resp., zˆ) for “pairs”, and by z3 (resp., zˆ3) for “triples”. We prove the following bounds and provide series of examples showing that these bounds are tight: • z≤zˆ≤z⋅O(log⁡nzlogσ⁡z) and z3≤zˆ3≤z3⋅O(log⁡nz3logσ⁡z3); • 12zˆ<zˆ3≤zˆ and 12z<z3≤z.
Unsupervised context extraction via region embedding for context-aware recommendations

Many context-aware recommendation methods extract contexts from reviews using supervised methods. However, this requires the optimal values for contexts to be predefined, which is not a trivial task. Although some approaches have avoided this by utilizing unsupervised methods, the extracted contexts have been limited to a unigram format. Moreover, most methods consider only the influence of context on the entire dataset, ignoring the fact that context might be relevant to individual users or items unequally. This work proposes a novel unsupervised context extraction method that uses predictive models for future ratings. Unlike previous work, we extract context from reviews automatically in the form of skip-grams by applying a region embedding technique. The predictive models utilize the interaction between contexts and users (and items) to model their influence on ratings. Experiments demonstrate that our models can outperform existing review-based recommendations that ignore contexts.
ELTS: A Brief Review for Extractive Learning-Based Text Summarizatoin Algorithms

Automatically capturing the main points from a single document or multiple documents is a challenging requirement. Extractive text summarization which refers to providing a brief summary extract significant sentences from text, deals with several issues. Recently a considerable amount of work has considered learning approaches as text summarization solutions. Intensive researches have surveyed different strategies for text summarization. This paper influenced by the merit performance of learning methods for this task, analytically reviewed current algorithms. In this paper we suggest a framework called "ELTS" including classification of existing learning based algorithm, introducing several criteria in order to make comparison between current models and an analysis based on these criteria. We offer ELTS with the aim to enhance future research which attempts to a) solve current methods defects, b) employ existing strategies according to their requirements or c) make analytical comparison between current and future work.
Automatic paper summary generation from visual and textual information

Due to the recent boom in artificial intelligence (AI) research, including computer vision (CV), it has become impossible for researchers in these fields to keep up with the exponentially increasing number of manuscripts. In response to this situation, this paper proposes the paper summary generation (PSG) task using a simple but effective method to automatically generate an academic paper summary from raw PDF data. We realized PSG by combination of vision-based supervised components detector and language-based unsupervised important sentence extractor, which is applicable for a trained format of manuscripts. We show the quantitative evaluation of ability of simple vision-based components extraction, and the qualitative evaluation that our system can extract both visual item and sentence that are helpful for understanding. After processing via our PSG, the 979 manuscripts accepted by the Conference on Computer Vision and Pattern Recognition (CVPR) 2018 are available1 . It is believed that the proposed method will provide a better way for researchers to stay caught with important academic papers.
RepoBee

The use of version control systems within computing education is growing in popularity. However, this is challenging because such systems are not particularly well designed to support educational situations, nor are they easy to use with confidence in teaching, as specialist knowledge and experience is required. This experience paper reports the development of the open source tool RepoBee, which assists in the use of Git/GitHub in an educational context. The tool provides a straightforward interface for managing batch tasks such as repository generation and cloning for setting and gathering assignments, opening and closing of issues to communicate with students, as well as facilitating peer reviews. Parts of RepoBee are open to integration with third party tools for additional tasks, such as running unit tests or static analysis on student repositories. We also include the perspectives of both teachers and teaching assistants who have been using the tool as part of a first year course for computer scientists.
Fault Tolerance in Automatic Semantic Web Service Composition based on QoS-awareness Using BTSC-DFS Algorithm

Quality-of-Service (QoS)-aware web service composition is very important in integrating individual services with respect to the functional and nonfunctional requirements. Despite the large number of candidate services, automation of the combination is essential in order to provide a good combination of service. Although many of the existing methods offer a solution that is optimal, most of them have little flexibility. In some cases, the compound service components fail, so the combination algorithm has to run again to find another optimal solution. Also in many situations, users prefer to have several alternative solutions. Therefore, providing a top-k service composition according to their QoS is more desirable. Because web services are unreliable, also since providing the transactional support in execution of a service composition is an important design requirement; so in this research, a fault management procedure is implemented to ensure the transaction execution of the service combinations. This procedure in the event of service failure undo the impact of this service by calling the equivalent service. The proposed method encounters these three issues: 1) the semantic selection of services; 2) QoS-aware web service composition with the top-k solution; and 3) fault-handling/recovery procedure. In the proposed method, ontology concept ranking algorithm is used in service selection, and top-k method is employed to solve service combination. Error handling procedure will then be reviewed and designed to ensure the transactional execution of the service composition. The evaluation results show that the proposed method not only finds optimal solutions but also can provide alternative solutions with optimal QoS.
Delineation of radiometric anomalies and conductive zones using gamma-ray spectrometric and electromagnetic methods, east Gabal El-Urf area, Northern Eastern Desert, Egypt

Ground gamma-ray spectrometric (GRS) and horizontal-loop electromagnetic (HLEM) surveys were carried out to determine the radioactive anomalous zones and to follow their lateral and vertical extensions in east Gabal (G.) El-Urf area, Northern Eastern Desert, Egypt. This area and its surroundings are mainly covered by basement rocks of Late Precambrian age, represented by quartz diorites and granite porphyry, which are intruded by pegmatites, quartz veins, and basic dykes. These rocks are traversed by dry wadis (dry valleys), which are filled by Quaternary sediments. The ground gamma-ray spectrometric data revealed that the study area possesses a wide range of radioelement concentrations, oscillating from 37 to 2164 μR/h, 0.9-170 ppm, 0.1-312 ppm, and 0.2%-7.8% for total count (TC), equivalent uranium (eU), equivalent thorium (eTh), and potassium (K), respectively. The eU composite image showed that high radioactive parts are mainly associated with the pegmatite rocks. Ground HLEM survey was performed at four frequencies (7040 Hz, 1760 Hz, 440 Hz, and 110 Hz) in the study area. The interpretation of the HLEM data indicated the presence of various conductive zones, mainly associated with pegmatite bodies. The parameters of source causative bodies, such as location, dip angle, width, and conductivity thickness were estimated for three conductive bodies. The calculated depths to the selected HLEM anomalies appear to be shallow, varying from 10 m to 22 m. All the anomalous bodies have dips ranging between 60° and 80° toward the south and west. The widths of the anomalous bodies range from medium conductors (13 m) to broad conductors (40 m or more). The conductivity thicknesses showed a wide range (1.14-72.7 Siemens), with the highest value associated with the highest eU anomaly.
A Game-Theoretic Framework for the Virtual Machines Migration Timing Problem

In a multi-tenant cloud, a number of Virtual Machines (VMs) are collocated on the same physical machine to optimize performance, power consumption and maximize profit. This, however, increases the risk of a malicious VM performing side-channel attacks and leaking sensitive information from neighboring VMs. As such, this paper develops and analyzes a game-theoretic framework for the VM migration timing problem in which the cloud provider decides <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">when</i> to migrate a VM to a different physical machine to reduce the risk of being compromised by a collocated malicious VM. The adversary decides the rate at which she launches new VMs to collocate with the victim VMs. Our formulation captures a data leakage model in which the cost incurred by the cloud provider depends on the duration of collocation with malicious VMs. It also captures costs incurred by the adversary in launching new VMs and by the defender in migrating VMs. We establish sufficient conditions for the existence of Nash equilibria for general cost functions, as well as for specific instantiations, and characterize the best response for both players. Furthermore, we extend our model to characterize its impact on the attacker’s payoff when the cloud utilizes intrusion detection systems that detect side-channel attacks. Our theoretical findings are corroborated with extensive numerical results in various settings as well as a proof-of-concept implementation in a realistic cloud setting.
Multiple Image Watermarking for Efficient Storage and Transmission of Medical Images

In today’s digital era, managing patient’s records can be one of the most challenging task for hospitals and health care centers. In hospitals, patient’s medical data like medical reports and images (like x-ray, CT and MRI) etc. are stored at different locations at server side that results in more memory utilization. Also, Medical applications like telemedicine require exchange of medical data between two health care centers that needs huge transmission bandwidth. As a result, the use of watermarking in medical applications would prove to be advantageous as multiple images can be hidden into a single image. Multiple image watermarking system is proposed in this paper which thereby can result in efficient memory and bandwidth utilization. The algorithm simultaneously embeds three different medical images as multiple watermarks into all the three color planes (Red, Green and Blue planes) of cover image there by reducing the memory for compact storage and bandwidth requirement during transmission thereby retaining the quality of images. For maintaining the confidentiality as well as security, Arnold’s scrambling method is used for encrypting the watermarks. The Performance indicators used for measuring the quality of watermarked image are peak signal to noise ratio (PSNR) and correlation coefficient.