Reviewerly/exHarmony huggingface dataset:
Optimizing Job Reliability Through Contention-Free, Distributed Checkpoint Scheduling

A datacenter that consists of hundreds or thousands of servers can provide virtualized environments to a large number of cloud applications and jobs that value the requirement of reliability very differently. Checkpointing a virtual machine (VM) is a proven technique to improve reliability. However, existing checkpoint scheduling techniques for enhancing reliability of distributed systems fails to achieve satisfactory results, either because they tend to offer the same, fixed reliability to all jobs, or because their solutions are tied up to specific applications and rely on centralized checkpoint control mechanisms. In this work, we first show that reliability can be significantly improved through contention-free scheduling of checkpoints. Then, inspired by the Carrier Sense Multiple Access (CSMA) protocol in wireless congestion control, we propose a novel framework for distributed and contention-free scheduling of VM checkpointing to provide reliability as a transparent, elastic service. We quantify reliability in closed form by studying system stationary behaviours, and maximize job reliability through utility optimization. Our design is validated via a proof-of-concept prototype that leverages readily available implementations in Xen hypervisors. The proposed checkpoint scheduling is shown to significantly reduce checkpointing interference and improve reliability by as much as one order of magnitude over contention-oblivious checkpoint schemes.
Efficient Data Sharing With Privacy Preservation Over Lattices for Secure Cloud Storage

Data sharing is a basic data processing mechanism in the cloud storage. Data confidentiality and the receiver privacy are two important requirements of data-sharing systems. Because the data owner does not wish anyone who is not authorized to access the shared data and the authenticated data receiver also does not wish anyone else to know that he/she has accessed the shared data from the data owner. Broadcast encryption gives a possible solution to these requirements of data-sharing systems. This article constructs a data-sharing system over lattice by designing an efficient identity-based broadcast encryption (IBBE) scheme which achieves the data confidentiality and the identity privacy simultaneously. Besides the data owner, no one knows the identities of the authenticated data receivers in the proposed data-sharing system. Data confidentiality is achieved by the indistinguishability under the selective identity and the chosen ciphertext attacks (IND-sID-CCA) of the proposed IBBE scheme. It is proven in the random oracle model that the IND-sID-CCA security is based on the hardness of the learning with errors problem which can resist the quantum attacks. Inherited from the proposed IBBE scheme, the proposed data-sharing system also has several other characters which are suitable to the practical application. The most important feature is that all the public parameters size, private key size, and the ciphertext length are constant for any data receivers set in the proposed system. Hence, the proposed system adapts to the situation of large receiver group. Moreover, the proposed data-sharing system supports efficient member joining. When a new user asks to access the uploaded data, the existing receiver does not need to update his/her private key. The decryption cost of the existing receiver will remain unchanged when the outsourced data have been updated. Detailed performance analysis and a simulated experiment of the designed core algorithm are given by using Java program on a personal computer Intel Core i7-8700 K (3.7 GHz), 32 G RAM which verifies the design results in this article.
Multi-view iterative random walker for automated salvageable tissue delineation in ischemic stroke from multi-sequence MRI

Non-invasive and robust identification of salvageable tissue (penumbra) is crucial for interventional stroke therapy. Besides identifying stroke injury as a whole, the ability to automatically differentiate core and penumbra tissues, using both diffusion and perfusion magnetic resonance imaging (MRI) sequences is essential for ischemic stroke treatment. A fully automated and novel one-shot multi-view iterative random walker (MIRW) method with an automated injury seed point detection is developed for lesion delineation. MIRW utilizes the heirarchical decomposition of multi-sequence MRI physical properties of the underlying tissue within the lesion to maximize the inter-class variations of the volumetric histogram to estimate the probable seed points. These estimates are further utilized to conglomerate the lesion estimations iteratively from axial, coronal and sagittal MRI volumes for a computationally efficient segmentation and quantification of salvageable and necrotic tissues from multi-sequence MRI. Comprehensive experimental analysis of MIRW is performed on three challenging adult(sub-)acute ischemic stroke datasets using performance measures like precision, sensitivity, specificity and Dice similarity score (DSC), which are computed with respect to the manual ground-truth. MIRW method resulted in a high DSC of 83.5% in a very less computational time of 98.23 s/volume, which is a significant improvement on the ISLES benchmark dataset for penumbra detection, compared to the state-of-the-art techniques. Quantitative measures demonstrate the promising potential of MIRW for computational analysis of adult stroke and quantifying penumbra in stroke patients which is essential for selecting the good candidates for recanalization.
Differentiation of Sliding Rescaled Ranges: New Approach to Encrypted and VPN Traffic Detection

We propose a new approach to traffic preprocessing called Differentiation of Sliding Rescaled Ranges (DSRR) expanding the ideas laid down by H.E. Hurst. We apply proposed approach on the characterizing encrypted and unencrypted traffic on the well-known ISCXVPN2016 dataset. We deploy DSRR for flow-base features and then solve the task VPN vs nonVPN with basic machine learning models. With DSRR and Random Forest, we obtain 0.971 Precision, 0.969 Recall and improve this result to 0.976 using statistical analysis of features in comparison with Neural Network approach that gives 0.93 Precision via 2D-CNN. The proposed method and the results can be found at https://github.com/AleksandrIvchenko/dsrr_vpn_nonvpn.
Cultural adaptation of “kawaii” in short mobile video applications: How the perception of “kawaii” is shaped by the cultural background of the viewer and the gender of the performer

Video-sharing social networking (VS-SN) platforms are a new communication tool especially popular among young people. Goal of our study was to examine the cultural adaptation of kawaii in VS-SN videos and its effect on viewers' social approach motivation to share the content. In a content analysis of 198 videos from a Japanese and Chinese VS-SN platform we identified two types of kawaii; “confident kawaii” typically found on the Chinese platform and “shy kawaii” typically found on the Japanese platform. These two types of kawaii were then tested in an online experiment comparing young male university students from Japan (n ​= ​107) and China (n ​= ​115). Chinese participants' kawaii ratings were higher for confident kawaii videos compared to Japanese participants’ ratings but no differences were found for “shy kawaii”. Furthermore, kawaii ratings had the highest prediction value for socially oriented behavior. Results of this study help to explain the impact kawaii has made worldwide and its popularity among young people on VS-SN platforms.
Secular: A Decentralized Blockchain-based Data Privacy-preserving Model Training Platform

The main actors for productizing machine learning models are machine learning model builders. Commonly, they aim to train their models on a large enough, suitable, convenient, and realistic data set to get a model of expected and satisfactory accuracy. However, one of their biggest challenges is how to maintain the security and privacy of the given dataset. Besides, Blockchain is one of the most discussed technologies of our time. It can be described as a radical new approach to delivering trust and confidence over exchanges of value without relying on a trusted third party. Hence, this article aims to propose Secular, a decentralized Blockchain-based data privacy-preserving model training platform. The main objective of the proposed platform is to guarantee data security and privacy while allowing model builders to upload their models for training as well as sharing the required data. We present, in this article, a high-level architecture for the data providing, model uploading, model validation, and model training phases. Finally, the Software Requirements Specification (SRS) and a prototype of Secular are given.
Optimal Load Balancing In Three Level Cloud Computing Using Osmotic Hybrid And Firefly Algorithm

Cloud Computing is an emerging paradigm of computing which facilitates computing as a service. It enables to use the computing facilities “on-demand”, for example, Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS) etc. This model is best suitable for modern application deployment in which the software /service is required to be deployed in a fast, efficient and cost effective manner. The underlying technology behind cloud computing is virtualization, which enables sharing of the computing resources across multiple users across the globe, connected with each other through internet. Service offering is provided by Cloud Service Provider Companies, some of which are big market players like Amazon (Amazon Web Services; AWS), Google (Google App Engine; GAE) and Microsoft (Microsoft Azure). Load Balancing in Cloud Environment is a central issue of research. This is critical as it leads to efficient utilization of cloud resources, thereby resulting into low cost per user through optimal utilization of resources. The contribution of this paper is two-fold. It extends the approach of using Osmotic Bio inspired algorithm at all the three level of task scheduling, viz, Physical Machine, Virtual Machine and at Task Level. Also, this paper enhances the Osmotic Algorithm with Firefly algorithm which has already been proved significant in load balancing in distributed environments. Also, in this paper, benchmark techniques of load balancing are discussed at depth, both for their effectiveness and limitations. Moreover, techniques are presented for load balancing among virtual machines using Opportunistic Load Balancing (OLB) and LBMM (Load Balance Min-min) scheduling approaches. The Simulation is performed over CloudSim and the results derived are compared to those of the analytical model to prove the validity of the approach.
Utility-based Vehicle Routing Integrating User Preferences

To fulfill Mark Weiser's vision that “the most profound technologies are those that disappear”, pervasive computing systems inherently have (i) to interact with the environment and the users but also (ii) to adapt their behavior to changes in their environment. Due to the dynamic, non-deterministic environment of pervasive computing systems, uncertainty arises. In this paper, we conduct a case study in the domain of driver assistance systems and show that uncertainty in proactive adaptations, i.e., adaptations in advance, increases with an extension of the planning horizon.
Image processing technology in high frequency vibration direction and amplitude measurement

In recent years, with the development of computer software and hardware and artificial intelligence technology, photoelectric imaging equipment has gradually developed in the direction of digitization and intelligence. This paper mainly studies the application of image processing technology in the measurement of high-frequency vibration direction and amplitude. According to the corresponding relationship between the vibration blur image and the still image in the frequency spectrum, the image is preprocessed. This article adopts the traditional camera calibration method, which uses a special standard template to calibrate the internal and external parameters of the camera. The three-dimensional search algorithm is used to analyze and compare the recognition images of the left and right cameras in each state, and finally calculate the internal and external parameters of the camera. The data shows that the fundamental frequency is about 4.9 Hz, and the fundamental frequency order basically fluctuates around 16. The calculated amplitudes are 0.89, 0.93 and 0.99μm, respectively. The results show that the image processing technology can restore image edge details to a certain extent, suppress noise, and effectively shorten the matching time between feature points.
An Automated Mammals Detection Based on SSD-Mobile Net

Abstract Animal detection techniques are one of the researcher’s interests and challenge. There are many difficulties faces by the researchers in this field that reduce the detection performance and efficiency, such as variation of image illumination, animal occlusion, the similarity of animal colors with background environment, etc. Multi-label Image Detection and classification of Mammals animals is the goal of this paper which we proposed to achieve in this proposal by using Single Shot Multi-Box Detector (SSD) and MobileNet v1 coco_2017 model. Localizing and classifying multiple objects (animals) of the Mammal category in digital images is another goal. The suggested SSD is regarded as a more accurate, fast, and efficient way to detect objects of different sizes based on deep learning technology. In this proposal, we used 2000 images in the network were collected from the standard dataset (such as Caltech 101) and the net. The SSD framework improves the detection and recognition processes of Convolution Neural Network (CNN). During the prediction time, the network introduces scores to the presence of every object class and bounded each object in the image with a box. Each box has a label that indicates the type of the object and the score represents the probability of the relationship of the object to that type. Boxes during the process are modified for getting the best matching to the object’s shape. The experimental results of this work proved the efficiency of classifying and detecting animals even in the variation of illumination, pose, and occlusion. Detection and classification accuracy is up to 98.7 %. This suggestion is more reliable and accurate than other similar works and detects a wide range of Mammals animals, unlike other similar works.
Investigating and recommending co-changed entities for JavaScript programs

JavaScript (JS) is one of the most popular programming languages due to its flexibility and versatility, but maintaining JS code is tedious and error-prone. In our research, we conducted an empirical study to characterize the relationship between co-changed software entities (e.g., functions and variables), and built a machine learning (ML)-based approach to recommend additional entity to edit given developers’ code changes. Specifically, we first crawled 14,747 commits in 10 open-source projects; for each commit, we created at least one change dependency graph (CDG) to model the referencer–referencee relationship between co-changed entities. Next, we extracted the common subgraphs between CDGs to locate recurring co-change patterns between entities. Finally, based on those patterns, we extracted code features from co-changed entities and trained an ML model that recommends entities-to-change given a program commit. According to our empirical investigation, (1) three recurring patterns commonly exist in all projects; (2) 80%–90% of co-changed function pairs either invoke the same function(s), access the same variable(s), or contain similar statement(s); (3) our ML-based approach CoRec recommended entity changes with high accuracy (73%–78%). CoRec complements prior work because it suggests changes based on program syntax, textual similarity, as well as software history; it achieved higher accuracy than two existing tools in our evaluation.
A Quality Assessment Model for Blockchain-Based Crowdsourcing System

In recent years, crowdsourcing has become a new business model. Quality assessment of crowdsourcing has also become a hot topic of interest for researchers. Most quality control methods are based on centralised platforms and cannot guarantee complete reliability. Therefore, this paper proposes a blockchain-based quality assessment model for crowdsourcing(BC-CQAM). A trusted mechanism is introduced to construct a reputation model, based on that we also propose a blockchain-based worker selection algorithm(BC-WS). A new quality assessment algorithm(BIV-EM) is proposed, resulting in more accurate evaluation results. The validity of the algorithm and the reliability of the worker selection have been demonstrated experimentally.
In-text citation’s frequencies-based recommendations of relevant research papers

From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models’ accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper’s text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.
A Compendious Analysis of Advances in HE Methods for Contrast Enhancement

Image enhancement is a crucial pre-processing technique for image processing applications such as satellite images, medical images, and aerial surveillance systems. The image enhancement aims to produce visual content of the image in more pleasing and more suitable for machine vision applications. Enhancement of image can be improved by either increasing the contrast of the image with a low dynamic range or by highlighting the prominent details of the image. In this paper, a comprehensive analysis of bi-histogram and two-dimensional histogram equalization-based contrast enhancement techniques is performed. The performance of the various algorithms has been validated through three different databases and various performance measures. From the compendious analysis, it can be interpreted that modeling an algorithm with lower absolute mean brightness error (AMBE) and higher contrast values can render a better-enhanced image. From the qualitative and quantitative analysis, edge enhancing bi-histogram equalization using guided image filter outperforms the other contrast enhancement techniques.
Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation

Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models’ inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose Random Forest Similarity Map (RFMap), a scalable interactive visual analytics tool designed to analyze RF ensemble models. RFMap focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values. The interactive nature of RFMap allows users to visually interpret model errors and decisions, establishing the necessary confidence and user trust in RF models and improving performance.
Learning Homogeneous and Heterogeneous Co-Occurrences for Unsupervised Cross-Modal Retrieval

Image-text retrieval, which focuses on unifying both visual and textual representations, is one of the major tasks of cross-modal information processing. With attention mechanism, previous methods performing well take advantage of not only the correspondence in image-text level but also the semantic alignment between the regions in images and corresponding words. However, few of them comprehend the importance of combing the semantic relationship between multimodalities and semantic correspondences in one modality at the same time. Inspired by the heterogeneous information learning of heterogeneous graph network, we propose a novel method called Homogeneous and Heterogeneous Co-Occurrences (H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> CO) which mainly consists of two modules to achieve modal co-occurrence in a query of corporations. Specifically, Homogeneous Co-Occurrence Module captures correspondences with neighbors from single modal of regions and words respectively, while Heterogeneous Co-Occurrence Module aims to learn the relations about neighbors across modalities. Finally, the proposed method can aggregate the neighborhood features from both intra modality and inter modality at the same time, thus performs better on image-text matching for considering much more semantic information. Extensive experimental results on MS COCO and Flickr30K show the superior performance of our proposed modal over the state-of-the-arts.
Smart auction system flow model for Agro-Based sector farmers using blockchain technology

Blockchain technology has wide area of application, this article is very particular on emphasising another important field of application named Agro-products supply chain. Wherein this era, and the future needs to nurture the agriculture sector along with latest technologies, so that nation would make a revolution in the process of food supply from the farm to the plate. The main drawback in the agriculture supply chain is lack of transparency in food production and distribution, costly middleman, limited financial resources & disconnection between supplier and retailer. So this article proposes, suitable theoretical auction flow model to overcome all the issues highlighted in this agro-sector, we would like to give the solution by improving the transparency in the supply chain, by providing farmers direct access to suppliers through an appropriate auction system with an option of immediate payment on delivery & transparent transaction information to them. Further we can involve government as a central authority to overcome the technical aspects, education, policies and regulatory frame works, in turn they can generate job opportunities to the unemployed graduates.