Reviewerly/exHarmony huggingface dataset:
Secure Distributed Storage: Rate-Privacy Trade-Off and XOR-Based Coding Scheme

We consider the problem of storing data in a distributed manner over T servers. We require the data (i) to be recoverable from the T servers, and (ii) to remain private from any T -1 colluding servers, where privacy is quantified in terms of mutual information between the data and all the information available at the T -1 colluding servers. For this model, we determine (i) the fundamental trade-off between storage size and the level of desired privacy, (ii) the optimal amount of local randomness necessary at the encoder, and (iii) an explicit low-complexity coding scheme that solely relies on XOR operations and that asymptotically (with the data size) matches the fundamental limits found.
The Need for Virtualization: When and Why Virtualization Took Over Physical Servers

In early days, industries used physical role-based servers but as they were hard to scale according to the load they are getting and it was hard to manage the infrastructure; if any server is failed, then the service that corresponds to that server also gets down and a plausible solution to all these problems was solved by virtualization. Different researchers have been contributing and showing their effort to make this worth in the past. In this work, the server has been deployed and Datadog tool has been used to record different parameters to check the performance. Different parameters which have been considered are CPU utilization, disk usage, disk latency, memory breakdown. The aim of this work is to show how servers are reacting when the servers are at full load and when the load is lesser. Further, the virtualized environment also provides advanced features like load management on servers in real time and it also enables the organization to make their environment more efficient and robust with more efficient backups and security.
Superb Monocular Depth Estimation Based on Transfer Learning and Surface Normal Guidance

Accurately sensing the surrounding 3D scene is indispensable for drones or robots to execute path planning and navigation. In this paper, a novel monocular depth estimation method was proposed that primarily utilizes a lighter-weight Convolutional Neural Network (CNN) structure for coarse depth prediction and then refines the coarse depth images by combining surface normal guidance. Specifically, the coarse depth prediction network is designed as pre-trained encoder-decoder architecture for describing the 3D structure. When it comes to surface normal estimation, the deep learning network was designed as a two-stream encoder-decoder structure, which hierarchically merges red-green-blue-depth (RGB-D) images for capturing more accurate geometric boundaries. Relying on fewer network parameters and simpler learning structure, better detailed depth maps are produced than the existing states. Moreover, 3D point cloud maps reconstructed from depth prediction images confirm that our framework can be conveniently adopted as components of a monocular simultaneous localization and mapping (SLAM) paradigm.
A novel hybrid approach towards movie recommender systems

AbstractAbstractCollaborative and content-based filtering are the majorly used approaches towards implementing recommender systems which can successfully predict the item(s) to be recommended to users based upon their preferences. Each of the methods has its own advantages and disadvantages that are specific to the situation they are used in. The hybrid approach aims to combine both techniques in multiple ways to optimize results and overcome shortcomings. In this paper, we propose a hybrid approach based recommendation engine to improve accuracy. While content based recommendations are made on the similarity of items' attributes, collaborative recommendations are based on similarity of users. Collaborative filtering is achieved by matrix factorization technique. One of the most effective algorithms for matrix factorization: Singular Value Decomposition (SVD) has been used to perform collaborative filtering and combined with content based model to predict the item ratings per user. The paper aims to achieve the final result with minimum possible errors as per user preferences and recommended items.Subject Classification: 68-0068P15Keywords: Recommender systemsCollaborative filteringcontent-based filteringHybrid approachSingular Value Decomposition
Uncertainty quantification for multilabel text classification

Abstract Deep neural networks have recently achieved impressive performance on multilabel text classification. However, the uncertainty in multilabel text classification tasks and their application in the model are often overlooked. To better understand and evaluate the uncertainty in multilabel text classification tasks, we propose a general framework called Uncertainty Quantification for Multilabel Text Classification framework. Based on the prediction results produced by traditional neural networks, the aleatory uncertainty of each classification label and the epistemic uncertainty of the prediction result can further be obtained by this framework. We design experiments to characterize the properties of aleatory uncertainty and epistemic uncertainty from the data characteristics and model features. The experimental results show that this framework is reasonable. Furthermore, we demonstrate how this framework allows us to define the model optimization criterion to identify policies that balance the expected training cost, model performance, and uncertainty sensitivity. This article is categorized under: Algorithmic Development &gt; Bayesian Models
A Conceptual Framework for Mobile Learning Development in Higher Education

The idea of introducing new technologies into education is not a new one and as a result research in this area has been conducted over the years. However, the pace at which the new technologies are introduced in higher education in Bulgaria is very slow. The purpose of this paper is to propose a conceptual framework for mobile learning development and in particular mobile application introduction in the educational process in higher education. This goal is accomplished by analyzing the existing measures used to improve the quality of education through the active use of information and communication technologies and the existing frameworks for mobile learning. The proposed framework includes three basic elements. It is important that each of them is going to be properly implemented and that there will be continuity between them.
Blockchain-Based Secured Collaborative Model for Supply Chain Resource Sharing and Visibility

Globalization, escalating competition, and demand for sustainable practices have required supply chain and production managers to consider various capabilities and value creation strategies for the customers. Rapid technological advancement in the current production environment calls for integrative and collaborative efforts for effective resource utilization and better visibility to gain competitive advantages. However, privacy risks and trust have always been a significant barrier for organizations’ efforts towards supply chain integration. Supply chain stakeholders fear these collaborate practices might weaken their bargaining power, accelerate risk of data manipulation and result in loss of information advantages. Addressing these issues, the study proposes a Blockchain-based collaborative model for production visibility and resource sharing. It demonstrates the framework for stakeholders’ interaction over a central procurement system backed with blockchain technology. The study further lays down the notion of production capacity backed smart contract rules. These smart contracts will run on the proposed blockchain network to reduce the possibilities of fraudulent transactions and capacity overbooking- leading to illegitimate subcontracting. The overall network will stimulate visibility and develop a technology-based trust among partners which ensuring sustainability by effective utilization of resource.
ESR-GAN: Environmental Signal Reconstruction Learning With Generative Adversarial Network

Monitoring the status of urban environmental phenomenon, which provides fundamental sensory information, is of great significance for various field of urban research. In this article, we propose a new framework, environmental signal reconstruction generative adversarial network, for reconstructing high-quality environmental signal via sensory data from sparsely distributed monitoring sites. Our framework is based on the generative adversarial network (GAN), in which a three-layer convolutional neural network (CNN)-based generative model is proposed to learn an end-to-end mapping between low- and high-quality signals and a discriminative model is introduced for quantizing the reconstruction accuracy. Considering the scattered distribution of sensory data, we further propose a metric called impact map for building loss function and guiding the adversarial training. Experiments with real-world air quality data of Beijing demonstrate that our method outperforms the state-of-the-art data inference techniques in terms of signal recovery accuracy.
Learning Discriminative Sentiment Representation from Strongly- and Weakly Supervised CNNs

Visual sentiment analysis is attracting increasing attention with the rapidly growing amount of images uploaded to social networks. Learning rich visual representations often requires training deep convolutional neural networks (CNNs) on massive manually labeled data, which is expensive or scarce especially for a subjective task like visual sentiment analysis. Meanwhile, a large quantity of social images is quite available yet noisy by querying social networks using the sentiment categories as keywords, where various types of images related to the specific sentiment can be easily collected. In this article, we propose a multiple kernel network for visual sentiment recognition, which learns representation from strongly- and weakly supervised CNNs. Specifically, the weakly supervised deep model is trained using the large-scale data from social images, whereas the strongly supervised deep model is fine tuned on the affecitve datasets with manual annotation. We employ the multiple kernel scheme on the multiple layers of CNNs, which can automatically select the discriminative representation by learning a linear combination from a set of pre-defined kernels. In addition, we introduce a large-scale dataset collected from popular comics of various countries, such as America, Japan, China, and France, which consists of 11,821 images with various artistic styles. Experimental results show that the multiple kernel network achieves consistent improvements over the state-of-the-art methods on the public affective datasets, as well as the newly established Comics dataset. The Comics dataset can be found at http://cv.nankai.edu.cn/projects/Comic.
Profile of Data Sharing in the Clinical Neurosciences

Importance: In today's climate of high healthcare costs and limited research resources, much attention has been given to inefficiency in research.Open access to research data has been proposed as a way to pool resources and make the most of research funding while also promoting transparency and scientific rigor.Objective: The clinical neurosciences stand to benefit greatly from the potential opportunities afforded by open data, and we sought to evaluate the current state of publicly available research findings and data sharing policies within the clinical neurosciences.Design: The Clarivate Analytics Web of Science journal citation reports for 2017 were used to sort journals in the category 'Clinical Neurosciences' by impact factor.The top 50 journals were selected and reviewed, but data was only collected from journals focused on original research (42/50).For each journal we reviewed the 10 most recent original research articles for 2016, 2017, and 2018 as designated by Scopus.Results: A data sharing policy existed for 60% (25/42) of the journals reviewed.Of the articles studied 41% (517/1255) contained source data, and the amount of articles with available source data increased from 2016 to 2018.Of all the articles reviewed, 49.4% (620/1255) were open access.Overall, 6.9% (87/1255) of articles had their source data accessible outside of the manuscript (e.g.registries, databases, etc.) and 8.9% (112/1255) addressed the availability of their source data within the publication itself.The availability of source data outside the manuscript and in-article discussion of source data availability both increased from 2016 to 2018.Only 3.9% (49/1255) of articles reviewed reported negative results for their primary outcome, and 7.6% (95/1255) of the articles could not be defined as primarily reporting positive or negative findings (characterization studies, census reporting, etc.).The distribution of negative versus positive results reported showed no significant trend over the years studied. Conclusion and Relevance:Our results demonstrate an opportunity for increased data sharing in neuroscience original research.These findings also suggest a trend towards increased adoption of open data sharing policies among journals and increased availability of unprocessed data in publications.This can increase the quality and speed at which new research is developed in the clinical neurosciences.
Learning Stable Graphs from Multiple Environments with Selection Bias

Nowadays graph has become a general and powerful representation to describe the rich relationships among different kinds of entities via the underlying patterns encoded in its structure. The knowledge (more generally) accumulated in graph is expected to be able to cross populations from one to another and the past to future. However the data collection process of graph generation is full of known or unknown sample selection biases, leading to spurious correlations among entities, especially in the non-stationary and heterogeneous environments. In this paper, we target the problem of learning stable graphs from multiple environments with selection bias. We purpose a Stable Graph Learning (SGL) framework to learn a graph that can capture general relational patterns which are irrelevant with the selection bias in an unsupervised way. Extensive experimental results from both simulation and real data demonstrate that our method could significantly benefit the generalization capacity of graph structure.
Image enhancement through multi-scale alpha-rooting processing for remote sensing application

We present a new near-infrared (NIR) image enhancement algorithm based on multi-scale alpha-rooting processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. The parameter alpha for every block driven through optimization of measure of enhancement (EME). Also, we propose a fusion scheme for alpha-rooting enhancement and edge detection results. Some experimental results are presented to illustrate the performance of the proposed algorithm on the RGB-NIR Scene Data.
Modulation Recognition of Underwater Acoustic Communication Signals Based on Joint Feature Extraction

Recently, the research on modulation identification of underwater acoustic (UWA) communication signals has attracted much attention. It is a challenge to identify the modulation of underwater acoustic communication signals in the environment of low signal-to-noise ratio (SNR), multipath interference, frequency and phase drift. In this paper, a joint feature extraction algorithm is proposed which combines the method of cyclic spectrum with high-order cumulant to perform the modulation recognition of UWA communication. In the algorithm, the higher-order cumulant features of the communication signals and their counterparts after the square transformation are used firstly for the intra-class modulation recognition of phase shift keying (PSK) and frequency shift keying (FSK), and the inter-class modulation recognition of PSK. Then the cross-section parameter of spectral correlation function of signals are used for inter-class recognition of FSK modulation. Simulation results show that BPSK, QPSK, 2FSK and 4FSK underwater acoustic communication signals can be identified at rate better than 90% by using the joint modulation recognition algorithm with a signal-to-noise ratio of -2dB.
Cryptanalysis of the Ivanov-Kabatiansky-Krouk-Rumenko Cryptosystems

Recently, Ivanov, Kabatiansky, Krouk, and Rumenko (IKKR) proposed three new variants of the McEliece cryptosystem (CBCrypto 2020, affiliated with Eurocrypt 2020). In this letter, it is shown that one of the IKKR cryptosystems is equal to the McEliece cryptosystem. Furthermore, a polynomial-time attack algorithm for the other two IKKR cryptosystems is proposed. The proposed attack algorithm utilizes the linearity of IKKR cryptosystems. Also, an implementation of the IKKR cryptosystems and the proposed attack is given. The proposed attack algorithm finds the plaintext within 0.2 sec that is faster than the elapsed time for legitimate decryption.
Data resource protection based on smart contract

In this paper, a data resource protection solution is proposed to eliminate the potential risk of data owners reselling others' data resources. By utilizing the key functions of Ethereum blockchain and smart contract, a data-for-sale mechanism is built. By this mechanism, data-for-sale information is received and processed, the information is broadcast to the nodes in the blockchain, and the feedback of the nodes to the information is handled. By introducing large-margin multi-task metric learning, a dispute resolution mechanism is constructed to solve data-for-sale disputes. By designing "data-for-sale", "access to market", "dispute resolution" algorithms, data resource protection is realized. By proposing sequence diagrams and algorithms, smart contract is finished. Finally, according to the solution, the test and validation of the smart contract is completed. The code for the smart contract and ABI interface is available on GitHub.
A real world study on employees' susceptibility to phishing attacks

Phishing email attacks have been around for fifteen years but they are still among the top security risks faced by organisations. The most common approach to mitigate these attacks is employees' education and awareness. Employees' awareness on phishing attacks is achieved by embedded training that educate employees when they fall for the attack. However, the effectiveness of embedded training in workplace settings is uncertain given the large number of employees that remain vulnerable to phishing email attacks. Similarly, the role of persuasion techniques in making employees vulnerable to phishing attacks is yet to be investigated in the workplace settings. Therefore, in this paper we investigate which persuasion technique between authority and urgency is more effective in making employees susceptible to phishing, the relation between employees' susceptibility and their demographic data, and the effectiveness of embedded training in reducing employees' susceptibility to phishing attacks. To this end, we conducted a real phishing study with 191 employees of an Italian company. We found that employees were more vulnerable to phishing attacks when urgency principle was exploited. The study also showed no significant effect of employees' demographic data on susceptibility to phishing. Embedded training was perceived as effective by employees but it did not reduce their susceptibility to phishing.
Effectiveness of dataset reduction in testing machine learning algorithms

Many machine learning algorithms examine large amounts of data to discover insights from hidden patterns. Testing these algorithms can be expensive and time-consuming. There is a need to speed up the testing process, especially in an agile development process, where testing is frequently performed. One approach is to replace big datasets with smaller datasets produced by random sampling. In this paper, we report a set of experiments that are designed to evaluate the effectiveness of using reduced datasets produced by random sampling for testing machine learning algorithms. In our experiments, we use as subject programs four supervised learning algorithms from the Waikato Environment for Knowledge Analysis (WEKA). We identify five datasets from Kaggle.com to run with the four learning algorithms. For each dataset, we generate reduced datasets of different sizes using two random sampling strategies, i.e., pure random and stratified random sampling. We execute our subject programs with the original and the reduced datasets, and measure test effectiveness using branch and mutation coverage. Our results indicate that in most cases, reduced datasets of even very small sizes can achieve the same or similar coverage achieved by the original dataset. Furthermore, our results indicate that reduced datasets produced by the two sample strategies do not differ significantly, and branch coverage correlates with mutation coverage.
Indirect Recognition of Predefined Human Activities

The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.
Unusual Crowd Activity Detection using OpenCV and Motion Influence Map

Suspicious behavior is dangerous in public areas that may cause heavy causalities. There are various systems developed on the basis of video frame acquisition where motion or pedestrian detection occur but those systems are not intelligent enough to identify the unusual activities even at real time. It is required to recognized scamper situation at real time from video surveillance for quick and immediate management before any casualties. Proposed system focuses on recognizing suspicious activities and target to achieve a technique which is able to detect suspicious activity automatically using computer vision. Here system uses OpenCV library for classifying different kind of actions at real time. The motion influence map has been used to represent the motion analysis that frequently changes the position from one place to another. System uses pixel level presentation for making it easy to understand or identify the actual situation.
Exploring Depth Information for Spatial Relation Recognition

It is always well believed that modeling the relative depth information between objects would be helpful for recognizing the spatial relations between pairs of objects in images, especially for the spatial relation like "behind" and "in front of." Nevertheless, there has not been evidence in support of the idea on spatial relation recognition. In this paper, we present a novel Depth-guided Spatial Relation Recognizer (DSRR) to predict the spatial predicate from object pairs under the umbrella of relative depth information in between. Particularly, DSRR capitalizes on the off-the-shelf depth estimator to predict the depth information for each object. The depth cues for each pair of objects are further integrated with language (object name) and 2D (bounding box coordinates) cues to perform spatial relation reasoning. Extensive experiments conducted on SpatialSense dataset validate our proposal and superior results are reported when comparing to state-of-the-art models.