Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Using Language Models: A Study

Sentiment analysis is concerned with extracting sentiment to ascertain the attitudes, and emotions associated with the text. It is broadly applied to voice of the customer as they convey their experience and feelings more blatantly which is why comprehending customer's emotions is a must. To identify and classify these unstructured emotions natural language processing (NLP) and machine learning approaches have been adopted in recent times. The main issue with the existing techniques is the inability to deal with the correct interpretation of context owing to lack of labeled data. In this paper, we studied deep neural network based language models to interpret and classify textual sequences into positive, negative or neutral emotions which remove the bottleneck of explicit human labeling. These models were analyzed and evaluations were performed on the Twitter US Airline Sentiment dataset. We have observed a considerable amount of improvements with respect to prior state-of-the-art approaches which closes the gap with supervised feature learning.
Line Chart Understanding with Convolutional Neural Network

Visual understanding of the implied knowledge in line charts is an important task affecting many downstream tasks in information retrieval. Despite common use, clearly defining the knowledge is difficult because of ambiguity, so most methods used in research implicitly learn the knowledge. When building a deep neural network, the integrated approach hides the properties of individual subtasks, which can hinder finding the optimal configurations for the understanding task in academia. In this paper, we propose a problem definition for explicitly understanding knowledge in a line chart and provide an algorithm for generating supervised data that are easy to share and scale-up. To introduce the properties of the definition and data, we set well-known and modified convolutional neural networks and evaluate their performance on real and synthetic datasets for qualitative and quantitative analyses. In the results, the knowledge is explicitly extracted and the generated synthetic data show patterns similar to human-labeled data. This work is expected to provide a separate and scalable environment to enhance research into technical document understanding.
Deep Learning-Based Fault Localization in Video Networks Using Only Client-Side QoE

Maintaining a satisfactory customer Quality of Experience (QoE) is of vital importance for video service providers such as Netflix or Amazon Prime Video. Network faults degrade QoE and must therefore be detected, isolated, and fixed. However, this is difficult because each part of the end-to-end path belongs to a different autonomous system (AS) that is typically owned by a different entity, such as the video streaming provider, the internet service provider (ISP), and the client's local network operator. Although the video service provider (VSP) is usually blamed by the customer when there is poor QoE, the VSP does not have access to many parts of the network to localize the issue. In this paper, we show that with the aid of AI, it is possible for the VSP to localize the network fault without having access to the faulty part and using only QoE metrics. We collected a dataset from an actual video streaming testbed, where multiple videos are streamed from a video server through a simplified ISP network to a client network. Actual faults were generated in both the ISP and the client networks. Using only the QoE metrics measured at the client side, we use the deep learning methods of multi-layer perceptron (MLP) and long-short-term memory (LSTM) to detect and localize the fault with an accuracy of 93–97%, depending on the situation. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—Technologically, our work impacts video/game streaming service providers such as Netflix, YouTube, Amazon Prime, Google Stadia, Sony PlayStation Now, Nvidia GeForce Now, and videoconferencing providers such as Zoom and Skype. Our work enables these providers to train similar AI systems that can localize network problems using only the video quality of experience (QoE) recorded by their client software. They can then take an appropriate action, such as rerouting traffic using Open Connect Appliances (OCA) if available, using another network provider if they have contracts with more than one, or informing the owner of the network segment with the fault, so they can fix the problem and maintain their customers’ QoE at a satisfactory level. Economically, our work can contribute to the market expansion of any video streaming solution because it will lead to better QoE, which is synonymous with more customers.
A New Class of Q-Ary Codes for the McEliece Cryptosystem

The McEliece cryptosystem is a promising candidate for post-quantum public-key encryption. In this work, we propose q-ary codes over Gaussian integers for the McEliece system and a new channel model. With this one Mannheim error channel, errors are limited to weight one. We investigate the channel capacity of this channel and discuss its relation to the McEliece system. The proposed codes are based on a simple product code construction and have a low complexity decoding algorithm. For the one Mannheim error channel, these codes achieve a higher error correction capability than maximum distance separable codes with bounded minimum distance decoding. This improves the work factor regarding decoding attacks based on information-set decoding.
OverSketched Newton: Fast Convex Optimization for Serverless Systems

Motivated by recent developments in serverless systems for large-scale computation as well as improvements in scalable randomized matrix algorithms, we develop OverSketched Newton, a randomized Hessian-based optimization algorithm to solve large-scale convex optimization problems in serverless systems. OverSketched Newton leverages matrix sketching ideas from Randomized Numerical Linear Algebra to compute the Hessian approximately. These sketching methods lead to inbuilt resiliency against stragglers that are a characteristic of serverless architectures. Depending on whether or not the problem is strongly convex, we propose different iteration updates using the approximate Hessian. For both cases, we establish convergence guarantees for OverSketched Newton, and we empirically validate our results by solving large-scale supervised learning problems on real-world datasets. Experiments demonstrate a reduction of ~50% in total running time on AWS Lambda, compared to state-of-the-art distributed optimization schemes.
Item-Based Collaborative Filtering and Association Rules for a Baseline Recommender in E-Commerce

In the ever-growing data-driven world today, data increases in many forms, e.g. e-commerce sites uploading new products, streaming services adding TV shows and movies, and music platforms uploading new songs. It would be highly infeasible for end users to quickly browse all this data. Hence recommender systems can benefit end users (individuals as well as companies) in efficiently finding suitable products. Rather than making end users search through a vast array of items, recommender systems can suggest suitable items to users based on popularity of the items and the respective users' buying behavior. Accordingly, in this paper we explore two techniques widespread in recommender systems, i.e. item-based collaborative filtering and association rule mining, over Amazon review data on cellphones and accessories, and build a baseline recommender system scalable to larger data. Association rule mining is explored using the Apriori algorithm to find patterns in the data from transaction history. Item-based collaborative filtering is deployed using a correlation matrix to find similar products. Both these techniques yield useful results as evident from our baseline experiments. This work constitutes an exploratory study with longtime products in e-commerce and sets the stage for mining online data on relatively new products pertinent to the Covid-19 pandemic. These include face masks, hand sanitizers, disinfectant sprays, antibacterial wipes etc. Since multiple vendors are designing such crucial products today, it is important to provide recommendations to potential buyers. An ultimate goal in our work is to build a recommender app for e-commerce based on interesting results from our findings. This work constitutes intelligent data mining scalable over big data in e-commerce. It makes broader impacts on smart cities, since this fits the smart living and smart economy characteristics.
Institutional Anomie Theory and Cybercrime—Cybercrime and the American Dream, Now Available Online

As the world becomes increasingly connected and interdependent upon technology, crimes are moving online. Research on cybercrime is beginning to test the applicability of traditional criminological theories for understanding crime in this new medium. Using a national sample of 215 self-admitted cybercriminals, we examine Messner and Rosenfeld’s institutional anomie theory. Negative binomial regressions reveal that expressed levels of institutional anomie correlate with increased cybercrime activity. A curvilinear relationship was found, such that low and high levels of institutional anomie lead to higher levels of cybercrime. Our findings reveal how the dark side of the American Dream can lead to online criminality. Specifically, the penetration of, and accommodation to economic values dictated by American capitalism can lead individuals to adopt values such as the fetishism of money that, in turn, affects their online behavior and criminality.
FASTA/Q data compressors for MapReduce-Hadoop genomics: space and time savings made easy

Storage of genomic data is a major cost for the Life Sciences, effectively addressed via specialized data compression methods. For the same reasons of abundance in data production, the use of Big Data technologies is seen as the future for genomic data storage and processing, with MapReduce-Hadoop as leaders. Somewhat surprisingly, none of the specialized FASTA/Q compressors is available within Hadoop. Indeed, their deployment there is not exactly immediate. Such a State of the Art is problematic.We provide major advances in two different directions. Methodologically, we propose two general methods, with the corresponding software, that make very easy to deploy a specialized FASTA/Q compressor within MapReduce-Hadoop for processing files stored on the distributed Hadoop File System, with very little knowledge of Hadoop. Practically, we provide evidence that the deployment of those specialized compressors within Hadoop, not available so far, results in better space savings, and even in better execution times over compressed data, with respect to the use of generic compressors available in Hadoop, in particular for FASTQ files. Finally, we observe that these results hold also for the Apache Spark framework, when used to process FASTA/Q files stored on the Hadoop File System.Our Methods and the corresponding software substantially contribute to achieve space and time savings for the storage and processing of FASTA/Q files in Hadoop and Spark. Being our approach general, it is very likely that it can be applied also to FASTA/Q compression methods that will appear in the future.The software and the datasets are available at https://github.com/fpalini/fastdoopc.
Educators Training in the Context of Socio-Economic and Technological Trends of Kazakhstan

The work presents the results of research carried out within the international project “Engineering educators pedagogical training” (hereinafter referred to as ENTER), co-financed by the Erasmus + European Union programme. There are questionnaires developed for research. The purpose of the survey is to identify ways to modernize the training system for engineering educators. 408 people took part in the survey: teachers of universities (63%), colleges and other educational institutions (23%), employees of state enterprises and corporations (5%), commercial/joint-stock enterprises (9%). An analysis of the results of the questionnaire showed that in the preparation of engineering educators in Kazakhstan, forms of organization of education through electronic education platforms are poorly used (8% of the proposed programs in questionnaire organizations), online courses and mixed-type programs (2% of proposed programs in questionnaire organizations). Not enough attention is paid to forms of networking. Based on the analysis of the requirements of the Kazakhstan labor market for engineering educators, the necessary professional competencies for them are determined. Based on the results of the questionnaire, it was concluded that educational programs should be modernized taking into account regional needs, requirements for key competencies of engineering teachers by employers, and assessment of students’ career expectations. For this purpose, constant monitoring of popular and promising areas of training, improvement of forms and methods of training is required. Educational programs and forms of upgrading qualifications should be focused on using the professional context in the educational process, forecasting and creating conditions for the professional development of teachers, providing them with the right to choose a methodological service and the possibility of implementing an individual educational route [1].
Coverless Video Steganography Based on Inter Frame Combination

In most coverless image steganography methods, the number of images increases exponentially with the increase of hidden message bits, which is difficult to construct such a dataset. And several images in semantic irrelevance are usually needed to represent more secret message bits, which are easy to cause the attacker’s attention and bring some insecurity. To solve these two problems, a coverless video steganography method based on inter frame combination is proposed in this manuscript. In the proposed method, the hash sequence of a frame is generated by the CNNs and hash generator. To hide more information bits in one video, a special mapping rule is proposed. Through this mapping rule, some key frames in one video are selected. In the selected frames, one or several frames are used to represent a piece of information with equal length. To quickly index out the corresponding frames, a three-level index structure is proposed in this manuscript. Since the proposed coverless video steganography method does not embed one bit in video, it can effectively resist steganalysis algorithms. The experimental results and analysis show that the proposed method has a large capacity, good robustness and high security.
Investigating Network Performance of a Multi-user Virtual Reality Environment for Mining Education

Virtual Reality provides the ability to immerse users in realistic environments, which enables utilization of the technology as an immersive educational tool. This is particularly useful for educational fields that require students to visit certain locations, or that concern hazardous situations and materials. The EIT Raw Materials Project MiReBooks intends to develop novel augmented and virtual reality teaching tools to mining education. Within the project, we developed an interactive multi-user VR environment, named MiReBooks VR, for teaching mining to students by simulating a VR mine and creating learning scenarios in it. In this paper, we briefly described MiReBooks VR, and then focused on determining the capacity of the server running in a head-mounted display by measuring latency. To assess the system's capacity to handle multiple students connected to a class session, client simulation tests of up to 30 simultaneous connections were conducted. The results suggests performance issues with respect to latency affecting all peers that could cause a negative effect to the VR user experience. In addition, the results indicate that the frame rate requirements for VR applications are difficult to maintain in multi-user environments using current off-the-shelf VR equipment. Based on the development experiences and the tests, we provide five lessons learned that can be of interest to software engineers and researchers working on the development of multi-user VR systems.
Superpixel-Based Convolutional Neural Network for Georeferencing the Drone Images

Information extracted from aerial photographs has been used for many practical applications such as urban planning, forest management, disaster relief, and climate modeling.In many cases, labeling of information in the photo is still performed by human experts, making the process slow, costly, and error-prone.This article shows how a convolutional neural network can be used to determine the location of ground control points (GCPs) in aerial photos, which significantly reduces the amount of human labor in identifying GCP locations.Two convolutional neural network (CNN) methods, sliding-window CNN with superpixel-level majority voting, and superpixel-based CNN are evaluated and analyzed.The results of the classification and segmentation show that both of these methods can quickly extract the locations of objects from aerial photographs, but only superpixel-based CNN can unambiguously locate the GCPs.
RETRACTED: Prediction of Presence of Breast Cancer Disease in the Patient using Machine Learning Algorithms and SFS

Abstract Breast cancer disease is recognized as the common extensive malignant tumor in between women. Identification of the initial stage of malignant growth may treatment of this disease. Early treatment helps to alleviate the disease and helps anticipate its recurrence in women. Experts have used some fact checks and different medical methods or equipment to improve the accuracy of conclusions in clinical medical service management. In this article, it extensively discussed the implementation of data mining strategies to detection as well as prediction of breast malignant tumors, including random forest (RF), support vector classifier (SVC), k-nearest neighbors (KNN), linear discriminant analysis (LDA), Gradient Boosting Classifier (GBC), Decision Tree (DT) In addition, principal component analysis (PCA) to underline changes and show strong patterns in the informational index. The connection framework is likewise used to show the level of close relationship between attributes. The sequential feature selection (SFS) method is used for comparing the accuracy of a data set with all features and the accuracy of a classifier with selected features. The results show that RF_sfs, KNN_sfs, SVC_rbf and SVC_sfs have the highest and equal accuracy, which is 97.66%. They perform well and can predict the growth of harmful malignant tumors.
An Ensemble Deep Neural Network for Footprint Image Retrieval Based on Transfer Learning

As one of the essential pieces of evidence of crime scenes, footprint images cannot be ignored in the cracking of serial cases. Traditional footprint comparison and retrieval require much time and human resources, significantly affecting the progress of the case. With the rapid development of deep learning, the convolutional neural network has shown excellent performance in image recognition and retrieval. To meet the actual needs of public security footprint image retrieval, we explore the effect of convolution neural networks on footprint image retrieval and propose an ensemble deep neural network for image retrieval based on transfer learning. At the same time, based on edge computing technology, we developed a footprint acquisition system to collect footprint data. Experimental results on the footprint dataset we built show that our approach is useful and practical.
Indian Sign Language Generation System

Sign language (SL) is used by people with a hearing impairment. To bridge the gap between those who do and do not use SL, we developed a system for real-time translation of the natural language into Indian SL. The system, tested using the BiLingual Evaluation Understudy score, has 95% accuracy.
Advancements in Image Splicing and Copy-move Forgery Detection Techniques: A Survey

Images are a staple in the world of digital media, the manipulation of digital images can pose a serious threat due to propagation of misinformation. With the increasing pace of technological innovation in digital image forensics, the quality of generated forged images has reached a point where forgeries are becoming imperceptible. Images can come from various sources and can take many forms, thus passive (blind) techniques of image forgery detection are preferred since these do not require any prior information about an image. The most common kind of passive image forgery detection include copy-move and image splicing forgery detection. An attempt is made to discuss recent research in both fields and to provide a glimpse into how state-of-the-art techniques employ the use of various processing algorithms to achieve adequate results. A detailed explanation of classification of image forgery detection techniques has been done along with a brief background on detection techniques, followed by a table summarizing the discussion. An assessment of the popular datasets used in image forgery detection is also mentioned.
Agrochain: Ascending Blockchain Technology Towards Smart Agriculture

The agriculture sectors play a pivotal role in Indian economy by contributing about 16% of the total GDP. It not only proffers food and raw material but also employment opportunities to a gigantic proportion of the population. But issues concerning crop production, harvest, damage and destruction have been hindering the development. Smart agriculture revolutionizes the crop sector by reorienting the system to ensure food security, agriculture production and income using diverse technology such as IoT, Bigdata, ML and AI. Each of these constantly monitor a myriad of crop production and storage factors, analyze them and help in better decision making. The security and privacy of this information while attained, analyzed and stored is of major concern. The fundamental characteristics of a blockchain make them the most lucrative platform to store valuable data essential for smooth functioning of smart agriculture while safeguarding it. This paper first studies the nitty-gritty of smart agriculture and then establishes how blockchain contributes toward its effective implementation.
Memory-Safe Elimination of Side Channels

A program is said to be isochronous if its running time does not depend on classified information. The programming languages literature contains much work that transforms programs to ensure isochronicity. The current state-of-the-art approach is a code transformation technique due to Wu et al., published in 2018. That technique has an important virtue: it ensures that the transformed program runs exactly the same set of operations, regardless of inputs. However, in this paper we demonstrate that it has also a shortcoming: it might add out-of-bounds memory accesses into programs that were originally memory sound. From this observation, we show how to deliver the same runtime guarantees that Wu et al. provide, in a memory-safe way. In addition to being safer, our LLVM-based implementation is more efficient than its original inspiration, achieving shorter repairing times, and producing code that is smaller and faster.