Reviewerly/exHarmony huggingface dataset:
Decentralized Neural Network Based Collaborative Filtering For Privacy Concern Recommendation Systems

The growing concern about the privacy of user data is inspiring the development of new privacy preserving machine learning approaches. Decentralized federated learning is such a method which can handle privacy concerns effectively. It consists of servers and clients. Here, a machine learning model is distributed to a number of clients and the clients use their locally stored data to train the model and send the model to the server for aggregation. Here, the client only shares the model parameters with the server. The server receives thousands of locally trained models from clients and performs aggregation to define a global model. Only the model parameters such as weights and biases are being shared between the server and the client. In this paper, we have proposed a decentralized privacy preserving technique for neural collaborative filtering which is widely used in rating prediction for recommendation systems. Here each client receives an initial neural network based collaborative filtering model from the server and trains the model locally with its own data and only sends the model and parameters back to the server for aggregation. This method will eliminate privacy concerns in modern recommendation systems.
Steganography With FileSystem-in-SlackSpace

With the constant expansion of the cyber-space due to digital convergence, so does its attack surface. Modern day cyber criminals, astute and technology savvy, have embraced steganography including it as an important weapon in their cyber arsenal. In this paper, we present a new steganographic method that leverages the file slack space, generated by the Operating System, for hiding data. Our proposed method hides an entire file system volume exploiting the file slack space as the cover-media (a.k. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{a}$</tex> . cover medium) deviating from previous works which focus on hiding individual files. The method is persistent across system reboots and restarts, has robust detection resistance, is portable and has minimum impact on the target system's performance adding to its stealth. We validate the proposed method through prototype implementation on a Ubuntu 20.04 with a ext4 file system, with FAT16 file system as the hidden file system.
Investigating sex offending representation among politicians through Twitter

Sex violence is a sensitive topic that provokes public debate, due in part to the influential role of the media in taking crime cases out of the courts to the general public. This has led to a punitive attitude toward sex-offenders that has fostered the view of ‘once a sex-offender, always a sex-offender’. Using a linguistic analysis of Tweets, we examined how sex-offending and sex-offenders are portrayed by 782 Italian politicians. Results show that politicians shared fewer positive posts about sex-offending than the average of posts found on Twitter, with an emotional negative overtone dominating the sex-offending narrative rather than eliciting a cognitive response. Among emotions, anger was most prevalent, followed by sadness and anxiety. In addition, politicians’ portrayals of sex-offenders were consistently negative regardless of their political orientation. Social communication is critical in conveying unbiased information and fostering fruitful discussion about how to deal with sex-offenders, especially because politicians are responsible for making decisions and influencing legislation on such a controversial topic. A less emotionally driven response could promote a more evidence-based approach to managing sex-offenders prior to their release into the community, thereby increasing the likelihood of desistance from crime.
Efficient Implementation of Stream Cipher SNOW 3G for Resource-Constrained Devices

SNOW 3G is one of the stream ciphers proposed by the standard to protect data in 3G and 4G mobile communications. In recent years, the possible adaptation of the algorithm to the requirements of 5G communications has been studied, resulting in more efficient implementations, although modifications of the cipher, such as SNOW-V and SNOW-Vi, have finally been proposed to achieve the throughput and security that 5G requires. In this article, an efficient implementation of SNOW 3G is presented. It takes advantage of the 32-bit architecture of the processors and employes n-grouped operations giving raised to similar performance than the SIMD instructions set employed in the most recent stream ciphers. The implementation is based on the use of the equivalent binary model of the LFSRs defined in $$GF(2^n)$$ . Although SNOW 3G seems ruled out for 5G communications, it is still interesting to have efficient implementations in 4G that allow its integration in devices with limited processors.
Deploying a Federated Learning Based AI Solution in a Hierarchical Edge Architecture

Edge computing enables the computation and analytics capabilities to be brought closer to data sources. The available literature on AI solutions for edge computing primarily addresses just two edge layers. The upper layer can directly communicate with the cloud and comprises one or more IoT edge devices that gather sensing data from IoT devices present in the lower layer. However, industries mainly adopt a multi-layered architecture, referred to as the ISA-95 standard, to isolate and safeguard their assets. In this architecture, only the upper layer is connected to the cloud, while the lower layers of the hierarchy get to interact only with the neighbouring layers. Due to these added intermediate layers (and IoT edge devices) between the top and lower layers, existing AI solutions for typical two-layer edge architectures may not be directly applicable in this scenario. Moreover, not all industries prefer to send and store their private data in the cloud. Implementing AI solutions tailored to a hierarchical edge architecture would increase response time and maintain the same degree of security by working within the ISA-95-compliant network architecture. This paper explores a possible strategy for deploying a centralized federated learning-based AI solution in a hierarchical edge architecture and demonstrates its efficacy through a real deployment scenario.
Weakly Supervised Learning for Network Traffic Classification

With advances in deep learning methods and an enormous amount of network traffic data, training deep neural networks for malware traffic classification directly with the raw traffic data has gained popularity and success. Obtaining labeled data to train deeper models, on the other hand, has recently become a significant challenge. Weakly supervised learning approaches that aim to improve the classification accuracy of classifiers previously trained with a small quantity of labeled data using widely available but unlabeled data have grown more popular as a solution to this problem. We propose employing several deep models as labeling functions instead of manual definition based on hand-crafted features, such as Snorkel approach, to estimate the labels that will be utilized to tune the classifier to increase its accuracy for network traffic classification using unlabeled data. In a multi-class classification scenario, our findings demonstrate that utilizing deep models to label the unlabeled chunk can enhance accuracy by 1.5 % and F1-score by 5%.
Dual Machine Reading Comprehension for Event Extraction

Event extraction aims to extract structured triggers and arguments from unstructured text. However, the accuracy of named entity recognition will directly affect the performance of event argument role recognition, which results in error propagation. Meanwhile, treating the event extraction task as a classification task ignores semantic information in sentences. In the paper, we propose a dual machine reading comprehension model (Dual-MRC) for event extraction, which converts the classification task into a span extraction task. The model consists of the part of speech of the candidate argument on the left and the imperative sentence on the right to form a question template, dramatically improving the ability of event extraction. Dual-MRC achieves an F1 value of 74.6% in the event trigger extraction and classification task.Our model performs excellently in the case of data-low scenarios, demonstrating the advantages of machine reading comprehension. Experimental results show that our method is effective on the ACE 2005 dataset, especially for multi-word trigger extraction. In addition, we publish a Chinese mine accident annotation dataset. To the best of our knowledge, this is the first Chinese mine accident event dataset, and we verify the performance of the model in Chinese event extraction on this dataset.
Interpretable GAM Models: Predicting Sepsis in ICU Patients

Sepsis is one of the major causes of morbidity, mortality, and cost overruns in critically ill patients. The survival of septic patients is greatly increased by early intervention with antibiotics. While machine learning models are helpful for health professionals in the diagnosis of patients, the lack of interpretability remains a barrier to the deployment of models in many healthcare applications. This study evaluates GAM models for sepsis prediction in terms of interpretability and predictive performance. Several variations of GAM models are included and offer a considerable improvement in predictive performance compared to logistic regression, while remaining interpretable. However, none of the GAM models achieved a predictive performance similar to that of a random forests classifier, which is highly effective but may not be sufficiently interpretable for end users. GAM models offer a balanced trade-off between explainability and predictive performance towards users.
Why Can We Obtain Such an Analysis?

Recently we have been able to dealt with very big data. Accordingly, it is possible to obtain results by Deep Learning in a certain quality. However, it is not good or, is dangerous to use the results blindly. For instance, by Google some persons pictures had automatically been labelled as golliras. Accordingly recently explainable deep learning has been proposed. It may be able to ask the system “why such a result is obtained?” by the method. Moreover, it will be better to explain the result logically. In this paper, we will propose an abductive method to explain the results for decision making.
Interactive Sensing and Sensing Interactions

Sensors are collecting data from various environments, various situations, not only machinery but also human beings, and these data have been employed for the basis of various services for general/individual purposes. Progress of IoT technology encourages collect and use of data under ordinary life, it’s mean the system obtains a chance to adapt human/community/localization. This chapter focused on sensing humans activity and describes the difficulties of acquiring eligible data, and shows the effectiveness of interaction.
Wind resistance aerial path planning for efficient reconstruction of offshore ship

When the unmanned aerial vehicle (UAV) is applied to three-dimensional (3D) reconstruction of the offshore ship, it faces two problems: the battery capacity limitation of the UAV and the disturbance of the wind in the environment. Wind disturbance is generally not considered in the path planning process of the existing UAV 3D reconstruction path planning research. Therefore, the planned path is only suitable for no-wind or light-wind scenarios. For the 3D reconstruction of ship targets, we propose a UAV path planning method that can satisfy both reconstruction efficiency and wind disturbance resistance requirements. Firstly, the concept of model surface complexity is proposed to generate a more efficient candidate view set. Secondly, the Min–Max strategy and a new viewpoint construction method are used to generate the initial path. Thirdly, combined with the wind field model, a method for generating a stable path against wind disturbance based on the idea of interval optimization is proposed. Experimental results demonstrate that our method can adaptively determine the number of sample points and viewpoints according to ship’s geometric characteristics and further reduce the number of viewpoints without significantly affecting the reconstruction quality; the path planned by our method is also stable against wind disturbance.
New theorems for inverting the functions of logic gates in digital circuits

The advancements in digital circuits have led to the development of very complex and large assemblies of logic gates that are electronically integrated together to synthesize highly effective electronics, microprocessors and computer systems. However, the complexity of these devices must be succinctly simplified to achieve simplest circuits. The functions of some logic gates in the circuits must be inverted to minimize the existence of noise and other sources of interference that can distort the performance of the circuits. Besides, most circuit designers and programmers often oversight thorough diagnosis of the Boolean expressions for designing digital circuits. This paper proposes 26 new theorems of digital logic. The proven theorems demonstrate that logic gates are prominently embedded within the elements of hexadecimal notations and the Boolean expressions that are used for designing digital circuits, synthesizing and inverting the functions of six different logic gates during the simplification of complex digital circuits.
Hierarchical Dirichlet and Pitman–Yor process mixtures of shifted‐scaled Dirichlet distributions for proportional data modeling

Abstract In this article, first, we propose a novel unsupervised learning method based on a hierarchical Dirichlet process mixture of shifted‐scaled Dirichlet (SSD) distributions. Second, we extend it to a hierarchical Pitman–Yor process mixture of SSD distributions. The goal is to find a model that properly fits complex real‐world data. Our models are based on SSD distributions that are more flexible than Dirichlet distribution in fitting proportional data. Simultaneous data fitting (parameter estimate) and model selection (model complexity determination) are possible with the suggested methods. We applied batch and online variational inference for learning the models. The online setting allows us to feed our models with large‐scale streaming data. The effectiveness of our proposed models is evaluated by four realistic and challenging applications, namely, spam email detection, texture clustering, traffic sign detection, and vehicle detection. Experimental results demonstrate the potential of our models to fit proportional data.
The Second-Order Shannon Total Generalized Variation for Image Restoration

In this paper, a Shannon-based second-order TGV (Shannon TGV) is proposed. Shannon first and second-order operators are defined and their corresponding adjoints are investigated leading to design a variational model as an optimization problem. We obtain the dual form of the proposed model and utilize it to formulate imaging problems, i.e., denoising and deconvolution. Moreover, we examine numerically the effectiveness of the proposed scheme in imaging problems and compare the results to the classic total variation (TV), the second-order total generalized variation (TGV) and Shannon total variation. The outcomes confirm that the proposed model retains the advantages of both TGV and Shannon TV in elimination of artifacts simultaneously and admit the greater capability of the new model to remove artifacts.
Geospatial exploration of geothermal reservoirs in the Souss Massa regions of the anti-atlas and Moroccan Sahara

Geothermal reservoirs refer to any heat stored, naturally, in the earth's crust; it is considered as resource coming from a natural complex depending on the geological, geophysical aspect and territories geochemistry. Although this natural resource remains distinguished by his characters to be a renewable, ecological, universal and sustainable mining energy, it stills not, yet, sufficiently exploitable worldwide (El Bouazouli et al., 2020). The same finding was brought up in Moroccan context, despite the presence of some hot resources and capitalizable scientific research to develop this potential. Prospecting for underground geothermal resources is an expensive project that deserves to mobilize scientific and technical methods in order to overcome this risky impasse. Thus, Geomatics has been used, drawing on Remote Sensing for geo-spatial exploration in order to guide prospecting through the development of geothermal deposits potential maps at very low and medium depth on Moroccan territories (El Bouazouli et al., 2019). Furthermore, we explore smart tools to support short and long-term decision-making in terms of creating of clean energy mix and complementary mining energy as well as investment in energy exploration operations. And, as an experiment, we chose for this study, areas distributed mainly in the Moroccan Sahara and the Souss Massa Region, known, probably, by a potentially high geothermal gradient, and therefore constituting geothermal energy reservoirs. As for the exploration methodology adopted, it consists in processing of the imageries: LANDSAT 8, MODIS AND ASTER, in order to extract exploitation indices such as the surface temperature, the layer of lineaments and cracks density, and evidence of hydrothermal alteration that may indicate geothermal activity. The correlation between these three factors, will lead us to conclude that the Moroccan Sahara area is considered as one of the most potential geothermal reservoir areas in Morocco. In short, the application of remote sensing and GIS will allow us to calculate surface temperature as well as hydrothermal alteration indices and lineaments to identify promising areas for low-energy geothermal (Zhang et al, 2007; El Bouazouli et al., 2020), (Ouzzaouit et al., 2017)[13]. Admittedly, the use of electromagnetic waves, in particular infrared, will have made it possible to identify potential geothermal sites on the three pilot sites Lagouera, Guergarat and Al Mahbes in the Moroccan Sahara.
A Survey of Different Approaches for Word Sense Disambiguation

Analysis of textual data helps to understand the perception of people by studying the various senses of words in the text. The sense of a polysemous word varies as per the context in the sentence. The technique for determining the correct interpretation of a polysemous word according to context is called as Word Sense Disambiguation (WSD). Recently, researchers have proposed many algorithms to solve this linguistic ambiguity problem in different languages. In this paper, we give a general summary of current trends in WSD in terms of automation of disambiguation approaches. We also mention the challenges and future directions for research for WSD systems. We also propose a system based on these future directions for research, which may increase the accuracy of WSD system for Indian languages.
Emotion Prediction of Comments in Twitch.Tv Livestream Environment

Livestreaming platforms are discernibly the most comprehensive sources of data in real time. Such websites enable users to broadcast content like the games which they are playing, while providing them the opportunity to interact with viewers watching the livestream. Twitch.tv is one of the most popular livestreaming platforms across the globe with millions of monthly active streamers and viewers. Owing to the COVID-19 pandemic, there has been a shift in the conventional lifestyle of the people, with them turning towards online alternatives like Twitch.tv for leisure. This change has led to an increase in the engagement of users in these livestreaming platforms by manifolds. Concurrently, a lot of data is generated from this sudden inflow, which can prove very useful in understanding the general consensus of the crowd. This data is very important, and there is a need to construe the true emotion of the people in real time, which is reflected in the comments made by them in the chat section of livestream. The streamers on Twitch.tv can consequently refine their content immediately based on the feedback that they can infer from the responses given by the users. But, due to the sheer volume of data and convoluted nature of the chat due to the use of emojis, emotes, and emoticons, there are bound to be inconsistencies, human errors, and other esoteric references which are exceedingly complex to dissect, making the task of language processing difficult and leading to incoherent results. Taking into account the hindrance posed by these issues, we have taken up the task to achieve fairly accurate emotion prediction by putting forward machine learning and deep learning techniques. This will involve the creation of a labelled dataset that can be used for training and evaluating the algorithms. Given how context-specific most comments are on the platform, this will be an extensive task. The project will also require the creation of an end-to-end system that performs emotion analysis and giving results in real time through feedback-loops.
Similar Incident Detection for IT Service Management (ITSM) in Multilingual Records Using Information Retrieval Model

Integrated IT service management (ITSM) follows ITIL service operations process to track service request, incident, problem, or change in existing application or infrastructure stack. These tickets are raised by end-user, developer, and tester or through proactive monitoring sources to report a problem or request new feature development. This work contributes to AIOps offering for site reliability engineer (SRE) team who needs to continuously improve the application availability, security, and performance by analyzing re-occurring issues, frequent changes, performance metrics, etc. This solution also has feedback mechanism which continuously trains the model on result relevance. This research work will help the SRE to identify re-occurring issues irrespective of language, accelerates root cause investigation using historical resolution details and permanently fix the problem hampering the application performance. It is noticed that 80% of the resolution time is spent in diagnosing and detecting the issue. With knowledge of historical similar issues and resolution information, the time to detect and troubleshoot reduces drastically from 80 to 20%.
Secure Messaging Application with Live Translation and Privacy

We all tend to use the native language as much as possible. Even though English is the most popular and professional language, merely 20% of the population can communicate using it. In this planned project, We have developed a secure messaging application with a translation feature considering privacy issues of the user. The goal of this application is to provide messaging functionality with a translation feature (translate the message from English to Hindi and vice versa), maintain privacy by blocking screenshot/s and screen recording, encrypting and decrypting message/s. To implement this, I have used Android OS as it is used by nearly 70% of the world’s population. We have also used the firebase platform and Google ML kit. Google ML kit helps to achieve translation features.