Reviewerly/exHarmony huggingface dataset:
Role of Non-functional Requirements in projects’ success

Non-functional Requirements (NFRs) are the vital part of software development. NFRs define the quality attributes of the software product. According to the researchers’ nonfunctional requirements are of equal importance as of functional requirements. But among practitioners it is debatable, as most of them do not give significance value to NFRs because of the rapid development nature of the projects. In the literature it is encouraged to provide proper attention to non-functional requirements during the development of the project because it could lead to new functional requirements and hence would tend to change the development approach. It is also believed that the absence of non-functional requirement could cause extra cost or customer’s dissatisfaction. Hence, this paper aims to conduct an industrial survey to gather the data about how software practitioners think about the importance of nonfunctional requirements in the project success. Furthermore, we will be applying statistical testing on our data to analyze the results to check the significance role of NFRs in the success of the software project.
Hardware Implementation of Lossless Hyperspectral Compression USING CCSDS 123.0.B-1

Remote sensing satellites with hyperspectral and multispectral payloads would require large data storage onboard and downlink bandwidth because the data volume collected in a spatial area has multiple spectral band data. With remote sensing satellites collecting hyper-spectral images, compression algorithms are required for efficient utilization of downlink bandwidth. Since increasing the storage or bandwidth is expensive onboard, data compression techniques are utilized for effective utilization of resources. With the knowledge of elemental absorption and reflectance characteristics, spectroscopy aids in identification and analysis of the target. Multispectral or hyperspectral imaging sensors capture the target spatial area in multiple bands. The resolution of bands is much larger (of the order of few 100nm) in multispectral bands. Band gap between adjacent bands in hyper spectral imaging is in 10nm range. Thus formed spectral cube has length and breadth defining the spatial coordinates while the depth of it defines the number of bands. Consultative Committee for Space Data Systems (CCSDS) recommends certain algorithms for lossless and lossy compression which is selected based on the application. This paper details about implementation of lossless compression algorithm defined in CCSDS 123.0-B-1 using predictor and block adaptive entropy coder. The implementation is done on space grade FPGA RTG4150 with an operation frequency of 89.694 MHz with varying pixel depth from 12 to 16bits. This gives a throughput more than 1Gbps.
Using Search-Based Software Testing to Guide the Strive for Robust Machine Learning Components: Lessons Learned Across Systems and Simulators in the Mobility Domain

This talk shares lessons learned from using search-based techniques for robustness testing in simulators.
Vehicle Loan Fraud Prediction using Data Science And Machine Learning Techniques

Nowadays, banks are gaining a lot of customers day by day and many have taken loans from them, and it is not possible to get hold of every customer who is not able to pay or won’t be able to pay in future. So now banks are needing classification models to predict in future whether a specific person will be able to pay their loan on time or not. It allows them to take necessary actions in advance like reminding the customer early to pay their debt, or they will face the consequences. In this way, the bank won’t suffer losses, and it will help them to maintain their reputation in terms of handling loans in the market.As the people grow the needs also grow which also increases the need to get the basic things. Vehicle is a very basic thing which has become a necessity and to fulfil that people take loans and complete the necessity.Our project will solely be focusing on vehicle loan default with the help of many classification models and choosing the one with better accuracy after a detailed comparison.
STUDY ON PSYCHOLOGICAL HARDINESS OF STATE LEVEL LONG JUMPER AND JAVELIN THROWER

The present study was made to find out the psychological hardiness between State Level Sprinter and Javelin Thrower”. A total of 30 sprinters and 30 javelin throwers were selected from different district of West Bengal. The age of the subject ranged from 16 to 21 yrs. Old. To measure the psychological hardiness of the player was used to Bengali version of psychological hardiness scale which was developed by Singh (2008) was administered on a selected sample. Descriptive statistics along with a t-test was used to analyze the result of the study. The level of significance was 0.05 levels. Results revealed that there was no significant difference between Sprinters and Javelin throwers. Key Words: Psychological Hardiness, Long Jumper, Javelin Thrower
Smart Applications of Internet of Things (IoT) in Healthcare

The Internet of Things (IoT) is an extensive network connecting the many intricacies of the public sphere to the digital domain. It can be defined as a structure of objects containing specific attributes that can communicate with each other with the help of the internet or any digital device. It has steadily been gaining traction across various fields in tandem with the growth of the technological era. One such field in which IoT has become a force to reckon with is that of healthcare. Due to a surmounting occurrence in the growth of the population around the globe, innovations in healthcare are the need of the hour. With the rise in problems such as demographic-specific health concerns, epidemics, disease outbreaks, and superbugs, cutting-edge technologies that can aid the human brain in diagnosis are extremely favorable for the well-being of mankind. Due to the capacity of the IoT to independently communicate with its objects and provide an intelligent analysis of the same, it assumes a position of paramount importance in the healthcare sector. The novel methodologies detailed in this article include healthcare systems to monitor ICU patients at risk, IoT-based approach for specially abled persons, IoT in palliative care, clinical decision support system (CDSS), electronic health record (EHR), IoT against the COVID-19 pandemic, and remote medical assistance, used in the implementation of IoT in the healthcare field.
Features of classes on geoinformation technologies with schoolchildren in the framework of additional education

The article reveals the issues of organizing classes on geoinformation technologies with schoolchildren in the framework of additional education. Additional education is considered as an activity of the House of Scientific Collaboration on the basis of Cherepovets State University. The purpose of the work is to describe the system of tasks within the framework of the program "Geoinformation technologies" through the prism of the travel motive. Based on the analysis of psychological and pedagogical literature, the article highlights the following provisions for the system of classes: mastering the concept of "geoinformation technologies"; taking into account the main characteristics of the concept of "travel"; relying on the personal experience of students and the emotional component; the use of active and interactive teaching methods. The paper describes creative cases on the educational program "Geoinformation technologies", which are aimed at studying new information, analyzing the information received, fixing and presenting the results obtained. Intersubject connections are the basis for planning classes. The complex of case tasks is aimed at the formation of personal, meta-subject and subject results, and also allows to develop interest in studying the geography and history of the planet Earth using modern information technologies. Google Maps and Yandex.Maps act as tools for solving case tasks.
Keynote Talk - Federated Learning: The Hype, State-of-the-Art and Open Challenges

The popularity of machine learning models has dramatically increased in a large variety of applications that affect people's daily lifes, including product recommendations, healthcare predictions and critical applications. This wide availability has at the same time raised questions about the trustworthiness, security, and privacy implications of using these systems. While novel technologies and methodologies have been emerging to protect the privacy and security of AI Systems, there are still open challenges that need to be addressed by the research community. Over the past years, my research has focused on the creation of defenses to protect the machine learning pipeline and the design of privacy-aware methodologies to enable the training of accurate machine learning models without transmitting the data to a central place. In this talk, I will focus on data privacy covering a game-changing paradigm known as federated learning [4], which to some extend addresses privacy concerns and regulations that prevent the free transmission and sharing of information. Federated learning is a technology that enables multiple participants owning private data to collaboratively train a single machine learning model while maintaining their training data locally. This is in sharp contrast to traditional machine learning where all data needs to be in a central place. Some argue that federated learning is a privacy-by-design technology given that it does not require data to be transmitted to a central place. However, there are still privacy risks that are relevant in some scenarios. Novel inference attacks that take advantage of the federated learning process have been demonstrated in the literature, resulting in a variety of defenses that aim to reduce these risks. I will present some of these attacks and several cryptographical and differential privacy techniques to deter them, including [5,7,8]. The plethora of defenses is particularly interesting given their diverse threat models and the divergent set of privacy requirements they address. In this talk I will demystify them. I will also explain some challenges related to manipulation attacks [6,9] and machine learning fairness [1] in the context of federated learning. Finally, I will touch upon transparency issues and how to enable accountability for regulated industries [2,3] and vertical federated learning [7]. This talk will go through the security and privacy challenges and solutions in federated learning systems.
Advancements in Weather Forecasting With Deep Learning

The changes in the weather play a significant role in people's planning. It has attracted the attention of several study communities due to the fact that it has an impact on human life all over the world. But weather forecasting is a challenging task because it is dependent on a variety of factors such as wind speed, wind direction, global warming, etc. Deep learning-based solutions have seen a lot of success in the geospatial domain over the last few years. In the past few years, a variety of deep learning-based weather forecasting models have been proposed. The forecasting techniques used traditionally are highly parametric and so are complex. In this chapter, deep learning techniques which are used for weather forecasting, such as Multilayer Perceptron, Jordan Recurrent Neural Network, Elman Recurrent Neural Network, etc., are discussed in detail. This chapter presents a comparative analysis of various deep learning-based weather forecasting models that are currently available.
BERT based Multiple Parallel Co-attention Model for Visual Question Answering

Humans can easily interpret visual and textual content whereas for a machine this is a challenging task. Visual question answering is a well-known problem in the field of computer vision and NLP where an image and a question related to the image are given and the machine has to generate a natural language answer. This paper explores the use of transformer BERT as a language model in VQA. The BERT-based multiple parallel co-attention visual question answering model has been proposed and the effect of introducing a powerful feature extractor like BERT for language modeling has been studied. From the experimental results, it is concluded that the proposed model improves over the original baseline VQA model by 3%.
Automatically Categorizing Software Technologies

The lack of informal language and software technology standard taxonomy makes it impossible to analyze technology trends on forums and other online sites. Researchers have done an in-depth study of the seven top-notch technology tools and proposed a better automated method to classify software technologies. e.g., Witt uses phrases that describe software tech or concept and put back a wide-ranging class that defines it (i. e., IDE). Furthermore, it defines its function (commercial, PHP). By extension, this method can dynamically compile the list using all technologies of a given type. In a same way working of WordNet, WebIsADb, WiBiTaxonomy, and few other tools is also studied by the researchers. Eventually, they compared these classification methodologies and establish that Witt, once used in software jargon, showed healthier results as compared to all other explanations assessed, lacking a conforming diminution in untrue alarm rates.
Implementation of Steganography Model in Data Transmission using LabVIEW

This paper presents information concealing technique called steganography. The proposed method is relevant to text information types. The fundamental point of this procedure is to encrypt the content information within a cover image so that presence of data is concealed. It deals with the encoding of information text data in a given picture without rolling out any visible changes to it. Here introduced a steganography technique using LabVIEW software. LabVIEW is a software advancement environment and language created by National Instruments. Most of the present data hiding software uses multimedia objects like pictures, sound, video, etc. In this method, the Least Significant Bit technique is used to hide secret data in an image. It shrouds the information data in a spread media without rolling out any obvious changes in it and the information media presence is covered.
SET-STAT-MAP: Extending Parallel Sets for Visualizing Mixed Data

Multi-attribute dataset visualizations are often designed based on attribute types, i.e., whether the attributes are categorical or numerical. Parallel Sets and Parallel Coordinates are two well-known techniques to visualize categorical and numerical data, respectively. A common strategy to visualize mixed data is to use multiple information linked view, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this paper, we design visualizations for mixed data, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution SET-STAT-MAP is a harmonious combination of three interactive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a geospatial map view (visualizes the spatial information). We augment these components with colors and textures to enhance users' capability of analyzing distributions of pairs of attribute combinations. To improve scalability, we merge the sets to limit the number of possible combinations to be rendered on the display. We demonstrate the use of Set-stat-map using two different types of datasets: a meteorological dataset and an online vacation rental dataset (Airbnb). To examine the potential of the system, we collaborated with the meteorologists, which revealed both challenges and opportunities for Set-stat-map to be used for real-life visual analytics.
How Agile Are the Organizations Against Cyber Threats?

Some of the tasks of organizations in the digital age include information formation, coding and increasing value, data mining, and encoding information to make it accessible to others. As disruptive technology permeates all aspects of social life, new threats and vulnerabilities emerge. Cyber threats and cyber-security incidents may affect organizations, whether public or private, individuals, and all social network actors. The idea of a system that must defend against all possible attacks has given rise to the cyber resilience phenomenon. In public organizations, cyber resilience is obtained in various ways such as storing private classified data assets and records on independent backup platforms. Regardless of whether one platform is in danger, the other can provide a copy of missing or maliciously encrypted data immediately. Given the preceding discussion, this chapter focuses on agility, which is now regarded as a core competency in organizations in terms of cyber management, cyber resilience, knowledge management, and artificial intelligence in the cyber cosmos.
Materialization and Reuse Optimizations for Production Data Science Pipelines

Many companies and businesses train and deploy machine learning (ML) pipelines to answer prediction queries. In many applications, new training data continuously becomes available. A typical approach to ensure that ML models are up-to-date is to retrain the ML pipelines following a schedule, e.g., every day on the last seven days of data. Several use cases, such as A/B testing and ensemble learning, require many pipelines to be deployed in parallel. Existing solutions train each pipeline separately, which generates redundant data processing. Our goal is to eliminate redundant data processing in such scenarios using materialization and reuse optimizations. Our solution comprises of two main parts. First, we propose a materialization algorithm that given a storage budget, materializes the subset of the artifacts to minimize the run time of the subsequent executions. Second, we design a reuse algorithm to generate an execution plan by combining the pipelines into a directed acyclic graph (DAG) and reusing the materialized artifacts when appropriate. Our experiments show that our solution can reduce the training time by up to an order of magnitude for different deployment scenarios.
Visualization for neural-network-based person re-identification

Given images of a person, person re- identification (Person ReID) techniques aim to find images of the same person from previously collected images. Because of large data sets of person images and the advance of deep learning, convolutional neural networks (CNNs) successfully boost the accuracy of Person ReID algorithms, but it can be difficult to explain and to troubleshoot issues due to the complexity of CNNs. In this paper, we present a visualization-based approach to understand a CNN-based Person ReID algorithm. As Person ReID algorithms are often designed to map images of the same person into similar feature vectors, given two images, we design an algorithm to estimate how much each element in a CNN layer contributes to the similarity between their feature vectors. Based on the estimation, we build a visualization tool to interactively locate and visualize the activation of highly-contributing elements, other than manually examining all. Our visualization tool also supports various user interaction widgets to explore a Person ReID data set, locate difficult cases, and analyze the reason behind their similarities. We show a use case with our tool to understand and troubleshoot issues in a CNN-based Person ReID algorithm.
Does Nice or Nasty Matter? The Intensity of Touch Modulates the Rubber Hand Illusion

Our sense of body ownership results from the ongoing integration of perceptual information coming from the different senses (i.e., multisensory integration). The Rubber Hand Illusion (RHI) has been extensively studied to investigate the malleability of body ownership through contrasting multisensory information. Indeed, during the RHI, stroking a visible rubber hand synchronously to participants' hand hidden from sight generates the illusion of ownership of the rubber hand (embodiment) and the mis-location of participants' hand as closer to the rubber hand (proprioceptive drift). It is well known that the RHI is optimally evoked by a pleasant stroking (affective) touch, but what of an unpleasant (painful) stroking touch - does hedonic valence matter? To this aim, participants repeated the RHI while receiving different types of touch: pleasant, painful, and neutral. Results showed, for the first time, that the subjective intensity of the tactile stimulation experienced across the different conditions modulates the strength of the proprioceptive drift. Notably, participants reported a stronger RHI (mis-placed body ownership) from stimulation rated as more intense and involving an interoceptive activation (pain and pleasantness vs. neutral). We propose that interoceptive information, regardless of the valence of the stimuli (positive or negative), are perceived as more intense and enhance, through the activation of the limbic system, multisensory integration. In the context of the RHI, this translates to a stronger illusion in terms of proprioceptive drift.
Tourist Spots Recommendation Method Corresponding to Place Names Appearing in Novel Contents

In general, when a place depicted in a novel exists, users may like to visit the site that appears in the story. There is a need to efficiently visit tourist spots in or around the places depicted in the novel when visiting them. When we realize to retrieve appropriate tourist spots in areas in stories, it is possible to increase the number of new sightseeing opportunities for users. This paper presents a tourist spot recommendation method corresponding to place names appearing in novel contents. Our system recommends some actual tourist spots corresponding to a novel selected by a user. The feature of our proposed method is to recommend tourist spots within the range that users can visit in a day, based on the place names that appear in the novel. We apply this method to realize seamless linking media between the creative world like a novel and the real-world.
NLP based Model for Classification of Complaints: Autonomous and Intelligent System

Artificial intelligence nowadays is playing a vital role in our society. It is just minimizing human labor and effort in every field. Industrial sector is feeding their large amount of structured and unstructured data to find out useful information for scientific research. The main alarming thing is how to operate the huge feedback data, which is in the form of complaints i.e., in text format. Here, we have proposed a model which automatically classifies the complaints by analyzing the text with the help of machine learning and NLP (Natural Language Processing) methods. We have initially collected a dataset from a portal containing complaints of citizens. For validation, we have also used another dataset of complaints from the Consumer Complaint Database. After tokenizing, stemming and lemmatization, different feature extraction techniques like count vectorizer and TF-IDF are used to convert all the textual data into numerical data. Then different machine learning algorithms are used to classify the complaints into their categories. In our gathered dataset, 10 different divisions for complaints are used and an accuracy of more than 70% is achieved with all classifiers. Similarly on the Consumer Complaint dataset, 86% accuracy has been achieved. The proposed model is helpful in saving a lot of time, as there is no need to go through each complaint and categorizing manually.