Reviewerly/exHarmony huggingface dataset:
Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods

Moral values as commonsense norms shape our everyday individual and community behavior.The possibility to extract moral attitude rapidly from natural language is an appealing perspective that would enable a deeper understanding of social interaction dynamics and the individual cognitive and behavioral dimension.In this work we focus on detecting moral content from natural language and we test our methods on a corpus of tweets previously labeled as containing moral values or violations, according to Moral Foundation Theory.We develop and compare two different approaches: (i) a framebased symbolic value detector based on knowledge graphs and (ii) a zero-shot machine learning model fine-tuned on a task of Natural Language Inference (NLI) and a task of emotion detection.Our approaches achieve considerable performances without the need for prior training.
Digital Humanities and Smart Pedagogy for Managing Intellectual Development in Higher Education

The concept “Digital Humanities” is still quite a new definition that denotes the digital humanities and reveals their role for modern educational systems and smart pedagogy (SmP). According to the scientific classification the humanities generally include linguistics, pedagogy, psychology, economics, management. Integration of any of these sciences with hard skills and soft skills based on digital society requirements for high intellectual human resources among university scholars is known as a significant factor for the further development at the international market and the university’s world ranking. The aim of the study is to review the possibilities of managing the processes of mastering innovation in the educational environment through the fostering the digital humanities at smart university. As the case study the paper represents a model for ensuring the status of the university in the international educational environment through the development and fostering foreign language competences in digital society both for the graduates and teaching staff at the university. The novelty of the proposed solutions is represented in the statistical assessment of the foreign language professional and communicative competence and its influence for knowledge export and development of knowledge export opportunities of the Russian education system for the international educational market.
Round-Optimal Byzantine Agreement

Byzantine agreement is a fundamental primitive in cryptography and distributed computing, and minimizing its round complexity is of paramount importance. It is long known that any randomized r-round protocol must fail with probability at least $$(c\cdot r)^{-r}$$ , for some constant c, when the number of corruptions is linear in the number of parties, $$t = \theta (n)$$ . On the other hand, current protocols fail with probability at least $$2^{-r}$$ . Whether we can match the lower bound agreement probability remains unknown. In this work, we resolve this long-standing open question. We present a protocol that matches the lower bound up to constant factors. Our results hold under a (strongly rushing) adaptive adversary that can corrupt up to $$t = (1-\epsilon )n/2$$ parties, and our protocols use a public-key infrastructure and a trusted setup for unique threshold signatures. This is the first protocol that decreases the failure probability (overall) by a super-constant factor per round.
Neural Networks and Deep Learning

Artificial neural network is a well-known machine learning technique inspired by biological neural network structures. It mimics the human brain’s working mechanism by artificially forming a neural network. In this book, artificial neural networks are referred to as neural networks. The principal idea of a neural network is to show transformation between input and output as connections between neurons in a sequence (arrangement) of layers (White L, Togneri R, Liu W, Bennamoun M (2019) Neural Representations of Natural Language, vol. 783. Singapore: Springer Singapore). Neural networks are mostly used for prediction, decision making, pattern recognition, and novelty detection (Si in Data Mining Techniques for the Life Sciences, Humana Press, Totowa, NJ, 2010). The first is that without domain expertise, neural networks may assist in estimating function structures and parameters (Si in Data Mining Techniques for the Life Sciences, Humana Press, Totowa, NJ, 2010).
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference

Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels due to its subjectivity.Several recent efforts have been made to acknowledge and embrace the existence of ambiguity, and explore how to capture the human disagreement distribution.In contrast with directly learning from gold ambiguity labels, relying on special resource, we argue that the model has naturally captured the human ambiguity distribution as long as it's calibrated, i.e. the predictive probability can reflect the true correctness likelihood.Our experiments show that when model is well-calibrated, either by label smoothing or temperature scaling, it can obtain competitive performance as prior work, on both divergence scores between predictive probability and the true human opinion distribution, and the accuracy.This reveals the overhead of collecting gold ambiguity labels can be cut, by broadly solving how to calibrate the NLI network.
Modeling the Adaptive Stability and Competitiveness of the University During Digital Changes

The purpose of this study is to analyze the evolutionary and adaptive development of the information space within the framework of the concept of “smart university” and knowledge models like “semantic network”. Key methods, approaches and technologies of adapting web-learning are considered, as well as didactic aspects. The main goals of adaptive vocational education are highlighted. The issues of self-organization, web-education, their adaptation to digital transformations in society, the environment of the educational system were investigated. The mathematical model of the student's adaptive profile is proposed, taking into account the learning scenario, learning success, the adaptation mechanism and the starting state.
Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs

The purpose of regulatory data minimization obligations is to limit personal data to the absolute minimum necessary for a given context. Beyond the initial data collection, storage, and processing, data minimization is also required for subsequent data releases, as it is the case when data are provided using query-capable Web APIs. Data-providing Web APIs, however, typically lack sophisticated data minimization features, leaving the task open to manual and all too often missing implementations. In this paper, we address the problem of data minimization for data-providing, query-capable Web APIs. Based on a careful analysis of functional and non-functional requirements, we introduce Janus, an easy-to-use, highly configurable solution for implementing legally compliant data minimization in GraphQL Web APIs. Janus provides a rich set of information reduction functionalities that can be configured for different client roles accessing the API. We present a technical proof-of-concept along with experimental measurements that indicate reasonable overheads. Janus is thus a practical solution for implementing GraphQL APIs in line with the regulatory principle of data minimization.
Violence Detection in Videos Using Deep Learning: A Survey

There is a significant need of intelligent surveillance systems to monitor people and recognize their violent behavior at public environments (like banks, hospitals, market centers, and railway stations, and so on). Therefore, violent activity recognition becomes an emerging topic among the researchers in the field of computer vision. Effective and successful approaches for identification of violence in videos are extremely needed for safety concerns. Over the past few years, several techniques based on handcrafted and deep learning features have been introduced for detection of these activities. This paper commences with common framework for violence recognition followed by review on different deep learning techniques and methods based on convolution neural network (CNN) and long short-term memory networks (LSTM) used for violence detection. Besides that, popular datasets and challenges of this topic are also discussed in this review. The findings of the research have been also discussed which may help to find future work in this domain.
Development of Deep Learning Systems: A Data Science Project Approach

TUsing DL can have unexpected consequences, with a lack of reliability, leading to systematic forms of development. The work addressed DL systems from the perspective of DS projects, presenting a comparison of the methodologies used in these projects.
Design of a Security System Based on Raspberry Pi with Motion Detection

Currently, the integration of IoT technology in various fields is very widely used, however, data security remains the essential point to be monitored especially in companies, and also in homes. To control and overcome security-related problems, we adopted Internet of Things technology based on a Raspberry pi4 as the main data processing element in this study. In this paper, we present a simple, efficient, and very reliable study for the monitoring of a video stream coming from a camera installed on a Raspberry pi4 which constitutes the essential element in our project. To reproduce this realization, we did not use a motion sensor, but we took advantage of the algorithm advantages of the Motion software integrated into the free operating system MotionEyeOs on a Raspberry pi4 to trigger motion detection by causing a beep to draw attention. On the other hand, our study was implemented without noticed difficulty, and with a great level of performance and stability which shows that our realization of the Video Stream Surveillance System is successful.
Attention Based Deep Learning Techniques for Question Classification in Question Answering Systems

The most desired feature in any Question Answering Systems (QAS) is to obtain suitable answers as a response to user questions. Question classification is one of the critical components in QAS as it plays a crucial role in efficient answer extraction. The purpose of this paper is to build a question classification model that can identify whether the question provided is easy, medium, or challenging level. Once the category is identified, then the corresponding answer is retrieved. If the question levels are categorized perfectly, the user can grab knowledge from the required level. Suppose the user is a beginner; he can’t directly move to hard level questions; the user must first start with an easy level question and gradually move to hard level questions. Two integrated deep learning techniques, namely BI-LSTM with attention and BIGRU-CNN with attention, have been implemented for more useful question classification in QAS, and it is observed that BIGRU-CNN with attention gives good performance with an accuracy of 84.5 than BI-LSTM with attention.
An Augmented Reality Framework for Visualization of Internet of Things Data for Process Supervision in Factory Shop-Floor

Modern industrial environments provide a high level of digitalization, enabling great volumes of information to be used for shopfloor monitoring and control purposes. In cases where human operators participate in the production process, it is important to support operator’s intervention with Internet of Things (IoT) data and empower process supervision with constant information over Key Performance Indicators (KPIs). Towards this end, the proposed framework exploits Augmented Reality (AR) for the provision of an IoT data visualization solution to support KPIs based process supervision in the smart factory environment. The solution utilizes IoT-enabled components, in order to receive dynamic information over the status of sensors and resources spread across the factory shop-floor. The AR application facilitates the need for visualization of information available on the network, on the actual shopfloor components, which offer transparency and ease the supervision of production processes. The development of the AR and cloud API has been based on CSharp and Java, in a reusable framework that can be reused for similar industrial cases. The reconfigurability and reuse of the AR solution is supported by a template configuration, allowing industry practitioners to configure application features and external connections without any additional software development. The solution was validated on a thermoforming process supervision case of a white goods production system. The main goal was to use the AR framework to support operators during the process parameters design phase, and dynamic supervision of the shopfloor environment. It is demonstrated that the proposed system can be used for KPIs monitoring of the production cycles and for empowering process supervision and optimization.
Calmbots: Exploring Madagascar Cockroaches as Living Ubiquitous Interfaces

We introduce Calmbots, insect-based interfaces comprising multiple functions (transportation, display, drawing, or haptics) for use in human living spaces by taking advantage of insects’ capabilities. We utilized Madagascar hissing cockroaches as robots because of advantages such as mobility, strength, hiding, and self-sustaining abilities. Madagascar hissing cockroaches, for instance, can be controlled to move on uneven cable-lines floors and push light-weight objects such as tablespoon. We controlled the cockroaches’ movement using electrical stimulation and developed a system for tracking and communicating with their backpacks using augmented reality markers and a radio-based station, the steps of controlling multiple cockroaches for reaching their goals and transporting objects, and customized optional parts. Our method demonstrated effective control over a group of three or five cockroaches, with, at least, 60% success accuracy in dedicated experimental environments involving over forty trials for each test. Calmbots could move on carpeted or cable-lines floor and did not become desensitized to stimulation under a certain break interval.
A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers

Handwritten character recognition is the most critical and challenging area of research in image processing.A computer's ability to detect handwriting input from various original sources, such as paper documents, images, touch screens, and other online and offline devices, may be classified as this recognition.Identifying handwriting in Indian languages like Hindi, Tamil, Telugu, and Kannada has gotten less attention than in other languages like English and Asian dialects like Japanese and Chinese.Adaptive Moment Estimation (ADAM), Root Mean Square Propagation (RMSProp) and Stochastic Gradient Descent (SGD) optimization methods employed in a Convolution Neural Network (CNN) have produced good recognition, accuracy, and training and classification times for Telugu handwritten character recognition.It's possible to overcome the limitations of classic machine learning methods using CNN.We used numerous handwritten Telugu guninthalu as input to construct our own data set used in our proposed model.Comparatively, the RMSprop optimizer outperforms ADAM and SGD optimizer by 94.26%.
A Blockchain-Based Architecture for Implementing Job Recruitment Operations

Blockchain technologies are accelerating at a greater velocity and are considered as one of the best technologies as it has the potential to transform and reshape data governance. These technologies are integrated into fields like public sector, supply chain, e-government etc. Blockchain provides a secure and fool-proof systems. The technology provides a platform to re-deploy the present systems in much better ways. The use of blockchain in various fields of operation of government is already being adopted in several countries. Considering the importance and the impact of blockchain technology, this research paper presents the architecture of a job recruitment platform based on blockchain. The case study of Sultanate of Oman is presented in this research. The existing approach in the Sultanate of Oman involves obtaining information on open job vacancies from several ministries and working with the human resource management system to publicize these positions in the media. Later, a short messaging service system was created, allowing job seekers to apply for preferred employment by texting a job code. But these approaches had various drawbacks, including the expense of publicizing job positions. This research paper presents a blockchain-based framework to expedite the process to improve the current time-consuming recruiting strategy. The proposed system will provide a centralized hub for all the job vacancies available in the ministries of Oman, easing the documentation works, time consumption as well as the efficiency of the present job recruitment platform.
Implicit Relation Linking for Question Answering over Knowledge Graph

Relation linking (RL) is a vital module in knowledge-based question answering (KBQA) systems.It aims to link the relations expressed in natural language (NL) to the corresponding ones in knowledge graph (KG).Existing methods mainly rely on the textual similarities between NL and KG to build relation links.Due to the ambiguity of NL and the incompleteness of KG, many relations in NL are implicitly expressed, and may not link to a single relation in KG, which challenges the current methods.In this paper, we propose an implicit RL method called ImRL, which links relation phrases in NL to relation paths in KG.To find proper relation paths, we propose a novel path ranking model that aligns not only textual information in the word embedding space but also structural information in the KG embedding space between relation phrases in NL and relation paths in KG.Besides, we leverage a gated mechanism with attention to inject prior knowledge from external paraphrase dictionaries to address the relation phrases with vague meaning.Our experiments on two benchmark and a newly-created datasets show that ImRL significantly outperforms several state-of-theart methods, especially for implicit RL.
Computing Longest (Common) Lyndon Subsequences

Given a string T with length n whose characters are drawn from an ordered alphabet of size $$\sigma $$ , its longest Lyndon subsequence is a longest subsequence of T that is a Lyndon word. We propose algorithms for finding such a subsequence in $$\mathcal {O}(n^3)$$ time with $$\mathcal {O}(n)$$ space, or online in $$\mathcal {O}(n^3 \sigma )$$ space and time. Our first result can be extended to find the longest common Lyndon subsequence of two strings of length n in $$\mathcal {O}(n^4 \sigma )$$ time using $$\mathcal {O}(n^3)$$ space.
Optimization of Meixner Moments by the Firefly Algorithm for Image Analysis

Discrete orthogonal polynomials are widely used for image processing, these polynomials are characterized by parameters. However, it was very important to optimize these parameters to obtain a good result, in this context; this study suggests a new approach to optimize Meixner polynomial (MPs) parameters (β, u) based on the Firefly Algorithm (FA), and we use them to reconstruct 2D images. Using the FA algorithm determines the optimal parameters of the MPs, which improves the quality of the reconstruction. The simulation results, on the one hand, suggest that the proposed method based on the FA algorithm offers a high image reconstruction quality. On other hand, the comparison carried out with other algorithms indicates the advantage of the proposed method.
Logic Traps in Evaluating Attribution Scores

Modern deep learning models are notoriously opaque, which has motivated the development of methods for interpreting how deep models predict.This goal is usually approached with attribution method, which assesses the influence of features on model predictions.As an explanation method, the evaluation criteria of attribution methods is how accurately it reflects the actual reasoning process of the model (faithfulness).Meanwhile, since the reasoning process of deep models is inaccessible, researchers design various evaluation methods to demonstrate their arguments.However, some crucial logic traps in these evaluation methods are ignored in most works, causing inaccurate evaluation and unfair comparison.This paper systematically reviews existing methods for evaluating attribution scores and summarizes the logic traps in these methods.We further conduct experiments to demonstrate the existence of each logic trap.Through both theoretical and experimental analysis, we hope to increase attention on the inaccurate evaluation of attribution scores.Moreover, with this paper, we suggest stopping focusing on improving performance under unreliable evaluation systems and starting efforts on reducing the impact of proposed logic traps.
Osteoarthritis Detection Using Densely Connected Neural Network

Osteoarthritis (OA) is progressive deterioration kind of bone joint disease. Knee OA is most common type of OA, which affect locomotion. Pain in joint, swelling, stiffness and trouble in walking happens to be foremost symptoms of knee OA. KOA detection by medical practitioner is most commonly done with the help of radiographs. The radiographic findings in knee OA are joint space narrowing, formation of bone spurs and increased bone density. In this study, we bring out a method to detect knee OA using knee x-ray images, implemented in MATLAB. The detection is based on clinical changes that happen in bone ends. Nine hundred and fifty knee radiographic images in antero-posterior projection are used, collected from Osteoarthritis Initiative dataset. Those images are used to train Dense Net Neural Network model. Feature maps are computed for the given X-ray images to classify into osteoarthritic knee or normal knee. The trained model gives the validation accuracy of 95.56%. These results indicate the proposed methodology can be used by medical practitioner for assessment of OA.
Tracking and Interception of a Spiralling Ballistic Target on Reentry

Interception of the high velocity spiralling target requires accurate estimation of their relative states. In this work, the dynamics of spiralling target is modelled by representing the target acceleration through sinusoidal function in the inertial frame. A system model which consists of nine state variables is considered here with the three relative positions, three relative velocities, inverse of ballistic coefficient, manoeuvring coefficient and spiralling frequency of target. Non-linear state estimators, namely the unscented Kalman filter (UKF), new sigma point Kalman filter (NSKF) and cubature Kalman filter (CKF), are applied to track the spiralling target using the measurements obtained from an inbuilt seeker of the interceptor missile. Further, the estimated states are provided to a guidance block for generating interceptor missile accelerations. Performance comparison of state estimators in combination with PNG law is done by calculating the root mean square error(RMSE) of relative states and average miss-distance.