Reviewerly/exHarmony huggingface dataset:
Using Terms and Conditions to apply Fundamental Rights to Content Moderation

Large online platforms provide an unprecedented means for exercising freedom of expression online and wield enormous power over public participation in the online democratic space. However, it is increasingly clear that their systems, where (automated) content moderation decisions are taken based on a platform's terms and conditions (T&amp;amp;Cs), are fundamentally broken. Content moderation systems have been said to undermine freedom of expression, especially where important public interest speech ends up suppressed, such as speech by minority and marginalized groups. Indeed, these content moderation systems have been criticized for their overly vague rules of operation, inconsistent enforcement, and an overdependence on automation. Therefore, in order to better protect freedom of expression online, international human rights bodies and civil society organizations have argued that platforms “should incorporate directly” principles of fundamental rights law into their T&amp;amp;Cs. Under EU law, and apart from a rule in the Terrorist Content Regulation, platforms had until recently no explicit obligation to incorporate fundamental rights into their T&amp;amp;Cs. However, an important provision in the Digital Services Act (DSA) will change this. Crucially, Article 14 DSA lays down new rules on how platforms can enforce their T&amp;amp;Cs, including that platforms must have “due regard” to the “fundamental rights” of users under the EU Charter of Fundamental Rights. In this article, we critically examine the topic of enforceability of fundamental rights via T&amp;amp;Cs through the prism of Article 14 DSA. We ask whether this provision requires platforms to apply EU fundamental rights law and to what extent this may curb the power of Big Tech over online speech. We conclude that Article 14 will make it possible, in principle, to establish the indirect horizontal effect of fundamental rights in the relationship between online platforms and their users. But in order for the application and enforcement of T&amp;amp;Cs to take due regard of fundamental rights, Article 14 must be operationalized within the framework of the international and European fundamental rights standards, and therefore allowing Article 14 to fulfil its revolutionary potential.
Building the business schools of the future with a strong focus on practical wisdom for a VUCA world

Business environments are now frequently described as VUCA - volatile, uncertain, complex, and ambiguous. The COVID-19 pandemic breaking out in 2020 serves as a case in point. Therefore, strategies, business models, tactics, and plans set for the year were challenged. In this situation, executives worldwide did not suffer from insufficient general knowledge about strategizing, business modelling, or planning. This research project posits that what practitioners need to survive and thrive is practical wisdom. The chapter provides insights based on exploratory research and presents a state-of-the-art framework for fostering such practical wisdom. The process foresees a three-act process and six concrete steps in order to 'phronesize', i.e., taking practical wisdom to the next level. The gained insights contribute to a better return on education (ROE). It also prepares the learner more aptly for this VUCA world.
INNOVATIVE POTENTIAL OF THE EDUCATIONAL SYSTEM

Today, Ukrainian education needs modern teachers who are capable of cooperation, mobilized, dynamic, who are able to make decisions independently. Today’s students’ space has also change dramatically: its values, attitudes, expectations, changes cannot be ignored in the process of education at the university. The main educational trend of a higher educational institution of the past is to study the past as a subject (in terms of the study of human society) of historical science. It is assumed that knowledge of the past forms the student's understanding of the present. Such understanding of the past in our education is generally recognized. By focusing on the past and partly on the present, the educational process has proven to be an obsolete mechanism of socio-economic development. Translating “educational knowledge” from previous generations to subsequent ones does not mean that it helps to solve today’s problems, and even tomorrow’s ones. The past is based on basic values in theformat of conservation (habit, hierarchy) and paternalism (control and punishment, justice). The future is development. Control is replaced by trust, creativity, and involvement. In addition, education as an innovation process should be advanced, paving the way to the future, as a result of which it should become innovative and advanced.
A novel architecture for knowledge mining from digitised document libraries

This paper examines a novel knowledge mining architecture based on the Azure cloud data and AI services, to extract data from the Emporium library, a modern art journal published between 1985 and 1964. The knowledge mining starts with Optical Character Recognition (OCR) and custom Name Entity Recognition (NER) on digitised images of the pages and provide the final user with an user-friendly search portal to navigates the hundreds of pages in milliseconds through a semantic query. The study proved how this architecture fits from an art scholar’s perspective and how it enables to build more comprehensive statistics and description of the document corpus.
“I Want My Tracker To Be Like a Mother": The Implications of Universalism of Health Trackers on IndianWomen

The data captured by health and fitness tools is immensely personal and is intertwined with one’s complex identity - however, by using a one-size-fits-all approach, the design of these health and fitness tools flattens the importance of gendered differences and personal needs. In response to this, through semi-structured interviews, we aim to better understand the implications of this normalization and universality of health trackers on Indian women.
Voice Based Authentication System for Web Applications using Machine Learning

Due to security concerns, the biometric trend is being used in many systems. Biometric authentication is a cheap, easy, and reliable technology for multi-factor authentication. Cryptosystems are one such example of using biometric data. However, this could be risky as biometric information is saved for authentication purposes. Therefore, voice biometric systems provide more efficient security and unique identity than commonly used biometric systems. Although, speech recognition-based authentication systems suffer from replay attacks. In this paper, we implement and analyze a text-independent voice-based biometric authentication system based on the randomly generated input text. Since the prompted text phrase is not known to the speaker in advance, it is difficult to launch replay attacks. The system uses Mel-Frequency Cepstrum Coefficients (MFCC) to extract speech features and Gaussian Mixture Models (GMM) for speaker modeling.
Agro Marketing

Agro marketing is an application which is useful for both the farmers and customers. In present marketing system farmers are not getting profit for their hard work. In today marketing system the farming products are uploaded in the market by giving low cost to the farmers. Customers buy the products with high cost from market and also they are not healthy. In this Agro marketing application farmers upload their products that are cultivated in their own fields. Customers also login to the website and buy the products that they want to buy. As the farmers upload natural and fresh products, customers get healthy and fresh quality products that to directly from framers rather than a market. In this website the schemes and that are useful for the farmers are also uploaded by the admin which helps farmers financially.
Dependency on Digital Gadget of Youth and their Side Effects

It is amazing that in India around 95% smartphone users check their social media as the First thing in every morning. Our youth seems worried without their digital gadgets especially smart phones .Even in sleeping time also almost 90% people keeps Smartphone near to their bed. As being a digital media driven society our south Indian gadget users are always trying to connect with digital or social media to have updates. This study aims at the habits of Smartphone users and how it moving to the so -called addiction. moreover, the paper observes the priming outcomes of recent media on youth and its impact on society. As part of the study the researcher found some basic answer to reduce the over use of internet or device. Via survey, the researcher found many reasons to become a gadget additive of course how we can reduce the excessive use of Smartphone by practice.
Bio-Inspired Controller Tuning/ Parameter Estimation of an Uncertain MIMO System

Bio-inspired algorithms are powerful intelligent optimization tools capable of handling complex real-world problems by providing optimal solutions (values) of system parameters in various complex engineering and scientific problems such as the complex and highly nonlinear Twin Rotor MIMO System (TRMS) plant. Practically due to the near impossibility of trial and error method in obtaining the optimal gains or parameter values of the controller, it becomes inevitable/essential to employ metaheuristics optimization techniques to intelligently determine these values by allowing the optimization iterative simulation converge to the local or global solutions with ease. In designing a control system for the TRMS in this research, Jumping Spider Optimization Algorithm (JSOA), a new and very recent population based bio-inspired metaheuristics approach is used. It was well able to tune the parameters of Proportional Integral and Derivative (PID) controllers by solving a nonlinear optimization problem using multi-objective functions, subject to several intrinsic constraints or restrictions. Unlike other deterministic algorithms that get caught in a local minimum, the JSOA evolved an optimal/global solution after searching the entire solution search space in a vectorized fashion. The global solution corresponds to the best possible values of the controller parameters within the search space. Compared with several other well-established and widely used controllers and optimization techniques, the statistical results obtained through simulations presented showed that JSOA was able to find the gains of the PID controllers outperforming the other optimization methods well.
Eleven Icebreaking Games Online for Distance Training Realizing ASN Free Learning

Introduction/Main Objective: The Distance Training (PJJ) pattern or blended learning has become a popular training alternative. Changes in training practices by utilizing online platforms are expected to be a means of independence for ASN in education and developing their competencies. Therefore, the instructor must adapt icebreaking, often applied in classical activities, to online icebreaking. So that PJJ can take place in a fun and meaningful way even though it is carried out virtually without involving the physical interaction of the participants. Background Problems: Icebreaking is applied in face-to-face or classical learning, but not all can be used in PJJ or online training. Widyaiswara needs an educative and interactive online icebreaking training reference. Research Method: Literature study. Novelty: This study presents 11 educational and interactive online Icebreaking games. Findings/Results: The eleven icebreaking online create an online learning atmosphere that is educative, interactive, exciting, and fun, thus creating training that is interesting, active, creative, fun, and meaningful. Eleven icebreaking fosters the interest and motivation of the training participants in the training subjects to be studied. Conclusion: Researchers believe that eleven icebreaking is an interactive and educational icebreaker that Widyaiswara can apply in online training. Glad that PJJ makes training fun and exciting, eliminates awkwardness between participants, improves concentration and focus, and creates creative and innovative activity that increases participants' learning motivation.
An Intelligent Assistant for Converting City Requirements to Formal Specification

As more and more monitoring systems have been deployed to smart cities, there comes a higher demand for converting new human-specified requirements to machine-understandable formal specifications automatically. However, these human-specific requirements are often written in English and bring missing, inaccurate, or ambiguous information. In this paper, we present CitySpec, an intelligent assistant system for requirement specification in smart cities. CitySpec not only helps overcome the language differences brought by English requirements and formal specifications, but also offers solutions to those missing, inaccurate, or ambiguous information. The goal of this paper is to demonstrate how CitySpec works. Specifically, we present three demos: (1) interactive completion of requirements in CitySpec; (2) human-in-the-loop correction while CitySepc encounters exceptions; (3) online learning in CitySpec.
Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia

Alzheimer's disease and Frontotemporal dementia are two major types of dementia. Their accurate diagnosis and differentiation is crucial for determining specific intervention and treatment. However, differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms. Therefore, the automatic classification of multiple types of dementia has an important clinical value. So far, this challenge has not been actively explored. Recent development of deep learning in the field of medical image has demonstrated high performance for various classification tasks. In this paper, we propose to take advantage of two types of biomarkers: structure grading and structure atrophy. To this end, we propose first to train a large ensemble of 3D U-Nets to locally discriminate healthy versus dementia anatomical patterns. The result of these models is an interpretable 3D grading map capable of indicating abnormal brain regions. This map can also be exploited in various classification tasks using graph convolutional neural network. Finally, we propose to combine deep grading and atrophy-based classifications to improve dementia type discrimination. The proposed framework showed competitive performance compared to state-of-the-art methods for different tasks of disease detection and differential diagnosis.
Chain-based Discriminative Autoencoders for Speech Recognition

In our previous work, we proposed a discriminative autoencoder (DcAE) for speech recognition. DcAE combines two training schemes into one. First, since DcAE aims to learn encoder-decoder mappings, the squared error between the reconstructed speech and the input speech is minimized. Second, in the code layer, frame-based phonetic embeddings are obtained by minimizing the categorical cross-entropy between ground truth labels and predicted triphone-state scores. DcAE is developed based on the Kaldi toolkit by treating various TDNN models as encoders. In this paper, we further propose three new versions of DcAE. First, a new objective function that considers both categorical cross-entropy and mutual information between ground truth and predicted triphone-state sequences is used. The resulting DcAE is called a chain-based DcAE (c-DcAE). For application to robust speech recognition, we further extend c-DcAE to hierarchical and parallel structures, resulting in hc-DcAE and pc-DcAE. In these two models, both the error between the reconstructed noisy speech and the input noisy speech and the error between the enhanced speech and the reference clean speech are taken into the objective function. Experimental results on the WSJ and Aurora-4 corpora show that our DcAE models outperform baseline systems.
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks

The generative adversarial network (GAN) is successfully applied to study the perceptual single image superresolution (SISR). However, the GAN often tends to generate images with high frequency details being inconsistent with the real ones. Inspired by conventional detail enhancement algorithms, we propose a novel prior knowledge, the detail prior, to assist the GAN in alleviating this problem and restoring more realistic details. The proposed method, named DSRGAN, includes a well designed detail extraction algorithm to capture the most important high frequency information from images. Then, two discriminators are utilized for supervision on image-domain and detail-domain restorations, respectively. The DSRGAN merges the restored detail into the final output via a detail enhancement manner. The special design of DSRGAN takes advantages from both the model-based conventional algorithm and the data-driven deep learning network. Experimental results demonstrate that the DSRGAN outperforms the state-of-the-art SISR methods on perceptual metrics and achieves comparable results in terms of fidelity metrics simultaneously. Following the DSRGAN, it is feasible to incorporate other conventional image processing algorithms into a deep learning network to form a model-based deep SISR.
Multi-View Correlation Distillation for Incremental Object Detection

In real applications, new object classes often emerge after the detection model has been trained on a prepared dataset with fixed classes. Due to the storage burden and the privacy of old data, sometimes it is impractical to train the model from scratch with both old and new data. Fine-tuning the old model with only new data will lead to a well-known phenomenon of catastrophic forgetting, which severely degrades the performance of modern object detectors. In this paper, we propose a novel \textbf{M}ulti-\textbf{V}iew \textbf{C}orrelation \textbf{D}istillation (MVCD) based incremental object detection method, which explores the correlations in the feature space of the two-stage object detector (Faster R-CNN). To better transfer the knowledge learned from the old classes and maintain the ability to learn new classes, we design correlation distillation losses from channel-wise, point-wise and instance-wise views to regularize the learning of the incremental model. A new metric named Stability-Plasticity-mAP is proposed to better evaluate both the stability for old classes and the plasticity for new classes in incremental object detection. The extensive experiments conducted on VOC2007 and COCO demonstrate that MVCD can effectively learn to detect objects of new classes and mitigate the problem of catastrophic forgetting.
Sparsest Univariate Learning Models Under Lipschitz Constraint

Beside the minimization of the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability. Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems. In our first approach, we use the Lipschitz constant as a regularizer, which results in an implicit tuning of the overall robustness of the learned mapping. In our second approach, we control the Lipschitz constant explicitly using a user-defined upper-bound and make use of a sparsity-promoting regularizer to favor simpler (and, hence, more interpretable) solutions. The theoretical study of the latter formulation is motivated in part by its equivalence, which we prove, with the training of a Lipschitz-constrained two-layer univariate neural network with rectified linear unit (ReLU) activations and weight decay. By proving representer theorems, we show that both problems admit global minimizers that are continuous and piecewise-linear (CPWL) functions. Moreover, we propose efficient algorithms that find the sparsest solution of each problem: the CPWL mapping with the least number of linear regions. Finally, we illustrate numerically the outcome of our formulations.
Neural Network Module Decomposition and Recomposition

We propose a modularization method that decomposes a deep neural network (DNN) into small modules from a functionality perspective and recomposes them into a new model for some other task. Decomposed modules are expected to have the advantages of interpretability and verifiability due to their small size. In contrast to existing studies based on reusing models that involve retraining, such as a transfer learning model, the proposed method does not require retraining and has wide applicability as it can be easily combined with existing functional modules. The proposed method extracts modules using weight masks and can be applied to arbitrary DNNs. Unlike existing studies, it requires no assumption about the network architecture. To extract modules, we designed a learning method and a loss function to maximize shared weights among modules. As a result, the extracted modules can be recomposed without a large increase in the size. We demonstrate that the proposed method can decompose and recompose DNNs with high compression ratio and high accuracy and is superior to the existing method through sharing weights between modules.
ROLE OF INVOLVEMENT WITH PROFESSIONAL OCCUPATION OF LIFE-SENSE ORIENTATIONS OF TODAY'S STUDENTS OF PEDAGOGICAL INSTITUTIONS

The article discusses students' personal growth and preparedness for their future professional occupation during their learning in a pedagogical university. The authors address the effects of involvement in professional occupation during their university study on the life-meaning areas. The following methods were used to conduct this study: questionnaire surveys; "Who am I?", a reflective self-assessment test developed by V.S. Mukhina; the life-sense orientation test developed by D.A. Leontiev; the Value Survey by Milton Rokeach; Occupational Motivation Methodology by C. Zamfir updated by A. Rean, and the statistical methods of empirical data processing: Pearson's x2 criterion and cluster analysis. The research covered the students of the pedagogical institute, with 100 students in the 1st through 4th year. The empirical research identified five subject clusters distinguished by their involvement in professional activities and life-sense orientations. The research allows us to view professional involvement during students' higher education periods as a factor of their professional motivation in the context of digital education.