Reviewerly/exHarmony huggingface dataset:
Testing quantum contextuality of binary symplectic polar spaces on a Noisy Intermediate Scale Quantum Computer

The development of Noisy Intermediate Scale Quantum Computers (NISQC) provides for the Quantum Information community new tools to perform quantum experiences from an individual laptop. It facilitates interdisciplinary research in the sense that theoretical descriptions of properties of quantum physics can be translated to experiments easily implementable on a NISCQ. In this note I test large state-independent inequalities for quantum contextuality on finite geometric structures encoding the commutation relations of the generalized N-qubit Pauli group. The bounds predicted by Non-Contextual Hidden Variables theories are strongly violated in all conducted experiences.
A Thought-Gear Model of Theorizing from Literature

The aim of this editorial is to provide a worm’s-eye view of the theorizing process, by which we mean a detailed explanation of the activities and phases involved in theory development based on literature. We develop and explain by example a model of theorizing for developing new theories. The model comprises theory genesis, four thought-gears—namely Informing Thoughts, Generating Thoughts, Materializing Thoughts, and Building Theory Elements from Thoughts—culminating in theory composition. While the theorizing model was developed based on our experience creating grand theory, we believe it can be applied to the development of other forms of theory.
Performance Investigation of digital filters for Image Enhancement

Abstract In poorly lit environmental conditions, the camera does not capture clear images. There are many enhancement processes for low light images, considering all those factors, it is inferred that the main problem that arises even after the enhancement process is the noise. Haze removal method is taken for the enhancement of the image after performing various studies on the various processes. Guided, Median, Gaussian and Weiner filters are deployed for removing the noise which is enhanced during the process in the low light image set. The performance analysis of the filters is carried out to measure the rate of enhancement with metrics such as SNR, PSNR and SSIM. The experimental results here show the performance of various filters taken into consideration and found that guided filter, median filter and wiener filter showed good enhanced results.
Intent Classification based on Deep Learning Language Model in Turkish Dialog Systems

Dialog systems are information retrieval tools that utilize artificial intelligence algorithms to facilitate humanmachine interactions in conversational or written form, deployed in a number of applications, such as conversational search and virtual assistants. Intent Classification, being the first step in dialog systems, models the task of determining the topic of the search or the command to be executed by the virtual assistant as a classification task. In this study, we use an automatic translation tool to adapt the existing English intent classification datasets for Turkish. Using the adapted datasets that belong to various application domains, we fine-tune Turkish, English and multilingual variations of the deep-learning-based BERT model for intent detection, which achieves competitive performance in various Natural Language Processing tasks. We employ Support Vector Machine with bag-of-words modeling and TF-IDF term weighting as a baseline. Experiment results show that deep-learning-based models outperform bag-of-words model and achieve state-of-theart results for Turkish intent classification.
Per una ricostruzione del concetto di bioetica

By means of a critical investigation of the concept of bioethics starting from Potter’s work, the present essay intends to highlight aporias and paradoxes in the dominant construction in the literature of the sector. The line of argumentation aims to point out that the current concept of bioethics is not able to meet the regulatory expectations that have arisen around it. In the pars construens of the essay I will try to trace the way for a reconstruction of the concept of bioethics, bringing it closer to the origins of Fritz Jahr’s speculation and to the traditional notion of ethics, intended as a reflective science rather than a pragmatic discipline.
TurCoins: Turkish Republic Coin Dataset

In this paper, we present a novel and comprehensive dataset which contains Turkish Republic coins minted since 1924 and present a deep learning based system that can automatically classify coins. The proposed dataset consists of 11080 coin images from 138 different classes. To classify coins, we utilize a pre-trained neural network (ResNet50) which is pre-trained on ImageNet. We train the pre-trained neural networks on our dataset by transfer learning. The imbalanced nature of the dataset causes the classifier to show lower performance in classes with fewer samples. To alleviate the imbalance problem, we propose a StyleGAN2-based augmentation method providing realisticfake coins for rare classes. The dataset will be published in http://turcoins.org.
Bengali Context–Question Similarity Using Universal Sentence Encoder

In natural language, the similarity between the two texts is judged by their similarity score. Some of the recent NLP application such as text summarization, question answering, text generation, and text mining are depended on the machine provided text. Accuracy of response text is measured by the similar with corresponding text or human given text. Comparing by two texts and measuring the similarity defines that the two texts are lexical or semantically similar. If two texts are related to each other with the word or character, this text is lexically similar. Also, if the texts are related in meaning but not in word or character level that are semantically similar. In this research, we measure the similarity of context and question for your question answering system. Then we find the most similar answer for the corresponding question. We used universal sentence encoder for embedding and measure the similarity using cosine distance of the text. We used deep averaging network for find the best similar text. For evaluation of similarity model, we calculate the Pearson correlation value for our dataset and achieve 0.41 coefficient.
Are Bayesian neural networks intrinsically good at out-of-distribution detection?

The need to avoid confident predictions on unfamiliar data has sparked interest in out-of-distribution (OOD) detection. It is widely assumed that Bayesian neural networks (BNN) are well suited for this task, as the endowed epistemic uncertainty should lead to disagreement in predictions on outliers. In this paper, we question this assumption and provide empirical evidence that proper Bayesian inference with common neural network architectures does not necessarily lead to good OOD detection. To circumvent the use of approximate inference, we start by studying the infinite-width case, where Bayesian inference can be exact considering the corresponding Gaussian process. Strikingly, the kernels induced under common architectural choices lead to uncertainties that do not reflect the underlying data generating process and are therefore unsuited for OOD detection. Finally, we study finite-width networks using HMC, and observe OOD behavior that is consistent with the infinite-width case. Overall, our study discloses fundamental problems when naively using BNNs for OOD detection and opens interesting avenues for future research.
Graph Reparametrizations for Enabling 1000+ Monte Carlo Iterations In Bayesian Deep Neural Networks

The development of Variational Inference (VI) and Bayesian Deep Neural Networks has increasingly led to the uncertainty estimation in deep models, critical to many real-world applications. Recent evidence suggests that existing solutions dependent on simple Gaussian formulations may not be sufficient. However, moving to other distributions necessitates Monte Carlo (MC) sampling to estimate statistical values such as the KL divergence: ultimately expensive and scaling poorly as the dimensions of both the input data and the model grow. This is directly related to the structure of the computation graph, which can grow linearly as a function of the number of MC samples needed. Here, we construct a framework to describe these computation graphs, and identify probability families where the graph size can be independent or only weakly dependent on the number of MC samples. These nice probability families correspond directly to large classes of distributions common in practice. Empirically, we find that we can run a much larger number of iterations for MC approximations for larger architectures widely used in vision with gains in performance measured in confident accuracy, stability of training, memory and training time.
A Complex-valued Encoding Seeker Optimization Algorithm for Constrained Engineering Problems

This article comes up with a complex-valued encoding seeker optimization algorithm (CSOA) base on the multi-chain method for the constrained engineering optimization problems. The complex value encoding and a multi-link strategy are leaded by the seeker optimization algorithm (SOA). The complex value encoding method is an influential global optimization strategy, and the multi-link is an enhanced local search strategy. These strategies enhance the individuals’ diversity and avert fall into the local optimum. This article chose fifteen benchmark functions, four PID control parameter models, and six constrained engineering problems to test. According to the experimental results, the CSOA algorithm can be used in the benchmark functions, PID control parameters optimization, and optimization constrained engineering problems. Compared to particle swarm optimization (PSO), simulated annealing base on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of CSOA are better.
A Survey on Cloud Resources Allocation Using Multi-agent System

Cloud computing has become a new generation of technology with greater potential in markets and companies. It involves providing resources as a service in the marketplace, it can be accessed and consumed in a transparent and flexible manner. Several cloud customers need a range of services according to their dynamically evolving needs. It is therefore the task of the cloud computing framework to facilitate the provision of services required for its users. One of the main challenges is to improve resource allocation process. This is a challenging task due to their growth in size, power consumption, and complexity. The goal of this paper is to undertake a study on the resource allocation of cloud using multi-agent systems which have relatively not studied intensively. Cloud Computing's features include advanced operational characteristics for structured multi-agent systems. In essence, the integration of multi-agent system into the core of the Cloud allows the convergence of various functionality to be improved.
Fault Detection of A Class of Nonlinear Uncertain Parabolic PDE Systems

This paper investigates the fault detection (FD) problem of a class of uncertain distributed parameter systems modeled by nonlinear parabolic partial differential equations (PDEs). A novel FD scheme is proposed with a neural network-based adaptive dynamics learning approach. Specifically, based on the Galerkin method, a finite dimensional ordinary differential equation (ODE) system is first derived to capture the dominant dynamics of the PDE system. An adaptive dynamics learning approach using radial basis function neural networks (RBF NN) is then developed to achieve locally accurate identification of the associated uncertain system dynamics. The learned knowledge can be obtained and stored in a bank of constant RBF NN models, which can be used to further construct a bank of FD estimators. A threshold is finally designed for real-time FD decision making. One important feature of the proposed FD scheme is that the spatio-temporal uncertain dominant dynamics of the parabolic PDE system can be locally accurately identified, such that occurring faults can be effectively distinguished from system uncertainties for accurate FD. Extensive simulation studies are conducted to verify the effectiveness and advantages of the proposed FD scheme.
Unsupervised Discovery of Fingerspelled Letters in Sign Language Videos

Automatic discovery and indexing in sign language videos would provide many valuable services such as search and retrieval in deaf social and news media which consists of sign language videos often with no associated texts available. As some of the context words in sign language conversations are fingerspelled, the discovery of fingerspelled letters plays a significant role. In this study, k-means clustering and Gaussian Mixture Model methods are used for the unsupervised discovery of fingerspelled letters. The clusers which mostly contain hand shapes corresponding to transitions between letters are eliminated based on their distortion level and Bayes information criterion values. The performance of the clustering methods are evaluated with the help of Adjusted Mutual Information metric. According to the results obtained with this metric, using a Gaussian Mixture Model for clustering yields better results than k-means clustering.
A Study on Automatic Machine Translation Tools: A Comparative Error Analysis Between DeepL and Yandex for Russian-Italian Medical Translation

The present research is aimed at conducting a study with regard to Russian-Italian medical translation on the current development of two Machine Translation tools that feature prominently in today’s Neural Machine Translation framework, namely DeepL and Yandex. For the purpose of our research, we have selected a number of Russian medical articles: three highly specialized and three popular-science articles concerning coronavirus pandemic. Such a choice is justified by the willingness not only to analyse recently published scientific documents but also to investigate the particular linguistic implications of 2020’s coronavirus pandemic outbreak. In fact, during the period of pandemic a set of terms has been introduced and coined in  every-day communication and entered the boundaries of scientific terminology. We have considered this existing linguistic phenomenon as a proper condition to test the performances of Machine Translation tools. In particular, we discuss the most relevant features of the comparative error analysis as well as the BLEU metric for both DeepL and Yandex.
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them.

Making classifiers robust to adversarial examples is hard. Thus, many defenses tackle the seemingly easier task of detecting perturbed inputs. We show a barrier towards this goal. We prove a general hardness reduction between detection and classification of adversarial examples: given a robust detector for attacks at distance {\epsilon} (in some metric), we can build a similarly robust (but inefficient) classifier for attacks at distance {\epsilon}/2. Our reduction is computationally inefficient, and thus cannot be used to build practical classifiers. Instead, it is a useful sanity check to test whether empirical detection results imply something much stronger than the authors presumably anticipated. To illustrate, we revisit 13 detector defenses. For 11/13 cases, we show that the claimed detection results would imply an inefficient classifier with robustness far beyond the state-of-the-art.
A Smarter Way to Improvise Learning Outcome based on Computer Application

In any educational quality improvement endeavor, measurement plays a major role since it provides the information for any decision making mechanism. There is always a need to devise a standard teaching model which can be used as a benchmark to evaluate the quality of education imparted. Based on the output, preventive as well as corrective line of action can be taken, if required. The present research deals with a standard prototype application developed for a leading academic institution. It has served as a basis of a simple, user-friendly interface to manage the entire training cycle with excellent results. The project has been tested at a user level. The project has been developed in a less known relational database called FileMaker which is a product of Apple Company. The current project which has been named as EduGain version 1.0 which be demonstrated practically during any research assignment.
How to limit capacity of timing covert channel by adding extra delays

Covert channel is a communication channel that was not designed for information transmission. It is proven that in some circumstances the attacker has an ability to build a covert channel that cannot be detected. That is why the method of capacity limitation of a potential covert channel seems to be an effective counteraction that can be used when allowable level of covert channel capacity is set up. To use such method, it is needed to estimate residual covert channel capacity. Random delays before packet sending are considered as a tool of noise insertion into a covert channel. In this paper authors investigated the way to estimate covert channel capacity taking into account network load conditions. The main topics of the research are cases when time intervals between packet sending and packet receiving in network comply with normal and exponential distributions.
Detecting Local Insights from Global Labels: Supervised and Zero-Shot Sequence Labeling via a Convolutional Decomposition

Abstract We propose a new, more actionable view of neural network interpretability and data analysis by leveraging the remarkable matching effectiveness of representations derived from deep networks, guided by an approach for class-conditional feature detection. The decomposition of the filter-n-gram interactions of a convolutional neural network (CNN) and a linear layer over a pre-trained deep network yields a strong binary sequence labeler, with flexibility in producing predictions at—and defining loss functions for—varying label granularities, from the fully supervised sequence labeling setting to the challenging zero-shot sequence labeling setting, in which we seek token-level predictions but only have document-level labels for training. From this sequence-labeling layer we derive dense representations of the input that can then be matched to instances from training, or a support set with known labels. Such introspection with inference-time decision rules provides a means, in some settings, of making local updates to the model by altering the labels or instances in the support set without re-training the full model. Finally, we construct a particular K-nearest neighbors (K-NN) model from matched exemplar representations that approximates the original model’s predictions and is at least as effective a predictor with respect to the ground-truth labels. This additionally yields interpretable heuristics at the token level for determining when predictions are less likely to be reliable, and for screening input dissimilar to the support set. In effect, we show that we can transform the deep network into a simple weighting over exemplars and associated labels, yielding an introspectable—and modestly updatable—version of the original model.
Authentic Leadership and Work Engagement: The Mediatory Role of Employees’ Trust and Occupational Self-Efficacy

Purpose: The aim of the study is to find the impact of Authentic Leadership on Employee work engagement and the mediating role of employee trust and occupational self-efficacy. Methodology: The sample size of this study was 200 employees from different organizations of Pakistan and the quantitative method was used which includes questionnaires with close-ended questions. Smart PLS software was used to measure the relationship among variables and different techniques for the analysis were adopted such as SEM Path Analysis and Confirmatory factor analysis. Findings: The results indicate a strong relationship among variables and supported that there is a positive influence of Authentic leadership on employee work engagement along with the mediating effects of employee trust and occupational self-efficacy. Conclusions: This research finds a strong and positive relationship among the variables and highlights the vital role that authentic leaders play in motivating employees to bring positive change in employee’s attitudes and behavior. The conclusions drawn will help to develop strategies so that the leaders may develop positive traits and workers may perform effectively and efficiently to enhance the productivity of an organization.
An Interactive Mixed Reality Platform for Inquiry-Based Education

Mixed reality technology presents new opportunities for educational applications through its combination of real and virtual worlds. Recently, a growing interest has been noted in inquiry-based learning that emphasizes active learning and personalized education. Accordingly, this paper proposed a mixed reality-based interactive media platform for an inquiry-based learning environment in which the users were able to explore a wide range of optional experiments under the instruction of the inspectors. A sequence of mixed reality technologies are combined in this environment. Users became immersed in an interactive media space, while the environment responded with the user via interactive displays fitted on the experimental utilities and furniture. User behavior was instantly recorded and interactively visualized for the inspector to make decisions on the instructions. A prototype was then designed for this environment, and an evaluation was conducted to test its applicability. Overall, the participants revealed that the 3D printing experimental utilities were effective in enhancing their sense of reality, while the virtual interactive 3D scene played a key role in extending the inquiry base. Furthermore, it enabled users to have more freedom in conducting exploration compared to traditional experimental environments.
Cloud Computing in Bangladeshi Higher Educational Institutions: Influential Factors and Adoption Model

In recent years, Cloud Computing (CC) is as one of the most relevant and significant research trends in Information technology (IT). This paper aims to identify the factors, benefits, and challenges while adopting CC in Higher Educational Institutions (HEIs) in Bangladesh. Many HEIs are shifting from traditional IT services to Clouds because it makes the academic process more efficient and effective. Cloud Computing offers huge benefits to HEIs such as boosts collaborative research activities, storage and backs up data, improves filling and 24/7 accessible facilities from remote locations, provides latest software and applications for financial and HR management, online learning management system, improves accountability and various services with minimum start-up cost. Besides the utmost benefits it offers for the HEIs, increasing financial pressure forces them to consider adopting CC because it reduces cost and pay-per-use pricing model. Globally researchers are showing interest in the adoption of CC in HEIs sector, but factors are still unexplored for Bangladeshi HEIs context. As a result, to fill this gap is required to identify influential factors, benefits and what challenges Bangladeshi HEIs face while adopting CC. Therefore, TAM and TOE theory is used to develop a conceptual model to identify influential factors for Bangladeshi HEIs.