Reviewerly/exHarmony huggingface dataset:
Developing a Rule-based Weighted Expert System to Choose Qualified Instructor in the University

Each head of university faculties should choose some qualified instructors for respected courses in each semester. In this respect, some factors are considered such as teaching experience, education evidences, qualify, etc. This task usually is done now by expert humans as head faculty which is time consumed. Different semi-automatic systems have been proposed to help heads so far. A full automatic rule-based expert system is developed in this paper. The proposed expert system consist three main stages. Firstly, the knowledge of the human experts are imported and designed as decision trees. In the second stage, an expert system is designed based provided rules of produced decision trees. In the third stage an algorithm is proposed to weight the tree results based on expert's qualities. To improve the performance of the expert system, a majority voting algorithm is prepared as post process stage to select the qualified instructor, who satisfied more expert's decision tree for each course. The quality of the proposed expert system is evaluated using real data from universities of Iran. The computed accuracy rate is 85.55, which is shown the robustness and accurately of the proposed system. The proposed system has low computation complexity in comparison with efficient related works. Also, simple implementation and clear box are other properties of the proposed system
About Philosophy today

The article is devoted to modern problems of philosophy, and first of all, the question of determining its relevance and scientificity. The urgency of this issue follows from the scientifico-technical and worldview changes that have engulfed the last decade of the 21st century. In this regard, the relationship between philosophy and science and their influence on each other is considered. The author intends to show that philosophy not only gives impetus and direction to scientific research, but also helps to understand what to do with them. At the same time, the purpose of the analysis is to form the reader’s idea of philosophy not just as a way of knowing and/or spiritual mastering the world, but also as an art — the art of looking at oneself and the world around critically, the desire to understand one's reality and perceive the reality of other people and act in accordance with it. The article also discusses the continental and analytical types of philosophy, while the continental philosophy is defined by the author as more common in the countries of the post-Soviet space. The author comes to the conclusion that the constructive interaction of continental and analytical philosophy, scientists and sensualists leads to a stronger philosophical academy, and, perhaps, to a more adequate attitude of science to philosophy. In connection with the rapid development of neuroscience, the role of epistemology in philosophy is also analyzed. It is argued that epistemology, as well as other areas of knowledge in the new information society, must be rethought and redesigned.
Is the media a means of knowing the world or manipulating consciousness?

The article highlights some aspects of the influence of the media on the human subconscious. It has been found that today the world community is moving from direct knowledge of the world around it to knowledge of the world — mediated by the media — the virtual world, which is increasingly pretended to be real.&#x0D; It is noted that today the main means of profit and struggle for power is mass disinformation, including through the media. Inequality in access to information and misinformation are the basis of market and power-democratic speculation.&#x0D; The dangers associated with the activities of the media are considered. In particular, raising the information threshold of perception in an aggressive information environment can be used for explicit and implicit methods of manipulation; means of communication contribute to the transformation of people into information addicts; latent neurolinguistic programming is widely used, etc.&#x0D; In the context of the influence of the media on the consciousness and psyche of man, the peculiarities of his inner world and the dynamics of self-consciousness are analyzed.&#x0D; The negative consequences of the mass media influence on users and possible ways of its neutralization are analyzed. It was emphasized that the example of foreign countries with negative manifestations and influences of domestic media should be combated by improving relevant laws, creating public oversight councils in the media, strengthening the requirements of public organizations and movements to the legislative, executive and judicial systems.
Lapbook and lapbooking - the alternative of digital aids and technologies of teaching foreign languages and cultures?

In this review lapbook and lapbookingare considered as modern aid and technology of teachingforeign language. The experience of using them by foreign language teachers and lecturers on different levelshas been assessed. Some advantages and disadvantages of lapbook and lapbooking are emphasized. It has beenconcluded that they are relevant in integration with other technologies of teaching foreign languages.
The Positive Influence and Suggestion of Activity Curriculum on Children

Physical activity lesson (also known as physical activity practice lesson) is a kind of education method which combines physical activity with learning content.It has been considered a promising education method to increase school sports activities without reducing education time.In early childhood education, arranging physical activity lessons in children's curriculum positively impacts children's physical and mental development.By consulting the relevant literature at home and abroad, this paper introduces and analyzes the influence of physical activity lessons on children's physical activity and the impact on preschool education and health.This paper presents the application of specific physical activity lessons and their advantages and limitations.This research and analysis promote the development of children's physical activity lessons to provide a reference.
New Approximation Algorithms for Forest Closeness Centrality -- for Individual Vertices and Vertex Groups

The emergence of massive graph data sets requires fast mining algorithms. Centrality measures to identify important vertices belong to the most popular analysis methods in graph mining. A measure that is gaining attention is forest closeness centrality; it is closely related to electrical measures using current flow but can also handle disconnected graphs. Recently, [Jin et al., ICDM'19] proposed an algorithm to approximate this measure probabilistically. Their algorithm processes small inputs quickly, but does not scale well beyond hundreds of thousands of vertices. In this paper, we first propose a different approximation algorithm; it is up to two orders of magnitude faster and more accurate in practice. Our method exploits the strong connection between uniform spanning trees and forest distances by adapting and extending recent approximation algorithms for related single-vertex problems. This results in a nearly-linear time algorithm with an absolute probabilistic error guarantee. In addition, we are the first to consider the problem of finding an optimal group of vertices w.r.t. forest closeness. We prove that this latter problem is NP-hard; to approximate it, we adapt a greedy algorithm by [Li et al., WWW'19], which is based on (partial) matrix inversion. Moreover, our experiments show that on disconnected graphs, group forest closeness outperforms existing centrality measures in the context of semi-supervised vertex classification.
CARE: Lightweight Attack Resilient Secure Boot Architecturewith Onboard Recovery for RISC-V based SOC

Recent technological advancements have proliferated the use of small embedded devices for collecting, processing, and transferring the security-critical information. The Internet of Things (IoT) has enabled remote access and control of these network-connected devices. Consequently, an attacker can exploit security vulnerabilities and compromise these devices. In this context, the secure boot becomes a useful security mechanism to verify the integrity and authenticity of the software state of the devices. However, the current secure boot schemes focus on detecting the presence of potential malware on the device but not on disinfecting and restoring the soft-ware to a benign state. This manuscript presents CARE- the first secure boot framework that provides detection, resilience, and onboard recovery mechanism for the com-promised devices. The framework uses a prototype hybrid CARE: Code Authentication and Resilience Engine to verify the software state and restore it to a benign state. It uses Physical Memory Protection (PMP) and other security enchaining techniques of RISC-V processor to pro-vide resilience from modern attacks. The state-of-the-art comparison and performance analysis results indicate that the proposed secure boot framework provides a promising resilience and recovery mechanism with very little 8 % performance and resource overhead
Multi-point dimensionality reduction to improve projection layout reliability

In ordinary Dimensionality Reduction (DR), each data instance in a high dimensional space (original space), or on a distance matrix denoting original space distances, is mapped to (projected onto) one point in a low dimensional space (visual space), building a layout of projected points trying to preserve as much as possible some property of data such as distances, neighbourhood relationships, and/or topology structures, with the ultimate goal of approximating semantic properties of data with preserved geometric properties or topology structures in visual space. In this paper, the concept of Multi-point Dimensionality Reduction is elaborated on where each data instance can be mapped to (projected onto) possibly more than one point in visual space by providing the first general solution (algorithm) for it as a move in the direction of improving reliablity, usability and interpretability of dimensionality reduction. Furthermore by allowing the points in visual space to be split into two layers while maintaining the possibility of having more than one projection (mapping) per data instance , the benefit of separating more reliable points from less reliable points is dicussed notwithstanding the effort to improve less reliable points. The proposed solution (algorithm) in this paper, named Layered Vertex Splitting Data Embedding (LVSDE), is built upon and extends a combination of ordinary DR and graph drawing techniques. Based on the experiments of this paper on some data sets, the particular proposed algorithm (LVSDE) practically outperforms popular ordinary DR methods visually (semantics, group separation, subgroup detection or combinational group detection) in a way that is easily explainable.
Needmining: Designing Digital Support to Elicit Needs from Social Media

Today's businesses face a high pressure to innovate in order to succeed in highly competitive markets. Successful innovations, though, typically require the identification and analysis of customer needs. While traditional, established need elicitation methods are time-proven and have demonstrated their capabilities to deliver valuable insights, they lack automation and scalability and, thus, are expensive and time-consuming. In this article, we propose an approach to automatically identify and quantify customer needs by utilizing a novel data source: Users voluntarily and publicly expose information about themselves via social media, as for instance Facebook or Twitter. These posts may contain valuable information about the needs, wants, and demands of their authors. We apply a Design Science Research (DSR) methodology to add design knowledge and artifacts for the digitalization of innovation processes, in particular to provide digital support for the elicitation of customer needs. We want to investigate whether automated, speedy, and scalable need elicitation from social media is feasible. We concentrate on Twitter as a data source and on e-mobility as an application domain. In a first design cycle we conceive, implement and evaluate a method to demonstrate the feasibility of identifying those social media posts that actually express customer needs. In a second cycle, we build on this artifact to additionally quantify the need information elicited, and prove its feasibility. Third, we integrate both developed methods into an end-user software artifact and test usability in an industrial use case. Thus, we add new methods for need elicitation to the body of knowledge, and introduce concrete tooling for innovation management in practice.
Adaptive Private Distributed Matrix Multiplication

We consider the problem of designing codes with flexible rate (referred to as rateless codes), for private distributed matrix-matrix multiplication. A master server owns two private matrices $\mathbf{A}$ and $\mathbf{B}$ and hires worker nodes to help computing their multiplication. The matrices should remain information-theoretically private from the workers. Codes with fixed rate require the master to assign tasks to the workers and then wait for a predetermined number of workers to finish their assigned tasks. The size of the tasks, hence the rate of the scheme, depends on the number of workers that the master waits for. We design a rateless private matrix-matrix multiplication scheme, called RPM3. In contrast to fixed-rate schemes, our scheme fixes the size of the tasks and allows the master to send multiple tasks to the workers. The master keeps sending tasks and receiving results until it can decode the multiplication; rendering the scheme flexible and adaptive to heterogeneous environments. Despite resulting in a smaller rate than known straggler-tolerant schemes, RPM3 provides a smaller mean waiting time of the master by leveraging the heterogeneity of the workers. The waiting time is studied under two different models for the workers' service time. We provide upper bounds for the mean waiting time under both models. In addition, we provide lower bounds on the mean waiting time under the worker-dependent fixed service time model.
Hostility Detection in Hindi leveraging Pre-Trained Language Models

Hostile content on social platforms is ever increasing. This has led to the need for proper detection of hostile posts so that appropriate action can be taken to tackle them. Though a lot of work has been done recently in the English Language to solve the problem of hostile content online, similar works in Indian Languages are quite hard to find. This paper presents a transfer learning based approach to classify social media (i.e Twitter, Facebook, etc.) posts in Hindi Devanagari script as Hostile or Non-Hostile. Hostile posts are further analyzed to determine if they are Hateful, Fake, Defamation, and Offensive. This paper harnesses attention based pre-trained models fine-tuned on Hindi data with Hostile-Non hostile task as Auxiliary and fusing its features for further sub-tasks classification. Through this approach, we establish a robust and consistent model without any ensembling or complex pre-processing. We have presented the results from our approach in CONSTRAINT-2021 Shared Task on hostile post detection where our model performs extremely well with 3rd runner up in terms of Weighted Fine-Grained F1 Score.
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms

Estimating conditional mutual information (CMI) is an essential yet challenging step in many machine learning and data mining tasks. Estimating CMI from data that contains both discrete and continuous variables, or even discrete-continuous mixture variables, is a particularly hard problem. In this paper, we show that CMI for such mixture variables, defined based on the Radon-Nikodym derivate, can be written as a sum of entropies, just like CMI for purely discrete or continuous data. Further, we show that CMI can be consistently estimated for discrete-continuous mixture variables by learning an adaptive histogram model. In practice, we estimate such a model by iteratively discretizing the continuous data points in the mixture variables. To evaluate the performance of our estimator, we benchmark it against state-of-the-art CMI estimators as well as evaluate it in a causal discovery setting.
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which rarely exploit this structure explicitly. Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states. PSM assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. We also present a contrastive representation learning procedure to embed any state similarity metric, which we instantiate with PSM to obtain policy similarity embeddings (PSEs). We demonstrate that PSEs improve generalization on diverse benchmarks, including LQR with spurious correlations, a jumping task from pixels, and Distracting DM Control Suite.
Machine-Assisted Script Curation

We describe Machine-Aided Script Curator (MASC), a system for human-machine collaborative script authoring. Scripts produced with MASC include (1) English descriptions of sub-events that comprise a larger, complex event; (2) event types for each of those events; (3) a record of entities expected to participate in multiple sub-events; and (4) temporal sequencing between the sub-events. MASC automates portions of the script creation process with suggestions for event types, links to Wikidata, and sub-events that may have been forgotten. We illustrate how these automations are useful to the script writer with a few case-study scripts.
Improving Run Length Encoding by Preprocessing

The Run Length Encoding (RLE) compression method is a long standing simple lossless compression scheme which is easy to implement and achieves a good compression on input data which contains repeating consecutive symbols. In its pure form RLE is not applicable on natural text or other input data with short sequences of identical symbols. We present a combination of preprocessing steps that turn arbitrary input data in a byte-wise encoding into a bit-string which is highly suitable for RLE compression. The main idea is to first read all most significant bits of the input byte-string, followed by the second most significant bit, and so on. We combine this approach by a dynamic byte remapping as well as a Burrows-Wheeler-Scott transform on a byte level. Finally, we apply a Huffman Encoding on the output of the bit-wise RLE encoding to allow for more dynamic lengths of code words encoding runs of the RLE. With our technique we can achieve a lossless average compression which is better than the standard RLE compression by a factor of 8 on average.
Strong polygamy of multi-party $q$-expected quantum correlations

We show that the polygamous nature of multi-party quantum correlations can be characterized in a {\em stronger} form based on Tsallis $q$-entropy and $q$-expectation value. By considering the amount of entanglement that can be distributed in multi-party systems, we establish a class of strong polygamy inequalities of multi-party entanglement in terms of Tsallis $q$-entropy and $q$-expectation for $q \geq 1$. Our new class of inequalities is in fact tighter than the usual polygamy inequalities of multi-party entanglement, and the tightness is explicitly illustrated by an example. Moreover, our new class of inequalities is concerned with the $q$-expected entanglement distributed between a single party and any possible subsets of the rest parties whereas the usual polygamy inequality only considers the entanglement between a single party and another. We further establish the equivalence between strong polygamy inequalities of quantum entanglement and quantum discord distributed in multi-party quantum systems.
End-to-End Speaker Height and age estimation using Attention Mechanism with LSTM-RNN

Automatic height and age estimation of speakers using acoustic features is widely used for the purpose of human-computer interaction, forensics, etc. In this work, we propose a novel approach of using attention mechanism to build an end-to-end architecture for height and age estimation. The attention mechanism is combined with Long Short-Term Memory(LSTM) encoder which is able to capture long-term dependencies in the input acoustic features. We modify the conventionally used Attention -- which calculates context vectors the sum of attention only across timeframes -- by introducing a modified context vector which takes into account total attention across encoder units as well, giving us a new cross-attention mechanism. Apart from this, we also investigate a multi-task learning approach for jointly estimating speaker height and age. We train and test our model on the TIMIT corpus. Our model outperforms several approaches in the literature. We achieve a root mean square error (RMSE) of 6.92cm and6.34cm for male and female heights respectively and RMSE of 7.85years and 8.75years for male and females ages respectively. By tracking the attention weights allocated to different phones, we find that Vowel phones are most important whistlestop phones are least important for the estimation task.
$C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal  Recommendation in the Post-GDPR Era

Mobile devices enable users to retrieve information at any time and any place. Considering the occasional requirements and fragmentation usage pattern of mobile users, temporal recommendation techniques are proposed to improve the efficiency of information retrieval on mobile devices by means of accurately recommending items via learning temporal interests with short-term user interaction behaviors. However, the enforcement of privacy-preserving laws and regulations, such as GDPR, may overshadow the successful practice of temporal recommendation. The reason is that state-of-the-art recommendation systems require to gather and process the user data in centralized servers but the interaction behaviors data used for temporal recommendation are usually non-transactional data that are not allowed to gather without the explicit permission of users according to GDPR. As a result, if users do not permit services to gather their interaction behaviors data, the temporal recommendation fails to work. To realize the temporal recommendation in the post-GDPR era, this paper proposes $C^3DRec$, a cloud-client cooperative deep learning framework of mining interaction behaviors for recommendation while preserving user privacy. $C^3DRec$ constructs a global recommendation model on centralized servers using data collected before GDPR and fine-tunes the model directly on individual local devices using data collected after GDPR. We design two modes to accomplish the recommendation, i.e. pull mode where candidate items are pulled down onto the devices and fed into the local model to get recommended items, and push mode where the output of the local model is pushed onto the server and combined with candidate items to get recommended ones. Evaluation results show that $C^3DRec$ achieves comparable recommendation accuracy to the centralized approaches, with minimal privacy concern.