Reviewerly/exHarmony huggingface dataset:
Effects of the COVID-19 Pandemic on E-learning Student’s Dropout Levels During Cybersecurity Programs: A Case Study

As the COVID-19 pandemic shakes the human society, most fields had to adapt to online systems in order to keep operating. Such is the case of the Education field, where e-learning became the main (and sometimes the only) tool available to ensure that teaching activities could continue. This work analyzes the case of a cybersecurity program taught online in Chile from 2019 to this day. The available data allowed us to observe some of the impacts that the pandemic had on the student’s dropout levels, highlighting some difference between the early months of the pandemic, versus later periods in time.
The novel image encryption scheme based on three- dimentional coupled chaotic system

Abstract In order to improve the complexity of chaotic mapping and the security of image encryption algorithms, this paper proposes a new coupled three-dimensional chaotic system based on sine mapping and lorenz mapping. Based on this chaotic system, a new color image encryption algorithm is proposed, which performs index position scrambling and Arnold scrambling on the bit plane of the plaintext image, and then performs an XOR diffusion operation on the scrambled ciphertext to obtain the final ciphertext image. Simulation experiments show that the algorithm has a large key space, a small image correlation coefficient, an information entropy value of 7.9921, and a good encryption effect.
An Evaluation of Foot Rowing Type Wheelchair for Elderly People by Using Questionnaire with Experiments

Providing useful and affordable mobility is one of the most important points for ideal aging society. Especially, mobility device for elderly, disabled and handicapped people should be provided. Current type of wheelchair is useful for those who can use their arms and hands. But, if their arms and hands are disabled or difficult to move, current type of wheelchair is not useful. In order to solve the problem, we developed the new type of wheelchair. The feature of the developed prototype wheelchair is foot rowing. We have developed the prototype considering elderly people and finally performed experiments for evaluation. In the experiments, the differences were evaluated between the developed wheelchairs and ordinary type of wheelchair, which is turned by user’s hands. The contents of questionnaire are ease of movement, comfortability of riding and getting on and off, each movement including forward, backward, braking, right and left turning, traveling on bump road, slope and slalom. Also, comments about every content and indoor and outdoor locations to use this wheelchair were obtained. From the experimental results, interesting points were found and these points are valuable for the future developing.
Using LIWC to Analyze Participants' Psychological Processing in Accounting JDM Research

SUMMARY This paper provides methodological guidance for judgment and decision-making (JDM) researchers in accounting who are interested in using the Linguistic Inquiry and Word Count (LIWC) text analysis program to analyze research participants' written responses to open-ended questions. We discuss how LIWC's measures of psychological constructs were developed and validated in psycholinguistic research. We then use data from an audit JDM study to illustrate the use of LIWC to guide researchers in identifying suitable measures, performing quality control procedures, and reporting the analysis. We also discuss research design considerations that will strengthen the inferences drawn from LIWC analysis. The paper concludes with examples where LIWC analysis has the potential to reveal participants' deep, complex, effortful psychological processing and affective states from their written responses.
Optimal Trajectory Generation of UAV Considering Attitude Constraints

The six degrees of freedom (6-DOF) trajectory planning of quadrotors with attitude constraints is studied. Based on active disturbance rejection control (ADRC) technology, a controller for quadrotors that can track both position and attitude is designed. Firstly, the nonlinear dynamic model of quadrotors is established. Next, the optimal trajectory with attitude constraints is solved under different performance indexes. Finally, based on ADRC method, a 6-DOF tracking control algorithm for quadrotors is designed. The control effect is simulated and compared with the traditional linear control method. The results indicate that ADRC method has superior control accuracy and stability for quadrotors with large attitude maneuvers.
Inferring Human Beliefs and Desires from their Actions and the Content of their Utterances

To create dialogue systems that provide information a user needs to know at an opportune moment, it is important to infer the user’s mental states such as his/her beliefs and desires. There are two types of study on inferring beliefs and desires: one type infers them from actions and the other infers them from the content of utterances. However, a method to infer beliefs and desires from both kinds of inference in an integrated way has not yet been established. In this paper, we propose Multimodal Inference of Mind Simultaneous Contextualization and Interpreting (MIoM SCAIN), a system for sequentially inferring users’ beliefs and desires on the basis of their walking behaviors and the content of their utterances. In our evaluation, we compared inferences of MIoM SCAIN with those of baselines that use either walking behaviors or the content of utterances. MIoM SCAIN’s predictions showed more correlation with subjective judgements compared with the baselines, indicating that the inference of beliefs and desires from both walking behaviors and utterance content is possible.
Flexible Product Innovation Cycles for Smart Medical Devices

The development of medical devices currently faces several challenges, like the urgent need for new products in a pandemic situation and the release of a new regulatory framework for medical devices in the European Union, which shall ensure the safety and effectiveness of newly developed medical devices. At the same time, the desire for smart, user-friendly products is growing. These can be realized through agile and flexible process models. Based on agile values and the integration of customer feedback, this need can be achieved in iterative development cycles. What at first sight does not seem to be combinable, however, be well integrated into the regulated area of medical technology through the targeted use of agile processes and the adaptation of individual steps. This paper presents a framework for the integration of Scrum into the regulated design control process for medical devices. This framework facilitates flexible product development of smart medical devices.
CSD-CMAD

Traditionally, clustering of multivariate data aims at grouping objects described with multiple heterogeneous attributes based on a suitable similarity (conversely, distance) function. One of the main challenges is due to the fact that it is not straightforward to directly apply mathematical operations (e.g., sum, average) to the feature values, as they stem from heterogeneous contexts.
ОСОБЕННОСТИ ПРИМЕНЕНИЯ АРТ-ПЕДАГОГИЧЕСКИХ ПРАКТИК В ПОДГОТОВКЕ СТУДЕНТОВ К ПРЕПОДAВАНИЮ ФРАНЦУЗСКОГО ЯЗЫКА / PECULIARITIES OF THE USE OF ARTPEDAGOGICAL PRACTICES IN PREPARING THE STUDENTS TO TEACH FRENCH

The article is devoted to the disclosure of the term "art-pedagogy" and the peculiarities of the use of art-pedagogical practices in teaching French as a foreign language. Recently, more and more non-traditional means in the upbringing of a personality have begun to be used. Art is a way of expressing emotions and thoughts. Human speech activity is directly related to the people’s ability to control their emotions, determine the context of communication and play their role, that is, correctly formulate their discourse. Since the target audience is students, future teachers of the French language, the use of art pedagogical practices gives them the opportunity to practice the language in real conditions, get used to the role and develop their personality.
Action Recognition with Spatiotemporal Analysis and Support Vector Machine for Elderly Monitoring System

Nowadays, action recognition is widely used in various aspects of life, one of which is for monitoring purposes. One of the challenges in this problem is the process of detecting and recognizing the actions of the elderly. In this research, an integrated system is proposed to detect and monitor the actions of the elderly based on Computer Vision. This system using joint skeleton coordinate data as a system input. The system can detect actions taken by the elderly based on the dataset we created. There are seven actions: standing, sitting, walking, falling, drinking, sleeping, and eating. This system uses the OpenPose library to detect skeletons from the elderly. Then for feature extraction using spatiotemporal to extract the joint position data of the elderly every 15 frames. For classification using the Support Vector Machine in this system. Then, the recognized action data will be sent by the system to the backend of the platform. The test shows that the system can detect seven actions of the elderly with an accuracy value of 87.81% on the training model used.
Batch Weighted Nuclear-Norm Minimization for Medical Image Sequence Segmentation

Recently, convolutional neural networks have shown their superior performance for biomedical image sequence segmentation. Most of the current state-of-the-art segmentation methods are designed for segmentation in static frames. However, misleading or missing features in static images may severely degrade segmentation performance. Effective incorporation of shape priors can alleviate this issue, which has been under-explored for deep models in previous attempts. In this paper, we explore the use of the continuity-based prior, either in temporal or spatial, with the aim of improving the robustness of the medical image sequence segmentation. Specifically, we firstly propose a nuclear norm minimization (NNM) based regularizer. However, nuclear norm tends to over-shrink the rank components, and all singular value are equally regularized. The singular values should be treated differently, as neighboring frames are more similar than distant frames. To rectify the weakness of NNM, we further propose to utilize the weighted nuclear norm minimization (WNNM), which achieves a better matrix rank approximation than NNMs and avoids over-regularization. To empirically investigate the effectiveness and robustness of the proposed sequential segmentation approach, we have performed extensive experiments on two different imaging modalities. The results demonstrate that our method can provide better robustness against missing features.
Research Visibility and Impact in the Next Generation Libraries

Archiving high quality documents with the best metadata is only halfway towards receiving libraries’ user attention in the future. The rest of the journey is dependent on disseminating the documents via proper utilization of the “Research Tools”. Library users expect access to library resources anytime and anywhere. Proper tools provide the chance for librarians to innovate, boost quality, measure success, and align services with the priorities of their organizations. Librarians should increase the research impact and visibility of their documents. The talk will concentrate on various techniques for improving the Research Visibility and Impact in the Next Generation Libraries. New techniques are required to support researchers in maximizing their research impact and to increase researchers’ awareness of open access publishing models, self-archiving, and Altmetrics benefits.
Optimizing VMs across Multiple Hosts with Transparent and Consistent Tracking of Unused Memory

Recently, Infrastructure-as-a-Service (IaaS) clouds provide virtual machines (VMs) with a large amount of memory. To make the migration of such large-memory VMs flexible, split migration has been proposed. It divides the memory of a VM into smaller pieces and transfers them to multiple destination hosts. After the migration, the VM runs across multiple hosts and its memory data is exchanged between hosts by remote paging. There is often unused memory in a large-memory VM, but data of even unused memory is transferred via the network. This paper proposes FCtrans to achieve efficient split migration and remote paging by considering unused memory. FCtrans avoids transferring data of unused memory to destination hosts on split migration. Similarly, it does not perform remote paging for unused memory and immediately continues the execution of the VM. To enable this, FCtrans keeps track of the memory usage of a VM after starting split migration. In addition, it transparently and consistently reclaims memory released after used by the guest operating system using VM introspection and deals with it as unused. We have implemented FCtrans in KVM and conducted experiments using a VM with 352 GB of memory on the StarBED testbed. It is shown that split migration became up to 29x faster and the memory access performance of a VM across multiple hosts improved by up to 85 %.
The Hungarian Sebeok

Abstract It was never a secret that Thomas A. Sebeok was born in Hungary, and he always referred to his Hungarian background. He emigrated from Hungary (1936) to England and later (1937) to the United States, where he Americanized his family name, Sebők. As a scholar, he started Finno-Ugric studies (not only in Hungarian, but also in Cheremis). Sebeok continued as a general linguist, and then as a communication expert. From the 1960s, he became a semiotician, a key figure in building an international semiotic network. Sebeok often visited Hungary in connection with his research activities. He was a foreign member of the Hungarian Academy of Sciences, and became Honorary Professor at Loránd Eötvöspapers University in Budapest. Such was the respect he garnered that an international congress was organized in Budapest in honor of his 70th birthday, and his papers and books were also translated into Hungarian. From his very wide range of interests, I mention here only the Hungarian context of studying animal signs. Prolific writer, excellent organizer, and eloquent speaker, Sebeok is unforgettable as a world-renowned person – with many ties to his home culture, which he referred to as his “Hungarian frame.”
Older Adults’ Voice Search through the Human-Engaged Computing Perspective

Human-Engaged Computing (HEC) is a framework that addresses “synergized interaction” sustaining both humans and computers in the right balance, a relationship that consciously honors human inner capabilities over device creativity. Due to the growing interest and demand on voice search for older adults, it is critical to research on how to engage older adults with voice search to improve their healthiness and wellbeing. This paper presents two case studies to discuss the approaches and thoughts about applying HEC to the current voice search systems, in particular, how HEC can engage older adults with interaction of voice search systems and how we can measure older adults’ engagement with such systems.
An End-to-End, Interactive Deep Learning Based Annotation System for Cursive and Print English Handwritten Text

With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction.
Resolving Implicit References in Instructional Texts

The usage of (co-)referring expressions in discourse contributes to the coherence of a text. However, text comprehension can be difficult when referring expressions are non-verbalized and have to be resolved in the discourse context. In this paper, we propose a novel dataset of such implicit references, which we automatically derive from insertions of references in collaboratively edited how-to guides. Our dataset consists of 6,014 instances, making it one of the largest datasets of implicit references and a useful starting point to investigate misunderstandings caused by underspecified language. We test different methods for resolving implicit references in our dataset based on the Generative Pre-trained Transformer model (GPT) and compare them to heuristic baselines. Our experiments indicate that GPT can accurately resolve the majority of implicit references in our data. Finally, we investigate remaining errors and examine human preferences regarding different resolutions of an implicit reference given the discourse context.
On Squares of Dihedral Codes

The Schur–Hadamard squares are of particular interest due to its cryptographic applications. Namely, many attacks on code–based public–key encryption protocols have been constructed by employing Schur–Hadamard product. In addition, the specific properties of Schur–Hadamard squares of codes play an important role in building linear secret sharing and secure multi-party computation protocols. In this paper, we prove that the Schur–Hadamard products of group codes are group codes. Next, we consider the Schur–Hadamard product of the dihedral codes, i.e. the left ideal of dihedral group algebras. We obtain explicit algebraic description of the primitive dihedral codes products. Next, this result is applied to describe the squares of dihedral codes.
On Programmatic Advertising Recommendation Based on CTR

The emergence of the digital technology has led to the transformation of marketing approach. The Internet platform which has strong business transformation capability, has become one of the major channels for advertisers to run ads, thus achieving product distribution. Through the overview of the programmatic advertising recommendation system and analysis of the pros and cons of the mainstream recommendation algorithms, this paper offers the programmatic advertising recommendation strategy based on CTR and provides, based on testing the results of the brand i advertising, reference for the related theoretical and practical research in the field of programmatic advertising recommendation.
A method of expressing associated semanteme of entities

To make up for the defects of semanteme expression about linked data, this paper proposes a semanteme expressing method of associated entities based on relationship diagram so as to realize the machine expression and recognition of associated semanteme in relational databases. Starting with the structure and relationship of relational schema, this paper analyzes the rich semanteme of associated entities and presents the semanteme parsing method based on the traversal path as well as its formal expression; the analysis of instance database is also carried out. Studies show that this method can comprehensively parse and express the associated semanteme of entities. This work has reference significance for the research of intelligent semanteme synthesis and for semanteme-oriented intelligent query.
MSM: Multi-stage Multicuts for Scalable Image Clustering

Correlation Clustering, also called the minimum cost Multicut problem, is the process of grouping data by pairwise similarities. It has proven to be effective on clustering problems, where the number of classes is unknown. However, not only is the Multicut problem NP-hard, an undirected graph G with n vertices representing single images has at most \(\frac{n(n-1)}{2}\) edges, thus making it challenging to implement correlation clustering for large datasets. In this work, we propose Multi-Stage Multicuts (MSM) as a scalable approach for image clustering. Specifically, we solve minimum cost Multicut problems across multiple distributed compute units. Our approach not only allows to solve problem instances which are too large to fit into the shared memory of a single compute node, but it also achieves significant speedups while preserving the clustering accuracy at the same time. We evaluate our proposed method on the CIFAR10 and CelebA image datasets. Furthermore, we also provide the proof for the theoretical speedup.
Spillover Today? Predicting Traffic Overflows on Private Peering of Major Content Providers

Large content providers and content distribution network operators usually connect with large Internet service providers (eyeball networks) through dedicated private peering. The capacity of these private network interconnects is provisioned to match the volume of the real content demand by the users. Unfortunately, in cases in which there is a surge in traffic demand, (e.g., due to trending content or massive software updates) the capacity of the private interconnect may deplete, requiring the content provider/distributor to reroute the excess traffic through transit providers. Although such overflow events are rare, they negatively impact content providers, Internet service providers, and end-users. Such impact includes unexpected delays and disruptions that reduce the quality of the user experience, as well as direct costs paid by the Internet service provider to the transit providers. In this article, we examine the problem of predicting an overflow event in order to enable content and Internet service providers to handle the excess traffic in a timely manner. We propose an ensemble of deep learning models trained to predict overflow events over a short-term horizon of 2–4 hours and predict the specific interconnections through which the excess traffic will enter the Internet service provider. Evaluated with 2.5 years (2017-2019) of traffic measurement data from a large European Internet service provider, the models were shown to successfully recall 65% of the events with precision of 51% on average. While the lockdowns imposed by the COVID-19 pandemic reduced the overflow prediction accuracy, the pandemic’s impact on the accuracy was temporary. Although the lockdown continued on and off, the performance of models trained before the pandemic regained their performance during April-May 2020.