Reviewerly/exHarmony huggingface dataset:
Towards a Better Understanding of the Resistance Factors of Cryptocurrency Spread

A cryptocurrency is a digital medium of exchange that operates independent of the central bank. It relies on cryptography to secure financial transactions and to check asset transfer. Several studies have shown that several factors play a role in inhibiting cryptocurrency spread, such as national culture, variables related to consumer psychology as well as factors related to regulation and monetary policy. Despite all the technological innovations, the inclusion of cryptocurrencies has become a challenge to governments and central banks since cryptocurrencies are often disfavored by rigorous regulation and monetary policy. On the other hand, psychological levels such as the need for human interaction, the organizational and functional barriers and many other variables related to power distance and uncertainty avoidance are the hindering factors for cryptocurrency adoption. In this chapter, we study the phenomenon of resistance to the spread of cryptocurrency. This study aims to explain how economic and psychological factors could discourage the consumer to use cryptocurrency.
Secure Block Level De-Duplication on Big Data using Proof of Ownership

De-duplication technology is commonly used to decrease the space and bandwidth requirements of services by eliminating repeated data and store only a single copy of them. Unfortunately, it raises issues relating to security and ownership such as, unauthorized users may claim as owner of that file and security threat from curious server itself. To overcome these issues, Secure Block level de-duplication with Proof of Ownership Scheme is proposed in this work. Proof-of-Ownership Scheme allows any owner of the same data to prove to the server that he owns the data in a robust way. This scheme uses convergent encryption and it protects the data from attackers and honest but curious server. It also reduces storage space efficiently by checking the uniqueness of data at block level
THE PROBLEM OF PERSONALIZATION AND PERSONIFICATION OF PROFESSIONAL TRAINING AND ITS IMPLEMENTATION IN HIGHER EDUCATION INSTITUTIONS: THEORETICAL ASPECT

Professional training in the context of reforming the education sector acquires new aspects and forms of implementation of educational programs, taking into account the personality-oriented trajectory of individual professional growth of each subject of the educational process. The solution of the problem is relevant nowadays: what is important in the process of personal and professional development – personalization or personification of the educational process? The article highlights the features of personalization and personification of professional training, reveals and clarifies their essence, provides an analysis of scientific publications on this issue; the need to introduce a personalized approach, which is an important means of achieving this goal. Theoretical bases of personalization and personification of professional training, features of the organization of educational activity covering cognitive, informational, scientific-innovative, social, administrative spheres are characterized. It is proved that important conditions for personalization of the educational process in higher education institutions are the cultural impulse in the idea of democratization and construction of society and man, the tradition of socio-humanitarian thought, individualization of education and so on.
FAN-MCCD: Fast and Accurate Network for Multi-Scale Chinese Character Detection

Inaccurate localization due to scale-variation during character detection causes a widespread issue overconfidence in results of the document analysis community, for the most part in historical and handwritten documents. In this work, we explored the performance of a state-of-the-art network with a simple pipeline that fast and accurately predicts handwritten Chinese characters in old documents. In order to adapt to locations of characters with multi-scale more precisely, excluding pre-processing and in-between steps, we utilized a network with multi-scale feature maps. Then, across each feature map, pre-selected boxes of unalike scales and aspect ratios were employed. The last step was to prune the bounding boxes, sending them to non-maximum suppression to yield the final results. Focusing on a well-designed neural network architecture and loss function that presents well-classified examples, we found our experiments on Caoshu, Character, and Src-images datasets demonstrated that detection performance was enhanced for the detection rate (DT), the false positive per character (FPPC), and the F-score in the order of 98.84%, 0.71, and 97.64%, respectively. In comparison with SSD (single-shot detector), the detection performance of a detection rate (DT), the false positive per character (FPPC), and the F-score were 61.12%, 6.12, and 60.33%, respectively.
Using an Embodiment Technique in Psychological Experiments with Virtual Reality: A Scoping Review of the Embodiment Configurations and their Scientific Purpose

Background: In recent years, psychological studies with virtual reality have increasingly involved some eEmbodiment tTechnique (ET) in which the users’ bodily movements are mapped on the movements of a digital body. However, this domain is very fragmented across disciplines and plagued by terminological ambiguity. Objective: This paper provides a scoping review of the psychological studies deploying some ET in VR. Methods: A total of 742 papers were retrieved from Scopus and the ACM Digital library using “embodiment” and “virtual reality” as keywords; after screening them, 79 were eventually retained. From each study, the following information was extracted: (a) the content of the virtual scenario, (b) the extent of the embodiment, and (c) the scientific purpose and measure of the psychological experience of embodiment. This information is summarized and discussed, as well as reported in tabular format for each study. Results: We first distinguished ET from other types of digital embodiment. Then we summarized the ET solutions in terms of the completeness of the digital body assigned to the user and of whether the digital body's appearance resembled the users' real one. Finally, we report the purpose and the means of measuring the users’sense of embodiment. Conclusion: This review maps the variety of embodiment configurations and the scientific purpose they serve. It offers a background against which other studies planning to use this technique can position their own solution and highlight some underrepresented lines of research that are worth exploring.
Exploration of Collaboration Filtering Techniques for Product Recommendation

Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.
TEAM-Net: Multi-modal Learning for Video Action Recognition with Partial Decoding

Most of existing video action recognition models ingest raw RGB frames. However, the raw video stream requires enormous storage and contains significant temporal redundancy. Video compression (e.g., H.264, MPEG-4) reduces superfluous information by representing the raw video stream using the concept of Group of Pictures (GOP). Each GOP is composed of the first I-frame (aka RGB image) followed by a number of P-frames, represented by motion vectors and residuals, which can be regarded and used as pre-extracted features. In this work, we 1) introduce sampling the input for the network from partially decoded videos based on the GOP-level, and 2) propose a plug-and-play mulTi-modal lEArning Module (TEAM) for training the network using information from I-frames and P-frames in an end-to-end manner. We demonstrate the superior performance of TEAM-Net compared to the baseline using RGB only. TEAM-Net also achieves the state-of-the-art performance in the area of video action recognition with partial decoding. Code is provided at https://github.com/villawang/TEAM-Net.
Research on blended teaching of English interpretation in vocational colleges under the background of informatization

With the development of educational informationization, the traditional English teaching model is faced with severe challenges in cultivating students' English communicative ability. In developing educational system, the traditional English teaching model has serious obstacles in developing the communication skills of pupils. The present paper analyses the current situation of English learning in vocational colleges for exploring the intent and application of the Blended English learning approach, to combine traditional classroom learning with online teaching, and to improve the quality of English learning in vocational colleges effectively. The way individuals get information has dramatically changed with the advent of the information era. It also has the wave of higher education information, with the continuously innovative methods of teaching and teaching content. Recently, researchers are concerned about the integration of information technology organically into education system for supporting learning experiences and for encouraging the refinement of new abilities. It is the need of the day to be focused on the education technology. The undertaken study aims at elaborating the significance of Blended teaching English in the present era. There are currently only a few courses using the blended teaching approach. With the continual update of teaching concepts and the latest technology, the focus of forthcoming research is expected to be on the individual development of the blended teaching mode.
Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives

Background: Electronic Health Records (EHRs) contain rich information of patients' health history, which usually include both structured and unstructured data. There have been many studies focusing on distilling valuable information from structured data, such as disease codes, laboratory test results, and treatments. However, relying on structured data only might be insufficient in reflecting patients' comprehensive information and such data may occasionally contain erroneous records. Objective: With the recent advances of machine learning (ML) and deep learning (DL) techniques, an increasing number of studies seek to obtain more accurate results by incorporating unstructured free-text data as well. This paper reviews studies that use multimodal data, i.e. a combination of structured and unstructured data, from EHRs as input for conventional ML or DL models to address the targeted tasks. Materials and Methods: We searched in the Institute of Electrical and Electronics Engineers (IEEE) Digital Library, PubMed, and Association for Computing Machinery (ACM) Digital Library for articles related to ML-based multimodal EHR studies. Results and Discussion: With the final 94 included studies, we focus on how data from different modalities were combined and interacted using conventional ML and DL techniques, and how these algorithms were applied in EHR-related tasks. Further, we investigate the advantages and limitations of these fusion methods and indicate future directions for ML-based multimodal EHR research.
Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically

Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field.However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises.In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL).Firstly, HiCapsRKL extracts two empirically designed text fragments from medical literature and encodes them into fragment representations respectively.Secondly, the capsule routing algorithm is applied to two fragment representations.Through the capsule computing and dynamic routing, each representation is processed into a new representation (denoted as caps-representation), and we integrate the caps-representations as information gain to associate knowledge with medical literature hierarchically.Finally, HiCapsRKL are validated on relevance prediction and medical literature retrieval test sets.The experimental results and analyses show that HiCapsRKL can more accurately associate knowledge with medical literature than the mainstream methods.In summary, HiCapsRKL can efficiently help selecting the most relevant knowledge to the medical literature, which may be an alternative attempt to improve knowledge-based text representation.Source code is released on GitHub 1 .
Minimality Notions via Factorization Systems

For the minimization of state-based systems (i.e. the reduction of the number of states while retaining the system's semantics), there are two obvious aspects: removing unnecessary states of the system and merging redundant states in the system. In the present article, we relate the two aspects on coalgebras by defining an abstract notion of minimality. The abstract notion minimality and minimization live in a general category with a factorization system. We will find criteria on the category that ensure uniqueness, existence, and functoriality of the minimization aspects. The proofs of these results instantiate to those for reachability and observability minimization in the standard coalgebra literature. Finally, we will see how the two aspects of minimization interact and under which criteria they can be sequenced in any order, like in automata minimization.
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets

Language models can generate harmful and biased outputs and exhibit undesirable behavior. We propose a Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets, an iterative process to significantly change model behavior by crafting and fine-tuning on a dataset that reflects a predetermined set of target values. We evaluate our process using three metrics: quantitative metrics with human evaluations that score output adherence to a target value, and toxicity scoring on outputs; and qualitative metrics analyzing the most common word associated with a given social category. Through each iteration, we add additional training dataset examples based on observed shortcomings from evaluations. PALMS performs significantly better on all metrics compared to baseline and control models for a broad range of GPT-3 language model sizes without compromising capability integrity. We find that the effectiveness of PALMS increases with model size. We show that significantly adjusting language model behavior is feasible with a small, hand-curated dataset.
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings

Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words.However, it remains a challenge for NMT to leverage broader context information like topics.In this paper, we propose heterogeneous ways of embedding topic information at the sentence level into an NMT model to improve translation performance.Specifically, the topic information can be incorporated as pre-encoder topic embedding, post-encoder topic embedding, and decoder topic embedding to increase the likelihood of selecting target words from the same topic of the source sentence.Experimental results show that NMT models with the proposed topic knowledge embedding outperform the baselines on the English → German and English → French translation tasks.
Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

The proportion of welding work in total man-hours required for shipbuilding processes has been perceived to be significant, and welding man-hours are greatly affected by working posture. Continuous research has been conducted to identify the posture in welding by utilizing the relationship between man-hours and working posture. However, the results that reflect the effect of the welding posture on man-hours are not available. Although studies on posture recognition based on depth image analysis are being positively reviewed, welding operation has difficulties in image interpretation because an external obstacle caused by arcs exists. Therefore, any obstacle element must be removed in advance. This study proposes a method to acquire work postures using a low-cost RGB-D camera and recognize the welding position through image analysis. It removes obstacles that appear as depth holes in the depth image and restores the removed part to the desired state. The welder’s body joints are extracted, and a convolution neural network is used to determine the corresponding welding position. The restored image showed significantly improved recognition accuracy. The proposed method acquires, analyzes, and automates the recognition of welding positions in real-time. It can be applied to all areas where image interpretation is difficult due to obstacles.
Opportunities and Challenges of Digital Learning Media During the Covid-19 Pandemic in Primary School

&lt;span&gt;During the Covid-19 Pandemic, the learning process used by teachers is in the form of digital media. &lt;/span&gt;&lt;span lang="EN"&gt;This research is focused on digital learning, barriers faced by teachers, and various approaches to overcome various obstacles faced by teachers in implementing digital learning during the covid 19 pandemic&lt;/span&gt;&lt;span&gt;. The type of research is qualitative research with a survey method. This research was conducted in Sumenep Regency with primary school teachers as the research object. &lt;/span&gt;&lt;span lang="EN"&gt;The data collection consists of questionnaires given to 100 teachers and interviews to 9 teachers to describe information related to the focus of the research. The results of this study show that teachers do not directly utilize various digital platforms of various learning technologies implemented in online learning during covid 19. &lt;/span&gt;&lt;span&gt;The obstacles experienced by the teachers include the teacher's low ability in applying the information technology, parents' economic factor, unstable internet networks, and the absence of information technology training given by schools. On the other hand, as time goes by teachers are able to implement the digital learning independently and adapt to the surrounding environment and understand the various characteristics of students in making use of digital in learning during the COVID-19 pandemic.&lt;/span&gt;
Evolving Evocative 2D Views of Generated 3D Objects

We present a method for jointly generating 3D models of objects and 2D renders at different viewing angles, with the process guided by ImageNet and CLIP -based models. Our results indicate that it can generate anamorphic objects, with renders that both evoke the target caption and look visually appealing.
Track Before Detect: A Novel Approach For Unsupervised Anomaly Detection In Time Series

The need for robust unsupervised anomaly detection techniques in streaming data increases rapidly in today’s era of smart devices. Many existing anomaly detection methods have difficulties to detect anomalies in streaming data since most of them are designed to use all features of the data which are not applicable in a streaming context such as IoT. To address this problem, we present a novel unsupervised anomaly detection approach (Track Before Detect) for time series data. Track Before Detect (TBD) is capable of detecting a wide range of anomalies such as point anomalies and collective anomalies. In addition, it can differentiate between anomalous behavior and environmental changes in time series data in an unsupervised setting and without affecting the running system. Experiments based on real world data sets demonstrate that TBD succeeded in detecting anomalies in time series data and outperformed existing methods.
Attendance System Implementation Using Real Time Face Recognition

In this paper, we propose Facial Recognition using Supervised Learning technique to implement an attendance system using a comparative study between different methods of face detection and face recognition. The first step that is required is face detection, i.e., detecting faces in an image, video or real time coverage consisting of different types of objects (distinguishing faces from non-faces). Once the face is detected, feature extraction, i.e., determining the uniqueness of the face by takin out the features is performed. Eventually, training and classification of the facial databases is done and is tested using various classification techniques based on the comparative performance and efficiency for recognizing faces. We attempt to use this facial recognition system on a data set of faces of institute/university students for which the attendance system will be implemented. Attentiveness Monitoring feature is added which monitors facial features such as eye blinks, yawn and eye movements, to determine the relative attentiveness among students. Consolidated attendance is easily available to view and download.
The Stochastic Boolean Function Evaluation problem for symmetric Boolean functions

We give two approximation algorithms solving the Stochastic Boolean Function Evaluation (SBFE) problem for symmetric Boolean functions. The first is an O(logn)-approximation algorithm, based on the submodular goal-value approach of Deshpande, Hellerstein and Kletenik. Our second algorithm, which is simple, is based on the algorithm solving the SBFE problem for k-of-n functions, due to Salloum, Breuer, and Ben-Dov. It achieves a (B−1) approximation factor, where B is the number of blocks of 0's and 1's in the standard vector representation of the symmetric Boolean function. As part of the design of the first algorithm, we prove that the goal value of any symmetric Boolean function is less than n(n+1)/2. Finally, we give an example showing that for symmetric Boolean functions, minimum expected verification cost and minimum expected evaluation cost are not necessarily equal. This contrasts with a previous result, given by Das, Jafarpour, Orlitsky, Pan and Suresh, which showed that equality holds in the unit-cost case.
Features Of The Media Environment In The Conditions Of Digitalization Of Social Communications

The paper explores the features of the media network environment and audience involvement based on factor analysis of comments to video blogs. It is shown that although the involvement of the audience is an insignificant part of the full potential, it significantly complements and distorts the main topic of network information. It is also shown that the popularity of content reduces the constructiveness of audience engagement.