Reviewerly/exHarmony huggingface dataset:
Sequence of discussion on the “Psychology of Art” by L. S. Vygotsky (analysis and Interpretation of literary works)

&amp;nbsp; This article reviews the polemic of the author with a number of well-known psychologists who consider L. S. Vygotsky a humanities scholar, and that the book &amp;ldquo;Psychology of Art&amp;rdquo; introduces psychologists to the humanities. The author clarifies his position, trying to show that in his methodological projects L. S. Vygotsky adheres to the natural scientific approach; however, in some works he actually does think as a humanist. The author also casts doubt on Vygotsky's concept of art, demonstrating that the offered mechanisms of aesthetic response and interpretation of literary works are problematic or unsatisfactory. In this regard, analysis is conducted on the position of V. Sobkin, which differs significantly from the views of L. S. Vygotsky. The author offers the original interpretation of art, which implies the three aspect: sociocultural that analyzes artistic communication; related to the philosophy of subjectivity (life from the perspective of an artist, viewer, or reader); and analysis of artistic reality, which includes consideration of the literary language and events. The author elucidates the corresponding concepts and problems that appear in such art task. For better understanding of the proposed ideas, the author reviews the examples from the &amp;ldquo;Psychology of Art&amp;rdquo; by L. S. Vygotsky, and cites a case from the own works. The discrepancy with L. S. Vygotsky he views as an motive to continue his research and the need for their critical reflection. &amp;nbsp;
Stad: Stable Video Depth Estimation

We present a method for estimating temporally stable depth video from a sequence of images. We extend the prior work aimed at video depth estimation, Neural-RGBD [1], which proposed to use temporal information by accumulating a depth probability volume over time. We propose three simple yet effective ideas to gain improvement: (1) temporal attention module to select and propagate only the meaningful temporal information, (2) geometric warping operation to warp neighbor features in the way of preserving geometry cues, and (3) scale-invariant loss to relieve the inherent scale ambiguity problem in monocular depth estimation task. We demonstrate the efficiency of proposed ideas by comparing our proposed network STAD with the state-of-the-arts. Moreover, we compare STAD with its per-frame network STAD-frame to show the importance of utilizing temporal information. The experimental results show that STAD significantly improved the baseline accuracy without a large parameter increase.
Development of Chinese Vocabulary Learning by utilizing Virtual Reality

This paper presents the development of a virtual reality (VR) application for learning Chinese language. A virtual supermarket is created by using Unity software and C# scripts to allow users to learn basic vocabularies consisting of vegetables, meat, drinks, seasoning, fruits, and bathroom accessories while enjoying shopping activity in an immersive 3D environment and instinctive interaction. This new instructional media which provides 3D objects, sound, and Chinese characters simultaneously are tested with undergraduates and postgraduates with no Chinese knowledge and found more efficiently as compared to traditional learning.
VAE-CE: Visual Contrastive Explanation Using Disentangled VAEs

The goal of a classification model is to assign the correct labels to data. In most cases, this data is not fully described by the given set of labels. Often a rich set of meaningful concepts exist in the domain that can describe each datapoint much more precisely. Such concepts can also be highly useful for interpreting the model’s classifications. In this paper we propose Variational Autoencoder-based Contrastive Explanation (VAE-CE), a model that represents data with high-level concepts and uses this representation for both classification and explanation. The explanations are contrastive, conveying why a datapoint is assigned to one class rather than an alternative class. An explanation is specified as a set of transformations of the input datapoint, where each step changes a concept towards the contrastive class. We build the model using a disentangled VAE, extended with a new supervised method for disentangling individual dimensions. An analysis on synthetic data and MNIST validates the utility of the approaches to both disentanglement and explanation generation. Code is available at https://github.com/yoeripoels/vce .
Multi-Task Learning By A Top-Down Control Network

As the range of tasks performed by a general vision system expands, executing multiple tasks accurately and efficiently in a single network has become an important and still open problem. Recent computer vision approaches address this problem by branching networks, or by a channel-wise modulation of the network feature-maps with task specific vectors. We present a novel architecture that uses a dedicated top-down control network to modify the activation of all the units in the main recognition network in a manner that depends on the selected task, image content, and spatial location. We show the effectiveness of our scheme by achieving significantly better results than alternative state-of-the-art approaches on four datasets. We further demonstrate our advantages in terms of task selectivity, scaling the number of tasks and interpretability.
Strong Anonymity is not Enough: Introducing Fault Tolerance to Planet-Scale Anonymous Communication Systems

Current Anonymous Communication Systems (ACS) lack fault tolerance and thus risk becoming unavailable when failures occur, forcing users offline or to less private messengers. In this work, we evaluate end-to-end message transmission latencies and resource demands of state-of-the-art mixnet Vuvuzela and CPIR system Pung under different network failure scenarios on an ACS test bed across four continents. We compare Vuvuzela and Pung to proof-of-concept mixnet FTMix that we equip with simple fault tolerance measures. Our analysis shows that FTMix maintains the smallest divergence of end-to-end latencies under failures from their respective baseline among all three ACS, while also achieving a balanced resource consumption trade-off. Thus, we consider fault tolerance effective in ensuring service availability and a crucial design principle for future ACS proposals.
Unsupervised Learning: Word Vector

In the field of NLP, as well as for the processing of text data, the first thing we need to consider is how we can express the text mathematically. Through this, we are then able to derive the concept of word vector or word embedding. Word vector, as its name implies, maps a word to a vector space, maximally retaining the original semantics of the word. In this important tool for understanding natural language, we can use a word vector as the smallest unit for mining corpus data or as an input to complex models.
Application Development Design “How To Make Money Online” Using The Wartefall Method (Case Study : CV Satoeasa For Indonesia)

CV Satoeasa is a organization engaged in training and consulting services in the context of empowering and developing business actors. Satoeasa is supported by personnel who have competency standards and experience in the fields of entrepreneurship and local economic development. CV Satoeasa Untuk Indonesia is committed to increasing entrepreneurial capacity and is passionate about realizing a growing and developing business. use of the application "How To Make Money Online" to increase competitiveness with other companies, improve the quality of service to customers because now customers have more options to browse applications, compare and analyze services and ratings before choosing an application to be installed on their phone device . For the development of the application "How To Make Money Online" CV Satoeasa For Indonesia plans to create a web-based application with the aim of making it easier for customers to access it without having to install their personal device first. This research uses the waterfall method. In the waterfall method there are several steps including requirements, design, implementation, verification and maintenance. This "How To Make Money Online" application is still based on Android, therefore the author intends to make a web-based "How To Make Money Online" application development design. The result of this research is an application development design. Application development is easier with the application design that will be developed. The conclusion is the "How To Make Money Online" application development design using HTML (HyperText Markup Language) programming. Before this application was developed, it was based on Android and after it was developed it became web-based. In this application there are several menus including the homepage, articles, contacts, about us and help. Provide solutions to CV Satoeasa Untuk Indonesia regarding the design of the application development "How To Make Money Online" using the waterfall method
Computer Cloud Computing Technology Corpus Application System Research

In electronic computer cloud computing, text is the most important information carrier, and the calculation of computer similarity of text is a very basic and critical issue in information processing. Text similarity is a basic link in text information processing, and its calculation results directly affect the effect of subsequent text mining. Combining the text vectorization idea of space vector model and Chinese semantic analysis technology, the article proposes a Chinese text similarity calculation method that combines corpus statistics and semantic analysis. Experiments show that the semantic similarity algorithm significantly improves the accuracy of text classification and sentence similarity calculation in the corpus system.
A Systematic Review of the ICTD Education and Learning Literature to Identify Scope, Impact, and Areas for Future Work

In the past couple of decades, the topic of ICT for development (ICTD) has received significant attention and across the globe, researchers, educators, and developers have engaged in projects to study, design, and apply technology to improve education and learning. The expectation that use of information and communication technologies (ICT) will improve education and learning, a critical social good, throughout the world have never been higher. Currently, there is no systematic review of existing literature to better understand what areas have been addressed, their impact, and what are the lessons learned for future work. This paper presents review of work on education and learning with ICT. We focus on the ICTD literature as it has a mature body of work and engages directly with the goal of doing good with technology. Through this review I illustrate the diversity of the work that has been undertaken on the topic and outline five areas of to guide future contributions: 1) appreciating the diversity of the domain; 2) taking a long-tern view of technology intervention; 3) understanding contexts and perspectives; 4) preparing for unintended consequences; and, 5) aiming for power parity between dominant stakeholders and intended beneficiaries. I also show that not all efforts have had a positive outcomes and many design and intervention efforts have been critiqued.
Short-term dependency of a class of nonlinear continuous time dynamic systems

Dynamic systems usually have long-term dependency. Recent work, however, shows that the long-term memory models like recurrent neural networks and short-term memory models like feed-forward neural networks have comparable performance in approximating the behavior of dynamic systems. While the pioneering researchers try to understand the short-term dependency in discrete time dynamic systems, this paper focuses on a class of continuous time dynamic systems characterized by ordinary differential equations. In this class of continuous time dynamic systems, only the variable can be observed, and its derivatives with respect to time cannot be measured directly. By analyzing the observability of the continuous time dynamic systems and the sampled systems, we show that the current output only depends on finite steps of history information when sampling fast. If the sampling is fast enough such that the Euler approximation can approximate the sampled system well, the current output only relies on the most recent n steps of history information. Here, n is the order of the ordinary differential equation. Later, we verified our results with the NARMAX method.
Analyzing Economic Effectiveness of Fisheries Patents by Technology Area:Focusing on NIFS’s R&amp;D Cases

In this study, we estimated the economic effectiveness of 8 representative patents out of the hightest-evaluated(S-level) NIFS’s patents in the four fields of ‘Aquaculture’, ‘Aquatic disease’, ‘Safety & processing’ and ‘Fisheries engineering’. In addition, we suggested a plan for utilizing the achievements of government-owned patents in fisheries fields by comparing characteristics of each research field. Patents in aquaculture field showed a higher R&D period, budget, market size and share compared to other fields. In fields of fisheries engineering and fisheries disease, patents have high effects in terms of IRR and BCR because the R&D period is short and budget is relatively lower than other fields. In case of safety & processing, a sufficient demand market has been formed. However, the results of economic effectiveness are low because R&D contribution rate and continuous investment are lower than those of other three fields. The technology commercialization of government-owned patents is difficult to obtain immediate R&D investment effects due to the characteristics of fundamental researches. Even though the economic feasibility is lower than that of others, it is necessary to continue to secure and utilize the source technology at the national level, taking into account the economic impact that national R&D has on the entire industry from a long-term perspective.
How Older Adults’ User Experiences of Smart Healthcare System are Affected by their Cultural Differences

Population aging is a challenging and cross-culture problem throughout the world. At present, developing smart healthcare systems is a critical way to help realize healthy aging. On the other hand, the integration of data acquisition modules in multiple non-invasive sensors and wireless local area networks makes it possible to timely find and detect health problems in older adults. This study aims at exploring the influence of older adults' culture differences on user experience design for the smart healthcare system. This paper also suggests that the user experience design of the smart healthcare system needs to be carried out based on a concrete analysis of older adults' cultural life to improve the health and wellbeing of older adults in their later years.
Application of the Topological Gradient to Parsimonious Neural Networks

In this paper, a new deep learning approach based on native parsimony using the topological gradient method is introduced. This approach reduces the number of links to be identified by the learning process by several orders of magnitude and consequently, the required amount of learning data is reduced proportionally. This is a significant development when compared to methods for the simplification of redundant networks. Redundant networks have evolved within the bubble of big data where this is not an issue, but big data is rarely a reality for many industrial and healthcare applications. Native parsimonious approaches outperform state-of-the-art methods when the response of the model is continuous and a high level of accuracy is expected, like long-term prediction and data compression. In redundant networks, to avoid overfitting, the learning process is terminated before its convergence. This explains why state-of-the-art methods focus on discrete problems such as classification, pattern recognition, and natural language processing. While these discrete problems are oriented to replacing human decision-making, the ability to accurately predict continuous phenomena works hand-in-hand with human intelligence, augmenting our decision-making capabilities.
Comprehensive Evaluation of Weapon Equipment Systems Operational Effectiveness Based on State Space Concept

Abstract In recent years, there are many researches on combat test of weapon equipment systems. However, in the process of comprehensive effectiveness evaluation, there are some shortcomings in the construction of index system, such as index redundancy, experts subjective weighting, which lead to the lack of objectivity and scientificity. Focusing on the objective state of the evaluated object, this paper puts forward an idea of comprehensive evaluation in the efficiency state space (ESS), and uses intelligent data processing method based on principal component analysis to examine the correlation of the underlying indicators of the evaluation index system (EIS), and attempts to conduct a comprehensive evaluation on this basis. Finally, we give the comparison the corresponding new results with the original evaluation results in the references. The data show that comprehensive evaluation under ESS can reduce efficiency calculation dimension from10 to 3. The final efficiency ranking is slightly different from that in the reference, but is much more rational and convincing.
From ASTs to Machine Code with LLVM

A compiler is a program that translates source code written in a particular language into another language. Internally, the whole process is typically split into multiple stages that handle one particular aspect of this translation. One of these consists of translating the high-level representation of the program, typically an abstract syntax tree, into a simpler form that is suitable for analysis, optimizations, and code generation. This tutorial paper focuses on this process, and uses LLVM to compile programs into optimized machine code. LLVM is a language-agnostic compiler toolchain that handles program optimization and code generation. It is based on its own internal representation, called LLVM IR, which is then transformed into machine code.
VR Invite: A Project-Independent Smartphone App for VR Observation and Interactivity

Virtual Reality (VR) is a promising immersive technology, which provides users with place and plausibility illusions in a virtual environment (VE). However, current immersive experiences are often limited to those users wearing a VR head-mounted display (HMD). In this paper we present VR Invite, a project-independent smartphone app, which allows multiple non-immersive bystanders to observe and interact with the VE and the HMD users. Our system renders multiple view ports of the scene on a host computer, and transmits the data via wireless local network to the mobile devices. Furthermore, the position and orientation of the smartphones is tracked to change the viewpoints accordingly.We conducted a user study with 26 participants in the context of rehabilitation for older adults in retirement homes, with a focus on bystander integration. In the study, a VR user had to play multiple rounds of a memory game, while a bystander provided support. We compared VR Invite with a TV-gamepad-combination as interaction medium for the support role regarding sense of presence, social presence, workload and usability, both with purely verbal and active assistance capabilities. The results indicate that the opportunity for direct interaction positively influences the bystander’s sense of presence in the VE and the reported usability of the Smartphone app. However, social presence was rated higher in passive conditions in which the real person was the center of attention, as opposed to the avatar on the screen. Furthermore, users valued the comfort of sitting down over active participation and agency with room-scale movement.
An Overview of Bitcoin and Ethereum White-Papers, Forks, and Prices

We will be reviewing the two most popular cryptocurrencies, Bitcoin and Ethereum. Specifically, we will be reviewing the basic concepts outlined in their respective white papers, as well as some of their most significant forks. Not only will we discuss the technical changes brought by the forks, but we will also analyze the effects these forks have on the coin’s performance in the markets. Our research will involve analyzing the white papers of these coins, and the changes made by significant forks that have been added over the years. In doing so, we hope to develop an understanding of what security protocols are primarily used by each coin, and how they differ. A lot of cryptocurrencies have a reputation of being both underrated and overhyped simultaneously, so we will be reviewing our coins in the context of demand as it relates to security and performance. We will be measuring hype, or public sentiment, using technical factors such as Market Cap, Daily Volume, and Circulating Volume. Finally, we will comment on whether the forks themselves had an impact on the demand of the currency, as measured by its technical factors. We will see if there is any correlation between a useful fork being released and any sudden movements in market sentiment. Our paper will consist of three parts: a summary of the chosen cryptocurrencies white papers, the changes brought on by significant forks, and the past and current market demand behind them.
Specific Domain Taxonomy Construction for Massive Documents Classification

Taxonomy exists in many practical scenarios. A reasonable taxonomy plays a vital role in multiple tasks such as natural language processing and information retrieval. There are already a large number of manually edited taxonomy, such as Wikipedia. However, in some specific domains, the cost of obtaining the latest taxonomy manually is relatively high. Therefore, the automatic construction of a taxonomy has become a problem to be solved. In this paper, we construct a taxonomy for terms in a specific domain, and use it in actual scenarios. Based on the model proposed above, we design a large number of experiments to construct a taxonomy. Specifically, we use the CORD-19 dataset containing coronavirus research literatures to conduct experiments. The experiment shows that the taxonomy obtained by using frequency statistics and social network analysis methods has the characteristic of more concentrated semantics of concept nodes.
MAT-Net: Representing Appearance-Irrelevant Warp Field by Multiple Affine Transformations

Warp-based methods for image animation estimate a warp field what do a rearrangement on the pixels of the input image to roughly align with the target image. Current methods predict accurate warp field by using manually annotated data. In this paper, we propose a simple method (MAT-net) to predict more precise warp field in self-supervised way. MAT-net decomposes complex spatial object movement between two images into multiple simple local motions (i.e. affine transformation) occurring in different areas of images. Sequentially, our model calculates a warp field depicting complex object movement by combining all local motions. MAT-net encodes appearance-irrelevant object movement accurately. Compared to the state-of-the-art method, MAT-net generates more realistic images with faster inference speed. We published the source code of our project online.