Reviewerly/exHarmony huggingface dataset:
Research on the Evaluation of Digital Academic Competence of Chinese Humanists

The entry of the humanities into the digital era characterizes the prominence of the two dimensions of technology and humanity, highlighting the trend of their mutual intersection and integration. The traditional research paradigm of humanists has been transformed. Based on literature research and semistructured interviews, this study adopts a grounded theory approach to identify the digital academic competence of humanists in China and finds it is composed of information acquisition competence, digital technology application competence, and digital sharing competence. On the basis of previous research, the paper uses a questionnaire and statistical method to locate the shortage of digital academic competence of humanists. Proposing the digital academic competence of humanists’ framework, the study involved a questionnaire related to the humanists’ digital academic competence and developing a competence evaluation indicator system. The findings of this study will help Chinese research institutions, especially university libraries, measure the digital academic competence of humanists and provide targeted guidance in the training of digital humanities.
Designing types for R, empirically

The R programming language is widely used in a variety of domains. It was designed to favor an interactive style of programming with minimal syntactic and conceptual overhead. This design is well suited to data analysis, but a bad fit for tools such as compilers or program analyzers. In particular, R has no type annotations, and all operations are dynamically checked at run-time. The starting point for our work are the two questions: what expressive power is needed to accurately type R code? and which type system is the R community willing to adopt? Both questions are difficult to answer without actually experimenting with a type system. The goal of this paper is to provide data that can feed into that design process. To this end, we perform a large corpus analysis to gain insights in the degree of polymorphism exhibited by idiomatic R code and explore potential benefits that the R community could accrue from a simple type system. As a starting point, we infer type signatures for 25,215 functions from 412 packages among the most widely used open source R libraries. We then conduct an evaluation on 8,694 clients of these packages, as well as on end-user code from the Kaggle data science competition website.
Algorithm for forming a suspicion of a new coronavirus infection based on the analysis of symptoms for use in medical decision support systems

The course of the COVID‑19 pandemic imposes a significant burden on healthcare systems, including on primary care, when it is necessary to correctly suspect and determine further management. The symptoms non-specificity and the manifestations versatility of the COVID‑19 impose difficulties in identifying suspicions. To improve the definition of COVID‑19 symptom checkers and medical decision support systems (MDSS) can potentially be useful. They can give recommendations for determining the disease management. The scientific analysis shows the manifestations versatility and the occurrence frequency COVID‑19. We structured the manifestations by occurrence frequency, classified them as “large” and “small”. The rules for their interaction were determined to calculate the level of suspicion for COVID‑19. Recommendations on patient management tactics were developed for each level of suspicion. NLP models were trained to identify the symptoms of COVID‑19 in the unstructured texts of electronic health records. The accuracy of the models on the F-measure metric ranged from 84.6% to 96.0%. Thus, a COVID‑19 prediction method was developed, which can be used in symptom checkers and MDSS to help doctors determine COVID‑19 and support tactical actions.
Scripts, frames, and other semantic objects

In this article I review the history of various approaches to semantics that share the premise that meaning is (to a greater or smaller extent) encoded in semantic “objects” (scripts, frames, schemata, MOPs, etc.) and their relations (semantic graph). I compare the various approaches, highlighting in particular the common, shared elements of Object-Oriented Semantics (OOS). I will then focus on some of the defining features of OOS, including, primelessness (i.e., the lack of basic, undefined or fundamental privileged objects), fractality (the fact that there exists the same level of complexity at all levels of the network), dynamicity (the fact that semantic objects are built by trial-and-error and are always subject to revision) and falsifiability.
Advancing Graph Convolution Network with Revised Laplacian Matrix

Graph convolution networks are extremely efficient on the graph-structure data, which both consider the graph and feature information. Most existing models mainly focus on redefining the complicated network structure, while ignoring the negative impact of lowquality input data during the aggregation process. This paper utilizes the revised Laplacian matrix to improve the performance of the original model in the preprocessing stage. The comprehensive experimental results testify that our proposed model performs significantly better than other off-the-shelf models with a lower computational complexity, which gains relatively higher accuracy and stability.
GRDP Growth Rate Clustering in Surabaya City uses the K-Means Algorithm

Gross Regional Domestic Product (GRDP) is an indicator used to measure economic performance in a period. GRDP is the amount of added value generated by all business units in a particular area. It can also be said to be the sum of the value of the final goods and services produced by all economic units. Therefore, this study aims to cluster the GRDP Growth Rate according to business fields in the city of Surabaya, so that it is known which sectors have high or low growth. The clustering algorithm used is K-Means. By using this method, the data will b,e grouped into several clusters, where the implementation of the K-Means Clustering process uses the Rapid Miner tools. The data used is the GRDP Growth Rate in Surabaya City by Business Field, 2010-2019 (Percent). The data is divided into 3 clusters: high, medium, and low. The results obtained are nine categories/sectors with high clusters, 5 categories / sectors with medium clusters, and three categories,s / sectors with low clusters. This can be input and information for the Surabaya City government to further maximize efforts to increase the GRDP Growth Rate in the area.
Cyber Security Analysis of Academic Services based on Domain Delivery Services and Support using Indonesian E-Government Ratings (PEGI)

Safe academic services are the most important part of universities. The security of academic services is very important to maintain information optimally and safely. Along with the development of technology, academic information services are often misused by some irresponsible parties that can cause threats. To prevent these things from happening, it is necessary to know the extent of governance of higher education academic information system security by evaluating. So the research was conducted to determine the maturity of the security of Higher Education academic information service security by using the COBIT 5 framework in the DSS05 domain. The DSS05 domain in COBIT 5 is a good framework for use in implementing and evaluating the security of academic information services. Meanwhile, to determine the achievement of the evaluation of the security level of academic information systems, the Indonesian e-government ranking (PEGI) method is required. The combination of the COBIT 5 framework in the DSS05 domain using the PEGI method in academic information security service is able to provide a level of achievement in the form of Customer Value. The results of the COBIT 5 framework analysis of the DSS05 domain using the PEGI method get a score of 3.50 so that the quality of academic information service security evaluation achievement is at a very good level. At this level, universities are increasingly open to technological development. Higher education has applied the concept of quantification in every process, and has always been monitored and controlled for its performance in the security of academic information systems.
Inter-theory dependency analysis for SMT string solvers

Solvers in the framework of Satisfiability Modulo Theories (SMT) have been widely successful in practice. Recently there has been an increasing interest in solvers for string constraints to address security issues in web programming, for example. To be practically useful, the solvers need to support an expressive constraint language over unbounded strings, and in particular, over string lengths. Satisfiability checking for these formulas, especially in the SMT context, is very hard; it is generally undecidable for a rich fragment. In this paper, we propose a form of dependency analysis for a rich fragment of string constraints including high-level operations such as length, contains to deal with their inter-theory interaction so as to solve them more efficiently. We implement our dependency analysis in the string theory of the Z3 solver to obtain a new one, called S3N. Finally, we demonstrate the superior performance of S3N over state-of-the-art string solvers such as Z3str3, CVC4, S3P, and Z3 on several large industrial-strength benchmarks.
Challenges and opportunities of social media research

Digital footprints from social media enable us to study the far right in novel ways. In contrast to traditional methods such as interviewing, the examination of online discourse is non-reactive: the data are ‘given’ and as such not influenced by researchers. Social media thus allow us to unobtrusively get an insight into real-life everyday discussions among far right supporters. Social media also provide far right leaders the opportunity to circumvent traditional news channels, making their voices heard without interference from gatekeepers and journalists. This chapter reviews the methodological opportunities and challenges of using social media as a source of data. We focus on online discourses of the far right on Twitter and Facebook. To put flesh to the bones, we apply our review to two concrete research questions. First, to what extent are there differences between and within far right parties and movements in the outgroups that their followers discuss? Second, to what extent do far right leaders moderate their ideological outlook once they become member of a government?
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models

We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving inverse problems with deep generative models. Instead of optimizing only over the initial latent code, we progressively change the input layer obtaining successively more expressive generators. To explore the higher dimensional spaces, our method searches for latent codes that lie within a small $l_1$ ball around the manifold induced by the previous layer. Our theoretical analysis shows that by keeping the radius of the ball relatively small, we can improve the established error bound for compressed sensing with deep generative models. We empirically show that our approach outperforms state-of-the-art methods introduced in StyleGAN-2 and PULSE for a wide range of inverse problems including inpainting, denoising, super-resolution and compressed sensing.
The use of active learning methods to stimulate student activity in the online course

The development of innovations and related changes in the labor market, as well as Ukraine’s aspiration toward Euro integration, have led to the need for transformation of teaching media and methods in higher education. The consequence of this is a massive transition to e-learning, both in the native language and in English, which, however, creates psychological and pedagogical problems. These are difficulties for students with self-organization and self-training, establishing interpersonal contacts, maintaining the motivation to learn, and meeting a certain level of foreign language proficiency.The problems for teachers include the need to develop new teaching methods and technologies, changing the role of the teacher and the methods of interaction between participants in the educational process. The article presents the experience of mitigating these problems in the online course, “Fundamentals of Vehicle Certification” in Ukrainian and English by using active learning methods, innovative methods of motivation, and organization of reflection and communication, presenting some sections both in the native and foreign language. The introduction of this course into the educational process in the extramural form of education showed that the methods proposed by the authors reduced these problems, while the number of A and B grades increased. In addition, it was concluded that the effective use of online courses requires their preliminary approbation and further improvement, taking into account the characteristics of the discipline, experience and goals of the students.
Temporal Memory Network Towards Real-Time Video Understanding

Action recognition is the basic task for video understanding. Although the action recognition has achieved impressive performance in the static image-based task (e.g. Stanford40) with deep learning, real-time video-based action recognition is still a challenging task due to video's high complexity and computation cost. Motivated by human's recognition ability with only a short glance, we propose the fast light-weighted Temporal Memory Network (TMNet) to achieve real-time video action recognition. The TMNet has a self-supervised structure for exploring both spatial and temporal information with a single video frame. TMNet has three main parts, the base backbone, the regression branch, and the classification branch. Specifically, the base backbone network is a shallow 2D CNN network to obtain the video's initial feature sequences. The classification branch is based on existing successful video recognition models(e.g. TSN, I3D). To make the TMNet learn the spatial-temporal information at a lower cost, we add a self-supervised regression branch. This branch is based on light-weighted 2D CNN and only uses one frame as input. In the training stage, the input of TMNet is a video sequence, the classification branch combined with the base backbone is responsible for learning the video sequence's spatial-temporal feature. Meanwhile, the self-supervised regression branch aims to learn the same spatial-temporal feature under the supervision of the classification branch's output. And the regression branch's input is a single-frame feature sampled from the encoded video sequence. In this way, the regression branch is forced to learn temporal information of adjacent frames with one frame. Therefore, TMNet only needs one frame to predict each video's spatial-temporal information in the inference stage. Finally, TMNet can achieve real-time action recognition and better accuracy by extracting temporal information from a static image. Abundant ablation experiments demonstrate TMNet has a good trade-off between accuracy and speed.
Local Context Attention for Salient Object Segmentation

Salient object segmentation aims at distinguishing various salient objects from backgrounds. Despite the lack of semantic consistency, salient objects often have obvious texture and location characteristics in local area. Based on this priori, we propose a novel Local Context Attention Network (LCANet) to generate locally reinforcement feature maps in a uniform representational architecture. The proposed network introduces an Attentional Correlation Filter (ACF) module to generate explicit local attention by calculating the correlation feature map between coarse prediction and global context. Then it is expanded to a Local Context Block (LCB). Furthermore, a one-stage coarse-to-fine structure is implemented based on LCB to adaptively enhance the local context description ability. Comprehensive experiments are conducted on several salient object segmentation datasets, demonstrating the superior performance of the proposed LCANet against the state-of-the-art methods, especially with 0.883 max F-score and 0.034 MAE on DUTS-TE dataset.
Bitcoin Alert System using Bolt IoT Integrated with Blockchain

The emergence of cryptocurrency has sparked an interesting debate on the future of financial transactions. Post-COVID, there will be unbelievable advancements in the most popular and promising technology, cryptocurrency particularly bitcoins, as it is more valuable than any form of cryptocurrencies. Our work is about bitcoin price alert system using bolt Internet of Things (IOT) and Blockchain. In this research paper, we will be writing a python program that checks the current price of the bitcoin and sends an alert if the current price of the bitcoin is higher than the set selling price. The program checks the current price of bitcoins whenever the price of bitcoins is updated. And at that same time it compares the current price with the bitcoin price that has been set as selling price. It alerts through buzzer, email and message. For this we use Bolt Internet of Things (IOT) module and ubuntu software for writing program in python.
Age Estimation from Face Images Based on Deep Learning

Age estimation via face images is widely used in different fields, including intelligent security, financial transactions, and public transportation. Even after many years' research, evaluating the specific age from facial images is still a challenging problem. Recently, deep learning-based methods are playing an important role in image processing, which provides a possible way to improve estimation models. We estimate accurate age and age range by testing several deep learning models, which include InceptionResNetV2, Xception, DenseNet121, ResNet152V2, VGG16, and InceptionV3. By comparing their performance, we discover that Xception has the highest accuracy of age estimation. To optimize the performance of these convolutional neural network, we apply the soft labelling technique and analyze the experiment results.
The Evaluation of Competency-Based Diagnosis System and Curriculum Improvement of Information Management

Teaching evaluation is an important issue in the learning process in higher education. In addition to the teaching evaluation on campus, feedback from alumni is also very important to instruction improvement. Undergraduates and graduates in universities are the main labor force in Taiwan; therefore, many higher education institutions pay attention to the feedback of competency. However, the written questionnaire has limitations and lacks sufficient evidence to improve curriculum planning and instruction activities. In the present study, a systematic survey in the ‘University Career and Competency Assessment Network' was applied to analyze the results. Data were collected from 1,080 participants. The results can be summarized as follows: (1) ‘Learn-Practice Fit' was positive to the ‘Satisfaction' at workplaces. (2) ‘Responsibility and discipline' were significantly positive concerning ‘Communication and Expression,' ‘Interpersonal Interaction', and ‘Teamwork.' (3) ‘Service' and ‘Information technology writing' need to be improved in the training of Information Management students.
The Information Bottleneck : Connections to Other Problems, Learning and Exploration of the IB Curve

In this thesis we study the information bottleneck (IB) method. This is an informationtheoretic framework which addresses the question of what are the relevant factors of arandom variable X to expl ...
Robust real-time pedestrian detection on embedded devices

Detection of pedestrians on embedded devices, such as those on-board of robots and drones, has many applications including road intersection monitoring, security, crowd monitoring and surveillance, to name a few. However, the problem can be challenging due to continuously-changing camera viewpoint and varying object appearances as well as the need for lightweight algorithms suitable for embedded systems. This paper proposes a robust framework for pedestrian detection in many footages. The framework performs fine and coarse detections on different image regions and exploits temporal and spatial characteristics to attain enhanced accuracy and real time performance on embedded boards. The framework uses the Yolo-v3 object detection [1] as its backbone detector and runs on the Nvidia Jetson TX2 embedded board, however other detectors and/or boards can be used as well. The performance of the framework is demonstrated on two established datasets and its achievement of the second place in CVPR 2019 Embedded Real-Time Inference (ERTI) Challenge.
COVID-19 and Contact Tracing: A Study in Regulation by Technology

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An exhaustive algorithm based on GPU to process a kNN query

The Nearest Neighbors search is a widely used technique with applications on several classification problems. Particularly, the k-nearest neighbor (kNN) algorithm is a well-known method used in modern information retrieval systems aiming to obtain relevant objects based on their similarity to a given query object. Although algorithms based on an exhaustive search have proven to be effective for the kNN classification, their main drawback is their high computational complexity, especially with high-dimensional data. In this work, we present a novel and parallel algorithm to solve kNN queries on a multi-GPU platform. The proposed method is comprised of two stages, which first is based on pivots using the value of K to reduce the search space, and the second one uses a set of heaps to return the final results. Experimental results showed that using between 1-4 GPUs, the proposed algorithm achieves speed-ups of 117x, 224x, 330x, and 389x, respectively. Besides, the obtained results were compared with previous approaches of the state-of-the-art (cp-select and CUB Library), evidencing the superiority of our proposal.
LM-DAE: Low-Memory Deterministic Authenticated Encryption for 128-bit Security

This paper proposes a new lightweight deterministic authenticated encryption (DAE) scheme providing 128-bit security. Lightweight DAE schemes are practically important because resource-restricted devices sometimes cannot afford to manage a nonce properly. For this purpose, we first design a new mode LM-DAE that has a minimal state size and uses a tweakable block cipher (TBC). The design can be implemented with low memory and is advantageous in threshold implementations (TI) as a side-channel attack countermeasure. LM-DAE further reduces the implementation cost by eliminating the inverse tweak schedule needed in the previous TBC-based DAE modes. LM-DAE is proven to be indistinguishable from an ideal DAE up to the O(2n) query complexity for the block size n. To achieve 128-bit security, an underlying TBC must handle a 128-bit block, 128-bit key, and 128+4-bit tweak, where the 4-bit tweak comes from the domain separation. To satisfy this requirement, we extend SKINNY-128-256 with an additional 4-bit tweak, by applying the elastic-tweak proposed by Chakraborti et al. We evaluate the hardware performances of the proposed scheme with and without TI. Our LM-DAE implementation achieves 3,717 gates, roughly 15% fewer than state-of-the-art nonce-based schemes, thanks to removing the inverse tweak schedule.