Reviewerly/exHarmony huggingface dataset:
Moving Towards Intelligent Telemedicine: Computer Vision Measurement of Human Movement

Background The use of telemedicine consultations in healthcare systems is rapidly increasing around the world, accelerated by the global COVID-19 pandemic. Digital cameras integrated in laptops, as well as smartphones and tablet computers, have provided an accessible method for patients to consult with clinicians. However, telemedicine consultations remain severely limited compared to standard faceto-face consultations, because clinicians cannot accurately examine patients remotely. This limitation, combined with the swift uptake of telemedicine globally, results in significant risks to patient safety. Thus, there is a growing and urgent need to extend the capabilities of technologies so that telemedicine consultations can better meet the needs of patients and clinicians. In all areas of healthcare, clinicians assess human movement throughout the life course of their patients as a fundamental part of clinical assessment. Currently, there are no methods to do this objectively via telemedicine.Objectives Laptops are one of the most common devices used by patients for telemedicine consultations, but it is unclear whether videos from laptop cameras (with relatively low frame rate) can be used to accurately measure human movements at a range of speeds. The objectives of this study were to determine the validity and reliability of deep learning computer vision methods applied to video collected via a laptop camera to measure finger tapping, a well validated test of human movement.Method Sixteen healthy adults (9 female, mean age 34.5 years; range 24-52) completed finger-tapping tests internally-paced ‘as big and fast as possible’ and externally-paced by an auditory metronome at frequencies of 0.5Hz, 1Hz, 2Hz and 3Hz. Hand movements were recorded simultaneously by a standard laptop camera at 30 frames per second (FPS) and by Optotrak, a high-speed 3D motion analysis system sampling at 250 FPS. Three DeepLabCut (deep learning-based artificial neural network) architectures were applied to the laptop video to track thumb-tip and index fingertip position and the extracted movement features of each method were compared to the ground truth Optotrak motion tracking.Results The computer vision methods showed excellent validity and reliability compared to the Optotrak between 0.5Hz and 4Hz tapping frequency. Over 97% (552/538) of the computer vision measures were within +/-0.5Hz of the Optotrak measures. At higher tapping frequencies, there was a progressive decline in accuracy, attributed to motion blur associated with the laptop camera’s low FPS. This study shows that deep learning computer vision methods using laptop cameras hold strong potential for providing objective measurements of human movements during telemedicine consultations. This technology could substantially augment remote clinical assessments in healthcare systems but further developments will be required to accurately measure the fastest movements.
Construction of CDIO Integrated Teaching Management System of Software Course Based on Internet of Things

According to the current situation of teaching theory and practice of software engineering and software course, the CDIO integrated teaching system of Internet of things is constructed. Through the establishment of three modules, including theoretical teaching management, practical teaching management, and paperless examination management and combining with teaching information management and Internet of things perception teaching and other technical means, the educational concept and Internet of things technologies are organically integrated, realizing the integrated management of three modules.
Computer-Aided Instruction for Efficient Academic Writing

The paper tries to solve the problem of poor academic writing due to insufficient authors’ experience in written academic communication in a foreign language, their poor knowledge of international academic writing culture, and underdeveloped language skills. This issue is especially relevant to the universities situated in non-English speaking countries. To solve the problem, we equip our readers and potential authors with user-friendly instruction of aiding their writing with computer tools, which allow for the automation of particular writing tasks. These include paper planning, text structuring, guided writing, assessment, and proofreading. Automation is achieved through the usage of open-source online academic writing services, freely distributed desktop programs, applications, and add-ons for browsers. Among them are CohVis, De-Jargonizer, Gingko, Grammark, Marking Mate (standard version), Paper rater, Rationale, Research Writing Tutor, Write & Improve, and Writefull. The paper also provides some case studies to illustrate the efficiency of the tools in focus in teaching and implementing academic writing, which makes up the paper’s pragmatic value. All these tools have been checked for applicability and efficiency in the academic writing process, as well as consistency and digital security.
Integrated IoT Blockchain-Based Smart Agriculture System

Smart irrigation and automated crop field cultivation are innovative applications in the field of IoT. In this paper, an IoT-based agricultural information monitoring system involving IoT devices is designed. The controller unit collects data related to crops by connecting with temperature, humidity and soil dampness sensors. It is customized with limit estimations of temperature and moisture content to improve the agricultural yields. Challenges faced in the design of such architectures are addressed in this paper. The data collected from the sensors are transferred to the ThingSpeak cloud platform, where it is processed. The network is trained using Linear Regression (LR) model to guide the farmers for increased yield in the upcoming years.
Dongba Scripture Segmentation Algorithm Based on Discrete Curve Evolution

Text line segmentation is an important preprocessing work for Dongba document analysis. It is the basis of analysis and collection work such as character segmentation, Dongba pictograph extraction, Dongba pictograph translation and annotation segmentation, and sample collection. Combining with the structural characteristics of Dongba hieroglyphic documents, we give a text line segmentation algorithm which is suitable for Dongba hieroglyphic documents. The algorithm combines K-means clustering on the basis of projection segmentation to realize the automatic determination of the height range of a single text line. At the same time, it also realizes the automatic segmentation of too large or too small text blocks. This will lay the foundation for the automatic collection of Dongba pictographs and the completion of further research on Dongba documents.
Crowd Counting in High Dense Images using Deep Convolutional Neural Network

Crowd counting plays a significant role in analyzing the crowd behavior in high density areas. Deep learning techniques may be utilized to count the crowd from given high density images. This gives situation awareness and facilitates in imposing necessary actions to control the crowd in various scenarios when needed. In this paper a deep convolutional neural network model has been developed for crowd counting. The model has been developed using VGG16 pre-trained model and it is tuned up for crowd counting using transfer learning. The dataset used in this work is ShanghaiTech crowd dataset, that contains 482 high density crowd images. Image augmentation is applied to enlarge the dataset. The model gives a training accuracy of 83% and 79% of validation accuracy.
Block Chain Technology: Risk and Corporate GovernanceBlock Chain Technology: Risk and Corporate Governance

Blockchain technology (BCT) is a transparent, distributed, decentralised technological chain growing at CAGR of 35% in 2017 – increasing to 42% in 2018 and expected to involve a spend of $12 billion by 2022.Though it has innumerable pros, it can also be a trajectory of illicit and unregulated activities. BCT can offer smart solutions to classical governance inefficiencies incorporates more strongly by eliminating the potentiality of corporate frauds and also benefiting institutional investors, shareholders, in-house/outdoor stakeholders and minorities.
<i>Dandelion</i>: Boosting DNN Usability Under Dataset Scarcity

The development of deep neural network (DNN) has provided transformative impacts on many fields, including computer vision and video recognition. However, the impact is limited by the need for large, labeled datasets to enable effective training. To address this fundamental problem, we propose a novel inter-network system (Dandelion), providing architecture support (Dandelion-architecture) for data augmentation that trains DNNs with rare images generated by the generative adversarial network (GAN) with orthogonal attributes modified (Dandelion-function. The approach can account for the latency requirement and resource limitation of target applications by exploiting data and computation reuses between the two networks; this amortizes the impact of bottleneck brought by GAN and facilitates design of inter-network accelerator. Moreover, we show how to implement two-network design on 3D architecture to further enhance the accelerator. Our results show that with the generated images, DNN yields 13.6% - 37.5% improvement on accuracy, depending on the data scarcity level. Our architecture achieves at least 30% speedup compared with the baseline while 40% of the overhead brought by the incorporation of GAN is reduced in our design compared with ScaleDeep, and 26.3% of performance improvement over TETRIS.
Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

In this study, a new feature is added to the smart message passing interface (SMPI) approach (SMPIA) based on the prioritization method, which can completely eliminate the task starvation and lack of sufficient resources problems through prioritizing the tasks.The proposed approach is based on prioritizing the tasks and the urgency of implementation.Tasks are prioritized based on execution time, workload, the task with a more sensitive priority is executed earlier by the free source.The idea of demand-bound functions (DBFs) was extended to the SMPIA setting based on partitions and caps.For each task, two DBFs are constructed, DBFLO and DBFHI, for the LO and HI criticality modes, respectively.The simulation results returned by MATLAB showed that with the optimized SMPIA (O-SMPIA), the parameters of maximum service execution time, response time, delay time, and throughput improved in this work.In addition, the results confirmed that the reduction of execution time, completion time, and resource consumption time did not affect the response time and throughput of workflow tasks and did not cause inefficient use of resources in virtual machines (VMs) and data centers (DCs).The evaluation of performance metrics showed that the delay, response time of the Greedy algorithm was less than that of Max-Min and Min-Min.At the same time, the execution time of Max-Min was less than the others and the throughput of the Greedy was longer.The effect and throughput of O-SMPIA became more obvious as change to the job count and the number of cloud workloads increased.It is also worth mentioning that one of the main advantages of the O-SMPIA to other methods is the efficient use of time to execute all the defined tasks by CPU.
Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.
A Relative Study on Existing Models of e-Commerce Sites Based on Analytics

On the e-commerce websites people buy/ sell products or services by making monetary transactions through online payment system like debit card, credit card, UPI, payment wallet, internet banking. By 2021, mostly company depends on analytics techniques to increase the ROI of the business. Today, online social networking websites are providing a platform to the businesses to promote their products or services on social media sites and e-commerce sites. Today e-commerce websites not only provides huge data for e-commerce companies, but also provide insights data using so many analytics techniques which shows how people behave after seeing their ads and many more. This integration of e-commerce using analytics is the main area of the present research. The growth in electronic commerce has contributed to a major rise in demand for information for businesses. E-commerce turns old economic relationships into modern economic relationships characterized by end-to-end relationship management solutions. In this paper we study and compare the existing models of E-Commerce that based on business analytics models.
Universal Perturbation for Flow Correlation Attack on Tor

Tor is a popular anonymous social network. However, it is also concerned by censors or other malicious attackers. A large body of work examines Tor&#x2019;s susceptibility to flow correlation attacks. Moreover, the existing methods to defend against such attacks have two inherent drawbacks. One is they will bring high delay to the system, the other is they lack of theoretical basis to prove their effectiveness.This paper conducts the first experimental study of how to effectively defeat flow correlation attacks on Tor. We propose a new universal perturbation generation algorithm, a defense to achieve the goal of flawing flow correlation attacks by apply tiny perturbations to the traffic. Our approach uses adversarial sample technique to incorporate Tor traffic constraints and avoid the two drawbacks mentioned above. We evaluate it over five typical flow correlation attacks. Our results show the effectiveness and high transferability of the generated perturbations. For instance, by applying a perturbation with a tiny variance of only 10ms, the TP of original flow correlation attack is reduced from 82% to 60% and the surrogate attack decreases from 71% to 52%.
Remote sensing image object detection based on rotatable bounding box

Remote sensing image object detection is widely used in military investigations, disaster relief and urban traffic management. However, unlike ordinary images, remote sensing images are acquired from aerial photography, resulting in a variety of directions where targets are arranged. This situation leads to the poor detection accuracy of general object detection algorithms on remote sensing images. To address the problem that existing object detection algorithms have difficulty in detecting targets in remote sensing images with high accuracy, an improved YOLOv5 algorithm (Rotate-YOLOv5) was proposed for detecting arbitrary-oriented object in remote sensing images. Firstly, YOLOv5m was chosen as the baseline to build the network model, four types of movable targets were selected from the public dataset DOTA: plane, small vehicle, large vehicle and ship. And the dataset images were cropped to a size of 1024×1024 and preprocessed with mosaic data enhancements. And the anchor box size was determined by the adaptive anchor box filtering method. The long-edge definition method based on circular smoothing labels was used to achieve the rotation of the bounding box. The effect of angular periodicity on training was addressed by converting the regression problem into a classification problem. Finally, the CIoU loss was used as the loss function of the bounding box to improve the detection accuracy on the basis of ensuring the detection speed. The results show that the proposed algorithm achieves an improvement of 13.4% in mean average precision over YOLOv5. This algorithm can improve the accuracy of remote sensing image object detection.
Side Channel Attack

We discuss the side channel attacks with details. Power consumption is among the most commonly utilized leakage. We discuss about power models and various analysis techniques, their computational aspects, used, among others. Additionally, we show how a side channel attack is carried out in practice.
Disentangled Feature Network for Fine-Grained Recognition

Most of fine-grained recognition researches are implemented based on generic classification models as the backbone. However, it is a sub-optimal choice because the differences between similar categories in this task are so small that the models must capture discriminative fine-grained subtle variances. In this paper, we design a dedicated backbone network for fine-grained recognition. To this end, we propose a novel Disentangled Feature Network (DFN) that gradually disentangles and incorporates coarse- and fine-grained features to explicitly capture multi-grained features. Thus, it promotes the models to learn more representative features that potentially determine the classification results via easily replacing the original inappropriate backbone. Moreover, we further present an optional error correction loss to adaptively penalize misclassification between extremely similar categories and guide to capture fine-grained feature diversity. Extensive experiments fully demonstrate that when adopting our DFN as the backbone, like freebies, the baseline models boost the performance by about 2% with negligible extra parameters on widely used CUB, AirCraft, and Stanford Car dataset.
Introduction to Generative Adversarial Networks Challenges and Solutions

Deep learning has received spectacular adoption in the field of artificial intelligence. With this many deep learning models have been developed. Generative Adversarial Networks (GAN) is one of the deep learning model, which is based on the two-player game from the Game theory. Two neural networks namely; generator and discriminator compete with each other. The proposition of the model variation is to achieve the data distribution through unsupervised learning to generate more realistic data. At present, GANs have been widely studied due to the extensive application prospects which include computer vision like generating lot of data, image to image translation etc. In this paper, the background of the GAN with theoretic model is introduced. Finally the existing research challenges of GANs are discussed with probable solutions giving a wider area of research.
Progressive Web Apps Development and Analysis with Angular Framework and Service Worker for E-Commerce System

E-commerce is a small, medium, and large business that sells and buys products online. In Indonesia, e-commerce has become one of the drivers of the nation's economic development. On the other hand, the development of information technology is growing rapidly. Many technologies have claimed to be reliable in developing web systems. In this research, reliable information technology has been implemented by using Progressive Web Apps (PWA). This technology combines the Angular framework as a user interface development, the Angularfire library as a link to the server, and a Service Worker to support the website in various network conditions. The process of performance testing and analysis of the system has been carried out with two types of systems, with Service Worker and without Service Worker, including the response time, throughput, and latency. Base on the results, it shows that this technology is fast and reliable, and it is suitable for the implementation of e-commerce systems.
Fault Analysis of the ARIA and uBlock Block Ciphers

The ARIA block cipher is a Korean national standard, and the uBlock block cipher is an award-winning algorithm of the recent National Cryptographic Algorithm Design Competition in China. In this paper, we analyse the security of the ARIA and uBlock block ciphers against fault analysis, and show that the user key of ARIA can be recovered with a time complexity of about 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">17</sup> simple table lookups by introducing about 512 byte faults on average to the last four rounds in a ciphertext-only scenario, and that the user key of uBlock can be recovered with a time complexity of about 196 simple table lookups by introducing about 17.8 nibble faults on average to the last round in a chosen-plaintext scenario. To the best of our knowledge, our attack on ARIA is the first published ciphertext-only fault analysis of ARIA, and our attack on uBlock is the first published fault analysis of uBlock.
Link Harvesting on the Dark Web

In this information age, web crawling on the internet is a prime source for data collection. And with the surface web already being dominated by giants like Google and Microsoft, much attention has been on the Dark Web. While research on crawling approaches is generally available, a considerable gap is present for URL extraction on the dark web. With most literature using the regular expressions methodology or built-in parsers, the problem with these methods is the higher number of false positives generated with the Dark Web, which makes the crawler less efficient. This paper proposes the dedicated parsers methodology for extracting URLs from the dark web, which when compared proves to be better than the regular expression methodology. Factors that make link harvesting on the Dark Web a challenge are discussed in the paper.
Blockchain, Cryptocurrencies, and Distributed Organizations

One panel in the International Conference of Information Systems (ICIS), held in December 2016 in Dublin, attracted attention that surpassed the hundreds of seats of the auditorium and forced the security to close the doors when the stairs became completely occupied.The audience, eager to join the debate on the research perspectives on the topic of blockchain in the information systems (IS) field, represented one of the first academic manifestations of the great expectations placed on this emergent topic in this research community.One year later, the organizers of this panel (Avital, Beck, King, Rossi, & Teigland, 2016) were the editors of what was probably the first special issue on blockchain in a relevant IS journal (Beck, Avital, Rossi, & Thatcher, 2017).Although these events indicate milestones for the IS field, the blockchain started to shake the world almost a decade before.
Dynamic Missing Data Recovery Method with Low Complexity in Internet of Things

For the large-scale and high-dimensional internet of things (IoT) data, the missing values are very intractable, which affect the performance of related applications greatly. Based on this, a dynamic missing data recovery method with low complexity is proposed in this paper. Firstly, we use matrix factorization (MF) technology to map the IoT data to low dimensional space for analysis and processing. Then, taking full advantage of the characteristics of IoT data, the objective function of MF is improved furtherly. Last, the dynamic updating mechanism with low complexity is proposed to update the missing data recovery model constructed when new sampled data comes. The simulation results on two real data sets indicate the proposed method can effectively recover the missing data.