Reviewerly/exHarmony huggingface dataset:
Extracting low-dimensional psychological representations from convolutional neural networks.

Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images. While the predictive power of these networks is high, it is often not clear whether they also offer useful explanations of the task at hand. Convolutional neural network representations have been shown to be predictive of human similarity judgments for images after appropriate adaptation. However, these high-dimensional representations are difficult to interpret. Here we present a method for reducing these representations to a low-dimensional space which is still predictive of similarity judgments. We show that these low-dimensional representations also provide insightful explanations of factors underlying human similarity judgments.
Inferring political preference from Twitter tweets

Commercial popularity of social media and free availability of vast data has enhanced the interests of the researchers in analyzing its contents. Many businesses make use of such data to harness the mindset and likes of their target audience, thereby improving their profits. Sentiment analysis of Twitter texts have proved to be an effective way of voicing the needs of large masses and is used by many prominent politicians in making better campaigning strategies. Multiple machine learning classifiers are implemented in this study to access the stance of US citizens towards Democrats/ Republicans to deduce which political party a user prefers from his tweets. Performance of a stacked ensemble is compared against a deep neural network for the mentioned problem domain.
Intermediate Value Size Aware Coded MapReduce

MapReduce is a commonly used framework for parallel processing of data-intensive tasks, but its performance usually suffers from heavy communication load incurred by the shuffling of intermediate values (IVs) among computing servers. Recently, the Coded MapReduce framework is proposed which uses a coding scheme named coded distributed computing (CDC) to trade the communication load with extra computation in MapReduce. CDC can achieve the optimal computation-communication tradeoff when all the IVs have the same size. However, in many practical applications, the sizes of IVs can vary over a large range, leading to inferior performance. In this paper, we introduce a generalized CDC scheme which takes the sizes of IVs into account and then propose a combinatorial optimization problem aiming to minimize the communication load when the computation load is fixed. We show that the problem is NP-hard, and further propose a very efficient algorithm which achieves an approximation ratio of 2. Experiments conducted on Alibaba Cloud show that, compared to the original CDC scheme, our proposed IV size aware approach can significantly reduce the communication load and achieve a lower total execution time.
A Perioperative Care Display for Understanding High Acuity Patients

The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types.In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms.We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions.Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries.This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.
Learning by playing Init2Winit: How alignment knowledge increases educational aspirations and college plans in high school

Init2Winit, a gamified mobile application designed to increase students’ alignment knowledge between college, career, and salary expectations has been beta-tested in midwestern urban and rural U.S. high schools (age 14–18). Students earn different points in the game when their educational expectations and expected annual income are matched with their desired careers. Propensity score matching was used with 481 high school students to compare the changes in 4-year college plans, college-going aspirations, and educational expectations between players and non-players. Results show that Init2Winit players significantly increased their 4-year college plans and educational aspirations from fall 2018 to spring 2019 compared to those students who did not play the game.
Research on Traffic Information Detection of the Visually Impaired Based on Improved YOLOv3

Abstract Among the target detection algorithms, YOLOv3 algorithm has fast detection speed and high accuracy, but it is difficult to directly deploy to small embedded devices because of its high requirements for the computing power of the device. In response to this problem, this paper combines the characteristics of EfficientNet-lite network and YOLOv3, and proposes an improved model of YOLOv3 combined with EfficientNet-lite network. This model takes advantage of the small size and high efficiency of the EfficientNet-lite network to reduce the size of the model, so that it can be applied to wearable devices to help blind people traveling to detect environmental information. Experimental results show that the model greatly reduces its size and its dependence on equipment performance under the premise of a small decrease in detection accuracy.
Kripto Paranın Değerini Etkileyen Değişkenlerin İncelenmesi

Technically cryptocurrencies often have Distributed Ledger Technology (DLT) and encryption based on infrastructure called blockchain that allows all nodes to verify the validity of a transaction. In terms of monetary theory, cryptocurrencies are currently the most developed virtual currencies that cannot perform all the basic functions of money such as the account, exchange and capital accumulation.The price of cryptocurrency is based on supply and demand, without an intervention of a central authority. Dynamics that affect the value of cryptocurrencies can be classified as internal and external variables. The internal dynamics of cryptocurrencies have been examined under the headings of economic infrastructure and technological infrastructure. External factors that are effective in determining the value are observed as popularity, security, volume, inflation, tax, crypto exchange accidents, perception, speculations / manipulations and news.
Blockchain-based Efficient Energy Trading Scheme for Smart-Grid Systems

Bidirectional flow of power has opened up numerous opportunities for efficient usage of electricity. Vehicle to Grid (V2G) support has enabled easy transfer of power from the batteries of electric vehicles and then to the consumers itself. In order to make this practically applicable, a well designed system is needed for peer-to-peer (P2P) exchange of electricity for currency. Since it involves monetary transactions, it is expected to be highly secure and auditable by the government. The blockchain technology provides a transparent and secure means to achieve this. The distributed nature of blockchain provides resiliency to the system and also makes it highly available. It also provides a means of enabling government audits and immutability of data ensures full transparency. Motivated from these facts in this paper, we propose an auction model to maximize the utilization of power by trading energy for currency based on blockchain. It uses geolocation as the prime parameter of visibility between the consumers and aggregators of power and aims to create an ecosystem of energy transfer. It ensures direct transactions between suppliers and consumers of electricity within a specified safe range and government permissioned transfer for larger transactions.
A Complete and Fast Scraping Method for Collecting Tweets

In this paper, we propose a scraping method for collecting tweets, which we call DeepScrap. DeepScrap provides the complete scraping for the recent tweets that can be viewed on a specific user's page and crawls with a fast speed that overcomes the limited rates in Twitter APIs. Especially, to improve the crawling speed of DeepScrap, we devise a multiprocessing architecture while assigning different IPs to the multiple processes to follow the robots.txt of Twitter. This allows us to maximize the parallelism of crawling in a machine. We show that DeepScrap can crawl the entire tweets that are crawled by Twitter standard APIs by analyzing the tweets on 97 users. Through extensive experiments, we show that DeepScrap can crawl the entire tweets of 97 users, which amounts to 222,194 tweets while Twitter standard API can crawl only 12,586 tweets of them because of the constraints. We also show that multiprocessing of DeepScrap improves single processing of DeepScrap by 2.97 times to crawl 222,194 tweets for 97 users when four processes are running simultaneously.
Supplement for "Me vs. Super(wo)man: Effects of Customization and Identification in a VR Exergame"

This supplement describes an approach that can be used to create an “enhanced“ avatar based on a) a realistic, current avatar (R) and b) an idealised, desired future avatar (I) of a user. The aim of the approach is to create avatars that reflect “enhancements” of the realistic avatar along a realistic trajectory. The realistic avatar is used as a starting point, and the idealised avatar as a “goal”.
A Study of Securing Healthcare Big Data using DNA Encoding based ECC

IT world is migrating towards utilizing cloud computing as an essential data storing and exchanging platform. With the amelioration of technology, a colossal amount of data is generating with time. Cloud computing provides an enormous data storage capacity with the flexibility of accessing it without the time and place restrictions with virtualized resources. Healthcare industries spawn intense amounts of data from various medical instruments and digital records of patients. To access data remotely from any geographical location, the healthcare industry is moving towards cloud computing. EHR and PHR are patient's digital records, which include sensitive information of patients. Apart from all the proficient service provided by cloud computing, security is a primary concern for various organizations. To address the security issue, several cryptographic techniques implemented by researchers worldwide. In this paper, a vigorous cryptographic method discussed which is implemented by combining DNA cryptography and Elliptic Curve Cryptography to protect sensitive data in the cloud.
Identification of Indian classical languages using Convolutional Recurrent Neural Networks

Language Identification (LID) is a well explored research problem, and it is useful as a front end for several speech processing applications. The task of LID system is to recognize the exact language in a given speech utterance. Several LID systems exist for global languages, trained using several hours of data. But it is a challenging task for Indian languages due to similarities of languages and scarcity of labelled data resources for training. For that reason, this paper aims to solve the LID task of Indian languages using a Convolutional Recurrent Neural Network (CRNN) that uses raw speech as the input. CRNN model is the combination of Convolutional Neural Network (CNN) along with Long-Short-Term-Memory (LSTM). As a result, the CRNN model is assumed to capture the information of both sequential and spatial features from the visual representation of sound. The paper focus on the implementation of a speaker-invariant LID system that classifies all the Indian classical languages namely Kannada, Malayalam, Odia, Sanskrit, Tamil and Telugu. Apart from the identification of six Indian languages, performance comparison of CNN models and CRNN models is also demonstrated in this paper. The LID system also compares with some existing LID techniques and discovers that CRNN model is very effective and significantly outperforms some of the existing LID models.
Skeleton-Based Methods for Speaker Action Classification on Lecture Videos

The volume of online lecture videos is growing at a frenetic pace. This has led to an increased focus on methods for automated lecture video analysis to make these resources more accessible. These methods consider multiple information channels including the actions of the lecture speaker. In this work, we analyze two methods that use spatio-temporal features of the speaker skeleton for action classification in lecture videos. The first method is the AM Pose model which is based on Random Forests with motion-based features. The second is a state-of-the-art action classifier based on a two-stream adaptive graph convolutional network (2S-AGCN) that uses features of both joints and bones of the speaker skeleton. Each video is divided into fixed-length temporal segments. Then, the speaker skeleton is estimated on every frame in order to build a representation for each segment for further classification. Our experiments used the AccessMath dataset and a novel extension which will be publicly released. We compared four state-of-the-art pose estimators: OpenPose, Deep High Resolution, AlphaPose and Detectron2. We found that AlphaPose is the most robust to the encoding noise found in online videos. We also observed that 2S-AGCN outperforms the AM Pose model by using the right domain adaptations.
Computer-based Classification of Student's Report

Abstract. Learning Analytics focuses on measuring and analyzing learners’ data, such as formative assessment of collaborative writing and individual students’ performance. This work applied machine learning approaches and natural language processing to assess university students’ reports in the knowledge building domain. Students (n = 32) wrote essays about knowledge building topics, and the professor used Dublin descriptors as assessment criteria to evaluate the students’ reports. This paper presents the results of a study on validating whether students’ reports are aligned with Dublin descriptors for qualifications awarded. We have used two classification models: Support Vector Machine (SVM) and Random Forest Classifier (RFC), to predict manual annotations from experts in students’ reports. Random Forest Classifier reached 73% accuracy. We concluded that machine learning algorithms and natural language processing (NLP) together are useful for automating the classification of the students’ reports using manual annotations.
The 1st International Workshop on Machine Reasoning

Recent years have witnessed the success of machine learning and especially deep learning in many research areas such as Vision and Language Processing, Information Retrieval and Recommender Systems, Social Networks and Conversational Agents. Though various learning approaches have demonstrated satisfying performance in perceptual tasks such as associative learning and matching by extracting useful similarity patterns from data, the area still sees a large amount of research needed to advance the ability of reasoning towards cognitive intelligence in the coming years. This includes but is not limited to neural logical reasoning, neural-symbolic reasoning, causal reasoning, knowledge reasoning and commonsense reasoning. The workshop focuses on the research of machine reasoning techniques and their application in various intelligent tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent progress in machine intelligence to a broader community, including but not limited to CV, IR, NLP, ML, DM, AI and beyond.
Medical Image Compression: A Leap on Recent Progress and Publications

Information management is crucial to the success of the modern health care system. The increased volume of image data generated by the modern diagnostic imaging modalities demands more storage space and bandwidth. It is mandatory that hospitals have to store the patient's medical record for further references. The traditional text-oriented patient information system is being replaced by cloud-based patient medical record. Cloud-based healthcare system relies much on the digital data for keeping track of patient's health status. In the telemedicine sector, we need to transmit this information over networks in a reliable and cost-effective way. The medical images have the disadvantage of the requirement of larger memory in the database. To overcome the deal with the storage, transmission and high-quality reconstruction problems of medical images, extensive compression techniques are required. This paper presents the concepts of image compression and an extensive survey on image compression techniques for the telemedicine sector.
Polynomial Based Representation for DNA Sequence Compression and Search

Data compression plays an important role in lesser expensive resource consumption. It is also cumbersome to send a huge amount of data through the network. The data compression techniques help in optimizing physical storage devices as well as it makes easy to transfer the compressed data or file faster through internet or network. Deoxyribonucleic acid or DNA is biological traits presents in human and almost all other living organisms. The DNA contains the genetic information of the living organisms. In this research, various compression algorithms for DNA sequences are studied and compared. The scheme proposed here for compression is the use of a burrows wheeler compression technique. Our proposed algorithm is the modification of the Burrows Wheeler Compression Algorithm (BWCA). The lexicographical sorting of the matrix is replaced by the polynomial base representation of the bases of DNA. This method reduces the time for compression positively. Validation was performed on various datasets that concur the efficiency of the proposed scheme.
Hybrid Cryptographic Approach to Safeguard Cloud Computing Services: A Survey

Cloud computing is a broad solution that cost-effectively delivers information technology as a service that encourages organizations and individuals to transfer their data and applications to the cloud. It may be used for storing and accessing sensitive information on servers beyond the management and control of the customers making this information susceptible to malicious attacks. Therefore, data security is a fundamental issue that limits the benefits of this technology. Cryptography plays an important role in achieving an appropriate level of data security on the cloud. Hybrid cryptography exploits the advantages of symmetric and asymmetric cryptosystems by blending them instead of applying separately. In this article, an experimental survey has been conducted on the trending hybrid cryptographic models to secure information on the cloud within the period from 2017 to 2020. Each model was reviewed and simulated separately by explaining the methodology, its advantages, weaknesses, and application areas. An important point in this article is the implementation of all of these models using the Python programming language then an analytical comparison of the results was obtained in terms of encryption time and throughput. We aim to provide a scientific contribution in the field of information security in the cloud computing environments.
Solar Panel Position Monitoring System for Maximum Power Generation

The research on power generation renewable energy sources are increasing; in this paper the proposing automatic position control system of solar panel is introduced as the position of sun is changing throughout the day, in order to maximize the generation, i.e, maximizing the conversion of solar energy to electrical energy. The solar panel has to be faced toward the sun in order to get maximum solar energy. The light dependent resistors (LDR) is used as the sensor to detect the intensity of sunlight. And the main heart of this project is arduino Uno, where all the processing is done to know the position of the sun. Servo motor is used to move the solar panel based on signal received from arduino. This system is eco-friendly and is operating at low cost. Finally, the result will show the effectiveness of this system when compared to regular solar system.
Development Trend of Blockchain Technology in Large-Scale Sports Events in the Information Age

With the arrival of the data era, the rapid development of Internet of Things technology, block chain technology was proposed. Blockchain has received extensive attention since its publication. Its tamper-proof, traceable, anonymous and open features have attracted more and more people’s interest, and many researchers and relevant practitioners have stepped into this field. In this paper, the relevant technological concepts of blockchain are expounded, and the blockchain technology is integrated into the industrial innovation of sports events, so as to provide new ideas for the sports industry economy. This article through to our country sports industry and the scale of chain industry development, and the block distribution chain industry data statistical analysis, obtained about block chain used in the sports industry can have very good development prospects, through the study of the data transmission rate and success rate of platform test, rate can be stabilized at about 0.17 S, the success rate can be stable at more than 95%, fully meet the market demand for technology.