Reviewerly/exHarmony huggingface dataset:
TaskHerder

Notifications have become a core component of the smart-phone as our ubiquitous companion. Many of these only require minimal interaction, for which the smartwatch is a helpful companion device. However, its design and placement is influenced by its traditional ancestors. For applications where the user is constrained because of a specific usage situation, or performs tasks with both hands simultaneously, interaction with the smartwatch can be cumbersome. In this paper, we propose a wearable armstrap for minimal interaction in long-lived tasks. Placed around the elbow, it is outside the hands' proximal working space which reduces interference. Its flexible e-ink display provides screen space to provide overview information at minimal energy consumption for longer uptime. We designed the wearable for a professional use-case, meaning that is can easily be placed above protective clothing as its flexible round shape easily adjusts to various diameters. Capacitive touch sensing allows gesture input even under rough conditions, e.g., with gloves.
Quid

Quid is a web modeling tool aiming to create a complete modeling environment for prototyping Web Components in the browser. The approach presented introduces a concise DSL based on minimal syntax and indentation to implement containment relationships. A WYSIWYG editor is also provided to allow users to explore in real-time the design been constructed. Model-driven techniques and code generation are used to explore different implementation choices with different Web Components frameworks.
Comparison of Accuracy between Convolutional Neural Networks and Naïve Bayes Classifiers in Sentiment Analysis on Twitter

The needs and demands of the community for the ease of accessing information encourage the increasing use of social media tools such as Twitter to share, deliver and search for information needed.The number of large tweets shared by Twitter users every second, making the collection of tweets can be processed into useful information using sentiment analysis.The need for a large number of tweets to produce information encourages the need for a classifier model that can perform the analysis process quickly and provide accurate results.One algorithm that is currently popular and is widely used today to build classifier models is Deep Learning.Sentiment analysis in this research was conducted on English-language tweets on the topic "Turkey Crisis 2018" by using one of the Deep Learning algorithms, Convolutional Neural Network (CNN).The resulting of CNN classifier model will then be compared with the Naïve Bayes Classifier (NBC) classifier model to find out which classifier model can provide better accuracy in sentiment analysis.The research methods that will be carried out in this research are data retrieval, pre-processing, model design and training, model testing and visualization.The results obtained from this research indicate that the CNN classifier model produces an accuracy of 0.88 or 88% while the NBC classifier model produces an accuracy of 0.78 or 78% in the testing phase of the data test.Based on these results it can be concluded that the classifier model with Deep Learning algorithm produces better accuracy in sentiment analysis compared to the Naïve Bayes classifier model.
Path Planning of Multiple Unmanned Aerial Vehicles Based on RRT Algorithm

An optimal path planning method for multiple autonomous UAVs have been based on the modification of Rapidly-exploring Random Tree (RRT) algorithm by using the Rapidly-exploring Random Tree (RRT). The proposed method improves the path of RRT algorithm in 2D configuration space. The improved RRT algorithm is used to form the same path for each iteration and generate the minimum distance between the nodes. Moreover, the path reconstruction strategy has been put further to handle the problem of multiple UAVs. The effectiveness of the proposed method has been demonstrated through simulation using MATLAB.
Recovery of the Opinions Through the Specificities of Documents Text

With the explosive growth of textual data from social media (social networks, forums, and blogs), the exploitation of these new sources of information has become crucial. Our work focuses on the sentiment analysis in this context of social media. To identify sentiments from messages, original text mining techniques must be proposed. This article presents a new method that integrates semantic and lexical knowledge for sentiment analysis. The proposed approach gives significant weight to the “sentiment words” for a classification task. Our study focuses on the 88milSMS corpus to highlight the specific characteristics of SMS data in the context of social media.
A Weighted Minimum Distance Classifier Based on Relative Offset

In many classifiers based on supervised learning, the Minimum Distance Classifier (MDC) is widely used because of its simplicity and effectiveness. But it has the disadvantage of the low accuracy on classification. To improve the performance of the MDC, this paper proposed the Weighted Minimum Distance Classifier based on Relative Offset (WMDCRO). Under the condition of the Fisher criterion, this method defined the weights of each category attribute in terms of the relative distance. It analyzed and judged the degree of clustering about data in the feature space by using the relative offset. The experiment results show that the accuracy of this classifier is higher than that of the traditional MDC. Compared with the Naive Bayes classifier, the classification stability of this classifier is better.
Environmental Accounting and Financial Performance of Oil and Gas Companies in Nigeria

Environmental accounting serves as a systematic approach in managing the environmental aspects of company activities.The shift in business economics towards environmental issues has led to the realization of the increasing importance of research in environmental accounting.This study employs the use of questionnaires in order to receive direct (primary) information from companies that produces environmentally friendly products.The findings in this study suggests among other things, that lack of environmental reporting and disclosure standards significantly affects the reporting and disclosure uniformity of environmental related information in financial statements, annual reports and accounts.Thus, environmentally friendly organizations who voluntary disclose their environmental activities enjoy high level of competiveness.Still, such disclosure is guided by the social responsibility and commitment on the part of entities that work as strong agents for polluting the environment.Conclusively, issues related to financial performance, managerial accounting, external and internal auditing, tax and financial accounting need to be studied further in order to deal with other environmental issues effectively.This study thereby gave some recommendations among others is the recommendation that government should make environmental reporting in annual reports compulsory since most organizations hardly report their environmental activities in their reports.
Multidimensional structure for the effect of innovation culture and knowledge sharing on the new product development process with emphasis on improving new product development performance

The main goal of the paper is to study the effect of innovation culture and knowledge sharing on the new-product development (NPD) process with emphasis on improving new-product development performance. The research has been implemented in software developer or producer companies as well as information technology companies in Tehran, Iran. This study is an applied research in the goal view, and contains qualitative variables; also, it is a descriptive-survey and casual-correlation research in the data-gathering view. The validity of the content of the questionnaire posited in this study was confirmed by academic and industrial experts and constructive validity indices. Moreover, the reliability of the questionnaire was confirmed using Cronbach's alpha and composite reliability indicators. Findings have shown that appropriate infrastructure for innovation has the most effect on creating an innovation culture; it was also revealed that there is a positive and meaningful relationship between innovation culture and new-product development performance which is made by the mediation of knowledge sharing and NPD process.
THE RELATION OF GENDER AND TRACK ON HIGH SCHOOL STUDENTS’ ATTITUDE TOWARD CONVERGENCE

Preparing a positive attitude toward convergence is essential to help students grow into future generations with the ability to solve various complex problems. This research aims to examine high school students' attitude toward convergence and the relation to gender and track. Responses from 1,186 Indonesian students in tenth (10th) and eleventh (11th) grade were purposively collected. Students were administered twenty-three items of attitude toward convergence covering five constructs: knowledge, personal relevance, social relevance, interest, and self-efficacy. Data were analyzed using IRT-Rasch analyses, two-way ANOVA, Pearson correlation analysis, and cluster analyses. The primary finding of this research presented that high school students’ attitude toward convergence on the construct of interest and self-efficacy is significantly related to gender and track. Female students show to be more interested in convergence than male students. However, female students have lower self-efficacy. Additionally, science track students tend to be more interested in convergence than humanities track and showing higher self-efficacy. After performing cluster analysis, students are divided into three particular groups according to their attitude toward convergence. Finally, customized learning is proposed to improve students’ attitude toward convergence. Keywords: attitude toward convergence, female, humanities track, male, science track.
Argumentation Reasoning via Circumscription with Pyglaf

The fundamental mechanism that humans use in argumentation can be formalized in abstract argumentation frameworks. Many semantics are associated with abstract argumentation frameworks, each one consisting of a set of extensions, that is, a set of sets of arguments. Some of these semantics are based on preference relations that essentially impose to maximize or minimize some property. This paper presents the argumentation reasoner PYGLAF, which provides a uniform view of many semantics for abstract argumentation frameworks in terms of circumscription. Specifically, several computational problems of abstract argumentation frameworks are reduced to circumscription by means of linear encodings, and a few others are solved by means of a sequence of calls to an oracle for circumscription. Finally, grounded extensions are obtained in polynomial time by unit propagation, and acceptance problems are addressed by first checking cardinality optimal models of circumscribed theories, so that the naive extension enumeration is possibly avoided.
Natural Obstacles and Biological Salmon Behaviors Link to Modelling Approaches of Computational Intelligence Procedures for the Standard System

Abstract Since classical mathematical approaches have been applied to many technical and theoretical problems, they are useful and accurate for searching solutions even they suffer from large systems and multi spaces. Recently, many algorithms have been proposed for introducing new approaches conducted to phenomena or entities in nature. Many biological behaviors and mechanisms are adopted to replace classical methods which are presented in various names as performed for the natural inspiration. In these works, the novel computational intelligence is explored in Artificial Salmon Tracking Algorithm (ASTA). ASTA is developed based on the natural obstacles and biological Salmon behaviors link to modeling approaches of computational intelligence procedures. Moreover, ASTA is applied to a standard system model considering environmental requirements for the global warming parameter. The system process is supported by suppliers to fulfill a sustainable operation while the productions are also subjected to reach clean and green targets. In these studies, ASTA is also used to optimize the system and to get an optimal portion of the balanced combination of the system results. The biological Salmon behavior presented in ASTA is also tested based on technical requirements; the results show that the solution is produced dynamically to feed the operation. The system model is balanced in various combination portions of the solution while ASTA has been demonstrated clearly to search for optimal solutions.
Sentimental text mining based on an additional features method for text classification

Owing to the emergence of the Internet and its rapid growth, people can use mobile devices on many social media platforms (blogs, Facebook forums, etc.), and the platforms provide well-known websites for people to express and share their daily activities and ideas on global issues. Many consumers utilize product review websites before making a purchase. Many well-known websites are searched for relevant product reviews and experiences of product use. We can easily collect large amounts of structured and unstructured product data and further analyze the data to determine the desired product information. For this reason, many researchers are gradually focusing on sentiment analysis or opinion exploration (opinion mining) and use this technique to extract and analyze customer opinions and emotions. This paper proposes a sentimental text mining method based on an additional features method to enhance accuracy and reduce implementation time and uses singular value decomposition and principal component analysis for data dimension reduction. This study has four contributions: (1) the proposed algorithm for preprocessing the data for sentiment classification, (2) the additional features to enhance the accuracy of the sentiment classification, (3) the application of singular value decomposition and principal component analysis for data dimension reduction, and (4) the design of five modules based on different features, with or without stemming, to compare the performance results. The experimental results show that the proposed method has better accuracy than other methods and that the proposed method can decrease the implementation time.
EverParse: Verified Secure Zero-Copy Parsers for Authenticated Message Formats

We present EverParse, a framework for generating parsers and serializers from tag-length-value binary message format descriptions. The resulting code is verified to be safe (no overflow, no use after free), correct (parsing is the inverse of serialization) and non-malleable (each message has a unique binary representation). These guarantees underpin the security of cryptographic message authentication, and they enable testing to focus on interoperability and performance issues. EverParse consists of two parts: LowParse, a library of parser combinators and their formal properties written in F*; and QuackyDucky, a compiler from a domain-specific language of RFC message formats down to low-level F* code that calls LowParse. While LowParse is fully verified, we do not formalize the semantics of the input language and keep QuackyDucky outside our trusted computing base. Instead, it also outputs a formal message specification, and F* automatically verifies our implementation against this specification. EverParse yields efficient zero-copy implementations, usable both in F* and in C. We evaluate it in practice by fully implementing the message formats of the Transport Layer Security standard and its extensions (TLS 1.0–1.3, 293 datatypes) and by integrating them into miTLS, an F* implementation of TLS. We illustrate its generality by implementing the Bitcoin block and transaction formats, and the ASN.1 DER payload of PKCS#1 RSA signatures. We integrate them into C applications and measure their runtime performance, showing significant improvements over prior handwritten libraries. EverParse is open-source and publicly available on GitHub. You can view Antoine Delignat-Lavaud’s presentation at USENIX Security 2019.
Accelerating Deep Learning Inference via Freezing.

Over the last few years, Deep Neural Networks (DNNs) have become ubiquitous owing to their high accuracy on real-world tasks. However, this increase in accuracy comes at the cost of computationally expensive models leading to higher prediction latencies. Prior efforts to reduce this latency such as quantization, model distillation, and any-time prediction models typically trade-off accuracy for performance. In this work, we observe that caching intermediate layer outputs can help us avoid running all the layers of a DNN for a sizeable fraction of inference requests. We find that this can potentially reduce the number of effective layers by half for 91.58% of CIFAR-10 requests run on ResNet-18. We present Freeze Inference, a system that introduces approximate caching at each intermediate layer and we discuss techniques to reduce the cache size and improve the cache hit rate. Finally, we discuss some of the open research challenges in realizing such a design.
Modular Ontology Learning with Topic Modelling over Core Ontology

Nowadays, modular domain ontology, where each module represents a subdomain of the ontology domain, facilitates the reuse of information and provides users with domain-specific knowledge. In this paper, we focus on modular taxonomy learning from text, where each module collects terms with the same topic insights, and in parallel we manage to discover hypernym and 'related' relations among those collected terms.However, it is difficult to automatically fit terms into modules and discover relations.We propose to employ twice trainedLDAto partition termsof each subdomain, and relate subdomains into modules of ontology. Meanwhile, we apply core concept replacement and subdomain knowledge supplementation as supportive information embedding technique over the corpus. This shows that the twice trained LDA strategy can effectively identify topic-relevant terms into subdomains, with nearly two-fold precision comparing to that of normal LDA training. The combination of core concept replacement and subdomain knowledge supplementation contributes to significant improvements in modular taxonomy learning.
Eye Gaze Sequence Analysis to Model Memory in E-education

Intelligent Tutoring Systems are now mature technologies that successfully help students to acquire new knowledge and competencies through various educational methods and in a personalized way. However, evaluating precisely what they recall at the end of the learning process remains a complex task. In this paper, we study if there are correlations between memory and gaze data in the context of e-education. Our long-term goal is to model the memory of students thank to an eye-tracker in a continuous and transparent way. These models could then be used to adapt recommendations of pedagogical resources to the students’ learning rate. So as to address this research question, we designed an experiment where students were asked to learn a short lesson about Esperanto. Our results show that some gaze characteristics are correlated with recall in memory.
Optimal Wavelet Coefficients Based Steganography for Image Security with Secret Sharing Cryptography Model

Image security on web exchanges is the major concern of the hour as the breaching attacks into the image databases are rising every year. Numerous researchers are investigated the image security with Steganography, cryptography, encryption, watermarking et cetera, our proposed model image Steganography with Secret Share cryptography (SSC) is considered to upgrade the security level, here Medical images are considered for stego image creation process. In the wake of inserting of secret data with cover image Optimal Discrete Wavelet Transform (DWT) used to transform the area, here Daubechies (db2) coefficients are utilized, in addition upgrading the PSNR Continues Harmony Search (CHS) used to enhance those coefficients. At last, SS are made for lower band stego images with high security process. Visual Cryptography is utilized to encrypt a secret image into redid adaptations of the first image which prompts computational unpredictability and furthermore create share. In view of above process the secret data or image hided and anchored, finally apply converse procedure to recover the first image. From the execution results, PSNR, hiding capacity, error rate is computed, its contrasted with existing Techniques.
Ownership Identification of Digital Video via Unseen-Visible Watermarking

Unseen visible watermarking has been emerged as a promising data hiding approach which emulates real-world watermarks and preserves the advantages of both visible and invisible watermarking. Recently, several methods have been proposed which attempt of improving the advantages of this promising technique, however most of them are focused on still images. In this paper we propose an unseen visible watermarking technique applied to digital video. Our proposed method takes into account spatial and temporal characteristics of digital video sequence to reduce computational cost. Several experiments have been carried out to demonstrate the advantages of the proposed method in terms of imperceptibility, which is especially important due to the high redundancy of video content, and readability of the watermark. The reported results validate that the proposed scheme is effective and appropriate to determine ownership identification of digital video.
Analysis and Improvement of MPEG-DASH-based Internet Live Broadcasting Services in Real-world Environments

Adaptive bitrate streaming is a crucial element in the implementation of high-quality streaming of media content over the Internet.Dynamic adaptive streaming over HTTP (MPEG-DASH) has lately emerged as the de facto solution for over-the-top (OTT) video streaming services.In this paper, we perform macro-level analysis on a real-world MPEG-DASH-based Internet live broadcasting service to gain insight into its behavior throughout the end-to-end service-provision chain, from broadcasters to viewers.Based on this analysis, we propose methods to improve the quality-of-experience (QoE) of MPEG-DASH-based services, particularly with regard to reducing broadcasting delays between recording and viewing of videos, as well as synchronizing client terminals.
Lost in Online Stores? Agent-Based Modeling of Cognitive Limitations of Elderly Online Consumers

We have developed an agent-based model of e-commerce platform users’ behavior with emphasis on reflecting decision-making characteristics of elderly adults. The model has been used to verify how cognitive deficits of older customers influence the effectiveness of collaborative filtering and content-based recommender systems. The results from our simulation suggest that the effectiveness of recommender systems in improving quality of elderly consumers choices is low for population of agents with strong cognitive deficits.
Andragogy as a Scientific Basis for Training Professionals in Information Security

The paper presents the results of research on the use of the basic provisions of andragogy to improve modern systems of training professionals in a particular professional field (information security). To do this, the characteristics of such systems were determined, the classification of trainees according to the criteria of adulthood was carried out, the possibilities of applying the basic provisions of andragogy to the object (an educational system) and the subject (processes, methods, and technologies of training) of research in the selected area were considered.