Reviewerly/exHarmony huggingface dataset:
Comparative Evaluation of User Perceived Quality Assessment of Design Strategies for HTTP-based Adaptive Streaming

HTTP-based Adaptive Streaming (HAS) is the dominant Internet video streaming application. One specific HAS approach, Dynamic Adaptive Streaming over HTTP (DASH), is of particular interest, as it is a widely deployed, standardized implementation. Prior academic research has focused on networking and protocol issues, and has contributed an accepted understanding of the performance and possible performance issues in large deployment scenarios. Our work extends the current understanding of HAS by focusing directly on the impacts of choice of the video quality adaptation algorithm on end-user perceived quality. In congested network scenarios, the details of the adaptation algorithm determine the amount of bandwidth consumed by the application as well as the quality of the rendered video stream. HAS will lead to user-perceived changes in video quality due to intentional changes in quality video segments, or unintentional perceived quality impairments caused by video decoder artifacts such as pixelation, stutters, or short or long stalls in the rendered video when the playback buffer becomes empty. The HAS adaptation algorithm attempts to find the optimal solution to mitigate the conflict between avoiding buffer stalls and maximizing video quality. In this article, we present results from a user study that was designed to provide insights into “best practice guidelines” for a HAS adaptation algorithm. Our findings suggest that a buffer-based strategy might provide a better experience under higher network impairment conditions. For the two network scenarios considered, the buffer-based strategy is effective in avoiding stalls but does so at the cost of reduced video quality. However, the buffer-based strategy does yield a lower number of quality switches as a result of infrequent bitrate adaptations. Participants in buffer-based strategy do notice the drop in video quality causing a decrease in perceived QoE, but the perceived levels of video quality, viewer frustration, and opinions of video clarity and distortion are significantly worse due to artifacts such as stalls in capacity-based strategy. The capacity-based strategy tries to provide the highest video quality possible but produces many more artifacts during playback. The results suggest that player video quality has more of an impact on perceived quality when stalls are infrequent. The study methodology also contributes a unique method for gathering continuous quantitative subjective measure of user perceived quality using a Wii remote.
A Novel Framework for Credit Card Fraud Prevention and Detection (CCFPD) Based on Three Layer Verification Strategy

In the banking sector, credit card based frauds are rising rapidly and causing a huge amount of financial losses every year. This problem highlights the requirement of an efficient system to prevent and detect fraud activities at the application level and transaction level. With these considerations in mind, a frame-work called Credit Card Fraud Prevention Detection (CCFPD) is proposed for the prevention and detection of application & transaction level frauds and the various adaptive credit card fraud prevention methods are discussed in details in this paper. The proposed framework is designed based on three-layer verification strategies to verify and reduced the credit card application and transaction frauds, for this purpose we used a communal detection, spike detection, genetic algorithm, and threshold values. To test the efficiency of the proposed framework, we used the German dataset and genetic algorithm. The genetic algorithm has selected the best fifteen features and the performance compared with Best First Search+SVM, Ranker Search+SVM, Random Search+SVM & Tabu Search+SVM methods. The outcomes show that the performance of the proposed approach is better than other methods. Also, a comprehensive list of various security guidelines is discussed in this paper that helps credit card users from preventing fraudulent activities. The framework can help the new researcher to discover credit card (financial) fraud more efficiently.
A Client-Biased Cooperative Search Scheme in Blockchain-Based Data Markets

Lots of privacy and security issues in the current cloud-based data markets will be eliminated by taking advantage of blockchain-based decentralized storage services, which can provide a new paradigm for safe data outsourcing and correct remote search. However, existing data markets are also questioned on their inflexible and opaque pricing, where the value of data ownership and the cost of query search are mixed. Thus, a better pricing model is necessarily needed in an emerging decentralized data market. In this paper, we envision an Ethereum-based data market, in which the pricing model for each query includes two parties: owner (paid for his data ownership) and miner (rewarded by query search). We study a new cooperative search scheme through a proxy to reduce cost on the client (user) side. Suppose each user query is charged based on the number of keywords in the query. The cost reduction is based on combining multiple queries into a group subject to the constraint that the resulting combined query is not significantly larger than any of its original query in terms of the number of keywords. The total price is based on total number of keywords in all groups. As the optimal grouping depends on the pricing of both owner and miner, we build a small testbed to analyze how price setting will affect grouping results. Since it is a cooperative model with shared resources, we also study various incentive properties on the client side, thereby yielding a cost sharing mechanism to split joint cost in a truth-revealing and fair manner.
Evaluating the Impact of Sampling-Based Nonlinear Manifold Detection Model on Software Defect Prediction Problem

Accurate prediction of defects is considered an essential factor, depending mainly on how efficiently testing of different prediction models has been done. Earlier, most of the models were restricted to the use of feature selection methods that had limited effects in solving this problem in initial stage of software development. To overcome it, the application of software defect prediction model using modern nonlinear manifold detection (nonlinear MD) combined with SMOTE using four machine learning classification approaches has been proposed in a way that the challenging task of defect prediction has been categorized as problem of high-dimensional datasets, problem of imbalanced class, and identification of most relevant and effective software attributes. Then, statistically evaluated and compared performance of prediction model with or without SMOTE-nonlinear MD approaches and results validated that proposed SMOTE-nonlinear MD approach prediction model predicts defect with better accuracy than others using RMSE, accuracy, and area under the curve.
Multivariate Maps—A Glyph-Placement Algorithm to Support Multivariate Geospatial Visualization

Maps are one of the most conventional types of visualization used when conveying information to both inexperienced users and advanced analysts. However, the multivariate representation of data on maps is still considered an unsolved problem. We present a multivariate map that uses geo-space to guide the position of multivariate glyphs and enable users to interact with the map and glyphs, conveying meaningful data at different levels of detail. We develop an algorithm pipeline for this process and demonstrate how the user can adjust the level-of-detail of the resulting imagery. The algorithm features a unique combination of guided glyph placement, level-of-detail, dynamic zooming, and smooth transitions. We present a selection of user options to facilitate the exploration process and provide case studies to support how the application can be used. We also compare our placement algorithm with previous geo-spatial glyph placement algorithms. The result is a novel glyph placement solution to support multi-variate maps.
Topic model for online communities’ interests prediction

This paper introduces the investigation of users interests by topic modeling. This work proposes a methodology for end-to-end unsupervised users topics investigation by textual data. Using additive regularization of topic models with large topic counts, tokens frequency filtering, lemmatization and communities information, one found interpretable interests. This paper shows differences of extracted topics from users posts and communities ones, from public user posts and hidden information from subscriptions. These topics differ significantly. This paper proposes a topics evaluation method by testing model prediction on the semi-automated labeled dataset. To get a more stable topic model one suggests using an averaged topic distribution based on users subscriptions. For cases with a large number of topics, one proposes an auto labeling approach solving as a classification problem with a constant number of classes.
Particle Swarm Optimized Ensemble Learning for Enhanced Predictive Sentiment Accuracy of Tweets

The mounting global interest of users in social media portals has reinforced research in knowledge discovery domains to mine useful information from the publicly available user-generated big data. Uncertainty is often linked with the online content, mostly owing to the diverse, noisy, or unstructured data which might be imprecise or vague. Determining appropriate features that could yield enhanced sentiment predictive accuracy becomes a tedious and an arduous task which motivates for automating the sentiment classification predictive task. The empirical study is investigated on the tweets fetched from two standard Twitter datasets namely SemEval 2016 & SemEval 2017. Consequently, we propose the use of swarm based feature selection for enhancing classifier efficiency. The proposed feature selection using PSO outperforms baseline ensemble learning algorithm trained using conventional tf-idf. An average 8.5% improvement in accuracy with 33% reduction in feature set is obtained by implementing particle swarm optimization.
Poster Abstract: Deep Learning Workloads Scheduling with Reinforcement Learning on GPU Clusters

With the recent widespread adoption of deep learning (DL) in academia and industry, more attention are attracted by DL platform, which can support research and development (R&D) of AI firms, institutes and universities. Towards an off-the-shelf distributed GPU cluster, prior work propose prediction-based schedulers to allocate resources for diverse DL workloads. However, the prediction-based schedulers have disadvantages on prediction accuracy and offline-profiling costs. In this paper, we propose a learning-based scheduler, which models the scheduling problem as a reinforcement learning problem, achieving minimum average job completion time and maximum system utilization. The scheduler contains the designs of state space, action space, reward function and update scheme. Furthermore, we will evaluate our proposed scheduler implemented as a plugin of Tensorflow on real cluster and large-scale simulation.
Improving Taxonomy of Errors in Chat-Oriented Dialogue Systems

In previous studies, top-down and bottom-up approachesHigashinaka, Ryuichiro have been proposed for creating taxonomies of errors in chat-orientedAraki, Masahiro dialogueTsukahara, Hiroshi systems. However, the reported $$\kappa $$ (kappa) value for the taxonomy basedMizukami, Masahiro on the top-down approach is low at 0.239, and no evaluation has been conducted for that based on the bottom-up approach. In this paper, we propose to revise these taxonomies to achieve better inter-annotator agreement. The revised taxonomy based on the bottom-up approach yielded a reasonable $$\kappa $$ of 0.44 (Fleiss’ $$\kappa $$ ), suggesting that this taxonomy can be used reliably to classify errors in chat-oriented dialogue systems.
Feature Extraction Mining for Student Performance Analysis

The performance of students was bad instead of the extreme effect of its teachers in a Portuguese school. The core problem is there low performance in two of the main subjects i.e., Portuguese language and mathematics. Now as the technology has improved in years so several machine learning techniques can be used based to analyze the performance. There are several factors which have directly and/or indirectly affect on student’s performance. The real world data was collected [16]. It consists of two main datasets of Portuguese and mathematics and the different features related to students and their marks in the form of G1, G2, and G3. To identify the features which are responsible for any task, is very important. Although student performance is deeply impacted by past evaluations predictive analysis has shown that there are also other relevant features. By this research, we came to know that more efficient algorithm can be used to make results more predictable and then more focusing on those factors the quality of education can be improved and also can be used to improve the resources of school management. Therefore there is need to apply machine learning models (i.e. Simple vector machines, Random Forest, Logistic regression and Support Vector Machines) to analyze the performance of the students based on certain features.
ITADP: An inter-tenant attack detection and prevention framework for multi-tenant SaaS

Software-as-a-service (SaaS) is a service-oriented Web application running on a Cloud environment. With the multi-tenancy, the SaaS provider can largely reduce the cost of resources and maintenance by sharing the application and database instances between its tenants (clients). This multi-tenancy affects the security of tenants, specifically, when several tenants use the same tables of a single database. Indeed, an important consequence of this full multi-tenancy is that a malicious tenant user can view or modify the rows of other tenants. Consequently, the detection and prevention of attacks among tenants is a key security requirement that should be addressed by the provider. In this sense, this paper proposes an inter-tenant attack detection and prevention framework, based on SQL syntactic analysis, for multi-tenant SaaS. This framework is integrated in Amazon Web Services (AWS) public Cloud and meets accuracy, portability, compatibility, and ease of integration requirements. The experiment results show that the framework works with small overhead on the virtual machines and minimal impact on the HTTP response time.
Evaluation of the readability of online texts related with autism spectrum disorder

Aim: There has been a rapid incline in the number of patients diagnosed with autism spectrum disorder (ASD), recently.The present study aims to analyze the content and readability of online Turkish texts, on Turkish websites related to autism spectrum disorder.Methods: In order to collect the necessary data of the present study, Google (http://www.google.com.tr), a commonly used internet search engine in our country, was used.Hence 300 websites from the first ten generated pages after writing the keywords autism, atypical autism, and autism spectrum disorder on the search engine were analyzed.Out of these 300 sites, sites containing only advertisement, videos, images and sites containing less than ten sentences, chat and forum sites, commercial sales sites and news sites that do not contain information about ASD were excluded.The mean number of syllables, the average number of words with four syllables and above, the average number of words and readability values were calculated according to Ateşman and Bezirci-Yılmaz. Results:The mean number of words in the analyzed texts was 14.42 ± 0.48, of words with four syllables and above was 4.52 ± 0.17, of syllables was 2.92 ± 0.01 and the readability value of the texts analyzed is according to Bezirci-Yılmaz was 14.68 ± 0, 52 and according to Ateşman it was 43.72 ± 1.45.The mean number of words among the study groups is (p = 0.538), the mean number of words with four and more syllables is (p = 0.548) and the mean number of syllables is (p = 0.652), Bezirci-Yılmaz readability value is (p = 0.772), Ateşman readability value is (p = 0.626) displaying no statistically significant difference. Conclusion:As a result of this study, it was found that the data presented on the internet about ASD was far beyond the health literacy level of our people.
Students Head-Pose Estimation Using Partially-Latent Mixture

Head-pose estimation considers the most popular area of research. Estimating the head pose variation in the correct way from faces shots is an essential step for building interactive vision-based applications. In this work, a head-pose estimation system that predicts a head-pose angle for students during studying is proposed. The bounding-box method for face detection is used and a high-dimensional vector-space of faces using HOG features is adopted. A mixture of linear regression technique is applied to determine how to map high-dimensional vector-space onto bounding-box shifts and both head-pose parameters. The performance of the proposed system is evaluated and checked using the absolute error between the predicted angle and the target angle, after that, the mean absolute error (MAE) is computed along with standard deviation (STD).
Prototyper: A Virtual Remote Prototyping Space

Collaborative virtual environment groupware is—despite of notable research efforts over several years—still not common in users’ workplaces. Reasons are high costs of engaging in collaboration next to the loss of information and capabilities that people enjoy in co-located settings. Low-fidelity prototyping is a way for co-located teams to create joint understandings and to gather feedback in early design stages. When it comes to geographically dispersed teams, dedicated tools are required that help to fulfill tasks at hand, while enabling team members as much as possible to apply working modes known from co-located settings. We present a web browser-based collaborative virtual environment that supports the joint real time creation of three-dimensional low-fidelity prototypes. It is a cross-platform application that runs on a multitude of hardware devices. While focusing on usage with virtual reality hardware, users may also freely participate when there are only traditional input and output devices available. The system provides enhanced awareness through visual remote user embodiment combined with spatial audio communication.
How do the Swiss Perceive Electronic Voting? Social Insights from an Exploratory Qualitative Research

Electronic voting is enjoying growing interest within the scientific community. However, the focus is on systems (algorithms, mathematical cryptographic models, user experience, reliability, traceability, security, etc.). Consequently, the purpose of this exploratory research on e-voting is not to address aspects that have already been well-studied by scientists, but rather to understand, through a qualitative research, bottlenecks and sociological obstacles. This understanding will help to explain the reasons that might prevent its adoption by Swiss citizens and also the dissemination of e-voting in the digital age. Based on 25 semi-directed interviews (in German, French and Italian) that we have analyzed, we are able to provide new insights that are more sociological than technological. These insights are essentially related to the social acceptance of e-voting. We observe in particular that the vote in Switzerland has an almost sacred dimension and that the trust that surrounds the voting “ritual” is of supreme importance.
Landau–Zener transitions in coupled qubits: Effects of coloured noise

The feasibility of stochastic Hamiltonians for interacting qubits to deliver consistent quantum speedups in quantum adiabatic optimisation remains an open question. This paper studies theoretically the noisy Landau–Zener model of a system of two interacting qubits. Exact analytic results are obtained for all transition probabilities via nonstationary perturbation technique, even where semiclassical trajectories interfere. Where the system is driven by slow classical Gaussian noise, destructive interference of the trajectories results in forbidden transitions. However, in the fast noise regime, these transitions have non-zero probabilities. We elucidate the roles of noise parameters on the decoherence of coupled qubits.
Motion Gaming AI using Time Series Forecasting and Dynamic Difficulty Adjustment

This paper proposes a motion gaming AI that encourages players to use their body parts in a well-balanced manner while promoting their player experience. The proposed AI is an enhanced version of our previous AI in two aspects. First, it uses time series forecasting to more precisely predict what actions the player will perform with respect to its candidate actions, based on which the amount of movement to be produced on each body part of the player against each of such candidates is derived; as in our previous work. the AI selects its action from those candidates with a goal of making the player’s movement of their body parts on both sides equal. Second, this AI employs Monte-Carlo tree search that finds candidate actions according to dynamic difficulty adjustment. Our results show that the proposed game AI outperforms our previous AI in terms of the player’s body-movement balancedness, enjoyment, engrossment, and personal gratification.
Automatic Generation of Level Maps with the Do What’s Possible Representation

Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what’s possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.
Robust Continuous Build-Order Optimization in StarCraft

To solve complex real-world planning problems it is often beneficial to decompose tasks into high-level and low-level components and optimize actions separately. Examples of such modularization include car navigation (a high-level path planning problem) and obstacle avoidance (a lower-level control problem), and decomposing playing policies in modern video games into strategic ("macro") and tactical ("micro") components. In real-time strategy (RTS) video games such as StarCraft, players face decision problems ranging from economic development to maneuvering units in combat situations. A popular strategy employed in building AI agents for complex games like StarCraft is to use this strategy of task decomposition to construct separate AI systems for each of these sub-problems, combining them to form a complete game-playing agent. Existing AI systems for such games often contain build-order planning systems that attempt to minimize makespans for constructing specific sets of units, which are typically decided by hand-coded human expert knowledge rules. Drawbacks of this approach include the human expert effort involved in constructing these rules, as well as a lack of online adaptability to unforeseen circumstances, which can lead to brittle behavior that can be exploited by more advanced opponents. In this paper we introduce a new robust build-order planning system for RTS games that automatically produces build-orders which optimize unit compositions toward strategic game concepts (such as total unit firepower), without the need for specific unit goals. When incorporated into an existing StarCraft AI agent in a real tournament setting, it outperformed the previous state-of-the-art planning system which relied on human expert knowledge rules for deciding unit compositions.