Reviewerly/exHarmony huggingface dataset:
Immersive Image QoE in Mobile Consumer Virtual Reality Settings

We evaluate the Quality of Experience (QoE) for spherical images presented in a multimedia consumer setting with commercial mobile devices. We find that approximations and initial predictions from the non-referential BRISQUE image quality metric is possible within ten percent of the common Likert-type scale ratings. A comparison of results with those obtained in augmented reality settings yields significant differences for the same content, requiring different approaches for approximations. Overall, our findings can readily applied in real world settings by network and content service providers in future offerings.
Improving Attitude Words Classification for Opinion Mining Using Word Embedding

Recognizing and classifying evaluative expressions is an important issue of sentiment analysis. This paper presents a corpus-based method for classifying attitude types (Affect, Judgment and Appreciation) and attitude orientation (positive and negative) of words in Spanish relying on the Attitude system of the Appraisal Theory. The main contribution lies in exploring large and unlabeled corpora using neural network word embedding techniques in order to obtain semantic information among words of the same attitude and orientation class. Experimental results show that the proposed method achieves a good effectiveness and outperforms the state of the art for automatic classification of attitude words in Spanish language.
Third workshop on theory-informed user modeling for tailoring and personalizing interfaces (HUMANIZE)

The third workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE)1 took place in conjunction with the 24th annual meeting of the intelligent user interfaces (IUI)2 community in Los Angeles, CA, USA on March 20, 2019. The goal of the workshop was to attract researchers from different fields by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of six papers were accepted for this edition of the workshop.
Fundamental Architectures of Datacenter ECR System Prototype by Applying Principle of Messaging Services

Interoperability is a component that contains information systems integration. It provides an interface from multiple data sources to support data and information integrity between various information systems. This study aims to: (1) Propose Architecture of Messaging services to support data integration of Datacenter ECR. (2) Present a fundamental architecture prototype Datacenter ECR with messaging services principle. This research is expected to provide the solution to the data integration architecture between systems that is running on HTTP protocol over internet. The integration model uses messaging services schemes and improve it with customization end-to-end encryption messages. This type of research is research and development (Research and Development) with Agile SCRUM Framework research model. It will provide any robust workflows to each step of development and management resources during design architecture and develop Datacenter ECR prototype. For the testing process, three (3) test process stages are performed: (1) White-box and (2) Black-box testing (3) Performance consistency testing
Simultaneous interaction with dimension reduction and clustering projections

Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide feedback to the system, demonstrating relationships that the analyst wishes to find within the projection. However, determining the precise intent of the analyst is a challenge; when an analyst interacts with a projection, the system could infer a variety of possible interpretations. In this work, we explore interaction design considerations for the simultaneous use of dimension reduction and clustering algorithms to address this challenge.
Text Mining Techniques for Intelligent Grievances Handling System: WECARE Project Improvements in EgyptAir

The current work provides quick responding and minimize the required time of processing of the incoming grievances by using automated categorization that analyses the English text contents and predict the category.This work built a model by text mining and NLP processing to extract the useful information from customer grievances data to be used as a guideline to air transport industry.A customer grievances' system in EGYPTAIR called WECARE has had large feeds of data which can be collected in data sets through various channels such as e-mail, website or mobile Apps.Then the incoming data sets are analyzed and assessed by organization's staff then it is assigned to related department through manual classification.Finally, it provides proposed solution for the issue.Thence grievances categorization that handled manually is time consuming process.So, this work decided a model to improve WECARE system in Egypt Airlines.Classification based data mining Techniques are used to identify data into groups of categories across the variable touch points.The system has 166 categories of problems, but for experimental purposes we decided to study six categories only.We have applied four commonly used classifiers, namely, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayesian and Decision Tree on our data set to classify the grievances data set then selecting the best of them to be the candidate grievances classifier in enhanced WECARE system.Among four classifiers applied on the dataset, KNN achieved the highest average accuracy (97.5%) with acceptable running time.Also, the work is extended to make hint to the system user, about how to solve this grievance issue based on previous issues saved in Knowledge Base (KB).Several experiments were conducted to test solution hint module by changing similarity score.The benefits of performing a thorough analysis of problems include better understanding of service performance.
Relation Extraction Datasets in the Digital Humanities Domain and Their Evaluation with Word Embeddings

In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models. The first step comprises the creation of unigram and n-gram datasets for two fantasy novel book series for two task types each, analogy and doesn’t-match. This is followed by the training of models on the two book series with various popular word embedding model types such as word2vec, GloVe, fastText, or LexVec. Finally, we evaluate the suitability of word embedding models for such specific relation extraction tasks in a situation of comparably small corpus sizes. In the evaluations, we also investigate and analyze particular aspects such as the impact of corpus term frequencies and task difficulty on accuracy. The datasets, and the underlying system and word embedding models are available on github and can be easily extended with new datasets and tasks, be used to reproduce the presented results, or be transferred to other domains.
Analysis of solar energy potentials at Nansha Guangzhou

The increasing energy supply is believed to exceed the capacity of three existing thermal power plants at Nansha Guangzhou for the rapid development. Thus constructing an eco-friendly energy structure will be one of the main themes during the region planning in near future. In this study, solar energy resources were assessed theoretically and simulated by PVsyst, PVGIS, Global Solar Atlas, RETScreen and SSE Release 6.0 as well. It drew a conclusion that from 2010 to 2014, sun exposure hour percentage averaged at 41.28% and demonstrated prominent higher from June to November, solar radiation was extremely stable and extremely abundant in this area. Furthermore, simulation results of horizontal irradiation carried out by SSE Release 6.0, PVsyst (based on NASA-SSE) and Global Solar Atlas were much similar, averaged at 3.93 kWh/m2/day; results provided by Global Solar Atlas (4.01 kWh/m2/day) were more visualized and representative if thinking about the latest database. For another, results calculated by PVGIS (4.22 kWh/m2/day) were relatively higher, and that of Meteonorm 6.1 (3.50 kWh/m2/day) were relatively conservative. In conclusion, it's feasible to offer stable solar energy supply at Nansha Guangzhou especially in summer and autumn.
Stable and Practical AS Relationship Inference with ProbLink

Knowledge of the business relationships between Autonomous Systems (ASes) is essential to understanding the behavior of the Internet routing system. Despite significant progress in the development of sophisticated relationship inference algorithms, the resulting datasets are impractical for many critical real-world applications, cannot offer adequate predictability in the configuration of routing policies, and suffer from inference oscillations. To achieve more practical and stable relationship inferences we first illuminate the root causes of the contradictions between these shortcomings and the near-perfect validation results of AS-Rank, the state-of-the-art relationship inference algorithm. Using a naive inference approach as a benchmark, we find that the available validation datasets over-represent AS links with easier inference requirements. We identify which types of links are harder to infer, and we develop appropriate validation subsets to enable more representative evaluation. We then develop a probabilistic algorithm, ProbLink, to overcome the inference barriers for hard links, such as non-valley-free routing, limited visibility, and non-conventional peering practices. To this end, we identify key interconnection features that provide stochastically informative and highly predictive relationship inference signals. Compared to AS-Rank, our approach reduces the error rate for all links by 1.6\times×, and importantly, by up to 6.1 times for different types of hard links. We demonstrate the practical significance of our improvements by evaluating their impact on three applications. Compared to the current state-of-the-art, ProbLink increases the precision and recall of route leak detection by 4.1 times and 3.4 times respectively, reveals 27% more complex relationships, and increases the precision of predicting the impact of selective advertisements by 34%.
Extracting Side-Channel Leakage from Round Unrolled Implementations of Lightweight Ciphers

Energy efficiency and security is a critical requirement for computing at edge nodes. Unrolled architectures for lightweight cryptographic algorithms have been shown to be energy-efficient, providing higher performance while meeting resource constraints. Hardware implementations of unrolled datapaths have also been shown to be resistant to side channel analysis (SCA) attacks due to a reduction in signal-to-noise ratio (SNR) and an increased complexity in the leakage model. This paper demonstrates optimal leakage models and an improved CFA attack which makes it feasible to extract first-order side-channel leakages from combinational logic in the initial rounds of unrolled datapaths. Several leakage models, targeting initial rounds, are explored and 1-bit hamming weight (HW) based leakage model is shown to be an optimal choice. Additionally, multi-band narrow bandpass filtering techniques in conjunction with correlation frequency analysis (CFA) is demonstrated to improve SNR by up to 4×, attributed to the removal of the misalignment effect in combinational logics and signal isolation. The improved CFA attack is performed on side channel signatures acquired for 7-round unrolled SIMON datapaths, implemented on Sakura-G (XILINX spartan 6, 45nm) based FPGA platform and a 24× reduction in minimum-traces-to-disclose (MTD) for revealing 80% of the key bits is demonstrated with respect to conventional time domain correlation power analysis (CPA). Finally, the proposed method is successfully applied to a fully-unrolled datapath for PRINCE and a parallel round-based datapath for Advanced Encryption Standard (AES) algorithm to demonstrate its general applicability.
Key regression from constrained pseudorandom functions

Key regression is a primitive that allows a content publisher to give a small amount of keying material, for time stamp i, to a party such that they can derive key ki and all keys kj for time stamps j<i, but not for time stamps j>i. Existing key-regression schemes based on random oracles, pseudorandom permutations, and pseudorandom generators require a publisher to maintain state of size linear in the number of potential keys. In previous work, constant sized publisher state has only been accessible through the use of trapdoor permutations (e.g., RSA). This paper presents a novel key-regression scheme where the publisher must store only a constant amount of state information without trapdoor permutations. This is achieved through the use of constrained pseudorandom functions, a primitive that allows for the delegation of partial evaluation without revealing the key.
V-tracer: a Vehicular Trace Generator for Future Predictive Maintenance

In this paper we present V-tracer, a vehicular trace generator aimed at generating realistic data about mobility of vehicles, as well as their daily operation and wear. The objectives of our approach are two: first, gathering real traces obtained by in-vehicle on-board units (OBUs), and second, as the first target is hard to achieve, generating synthetic data. The final goal will be getting all the information that would be very useful to infer and predict vehicle failures. The traces provided by our generator may be used to perform the predictive maintenance of vehicles in the near future.
Speech Synthesis for Conversational News Contents Delivery

We have been developing a speech-based “news-delivery system”, which can transmit news contents via spoken dialogues. In such a system, a speech synthesis sub system that can flexibly adjust the prosodic features in utterances is highly vital: the system should be able to highlight spoken phrases containing noteworthy information in an article; it should also provide properly controlled pauses between utterances to facilitate user’s interactive reactions including questions. To achieve these goals, we have decided to incorporate the position of the utterance in the paragraph and the role of the utterance in the discourse structure into the bundle of features for speech synthesis. These features were found to be crucially important in fulfilling the above-mentioned requirements for the spoken utterances by the thorough investigation into the news-telling speech data uttered by a voice actress. Specifically, these features dictate the importance of information carried by spoken phrases, and hence should be effectively utilized in synthesizing prosodically adequate utterances. Based on these investigations, we devised a deep neural network-based speech synthesis model that takes as input the role and position features. In addition, we designed a neural network model that can estimate an adequate pause length between utterances. Experimental results showed that by adding these features to the input, it becomes more proper speech for information delivery. Furthermore, we confirmed that by inserting pauses properly, it becomes easier for users to ask questions during system utterances.
Evaluating expressiveness of a voice-guided speech re-synthesis system using vocal prosodic parameters

Contour is a voice-guided speech re-synthesis system we previously developed for efficient TTS (Text-to-Speech) content production. In this follow-up evaluation study, we investigate qualities of synthetic speech produced using Contour against a conventional parametric-based workflow by evaluating expressive dimensions of produced TTS content using vocal prosodic parameters. Based on the quantitative and qualitative results, we discuss user preferences between these two workflows for producing TTS content.
An Effective Approach Based on a Subset of Skeleton Joints for Two-Person Interaction Recognition

In this article, the problem of human interaction recognition is addressed. A novel methodology is presented to model the interaction between two people using a Kinect sensor. It is proposed to analyse the distances between a subset of skeleton joints to determine their contribution to the recognition of interaction. Subsequently, the most representative joints are taken into account for the formation of a pentagon for each person. Five Euclidean distances are extracted between the vertices of two pentagons corresponding to the people to analyse. Finally, the SVM is used for the recognition of human interaction. Experimental results demonstrate the effectiveness of this work compared to recently published proposals.
MultiDomainBenchmark: a multi-domain query and subject database suite

Genetic sequence database retrieval benchmarks play an essential role in evaluating the performance of sequence searching tools. To date, all phylogenetically diverse benchmarks known to the authors include only query sequences with single protein domains. Domains are the primary building blocks of protein structure and function. Independently, each domain can fulfill a single function, but most proteins (>80% in Metazoa) exist as multi-domain proteins. Multiple domain units combine in various arrangements or architectures to create different functions and are often under evolutionary pressures to yield new ones. Thus, it is crucial to create gold standards reflecting the multi-domain complexity of real proteins to more accurately evaluate sequence searching tools.This work introduces MultiDomainBenchmark (MDB), a database suite of 412 curated multi-domain queries and 227,512 target sequences, representing at least 5108 species and 1123 phylogenetically divergent protein families, their relevancy annotation, and domain location. Here, we use the benchmark to evaluate the performance of two commonly used sequence searching tools, BLAST/PSI-BLAST and HMMER. Additionally, we introduce a novel classification technique for multi-domain proteins to evaluate how well an algorithm recovers a domain architecture.MDB is publicly available at http://csc.columbusstate.edu/carroll/MDB/ .
Secure Trick-Taking Game Protocols

Trick-Taking Games (TTGs) are card games in which each player plays one of his cards in turn according to a given rule. The player with the highest card then wins the trick, i.e., he gets all the cards that have been played during the round. For instance, Spades is a famous TTG proposed by online casinos, where each player must play a card that follows the leading suit when it is possible. Otherwise, he can play any of his cards. In such a game, a dishonest user can play a wrong card even if he has cards of the leading suit. Since his other cards are hidden, there is no way to detect the cheat. Hence, the other players realize the problem later, i.e., when the cheater plays a card that he is not supposed to have. In this case, the game is biased and is canceled. Our goal is to design protocols that prevent such a cheat for TTGs. We give a security model for secure Spades protocols, and we design a scheme called SecureSpades. This scheme is secure under the Decisional Diffie-Hellman assumption in the random oracle model. Our model and our scheme can be extended to several other TTGs, such as Belotte, Whist, Bridge, etc.
Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation

We propose a novel approach for modeling semantic contextual relationships in videos. This graph-based model enables the learning and propagation of higher-level spatial-temporal contexts to facilitate the semantic labeling of local regions. We introduce an exemplar-based nonparametric view of contextual cues, where the inherent relationships implied by object hypotheses are encoded on a similarity graph of regions. Contextual relationships learning and propagation are performed to estimate the pairwise contexts between all pairs of unlabeled local regions. Our algorithm integrates the learned contexts into a Conditional Random Field (CRF) in the form of pairwise potentials and infers the per-region semantic labels. We evaluate our approach on the challenging YouTube-Objects dataset which shows that the proposed contextual relationship model outperforms the state-of-the-art methods.
Cognitive load during gaze typing

Cognitive load derived from pupillary measures has been studied extensively in the human-computer interaction community, using various kinds of tasks and measures. In this paper, we present preliminary results on the cognitive load due to a gaze typing task of varying complexities. A pilot study was conducted to observe if a task on gaze typing sentences, of varying complexities, could generate different cognitive load in the participants. The data obtained was from pupils, blink data, pupil oscillation measured in form of Index of Pupillary Activity and subjective scores. Logistic regression was performed to observe the difference between easy and difficult tasks and they were successfully differentiated, with 75% accuracy, using the measures of absolute pupil size, relative pupil size and subjective scores given by the participants.
Super resolution projection: leveraging the MEMS speed to double or quadruple the resolution

Texas Instruments’ digital mirror device (DMD) uses thousands to millions of individual micromirrors to direct light as a Spatial Light Modulator (SLM). The Tilt-Roll-Pixel (TRP) is currently the smallest DLP Products pixel node at 5.4&mu;m pitch. The small micromirror size, which enables fast switching speed, and precise tilt angles, exploits this speed on a system level to double or quadruple the resolution by using super-resolution projection. Super-resolution projection overlays multiple sub-sampled images, each shifted on the screen by a fraction of a pixel, and as long as the shifting occurs at a rate faster than the critical flicker fusion threshold, the human visual system will act as a temporal low pass filter and naturally integrate all low-resolution SLM images into a single super-resolution result. This paper will discuss the operation of the TRP node, how this node can be operated more quickly, how super-resolution projection works, and how we modified the optical architecture to leverage the switching speed for super-resolution projection.
Enhance Rating Algorithm for Restaurants

More and more people make their purchase decisions by referring to reviews and ratings provided in online platforms. Visitors to restaurants use online reviews on a larger scale compared with the users of other industries. However, for these visitors, evaluating numerous reviews is a hassle and time consuming as it involves a process of reading through all the reviews, identifying the date of review posting, understanding the reviewer’s credibility and identifying the rating of the reviewer and the restaurant before making the decision. This research proposes an enhanced rating algorithm which will calculate an overall rating. Apart from the standard point rating the solution will include the aspect, sentiment, time factor and user credibility of a review. The enhanced algorithm uses Natural Language Processing and Sentiment analysis used with machine learning to identify the thoughts of the user regarding the restaurants. The algorithm is tested with a web-based solution that gives an overall idea of the current performance of a particular restaurant utilizing reviews of those restaurants. The new algorithm gives a much credible rating than the conventional rating systems.