Reviewerly/exHarmony huggingface dataset:
Energy-Oriented Designs of an Augmented-Reality Application on a VUZIX Blade Smart Glass

The advent of wearable devices introduces many opportunities with unconventional computing paradigms. In this work, we investigate energy-efficient designs of an augmented- reality application, FaceReminder, on a VUZIX Blade <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> smart glass by exploiting its unique see-through display and on-board camera. Powered with well-known facial recognition techniques, FaceReminder aims at helping people with prosopagnosia (i.e., face blindness) or short-memory of face-name connection by showing the names of the person on the see-through display. To cope with the limited resources (especially battery) of the smart glass, we explore designs that offload portion of the computation in facial detection and recognition to another mobile device (e.g., smart phone), which pairs with the glass via Bluetooth. Several optimization techniques, such as resolution adjustment and cropping, have been investigated to improve the latency and energy efficiency with reduced image sizes. We implemented FaceReminder and empirically evaluated its accuracy, latency and energy consumption of the major steps (including photo taking, resizing/cropping, Bluetooth transmission, facial detection and recognition). Compared to the baseline Glass-Only design, the most efficient Paired Glass-Device design with photos of reduced resolution and MTCNN facial detection technique can reduce the average latency by 73% and energy consumption by 78.9% (i.e., about 5× battery life improvement) while maintaining more than satisfactory 80% recognition accuracy.
Perspective Distortion Modeling for Image Measurements

A perspective distortion modelling for monocular view that is based on the fundamentals of perspective projection is presented in this work. Perspective projection is considered to be the most ideal and realistic model among others, which depicts image formation in monocular vision. There are many approaches trying to model and estimate the perspective effects in images. Some approaches try to learn and model the distortion parameters from a set of training data that work only for a predefined structure. None of the existing methods provide deep understanding of the nature of perspective problems. Perspective distortions, in fact, can be described by three different perspective effects. These effects are pose, distance and foreshortening. They are the cause of the aberrant appearance of object shapes in images. Understanding these phenomena have long been an interesting topic for artists, designers and scientists. In many cases, this problem has to be necessarily taken into consideration when dealing with image diagnostics, high and accurate image measurement, as well as accurate pose estimation from images. In this work, a perspective distortion model for every effect is developed while elaborating the nature of perspective effects. A distortion factor for every effect is derived, then followed by proposed methods, which allows extracting the true target pose and distance, and correcting image measurements.
A NONLINEAR MODEL FOR A SMART SEMANTIC BROWSER BOT FOR A TEXT ATTRIBUTE RECOGNITION

In spite of the advances in the state of the art in semantic artificial intelligence applications, there is still a long way to go to bring it to a level of mass adoption. Thus, in order to contribute to the advancement of this topic, this study develops a feasible model with a potential scalability for semantic applications’ mass adoption, specifically for news or statement cluster attribute identification, either positive, negative or neutral. This paper proposes a disruptive system based on Blockchain using a Semantic Browser Expert System Bot with artificial intelligence called Blockchain Semantic Browser Expert System (BSBES) to look for and analyze relevant information that significantly represents the cryptocurrencies adoption patterns. The artificial intelligence in this study consists of a deep learning neural network to process the input information to identify the news pattern in a semantic way using deep learning based on two aspects of the news: technical aspect and adoption aspect of the cryptocurrencies. BSBES performance is achieved based on deep learning tools, and scalability is supported by a blockchain system including a stability study.
Network Quality-Aware Architecture for Adaptive Video Streaming From Drones

Video streaming over the IP networks presents several challenges for remote drone piloting. To achieve a High Quality of Experience, minimal latency is mandatory. However, wireless links usually impose dynamic changes to the Quality of Service conditions. Moreover, bandwidth limitations can increase both the final perceived latency and packet loss during video streaming. These circumstances require an architecture capable of estimating network performance and applying corrective actions in a timely manner to optimize application-level quality. In this article, we present such an architecture, and discuss the results of its application in video streaming for remote drone piloting. Our proposal offers a framework with low coupling between its functional blocks and high adaptability to dynamic scenarios. Accordingly, we aim to pave the way for reactive applications that leverage edge-computing elements and adapt to network conditions.
Improved Cryptanalysis of Reduced-Version QARMA-64/128

QARMA is a new tweakable block cipher used for memory encryption, the generation of short tags and the construction of the keyed hash functions in future.It adopts a three-round Even-Mansour scheme and supports 64 and 128 bits of block size, denoted by QARMA-64 and QARMA-128, respectively.Their tweak lengths equal the block sizes and their keys are twice as long as the blocks.In this paper, we improve the security analysis of reduced-version QARMA against impossible differential and meet-in-the-middle attacks.Specifically, first exploit some properties of its linear operations and the redundancy of key schedule.Based on them, we propose impossible differential attacks on 11-round QARMA-64/128, and meet-in-themiddle attacks on 10-round symmetric QARMA-128 and the last 12 rounds of asymmetric QARMA-128.Compared with the previously best known results on QARMA-64, our attack can recover 16 more bits of master key with the almost complexities.Compared with the previously best known results on symmetric QARMA-128, the memory complexity of our attack in Section IV is reduced by a factor of 2 48 .Moreover, the meet-in-the-middle attack on 12-round QARMA-128 is the best known attack on QARMA-128 in terms of the number of rounds.
An Efficient Chain Structure to Mine High-Utility Sequential Patterns

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns from the quantitative databases. Several works have been presented to speed up computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art USpan, HUS-Span and HUSP-ULL approaches and the results of the developed structure can achieve good mining performance than the existing algorithms.
Vehicle Brands and Types Detection Using Mask R-CNN

Detection and classification of vehicles are inseparable parts of Intelligent Transportation Systems (ITS), various kinds of information technology applications are used to be able to detect and classify these vehicles, starting with the use of ultrasonic sensors, laser scanners, induction loops, magnetic sensors, range sensors, pressure sensors and CCTV cameras, but the circulation of vehicles with the same design from different manufacturing companies makes the classification of vehicles to determine the vehicle brands and types difficult to do. In this paper, deep learning framework Mask Regional-Convolutional Neural Network (Mask R-CNN) is used to solve the problem. Experiments have been conducted twice by using a combination of different datasets and detection algorithms. To be able to distinguish cars with similar shapes from different manufacturers, we use vehicle logos as one of the features that distinguish the manufacturer. The best detection and classification results were obtained in dataset training using 60 epoch, 400 step iterations with an accuracy value of 0.91 and mAP (Mean Average Precision) of 0.89.
Recognition and Extraction of Rain Drops in an Image Using Hough Transform

Bad weather especially rain drops degrades the perceptual image quality. It is clear that raindrops destroy the visibility of a scene. In steady bad weather, constituent droplets are very small (1–10 μm) and steadily float in the air. Individual detection of these droplets by the camera is very difficult. In dynamic bad weather, constituent droplets are 1000 times larger than those of the steady weather. Due to this large size, these droplets are visible to the video capturing camera which affects the quality of the image. Thus, there is a need for recognition and extraction of rain drops to enhance the image quality. The objective of this paper is to identify the location of the damage sample as precisely as possible and to remove the detected rain pixels from the image in order to get a clearer and brighter image. In this paper, we present a hybrid approach to identify and extract raindrops from the rainy image in order to restore the image with its original back ground. The algorithm is framed in order to recognize the rain droplets using clustering and shape modeling of raindrops. Proposed framework is based on K-means clustering and Gaussian filter for the efficient retrieval of rain droplets from the rainy image. The k-means clustering results in highest correct clustering rate and Hough transform is used to detect and remove the raindrops from single image. The proposed system showed better results than available approaches for raindrop recognition and deletion.
Double bordered constructions of self-dual codes from group rings over Frobenius rings

Abstract In this work, we describe a double bordered construction of self-dual codes from group rings. We show that this construction is effective for groups of order 2 p where p is odd, over the rings $\mathbb {F}_{2}+u\mathbb {F}_{2}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>u</mml:mi> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> and $\mathbb {F}_{4}+u\mathbb {F}_{4}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>u</mml:mi> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> . We demonstrate the importance of this new construction by finding many new binary self-dual codes of lengths 64, 68 and 80; the new codes and their corresponding weight enumerators are listed in several tables.
Synthesis of Screentone Patterns of Manga Characters

Manga or Japanese comics are a popular medium and their images comprise line drawings and screentones. This study investigates the screentone synthesis task that involves translation from line drawings to manga images. Screentones have regular patterns that are difficult to synthesize. To address this problem, we propose a method to translate line drawings into manga images by generating pixel-wise screentone class labels instead of generating manga images directly. To train a screentone label generator, we create paired data of line drawings and pixel-wise screentone class labels that we obtain by applying to manga images a screentone removal and a screentone classifier, respectively. We train the screentone classifier using paired data of simulated manga images and pixel-wise screentone class labels. In tests, we conduct post-processing to reduce noise in the generated pixel-wise screentone labels. Experiments show that our proposed method produces reasonable screentone patterns. In comparison with results obtained using a baseline method of image-to-image translations, our results are comparable or more visually appealing.
Stance Detection in Hindi-English Code-Mixed Data

Social media sites such as Twitter, Facebook, and many other microblogging forums have emerged as a platform for people to express their opinions and perspectives on different events. People often tend to take a stance; in favor, against or neutral towards a particular topic on these platforms. Hindi and English are the most widely used languages on social media platforms in India, and the user predominantly expresses their opinions in Hindi-English code-mixed texts. As a result, knowing the diverse opinions of the masses is difficult. We target to classify Hindi-English code-mixed tweets based on their stance. A dataset consisting of 3545 English-Hindi code-mixed tweets with Demonetisation in the target is used in the experiments so far. We present a new stance annotated dataset of English-Hindi 4219 code-mixed tweets with the abrogation of article 370 in focus.
Maturity Level Analysis of Governance and Integration IT of Simkeuda in Pamekasan Regency Using COBIT 4.1

Pamekasan Government has not analyzed the maturity level of Simkeuda governance so that it is difficult to measure alignment of IT investment with organizational goals. Based on the recommendations of the Audit Board (BPK) of the East Java Province, Pamekasan Government needs to analyze the maturity level of Simkeuda governance, as well as integrate with Salary SIM, Asset SIM (Simasda), and Regional Tax SIM (Simpad). The research methodology began with a survey using the Likert scale questionnaire, Pearson product moment validity test, Cronbach's alpha reliability test, and data analysis to measure the maturity level of the Simkeuda governance. Comparison of the maturity level of simkeuda governance before and after integration becomes novelty in IT governance research. The maturity level of governance before integration was at the level of Repeatable but Intuitive for subdomains PO3, AI2, and AI5. Whereas after integration remained at the Repeatable but Intuitive level but experienced an increase in the maturity index of 3,1% for PO3, 13,21% for AI2, and 8,9% for AI5. The conclusion of this study is integration of Simkeuda with Salary SIM, Asset SIM (Simasda), and Regional Tax SIM (Simpad), can increase Simkeuda governance maturity index in all subdomains.
The Limited Effect of Graphic Elements in Video and Augmented Reality on Children’s Listening Comprehension

There is currently significant interest in the use of instructional strategies in learning environments thanks to the emergence of new multimedia systems that combine text, audio, graphics and video, such as augmented reality (AR). In this light, this study compares the effectiveness of AR and video for listening comprehension tasks. The sample consisted of thirty-two elementary school students with different reading comprehension. Firstly, the experience, instructions and objectives were introduced to all the students. Next, they were divided into two groups to perform activities—one group performed an activity involving watching an Educational Video Story of the Laika dog and her Space Journey available by mobile devices app Blue Planet Tales, while the other performed an activity involving the use of AR, whose contents of the same history were visualized by means of the app Augment Sales. Once the activities were completed participants answered a comprehension test. Results (p = 0.180) indicate there are no meaningful differences between the lesson format and test performance. But there are differences between the participants of the AR group according to their reading comprehension level. With respect to the time taken to perform the comprehension test, there is no significant difference between the two groups but there is a difference between participants with a high and low level of comprehension. To conclude SUS (System Usability Scale) questionnaire was used to establish the measure usability for the AR app on a smartphone. An average score of 77.5 out of 100 was obtained in this questionnaire, which indicates that the app has fairly good user-centered design.
Relations between single and repeated qubit gates: coherent error amplification for high-fidelity quantum-gate tomography

Abstract In quantum computation the target fidelity of the qubit gates is very high, with the admissible error being in the range from 10 −3 to 10 −4 and even less, depending on the protocol. The direct experimental determination of such a tiny error is very challenging by standard quantum-process tomography. Instead, the method of randomized benchmarking, which uses a random sequence of Clifford gates, has become a standard tool for determination of the average gate error as the decay constant in the exponentially decaying fidelity. In this paper, the task for determining a tiny error is addressed by sequentially repeating the same gate multiple times, which leads to the coherent amplification of the error, until it reaches large enough values to be measured reliably. If the transition probability is p = 1 − ϵ with ϵ ≪ 1 in the single process, then classical intuition dictates that the probability after N passes should be <?CDATA ${P}_{N}\approx 1-N\epsilon $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:msub> <mml:mo>≈</mml:mo> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mi>N</mml:mi> <mml:mi>ϵ</mml:mi> </mml:math> . However, this classical expectation is misleading because it neglects interference effects. This paper presents a rigorous theoretical analysis based on the SU(2) symmetry of the qubit propagator, resulting in explicit analytic relations that link the N -pass propagator to the single-pass one in terms of Chebyshev polynomials. In particular, the relations suggest that in some special cases the N -pass transition probability degrades as <?CDATA ${P}_{N}=1-{N}^{2}\epsilon $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:msup> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mi>ϵ</mml:mi> </mml:math> , i.e. dramatically faster than the classical probability estimate. In the general case, however, the relation between the single-pass and N -pass propagators is much more involved. Recipes are proposed for unambiguous determination of the gate errors in the general case for both Clifford and non-Clifford gates.
A Text-Based Regression Approach to Predict Bug-Fix Time

PredictingArdimento, Pasquale bug-fixing time can help project managers to select the adequate resources in bug assignment activity. In this work, we tackle the problem of predicting the bug-fixing time by a multiple regression analysis using as predictor variablesBoffoli, Nicola the textual information extracted from the bug reports. Our model selects all and only the features useful for prediction, also using statistical procedures, such as the Principal Component Analysis (PCA). To validate our model, weMele, Costantino performed an empirical investigation using the bug reports of four well-known open source projects whose bugs are stored in Bugzilla installations, where Bugzilla is an online open-source Bug Tracking System (BTS). For each project, we built a regression model using the M5P model tree, Support Vector Machine (SVM) and Random Forests algorithms. Experimental results show the model is effective, in fact, they are slightly better than all the ones known in the literature. In the future, we will use and compare other different regression approaches to select the best one for a specific data set.
Integrating neural and syntactic features on the helpfulness analysis of the online customer reviews

Before purchasing a product online, customers often read the reviews posted by people who also brought the product. Customer reviews provide opinions and relevant information such as comparisons among similar products or usage experiences about the product. Previous studies addressed on the prediction of the helpfulness of customer reviews to predict the helpfulness voting results. However, the voting result of an online review is not a constant over time; predicting the voting result based on the analysis of text is not practical. Therefore, we collect the voting results of the same online customer review over time, and observe whether the number of votes will increase or not. We construct a dataset with 10,195 online reviews in six different product categories (Computer Hardware, Drink, Makeup, Pen, Shoes, and Toys) from Amazon.cn with the voting result on the helpfulness of the reviews, and monitor the helpfulness voting in six weeks. Experiments are conducted on the dataset to predict whether the helpfulness voting result of each review will increase or not. We propose a classification system that can classify the online reviews into more helpful ones, based on a set of syntactic features and neural features trained via CNN. The results show that integrating the syntactic features with the neural features can get better result.
Feature-Interaction Aware Configuration Prioritization for Configurable Code

Unexpected interactions among features induce most bugs in a configurable software system. Exhaustively analyzing all the exponential number of possible configurations is prohibitively costly. Thus, various sampling techniques have been proposed to systematically narrow down the exponential number of legal configurations to be analyzed. Since analyzing all selected configurations can require a huge amount of effort, fault-based configuration prioritization, that helps detect faults earlier, can yield practical benefits in quality assurance. In this paper, we propose CoPro, a novel formulation of feature-interaction bugs via common program entities enabled/disabled by the features. Leveraging from that, we develop an efficient feature-interaction-aware configuration prioritization technique for a configurable system by ranking the configurations according to their total number of potential bugs. We conducted several experiments to evaluate CoPro on the ability to detect configuration-related bugs in a public benchmark. We found that CoPro outperforms the state-of-the-art configuration prioritization techniques when we add them on advanced sampling algorithms. In 78% of the cases, CoPro ranks the buggy configurations at the top 3 positions in the resulting list. Interestingly, CoPro is able to detect 17 not-yet-discovered feature-interaction bugs.