Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Software Test Laboratory Based on Cloud Platform

Software testing has become an essential indicator of students' professional level. To improve students' software testing ability, the construction of software testing laboratory has become an urgent need for many institutions. With the development and popularization of cloud computing, more and more software industries rely on cloud computing, which also brings new ideas for the construction and development of software testing laboratories in colleges and universities. This paper designs and implements a software testing laboratory based on a cloud platform, which can reuse the traditional hardware devices such as the servers in the laboratory. It is an intelligent and convenient laboratory environment with rich functions and powerful management functions, such as antagonistic experiments, programming communities, developer testing, and so on. Students can also connect to the cloud platform through the campus network to carry out various experiments. The experimental results of students will be stored on the cloud platform to facilitate the management of teachers, which can effectively improve the level of resource utilization of university laboratories. Through the cloud platform, we can arouse the enthusiasm of students and improve their professional skills, and improve the quality of experimental teaching in colleges and universities as well.
Architecting Effectual Computation for Machine Learning Accelerators

Inference efficiency is the predominant design consideration for modern machine learning accelerators. The ability of executing multiply-and-accumulate (MAC) significantly impacts the throughput and energy consumption during inference. However, MAC operation suffers from significant ineffectual computations that severely undermines the inference efficiency and must be appropriately handled by the accelerator. The ineffectual computations are manifested in two ways: first, zero values as the input operands of the multiplier, waste time and energy but contribute nothing to the model inference; second, zero bits in nonzero values occupy a large portion of multiplication time but are useless to the final result. In this article, we propose an ineffectual-free yet cost-effective computing architecture, called split-and-accumulate (SAC) with two essential bit detection mechanisms to address these intractable problems in tandem. It replaces the conventional MAC operation in the accelerator by only manipulating the essential bits in the parameters (weights) to accomplish the partial sum computation. Besides, it also eliminates multiplications without any accuracy loss, and supports a wide range of precision configurations. Based on SAC, we propose an accelerator family called Tetris and demonstrate its application in accelerating state-of-the-art deep learning models. Tetris includes two implementations designed for either high performance (i.e., cloud applications) or low power consumption (i.e., edge devices), respectively, contingent to its built-in essential bit detection mechanism. We evaluate our design with Vivado HLS platform and achieve up to 6.96× performance enhancement, and up to 55.1× energy efficiency improvement over conventional accelerator designs.
An Approach to Reading Assistance with Eye Tracking Data and Text Features

Unknown words and long and difficult sentences with complex structures often cause difficulties in reading comprehension. This paper proposed a reading assistance approach based on analyzing the eye tracking data and the text features of the gazed area in the process of reading. This approach could automatically detect the user's intention in terms of word translation or long sentence summary, and then display the meaning of the word or the summary of the sentences in the form of annotations. The pilot study results showed that the average accuracy of this approach reached 80.6%±6.3%, and the automatically generated annotation improved the user's reading efficiency and subjective experience.
Continuous Process Monitoring Through Ensemble-Based Anomaly Detection

In many production processes, a complete quality inspection of all products is not feasible due to technological and organizational restrictions. In order to ensure zero-defect products, monitoring process parameters in real time and using them to predict product quality by supervised learning methods is a very established approached. However, this approach requires a joining of process parameters and quality features. In order to guarantee high-quality products even in the absence of traceability, a continuous process monitoring approach based on an anomaly detection ensemble method is beneficial.
Aggressive Energy Reduction for Video Inference with Software-only Strategies

In the past years, several works have proposed custom hardware and software-based techniques for the acceleration of Convolutional Neural Networks (CNNs). Most of these works focus on saving computations by changing the used precision or modifying frame processing. To reach a more aggressive energy reduction, in this paper we propose software-only modifications to the CNNs inference process. Our approach exploits the inherent locality in videos by replacing entire frame computations with a movement prediction algorithm. Furthermore, when a frame must be processed, we avoid energy-demanding floating-point operations, and at the same time reduce memory accesses by employing look-up tables in place of the original convolutions. Using the proposed approach, one can reach significant energy gains of more than 25× for security cameras, and 12× for moving vehicles applications, with only small software modifications.
Towards Issue Recommendation for Open Source Communities

In open source software development, a major challenge is the prioritization of new requirements as well as the identification of responsible developers for their implementation. Unlike conventional industrial software development, where requirements engineers have to explicitly define who implements what, in the context of open source development, developers (contributors) usually decide on their own which requirements to implement next. Contributors have to deal with a huge number of requirements where the recognition of the most relevant ones often becomes a crucial task with a high impact on the success of a software project. This fact defines our major motivation for the development of a prioritization tool for the Eclipse community which recommends relevant requirements (issues/bugs) to open source developers. Our tool uses real-world data from Eclipse in order to build a prediction model. We trained and tested our tool with different classifiers such as Naive Bayes (representing our baseline), Decision Tree, and Random Forest. The evaluation results indicate that the Random Forest classifier correctly predicts issues with a precision of 0.88 (F1-score 0.68).
Eyelid and Pupil Landmark Detection and Blink Estimation Based on Deformable Shape Models for Near-Field Infrared Video

The eyelid contour, pupil contour and blink event are important features of eye activity, and their estimation is a crucial research area for emerging wearable camera-based eyewear in a wide range of applications e.g. mental state estimation. Current approaches often estimate a single eye activity, such as blink or pupil center, from far-field and non-infrared (IR) eye images, and often depend on the knowledge of other eye components. This paper presents a unified approach to simultaneously estimate the landmarks for the eyelids, the iris and the pupil, and detect blink from near-field IR eye images based on a statistically learned deformable shape model and local appearance. Unlike the facial landmark estimation problem, by comparison, different shape models are applied to all eye states – closed eye, open eye with iris visible, and open eye with iris and pupil visible – to deal with the self-occluding interactions among the eye components. The most likely eye state is determined based on the learned local appearance. Evaluation on three different realistic datasets demonstrates that the proposed three-state deformable shape model achieves state-of-the-art performance for the open eye with iris and pupil state, where the normalized error was lower than 0.04. Blink detection can be as high as 90% in recall performance, without direct use of pupil detection. Cross-corpus evaluation results show that the proposed method improves on the state-of-the-art eyelid detection algorithm. This unified approach greatly facilitates eye activity analysis for research and practice when different types of eye activity are required rather than employ different techniques for each type. Our work is the first study proposing a unified approach for eye activity estimation from near-field IR eye images and achieved the state-of-the-art eyelid estimation and blink detection performance.
A Corpus-Free State2Seq User Simulator for Task-Oriented Dialogue

Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotated dialogue corpora are often unavailable. One of the popular approaches addressing this is to train a dialogue agent with a user simulator. Traditional user simulators are built upon a set of dialogue rules and therefore lack response diversity. This severely limits the simulated cases for agent training. Later data-driven user models work better in diversity but suffer from data scarcity problem. To remedy this, we design a new corpus-free framework that taking advantage of their benefits. The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data. To enhance the performance, we propose the State2Seq user simulator model to efficiently leverage dialogue state and history. Experiment results on an open dataset show that our user simulator helps agents achieve an improvement of $$6.36\%$$ on success rate. State2Seq model outperforms the seq2seq baseline for 1.9 F-score.
Bridging Between Emojis and Kaomojis by Learning Their Representations from Linguistic and Visual Information

Small images of emojis have unique characteristics as additional information in understanding writers’ intentions. They enable social media users to emphasize their emotions and to express gestural movements in their posts. In addition to emojis, kaomojis (emoticons or facemarks) also behave in a similar way. They are composed of a sequence of characters, which are popularized especially in Asian countries. Although both emojis and kaomojis fulfill similar functions and share the same meaning that can be clues in opinion mining or sentiment analysis, the previous researches have been biased to explore emojis and kaomojis separately. In this paper, we align emojis and kaomojis together as a single token in the Japanese context to offer a bridge between them. Specifically, we aim to judge whether emojis and kaomojis share the same meaning or are similar with each other. We assume that emojis and kaomojis are both a single word in order to obtain their linguistic information with the skip-gram model. Furthermore, we present a new approach to consider the appearances of emojis and kaomojis in themselves, meaning that we explore the information of their visually similar shapes. We regard both of them as a single image to take into account their visual information with the CNN model. We merge two different perspectives toward emojis and kaomojis by exploring their linguistic and visual information simultaneously on the same space. The experimental results showed that we can align an unlimited number of emojis and kaomojis together with their representations (embeddings), and adding the visual information to the linguistic information can improve their representations.
Speech Emotion Recognition: Models Implementation &amp; Evaluation

Speech emotion recognition (SER) is a new emergent field of research that has several possible applications in both human-computer and human-human interaction systems. The body of work on emotion detection from speech signal is relatively limited. Nowadays, researchers are yet augmenting on what features effects the identification of emotion in speech. There is a significant ambiguity as to the finest algorithm for emotion's classification, and which emotions to class together as well. In this work, we seek to address these matters. We use Support Vector Machines (SVMs) and Artificial Neuron Network (ANN) to classify opposite emotions. There is a variability of temporal and spectral characteristics that can be extracted from human speech. We focus only on Mel Frequency Cepstral Coefficients (MFCCs) as inputs to the classification algorithms. The classification reports obtained from the conducted experiments allow us to say that, for the given parameters, the ANN model was better detecting the speech carried emotional information than the SVM.
Error-disturbance relation in Stern-Gerlach measurements

Although Heisenberg's uncertainty principle is represented by a rigorously proven relation about intrinsic uncertainties in quantum states, Heisenberg's error-disturbance relation (EDR) has been commonly believed to be another aspect of the principle. Based on the recent development of universally valid reformulations of Heisenberg's EDR, we study the error and disturbance of Stern-Gerlach measurements of a spin-1/2 particle. We determine the range of the possible values of the error and disturbance for arbitrary Stern-Gerlach apparatuses with the orbital degree prepared in an arbitrary Gaussian state. We show that their error-disturbance region is close to the theoretical optimal and actually violates Heisenberg's EDR in a broad range of experimental parameters. We also show the existence of orbital states in which the error is minimized by the screen at a finite distance from the magnet, in contrast to the standard assumption.
Extramission: A Large Scale Interactive Virtual Environment Using Head Mounted Projectors and Retro-reflectors

We present Extramission, a method to a large scale interactive virtual environment. It consists of dual head mounted pico projectors and retro-reflective materials. With high-accuracy retro-reflective materials, laser beams scanned on user’s retina makes clear and free-focus vision. In this retinal scanning configuration, even if the luminance of the projector is low, scanned images can be seen clearly, which helps to evade overlaps between projected images. Due to small overlaps, Extramission can provide multi-user virtual experiences showing different images to each individual, and dual pico projectors can provide each user with stereoscopic vision. Moreover, the tolerance of low luminance allows larger distance between users and retro-reflectors, which is required for large scale virtual experiences using head mounted projectors. In this paper, we describe the principle and the implementation of Extramission. We also see its performance of displaying images.
Integration of the Alexa Assistant as a Voice Interface for Robotics Platforms

Virtual assistants such as Cortana or Google Assistant are becoming familiar devices in everyday environments, where they are used to control real devices through natural language. This paper extends this application scenario, and it describes the use of the Alexa assistant from Amazon through an Echo dot device to drive the behaviour of a robotic platform. The paper focuses on the description of the technologies employed to set such ecosystem. Significantly, the proposed architecture is based, from the remote server to the on-board controllers, in Low-Energy (LE) hardware and a scalable software platform. This approach will ease programmers integrating different platforms, e.g. mobile-based applications to control robots or home-made devices.
The improved level set evolution for ultrasound image segmentation in the high-intensity focused ultrasound ablation therapy

An improved level set evolution model is proposed to segment the ultrasound images accurately and efficiently in this paper. At first, the multi-scale gradient vector field is employed to reduce the image noise. We assign the standard deviation as a scale to set up several scales images, and use the gradient direction information between the adjacent scales to refine the multi-scale gradient vector field, which highlights the real boundaries and reduces the noise. Besides, the divergence operator of the multi-scale gradient vector field is used to overcome the sensitivity problem of initialization and avoid boundary leakage. The experiments results demonstrate that the performance of the proposed method is very close to the ground truths obtained from manual segmentation by specialists with higher area similarity measure (ASM) and lower mean contour distance (MCD), which demonstrated that the proposed method is reliable, accurate and robust for the tumor boundary segmentation in ultrasound images.
Quasi doubly stochastic operator on <i>l</i><sup>1</sup> and Nielsen’s theorem

In this paper, we introduce the quasidoubly stochastic operator, which is between doubly stochastic operators and column stochastic operators, so as to apply to characterized operator S on l1 such that Sf is majorized by f for every f ∈ l1. We present some classes of majorization preservers on l1 under quasi doubly stochastic operators. Moreover, as an application of our result in quantum physics, the convertibility of pure states of a composite system by local operations and classical communication has been considered.
Temporal Methods for Eye Movement Analysis

This chapter is aimed at introducing the reader to current methods for the spatial and temporal analysis of eye movements. There are four main parts. In the first part of the chapter, we introduce the relation between attention and eye movements and then review the foundations of attention research, social attention and the effect of stimulus saliency on attention. In the second part, we first review traditional characterizations of eye movements, specifically measures of fixations and saccades, and then we review the major methods for the spatial analysis of eye movements, including heat maps and area-of-interest analyses. In the third part, we introduce a new temporal analysis of eye movements, recurrence quantification analysis. We review the basic methods and measures of this analysis, and we discuss several applications. In the last part of the chapter, we provide an overview of methods for the comparison of scanpaths and show that recurrence quantification analysis can be generalized successfully to scanpath comparisons. With the information provided in this chapter, readers will be able to understand
Recognition of Japanese Historical Hand-Written Characters Based on Object Detection Methods

We consider the recognition problem of Japanese historical handwritten characters called "Kuzushiji". Unlike modern characters, Kuzushiji characters are harder to recognize partly because many of them are connected and not separated by spaces without any segmentation information. We propose two methods for segmentation and recognition of Kuzushiji characters. The first method learns segmentation rules and character classifiers simultaneously from data sets with character labels and segmentation information. Second method is for segmentation and can be used with any single character recognizer. Our methods outperform other baselines and achieve the state-of-the-art accuracy on both segmentation and recognition tasks on data sets of three consecutive Kuzushiji characters.
An Evaluation of Knowledge Translation in Software Engineering

Knowledge translation is defined, in health sciences, as “the exchange, synthesis and ethically sound application of research results in practice”. The objective of this paper is to implement and conduct a feasibility evaluation of a knowledge translation framework in software engineering. We evaluated the outcome of the knowledge translation framework in an industrial setting, along with the effectiveness of the interventions undertaken as part of knowledge translation in a multi-case study. The results of the evaluation suggest that the practitioners perceive the knowledge translation framework to be valuable and useful. In conclusion, this paper contributes towards the reporting of a systematic implementation of knowledge translation and evaluating its use in software engineering.
Real-time Safe Navigation Strategy for Mobile Robots in 3D:A Deforming Path Approach

This work considers the 3D safe navigation problem of autonomous vehicles such as flying drones and underwater vehicles. Such a problem is very challenging especially when dealing with unknown and/or dynamic obstacles. In these situations, the vehicle is required to take quick actions to avoid collisions with detected obstacles. Therefore, the proposed strategy adopts a sense and avoid paradigm to produce collision-free motions. This is achieved by applying real-time deformations to the vehicle's reference path around obstacles based on sensory information which can provide better computational solutions and quicker actions compared with map-based navigation approaches. Also, quintic Bezier splines are utilized to generate smooth deformations. Computer simulations have been conducted to demonstrate the effectiveness of the proposed strategy with unknown/dynamic obstacles.
Thesauri and Semantic Web: Discussion of the Evolution of Thesauri Toward Their Integration With the Semantic Web

Thesauri are Knowledge Organization Systems (KOS), that arise from the consensus of wide communities. They have been in use for many years and are regularly updated. Whereas in the past thesauri were designed for information professionals for indexing and searching, today there is a demand for conceptual vocabularies that enable inferencing by machines. The development of the Semantic Web has brought a new opportunity for thesauri, but thesauri also face the challenge of proving that they add value to it. The evolution of thesauri toward their integration with the Semantic Web is examined. Elements and structures in the thesaurus standard, ISO 25964, and SKOS (Simple Knowledge Organization System), the Semantic Web standard for representing KOS, are reviewed and compared. Moreover, the integrity rules of thesauri are contrasted with the axioms of SKOS. How SKOS has been applied to represent some real thesauri is taken into account. Three thesauri are chosen for this aim: AGROVOC, EuroVoc and the UNESCO Thesaurus. Based on the results of this comparison and analysis, the benefits that Semantic Web technologies offer to thesauri, how thesauri can contribute to the Semantic Web, and the challenges that would help to improve their integration with the Semantic Web are discussed.
Tangible Projection Mapping onto Deformable Moving Thin Plants via Markerless Tracking

We propose a system that automatically generates a projection mapping onto natural objects such as leaves and flowers, for which it is currently difficult to apply tracking markers. Our PM allows user interactions such as contact and covering. We describe our results with some animated effects on various moving leaves and flowers.