Reviewerly/exHarmony huggingface dataset:
Towards Formal Specification for AADL with Behavior Annex in Isabelle

Abstract The analysis of safety-critical systems designed by architectural languages such as AADL (Architecture Analysis and Design Language) is a challenging research topic. In such a context, formal methods become an advocated practice in software engineering for rigorous analysis. Moreover, they are applied on specific formalisms to be analyzed on dedicated tools. This paper studies the comprehensive formal specification for AADL language, in particular supporting major components of AADL and Behavior Annex. The presentation of this specification and modeling is the aim of this paper. This work is illustrated with a ARINC653 case study. As a study case, this work develops an AADL model from an ARINC653, specify a set of critical properties of the model and perform formal modeling in in Isabelle/HOL.
A Comparative Analysis of Edge-Preserving Approaches for Image Filtering

Noise removal while sustaining the integrity of important details is a pedantic problem in image enhancement and reclamation. The vital stage to improve image quality which is needed for quantitative imaging study is filtering. Edges preserving filtering techniques are used extensively in computer vision to serve denoising efficiently in prominent research areas for enhancing the quality of low-level vision images, medical images, industrial images, geological images, and so on. This review paper is presenting the popular edge-preserving approaches for the enhancement of speckle contaminated images and to enhance peak signal-to-noise ratio (PSNR) of images. Various image quality metrics for proving the worth of these approaches are discussed. The main challenges in these filtering approaches are also covered along with how those challenges are inscribed by researchers. This review contributes to research problems to other fellow researchers who are keen to work in this area.
Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

In the field of facial attractiveness prediction, while deep models using static pictures have shown promising results, little attention is paid to dynamic facial information, which is proven to be influential by psychological studies. Meanwhile, the increasing popularity of short video apps creates an enormous demand for facial attractiveness prediction from short video clips. In this paper, we target on the dynamic facial attractiveness prediction problem. To begin with, a large-scale video-based facial attractiveness prediction dataset (VFAP) with more than one thousand clips from TikTok is collected. A two-stream temporal convolutional network (2S-TCN) is then proposed to capture dynamic attractiveness features from both facial appearance and landmarks. We employ attentive feature enhancement along with specially designed modality and temporal fusion strategies to better explore the temporal dynamics. Extensive experiments on the proposed VFAP dataset demonstrate that 2S-TCN has a distinct advantage over the state-of-the-art static prediction methods.
A Novel Deep-learning Pipeline for Light Field Image Based Material Recognition

The primitive basis of image based material recognition builds upon the fact that discrepancies in the reflectances of distinct materials lead to imaging differences under multiple viewpoints. LF cameras possess coherent abilities to capture multiple sub-aperture views (SAIs) within one exposure, which can provide appropriate multi-view sources for material recognition. In this paper, a unified “Factorize-Connect-Merge” (FCM) deep-learning pipeline is proposed to solve problems of light field image based material recognition. 4D light-field data as input is initially decomposed into consecutive 3D light-field slices. Shallow CNN is leveraged to extract low-level visual features of each view inside these slices. As to establish correspondences between these SAIs, Bidirectional Long-Short Term Memory (Bi-LSTM) network is built upon these low-level features to model the imaging differences. After feature selection including concatenation and dimension reduction, effective and robust feature representations for material recognition can be extracted from 4D light-field data. Experimental results indicate that the proposed pipeline can obtain remarkable performances on both tasks of single-pixel material classification and full-image material segmentation. In addition, the proposed pipeline can potentially benefit and inspire other researchers who may also take LF images as input and need to extract 4D light-field representations for computer vision tasks such as object classification, semantic segmentation and edge detection.
Efficient Typing on Ultrasmall Touch Screens With In Situ Decoder and Visual Feedback

Typing on a smartwatch is challenging because of the fat-finger problem. Rising to the challenge, we present a soft keyboard for ultrasmall touch screen devices with efficient visual feedback integrated with autocorrection and prediction techniques. After exploring the design space to support efficient typing on smartwatches, we designed a novel and space-saving text entry interface based on an in situ decoder and prediction function that can run in real-time on a smartwatch such as LG Watch Style. We outlined the details implemented through performance optimization techniques and released interface code, APIs, and libraries as open source. We examined the design decisions with the simulations and studied the visual feedback methods in terms of performance and user preferences. The experiment showed that users could type more accurately and quickly on the target device with our best-performing visual feedback design and implementation. The simulation result showed that the single word suggestion could yield a sufficiently high hit ratio using the optimized word suggestion algorithm.
Robust Lexicon-Free Confidence Prediction for Text Recognition

Benefiting from the success of deep learning, Optical Character Recognition (OCR) is booming in recent years. As we all know, the text recognition results are vulnerable to slight perturbation in input images, thus a method for measuring how reliable the results are is crucial. In this paper, we present a novel method for confidence measurement given a text recognition result, which can be embedded in any text recognizer with little overheads. Our method consists of two stages with a coarse-to-fine style. The first stage generates multiple candidates for voting coarse scores by a Single-Input Multi-Output network (SIMO). The second stage calculates a refined confidence score referred by the voting result and the conditional probabilities of the Top-1 probable recognition sequence. Highly competitive performance is achieved on several standard benchmarks which validate the efficiency and effectiveness of the proposed method. Moreover, it can be adopted in both Latin and non-Latin languages.
Conversational Voice User Interfaces: Connecting Engineering Fundamentals to Design Considerations

HCI research has for long been dedicated to better and more naturally facilitating information transfer between humans and machines. Unfortunately, humans' most natural form of communication, speech, is also one of the most difficult modalities to be understood by machines – despite, and perhaps, because it is the highest-bandwidth communication channel we possess. While significant research efforts, from engineering, to linguistic, and to cognitive sciences, have been spent on improving machines' ability to understand speech, the CHI community (and the HCI field at large) has only recently started embracing this modality as a central focus of research. This can be attributed in part to the unexpected variations in error rates when processing speech, in contrast with often-unfounded claims of success from industry, but also to the intrinsic difficulty of designing and especially evaluating speech and natural language interfaces. As such, the development of interactive speech-based systems is mostly driven by engineering efforts to improve such systems with respect to largely arbitrary performance metrics. Such developments have often been void of any user-centered design principles or consideration for usability or usefulness in the same ways as graphical user interfaces have benefited from heuristic design guidelines.
Discovering Traffic Anomaly Propagation in Urban Space Using Traffic Change Peaks

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as STOTree, are not accurate for two reasons. First, they discover the propagation pattern based on the detected anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal neighborhoods rather than considering from a global perspective, and thus cannot find a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using traffic change peaks, which can visualize the change of traffic anomalies (e.g., congestion and evacuation area) and thus accurately captures traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time period can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0–255) of pixels. An adaptive filter is developed to generate traffic change graphs from grid traffic images in consecutive periods, and clustering traffic change peaks along the road is to discover the propagation of traffic anomalies. The effectiveness of the proposed method has been demonstrated using a real-world GPS trajectory dataset.
Challenges of Implementing Software Variability in Eclipse OMR: An Interview Study

Software variability is the ability of a software system to be customized or configured for a particular context. In this paper, we discuss our experience investigating software variability implementation challenges in practice. Eclipse OMR, developed by IBM, is a set of highly configurable C++ components for building language runtimes; it supports multiple programming languages and target architectures. We conduct an interview study with 6 Eclipse OMR developers and identify 8 challenges incurred by the existing variability implementation, and 3 constraints that need to be taken into account for any reengineering effort. We discuss these challenges and investigate the literature and existing open-source systems for potential solutions. We contribute a solution for one of the challenges, namely adding variability to enumerations and arrays. We also share our experiences and lessons learned working with a large-scale highly configurable industry project. For example, we found that the "latest and greatest" research solutions may not always be favoured by developers due to small practical considerations such as build dependencies, or even C++ version constraints.
Tor Traffic Classification Based on Encrypted Payload Characteristics

Tor is increasingly used on the Internet as a means of accessing illicit or illegal services. If enacted by employees, such use may lead to negative impact on the organization. By its nature, Tor traffic is encrypted multiple times before being sent across networks to reach a destination. Therefore it may be impossible to detect the nature of a Tor user's online activities. Nevertheless, such users cannot hide the fact that they are using Tor. This paper proposes a novel data payload analysis as a means of classifying Tor traffic using machine learning. To this end, we consider the characteristics of the encrypted data payload for Tor and encrypted nonTor packets from 8 different applications and extract features to train our machine learning model. Our results indicate that, contrary to the commonsense assumption that Tor packets resemble other encrypted packets, such payload content can be used to distinguish between Tor and nonTor packets.
GazeTance Guidance: Gaze and Distance-Based Content Presentation for Virtual Museum

The increasing popularity of virtual reality provides new opportunities for online exhibitions, especially for fragile artwork in museums. However, the limited guidance approaches of virtual museums might hinder the acquisition of knowledge. In this paper, a novel interaction concept is proposed named GazeTance Guidance, which leverages the user's gaze point and interact-distance towards the region of interest (ROI) and helps users appreciate artworks more organized. We conducted a series of comprehension tasks on several long scroll paintings and verified the necessity of guidance. Comparing with no-guidance mechanisms, participants showed a better memory performance on the ROIs without compromising presence and user experience.
Uses of Machine Translation

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Optimization and implementation of the number theoretic transform butterfly unit for large integer multiplication

Fully homomorphic encryption (FHE) is a technique enabling processing to be performed directly on encrypted data in a commercial cloud environment, thereby preserving privacy. Large integer multiplication is the most time-consuming operation during the FHE. Concerning this issue, this paper proposes an operands merging method of the number theoretic transform (NTT) multiplication butterfly unit. By using the operands merging method and a fast modulo method, the operands of the radix-16 units are reduced to 43.8%. The hardware architecture of the NTT radix-16 unit is designed and implemented. The proposed design has been synthesized using 90-nm process technology and Xilinx Kintex UltraScale+ FPGA. The results show that the maximum frequency of the circuit is 600 MHz at the cost of 243k gates and 144 mW and 430 MHz at the cost of 19.6k CLB LUTs, 11.5k CLB registers, and 1.2k CARRY8 respectively. The multiplier implementation results also show that the optimization methods improve the area requirements and performance.
Everyday design-driven innovation: Exploring meaning change in IKEA hacking

Although much work exists to describe design-driven meaning innovation, few provide objective tools to identify and measure meaning innovation. Within the context of everyday design and adopting conceptual blending theory, we identify and assesses design-driven innovation in meaning within cases of IKEA product hacking as examples of higher-level everyday design. Conceptual blending is used as a scaffold for a content analysis of 100 IKEA hacks to identify 60 cases of meaning change. These cases are further assessed through objective meaning innovation scores derived from conceptual blending theory. A user evaluation is then conducted through a pair-wise comparison of the 60 cases to derive meaning innovation scores. The results indicate, although personal biases, idiosyncrasies, and life experiences inevitably influence evaluation of innovation through conceptual blending, that the approach has potential as an objective means to identify and evaluate design-driven meaning innovation in cases of everyday design. Finally, broader implications for assessing meaning innovation are discussed.
Utilizing BERT for Detecting Aspect Categories on TABSA via Adjusting Self-attention among Words

Aspect-based sentiment analysis (ABSA) has become a popular research topic in recent years due to its strong function of breaking down text into aspects and identifying sentiment polarity towards a specific target, generating a significant amount of discussion among researchers. Motivated by recent work of application of sentence-pair classification task into ABSA, this article discusses how to further fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model and obtain the results on SentiHood dataset. In a contrast to the previous work, this article considers that the sentiment analysis has relations to every single word in each sentence and shows the process of modifying the forward network in BERT to create self-attention between words. The proposed model demonstrates a certain degree of improvement in some aspects, in particular to aspect category detection.
Mask detection device based on YOLOv3 framework

In early 2020, novel coronavirus pneumonia broke out. In order to prevent the spread of the disease, governments around the world asked the masses to wear masks. However, there are still many people who do not wear masks in public places. To solve this problem, this paper proposes a mask detection device based on yolo3 framework. The device uses the yolov3 algorithm to extract the face prediction area, and uses the gray image to calculate the skin exposure rate of the mouth and nose of the face, so as to judge whether the recognized person is wearing a mask or not and whether the mask is wearing correctly. The model is deployed on the hardware to facilitate the staff to carry the detection. The experimental results show that the recognition rate is 86.6%.
MagicChem: A Multi-modal Mixed Reality System Based on Needs Theory for Chemical Education

Due to the reasons such as the complicated preparation of reagents, danger, pollution, and lack of teacher resources, there are few chemical experiments that students can actually operate during the chemistry learning, which is not conducive to the chemistry learning and the phenomena principle understanding. Fortunately, MR technology provides us with the possibility of solving the safety issues and the space-time constraints, while the theory of human needs provides us with a way to think about designing a comfortable and stimulant MR system with realistic visual presentation and interaction. This study combines with the theory of human needs to propose a new needs model for virtual experiment. Based on this needs model, we design and develop a comprehensive MR system called MagicChem to verify the needs model. User study shows MagicChem that satisfies the needs model is better than the MR experimental environment that partially meet the needs model. In addition, we explore the application of the needs model in VR environment.
GLDraw: A Platform for Graph Visualization

Graph visualization refers to the representation of a graph, available as data, in an understandable human manner, such as a diagram of an abstract graph. These diagrams aimed at providing better readability to the user. Finding the best drawing strategy for a visually pleasing diagram is really challenging as it depends upon the quality and allowed time expected in the output. Almost all the existing graph drawing algorithms have a linear time-variant, but their practical implementation time differs widely. Existing platforms provide only a small number of algorithmic choices, so there is a difficulty in comparing all the algorithms for a particular task. In this paper, we describe GLDraw, our graph visualization tool. GLDraw (Graph Layout Drawing) provides the user, a choice of different graph drawing layouts and heuristics to improve the quality of the drawing. We have used GLDraw to find out the best strategy to draw planar graphs and perform a comparative study based on the time taken to produce the output for different graph drawing algorithms (or layouts). GLDraw also supports simultaneous drawing of shared graphs as well as multiple drawings of different graphs. The drawing techniques provided are hierarchical, orthogonal, grid, and force-directed methods. We have found that for different types of graphs, choosing an appropriate layout plays a vital role in achieving a quality drawing, for example, in case of tree graphs planarization grid and for general planar graphs force-directed would be the better option.
Meals for Monsters: a Mobile Application for the Feasibility of Gaming and Social Mechanisms

poster Share on Meals for Monsters: a Mobile Application for the Feasibility of Gaming and Social Mechanisms Authors: Samantha Olinsky Fashion Institute of Technology, United States Fashion Institute of Technology, United StatesView Profile , Pooja M. Desai Department of Biomedical Informatics Columbia University Irving Medical Center, United States Department of Biomedical Informatics Columbia University Irving Medical Center, United StatesView Profile , Selen Turkay Queensland University of Technology, Australia Queensland University of Technology, AustraliaView Profile , Elizabeth M Heitkemper School of Nursing The University of Texas at Austin, United States School of Nursing The University of Texas at Austin, United StatesView Profile , Elliot G Mitchell Department of Biomedical Informatics Columbia University, United States Department of Biomedical Informatics Columbia University, United StatesView Profile , Lena Mamykina Department of Biomedical Informatics Columbia University, United States Department of Biomedical Informatics Columbia University, United StatesView Profile , Maria L. Hwang Science and Math Fashion Institute of Technology, United States Science and Math Fashion Institute of Technology, United StatesView Profile Authors Info & Claims CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing SystemsMay 2021 Article No.: 442Pages 1–9https://doi.org/10.1145/3411763.3451789Published:08 May 2021Publication History 1citation153DownloadsMetricsTotal Citations1Total Downloads153Last 12 Months75Last 6 weeks15 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Chaotic Grasshopper Optimization with Wavelet Kernel Extreme Learning Machine for Medical Data Classification

Medical data classification acts as a vital part to enhance the survival rate of the patients and control the health condition to a certain level depending upon the clinical and non-clinical profiles of the patients. To improve the survival rate and decision making process in healthcare sector, necessary disease diagnosis models become essential. This paper presents an efficient Chaotic Grasshopper Optimization Algorithm (CGOA) with Wavelet Kernel Extreme Learning Machine (WKELM), called CGOA-WELM for Medical Data Classification. The CGOA-WKELM model involves preprocessing at the beginning stage to raise the data quality. In addition, WKELM based classification process gets executed which involves the weighted approach to cost function for obtaining the identical effects of weighted ELM. At the same time, in order to effectively set the parameters of the WKELM model, CGOA is applied as a parameter optimizer in such a way to enhance the classification performance. An extensive experimental validation process gets executed to determine the betterment of the CGOA-WKELM model and the results are investigated interms of distinct aspects. The attained experimental values verified the superior performance of the CGOA-WKELM model on all the applied three datasets.