Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Chinese Short Text Based on Multiple Features

With the rapid development of mobile internet and the increase of social platforms, higher performance requirements are put forward for sentiment analysis of Chinese short texts. The traditional deep learning models based on CNN, LSTM and other frameworks face the problem of unable to extract all the effective information contained in the text because of the single direction of text parsing. However, complex model which combines framework in serial way exist a problem that cannot be ignored, that is it can not get effective training. To solve the above problems, this paper proposes an interpretable emotion analysis framework MIX-CNN-BiLSTM-Attention-Transformer (MCBAT Model), which can extract different features from multiple models. From the three dimensions of the fixed collocation of words, the context information and the importance of words in the text, CNN, BiLSTM-Attention and Transformer model are used to extract the above three different features. After vector splicing, the classification results are obtained by the classifier through the full connection layer. The accuracy and stability of the MCBAT model are improved compared with other classical emotion analysis models (CNN, BiLSTM, CNN-BiLSTM, etc.) and LSTM-LDA model. The model based on multi feature consideration is of great significance to emotion analysis task, and provides a method support for further development in the future.
Real-Time Garbage Object Detection With Data Augmentation and Feature Fusion Using SUAV Low-Altitude Remote Sensing Images

Recently, a number of nature reserves have been shut down because of serious pollution from tourist garbage. Garbage monitoring in high-altitude natural reserves using small unmanned aerial vehicle (SUAV) remote sensing is an important and urgent need for environmental protection. In order to help cleaners to eliminate garbage more conveniently and quickly, a novel approach is proposed to detect scattered garbage regions in real time using low-altitude remote sensing videos captured by SUAVs. First, the high-resolution, low-altitude, multitemporal remote sensing images and videos containing scattered garbage were collected through SUAV and then proposed a data augmentation method to expand the training samples. Second, the Yolov4 detection network was used to classify the scattered garbage regions. Finally, the location of the object was roughly calculated according to the altitude, flight direction, global positioning system, and digital elevation model (DEM). Then, the garbage object was marked on the video, while the object location was marked on the map. Experimental results show that the proposed method achieves a mean accuracy of 91.34% and provides better performances on the real data set compared with state-of-the-art methods.
An Overview of Identity Relationship Management in the Internet of Things

Identity and Access Management (IAM) is essential and a core of any information system in enterprise networks. IAM systems manage users on premises, focus on the needs within enterprises, and utilize static intelligence for authentication and access control. Today's IAM requirements are much more complex due to the Internet of Things (IoT). Boundaries between home and work, on-premises and cloud, personal devices and corporate owned devices are fading away. These changes have driven the shift of IAM to Identity Relationship Management (IRM). IRM aims to meet the demands not only within enterprises, but also from the Internet. It manages not only users, but also devices. It utilizes not only static intelligence but also dynamic intelligence for authentication and access control. This paper introduces and discusses the needs of IRM for the IoT. It addresses four fundamental issues related to relationships in the IRM, i.e., what relationships need to be supported in the IRM, how relationships can be used for authentication, how relationships can be utilized for access control, and what infrastructure is required to support the IRM. The paper also differentiates IAM and IRM and points out future research directions in the IRM.
DualBox: Generating BBox Pair with Strong Correspondence via Occlusion Pattern Clustering and Proposal Refinement

Despite the rapid development of pedestrian detection, the problem of dense pedestrian detection is still unsolved, especially the upper limit of Recall caused by Non-Maximum-Suppression (NMS). Out of this reason, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NMS [1] is proposed to simultaneously detect full and visible body bounding boxes, by replacing the full body BBoxes with less occluded visible body BBoxes in the NMS algorithm, achieving a higher recall. However, the P-RPN and P-RCNN modules proposed in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NMS for simultaneous high quality full and visible body prediction require non-trivial positive/negative assigning strategies for anchor BBoxes. To simplify the prerequisites and improve the utility of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NMS, we incorporate clustering analysis into the learning of visible body proposals from full body proposals. Furthermore, to reduce the computation complexity caused by the large number of potential visible body proposals, we introduce a novel occlusion pattern prediction branch on top of the R-CNN module (i.e. F-RCNN) to select the best matched visible proposals for each full body proposals and then feed them into another R-CNN module (i.e. V-RCNN). Incorporated with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NMS, our DualBox model can achieve competitive performance while only requires few hyper-parameters. We validate the effectiveness of the proposed approach on the CrowdHuman [2] and CityPersons [3] datasets. Experimental results show that our approach achieves promising performance for detecting both non-occluded and occluded pedestrians, especially heavily occluded ones.
Revelation of Geospatial Information using Augmented Reality

Augmented Reality (AR) is the technology that enables us to combine digital knowledge into the user's realworld space. Augmented Reality uses the existing environment to overlay digital content and information onto the physical world. The Proposed method demonstrates AR's use to provide a lively and visual representation of information about the geography of different masses on the globe and how AR can be utilized to enhance research studies and education by visualizing data collected by various statistical sources. This work helps to visualize the globe in Augmented Reality where one can overlay any OGC (Open Geospatial Consortium) Web Map Service (WMS) layer onto the globe's surface and interact with it to understand the visualization of statistics around the world. Users can utilize any pre-defined marker, target or placeholders of the object, which is trained beforehand to be recognized later in the camera stream. When seeing the placeholder from the camera of phone, an augmented reality globe must appear over the placeholder. Moving the phone camera, it have to be possible to overlay any Web Outline on the globe. This application must provide an interface to choose the data such as Climate, Sea Floor Temperature, Social Progress, World Literacy information to be overlaid on the globe. Real-time information changes according to the observer's movement as if that information truly exists in the real world.
Multimodal Punctuation Prediction with Contextual Dropout

Automatic speech recognition (ASR) is widely used in consumer electronics. ASR greatly improves the utility and accessibility of technology, but usually the output is only word sequences without punctuation. This can result in ambiguity in inferring user-intent. We first present a transformer-based approach for punctuation prediction that achieves 8% improvement on the IWSLT 2012 TED Task, beating the previous state of the art [1]. We next describe our multimodal model that learns from both text and audio, which achieves 8% improvement over the text-only algorithm on an internal dataset for which we have both the audio and transcriptions. Finally, we present an approach to learning a model using contextual dropout that allows us to handle variable amounts of future context at test time.
Revisiting the nodal voltage method for both human comprehension and software implementation: Towards a teaching/self‐learning simulation tool

Abstract This paper outlines an application—dubbed U=RIsolve (read as “you resolve”)—for helping students to learn the Nodal (or Node) Voltage Analysis method (NVM, for short) for electrical circuit analysis. The NVM enables determining the voltage in every Node in the circuit, related to a reference (Ground) Node. In turn, this enables to compute the current in every Branch (and therefore, the voltage across every component). The U = RIsolve application goes far beyond the capabilities of traditional circuit simulators; as it outputs the fundamental circuit information, the methodology, the equations, and the results related to the NVM for a given circuit previously simulated in Quite Universal Circuit Simulator (QUCS). QUCS can generate a text file (called netlist ) that identifies all the circuit components/connections and that serves as an input to the U = RIsolve application. Additionally, users can also upload the circuit schematics to facilitate the analysis of the U = RIsolve output. Then, the user just needs to click a button to get all the reasoning of the NVM analysis.
Living as a Service (LaaS): An Experiment to Evaluate Living Experience in Connected Homes in the Netherlands

User-centric approaches to technological interfaces have increasingly surfaced in the recent times. In smart homes, this manifests in the form of how people adapt and interact with devices, and the set up as a whole. This study aims to analyze the behavioral aspects of living in a smart home through interview and structured survey-based interactions with 21 families in the Netherlands who were provided a predefined set of smart devices for about three weeks. We evaluate user experience with smart devices through four stages of smart home adoption. Based on user behavior and motivations, we find four personas emerging, namely convenience-oriented, safety-oriented, cautiously supportive and sustainability-oriented. The study finds a positive experience predominantly with smart home devices when they are delivered and integrated in a manner that serves user's priorities in a home. Future directions incorporating user preferences in smart home design are discussed.
Predictive Coding for Lossless Dataset Compression

Lossless compression of datasets is a problem of significant theoretical and practical interest. It appears naturally in the task of storing, sending, or archiving large collections of information for scientific research. We can greatly improve encoding bitrate if we allow the compression of the original dataset to decompress to a permutation of the data. We prove the equivalence of dataset compression to compressing a permutation-invariant structure of the data and implement such a scheme via predictive coding. We benchmark our compression procedure against state-of-the-art compression utilities on the popular machine-learning datasets MNIST and CIFAR-10 and outperform for multiple parameter sets.
Experiential Persona: Towards Supporting Richer and Unfinalized Representations of People

The Persona, as a 2D poster, is a commonly created and used tool in user-centred design activities. Whilst popular, many in HCI have critiqued its depictions of ‘the user’ as reductive, shallow, and static. Yet, there are not many alternatives. In this late-breaking work, we present our efforts to imagine and create an alternative to this 2D persona - one that consists of a carefully curated, staged collection of artifacts. We call this an Experiential Persona because it allows designers to interact with and explore the artifacts, individually and as a collection, to imagine and experience the world of ‘the user’. This more embodied, interactive and open-ended persona can potentially support richer sense making; encouraging a more open, emergent, and unfinalized view of people we design for. This study contributes to extending design tools, and exploring novel use of tangible artifacts to support design, as well as a way to represent design knowledge.
Vehicle-Pedestrian Interaction: Distributed intelligence framework

In natural systems, many species are able to coordinate their individual intelligences to perform complex tasks. Swarm Intelligence also denotes the concept of distributed intelligence which relies on the coordination between a massive number of individual intelligences. In this paper, we develop a logical approach to consider distributed intelligence. Our proposal is based on a pedestrian safety issue. So, we extend the bases of a distributed non-monotonic theory to model the “vehicle-pedestrian” interaction system. This extension concerns the preliminary approach proposed in our previous work. Non-monotonic reasoning processes for distributed intelligent logic agents strongly contribute to the development of Pedestrian Collision Warning Systems. These systems benefit greatly from the efficiency of pedestrian detection which has reached a high level thanks to several technologies. We demonstrate the validity of our construction and we specify the knowledge representation of driver-pedestrian interaction in the context of the proposed theory.
Ontology-Based Sentiment Analysis on News Title

News are one source of information that is easily accessible for the public. News can build an image of or shape the public opinion on the reported object. As such, news is an important medium in General Election (GE) for both the public and the candidates, to know what instruments are being put in place and how they fare. In this research, we performed an ontology-based sentiment analysis to find out whether the 2019 GE in Indonesia has received a positive or a negative response according to the different instruments used. We performed two kinds of experiment, one was based on lexicon and another was using Support Vector Machine (SVM) algorithm. The results of our experiments show that SVM has a better classification performance. For lexicon-based method, the overall recall score was 28.8% and the overall precision score was 46.3%. Whereas for SVM-based method, the scores were 82.4% and 83.3% respectively. These differences may be accounted for the fact that SVM considered our training input instead of just a fixed list of words.
Learning Binary Semantic Embedding for Breast Histology Image Classification and Retrieval

With the development of medical imaging technology and machine learning, the computer-assisted diagnosis has attracted extensive research attention, which can provide beneficial reference to pathologists. However, the exponential growth of medical images and uninterpretability of traditional classification models have hindered the applications of the computer-assisted diagnosis. To address this issues, we propose a novel method for Learning Binary Semantic Embedding (LBSE). Based on this efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Furthermore, double supervision, bit uncorrelation and balance constraint, asymmetric strategy and discrete optimization are seamlessly integrated in the proposed method for learning binary embedding. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
Content and knowledge based Food Recommending Intelligence Enabled System (FRIES)

In this day and age, a consumer is exposed to a myriad of alternatives for even the most minuscule decisions. Making the optimum choice has become a painstaking process or perusing and reviewing the plethora of available options. This conundrum extends to the choice of food from the ever increasing and byzantine menus of sumptuous restaurants. Ordering food from a litany of items in a menu may demand more time than the user is willing to expend, especially whilst ordering food online. Further the dearth of description regarding the ingredients and taste palette of a dish waxes the probability of the customer being discontent with his ordered dish. This paper explores the use of content and knowledge based recommendation systems to assay and recommend food tailored to the menu of Utsav Restaurant. Further the paper discusses the used methodology and presents its findings.
Semi-automatic System for Rapid Annotation of Moving Objects in Surveillance Videos using Deep Detection and Multi-object Tracking Techniques

With the recent advances in video sensor technologies, emergence of new applications associated with these technologies, and demand for automated video analytics have increased the need for ground-truth annotations. Researchers attempt to explore different methodologies and algorithms on different challenging datasets. Ground-truth annotations are needed for quantitative evaluation and comparison of video analysis methods, and training machine learning approaches. Ground Truth Generator (GTG) is our semi-automatic solution for rapid moving object annotation. GTG allows annotation of high densities of moving objects in a video with considerably fewer human interactions, and greatly reduces annotation effort and time. For each individual moving object in a video GTG generates bounding boxes, object classes, and motion trajectories. GTG integrates a fully-automated detection, tracking, and fusion pipeline with an interactive visualization, annotation, and editing interface. Our proposed system was tested on FPSS [1] and VisDrone [2] datasets and showed promising results.
Adaptive function-on-scalar regression with a smoothing elastic net

This paper presents a new methodology, called AFSSEN, to simultaneously select significant predictors and produce smooth estimates in a high-dimensional function-on-scalar linear model with sub-Gaussian errors. Outcomes are assumed to lie in a general real separable Hilbert space, H, while parameters lie in a subspace known as a Cameron–Martin space, K, which are closely related to Reproducing Kernel Hilbert Spaces, so that the parameter estimates inherit particular properties, such as smoothness or periodicity, without enforcing such properties on the data. We propose a regularization method in the style of an adaptive Elastic Net penalty that involves mixing two types of functional norms, providing a fine tune control of both the smoothing and variable selection in the estimated model. Asymptotic theory is provided in the form of a functional oracle property, and the paper concludes with a simulation study demonstrating the advantages of using AFSSEN over existing methods in terms of prediction error and variable selection.
Understanding Art through Augmented Reality: Exploring Mobile Tools for Everyone's Use

This work seeks to register in real-time the dance movement through the principles of Augmented Reality. From the reading and analyzing of this movement is intended to explore different visual approaches, such as drawing, painting, or an abstract cartography. For this purpose, this work aims to develop a mobile system that detects, different parts of the human body (body-parts) and records the movement made by these points. Through the movement detection this system will interpret movement parameters, analyze and classify them into new values to then generate a new visual representation. The new visual composition will be presented in real-time and in a three-dimensional format allowing the participants a closer and more detailed art perspective. This work is intended to give everyone a better understanding of art, but also to contribute with a tool that can stimulate the creative process, the physical and emotional well- being, through of a multidisciplinary work.
Spacewalker: Rapid UI Design Exploration Using Lightweight Markup Enhancement and Crowd Genetic Programming

User interface design is a complex task that involves designers examining a wide range of options. We present Spacewalker, a tool that allows designers to rapidly search a large design space for an optimal web UI with integrated support. Designers first annotate each attribute they want to explore in a typical HTML page, using a simple markup extension we designed. Spacewalker then parses the annotated HTML specification, and intelligently generates and distributes various configurations of the web UI to crowd workers for evaluation. We enhanced a genetic algorithm to accommodate crowd worker responses from pairwise comparison of UI designs, which is crucial for obtaining reliable feedback. Based on our experiments, Spacewalker allows designers to effectively search a large design space of a UI, using the language they are familiar with, and improve their design rapidly at a minimal cost.
Double Manifolds Regularized Non-negative Matrix Factorization for Data Representation

Non-negative matrix factorization (NMF) is an important method in learning latent data representation. The local geometrical structure can make the learned representation more effectively and significantly improve the performance of NMF. However, most of existing graph-based learning methods are determined by a predefined similarity graph which may be not optimal for specific tasks. To solve the above problem, we propose the Double Manifolds Regularized NMF (DMR-NMF) model which jointly learns an adaptive affinity matrix with the nonnegative matrix factorization. The learned affinity matrix can guide the NMF to fit the clustering task. Moreover, we develop the iterative updating optimization schemes for DMR-NMF, and provide the strict convergence proof of our optimization strategy. Empirical experiments on four different real-world data sets demonstrate the state-of-the-art performance of DMR-NMF in comparison with the other related algorithms.
Parallelizing Image Encryption Algorithm for Wireless Sensor Networks

In this paper, techniques to improve the overall efficiency of an image encoding algorithm that is specifically designed to be used in Wireless Sensor Networks (WSN) are proposed. The inherent nature of WSN, which contains numerous network nodes each having its computational resources, makes the algorithm is being underutilized due to its sequential nature and hence there is a scope to improve speedup by applying the concepts of parallelization on it. The other avenues which are pertinent to the task are the usage of GPU for computation of large arrays as well as the concept of cluster computing to run the algorithm on a cluster of nodes. Another point of importance for such an algorithm is the utilization of memory and how to make its usage more efficient. Hence, the concept of pre-allocation is also applied to the algorithm to make it run faster. An in-depth study of the algorithm is conducted by considering various parameters such as the size and complexity of the key, histogram analysis, correlation values, etc.