Reviewerly/exHarmony huggingface dataset:
Prefigurative Politics and Passionate Witnessing

SIGACCESS and SIGCHI are being shaped by new members as they integrate feminist theory, critical race theory, postcolonial studies, and critical disability studies into the field. What happens when we begin to hold each other accountable to the impact our work has on marginalized users and communities? How do we develop a community of collaborative personal growth and institutional transformation? In this experience report, the authors retell the tangled events that brought them together. Through the passionate witnessing of critical analysis and critique, what might have been a contentious adversarial relationship became a potent partnership devoted to pushing the field toward a transformative future. From our story, we present reflections for the "Prefigurative Politics" of socially conscious computing.
Domain-Specific Embedding Network for Zero-Shot Recognition

Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or unseen domain by projecting the image data and semantic labels into a joint embedding space. However, most existing methods directly adapt a well-trained projection from one domain to another, thereby ignoring the serious bias problem caused by domain differences. To address this issue, we propose a novel Domain-Specific Embedding Network (DSEN) that can apply specific projections to different domains for unbiased embedding, as well as several domain constraints. In contrast to previous methods, the DSEN decomposes the domain-shared projection function into one domain-invariant and two domain-specific sub-functions to explore the similarities and differences between two domains. To prevent the two specific projections from breaking the semantic relationship, a semantic reconstruction constraint is proposed by applying the same decoder function to them in a cycle consistency way. Furthermore, a domain division constraint is developed to directly penalize the margin between real and pseudo image features in respective seen and unseen domains, which can enlarge the inter-domain difference of visual features. Extensive experiments on four public benchmarks demonstrate the effectiveness of DSEN with an average of $9.2%$ improvement in terms of harmonic mean. The code is available in \urlhttps://github.com/mboboGO/DSEN-for-GZSL.
GADM: Manual fake review detection for O2O commercial platforms

O2O (online to offline) commercial platforms, such as Yelp, play a crucial role in our daily purchases. Seeking fame and profit, some people try to manipulate the O2O market by opinion spamming, i.e., engaging in fraudulent behavior such as writing fake reviews, which affects the online purchasing environment. Manual fake reviews imitate honest reviews in many ways; hence they are more deceptive and harmful than botnet reviews. Several efficient methods have been proposed to detect fake reviews, but manual fake reviewers are evolving rapidly. They pretend to be benign users, control the velocity of review fraud actions, and deceive detection systems. Previous work has focused on the contents of reviews or the information of reviewers. We find that geolocation factors have potential and have been neglected in most studies. Our research indicates that geolocation can well distinguish between fake reviewers and benign users on an O2O platform. We propose a manual fake review detection model, the geolocation-based account detection model (GADM), which combines the AdaBoost model and a long short-term memory (LSTM) neural network to analyze a user's account and geolocation information, achieving 83.3% accuracy and an 86.2% F1-score on a Yelp dataset. We also propose a high-efficiency algorithm to detect review fraud groups.
Unpacking the Audio Game Experience

People with or without visual impairments play and enjoy audio games. While this genre of computer games has attracted a strong fan base and some attention in HCI, little research has been dedicated to the people who actually play audio games in their daily life. There is a pressing need to capture the viewpoints of authentic or expert players, designers and developers to advance audio game design. Thus, we give voice to seven game veterans of sound-based gaming, i.e., people who each have more than a decade of profound experience in playing or designing audio games. We conducted a total of 14 interviews and employed grounded theory methods to unpack their experiences. We found that audio games enriched their life through creativity, play, and social exchange. Those core concepts were influenced by peripheral concepts like, inter alia, aesthetics & enjoyability, accessibility, or the availability of audio games. We show how they relate to each other and discuss design implications.
Fast Pedestrian Detection With Attention-Enhanced Multi-Scale RPN and Soft-Cascaded Decision Trees

Pedestrian detection has attracted more attention in the fields of computer vision and artificial intelligence. A variety of real-world applications involving pedestrian detection have been promoted, such as Advanced Driving Assistant System (ADAS). Although both two-stage and single-stage deeply learned object detectors have shown outstanding performance for general object detection, they are still facing the problem of poor accuracy in single-class detection senario because they are designed to distinguish objects from different categories rather than pay attention to various appearances of pedestrians. Previous leading pedestrian detectors F-DNN and F-DNN v2 fuse several neural networks like SSD, VGG16 and GoogLeNet to generate ROIs and supress false alarms with cascaded structure, resulting in low miss rate but high complexity. In this paper we propose a novel framework called Attention-Enhanced Multi-Scale Region Proposal Network (AEMS-RPN) for ROI generation, which also acts as first-stage classification. Inspired by the success of traditional pedestrian detectors, we use soft-cascaded decision trees instead of cascaded deep neural networks to achieve high accuracy and fast detection speed simultaneously. The decision tree classifier is used and enables us to combine features from different layers with various resolutions for classification and incorporate effective bootstrapping for mining hard negatives. We test our method on several pedestrian detection datasets and the experimental results certify the effectiveness of the proposed AEMS-RPN. Compared with the state-of-the-art, we obtain the competitive accuracy with near real-time efficiency.
Energy-Efficient Pattern Recognition Hardware With Elementary Cellular Automata

The development of power-efficient Machine Learning Hardware is of high importance to provide Artificial Intelligence (AI) characteristics to those devices operating at the Edge. Unfortunately, state-of-the-art data-driven AI techniques such as deep learning are too costly in terms of hardware and energy requirements for Edge Computing (EC) devices. Recently, Cellular Automata (CA) have been proposed as a feasible way to implement Reservoir Computing (RC) systems in which the automaton rule is fixed and the training is performed using a linear regression model. In this work we show that Reservoir Computing based on CA may arise as a promising AI alternative for devices operating at the edge due to its intrinsic simplicity. For this purpose, a new low-power CA-based reservoir hardware is proposed and implemented in a FPGA (known as ReCA circuitry). The use of Elementary Cellular Automata (ECA) is able to further simplify the RC structure to implement a power efficient AI system suitable to be implemented in EC applications. Experiments have been conducted on the well-known MNIST handwritten digits database, obtaining competitive results in terms of processing time, circuit area, power and inference accuracy.
Content-Based Visual Summarization for Image Collections

With the surge of images in the information era, people demand an effective and accurate way to access meaningful visual information. Accordingly, effective and accurate communication of information has become indispensable. In this article, we propose a content-based approach that automatically generates a clear and informative visual summarization based on design principles and cognitive psychology to represent image collections. We first introduce a novel method to make representative and nonredundant summarizations of image collections, thereby ensuring data cleanliness and emphasizing important information. Then, we propose a tree-based algorithm with a two-step optimization strategy to generate the final layout that operates as follows: (1) an initial layout is created by constructing a tree randomly based on the grouping results of the input image set; (2) the layout is refined through a coarse adjustment in a greedy manner, followed by gradient back propagation drawing on the training procedure of neural networks. We demonstrate the usefulness and effectiveness of our method via extensive experimental results and user studies. Our visual summarization algorithm can precisely and efficiently capture the main content of image collections better than alternative methods or commercial tools.
Facial Image-to-Video Translation by a Hidden Affine Transformation

There has been a prominent emergence of work on video prediction, aiming to extrapolate the future video frames from the past. Existing temporal-based methods are limited to certain numbers of frames. In this paper, we study video prediction from a single still image in the facial expression domain, a.k.a, facial image-to-video translation. Our main approach, dubbed AffineGAN, associates each facial image with an expression intensity and leverages an affine transformation in the latent space. AffineGAN allows users to control the number of frames to predict as well as the expression intensity for each of them. Unlike previous intensity-based methods, We derive an inverse formulation to the affine transformation, enabling automatic inference of the facial expression intensities from videos --- manual annotation is not only tedious but also ambiguous as people express in various ways and have different opinions about the intensity of a facial image. Both quantitative and qualitative results verify the superiority of AffineGAN over the state of the arts. Notably, in a Turing test with web faces, more than 50% of the facial expression videos generated by AffineGAN are considered real by the Amazon Mechanical Turk workers. This work could improve users' communication experience by enabling them to conveniently and creatively produce expression GIFs, which are popular art forms in online messaging and social networks.
Firm Constraints on the Link between Proactive Innovation, Open Innovation and Firm Performance

This study aims to examine the impacts of firm constraints and proactive innovation on firm performance, using a sample of 3504 small and medium enterprises (SMEs) in Vietnam from 2011-2015.Our findings suggest that technological innovations in general are beneficial to firm performance, increasing firm sales and profits.Further filtering innovations into two categories of proactive and reactive ones, we find that reactive innovation negatively affects firm performance, consistent with the view that proactive entrepreneurial behavior is a highly sought-after characteristic or a valuable resource for a firm as specified in resources-based theory.Finally, our result implies that if firms have low constraint or have sufficient resources, proactive strategies should be the choice if firms seek to improve their performance.
AlphaSeq: Sequence Discovery With Deep Reinforcement Learning

Sequences play an important role in many applications and systems. Discovering sequences with desired properties has long been an interesting intellectual pursuit. This article puts forth a new paradigm, AlphaSeq, to discover desired sequences algorithmically using deep reinforcement learning (DRL) techniques. AlphaSeq treats the sequence discovery problem as an episodic symbol-filling game, in which a player fills symbols in the vacant positions of a sequence set sequentially during an episode of the game. Each episode ends with a completely filled sequence set, upon which a reward is given based on the desirability of the sequence set. AlphaSeq models the game as a Markov decision process (MDP) and adapts the DRL framework of AlphaGo to solve the MDP. Sequences discovered improve progressively as AlphaSeq, starting as a novice, and learns to become an expert game player through many episodes of game playing. Compared with traditional sequence construction by mathematical tools, AlphaSeq is particularly suitable for problems with complex objectives intractable to mathematical analysis. We demonstrate the searching capabilities of AlphaSeq in two applications: 1) AlphaSeq successfully rediscovers a set of ideal complementary codes that can zero-force all potential interferences in multi-carrier code-division multiple access (CDMA) systems and 2) AlphaSeq discovers new sequences that triple the signal-to-interference ratio-benchmarked against the well-known Legendre sequence-of a mismatched filter (MMF) estimator in pulse compression radar systems.
Cramér–Rao Bounds for Filtering Based on Gaussian Process State-Space Models

Posterior Cramér-Rao bounds (CRBs) are derived for the estimation performance of three Gaussian process-based state-space models. The parametric CRB is derived for the case with a parametric state transition and a Gaussian process-based measurement model. We illustrate the theory with a target tracking example and derive both parametric and posterior filtering CRBs for this specific application. Finally, the theory is illustrated with a positioning problem, with experimental data from an office environment where the obtained estimation performance is compared to the derived CRBs.
MSNet: Structural Wired Neural Architecture Search for Internet of Things

The prosperity of Internet of Things (IoT) calls for efficient ways of designing extremely compact yet accurate DNN models. Both the cell-based neural architecture search methods and the recently proposed graph based methods fall short in finding high quality IoT models due to the search flexibility, accuracy density, and node dependency limitations. In this paper, we propose a new graphbased neural architecture search methodology MSNAS for crafting highly compact yet accurate models for IoT devices. MSNAS supports flexible search space and can accumulate learned knowledge in a meta-graph to increase accuracy density. By adopting structural wiring architecture, MSNAS reduces the dependency between nodes, which allows more compact models without sacrificing accuracy. The preliminary experimental results on IoT applications demonstrate that the MSNet crafted by MSNAS outperforms MobileNetV2 and MnasNet by 3.0% in accuracy, with 20% less peak memory consumption and similar Multi-Adds.
Co-saliency Detection Based on Hierarchical Consistency

As an interesting and emerging topic, co-saliency detection aims at discovering common and salient objects in a group of related images, which is useful to variety of visual media applications. Although a number of approaches have been proposed to address this problem, many of them are designed with the misleading assumption, suboptimal image representation, or heavy supervision cost and thus still suffer from certain limitations, which reduces their capability in the real-world scenarios. To alleviate these limitations, we propose a novel unsupervised co-saliency detection method, which successively explores the hierarchical consistency in the image group including background consistency, high-level and low-level objects consistency in a unified framework. We first design a novel superpixel-wise variational autoencoder (SVAE) network to precisely distinguish the salient objects from the background collection based on the reconstruction errors. Then, we propose a two-stage clustering strategy to explore the multi-level salient objects consistency by using high-level and low-level features separately. Finally, the co-saliency results are refined by applying a CRF based refinement method with the multi-level salient objects consistency. Extensive experiments on three widely datasets show that our method achieves superior or competitive performance compared to the state-of-the-art methods.
DAViS Camera Optical Flow

Frame-based optical flow (OF) methods struggle in the presence of large motion and occlusion due to slow frame rates. Optical flow of dynamic vision sensor (DVS) has gained attention recently as a way to overcome these shortcomings. DVS reports “events” corresponding log intensity changes exceeding a specific threshold, with an accuracy of microsecond order. The increased temporal sampling rate are indeed helpful, but the poor spatial fidelity of DVS outputs make events-based OF less reliable overall. In this work, we consider a new sensor called DAViS that combines the conventional active pixel sensor (APS) and DVS circuitries, yielding a conventional intensity image frames as well as the events. We propose a novel optical flow method designed specifically for a DAViS camera that leverages the high spatial fidelity of intensity image frames and the high temporal resolution of events generated by DVS. Hence, the proposed DAViS-OF method yields reliable motion vector estimates while overcoming the fast motion and occlusion problems. The proposed DAViS-OF method is computationally efficient and is suitable for real-time implementation.
Image Classification and Recognition on the Base of Autoassociative Neural Network Usage

The problem of images classification is considered. It is proposed to use the autoassociative neural networks based on the model of successive geometric transformations for this problem solving. The advantages and disadvantages of this approach are considered, as well as ways to improve the accuracy of image classification.
Distributed Selection of Continuous Features in Multilabel Classification Using Mutual Information

Multilabel learning is a challenging task demanding scalable methods for large-scale data. Feature selection has shown to improve multilabel accuracy while defying the curse of dimensionality of high-dimensional scattered data. However, the increasing complexity of multilabel feature selection, especially on continuous features, requires new approaches to manage data effectively and efficiently in distributed computing environments. This article proposes a distributed model for mutual information (MI) adaptation on continuous features and multiple labels on Apache Spark. Two approaches are presented based on MI maximization, and minimum redundancy and maximum relevance. The former selects the subset of features that maximize the MI between the features and the labels, whereas the latter additionally minimizes the redundancy between the features. Experiments compare the distributed multilabel feature selection methods on 10 data sets and 12 metrics. Results validated through statistical analysis indicate that our methods outperform reference methods for distributed feature selection for multilabel data, while MIM also reduces the runtime in orders of magnitude.
Small and Dense Commodity Object Detection with Multi-Scale Receptive Field Attention

Small and dense commodity object detection is highly valued to the applications in practical scenario. Unlike existing approaches mostly focus on detecting generic objects, this paper studies the problem of specific commodity detection, which is characterized by searching for small and dense instances with similar appearances. Since there is no available dataset or benchmark specialized for exploring this issue, we release a Small and Dense Object Dataset of Milk Tea (SDOD-MT) for promoting the research. Besides, our main solutions for mitigating the detection performance drop caused by the existence of small and dense objects can be concluded as two items. First, for the sake of highlighting the information of positive objects in the feature map, we propose a Multi-Scale Receptive Field (MSRF) attention to generate an attention map to weight the importance on each location of the image feature. Second, for eliminating the negative impact for detection performance brought by the issue of sample imbalance, we present a new loss function named ω-focal loss, which significantly improves the detection accuracy of the categories with few objects. Incorporating these two components into an end-to-end deep architecture, we propose a one-stage detecting framework, dubbed CommodityNet. Extensive experimental results on SDODMT demonstrate that the proposed approach achieves a superior performance on small dense object detection.
Imbalance-aware Pairwise Constraint Propagation

Pairwise constraint propagation (PCP) aims to propagate a limited number of initial pairwise constraints (PCs, including must-link and cannot-link constraints) from the constrained data samples to the unconstrained ones to boost subsequent PC-based applications. The existing PCP approaches always suffer from the imbalance characteristic of PCs, which limits their performance significantly. To this end, we propose a novel imbalance-aware PCP method, by comprehensively and theoretically exploring the intrinsic structures of the underlying PCs. Specifically, different from the existing methods that adopt a single representation, we propose to use two separate carriers to represent the two types of links. And the propagation is driven by the structure embedded in data samples and the regularization of the local, global, and complementary structures of the two carries. Our method is elegantly cast as a well-posed constrained optimization model, which can be efficiently solved. Experimental results demonstrate that the proposed PCP method is capable of generating more high-fidelity PCs than the recent PCP algorithms. In addition, the augmented PCs by our method produce higher accuracy than state-of-the-art semi-supervised clustering methods when applied to constrained clustering. To the best of our knowledge, this is the first PCP method taking the imbalance property of PCs into account.
Navigation Graph for Tiled Media Streaming

After the emergence of video streaming services, more creative and diverse multimedia content has become available, and now the capability of streaming 360-degree videos will open a new era of multimedia experiences. However, streaming these videos requires larger bandwidth and less latency than what is found in conventional video streaming systems. Rate adaptation of tiled videos and view prediction techniques are used to solve this problem. In this paper, we introduce the Navigation Graph, which models viewing behaviors in the temporal (segments) and the spatial (tiles) domains to perform the rate adaptation of tiled media associated with the view prediction. The Navigation Graph allows clients to perform view prediction more easily by sharing the viewing model in the same way in which media description information is shared in DASH. It is also useful for encoding the trajectory information in the media description file, which could also allow for more efficient navigation of 360-degree videos. This paper provides information about the creation of the Navigation Graph and its uses. The performance evaluation shows that the Navigation Graph based view prediction and rate adaptation outperform other existing tiled media streaming solutions. Navigation Graph is not limited to 360-degree video streaming applications, but it can also be applied to other tiled media streaming systems, such as volumetric media streaming for augmented reality applications.