Reviewerly/exHarmony huggingface dataset:
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation

Optical character recognition (OCR) systems performance have improved significantly in the deep learning era. This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design. That said, deep learning based HTR is limited, as in every other task, by the number of training examples. Gathering data is a challenging and costly task, and even more so, the labeling task that follows, of which we focus here. One possible approach to reduce the burden of data annotation is semi-supervised learning. Semi supervised methods use, in addition to labeled data, some unlabeled samples to improve performance, compared to fully supervised ones. Consequently, such methods may adapt to unseen images during test time. We present ScrabbleGAN, a semi-supervised approach to synthesize handwritten text images that are versatile both in style and lexicon. ScrabbleGAN relies on a novel generative model which can generate images of words with an arbitrary length. We show how to operate our approach in a semi-supervised manner, enjoying the aforementioned benefits such as performance boost over state of the art supervised HTR. Furthermore, our generator can manipulate the resulting text style. This allows us to change, for instance, whether the text is cursive, or how thin is the pen stroke.
Context-Aware Term Weighting For First Stage Passage Retrieval

Term frequency is a common method for identifying the importance of a term in a document. But term frequency ignores how a term interacts with its text context, which is key to estimating document-specific term weights. This paper proposes a Deep Contextualized Term Weighting framework (DeepCT) that maps the contextualized term representations from BERT to into context-aware term weights for passage retrieval. The new, deep term weights can be stored in an ordinary inverted index for efficient retrieval. Experiments on two datasets demonstrate that DeepCT greatly improves the accuracy of first-stage passage retrieval algorithms.
KERL

For sequential recommendation, it is essential to capture and predict future or long-term user preference for generating accurate recommendation over time. To improve the predictive capacity, we adopt reinforcement learning (RL) for developing effective sequential recommenders. However, user-item interaction data is likely to be sparse, complicated and time-varying. It is not easy to directly apply RL techniques to improve the performance of sequential recommendation.
Efficient Processing of Deep Neural Networks

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems.
A Systematic Review of Gesture Elicitation Studies

Gesture elicitation studies represent a popular and resourceful method in HCI to inform the design of intuitive gesture commands, reflective of end-users' behavior, for controlling all kinds of interactive devices, applications, and systems. In the last ten years, an impressive body of work has been published on this topic, disseminating useful design knowledge regarding users' preferences for finger, hand, wrist, arm, head, leg, foot, and whole-body gestures. In this paper, we deliver a systematic literature review of this large body of work by summarizing the characteristics and findings ofN=216gesture elicitation studies subsuming 5,458 participants, 3,625 referents, and 148,340 elicited gestures. We highlight the descriptive, comparative, and generative virtues of our examination to provide practitioners with an effective method to (i) understand how new gesture elicitation studies position in the literature; (ii) compare studies from different authors; and (iii) identify opportunities for new research. We make our large corpus of papers accessible online as a Zotero group library at https://www.zotero.org/groups/2132650/gesture_elicitation_studies.
AUTSL: A Large Scale Multi-Modal Turkish Sign Language Dataset and Baseline Methods

Sign language recognition is a challenging problem where signs are identified by simultaneous local and global articulations of multiple sources, i.e. hand shape and orientation, hand movements, body posture, and facial expressions. Solving this problem computationally for a large vocabulary of signs in real life settings is still a challenge, even with the state-of-the-art models. In this study, we present a new largescale multi-modal Turkish Sign Language dataset (AUTSL) with a benchmark and provide baseline models for performance evaluations. Our dataset consists of 226 signs performed by 43 different signers and 38,336 isolated sign video samples in total. Samples contain a wide variety of backgrounds recorded in indoor and outdoor environments. Moreover, spatial positions and the postures of signers also vary in the recordings. Each sample is recorded with Microsoft Kinect v2 and contains RGB, depth, and skeleton modalities. We prepared benchmark training and test sets for user independent assessments of the models. We trained several deep learning based models and provide empirical evaluations using the benchmark; we used CNNs to extract features, unidirectional and bidirectional LSTM models to characterize temporal information. We also incorporated feature pooling modules and temporal attention to our models to improve the performances. We evaluated our baseline models on AUTSL and Montalbano datasets. Our models achieved competitive results with the state-of-the-art methods on Montalbano dataset, i.e. 96.11% accuracy. In AUTSL random train-test splits, our models performed up to 95.95% accuracy. In the proposed user-independent benchmark dataset our best baseline model achieved 62.02% accuracy. The gaps in the performances of the same baseline models show the challenges inherent in our benchmark dataset. AUTSL benchmark dataset is publicly available at https://cvml.ankara.edu.tr.
Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

Remote sensing targets have different dimensions, and they have the characteristics of dense distribution and a complex background. This makes remote sensing target detection difficult. With the aim at detecting remote sensing targets at different scales, a new You Only Look Once (YOLO)-V3-based model was proposed. YOLO-V3 is a new version of YOLO. Aiming at the defect of poor performance of YOLO-V3 in detecting remote sensing targets, we adopted DenseNet (Densely Connected Network) to enhance feature extraction capability. Moreover, the detection scales were increased to four based on the original YOLO-V3. The experiment on RSOD (Remote Sensing Object Detection) dataset and UCS-AOD (Dataset of Object Detection in Aerial Images) dataset showed that our approach performed better than Faster-RCNN, SSD (Single Shot Multibox Detector), YOLO-V3, and YOLO-V3 tiny in terms of accuracy. Compared with original YOLO-V3, the mAP (mean Average Precision) of our approach increased from 77.10% to 88.73% in the RSOD dataset. In particular, the mAP of detecting targets like aircrafts, which are mainly made up of small targets increased by 12.12%. In addition, the detection speed was not significantly reduced. Generally speaking, our approach achieved higher accuracy and gave considerations to real-time performance simultaneously for remote sensing target detection.
Intelligent waste management system using deep learning with IoT

Waste management leads to the demolition of waste conducted by recycling and landfilling. Deep learning and the Internet of things (IoT) confer an agile solution in classification and real-time data monitoring, respectively. This paper reflects a capable architecture of the waste management system based on deep learning and IoT. The proposed model renders an astute way to sort digestible and indigestible waste using a convolutional neural network (CNN), a popular deep learning paradigm. The scheme also introduces an architectural design of a smart trash bin that utilizes a microcontroller with multiple sensors. The proposed method employs IoT and Bluetooth connectivity for data monitoring. IoT enables control of real-time data from anywhere while Bluetooth aids short-range data monitoring through an android application. To examine the efficacy of the developed model, the accuracy of waste label classification, sensors data estimation, and system usability scale (SUS) are enumerated and interpreted. The classification accuracy of the proposed architecture based on the CNN model is 95.3125%, and the SUS score is 86%. However, this smart system will be adjustable to household activities with real-time waste monitoring.
Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.
An empirical study on anthropomorphism and engagement with disembodied AIs and consumers' re‐use behavior

Abstract The integration of artificial intelligence (AI) into the human race has gradually become a norm. AI entails technology assemblages such as machine learning, natural language processing, and reasoning. The influence of AI systems has intensified in consumers' daily lives. Many consumers have interacted with the notions of AI through advertisements or having personal experiences. Many consumers are curious about the use of AI. This paper reports three studies conducted to determine whether anthropomorphism (ANTH) and engagement play a role in consumers' intention to re‐use a voice assistant (VA; a machine‐learning AI). The second study will determine if ANTH and engagement play a role when consumers are using the VAs for different activities (task completion vs. information gathering). In addition, in Study 2, actual re‐use behavior was also tested in the model, which encouraged a stronger overall model fit. The results show that in general effort expectation (EE) has a strong positive impact on consumers' usage experience of the VA. However, between the two types of activities, EE has a stronger impact on consumers' usage experience for information‐gathering activities, whereas performance expectation has a stronger impact on usage experience when consumers use the VA for task‐completion purposes. The third study used internet usage experience as a moderating variable to determine the boundaries of the mediating effects in the study. The results show that the mediators yield results similar to prior studies.
Interaction between hotel service robots and humans: A hotel-specific Service Robot Acceptance Model (sRAM)

The growing implementation of robotics in hospitality and tourism requires broader research into customers' experience with service robots. This study explores human-robot interaction (HRI) in the context of tourists interacting with hotel service robots. The data, 7994 online TripAdvisor reviews of 74 hotels, were subjected to a content analysis based on the Service Robot Acceptance Model (sRAM) and its dimensions (functional, social-emotional and relational). A sentiment analysis was also carried out. The results identify the principal dimensions and variables involved in HRI and the feelings robots inspire in different types of travellers. Guests most often comment on the functional dimension. Robots' functions determine this experience and influence the interaction between robots and hotel guests.
Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss

Parsing very high resolution (VHR) urban scene images into regions with semantic meaning, e.g. buildings and cars, is a fundamental task in urban scene understanding. However, due to the huge quantity of details contained in an image and the large variations of objects in scale and appearance, the existing semantic segmentation methods often break one object into pieces, or confuse adjacent objects and thus fail to depict these objects consistently. To address these issues uniformly, we propose a standalone end-to-end edge-aware neural network (EaNet) for urban scene semantic segmentation. For semantic consistency preservation inside objects, the EaNet model incorporates a large kernel pyramid pooling (LKPP) module to capture rich multi-scale context with strong continuous feature relations. To effectively separate confusing objects with sharp contours, a Dice-based edge-aware loss function (EA loss) is devised to guide the EaNet to refine both the pixel- and image-level edge information directly from semantic segmentation prediction. In the proposed EaNet model, the LKPP and the EA loss couple to enable comprehensive feature learning across an entire semantic object. Extensive experiments on three challenging datasets demonstrate that our method can be readily generalized to multi-scale ground/aerial urban scene images, achieving 81.7% in mIoU on Cityscapes Test set and 90.8% in the mean F1-score on the ISPRS Vaihingen 2D Test set. Code is available at: https://github.com/geovsion/EaNet.
Dual Mixup Regularized Learning for Adversarial Domain Adaptation

Recent advances on unsupervised domain adaptation (UDA) rely on adversarial learning to disentangle the explanatory and transferable features for domain adaptation. However, there are two issues with the existing methods. First, the discriminability of the latent space cannot be fully guaranteed without considering the class-aware information in the target domain. Second, samples from the source and target domains alone are not sufficient for domain-invariant feature extracting in the latent space. In order to alleviate the above issues, we propose a dual mixup regularized learning (DMRL) method for UDA, which not only guides the classifier in enhancing consistent predictions in-between samples, but also enriches the intrinsic structures of the latent space. The DMRL jointly conducts category and domain mixup regularizations on pixel level to improve the effectiveness of models. A series of empirical studies on four domain adaptation benchmarks demonstrate that our approach can achieve the state-of-the-art.
Factors Affecting Organizations’ Resistance to the Adoption of Blockchain Technology in Supply Networks

From a supply chain perspective, new technologies such as blockchain can improve the efficiency and competitiveness of logistics and increase customer satisfaction. Although blockchain technology has been lauded as a way for firms to build sustainable supply chain networks, the rate of acceptance of this technology remains low. Therefore, this study seeks to identify the factors that discourage firms from merging blockchain with the supply chain. Instead of providing further reasons for adopting blockchain technology, we try to understand what deters firms from adding blockchain to their operations. Following the deductive approach, a confirmatory factor analysis is conducted on pre-test questionnaires to test, improve, and verify the constructs (questions) to measure the hypothesized factors. A theoretical model is proposed based on the hypotheses, and structural equation modeling is applied. The results are estimated using the partial least squares approach and a sample of 83 respondents. Our findings based on our empirical data support most of our hypotheses. We find that various factors impede the adoption of blockchain technologies, including technological barriers, constraints rooted in organizations and the environment, and system-related governmental barriers. In addition, various factors are critical determinants of resistance to blockchain in the technological, organizational, and environmental dimensions.
Quantum gradient descent and Newton’s method for constrained polynomial optimization

Abstract Optimization problems in disciplines such as machine learning are commonly solved with iterative methods. Gradient descent algorithms find local minima by moving along the direction of steepest descent while Newton’s method takes into account curvature information and thereby often improves convergence. Here, we develop quantum versions of these iterative optimization algorithms and apply them to polynomial optimization with a unit norm constraint. In each step, multiple copies of the current candidate are used to improve the candidate using quantum phase estimation, an adapted quantum state exponentiation scheme, as well as quantum matrix multiplications and inversions. The required operations perform polylogarithmically in the dimension of the solution vector and exponentially in the number of iterations. Therefore, the quantum algorithm can be useful for high-dimensional problems where a small number of iterations is sufficient.
Quantifying Differential Privacy in Continuous Data Release Under Temporal Correlations

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives to continuously release private data for protecting privacy at each time point (i.e., event-level privacy), which assume that the data at different time points are independent, or that adversaries do not have knowledge of correlation between data. However, continuously generated data tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations. First, we analyze the privacy leakage of a DP mechanism under temporal correlation that can be modeled using Markov Chain. Our analysis reveals that, the event-level privacy loss of a DP mechanism may increase over time. We call the unexpected privacy loss temporal privacy leakage (TPL). Although TPL may increase over time, we find that its supremum may exist in some cases. Second, we design efficient algorithms for calculating TPL. Third, we propose data releasing mechanisms that convert any existing DP mechanism into one against TPL. Experiments confirm that our approach is efficient and effective.
Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features

With the prevalent use of Deep Neural Networks (DNNs) in many applications, security of these networks is of importance. Pre-trained DNNs may contain backdoors that are injected through poisoned training. These trojaned models perform well when regular inputs are provided, but misclassify to a target output label when the input is stamped with a unique pattern called trojan trigger. Recently various backdoor detection and mitigation systems for DNN based AI applications have been proposed. However, many of them are limited to trojan attacks that require a specific patch trigger. In this paper, we introduce composite attack, a more flexible and stealthy trojan attack that eludes backdoor scanners using trojan triggers composed from existing benign features of multiple labels. We show that a neural network with a composed backdoor can achieve accuracy comparable to its original version on benign data and misclassifies when the composite trigger is present in the input. Our experiments on 7 different tasks show that this attack poses a severe threat. We evaluate our attack with two state-of-the-art backdoor scanners. The results show none of the injected backdoors can be detected by either scanner. We also study in details why the scanners are not effective. In the end, we discuss the essence of our attack and propose possible defense.
Ratio-and-Scale-Aware YOLO for Pedestrian Detection

Current deep learning methods seldom consider the effects of small pedestrian ratios and considerable differences in the aspect ratio of input images, which results in low pedestrian detection performance. This study proposes the ratio-and-scale-aware YOLO (RSA-YOLO) method to solve the aforementioned problems. The following procedure is adopted in this method. First, ratio-aware mechanisms are introduced to dynamically adjust the input layer length and width hyperparameters of YOLOv3, thereby solving the problem of considerable differences in the aspect ratio. Second, intelligent splits are used to automatically and appropriately divide the original images into two local images. Ratio-aware YOLO (RA-YOLO) is iteratively performed on the two local images. Because the original and local images produce low- and high-resolution pedestrian detection information after RA-YOLO, respectively, this study proposes new scale-aware mechanisms in which multiresolution fusion is used to solve the problem of misdetection of remarkably small pedestrians in images. The experimental results indicate that the proposed method produces favorable results for images with extremely small objects and those with considerable differences in the aspect ratio. Compared with the original YOLOs (i.e., YOLOv2 and YOLOv3) and several state-of-the-art approaches, the proposed method demonstrated a superior performance for the VOC 2012 comp4, INRIA, and ETH databases in terms of the average precision, intersection over union, and lowest log-average miss rate.
NSGANetV2: Evolutionary Multi-objective Surrogate-Assisted Neural Architecture Search

In this paper, we propose an efficient NAS algorithm for generating task-specific models that are competitive under multiple competing objectives. It comprises of two surrogates, one at the architecture level to improve sample efficiency and one at the weights level, through a supernet, to improve gradient descent training efficiency. On standard benchmark datasets (C10, C100, ImageNet), the resulting models, dubbed NSGANetV2, either match or outperform models from existing approaches with the search being orders of magnitude more sample efficient. Furthermore, we demonstrate the effectiveness and versatility of the proposed method on six diverse non-standard datasets, e.g. STL-10, Flowers102, Oxford Pets, FGVC Aircrafts etc. In all cases, NSGANetV2s improve the state-of-the-art (under mobile setting), suggesting that NAS can be a viable alternative to conventional transfer learning approaches in handling diverse scenarios such as small-scale or fine-grained datasets. Code is available at https://github.com/mikelzc1990/nsganetv2 .