Reviewerly/exHarmony huggingface dataset:
Visual Object Tracking in Drone Images with Deep Reinforcement Learning

There is an increasing demand on utilizing camera equipped drones and their applications in many domains varying from agriculture to entertainment and from sports events to surveillance.In such drone applications, an essential and a common task is tracking an object of interest visually.Drone (or UAV) images have different properties when compared to the ground taken (natural) images and those differences introduce additional complexities to the existing object trackers to be directly applied on drone applications.Some important differences among those complexities include (i) smaller object sizes to be tracked and (ii) different orientations and viewing angles yielding different texture and features to be observed.Therefore, new algorithms trained on drone images are needed for the drone-based applications.In this paper, we introduce a deep reinforcement learning (RL) based single object tracker that tracks an object of interest in drone images by estimating a series of actions to find the location of the object in the next frame.This is the first work introducing a single object tracker using a deep RL-based technique for drone images.Our proposed solution introduces a novel reward function that aims to reduce the total number of actions taken to estimate the object's location in the next frame and also introduces a different backbone network to be used on low resolution images.Additionally, we introduce a set of new actions into the action library to better deal with the above-mentioned complexities.We compare our proposed solutions to a state of the art tracking algorithm from the recent literature and demonstrate up to 3.87% improvement in precision and 3.6% improvement in IoU values on the VisDrone2019 data set.We also provide additional results on OTB-100 data set and show up to 3.15% improvement in precision on the OTB-100 data set when compared to the same previous state of the art algorithm.Lastly, we analyze the ability to handle some of the challenges faced during tracking, including but not limited to occlusion, deformation, and scale variation for our proposed solutions.
A performance study of meta‐heuristic approaches for quadratic assignment problem

Abstract The quadratic assignment problem (QAP) is a well‐known challenging combinatorial optimization problem that has received many researchers' attention with varied real‐world and industrial applications areas. It is noteworthy to mention that a plethora of nature‐inspired optimization algorithms have successfully been used to solve various optimization problems, including several variants of the QAPs. In this article, a comprehensive literature review is presented to show the most relevant nature‐inspired algorithms that have been used in solving the QAP. More so, extensive experiments are conducted and analyzed to show the performance of the well‐known state‐of‐the‐art nature‐inspired meta‐heuristic optimization algorithms in solving the QAP, including the ant colony optimization (ACO), bat algorithm, genetic algorithm (GA), particle swarm optimization (PSO), and tabu search algorithm. Besides, a modified variant of the discrete PSO algorithm is implemented and compared with existing approaches. The six selected algorithms' performances, including the modified PSO, are validated on eight commonly used QAP instances of varying complexity and size, considering the quality of solutions achieved and computational time consumed by the representative algorithms. The numerical results revealed that the most competitive algorithm was ACO, while the GA appeared to be the worst performed algorithm among the six compared meta‐heuristic algorithms. However, based on the extensive analysis conducted on the tested algorithms, further improvements are suggested, including implementing new modified versions of the tested algorithms to tackle the QAP and its variant instances.
F3RNet: full-resolution residual registration network for deformable image registration

Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream high-to-low, low-to-high network structure and can achieve satisfactory overall registration results. However, accurate alignments for some severely deformed local regions, which are crucial for pinpointing surgical targets, are often overlooked. Consequently, these approaches are not sensitive to some hard-to-align regions, e.g., intra-patient registration of deformed liver lobes.We propose a novel unsupervised registration network, namely full-resolution residual registration network (F3RNet), for deformable registration of severely deformed organs. The proposed method combines two parallel processing streams in a residual learning fashion. One stream takes advantage of the full-resolution information that facilitates accurate voxel-level registration. The other stream learns the deep multi-scale residual representations to obtain robust recognition. We also factorize the 3D convolution to reduce the training parameters and enhance network efficiency.We validate the proposed method on a clinically acquired intra-patient abdominal CT-MRI dataset and a public inspiratory and expiratory thorax CT dataset. Experiments on both multimodal and unimodal registration demonstrate promising results compared to state-of-the-art approaches.By combining the high-resolution information and multi-scale representations in a highly interactive residual learning fashion, the proposed F3RNet can achieve accurate overall and local registration. The run time for registering a pair of images is less than 3 s using a GPU. In future works, we will investigate how to cost-effectively process high-resolution information and fuse multi-scale representations.
Presence Effects in Virtual Reality Based on User Characteristics: Attention, Enjoyment, and Memory

Presence refers to the emotional state of users where their motivation for thinking and acting arises based on the perception of the entities in a virtual world. The immersion level of users can vary when they interact with different media content, which may result in different levels of presence especially in a virtual reality (VR) environment. This study investigates how user characteristics, such as gender, immersion level, and emotional valence on VR, are related to the three elements of presence effects (attention, enjoyment, and memory). A VR story was created and used as an immersive stimulus in an experiment, which was presented through a head-mounted display (HMD) equipped with an eye tracker that collected the participants’ eye gaze data during the experiment. A total of 53 university students (26 females, 27 males), with an age range from 20 to 29 years old (mean 23.8), participated in the experiment. A set of pre- and post-questionnaires were used as a subjective measure to support the evidence of relationships among the presence effects and user characteristics. The results showed that user characteristics, such as gender, immersion level, and emotional valence, affected their level of presence, however, there is no evidence that attention is associated with enjoyment or memory.
UAV path planning based on improved particle swarm algorithm

As one of the key technologies to realize the autonomy of UAVs, path planning is one of the key issues in the field of UAVs. The results of the existing traditional algorithms for path planning are difficult to meet the increasingly complex environment and task requirements. Aiming at the shortcomings of traditional particle swarm algorithm, this research improves the weight and learning factor of particle swarm. The proposed new algorithm can efficiently plan autonomous paths for UAVs, thereby effectively improving the operating environment of UAVs and providing help for applications in related industries. Finally, this research was simulated by MATLAB software to verify the feasibility and effectiveness of the proposed algorithm.
Online Course: Application Models in the Educational Process

Digitalization of education and the development of electronic pedagogy as a branch of pedagogical science are the conditions that actualize the analysis of online courses application mo-dels which is the purpose of this study. Based on the analysis of scientific pedagogical literature, the article addresses the typology of online courses integration models implemented in the traditional (face-to face) educational process. The author has studied and summarized the various approaches to selecting integration models of online courses in Russian and foreign scientific and pedagogical literature and developed a typology of patterns of online courses application based on three criteria: the organization of the educational process (characteristics: the character of the online course incorporating in the educational process, a way of organizing the educational process), the contents of traditional face-to-face and online components in the learning content (characteristics: a change in the structure of educational process, forms and methods of training), and the organization of interaction between the subjects of the educational process (characteristics: organization of pedagogical support, organization of pedagogical interaction between the subjects). This approach made it possible to pinpoint thirty-four models of online courses application and integration. Each model implemented in practice can be identified unambiguously when typologizing by one characteristic of the corresponding criterion and refer to several types when considering its different characteristics.
Beyond Show of Hands: Engaging Viewers via Expressive and Scalable Visual Communication in Live Streaming

Live streaming is gaining popularity across diverse application domains in recent years. A core part of the experience is streamer-viewer interaction, which has been mainly text-based. Recent systems explored extending viewer interaction to include visual elements with richer expression and increased engagement. However, understanding expressive visual inputs becomes challenging with many viewers, primarily due to the relative lack of structure in visual input. On the other hand, adding rigid structures can limit viewer interactions to narrow use cases or decrease the expressiveness of viewer inputs. To facilitate the sensemaking of many visual inputs while retaining the expressiveness or versatility of viewer interactions, we introduce a visual input management framework (VIMF) and a system, VisPoll, that help streamers specify, aggregate, and visualize many visual inputs. A pilot evaluation indicated that VisPoll can expand the types of viewer interactions. Our framework provides insights for designing scalable and expressive visual communication for live streaming.
Sentiment Analysis of Persian-English Code-mixed Texts

The rapid production of data on the internet and the need to understand how users are feeling from a business and research perspective has prompted the creation of numerous automatic monolingual sentiment detection systems. More recently however, due to the unstructured nature of data on social media, we are observing more instances of multilingual and code-mixed texts. This development in content type has created a new demand for code-mixed sentiment analysis systems. In this study we collect, label and thus create a dataset of Persian-English code-mixed tweets. We then proceed to introduce a model which uses BERT pretrained embeddings as well as translation models to automatically learn the polarity scores of these Tweets. Our model outperforms the baseline models that use Naïve Bayes and Random Forest methods.
Analyzing Transaction Confirmation in Ethereum Using Machine Learning Techniques

Ethereum has emerged as one of the most important cryptocurrencies in terms of the number of transactions. Given the recent growth of Ethereum, the cryptocurrency community and researchers are interested in understanding the Ethereum transactions behavior. In this work, we investigate a key aspect of Ethereum: the prediction of a transaction confirmation or failure based on its features. This is a challenging issue due to the small, but still relevant, fraction of failures in millions of recorded transactions and the complexity of the distributed mechanism to execute transactions in Ethereum. To conduct this investigation, we train machine learning models for this prediction, taking into consideration carefully balanced sets of confirmed and failed transactions. The results show high-performance models for classification of transactions with the best values of F1-score and area under the ROC curve approximately equal to 0.67 and 0.87, respectively. Also, we identified the gas used as the most relevant feature for the prediction.
Rupture Passing Probabilities at Fault Bends and Steps, with Application to Rupture Length Probabilities for Earthquake Early Warning

ABSTRACT Earthquake early warning (EEW) systems can quickly identify the beginning of a significant earthquake rupture, but the first seconds of seismic data have not been found to predict the final rupture length. We present two approaches for estimating probabilities of rupture length given the rupture initiation from an EEW system. In the first approach, bends and steps on the fault are interpreted as physical mechanisms for rupture arrest. Arrest probability relations are developed from empirical observations and depend on bend angle and step size. Probability of arrest compounds serially with increasing rupture length as bends or steps are encountered. In the second approach, time-independent rates among ruptures from the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), are interpreted to apply to the time-dependent condition in which rupture grows from a known starting point. Length probabilities from a Gutenberg–Richter magnitude–frequency relation provide a reference of comparison. We illustrate the new approach using the discretized fault model for California developed for UCERF3. For the case of rupture initiating on the southeast end of the San Andreas fault we find the geometric complexity of the Mill Creek section impedes most ruptures, and only ∼5% are predicted to reach to San Bernardino on the eastern edge of the greater Los Angeles region. Conditional probabilities of length can be precompiled in this manner for any initiation point on the fault system and thus are of potential value in seismic hazard and EEW applications.
“Good Enough!”: Flexible Goal Achievement with Margin-based Outcome Evaluation

Traditional goal setting simply assumes a binary outcome for goal evaluation. This binary judgment does not consider a user's effort, which may demotivate the user. This work explores the possibility of mitigating this negative impact with a slight modification on the goal evaluation criterion, by introducing a 'margin' that is widely used for quality control in the manufacturing fields. A margin represents a range near the goal where the user's outcome will be regarded as 'good enough' even if the user fails to reach it. We explore users' perceptions and behaviors through a large-scale survey study and a small-scale field experiment using a coaching system to promote physical activity. Our results provide positive evidence on the margin, such as lowering the burden of goal achievement and increasing motivation to make attempts. We discuss practical design implications on margin-enabled goal setting and evaluation for behavioral change support systems.
Multi-Scale Attention-Guided Network for mammograms classification

Abstract For the breast mass segmentation in whole mammograms, in our studies, we observe that there is an enormous performance reduction in the case of considering the normal data during training. Therefore, the mammogram classification (normal vs. abnormal) is essential for boosting the breast mass segmentation performance in whole mammograms and is our research topic in this paper. Due to the breast lesions with a variety of sizes, the mammogram classification (normal vs. abnormal) is a challenging task. To improve the mammogram classification performance, we propose an end-to-end convolutional neural network, namely Multi-Scale Attention-Guided Network (MSANet). Specifically, MSANet can be constructed by stacking several Multi-Scale Attention (MSA) bottlenecks. Each MAS bottleneck consists of a Scale Aggregation (SA) unit and a Multi-Scale Attention Module (MSAM). The SA unit is used to generate multiple feature maps of different scales, and the MSAM is used to allocate the suitable size of receptive field for objects of different sizes. According to the extensive experiments, our proposed MSANet-50 achieves a fully automated classification AUC of 0.942 on the DDSM database, which outperforms several approaches.
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR

Recently, an end-to-end speaker-attributed automatic speech recognition (E2E SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech. In the previous study, the model parameters were trained based on the speaker-attributed maximum mutual information (SA-MMI) criterion, with which the joint posterior probability for multi-talker transcription and speaker identification are maximized over training data. Although SA-MMI training showed promising results for overlapped speech consisting of various numbers of speakers, the training criterion was not directly linked to the final evaluation metric, i.e., speaker-attributed word error rate (SA-WER). In this paper, we propose a speaker-attributed minimum Bayes risk (SA-MBR) training method where the parameters are trained to directly minimize the expected SA-WER over the training data. Experiments using the LibriSpeech corpus show that the proposed SA-MBR training reduces the SA-WER by 9.0 % relative compared with the SA-MMI-trained model. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Deep Deterministic Information Bottleneck with Matrix-Based Entropy Functional

We introduce the matrix-based Rényi’s α-order entropy functional to parameterize Tishby et al. information bottleneck (IB) principle [1] with a neural network. We term our methodology Deep Deterministic Information Bottleneck (DIB), as it avoids variational inference and distribution assumption. We show that deep neural networks trained with DIB outperform the variational objective counterpart and those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack. Code available at https://github.com/yuxi120407/DIB.
Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization

Federated learning (FL) is encountered with the challenge of training a model in massive and heterogeneous networks. Model averaging (MA) has become a popular FL paradigm where parallel (stochastic) gradient descent (GD) is run on a small sampled subset of clients multiple times before uploading the local models to a server for averaging, which has been proven effective in reducing the communication cost for achieving a good model. However, MA has not been considered for the important matrix factorization (MF) model, which has vast signal processing and machine learning applications. In this paper, we investigate the federated MF problem and propose a new MA based algorithm, named FedMAvg, by judiciously combining the alternating minimization technique and MA. Through analysis, we show that gradually decreasing the number of local GD and only allowing partial clients to communicate with the server can greatly reduce the communication cost, especially in heterogeneous networks with non-i.i.d. data. Experimental results by applying FedMAvg to data clustering and item recommendation tasks demonstrate its efficacy in terms of both task performance and communication efficiency.
Region-based Non-local Operation for Video Classification

Convolutional Neural Networks (CNNs) model long-range dependencies by deeply stacking convolution operations with small window sizes, which makes the optimizations difficult. This paper presents region-based non-local (RNL) operations as a family of self-attention mechanisms, which can directly capture long-range dependencies without using a deep stack of local operations. Given an intermediate feature map, our method recalibrates the feature at a position by aggregating the information from the neighboring regions of all positions. By combining a channel attention module with the proposed RNL, we design an attention chain, which can be integrated into the off-the-shelf CNNs for end-to-end training. We evaluate our method on two video classification benchmarks. The experimental results of our method outperform other attention mechanisms, and we achieve state-of-the-art performance on the Something-Something V1 dataset. The code is available at: https://github.com/guoxih/region-based-non-local-network.
Fcl-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech Synthesis

Sequence-to-sequence (seq2seq) learning has greatly improved text-to-speech (TTS) synthesis performance, but effective implementation on resource-restricted devices remains challenging as seq2seq models are usually computationally expensive and memory intensive. To achieve fast inference speed and small model size while maintain high-quality speech, we propose FCL-taco2, a Fast, Controllable and Lightweight (FCL) TTS model based on Tacotron2. FCL-taco2 adopts a novel semi-autoregressive (SAR) mode for phoneme level based parallel mel-spectrograms generation conditioned on prosody features, leading to faster inference speed and higher prosody controllability than Tacotron2. Besides, knowledge distillation (KD) is leveraged to compress a relatively large FCL-taco2 model to its small version with minor loss of speech quality. Experimental results on English (EN) and Chinese (CN) datasets show that the small version of FCL-taco2 achieves comparable performance with Tacotron2 in terms of speech quality, while it has a 4.8× smaller footprint with 17.7× and 18.5× faster inference speeds on average for EN and CN experiments respectively. Besides, execution on mobile devices shows that the proposed model can achieve faster than real-time speech synthesis. Our code and audio samples are released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Transgender People’s Technology Needs to Support Health and Transition

Health and well-being are integral parts of the human experience, and yet due to numerous factors are inaccessible for many communities. The transgender community is no exception and faces increased risk for both physical and mental illness. This population faces many unique challenges before, during, and after transition. To gain a deeper understanding of the trans community's health and well-being needs, we conducted twenty-one interviews with transgender individuals to determine how they navigated their identities and transitions. From our interviews, we examine and highlight the unique needs of the trans population with respect to health, well-being, identity, and transition. We discuss how designers can better understand and accommodate the diversity of this community, give suggestions for the design of technologies for trans health and well-being, and contribute open areas of research.