Reviewerly/exHarmony huggingface dataset:
Hard Occlusions in Visual Object Tracking

Visual object tracking is among the hardest problems in computer vision, as trackers have to deal with many challenging circumstances such as illumination changes, fast motion, occlusion, among others. A tracker is assessed to be good or not based on its performance on the recent tracking datasets, e.g., VOT2019, and LaSOT. We argue that while the recent datasets contain large sets of annotated videos that to some extent provide a large bandwidth for training data, the hard scenarios such as occlusion and in-plane rotation are still underrepresented. For trackers to be brought closer to the real-world scenarios and deployed in safety-critical devices, even the rarest hard scenarios must be properly addressed. In this paper, we particularly focus on hard occlusion cases and benchmark the performance of recent state-of-the-art trackers (SOTA) on them. We created a small-scale dataset (Dataset can be accessed at https://github.com/ThijsKuipers1995/HTB2020 ) containing different categories within hard occlusions, on which the selected trackers are evaluated. Results show that hard occlusions remain a very challenging problem for SOTA trackers. Furthermore, it is observed that tracker performance varies wildly between different categories of hard occlusions, where a top-performing tracker on one category performs significantly worse on a different category. The varying nature of tracker performance based on specific categories suggests that the common tracker rankings using averaged single performance scores are not adequate to gauge tracker performance in real-world scenarios.
Code-Aided Modulation Classification Algorithm for Multiuser Uplink SC-FDMA Systems

Modulation classification has attracted great interest in rapid deployment in wireless radios for civilian and military applications. While there is a massive amount of works on this topic for single-signal transmissions, a few works are reported in the literature for multi-signal transmissions. Therefore, there is an urgent need to explore different modulation classification algorithms for such scenarios. In this work, we propose a novel modulation classification algorithm for multiuser uplink single-carrier frequency division multiple access systems exploiting the channel decoder's soft information as a beneficial resource to improve the classification performance. Starting from the maximum-likelihood principle, we analytically design the proposed algorithm using a space-alternating generalized expectation-maximization approach. Channel estimation is also developed as an auxiliary task for the proposed algorithm. Simulation results indicate that the proposed algorithm is superior to the traditional algorithms with reduced processing time.
Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

Predictive power allocation is conceived for energyefficient video streaming over mobile networks using deep reinforcement learning.The goal is to minimize the accumulated energy consumption of each base station over a complete video streaming session under the constraint that avoids video playback interruptions.To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem.In contrast to previous predictive power allocation policies that first predict future information with historical data and then optimize the power allocation based on the predicted information, the proposed policy operates in an on-line and end-to-end manner.By judiciously designing the action and state that only depend on slowly-varying average channel gains, we reduce the signaling overhead between the edge server and the base stations, and make it easier to learn a good policy.To further avoid playback interruption throughout the learning process and improve the convergence speed, we exploit the partially known model of the system dynamics by integrating the concepts of safety layer, post-decision state, and virtual experiences into the basic DDPG algorithm.Our simulation results show that the proposed policies converge to the optimal policy that is derived based on perfect large-scale channel prediction and outperform the first-predictthen-optimize policy in the presence of prediction errors.By harnessing the partially known model, the convergence speed can be dramatically improved.
Alignment and granularity of requirements and architecture in agile development: A functional perspective

Requirements engineering and software architecture are tightly linked disciplines. The Twin Peaks model suggests that requirements and architectural components should stay aligned while the system is designed and as the level of detail increases. Unfortunately, this is hardly the case in practical settings. We surmise that a reason for the absence of conjoint evolution is that existing models, such as the Twin Peaks, do not provide concrete guidance for practitioners. We propose the Requirements Engineering for Software Architecture (RE4SA) model to assist in analyzing the alignment and the granularity of functional requirements and architectural components. After detailing the RE4SA model in notation-independent terms, we propose a concrete instance, called RE4SA-Agile, that connects common artifacts in agile development, such as user stories and features. We introduce metrics that measure the alignment between the requirements and architecture, and we define granularity smells to pinpoint situation in which the granularity of one high-level requirement or high-level component is not uniform with the norm. We show two applications of RE4SA-Agile, including the use of the metrics, to real-world case studies. Our applications of RE4SA-Agile, which were discussed with representatives from the development teams, prove to be able to pinpoint problematic situations regarding the relationship between functional requirements and architecture. RE4SA and its metrics can be seen as a first attempt to provide a concrete approach for supporting the application of the Twin Peaks model. We expect future research to apply our metrics to additional cases and to provide variants for RE4SA that support different concrete notations, and extend the perspective beyond the functional perspective of this research, similar to what we did with RE4SA-Agile in this paper.
Latent Distribution-Based 3D Hand Pose Estimation From Monocular RGB Images

In this article, we propose a novel compressed latent distribution representation for 3D hand pose estimation from monocular RGB images to alleviate the channel correspondence problem. The channel correspondence problem occurs when the 2D and depth coordinates are estimated from independent feature maps, which means the 2D and depth channel sequences may not match during the cross-dataset inference. In contrast, we propose a compressed latent distribution representation that the 2D and depth feature maps for each joint are interconnected and inter-constrained more directly, effectively alleviating the channel correspondence problem and improving cross-dataset performance. Moreover, we design an efficient encoder-decoder network that can maintain the resolution of feature maps to enable better hand feature extraction from monocular RGB images. In this work, the overall pipeline contains two branches: one is the 2D hand pose estimation branch based on a latent heatmap representation (LHR); the other is the 3D hand pose estimation branch based on our proposed latent distribution representation (LDR). In this way, the 2D estimation branch serves as guidance for the 3D branch, which simplifies the optimization of the overall network and results in a more rapid convergence during training. The results on several benchmark datasets (including STB, RHD, and the most recently released InterHand2.6M) demonstrate that our proposed method achieves state-of-the-art (SOTA) performance.
Quick-CapsNet (QCN): A Fast Alternative to Capsule Networks

The basic computational unit in Capsule Network (CapsNet) [1] is a capsule (vs. neurons in Convolutional Neural Networks (CNNs)). A capsule is a set of neurons, which form a vector. CapsNet is used for supervised classification of data and has achieved state-of-the-art accuracy on MNIST digit recognition dataset, outperforming conventional CNNs in detecting overlapping digits. Moreover, CapsNet shows higher robustness towards affine transformation when compared to CNNs for MNIST datasets. One of the drawbacks of CapsNet, however, is slow training and testing. This can be a bottleneck for applications that require a fast network, especially during inference. In this work, we introduce Quick-CapsNet (QCN) as a fast alternative to CapsNet, which can be a starting point to develop CapsNet for fast real-time applications. QCN builds on producing a fewer number of capsules, which results in a faster network. QCN achieves this at the cost of marginal loss in accuracy. Inference is 5x faster on MNIST, F-MNIST, SVHN and Cifar-10 datasets. We also further enhanced QCN by employing a more powerful decoder instead of the default decoder to further improve QCN.
Sequential Learning for Domain Generalization

In this paper we propose a sequential learning framework for Domain Generalization (DG), the problem of training a model that is robust to domain shift by design. Various DG approaches have been proposed with different motivating intuitions, but they typically optimize for a single step of domain generalization – training on one set of domains and generalizing to one other. Our sequential learning is inspired by the idea lifelong learning, where accumulated experience means that learning the $$n^{th}$$ thing becomes easier than the $$1^{st}$$ thing. In DG this means encountering a sequence of domains and at each step training to maximise performance on the next domain. The performance at domain n then depends on the previous $$n-1$$ learning problems. Thus backpropagating through the sequence means optimizing performance not just for the next domain, but all following domains. Training on all such sequences of domains provides dramatically more ‘practice’ for a base DG learner compared to existing approaches, thus improving performance on a true testing domain. This strategy can be instantiated for different base DG algorithms, but we focus on its application to the recently proposed Meta-Learning Domain generalization (MLDG). We show that for MLDG it leads to a simple to implement and fast algorithm that provides consistent performance improvement on a variety of DG benchmarks.
Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems

SQL-on-Hadoop engines such as Hive provide a declarative interface for processing large-scale data over computing frameworks such as Hadoop. The underlying frameworks contain a large number of configuration parameters that can significantly impact performance, but which are hard to tune. The problem of automatic parameter tuning has become a lively research area and several sophisticated tuning advisors have been proposed for Hadoop. In this paper, we conduct an experimental study to explore the impact of Hadoop parameter tuning on Hive. We reveal that the performance of Hive queries does not necessarily improve when using Hadoop-focused tuning advisors out-of-the-box, at least when following the current approach of applying the same tuning setup uniformly for evaluating the entire query. After extending the Hive query processing engine, we propose an alternative tuning approach and experimentally show how current Hadoop tuning advisors can now provide good and robust performance for Hive queries, as well as improved cluster resource utilization. We share our observations with the community and hope to create an awareness for this problem as well as to initiate new research on automatic parameter tuning for SQL-on-Hadoop systems.
Gadget Recommendation System using Data Science

Electronic gadgets have become vital in basic need of individual. The advances in technology, makes it necessary to satisfy different functional needs of users. Therefore, it is important to suggest gadgets for users according to personal preference and various constraints of electronic gadgets. A gadget recommendation system is proposed by integrating various requirements from users.
A novel user-based gesture vocabulary for conceptual design

Research into hand gestures for human computer interaction has been prolific recently, but within it research on hand gestures for conceptual design has either focused on gestures that were defined by the researchers rather than the users, or those that were heavily influenced by what can be achieved using currently available technology. This paper reports on the study performed to identify a user elicited vocabulary of gestures for conceptual design, disassociated from the currently available technology, and its subsequent evaluation. The study included 44 product design engineering students (3rd, 4th year and recent graduates) and identified 1772 gestures that were analysed to build a novel gesture consensus set of vocabulary of hand gestures for conceptual design. This set is then evaluated by 10 other professionals, in order to generalise this set for a wider range of users and possibly reduce the need for training. The evaluation has shown that majority of gestures added to the vocabulary were easy to perform and appropriate for the activities, but that at the implementation stage the vocabulary will require another round of evaluation to account for the technology capabilities. The aim of this work is to create a starting point for a potential future system that could adapt to individual designers and allow them to use non-prescribed gestures that will support rather than inhibit their conceptual design thinking processes, akin to the developments that happened in hand writing recognition or predictive texting.
Truth or Dare: Understanding and Predicting How Users Lie and Provide Untruthful Data Online

Individuals are known to lie and/or provide untruthful data when providing information online as a way to protect their privacy. Prior studies have attempted to explain when and why individuals lie online. However, no work has examined into how people lie or provide untruthful data online, i.e. the specific strategies they follow to provide untruthful data, or attempted to predict whether people would be truthful or not depending on the specific question/data. To close this gap, we present a large-scale study with over 800 participants. Based on it, we show that it is possible to predict whether users are truthful or not using machine learning with very high accuracy (89.7%). We also identify four main strategies people employ to provide untruthful data and show the factors that influence the choices of their strategies. We discuss the implications of findings and argue that understanding privacy lies at this level can help both users and data collectors.
Transductive Semisupervised Deep Hashing

Deep hashing methods have shown their superiority to traditional ones. However, they usually require a large amount of labeled training data for achieving high retrieval accuracies. We propose a novel transductive semisupervised deep hashing (TSSDH) method which is effective to train deep convolutional neural network (DCNN) models with both labeled and unlabeled training samples. TSSDH method consists of the following four main ingredients. First, we extend the traditional transductive learning (TL) principle to make it applicable to DCNN-based deep hashing. Second, we introduce confidence levels for unlabeled samples to reduce adverse effects from uncertain samples. Third, we employ a Gaussian likelihood loss for hash code learning to sufficiently penalize large Hamming distances for similar sample pairs. Fourth, we design the large-margin feature (LMF) regularization to make the learned features satisfy that the distances of similar sample pairs are minimized and the distances of dissimilar sample pairs are larger than a predefined margin. Comprehensive experiments show that the TSSDH method can produce superior image retrieval accuracies compared to the representative semisupervised deep hashing methods under the same number of labeled training samples.
Secure Outsourced Computation of Matrix Determinant Based on Fully Homomorphic Encryption

Fully homomorphic encryption enables to perform arbitrary computation over encrypted data, providing a powerful tool for secure outsourced computation on an untrusted environment. This paper investigates secure outsourced computation of matrix determinant based on fully homomorphic encryption. We propose an efficient secure outsourced computation scheme for matrix determinant based on an efficient matrix encoding technique called hypercube structure which packs a matrix into a single ciphertext. Experimental results show that our scheme efficiently computes the matrix determinant in the ciphertext domain. Finally, we show that our proposed scheme can be easily applied as a submodule in the high-level applications.
Dynamic Warping Network for Semantic Video Segmentation

A major challenge for semantic video segmentation is how to exploit the spatiotemporal information and produce consistent results for a video sequence. Many previous works utilize the precomputed optical flow to warp the feature maps across adjacent frames. However, the imprecise optical flow and the warping operation without any learnable parameters may not achieve accurate feature warping and only bring a slight improvement. In this paper, we propose a novel framework named Dynamic Warping Network (DWNet) to adaptively warp the interframe features for improving the accuracy of warping-based models. Firstly, we design a flow refinement module (FRM) to optimize the precomputed optical flow. Then, we propose a flow-guided convolution (FG-Conv) to achieve the adaptive feature warping based on the refined optical flow. Furthermore, we introduce the temporal consistency loss including the feature consistency loss and prediction consistency loss to explicitly supervise the warped features instead of simple feature propagation and fusion, which guarantees the temporal consistency of video segmentation. Note that our DWNet adopts extra constraints to improve the temporal consistency in the training phase, while no additional calculation and postprocessing are required during inference. Extensive experiments show that our DWNet can achieve consistent improvement over various strong baselines and achieves state-of-the-art accuracy on the Cityscapes and CamVid benchmark datasets.
Enhanced Faster-RCNN Algorithm for Object Detection in Aerial Images

In recent years, a lot of research has been conducted in the field of object detection for aerial image, and many effective methods have emerged. This paper enhances the existing Faster RCNN model to achieve better detection accuracy. We improve the feature extraction of Faster RCNN model using multi-scale feature fusion. Compared with the traditional Faster RCNN model, the accuracy on mAP is improved by 1.06% by applying the proposed method. The visual effects and numerical results of object detection verify the improvement of the proposed method.
Refining Word Embeddings Based on Improved Genetic Algorithm for Sentiment Analysis

Word embeddings have been extensively used for sentiment analysis tasks. However, typical existing algorithms only model the syntactic context of words but fail to capture sufficient sentiment information of text, which affects the performance of sentiment analysis. Therefore, this paper presents an word vector refinement model based on improved genetic algorithm, which uses sentiment lexicon to obtain the sentiment ranking of the semantic nearest neighbors of the target word, and uses an improved genetic algorithm to optimize the vector representations of words such that they get the sentiment information of the word. Experimental results show that the proposed model can improve conventional word embeddings for binary classification on Internet Movie Database (IMDB) and fine-grained classification on Stanford Sentiment Treebank (SST).
From FiLM to Video: Multi-turn Question Answering with Multi-modal Context

Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems. The Audio Visual Scene-aware Dialog (AVSD) challenge, organized as a track of the Dialog System Technology Challenge 7 (DSTC7), proposes a combined task, where a system has to answer questions pertaining to a video given a dialogue with previous question-answer pairs and the video itself. We propose for this task a hierarchical encoder-decoder model which computes a multi-modal embedding of the dialogue context. It first embeds the dialogue history using two LSTMs. We extract video and audio frames at regular intervals and compute semantic features using pre-trained I3D and VGGish models, respectively. Before summarizing both modalities into fixed-length vectors using LSTMs, we use FiLM blocks to condition them on the embeddings of the current question, which allows us to reduce the dimensionality considerably. Finally, we use an LSTM decoder that we train with scheduled sampling and evaluate using beam search. Compared to the modality-fusing baseline model released by the AVSD challenge organizers, our model achieves a relative improvements of more than 16%, scoring 0.36 BLEU-4 and more than 33%, scoring 0.997 CIDEr.
AtNE-Trust: Attributed Trust Network Embedding for Trust Prediction in Online Social Networks

Trust relationship prediction among people provides valuable supports for decision making, information dissemination, and product promotion in online social networks. Network embedding has achieved promising performance for link prediction by learning node representations that encode intrinsic network structures. However, most of the existing network embedding solutions cannot effectively capture the properties of a trust network that has directed edges and nodes with in/out links. Furthermore, there usually exist rich user attributes in trust networks, such as ratings, reviews, and the rated/reviewed items, which may exert significant impacts on the formation of trust relationships. It is still lacking a network embedding-based method that can adequately integrate these properties for trust prediction. In this work, we develop an AtNE-Trust model to address these issues. We firstly capture user embedding from both the trust network structures and user attributes. Then we design a deep multi-view representation learning module to further mine and fuse the obtained user embedding. Finally, a trust evaluation module is developed to predict the trust relationships between users. Representation learning and trust evaluation are optimized together to capture high-quality user embedding and make accurate predictions simultaneously. A set of experiments against the real-world datasets demonstrates the effectiveness of the proposed approach.