Reviewerly/exHarmony huggingface dataset:
Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing

For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization loss. A well-established hashing approach is Iterative Quantization (ITQ), which addresses these two requirements in separate steps. In this paper, we revisit the ITQ approach and propose novel formulations and algorithms to the problem. Specifically, we propose a novel approach, named Simultaneous Compression and Quantization (SCQ), to jointly learn to compress (reduce dimensionality) and binarize input data in a single formulation under strict orthogonal constraint. With this approach, we introduce a loss function and its relaxed version, termed Orthonormal Encoder (OnE) and Orthogonal Encoder (OgE) respectively, which involve challenging binary and orthogonal constraints. We propose to attack the optimization using novel algorithms based on recent advance in cyclic coordinate descent approach. Comprehensive experiments on unsupervised image retrieval demonstrate that our proposed methods consistently outperform other state-of-the-art hashing methods. Notably, our proposed methods outperform recent deep neural networks and GAN based hashing in accuracy, while being very computationally-efficient.
A bag of constrained informative deep visual words for image retrieval

In this paper, we propose a bag of constrained informative deep visual words (BoCIDVW) model for image retrieval. Informative patches from each image are first obtained using patch entropy values. Each such patch is represented by deep features extracted through VGG16-Net. Two sets of constraints, namely, the must-link (ML) and the cannot-link (CL), are obtained for each deep informative patch in an unsupervised manner from its mutual information values (with other patches). The patches are then quantized using the Linear-time Constrained Vector Quantization Error (LCVQE), a fast yet accurate constrained K-means algorithm. The resulting clusters, which we term constrained informative deep visual words, are employed to label each patch. Finally, a bag (histogram) of constrained informative visual words is developed for image retrieval. Experiments on three different publicly available datasets demonstrate the merit of the proposed formulation.
Recent advances in unmanned aerial vehicles real-time trajectory planning

The growing interest in unmanned aerial vehicles (UAV) can be attributed to many factors, particularly the potential for them to operate beyond visual line of sight and at increasingly higher levels of autonomy. Trajectory planning defines the extent to which a vehicle is “autonomous”, therefore, to operate at high levels of autonomy, UAV will require real-time embedded trajectory planning that respects a minimum of flyability. This paper presents a review of recent advances in UAV trajectory planning and seeks to clarify the flyability, the real-time, and the embedded aspects, which are not adequately considered by previous survey papers. This work, which specifically focuses on Class II (≥150 and ≤600 kg) and Class III (&gt;600 kg) fixed-wing UAV, analyses 60 papers from the last decade that mention some real-time achievement with respect to UAV trajectory planning. From this analysis, we highlight some challenges and some suggested orientations for future works.
A Simplified Speaker Recognition System Based on FPGA Platform

Speaker recognition is a crucial bio-identification technology, which is extensively used in our daily life. With the development of deep learning, convolutional neural networks (CNNs) are applied to speaker recognition tasks given their excellent performance. However, in real life, speaker recognition systems are frequently deployed on end-devices. Therefore, while obtaining recognition accuracy, the model of speaker recognition is expected to be as simple as possible. Inspired by 1-max pooling CNN and Gaussian mixture model-universal background model (GMM-UBM), this study proposes a one dimension convolutional neural networks (1D CNN) on the basis of original 2D CNN. The proposed model reduces the computational complexity of ResNet20 by 64% and the amount of parameters by 53%. In comparison with the original ResNet20 models, the recognition accuracy will be reduced by about one percent on the 15s data set. Then, on the basis of the 1D CNN, we propose a pyramid layer-folding pipeline structure and implement it on the Xilinx VC709 platform. According to the time-dimension partition, the proposed pyramid pipeline structure can process speech data of various lengths. Moreover, our accelerator is 5.1× faster on 3s dataset and 6.8× quicker on 15s dataset than those of the CPU platform.
Topologies on Z n that Are Not Homeomorphic to the n-Dimensional Khalimsky Topological Space

The present paper deals with two types of topologies on the set of integers, Z : a quasi-discrete topology and a topology satisfying the T 1 2 -separation axiom. Furthermore, for each n ∈ N , we develop countably many topologies on Z n which are not homeomorphic to the typical n-dimensional Khalimsky topological space. Based on these different types of new topological structures on Z n , many new mathematical approaches can be done in the fields of pure and applied sciences, such as fixed point theory, rough set theory, and so on.
Automated Selection and Quality Assessment of Primary Studies

Researchers use systematic literature reviews (SLRs) to synthesize existing evidence regarding a research topic. While being an important means to condense knowledge, conducting an SLR requires a large amount of time and effort. Consequently, researchers have proposed semi-automatic techniques to support different stages of the review process. Two of the most time-consuming tasks are (1) to select primary studies and (2) to assess their quality. In this article, we report an SLR in which we identify, discuss, and synthesize existing techniques of the software-engineering domain that aim to semi-automate these two tasks. Instead of solely providing statistics, we discuss these techniques in detail and compare them, aiming to improve our understanding of supported and unsupported activities. To this end, we identified eight primary studies that report unique techniques that have been published between 2007 and 2016. Most of these techniques rely on text mining and can be beneficial for researchers, but an independent validation using real SLRs is missing for most of them. Moreover, the results indicate the necessity of developing more reliable techniques, providing access to their implementations, and extending their scope to further activities to facilitate the selection and quality assessment of primary studies.
Smartphone Apps of Obstacle Detection for Visually Impaired and its Evaluation

This study deals with a smartphone application to support the walking of visually impaired people. It recognizes surrounding situation from image acquired by camera and notifies it of any danger.
'I'm going to hell for laughing at this'

The subreddit r/RoastMe presents an intriguing case of how alternative norms can emerge in subversive online communities, allowing behaviours conventionally condemned as inappropriate to be reframed as acceptable. In this community, users post photos of themselves with the explicit expectation of being mocked or ridiculed by others. This mixed-methods, within-subjects experiment explores the influence of three factors that allow negative comments to be framed as acceptable and appropriate within RoastMe: humour, a mean (but funny) normative tone, and explicit articulation of these norms. 117 participants read, rated and reported their intended responses to humorous and non-humorous comments presented as being from RoastMe (explicitly mean but funny), ToastMe (explicitly positive), and two fictionalised communities where the normative tone was not explicitly defined (the mean but funny RateMe, and the positive DescribeMe). Results indicated clear interaction effects between community tone and norm explicitness, whereby comments from RoastMe were consistently rated and responded to most positively, and separate effects of humour on comment ratings and responses. Individual-level moral disengagement appeared central in allowing participants to excuse negative comments in humorous or permissive contexts. Consistent with benign violation theory, the explicitly negative tone of RoastMe was seen to create a shared understanding that users posting photos would expect and not be harmed by comments, allowing participants to reframe interactions as safe, acceptable and funny.
Recommendation system in social networks with topical attention and probabilistic matrix factorization

Collaborative filtering (CF) is a common recommendation mechanism that relies on user-item ratings. However, the intrinsic sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. At present, most algorithms use two-valued trust relationship of social network to improve recommendation quality but fail to take into account the difference of trust intensity of each friend and user's comment information. To this end, the recommendation system within a social network adopts topical attention and probabilistic matrix factorization (STAPMF) is proposed. We combine the trust information in social networks and the topical information from review documents by proposing a novel algorithm combining probabilistic matrix factorization and attention-based recurrent neural networks to extract item underlying feature vectors, user's personal potential feature vectors, and user's social hidden feature vectors, which represent the features extracted from the user's trusted network. Using real-world datasets, we show a significant improvement in recommendation performance comparing with the prevailing state-of-the-art algorithms for social network-based recommendation.
CoachAI: A Project for Microscopic Badminton Match Data Collection and Tactical Analysis

Computer vision based object tracking has been used to annotate and augment sports video. For automatically and systematically competition data collection and tactical analysis. The proposed project also includes research of data visualization, connected training auxiliary devices, and data warehouse. Deep learning techniques will be used to develop video-based real-time microscopic competition data collection based on broadcast competition video. Machine learning techniques will be used to develop tactical analysis. In addition, training auxiliary devices including smart badminton rackets and connected serving machines will be developed based on the IoT technology to further utilize competition data and tactical data and boost training efficiency. Especially, the connected serving machines will be developed to perform specified tactics and to interact with players in their training.
DRAN: Deep recurrent adversarial network for automated pancreassegmentation

Automated pancreas segmentation in abdominal computed tomography (CT) scans is of high clinical relevance (i.e. pancreas cancer diagnosis and prognosis), but extremely difficult because the pancreas is a soft, small, and flexible abdominal organ with high anatomical variability, which causes the previous segmentation methods to result in low precision. In this study, the authors present a new deep recurrent adversarial network (DRAN) to tackle this challenge. DRAN contains three steps: (i) preserving global resolution of CT scans and modifying the receptive field of kernel adaptively through a dilated convolution autoencoder module; (ii) modelling contextual spatial correlation between neighbouring CT scan patches benefits from a specially designed local long short-term memory module; and (iii) improving the performance and generalisation by leveraging an adversarial module, which can constrain the spatial smoothness consistency between continuous CT scans based on the long-range spatial interaction. The system is evaluated on a dataset of 80 manually segmented CT volumes, using four-fold cross-validation. Its performance surpasses other state-of-the-art methods, with the Dice similarity coefficient of and pixel-wise accuracy of . Also, they perform a qualitative evaluation by an expert further revealing the effectiveness and potential of their DRAN as a clinical segmentation tool.
A social network analysis of ICTD conferences (2006–2017)

Information and Communication Technologies for Development is an interdisciplinary area of research associated with engineering, application, and adoption of ICTs in developing regions and/or for development. The International Conference on Information and Communication Technologies and Development (ICTD) was started in 2006 with the objective to build a community of scholars and practitioners from diverse disciplinary backgrounds. In this paper, we examine the social dimension of ICTD as manifest in co-authorship ties in the papers published in conference proceedings. This research community has 1053 unique authors from 302 institutions in 55 countries. Almost 85% of 456 papers are co-authored by two or more people. Initially, the research community displayed small-world characteristics but the social network subsequently displays a distinct core-periphery structure. Further, collaborative ties among academic institutions in developing countries are comparatively less. A key implication is that institutional support is imperative to initiate and maintain collaborative research ties.
High-quality face image generation based on generative adversarial networks

Conventional face image generation using generative adversarial networks (GAN) is limited by the quality of generated images since generator and discriminator use the same backpropagation network. In this paper, we discuss algorithms that can improve the quality of generated images, that is, high-quality face image generation. In order to achieve stability of network, we replace MLP with convolutional neural network (CNN) and remove pooling layers. We conduct comprehensive experiments on LFW, CelebA datasets and experimental results show the effectiveness of our proposed method.
Unsupervised depth estimation from monocular videos with hybrid geometric-refined loss and contextual attention

Most existing methods based on convolutional neural networks (CNNs) are supervised, which require a large amount of ground-truth data for training. Recently, some unsupervised methods utilize stereo image pairs as input by transforming depth estimation into a view synthesis problem, but need stereo camera as an additional equipment for data acquisition. Therefore, we use more available monocular videos captured from monocular camera as our input, and propose an unsupervised learning framework to predict scene depth maps from monocular video frames. First, we design a novel unsupervised hybrid geometric-refined loss, which can explicitly explore more accurate geometric relationship between the input color image and the predicted depth map, and preserve depth boundaries and fine structures in depth maps. Then, we design a contextual attention module to capture nonlocal dependencies along the spatial and channel dimensions in a dual path, which can improve the ability of feature representation and further preserve fine depth details. In addition, we also utilize an adversarial loss to discriminate synthetic or realistic color images by training a discriminator so as to produce realistic results. Experimental results demonstrate that the proposed framework achieves comparable or even better results than those trained with monocular videos or stereo image pairs.
SpaRTA Tracking Across Occlusions via Partitioning of 3D Clouds of Points

Any 3D tracking algorithm has to deal with occlusions: multiple targets get so close to each other that the loss of their identities becomes likely; hence, potentially affecting the very quality of the data with interrupted trajectories and identity switches. Here, we present a novel tracking method that addresses the problem of occlusions within large groups of featureless objects by means of three steps: i) it represents each target as a cloud of points in 3D; ii) once a 3D cluster corresponding to an occlusion occurs, it defines a partitioning problem by introducing a cost function that uses both attractive and repulsive spatio-temporal proximity links; and iii) it minimizes the cost function through a semi-definite optimization technique specifically designed to cope with the presence of multiminima landscapes. The algorithm is designed to work on 3D data regardless of the experimental method used: multicamera systems, lidars, radars, and RGB-D systems. By performing tests on public data-sets, we show that the new algorithm produces a significant improvement over the state-of-the-art tracking methods, both by reducing the number of identity switches and by increasing the accuracy of the estimated positions of the targets in real space.
Live migration of trans-cloud applications

The development of applications independent of the cloud providers where they are going to be deployed is still an open issue. In fact, cloud agnostic software development presents important challenges to be solved. One of these issues is the runtime migration of components. Even more difficult is dealing with the interoperability issues when the migration also implies a change of provider or service level. This paper presents a solution for the component-wise migration of cloud applications. The migration is performed component-wise in the sense that each component of the application to be migrated, which may be deployed on a specific service on a specific provider, may individually be moved to a different one. Our solution relies on the three key ingredients of the trans-cloud approach, where the CAMP and TOSCA standards play a central role: A CAMP-based unified API, TOSCA-based agnostic topology descriptions, and mechanisms for the independent specification of target service locations. The effort and the time required for restoring the activity of applications are used as metrics to evaluate the performance of the proposed migration orchestrator. Although the time required for the migration operation is directly related to the topology of the application, the affected components, and their previous and target locations, the downtimes are significantly reduced. Moreover, thanks to the abstraction level at which it operates and the automation provided, the effort needed from the user for a migration operation is almost zero. We present an implementation of our proposed solution and illustrate it with a case study and experimental results.
Increase in Use Values of Future Engineers through the Education Technology Trends Implementation

The paper deals with the ways to increase the use values of future engineers. The forecast of the most sought after educational technologies are considered Global Education Market Expenditure was identified. An overview of innovative educational trends has been made such as Blockchain, Virtual Reality and Augmented reality, artificial intelligence and 3D printing. The state of implementation of technological trends in the Ukrainian educational process was analyzed.
Improving Resource Utilization in Data Centers using an LSTM-based Prediction Model

Data centers are centralized facilities where computing and networking hardware are aggregated to handle large amounts of data and computation. In a data center, computing resources such as CPU and memory are usually managed by a resource manager. The resource manager accepts resource requests from users and allocates resources to their applications. A commonly known problem in resource management is that users often request more resources than their applications actually use. This leads to the degradation of overall resource utilization in a data center. This paper aims to improve resource utilization in data centers by predicting the required resource for each application. We designed and implemented a neural network model based on Long Short-Term Memory (LSTM) to predict more efficient resource allocation for a job based on historical data. Our model has two LSTM layers each of which learns the relationship between: (1) allocation and usage, and (2) CPU and memory. We used Googles cluster-usage trace, which contains a trace of resource allocation and usage for each job executed on a Google data center, to train our neural network. Googles cluster scheduler simulator was used to evaluate our proposed method. Our simulation indicated that the proposed method improved the CPU utilization and memory utilization by 10.71% and 47.36%, respectively, compared to a conventional resource manager. Moreover, we discovered that increasing the memory cell size of our LSTM model improves the accuracy of the prediction in return for longer training time.
Exploring Word Embeddings in CRF-based Keyphrase Extraction from Research Papers

Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections. However, keyphrases are not always provided with the papers, but they need to be extracted from their content. In this paper, we explore keyphrase extraction formulated as sequence labeling and utilize the power of Conditional Random Fields in capturing label dependencies through a transition parameter matrix consisting of the transition probabilities from one label to the neighboring label. We aim at identifying the features that, by themselves or in combination with others, perform well in extracting the descriptive keyphrases for a paper. Specifically, we explore word embeddings as features along with traditional, document-specific features for keyphrase extraction. Our results on five datasets of research papers show that the word embeddings combined with document specific features achieve high performance and outperform strong baselines for this task.
Lightweight adversarial network for salient object detection

Recent advance on salient object detection benefits mostly from the revival of Convolutional Neural Networks (CNNs). However, with these CNN based models, the predicted saliency map is usually incomplete, that is, spatially inconsistent with the corresponding ground truth, because of the inherent complexity of the object and the inaccuracy of object boundary detection resulted from regular convolution and pooling operations. Besides, the breakthrough on saliency detection accuracy of current state-of-the-art deep models comes at the expense of high computational cost, which contradicts its role as a pretreatment procedure for other computer vision tasks. To alleviate these issues, we propose a lightweight adversarial network for salient object detection, which simultaneously improves the accuracy and efficiency by enforcing higher-order spatial consistency via adversarial training and lowering the computational cost through lightweight bottleneck blocks, respectively. Moreover, multi-scale contrast module is utilized to sufficiently capture contrast prior for visual saliency reasoning. Comprehensive experiments demonstrate that our method is superior to the state-of-the-art works on salient object detection in both accuracy and efficiency.