Reviewerly/exHarmony huggingface dataset:
Dense Broad Learning System based on Conjugate Gradient

Conventional training mechanism for deep learning, which is based on gradient descent, suffers from many notorious issues such as low convergence rate, over-fitting, and time-consuming. To alleviate these problems, a novel deep learning algorithm with a different learning mechanism named Broad Learning System (BLS) was proposed by Prof. C. L. Philip Chen in 2017. BLS randomly selects the parameters of the feature nodes and enhancement nodes during its training process and uses the ridge regression theory to solve its output weights. BLS has been widely used in many fields because of its high efficiency. However, there is a fundamental problem that has not yet been solved, that is, the appropriate value of the parameter λ for the ridge regression operation of BLS is difficult to be set properly, which often leads to the problem of over-fitting and seriously limits the development of BLS. To solve this problem, we proposed a novel Dense BLS based on Conjugate Gradient (CG-DBLS) in this paper, in which each feature node is connected to other feature nodes and each enhancement node is connected to other enhancement nodes in a feed-forward fashion. The recursive least square method and conjugate gradient method are used to calculate the output weights of the feature nodes and enhancement nodes respectively. Experiment studies on four benchmark regression problems from UCI repository show that CG-DBLS can achieve much lower error and much higher stability than BLS and its variants.
3D Point Cloud Enhancement Using Graph-Modelled Multiview Depth Measurements

A 3D point cloud is often synthesized from depth measurements collected by sensors at different viewpoints. The acquired measurements are typically both coarse in precision and corrupted by noise. To improve quality, previous works denoise a synthesized 3D point cloud a posteriori, after projecting the imperfect depth data onto the 3D space. Instead, we enhance depth measurements on the sensed images a priori, exploiting inherent 3D geometric correlation across views, before synthesizing a 3D point cloud from the improved measurements. By enhancing closer to the actual sensing process, we benefit from optimization targeting specifically the depth image formation model, before subsequent processing steps that can further obscure measurement errors. Mathematically, for each pixel row in a pair of rectified viewpoint depth images, we first construct a graph reflecting inter-pixel similarities via metric learning using data in previous enhanced rows. To optimize left and right viewpoint images simultaneously, we write a non-linear mapping function from left pixel row to the right based on 3D geometry relations. We formulate a MAP optimization problem, which, after suitable linear approximations, results in an unconstrained convex and differentiable objective, solvable using fast gradient method (FGM). Experimental results show that our method noticeably outperforms recent denoising algorithms that enhance after 3D point clouds are synthesized.
Deep Categorization with Semi-Supervised Self-Organizing Maps

Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Labeling is an expensive, time-consuming, and difficult task. It is usually done manually, which collaborates with the incorporation of noise and errors to the data. Hence, it is of great importance to developing intelligent models that can benefit from both labeled and unlabeled data. Currently, works on unsupervised and semi-supervised learning are still being overshadowed by the successes of purely supervised learning. However, it is expected that they become far more important in the longer term. This article presents a semi-supervised model, called Batch Semi-Supervised Self-Organizing Map (Batch SS-SOM), which is an extension of a SOM incorporating some advances that came with the rise of Deep Learning, such as batch training. The results show that Batch SS-SOM is a good option for semi-supervised classification and clustering. It performs well in terms of accuracy and clustering error, even with a small number of labeled samples, as well as when presented to unsupervised data, and shows competitive results in transfer learning scenarios in traditional image classification benchmark datasets.
One Shot Radial Distortion Correction by Direct Linear Transformation

A novel method is proposed to estimate and correct image radial distortion. The solution is based upon an algebraic expansion of the homographic relationship between a planar pattern and its distorted projection into a single image, which is solved using the Direct Linear Transformation. The method requires ten point correspondences, is fully automatic and estimates both the first two parameters of the division model, and the center of distortion. Experimental results show the method to be more accurate than other recent one- and two-parameter point-based and plumb-line-based approaches.
Quantum ensemble of trained classifiers

Through superposition, a quantum computer is capable of representing an exponentially large set of states, according to the number of qubits available. Quantum machine learning is a subfield of quantum computing that explores the potential of quantum computing to enhance machine learning algorithms. An approach of quantum machine learning named quantum ensembles of quantum classifiers consists of using superposition to build an exponentially large ensemble of classifiers to be trained with an optimization-free learning algorithm. In this work, we investigate how the quantum ensemble works with the addition of an optimization method. Experiments using benchmark datasets show the improvements obtained with the addition of the optimization step.
Web Service to Retrieve and Semantically Enrich Datasets for Theses From Open Educational Repositories

The paper describes the design and implementation of a semantic web service that retrieves theses and extends the keyword based-search of a DSpace repository taking into account the roles of advisors and steering committee members formally represented into a custom-made ontology. The service uses SPARQL queries and the serialization module of RDF DSpace, this links the item submission process and the ontology, thus the more theses are added into a repository, the more instances are inserted into the ontology. The paper provides empirical insights about how to reuse theses metadata and includes the results of an exploratory and self-management survey of usability heuristic evaluation of a web site that enables to access the proposed service. Heuristics were estimated with a purposive sample of students, teachers, and managers, the results indicated a high satisfaction level and showed that the service increased theses accessibility in the web environment. The service also generates semantically enriched datasets that coexist with the repository, they are of utility and value to educational organizations as they give institutional visibility.
A Bit-Vector Differential Model for the Modular Addition by a Constant

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR, which achieve the best software performances in low-end microcontrollers. To evaluate the resistance of an ARX cipher against differential cryptanalysis and its variants, the recent automated methods employ constraint satisfaction solvers, such as SMT solvers, to search for optimal characteristics. The main difficulty to formulate this search as a constraint satisfaction problem is obtaining the differential models of the non-linear operations, that is, the constraints describing the differential probability of each non-linear operation of the cipher. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this operation from being evaluated with automated methods. In this paper, we present the first bit-vector differential model for the n-bit modular addition by a constant input. Our model contains $$O(\log _2(n))$$ basic bit-vector constraints and describes the binary logarithm of the differential probability. We also represent an SMT-based automated method to look for differential characteristics of ARX, including constant additions, and we provide an open-source tool ArxPy to find ARX differential characteristics in a fully automated way. To provide some examples, we have searched for related-key differential characteristics of TEA, XTEA, HIGHT, and LEA, obtaining better results than previous works. Our differential model and our automated tool allow cipher designers to select the best constant inputs for modular additions and cryptanalysts to evaluate the resistance of ARX ciphers against differential attacks.
Asynchronous in Parallel Detection and Tracking (AIPDT): Real-Time Robust Polyp Detection

Automatic polyp detection during colonoscopy screening test is desired to reduce polyp miss rate and thus lower patients’ risk of developing colorectal cancer. Previous works mainly focus on detection accuracy, however, real-time and robust polyp detection is as important to be adopted in clinical workflow. To maintain accuracy, speed and robustness for polyp detection at the same time, we propose a framework featuring two novel concepts: (1) decompose the task into detection and tracking steps to take advantage of both high resolution static images for accurate detection and the temporal information between frames for fast tracking and robustness. (2) run detector and tracker in two parallel threads asynchronously so that a heavy but accurate detector and a light tracker can efficiently work together. We also propose a robustness metric to evaluate performance in realistic clinical setting. Experiments demonstrated that our method outperformed the state-of-the-art results in terms of accuracy, robustness and speed.
Intelligent Data Prognosis of Recurrent of Depression in Medical Diagnosis

Depression normally smites more than once in a person's life and it can become a persistent recurrent disorder. Prevention of relapse and recurrence of depression would be highly advisable. Therefore, researchers have started to recognize the chances of recurrence factors of depression. Real-time patient data diagnosis can solve some unknown truth in the medical investigation. This paper focuses on the requirement of intelligent data analysis in medical diagnosis and examines different classification of intelligent data analysis techniques on 109 numbers of sample patient data from the National Institute of Health Organization (NIH). The paper concludes with the accuracy of implemented intelligent data analysis methods and their suitability in medical research.
Learning Navigation Costs from Demonstration in Partially Observable Environments

This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments. The objective is to infer a cost function that explains expert-demonstrated navigation behavior while relying only on the observations and state-control trajectory used by the expert. We develop a cost function representation composed of two parts: a probabilistic occupancy encoder, with recurrent dependence on the observation sequence, and a cost encoder, defined over the occupancy features. The representation parameters are optimized by differentiating the error between demonstrated controls and a control policy computed from the cost encoder. Such differentiation is typically computed by dynamic programming through the value function over the whole state space. We observe that this is inefficient in large partially observable environments because most states are unexplored. Instead, we rely on a closed-form subgradient of the cost-to-go obtained only over a subset of promising states via an efficient motion-planning algorithm such as A* or RRT. Our experiments show that our model exceeds the accuracy of baseline IRL algorithms in robot navigation tasks, while substantially improving the efficiency of training and test-time inference.
Information Technology of Motor Vehicle Databases Use to Prevent Terrorist Emergencies

Based on the analysis of gradation scheme of motor vehicles situational background near a protected object of a critical infrastructure, the process of data collecting and filling the database and knowledgebase of the protected object main server as well as the procedure of violators' identification and collecting data on them is considered in the paper. After that, the structure of the control algorithm is described. The algorithm defines the application of information technology for the use of motor vehicles databases to prevent the terrorist emergencies.
Classification and Analysis of Arabic Natural Language Inference Systems

In natural language, the same meaning can be expressed by different texts. The process of determining the inference relationship occurring between a text T and a hypothesis H is called Natural Language Inference (NLI). The NLI task aims to provide a generic framework that captures, in a unifying manner, the inference across Natural Language Processing applications such as question answering, summarization, information retrieval, and machine translation. Many tasks and datasets have been created to support the development and evaluation of the ability of the NLI task in different languages. For the Arabic language, interest in this field is gradually increasing. This paper aims to provide an overview of state-of-the-art NLI approaches for Arabic, the relevant knowledge resources and the used tools in order to support a better understanding of this growing field. Moreover, this paper points to classify the proposed approaches for Arabic NLI and compare existing NLI systems.
High Efficiency Live Video Streaming With Frame Dropping

HTTP based video streaming is widely adopted in video service and its adaptive bitrate algorithms have attracted a lot of studies in recent years. However, most of the algorithms are designed for on-demand video service and are not suitable for live video streaming service, which is sensitive to end-to-end latency. In this paper, we propose a live video streaming framework based on HTTP/2 which enables frame dropping for low latency environment. Firstly, we formulate live video streaming model and QoE model considering frame dropping. Then, an optimization problem is formulated aiming at high quality of video streaming. Furthermore, to solve this problem, we propose a live video streaming adaptation algorithm with frame dropping based on Model Predictive Control (MPC). Finally, extensive experiments are conducted to evaluate the proposed method over realistic traces with general Adaptive Bitrate Algorithms (ABR). Compared with the optimal solution, the proposed method achieves comparable performance with only 8.06% quality loss.
Large-Scale Internet User Behavior Analysis of a Nationwide K-12 Education Network Based on DNS Queries

To the best of our knowledge, this paper presents the first Internet Domain Name System (DNS) queries data study from a national K-12 Education Service Provider. This provider, called Plan Ceibal, supports a one-to-one computing program in Uruguay. Additionally, it has deployed an Information and Communications Technology (ICT) infrastructure in all of Uruguay’s public schools and high-schools, in addition to many public spaces. The main development is wireless connectivity, which allows all the students (whose ages range between 6 and 18 years old) to connect to different resources, including Internet access. In this article, we use 9,125,888,714 DNS-query records, collected from March to May 2019, to study Plan Ceibal user’s Internet behavior applying unsupervised machine learning techniques. Firstly, we conducted a statistical analysis aiming at depicting the distribution of the data. Then, to understand users’ Internet behavior, we performed principal component analysis (PCA) and clustering methods. The results show that Internet use behavior is influenced by age-group and time of the day. However, it is independent of the geographical location of the users. Internet use behavior analysis is of paramount importance for evidence-based decision making by any education network provider, not only from the network-operator perspective but also for providing crucial information for learning analytics purposes.
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

In the context of model-based reinforcement learning and control, a large number of methods for learning system dynamics have been proposed in recent years. The purpose of these learned models is to synthesize new control policies. An important open question is how robust current dynamics-learning methods are to shifts in the data distribution due to changes in the control policy. We present a real-robot dataset which allows to systematically investigate this question. This dataset contains trajectories of a 3 degrees-of-freedom (DOF) robot being controlled by a diverse set of policies. For comparison, we also provide a simulated version of the dataset. Finally, we benchmark a few widely-used dynamics-learning methods using the proposed dataset. Our results show that the iid test error of a learned model is not necessarily a good indicator of its accuracy under control policies different from the one which generated the training data. This suggests that it may be important to evaluate dynamics-learning methods in terms of their transfer performance, rather than only their iid error.
Joint Representation Learning with Deep Quadruplet Network for Real-Time Visual Tracking

Recently, trackers based on Siamese networks have attracted spread attention in the field of tracking because of a balance between accuracy and speed. Learning powerful representation via effective offline training strategy is critical for constructing high performance Siamese trackers. However, features extracted in most networks cannot accurately distinguish a tracked target from the background with semantic information in some challenging scenes. In this paper, we develop a Fully-Convolutional deep Quadruple Network (QuadFC) to learn more expressive representation via a novel multi-task loss function composed of a differential pairwise loss for tracking and a constructed triplet loss for similarity learning, which can be trained offline in an end-to-end mode. During inference, the proposed deep architecture does not need to update model and the positive-negative branches are removed to avoid unnecessary calculations. In particular, our approach is able to extract more discriminative features and perform robust visual tracking, due to joint representation learning and taking full use of original samples via the combination of positive-negative pairs. Furthermore, theoretical analysis of QuadFC is carried out through comparing the gradients of different loss functions. Extensive experiments on several tracking benchmarks, show that the proposed tracker achieves the state-of-the-art tracking performance while running at 68 FPS. The code can be available at https://github.com/DavidZhangdw/QuadFC.
Mixed Labeling: Integrating Internal and External Labels

In this article, we present an algorithm capable of mixed labeling of 2D and 3D objects. In mixed labeling, the given objects are labeled with both internal labels placed (at least partially) over the objects and external labels placed in the space around the objects and connected with the labeled objects with straight-line leaders. The proposed algorithm determines the position and type of each label based on the user-specified ambiguity threshold and eliminates overlaps between the labels, as well as between the internal labels and the straight-line leaders of external labels. The algorithm is a screen-space technique; it operates in an image where the 2D objects or projected 3D objects are encoded. In other words, we can use the algorithm whenever we can render the objects to an image, which makes the algorithm fit for use in many domains. The algorithm operates in real-time, giving the results immediately. Finally, we present results from an expert evaluation, in which a professional illustrator has evaluated the label layouts produced with the proposed algorithm.
Single-Shot Deep Volumetric Regression for Mobile Medical Augmented Reality

Augmented reality for medical applications allows physicians to obtain an inside view into the patient without surgery. In this context, we present an augmented reality application running on a standard smartphone or tablet computer, providing visualizations of medical image data, overlaid with the patient, in a video see-through fashion. Our system is based on the registration of medical imaging data to the patient using a single 2D photograph of the patient. From this image, a 3D model of the patient’s face is reconstructed using a convolutional neural network, to which a pre-operative CT scan is automatically registered. For efficient processing, this is performed on a server PC. Finally, anatomical and pathological information is sent back to the mobile device and can be displayed, accurately registered with the live patient, on the screen. Hence, our cost-effective, markerless approach needs only a smartphone and a server PC for image processing. We present a qualitative and quantitative evaluation using real patient photos and CT from the clinical routine in facial surgery, reporting overall processing times and registration errors.
DLchain: Blockchain with Deep Learning as Proof-of-Useful-Work

Blockchains based on Proof-of-Work can maintain a distributed ledger with a high security guarantee but also lead to severe energy waste due to the useless hash calculation. Proof-of-Useful-Work (PoUW) mechanisms are alternatives, but finding hard puzzles with easy verification and useful results is challenging. Recent popular deep learning algorithms require large amount of computation resources due to the large-scale training datasets and the complexity of the models. The work of deep learning training is useful, and the model verification process is much shorter than its training process. Therefore, in this paper, we propose DLchain, a PoUW-based blockchain using deep learning training as the hard puzzle. Theoretical analysis shows that DLchain can achieve a security level comparable to existing PoW-based cryptocurrency when the miners' best interest is to maximize their revenue. Notably, this is achieved without relying on common assumptions made in existing PoUW-based blockchain such as globally synchronized timestamps. Simulated experiments also show that the extra network delay caused by data transfer and the full nodes' validation is acceptable.
Neural Network Compression and Acceleration by Federated Pruning

In recent years, channel pruning is one of the important methods for deep model compression. But the resulting model still has tremendous redundant feature maps. In this paper, we propose a novel method, namely federated pruning algorithm, to achieve narrower model with negligible performance degradation. Different from many existing approaches, the federated pruning algorithm removes all filters in the pre-trained model together with their connecting feature map by combining the weights with the importance of the channels, rather than pruning the network in terms of a single criterion. Finally, we fine-tune the resulting model to restore network performance. Extensive experiments demonstrate the effectiveness of federated pruning algorithm. VGG-19 network pruned by federated pruning algorithm on CIFAR-10 achieves 92.5% reduction in total parameters and \(13.58\times \) compression ratio with only 0.23% decrease in accuracy. Meanwhile, tested on SVHN, VGG-19 achieves 94.5% reduction in total parameters and \(18.01\times \) compression ratio with only 0.43% decrease in accuracy.