Reviewerly/exHarmony huggingface dataset:
SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images

Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years, they are mainly designed for haze removal in daytime. In nighttime, dehazing is more challenging where most daytime dehazing methods become invalid due to multiple scattering phenomena, and non-uniformly distributed dim ambient illumination. While a few approaches have been proposed for nighttime image dehazing, low ambient light is actually ignored. In this paper, we propose a novel unified nighttime hazy image enhancement framework to address the problems of both haze removal and illumination enhancement simultaneously. Specifically, both halo artifacts caused by multiple scattering and non-uniformly distributed ambient illumination existing in low-light hazy conditions are considered for the first time in our approach. More importantly, most current daytime dehazing methods can be effectively incorporated into nighttime dehazing task based on our framework. Firstly, we decompose the observed hazy image into a halo layer and a scene layer to remove the influence of multiple scattering. After that, we estimate the spatially varying ambient illumination based on the Retinex theory. We then employ the classic daytime dehazing methods to recover the scene radiance. Finally, we generate the dehazing result by combining the adjusted ambient illumination and the scene radiance. Compared with various daytime dehazing methods and the state-of-the-art nighttime dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate the superiority of our framework in terms of halo mitigation, visibility improvement and color preservation.
Digital detox of the youth (on the example of social networks)

The article considers practices of using virtual social networks and attitudes of the student and working youth to the digital detox under the influence of objective and subjective factors (age, social status, competitiveness, social trust). The research was conducted with the survey of students (high school, vocational and higher education) and working youth in the Tyumen Region. The sample consisted of 10th-11th grade students (N=1130), university students (N=1097) and working youth (N=942). The results show that the main purpose of using social networks is to get new information (81%), entertaining video- and audio-content (72%); only 32% of students satisfy their educational needs. When getting older, the intensity of the youths search for identity decreases together with the desire to keep ones social status in the virtual reality. The high school students show the highest level of readiness for digital detox for a short period (84%), the long-term detox is more typical for the working youth (32% vs. a quarter in two other groups). The lower the level of the general social trust, the easier respondents choose digital detox from social networks. The results of the survey prove the following trend in using virtual social networks: those who accept a day detox are rarely ready for a month detox; those who accept a week detox from virtual communication consider it quite realistic to extend it to a month.
On the Assessment of Completeness and Timeliness of Actionable Cyber Threat Intelligence Artefacts

In this paper we propose an approach for hunting adversarial tactics, techniques and procedures by leveraging information described in structured cyber threat intelligence models. We focused on the properties of timeliness and completeness of cyber threat intelligence indicators to drive the discovery of tactics, techniques and procedures placed highly on the so-called Pyramid of Pain.We used the unit 42 playbooks dataset to evaluate the proposed approach and illustrate the limitations and opportunities of a systematic intelligence sharing process for high pain tactics, techniques and procedures discovery. We applied the Levenshtein Distance in order to present a metric between the attack vectors constructed from the kill chain phases for completeness and timeliness.
The Information Technology for Building a Test Sequence to Control the Technical Condition of Digital Devices

Modern digital devices used in many electronic systems are characterized by versatility and complexity, due to the volume and nature of the tasks they solve, as well as the widespread introduction of various technical devices based on the elemental fifth and sixth generations -large integrated circuits, ultra-large integrated circuits, microprocessor integrated circuits.The purpose of the article is to develop the information technology for building a test sequence to control the technical condition of digital devices.
An Augmented Reality Game for Helping Elderly to Perform Physical Exercises at Home

Abstract People are living longer nowadays. Unfortunately, this positive tendency is marred by various age-related health issues, which people experience. Falling is one of the most serious and common of them. Falls negatively influences elderly’ everyday living and significantly decreases quality of their life. Physical exercises is a proven method for preventing falls. However, it is only effective when training is regular and exercise techniques are correct. This paper presents a prototype of an augmented reality exergame for elderly people to perform physical exercise at home. The research is focusing on developing a solution for both above-mentioned issues: augmentation with Microsoft Kinect and various sensors assists in creating a safe game environment, which can helps to perform exercises with right technique; gamification elements contribute to users’ motivation to train regularly. A user-centered design approach was adopted to guide the design and development iterative process. User testing of the first prototype was performed and demonstrated positive attitudes from participants. Feedback from user testing will be used for the next development iterations.
Deep Neural Network Compression Method Based on Product Quantization

In this paper a method based on the combination of product quantization and pruning to compress deep neural network with large size model and great amount of calculation is proposed. First of all, we use pruning to reduce redundant parameters in deep neural network, and then refine the tune network for fine tuning. Then we use product quantization to quantize the parameters of the neural network to 8 bits, which reduces the storage overhead so that the deep neural network can be deployed in embedded devices. For the classification tasks in the Mnist dataset and Cifar10 dataset, the network models such as LeNet5, AlexNet, ResNet are compressed to 23 to 38 times without losing accuracy as much as possible.
Noise and acoustic modeling with waveform generator in text-to-speech and neutral speech conversion

Abstract This article focuses on developing a system for high-quality synthesized and converted speech by addressing three fundamental principles. Although the noise-like component in the state-of-the-art parametric vocoders (for example, STRAIGHT) is often not accurate enough, a novel analytical approach for modeling unvoiced excitations using a temporal envelope is proposed. Discrete All Pole, Frequency Domain Linear Prediction, Low Pass Filter, and True envelopes are firstly studied and applied to the noise excitation signal in our continuous vocoder. Second, we build a deep learning model based text–to–speech (TTS) which converts written text into human-like speech with a feed-forward and several sequence-to-sequence models (long short-term memory, gated recurrent unit, and hybrid model). Third, a new voice conversion system is proposed using a continuous fundamental frequency to provide accurate time-aligned voiced segments. The results have been evaluated in terms of objective measures and subjective listening tests. Experimental results showed that the proposed models achieved the highest speaker similarity and better quality compared with the other conventional methods.
Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism

Marine object tracking is critical for search and rescue activities in the complex marine environment. However, the complex marine environment poses a huge challenge to the effect of tracking, such as the variability of light, the impact of sea waves, the occlusion of other ships, etc. Under these complex marine environmental factors, how to design an efficient dynamic visual tracker to make the results accurate, real time and robust is particularly important. The parallel three-branch correlation filters for complex marine environmental object tracking based on a confidence mechanism is proposed by us. The proposed tracker first detects the appearance change and position change of the object by constructing parallel three-branch correlation filters, which enhances the robustness of the correlation filter model. Through the weighted fusion of response maps, the center position of the object is accurately located. Secondly, the Gaussian-triangle joint distribution is used to replace the original Gaussian distribution in the training phase. Finally, a verification mechanism of confidence metric is embedded in the filter update section to analyze the tracking effect of the current frame, and to update the filter sample from verification result. Thus, a more accurate correlation filter is trained to prevent model drift and achieve a good tracking effect. We found that the effect of various interferences on the filter is effectively reduced by comparing with other trackers. The experiments prove that the proposed tracker can play an outstanding role in the complex marine environment.
Deep Learning-Based Lane Marking Detection using A<sup>2</sup>-LMDet

Automated lane marking detection is essential for advanced driver assistance system (ADAS) and pavement management work. However, prior research has mostly detected lane marking segments from a front-view image, which easily suffers from occlusion or noise disturbance. In this paper, we aim at accurate and robust lane marking detection from a top-view perspective, and propose a deep learning-based detector with adaptive anchor scheme, referred to as A 2 -LMDet. On the one hand, it is an end-to-end framework that fuses feature extraction and object detection into a single deep convolutional neural network. On the other hand, the adaptive anchor scheme is designed by formulating a bilinear interpolation algorithm, and is used to guide specific-anchor box generation and informative feature extraction. To validate the proposed method, a newly built lane marking dataset contained 24,000 high-resolution laser imaging data is further developed for case study. Quantitative and qualitative results demonstrate that A 2 -LMDet achieves highly accurate performance with 0.9927 precision, 0.9612 recall, and a 0.9767 [Formula: see text] score, which outperforms other advanced methods by a considerable margin. Moreover, ablation analysis illustrates the effectiveness of the adaptive anchor scheme for enhancing feature representation and performance improvement. We expect our work will help the development of related research.
CoNTe: A Core Network Temporal Blockchain for 5G

Virtual Network Functions allow the effective separation between hardware and network functionality, a strong paradigm shift from previously tightly integrated monolithic, vendor, and technology dependent deployments. In this virtualized paradigm, all aspects of network operations can be made to deploy on demand, dynamically scale, as well as be shared and interworked in ways that mirror behaviors of general cloud computing. To date, although seeing rising demand, distributed ledger technology remains largely incompatible in such elastic deployments, by its nature as functioning as an immutable record store. This work focuses on the structural incompatibility of current blockchain designs and proposes a novel, temporal blockchain design built atop federated byzantine agreement, which has the ability to dynamically scale and be packaged as a Virtual Network Function (VNF) for the 5G Core.
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring

With the success of big data and artificial intelligence in many fields, the applications of big data driven models are expected in financial risk management especially credit scoring and rating. Under the premise of data privacy protection, we propose a projected gradient-based method in the vertical federated learning framework for the traditional scorecard, which is based on logistic regression with bounded constraints, namely FL-LRBC. The latter enables multiple agencies to jointly train an optimized scorecard model in a single training session. It leads to the formation of the model with positive coefficients, while the time-consuming parameter-tuning process can be avoided. Moreover, the performance in terms of both AUC and the Kolmogorov-Smirnov (KS) statistics is significantly improved due to data enrichment using FL-LRBC. At present, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.
Multi-label text classification algorithm based on semi-supervised learning

Abstract The standard Co-Training algorithm can effectively improve the performance of the classifier by using unlabeled data. In fact, however, it is difficult to meet the requirement of sufficient and redundant views At the same time, the constraints of Co-Training is further relaxed to solve the problem of inaccurate confidence estimation of unlabeled samples. In this paper, imml-knn and mlplsa-knn are used to train the basic classifiers with great difference. By using unlabeled data in the way of providing Pseudotag sample to each other, a Multi-Label Text Classification Algorithm CT-MLTC based on Co-Training is proposed. Experimental results show that the algorithm has good performance in multi label text classification tasks with more unlabeled data.
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance. However, evaluating the robustness only under the worst-case scenarios based on known attacks is not comprehensive, not to mention that some of them even rarely occur in the real world. In addition, the distribution of safety-critical data is usually multimodal, while most traditional attacks and evaluation methods focus on a single modality. To solve the above challenges, we propose a flow-based multimodal safety-critical scenario generator for evaluating decisionmaking algorithms. The proposed generative model is optimized with weighted likelihood maximization and a gradient-based sampling procedure is integrated to improve the sampling efficiency. The safety-critical scenarios are generated by querying the task algorithms and the log-likelihood of the generated scenarios is in proportion to the risk level. Experiments on a self-driving task demonstrate our advantages in terms of testing efficiency and multimodal modeling capability. We evaluate six Reinforcement Learning algorithms with our generated traffic scenarios and provide empirical conclusions about their robustness.
Empirical Exploration of the Challenges in Temporal Relation Extraction from Clinical Text

Time is an important concept in human-cognition, fundamental to a wide range of reasoning tasks in the clinical domain. Results of the Clinical TempEval 2016 challenge, a set of shared tasks that evaluate temporal information extraction systems in the clinical domain, indicate that current state-of-the-art systems do well in solving event and time expression identification but perform poorly in temporal relation extraction. This study aims to identify and analyze the reason(s) for this uneven performance. It adapts a general domain tree-based bidirectional long short-term memory recurrent neural network model for semantic relation extraction to the task of temporal relation extraction in the clinical domain, and tests the system in a binary and multi-class classification setting by experimenting with general and in-domain word embeddings. Its results outperform the best Clinical TempEval 2016 system and the current state-of-the-art model. However, there is still a significant gap between the system and human performance. Consequently, this study delivers a deep analysis of the results, identifying a high incidence of nouns as events and class overlapping as posing major challenges in this task.
Melanoma Cell Detection in Lymph Nodes Histopathological Images using Deep Learning

Histopathological images are widely used to diagnose diseases including skin cancer.As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment.In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images.The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus.The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier.Experimental results show that the CNN can segment the nuclei with more than 90% accuracy.The proposed technique has a low computational complexity.
Research on Trajectory Tracking and Obstacle Avoidance of Nonholonomic Mobile Robots in a Dynamic Environment

This paper presents an obstacle-avoidance trajectory tracking method based on a nonlinear model prediction, with a dynamic environment considered in the trajectory tracking of nonholonomic mobile robots for obstacle avoidance. In this method, collision avoidance is embedded into the trajectory tracking control problem as a nonlinear constraint of the position state, which changes with time to solve the obstacle-avoidance problem in dynamic environments. The CasADi toolkit was used in MATLAB to generate a real-time, efficient C++ code with inequality constraints to avoid collisions. Trajectory tracking and obstacle avoidance in dynamic and static environments are trialed using MATLAB and CasADi simulations, and the effectiveness of the proposed control algorithm is verified.
Complexity of Deep Convolutional Neural Networks in Mobile Computing

Neural networks employ massive interconnection of simple computing units called neurons to compute the problems that are highly nonlinear and could not be hard coded into a program. These neural networks are computation-intensive, and training them requires a lot of training data. Each training example requires heavy computations. We look at different ways in which we can reduce the heavy computation requirement and possibly make them work on mobile devices. In this paper, we survey various techniques that can be matched and combined in order to improve the training time of neural networks. Additionally, we also review some extra recommendations to make the process work for mobile devices as well. We finally survey deep compression technique that tries to solve the problem by network pruning, quantization, and encoding the network weights. Deep compression reduces the time required for training the network by first pruning the irrelevant connections, i.e., the pruning stage, which is then followed by quantizing the network weights via choosing centroids for each layer. Finally, at the third stage, it employs Huffman encoding algorithm to deal with the storage issue of the remaining weights.
Metamorphic filtering of black-box adversarial attacks on multi-network face recognition models

Adversarial examples pose a serious threat to the robustness of machine learning models in general and of deep learning models in particular. These carefully designed perturbations of input images can cause targeted misclassifications to a label of the attacker's choice, without being detectable to the naked eye. A particular class of adversarial attacks called black box attacks can be used to fool a target model despite not having access to the model parameters or to the input data used to train the model. In this paper, we first build a black box attack against robust multi-model face recognition pipelines and then test it against Google's FaceNet. We then present a novel metamorphic defense pipeline relying on nonlinear image transformations to detect adversarial attacks with a high degree of accuracy. We further use the results to create probabilistic metamorphic relations that define efficient decision boundaries between the safe and adversarial examples; achieving adversarial classification accuracy of up to 96%.
Sets of Attacking Arguments for Inconsistent Datalog Knowledge Bases

Logic-based argumentation is a well-known approach for reasoning with inconsistent logic knowledge bases. Such frameworks have been shown to suffer from a major practical drawback consisting of a large number of arguments and attacks. To address this issue, we provide an argumentation framework that considers sets of attacking arguments and provide a theoretical analysis of the new framework with respect to its syntactic and semantic properties. We provide a tool for generating such argumentation frameworks from a Datalog knowledge base and study their characteristics.
Synchronizing Embedding Changes in Side-Informed Steganography

Historically, two different strategies have been proposed for improving steganographic security by allowing each cover element to be modified by +1 or &#x2212;1 with unequal probabilities: side-informed steganography and methods that cluster the polarity of neighboring changes. In the first strategy, the sender typically uses the knowledge of quantization errors when developing / processing the cover before embedding. In the latter, embedding on disjoint sub-lattices employs heuristic rules to increase the probability that the polarities of neighboring changes align. In this paper, we propose a method for combining both strategies and experimentally show an improvement in empirical security for several types of side information on two datasets when steganalyzing with rich models as well as convolutional neural networks.
A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods.

Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge towards the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.