Reviewerly/exHarmony huggingface dataset:
Logistic Similarity Metric Learning via Affinity Matrix for Text-Independent Speaker Verification

This paper proposes a novel objective function, called Logistic Affinity Loss (Logistic-AL), to optimize the end-to-end speaker verification model. Specifically, firstly, the cosine similarities of all pairs in a mini-batch of speaker embeddings are passed through a learnable logistic regression layer and the probability estimation of all pairs is obtained. Then, the supervision information for each pair is formed by their corresponding one-hot speaker labels, which indicates whether the pair belongs to the same speaker. Finally, the model is optimized by the binary cross entropy between predicted probability and target. In contrast to the other distance metric learning methods that push the distance of similar/dissimilar pairs to a pre-defined target, Logistic-AL builds a learnable decision boundary to distinguish the similar pairs and dissimilar pairs. Experimental results on the VoxCeleb1 dataset show that the x-vector feature extractor optimized by Logistic-AL achieves state-of-the-art performance.
A Novel Image Dehazing and Assessment Method

Images captured in hazy weather conditions often suffer from color contrast and color fidelity. This degradation is represented by transmission map which represents the amount of attenuation and airlight which represents the color of additive noise. In this paper, we have proposed a method to estimate the transmission map using haze levels instead of airlight color since there are some ambiguities in estimation of airlight. Qualitative and quantitative results of proposed method show competitiveness of the method given. In addition we have proposed two metrics which are based on statistics of natural outdoor images for assessment of haze removal algorithms.
Language Identification and Context-based Analysis of Code-switching Behaviors in Social Media Discussions

Social media discussions see the participation of multilingual individuals: who tend to utilize alternate languages in a single post (code-switching) for effective communication in a discussion. This paper attempts to characterize such discussions to analyze contextual factors related to multilingual communities. Features extracted from the posts are used to train a CRF-based sequence labeling algorithm for language identification in an intra-sentential code-switching scenario. The context of a sentence in a discussion is modeled in defining relevance through Term Frequency Inverse Document Frequency (TF-IDF). Further context of a multilingual sentence with respect to the discussion such as agreement and questioning between pairs of posts is also modeled.
An encoder–decoder approach to mine conditions for engineering textual data

Data engineering seeks to support artificial intelligence processes that extract knowledge from raw data. Many such data are rendered in natural language from which entity-relation extractors extract facts and opinion miners extract opinions; the goal of condition mining is to mine the conditions that have an influence on them. In this article, a new condition mining method is proposed. It relies on a deep neural network and attempts to overcome the limitations of existing methods for condition mining that we reviewed. The materials used include readily-available software components for natural language processing and a large multi-lingual, multi-topic dataset. The common information retrieval performance measures were used to assess the results, namely: precision, which is the fraction of correct conditions to the mined ones, recall, which is the fraction of correct conditions that have been mined to the total number of correct conditions, and the F1 score, which is the harmonic mean of precision and recall. The results of the experimental analysis prove that the new proposal can attain an F1 score that is significantly greater than with existing methods. Furthermore, a comprehensive analysis of the dataset was performed, which revealed two key findings: the connectives follows a long-tail distribution and the conditions are quite dissimilar from a semantic point of view.
Multi-objective Evaluation of Deep Learning Based Semantic Segmentation for Autonomous Driving Systems

Recent applications of deep learning (DL) architectures for semantic segmentation had led to a significant development in autonomous driving systems (ADS). Most of the semantic segmentation applications for ADS consider a plethora of classes. Nevertheless, we believe that focusing only on the segmentation of drivable roads, sidewalks, traffic signs, and cars, can drive the improvement of navigation and control techniques in autonomous vehicles. In this study, some state-of-the-art topologies are analyzed to find a strategy that can achieve a uniform performance for the four classes. We propose a multiple objective evaluation method with the purpose of finding the non-dominated solutions in different DL architectures. Numerical results are shown using CityScapes, SYNTHIA, and CamVid datasets.
Generalised Atanassov Intuitionistic Fuzzy Sets Are Actually Intuitionistic Fuzzy Sets

The purpose of this paper is to investigate the relationship between the recently introduced Generalised Atanassov Intuitionistic Fuzzy Sets and the Intuitionistic Fuzzy Sets (also sometimes called Atanassov sets). As a result it will be established that the two notions are completely equivalent.
Trust Inference for Rideshare through Co-training on Social Media Data

Ridesharing can save energy, reduce pollution, and alleviate congestion. These societal benefits can only occur, however, if sufficient trust is established between rideshare partners. Trust judgments heavily influence the usage of ridesharing platforms. Towards trust building, ridesharing systems are starting to allow drivers to provide personal information about themselves. Some previous work already built a trust prediction model using information from Instagram profiles. Their results indicated both social account information and picture information factors influenced judgments of trust of potential rideshare partners. In this paper, we advance these findings by describing a co-training approach that uses social account information and picture information as two independent views to predict trust judgments. Specifically, this analysis addresses the problem of accurate labeling of complex pictures from social media by using the hashtags of pictures for trust inference. We use a topic model to summarize the hashtags of pictures into a small number of groups called topics. The final results show that using hashtags as picture information yields a better result than the use of picture category. The co-training approach with social account information and hashtags outperforms supervised and semisupervised classifiers. Often, in order to make an accurate trust prediction model, large amounts of expensive labeled training data are needed. The co-training approach described allows for faster labeling of large datasets, saving resources by avoiding intensive human labeling effort.
Difficult XSS Code Patterns for Static Code Analysis Tools

We present source code patterns that are difficult for modern static code analysis tools. Our study comprises 50 different open source projects in both a vulnerable and a fixed version for XSS vulnerabilities reported with CVE IDs over a period of seven years. We used three commercial and two open source static code analysis tools. Based on the reported vulnerabilities we discovered code patterns that appear to be difficult to classify by static analysis. The results show that code analysis tools are helpful, but still have problems with specific source code patterns. These patterns should be a focus in training for developers.
AutoGstr: Relatively Accurate Sign Language Interpreter

Hand gesture recognition is the one of the method to identify the hand position, its pattern and then translate it to the corresponding meaning or purpose. This paper contributes a real time sign language interpretation of hand gestures based on deep convolutional neural networks with focus on development of a cost-effective and efficient hardware prototype for communication ease with deaf and dumb people.
Unsupervised Single Image Dehazing via Disentangled Representation

Image dehazing aims to recover the latent clear content from the corresponding degraded hazy image. In this paper, we propose an unsupervised method for single image dehazing based on disentangled representation. Our proposed method does not rely on the physical scattering model and does not need paired of training data. We propose a content encoder and a haze encoder to disentangle the content and hazy information from a hazy image respectively. We propose a latent regression loss to encourage the generated image to preserve haze information and to force the haze encoder to extract haze information from the haze image. The cycle-consistency loss are introduced to ensure that the dehazed images have the same content structures with the original images. We also use an adversarial loss on the dehazed images to guarantee haze free and visually realistic. Extensive experiment results on the public dehazing dataset RESIDE demonstrate that the proposed method outperforms state-of-the-art unsupervised methods, and can achieve comparable performance with the state-of-the- art supervised methods.
Distributed Discrete Hashing by Equivalent Continuous Formulation

Hashing based approximate nearest neighbor search has attracted considerable attention in various fields. Most of the existing hashing methods are centralized, which cannot be used for many large-scale applications with the data stored or collected in a distributed manner. In this article, we consider the distributed hashing problem. The main difficulty of hashing is brought by its inherent binary constraints, which makes the problem generally NP-hard. Most of the existing distributed hashing methods chose to relax the problem by dropping the binary constraints. However, such a manner will bring additional quantization error, which makes the binary codes less effective. In this paper, we propose a novel distributed discrete hashing method, which learns effective hash codes without using any relaxations. Specifically, we give a method to transform the discrete hashing problem into an equivalent distributed continuous optimization problem. After transformation, we devise a distributed discrete hashing (dDH) algorithm based on the idea of DC programming to solve the problem. To obtain more efficient hash codes, we further add bits balance and uncorrelation constraints to the hashing problem, and we also propose a distributed constrained discrete hashing algorithm (dCDH) to solve this problem. Extensive experiments are provided to show the superiority of the proposed methods.
Automatic Determination of K in Distributed K-Means Clustering

Traditional K-Means based distributed data clustering require number of clusters as input which is difficult to obtain in case of a real life application like wireless sensor network. To mitigate this issue here an Automatic Distributed K-Means (ADK-Means) algorithm is proposed. In this algorithm cluster assignment is carried out with point symmetry based distance instead of Euclidean distance, to effectively detect arbitrary shaped clusters. The performance of proposed algorithm is demonstrated on four synthetic and one practical dataset. Three cluster quality evaluation methods have been employed and comparative analysis is done with existing distributed K-Means. The improved performance is up to 12.6% in terms of Silhouette index reported as compared to existing approach.
Walk2Privacy: Limiting target link privacy disclosure against the adversarial link prediction

The disclosure of an important yet sensitive link may cause serious privacy crisis between two users of a social graph. Only deleting the sensitive link referred to as a target link which is often the attacked target of adversaries is not enough, because the adversarial link prediction can deeply forecast the existence of the missing target link. Thus, to defend some specific adversarial link prediction, a budget limited number of other non-target links should be optimally removed. We first propose a path-based dissimilarity function as the optimizing objective and prove that the greedy link deletion to preserve target link privacy referred to as the GLD2Privacy which has monotonicity and submodularity properties can achieve a near optimal solution. However, emulating all length limited paths between any pair of nodes for GLD2Privacy mechanism is impossible in large scale social graphs. Secondly, we propose a Walk2Privacy mechanism that uses self-avoiding random walk which can efficiently run in large scale graphs to sample the paths of given lengths between the two ends of any missing target link, and based on the sampled paths we select the alternative non-target links being deleted for privacy purpose. Finally, we compose experiments to demonstrate that the Walk2Privacy algorithm can remarkably reduce the time consumption and achieve a very near solution that is achieved by the GLD2Privacy.
Analysis of Different Speckle-scene Models for SONAR Image Despeckling

Among the different models investigated for SONAR image denoising, the analysis considers two noise models, the signal independent additive noise model and the multiplicative noise model. For the signal-independent additive model, in the transform domain for the denoising a mutiresolution analysis method and the sparsity of the natural sonar images are exploited. For the same model in the spatial domain a side scan sonar image is denoised by Kalman filter-based estimation method. For the multiplicative noise model, in the transform domain a mixed noise removal based on probabilistic patch-based processing is employed and in the spatial domain, the fractional integral mask based method and an unscented Kalman filter based estimation method is adopted.
Multi-person Pose Estimation with Mid-Points for Human Detection under Real-World Surveillance

This paper introduces the design and usage of a multi-person pose estimation system. The system is developed targeting some challenging issues in real-world surveillance such as (i) low image resolution, and (ii) people captured in crowded situation. Under such conditions, we evaluated the system’s performance on human detection by comparing to other state-of-art algorithms. The leading results by using the proposed system are accomplished by several features in the system’s design: (i) training and inference of mid-point, which is the center of two body region points defined in human pose, (ii) core-of-pose which is association of a plurality of body region points, and used as root of each individual person during parsing multiple people under crowded situation. The proposed system is also fast and has the potential for industrial use.
Adaptive Data Center Network Traffic Management for Distributed High Speed Storage

The emerging high-speed storage technologies and the ever-growing need for data storage are placing increasing pressure on data center networks. In this paper, we develop novel mechanisms for dynamically deciding when to move storage chunks or alter the number of active copies to alleviate congestion during high traffic episodes and to enable traffic consolidation (and hence network energy savings) during low traffic periods. Both actions are essential due to increasing burstiness of data center traffic. Using extensive simulations with modified NS3 network simulator we provide deep insights into the migration vs. replication tradeoff. We also show that under traffic bursts, a careful replication can provide up to 47% improvement in latency with read-dominated traffic, and strategic data migration to a low utilized node can provide up to 22% improvement in delays with write dominated traffic.
Peer Firm Identification Using Word Embeddings

In the task of peer firm identification, researchers have relied on existing industry classification system regardless of their critical limitations. In the existing industry classification system, a company should be categorized into one group regardless of the number of products and services it offers. Furthermore, it is not possible to measure the similarity of companies belonging to the same group. The systems are revised manually, rendering it difficult for them to keep up with the fast-changing industry landscape. In this paper, we propose a novel peer firm identification method based on Word 2Vec. By computing the cosine similarity of word embedding vectors trained on a 10-year corpus of financial news articles, we developed a method that produces peer firms with their numeric similarity scores. Our approach allows us to observe chronological changes in the peer firms by having firm words that appear in news articles from different periods in the same vector space. Last but not least, our Word 2Vec-based method produced more economically homogeneous groups of peer firms compared to the existing classification systems.
Self-Adaptive Parameter Control in Genetic Algorithms Based on Entropy and Rules of Nature for Combinatorial Optimization Problems

The parameter control problem is crucial to the performance of genetic algorithms. In this paper, we propose a self-adaptive approach based on entropy and rules of nature to control the parameters of algorithms. This approach utilizes the entropy of both the population and each genetic locus as the feedback to evaluate the state of algorithms. Then, parameters are adjusted according to the state of algorithms and rules of nature. This strategy avoids the impact of randomness when evaluating the status of algorithms and tracks the development of each gene in time to prevent premature and nonconvergence on a certain gene. Furthermore, this method can not only maintain the solutions with good quality but also increase the probability that the solutions with poor quality change. The experimental results demonstrate that the proposed parameter-controlling strategy is valid for the algorithm to enhance the performance for solving a variety of combinatorial optimization problems.
Image Dehazing using Improved Dark Channel Prior and Relativity of Gaussian

Bad climatic weather conditions occur in our daily life are snow, sandstorm, mist, haze and fog etc. These various atmospheric particles affect the natural scene and the visibility will be tampered. Different dehazing methods are used to restore the visual perception of the image, increase the natural panoramic view and preserve the structural features of the image. Poor visibility becomes a significant issue for most outdoor vision applications. Image dehazing is a highlighted research area because of its real time applications in surveillance systems, driver assistance system especially for the people who resides in hilly areas where mist and haze is prominent. This paper presents an modified dark channel prior based image dehazing system with an enhanced and refined transmission map constructed by utilizing the RGB and YCrCb color system. Transmission map is constructed and estimated from combined airlight component obtained from the above said color models. In this work, we compute the Relativity of Guassian for RGB and YCbCr color space to produce guidance image. The both guidance images thus obtained are applied to guided filter for smoothening. Finally, hazeless image reconstruction is performed by utilizing the guidance images obtained from the previous step. In this proposed work, we focus on improving the transmission map by utilizing the concept of Relativity of Gaussian and Guided filter. We can find that the proposed method does not over enhance the images. Results analysis focuses on investigating the efficiency of proposed method by reconstructing the image without distortion, with no color artifacts and better aesthetic visual image
A 3D roaming and collision detection algorithm applicable for massive spatial data

In this paper, a novel 3D roaming algorithm considering collision detection and interaction is proposed that adopts a triangle mesh to organize and manage massive spatial data and uses a customized bounding box intersector to rapidly obtain the potential collided triangles. The proposed algorithm can satisfy the requirements of timeliness and practicability during complicated large 3D scene collision detection. Moreover, we designed a method to calculate the collision point coordinates according to the spatial position relation and distance change between the virtual collision detection sphere and triangles, with the triangle edges and three vertices being considered. Compared to the methods that use the native intersector of OpenSceneGraph (OSG) to obtain the collision point coordinates, the calculation efficiency of the proposed method is greatly improved. Usually, when there is a big split/pit in the scene, the viewpoints will fly off the scene due to the fall of the collision detection sphere, or the region interior cannot be accessed when the entrance of some local region (e.g., internal grotto) of the scene is too small. These problems are solved in this paper through 3D scene-path training and by self-adaptively adjusting the radius of the virtual collision detection sphere. The proposed 3D roaming and collision detection method applicable for massive spatial data overcomes the limitation that the existing roaming and collision detection methods are only applicable to 3D scenes with a small amount of data and simple models. It provides technical supports for freewill browsing and roaming of indoor/outdoor and overground/underground of the 3D scene in cases of massive spatial data.
Blockchain and Internet of Things: An Overview

Blockchain is the technique that underpins the digital currencies like bitcoin, litecoin, and ethereum, and so on. The data that are stored within a block depends on the category of blockchain. A Bitcoin block has data about the sender, receiver, and total number of bitcoins to be sent. Bitcoin is a currency that is used digitally and globally as a system currency. It permits people to send and receive cash through the internet, even to a less-trusted person. Money can also be exchanged without knowing the real identity. The mathematics of cryptography form the fundamentals of bitcoin’s security. Cryptocurrency wallets also get hacked usually, but the hack is not the usual one, outstanding at the blockchain itself. Chain is a company that creates cryptographic ledgers like blockchains for security in financial services. The tools helped banks and credit-card companies to securely store and manage their assets. The convergence of Internet of Things (IoT) and blockchain is of significant use in the industrial world. This chapter gives an overview of IoT, its applications and challenges, blockchain technology, its pros and cons, the convergence of blockchain technology with IoT, the different architectures, evaluation techniques, the advanced scenarios, and use-cases.