Reviewerly/exHarmony huggingface dataset:
State-of-the-art fuzzy active contour models for image segmentation

Image segmentation is the initial step for every image analysis task. A large variety of segmentation algorithm has been proposed in the literature during several decades with some mixed success. Among them, the fuzzy energy-based active contour models get attention to the researchers during last decade which results in development of various methods. A good segmentation algorithm should perform well in a large number of images containing noise, blur, low contrast, region in-homogeneity, etc. However, the performances of the most of the existing fuzzy energy-based active contour models have been evaluated typically on the limited number of images. In this article, our aim is to review the existing fuzzy active contour models from the theoretical point of view and also evaluate them experimentally on a large set of images under the various conditions. The analysis under a large variety of images provides objective insight into the strengths and weaknesses of various fuzzy active contour models. Finally, we discuss several issues and future research direction on this particular topic.
Secure Fine-Grained Multi-Keyword Search with Protection from Key Abusers in the Cloud

A combination of attribute-based encryption and searchable encryption is used to enable fine-grained searching capability. This combination leads to a powerful tool which supports fine-grained search functionality in multi-user scenario. In this paper, we have used key-policy (KP) design framework of attribute based encryption (ABE) to construct a multi-keyword search scheme where access rights assigned to a data user are associated with his/her secret key. This leads to a situation where data user can abuse his secret key to distribute it illegally to the unauthorized users to perform search over the shared data which are not intended for him/her. So, to track such kind of key abusers we have embedded an extra functionality of tracing the traitors.
Behavioral Cyber Deception: A Game and Prospect Theoretic Approach

This paper aims to characterize deception based attack-defense strategies when a system and an attacker is behavioral in nature under strategic considerations. The paper uses Prospect Theory to model the behavioral nature of the system and the attacker, and Game Theory to model the strategic interactions between them. The paper first considers the availability of multiple computing devices that can be used to deceive a behavioral attacker into attacking a device being unused by a behavioral system and characterizes the Nash Equilibrium (NE) based attack-defense strategy in such a scenario under cost considerations. The paper then considers the problem of protecting a target device that is accessible via a tree network and proposes the idea of deceptive routing to mislead a behavioral attacker towards a fake target present in the network. The NE based attack-defense strategies in the context of the proposed deceptive routing technique is also characterized. Numerical results provide insights into the strategic deception techniques presented in this paper.
SMART: security model adversarial risk-based tool for systems security design evaluation

Abstract As development and deployment of secure systems continue to grow at scale, there is an equal need to evaluate these systems for vulnerabilities and other problems. However, the process of evaluating these designs is complicated and mainly proprietary to the group performing the evaluation. Generally, one follows the generic risk equation of probability and impact. In addition, one should examine the costs related to the adversary and the defender of a system. Without accounting for all of these different aspects, one cannot expect to properly assess the security of a system model or design. This work presents a security model adversarial risk-based tool (SMART) for systems security design evaluation. Our tool reads in a systems security model an attack graph and collects the necessary information for the purpose of determining the best solution based on a calculated security risk represented as a monetary amount. The advantage of the tool is the level of automation provided in the evaluation of security attack trees while providing meaningful metrics that are effortless to compare and contrast.
Towards analyzing large graphs with quantum annealing

The use of quantum computing in graph community detection and regularity checking related to Szemerédi's Regularity Lemma (SRL) are demonstrated with D-Wave Systems' quantum annealer and simulations. We demonstrate the capability of quantum computing in solving hard problems relevant to big data. A new community detection algorithm based on SRL is also introduced and tested.
Deep text classification of Instagram data using word embeddings and weak supervision1

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing T witter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on training data annotated manually by humans, which in practice is both difficult and expensive to obtain. In this paper, we present methods for weakly supervised text classification of Instagram text. We analyze a corpora of Instagram posts from the fashion domain and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we demonstrate that in absence of annotated training data, using weak supervision to train models is a viable approach. With weak supervision we were able to label a large dataset in hours, something that would have taken months to do with human annotators. Using the dataset labeled with weak supervision in combination with generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance.
Deep Group-Shuffling Dual Random Walks With Label Smoothing for Person Reidentification

Person reidentification (ReID) is a challenging task of finding a target pedestrian in a gallery set collected from multiple nonoverlapping camera views. Recently, state-of-the-art ReID performance has been achieved via an end-to-end trainable deep neural network framework, which integrates convolution feature extraction, similarity learning and reranking into a joint optimization framework. In such a framework, the similarity is learned via an embedding network, the reranking is conducted with a random walk, and the whole framework is optimized with a cross-entropy-based verification loss. Unfortunately, the embedding net is difficult to train well because their two-dimensional outputs mutually interfere each other when using the conventional random walk. In addition, the supervision information has not been fully exploited during the training phase due to the binary nature of the verification loss. In this paper, we propose a novel approach, called group-shuffling dual random walks with label smoothing (GSDRWLS), in which random walks are performed separately on two channels-one for positive verification and one for negative verification-and the binary verification labels are properly modified with an adaptive label smoothing technique before feeding into the verification loss in order to train the overall network effectively and to avoid the overfitting problem. Extensive experiments conducted on three large benchmark datasets, including CUHK03, Market-1501 and DukeMTMC, confirm the superior performance of our proposal.
Landslide detection system using computer vision approach and Raspberry Pi

Landslide is a natural hazard, which badly affects people's safety and property. Continuous monitoring of such a disaster may lead to reducing of losses of human lives. With this aim, in this paper, we have proposed a surveillance system for real time landslide monitoring using computer vision algorithms. Here we have used a video camera to acquire live view of landslide site and a small computer board `raspberry Pi' to run the algorithm we will introduce here. When landslide is detected SMS text messages are transmitted using a GSM modem. Due to video camera and Raspberry Pi, this method is inexpensive yet efficient and also requires low power, therefore this method can be used in any region. We have used median filtering to remove noise present in the detection algorithm. Here we have also proposed a new implementation method for median filtering using a linked list and parallel processing which is very time efficient.
Sentence Compression for Portuguese

The task of Sentence Compression aims at producing a shorter version of a given sentence. This task may assist many other applications, as Automatic Summarization and Text Simplification. In this paper, we investigate methods for Sentence Compression for Portuguese. We focus on machine learning-based algorithms and propose new strategies. We also create reference corpora/datasets for the area, allowing to train and to test the methods of interest. Our results show that some of our methods outperform previous initiatives for Portuguese and produce competitive results with a state of the art method in the area.
Parallel Gradient Boosting based Granger Causality Learning

Granger causality and its learning algorithms have been widely used in many disciplines to study cause-effect relationship among time series variables. In this paper, we address computing challenges of state-of-art Granger causality learning algorithms, specially when facing increasing dimensionality of available datasets. We study how to leverage gradient boosting meta machine learning techniques to achieve accurate causality discovery and big data parallel techniques for efficient causality discovery from large temporal datasets. We propose two main algorithms for gradient boosting based causality learning, and parallel gradient boosting based causality learning. Our experiments show our proposed algorithms can achieve efficient learning in distributed environments with good learning accuracy.
Topological quantum walks in cavity-based quantum networks

We present a protocol to implement discrete-time quantum walks and simulate topological insulator phases in cavity-based quantum networks, where a single photon is the quantum walker and multiple cavity input–output processes are employed to realize a polarization-dependent translation operation. Different topological phases can be simulated through tuning the single-photon polarization rotation angles. We show that both the topological boundary states and topological phase transitions can be directly observed via measuring the final photonic density distribution. Moreover, we also demonstrate that these topological signatures are quite robust to practical imperfections. Our work opens a new prospect using cavity-based quantum networks as quantum simulators to study discrete-time quantum walks and mimic condensed matter physics.
A Privacy-Preserving Similarity Search Scheme over Encrypted Word Embeddings

Recent evolution in cloud computing platforms have attracted the largest amount of data than ever before. Today, even the most sensitive data are being outsourced, thus, protection is essential to ensure that privacy is not traded for the convenience provided by cloud platforms. Traditional symmetric encryption schemes provide good protection; however, they ruin the merits of cloud computing. Attempts have been made to obtain a scheme where both functionality and protection can be achieved. However, features provided in existing searchable encryption schemes tend to be left behind the latest findings in the information retrieval (IR) area.
A Secure Used Car Trading System based on Blockchain Technology

Although Myanmar's car import policy led shocks in the automotive market due to the Policy changes to import left-hand drive (new car), many consumers in Myanmar still more desire using second-hand cars. Car history is essential in the used car market which is a popular business in Myanmar. Online car trading requires a reliable supplier which may grantee the integrity of car grading. This problem can be solved by using a pure decentralized mechanism called blockchain which has become a hot research area to achieve an open and safe environment. In this paper, the system is aimed to develop a blockchain-based car trading system that can speed up the verification of car information and currency transfers. Besides the proposed car trading system is not only reliable but also protects from the 51% attacks even if 51% of car owners, insurance companies and repair services in this cooperation are simultaneously compromised.
Virtual Identity in Blockchain

Blockchain is a technology that helps produce virtual identities in the digital environment using the real identity and allows using these identities both in digital environment and in real environment. In a way, this has brought about a different dimension for the virtual identity concept and content produced in social network environments. Today, a blockchain-based virtual identity is no longer a really different identity; instead, it now has a form that allows individuals to produce and manage their virtual identities and facilitates their lives. In addition, its usage potential has led to the use of blockchain for institutional purposes. It is now possible for institutions to interact with their stakeholders via their institutional virtual identities. Virtual identities play an important facilitator role in communication to be established by educational institutions with their students especially at higher education level. The facilitator effects of technology in individuals' real lives will make important contributions to the usage potential of the blockchain technology.
Integer Wavelet Transform and Arnold Transform based image steganography with cryptanalysis

The rapid development on the field of communication require a secure exchange of information between the sender and the reciepient. Information hiding techniques are widely used in the field of communication such as military applications, scientific research etc. Information hiding is a general process or term which deals with embedding the message for communication in the content. Otherwise we can say that it refers to either producing the data to be hidden making the existence of secret information to be unnoticeable. This process leads to prevent unauthorized access. There are two inputs in the steganography process, one is the cover object and the other is secret object, both these inputs may be image, video, audio e.t.c. Depending upon these cover objects, it can be categorized into image steganography, video steganography, audio steganography e.t.c. In this paper both cover and secret objects are image. In this paper image steganography is performed with IWT (Integer Wavelet Transform). Normally the steganography process is performed with DWT (Discrete Wavelet Transform), while applying this transform there exists floating point coefficients,which leads to floating point truncation results in loss of information, to avoid this problem we choose IWT. Arnold transform is an image scrambling technique applied to the secret image to ensure security to it.
Optimal‐switched extended <i>H</i><sub>∞</sub> filter for nonlinear systems with stochastic uncertainties

Summary The extended H ∞ filter (EH ∞ F) is a conservative solution with infinite‐horizon robustness for the state estimation problem regarding nonlinear systems with stochastic uncertainties, which leads to excessive costs in terms of filtering optimality and reduces the estimation precision, particularly when uncertainties related to external disturbances and noise appear intermittently. In order to restore the filtering optimality lost due to the conservativeness of the EH ∞ F design, we developed an optimal‐switched (OS) filtering mechanism based on the standard EH ∞ F to obtain an optimal‐switched extended H ∞ filter (OS‐EH ∞ F). The OS mechanism has an error‐tolerant switched (ETS) structure, which switches the filtering mode between optimal and H ∞ robust by setting a switching threshold with redundancy to uncertainties, and a robustness‐optimality cost function (ROCF) is introduced to determine the threshold and optimize the ETS structure online. The ROCF is the weighted sum of the quantified filtering robustness and optimality. When a weight is given, the proposed OS‐EH ∞ F can obtain the optimal state estimates while maintaining the filtering robustness at an invariant ratio. A simulation example of space target tracking has demonstrated the superior estimation performance of the OS‐EH ∞ F compared with some other typical filters, thereby verifying the effectiveness of using the weight to evaluate the estimation result of the filters.
Use beyond usability: integrating user experience design and human factors to improve the usability of a cochlear implant charging system

Use error is a leading cause of medical device failure, with regulatory guidance now provided for usability and human factors. In the wider design field, usability and user experience are now well recognized as a primary focus of industrial design practitioners. This article presents a case study detailing the design of two medical devices (battery recharging devices for a cochlear implant system). Discussing and comparing the application of different methodologies from regulatory guidance (from the medical device field) and user experience methodologies (from the design field) to reduce user error. This article positions the concept of use beyond that of just ‘successful use’ and usability, and into the realm of experience. As medical devices become increasingly integrated as part of our daily lives, they are being used by more diverse user groups (not just trained doctors) in more diverse use environments (homes and workplaces; not just hospitals and clinics) through more complex interfaces (touch, voice, gesture, automation and AI). The diversity of these contexts complicates designing for use. This article explores how experience design can augment and enhance human factors design approaches. Enabling medical devices to be designed for more flexible user experiences that reduce error and enhance use.
Blockchain Implementation Using Smart Grid-Based Smart City

Blockchain is a trusted distributed ledger system across a network of users. It is a system, where the parties cooperate to ease the transaction process, make it more anonymous and yet more secure. Carbon credit token speaks to credits to be exchanged on a blockchain record, making an increasingly proficient and straightforward strategy for trading the significant authentications attached to lessening ozone harming substances. This chapter exhibits the usage and investigation of burden the executives by utilizing the stage of smart grids for private buyer setup, a case study of utility in Puducherry under open joint effort. The smart meters introduced in the utility, which have highlights of two-way correspondence, surveying interim and information estimation, stockpiling and show of utilization information to customers, support for variable duty. The chapter focuses on the blockchain smart grid framework in smart cities with its analysis and features.
An Investigation of LSTM-CTC based Joint Acoustic Model for Indian Language Identification

In this paper, phonetic features derived from the joint acoustic model (JAM) of a multilingual end to end automatic speech recognition system are proposed for Indian language identification (LID). These features utilize contextual information learned by the JAM through long short-term memory-connectionist temporal classification (LSTM-CTC) framework. Hence, these features are referred to as CTC features. A multi-head self-attention network is trained using these features, which aggregates the frame-level features by selecting prominent frames through a parametrized attention layer. The proposed features have been tested on IIITH-ILSC database that consists of 22 official Indian languages and Indian English. Experimental results demonstrate that CTC features outperformed i-vector and phonetic temporal neural LID systems and produced an 8.70% equal error rate. The fusion of shifted delta cepstral and CTC feature-based LID systems at the model level and feature level further improved the performance.
Optimization for Type-1 and Interval Type-2 Fuzzy Systems for the Classification of Blood Pressure Load Using Genetic Algorithms

In this paper, the design of type-1 and interval type-2 fuzzy systems using genetic algorithm is defined. Fuzzy systems are built from the experience of an expert, in this case a cardiologist. The main contribution of this work is to provide the optimization structure for the classification of the blood pressure load in a patient. The decision was made to use genetic algorithms for the optimization of membership functions, which help to improve the classification and provide a better diagnosis to the patient. In addition, the fuzzy systems have fuzzy rules, which are designed from the categories already defined by an expert. After performing some experiments with different type-1 and type-2 fuzzy systems for the classification of blood pressure load, it was concluded that it is necessary to optimize the membership functions to have better results.
Matrix-valued twin-multistate Hopfield neural networks

A complex-valued Hopfield neural network (CHNN) has been widely used for the storage of image data. The CHNN has been extended using hypercomplex numbers. A couple of hypercomplex-valued Hopfield neural networks employ a twin-multistate activation function to reduce the numbers of weight parameters. In this work, we propose a matrix-valued twin-multistate Hopfield neural network (MTMHNN), whose neuron states and weights are 2 × 2 matrices. Computer simulations show that the MTMHNN has better noise tolerance than the hypercomplex-valued twin-multistate Hopfield neural networks.
A Comparison of Feature Extractors for Panorama Stitching in an Autonomous Car Architecture

Panorama stitching consists on frames being merged to create a 360° view. This technique is proposed for its implementation in autonomous vehicles instead of the use of an external 360-degree camera, mostly due to its reduced cost and improved aerodynamics. This strategy requires a fast and robust set of features to be extracted from the images obtained by the cameras located around the inside of the car, in order to effectively compute the panoramic view in real time and avoid hazards on the road. This paper compares and creates discussion of three feature extraction methods (i.e. SIFT, BRISK and SURF) for image feature extraction, in order to decide which one is more suitable for a panorama stitching application in an autonomous car architecture. Experimental validation shows that SURF exhibits an improved performance under a variety of image transformations, and thus appears to be the most suitable of these three methods, given its accuracy when comparing features between both images, while maintaining a low time consumption. Furthermore, a comparison of the results obtained with respect to similar work allows us to increase the reliability of our methodology and the reach of our conclusions.