Reviewerly/exHarmony huggingface dataset:
Segmentation of Breast Microcalcification Using Hybrid Method of Canny Algorithm with Otsu Thresholding and 2D Wavelet Transform

Nowadays, the segmentation process is very important in the detection of microcalcification in mammogram images. However, the microcalcification in breasts is difficult to detect because of the tiny size and subtle nature abnormalities. In this study, the hybrid method of Canny Edge Detection, Otsu Thresholding, and 2D Wavelet Transform was used by exploiting the strength of each respective methods. The hybrid method is an improvisation of the traditional Canny Edge Detection method that requires a manual setting of the threshold. The quality of the edge detection method will be seriously affected if the manual setting of the threshold is not precise. The proposed hybrid method has overcome this problem by determining the high and low thresholds of the Canny algorithm automatically. The hybrid method was tested with 30 mammogram images, whereby the microcalcifications were already confirmed by radiologists. The performance of the segmentation results was measured in terms of accuracy, sensitivity, F-measure, and error rate. All the experimental results generated outstanding results, where the hybrid method produced 97.50% for accuracy, 0.9280 for F-measure, and 0.1375 for error rate. Hence, the proposed hybrid method has proven its reliability to segment the microcalcification efficiently and is relevant to be used in the medical industry for the segmentation of the region of interest in the near future.
Color Stereo Image Zero-Watermarking using Quaternion Radial Tchebichef Moments

This paper presents a new zero-watermarking scheme, for the copyright protection of stereo color images, based on the Quaternion Radial Tchebichef Moments (QRTMs) and on a chaotic system very sensitive to the initial values. The novelties of this scheme are as follows: (1) QRTMs are applied for the first time in the zero-watermarking scheme for stereo color image; (2) The QRTMs used in the proposed scheme have no numerical instability, which effectively improves the performance of the zero-watermarking scheme; (3) the proposed scheme based on QRTMs effectively resists geometric attacks such as: rotation, uniform scaling and non-uniform scaling; and (4) The chaotic system used is extremely dependent on the initial values, which offers the proposed scheme a high level of security. Experimental results show that the performance of the proposed scheme is superior to that of similar zero-watermarking schemes and other watermarking schemes of stereo color images.
Towards the Utilization of Cost-Effective Off-The-Shelf Devices for Achieving Energy Savings in Existing Buildings

The optimization of energy usage, improvement of energy efficiency, and lowering the carbon footprint are today's hot topics in many fields of research. Research shows that in many countries the cooling and heating of buildings can consume significant amounts of energy. For example, in Finland the heating of buildings uses over a quarter of the total energy produced. Furthermore, most of the older, existing buildings lack any means of measuring indoor conditions and are rarely equipped with systems capable of adequately controlling the conditions within working or living environments. Commercial products do exist for monitoring larger buildings or building complexes, but these tend to be quite expensive and in many cases are targeted for use in newer, so-called "smart buildings", and may not be directly applicable for retrofitting into older buildings. This paper presents an ongoing research study that aims to use cost-effective, off-the-shelf components for measuring indoor air quality with the ultimate goal of utilizing the collected data for optimizing energy usage in existing building stock while preserving good living and working conditions. The paper introduces the overall system architecture and describes the components used in implementing the sensor system. The use of the system is illustrated through two use cases: a university classroom and an apartment building used for eldercare. The requirements that were observed for the system as well as the experiences and challenges faced in designing, implementing, and installing the system in real-life locations are discussed in this paper.
PTEM: A popularity-based topical expertise model for community question answering

Community Question Answering (CQA) websites are widely used in sharing knowledge, where users can ask questions, reply answers and evaluate answers. So far, the evaluation of answers has been explained by the contents of answers through the investigation of users' topics of interest and expertise levels. In this paper we focus on modeling the user's evaluation behavior, in that users can see the answerer's profile as well as the answer content before evaluating the quality of the answer. We propose a model called Popularity-based Topical Expertise Model (PTEM), a generative model to analyze the rich-get-richer phenomenon that popular user's answers are more recommended. We can simultaneously estimate the topical expertise of each user and the strength of the rich-get-richer effect through the EM algorithm combined with collapsed Gibbs sampling. Experiments are performed on the StackExchange data, and the results demonstrate a rich-get-richer phenomenon in the community. We further discuss the superiority and usefulness of the proposed model through analysis in the discipline of philosophy.
Proposal for an Interactive Software System Design for Learning Mexican Sign Language with Leap Motion

The main objective of this work is to carry out a proposal of an interactive software system (ISS) design that makes use a Hardware device (Hw) "Leap Motion" and is centered average users as an interactive solution for communication with deaf people is feasible.
Towards Generating SPARK from Event-B Models

This paper presents an approach to generate SPARK code from Event-B models. System models in Event-B are translated into SPARK packages including proof annotations. Properties of the Event-B models such as axioms and invariants are also translated and embedded in the resulting models as pre- and post-conditions. This helps with generating SPARK proof annotations automatically hence ensuring the correct behaviour of the resulting code. A prototype plug-in for the Rodin has been developed and the approach is evaluated on different examples. We also discuss the possible extensions including to generate scheduled code and data structures such as records.
Silhouette-free multiple image encryption using coherent superposition and Fresnel transform

This work proposes an asymmetric scheme for silhouette-free multiple image encryption using coherent superposition and Fresnel transform. Our encryption process is divided into ciphertext step and private key step, where the ciphertexts and the private keys have been obtained with the aid of one random mask. In the process of separately generating the ciphertexts and the private keys, the functions have been constructed for enhancing the security. Each different image is decrypted by employing a different private key. Moreover, the discerning of the information regarding any one of the original images, including its silhouette, is prohibited despite the use of one or two of the masks for decryption. Our scheme is also immune to the cross-talk. Numerical simulations have been employed for demonstrating the feasibility and security of our scheme.
A database for developing machine learning based disruption predictors

Machine learning based disruption prediction methods have exhibited good prediction performance with higher success rate, lower false alarm rate and earlier warning time than physical based methods. One important thing pushed recent advances in machine learning field is high-quality training data. So a database with rich set of accurate disruption related information is crucial to the development of a high performance disruption predictor. In order to develop machine learning based disruption predictors fast and iteratively on J-TEXT, a database dedicated for machine learning disruption prediction has been built. This database provides not only disruption related labels, interfaces for querying the data based on user proved filter and auto generation of training and test data, but also interface for benchmark the predictors. Its modular design allows us to plug-in various shot analysis modules which process diagnostic signals and generate different labels automatically. These modules can be scheduled and run parallel on a cluster which will speed up the shot analysis process. The generated labels are inserted into a MongoDB NoSQL database for later querying. But one major hurdle for machine learning disruption prediction is they perform un-acceptably poor on devices other than they are trained on. It requires data from many different tokamaks to possibly develop a cross machine predictor, so this database also has a data import module which reads diagnostic data from different data sources such as MDSplus and store them as HDF5 files with unified data structure on a parallel file system. It is easy to import data from different machines and provide a unified data access interface.
A Unifying Approximate Method of Multipliers for Distributed Composite Optimization

This article investigates solving convex composite optimization on an undirected network, where each node, privately endowed with a smooth component function and a nonsmooth one, is required to minimize the sum of all the component functions throughout the network. To address such a problem, a general approximate method of multipliers (AMM) is developed, which attempts to approximate the method of multipliers by virtue of a surrogate function with numerous options. We then design the possibly nonseparable, time-varying surrogate function in various ways, leading to different distributed realizations of AMM. We demonstrate that AMM generalizes more than ten state-of-the-art distributed optimization algorithms, and certain specific designs of its surrogate function result in a variety of new algorithms to the literature. Furthermore, we show that AMM is able to achieve an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(1/k)$</tex-math></inline-formula> rate of convergence to optimality, and the convergence rate becomes linear when the problem is locally restricted strongly convex and smooth. Such convergence rates provide new or stronger convergence results to many prior methods that can be viewed as specializations of AMM.
Cyber-Manufacturing System Testbed Development: Adversarial Insider Manipulation

Insiders are one of the most difficult attackers to deal with in manufacturing systems because of their legitimate access to critical manufacturing components, data, and information. Even a Cyber-Manufacturing System (CMS) that is equipped with the most effective intrusion detection system is vulnerable to insider threats. Insiders who are in the security parameter of a manufacturing system can easily incapacitate it with their authenticated access to the system. In order to devise ways to address the insider threats in CMS, a testbed was developed. To validate and evaluate the usefulness of the testbed for adversarial insider attacks, three physical audits and their results are presented with two case studies.
Top-k Frequent Itemsets Publication of Uncertain Data Based on Differential Privacy

Privacy preserving frequent itemset mining (PPFIM) on uncertain data is booming with the increasing attention to data privacy. Existing methods use a filter function satisfies exponential differential privacy to obtain top-k frequent itemsets, and add Laplace noise to their supports to achieve the privacy protection release of top-k frequent itemsets. The privacy protection mechanism is independent of the mining process, resulting in the accuracy of the top-k frequent itemsets being affected by the k value. When the algorithm is applied to large-scale frequent itemsets, balance the data availability and privacy security is difficult. In view of the above deficiencies, the privacy protection mechanism and the mining process are integrated to design a PPFIM strategy, achieve the separation of noise addition and top-k filtering, and avoid the dependence of the algorithm accuracy on the k value. A candidate level information extraction strategy is designed to reduce the search space and effectively reduce the privacy budget by utilizing the feature of upper limit threshold in uncertain data sets. On this basis, a novel algorithm Uncertain difference privacy level frequent itemset mining (UDP-LFIM) is proposed. Theoretical analysis and experimental demonstrate that the top-k itemsets published by the algorithm can guarantee the accuracy on the premise of satisfying differential privacy.
Space/time-efficient RDF stores based on circular suffix sorting

The resource description framework (RDF) has gained popularity as a format for the standardized publication and exchange of information in the Web of Data. In this paper, we introduce RDFCSA, a compressed representation of RDF datasets that in addition supports efficient querying. RDFCSA regards the triples of the RDF store as short circular strings and applies suffix sorting on those strings, so that triple-pattern queries reduce to prefix searching on the string set. The RDF store is then represented compactly using a compressed suffix array (CSA), a proved technology in text indexing that efficiently supports prefix searches. Our experiments show that RDFCSA is competitive with state-of-the-art alternatives. It compresses the raw data to 60% of its size, close to the most compact alternatives. While most alternatives perform better in some kinds of triple-patterns than in others, RDFCSA features fast and consistent query times, a few microseconds per result in all cases. This enables efficiently supporting join queries by using either merge- or chaining-join strategies over the triple patterns coupled with some specific optimizations such as variable filling. Our experiments on binary joins show that RDFCSA is faster than the alternatives in most cases.
LSTM based Deep RNN Architecture for Election Sentiment Analysis from Bengali Newspaper

This work presents the sentiment analysis of the political news articles in Bengali. This domain is quite complicated, because the political news is subjective and depends on several socio economic factors and largely over the geographical areas, communities, etc. The work is conducted in two steps the pre-processing and the classifications. In the pre-processing phase, the data is prepared. It is an automated process, which collects data from the web and identifies the relevant news article. A special type of Recurrent Neural Network (RNN) based deep learning approach called Long Short Term Memory (LSTM) is used for election sentiment classification and the result is compared with other supervised classifiers like Naive Bayes, SVM, and Decision Tree. Different types of word embedding models are compared for election sentiment analysis. Results demonstrate that LSTM based deep RNN architecture with context encoder provides 85% of accuracy which dominates others. The result shows that it is close to the existing state-of-the-art and it also gives very attractive insights.
Phrase-Based Machine Translation of Digaru-English

The Natural Language Processing (NLP) is mainly associated with the interactivity of computers and natural languages in order to process and analyze natural language data. The machine translation is a subdomain of the NLP which helps us settle language inconceivable issues. The high resourced language such as the European language has been widely studied and used within the discipline of computational linguistics and for Machine Translation whereas several low resourced language such as Digaru spoken by the Tawra Mishmi Tribe of Arunachal Pradesh has yet to be introduced in the Machine Translation field of NLP due to unavailability of data in this data-driven world. In this paper, we have provided freshly collected Digaru-English translation of about 5057 aligned sentence pairs and measured the translation accuracy of the freshly collected data through Statistical Approach of the Machine Translation and provided the unigram score 0.4063 calculated by the BLEU metrics.
Multisensor distributed out-of-sequence-tracks fusion with track origin uncertainty

The increased trend toward multisensor target tracking system is driving an interest for distributed tracks fusion algorithm. In such a system, local tracks formed by each sensor may arrive in the fusion center with temporal out-of-sequence because of different sensor data processing time and random communication delay, moreover, the priori information on the origins of tracks to be fused is usually unknown due to clutter disturbance and presence of multitargets. This paper considers the problem of fusing out-of-sequence tracks (OOSTs) with track origin uncertainty in a distributed fusion setup, and proposes a novel all neighbor fusion-integrated forward prediction fusion and decorrelation (ANF-IFPFD) method. The proposed ANF-IFPFD enumerates and probabilistically evaluates all feasible track-to-track association events, and fuses the central tracks with extracted information purely contributed by the local OOSTs through an information decorrelation process. Furthermore, it enables to operate the false track discrimination (FTD) in the fusion center by using the fused probability of target existence as a track quality measure. Additionally, two implementation configurations of the proposed ANF-IFPFD method are also designed to provide a trade-off between the tracking performance and the system communication bandwidth consumption. Monte Carlo simulations are carried out to validate the superiority of the proposed ANF-IFPFD over the enhanced state-of-art methods, in terms of both tracking performance and also computation-storage requirement. Compared to the configuration without any information feedback, the partial information feedback configuration of the ANF-IFPFD method is verified to deliver intensive improvements for both local tracking and tracks fusing.
Anonymity in Cryptocurrencies

Cryptocurrencies are defined as anonymous digital currencies. It is widely assumed that the real-world identities of the persons who engage in cryptocurrency transactions are secret and will remain secret. Digital signatures serve as identities of the parties involved in cryptocurrency transactions. They are essentially composed of a pair of keys, the secret key and the public key, which are used for signing transactions. These keys are issued as many times as needed thus creating different identities for the same person. Since transactions are recorded with these pseudo names, it is thought that the real identities may not be linked to the transactions. Unlinkability has to be achieved for complete anonymity. However, recent studies show that real-life identities can be linked to addresses of these cryptocurrencies and transactions which use them. Therefore, it is safe to say that most cryptocurrencies are pseudonymous rather than anonymous. In this chapter, the problem with anonymity and its implications in accounting will be discussed. In addition to this, protocols and services, which make it harder or even impossible to link addresses and transactions to their senders and recipients, will be reviewed.
Chunker for Gujarati Language Using Hybrid Approach

For free word order languages, chunking is quite challenging as they have relatively unrestricted phrase structures. A robust chunker helps in other NLP applications. This paper presents a Hybrid chunker for Gujarati Language. Contextual information in the form of last two unicodes of the word and of part-of-speech (POS) is used as the key features in developing the chunker using Machine learning approach. Four different statistical techniques, namely, SVM, CRF, Naïve Bayes, and HMM have been implemented to identify the most appropriate technique for Chunking the text in Gujarati language. Further, to improve performance, linguistic rules have been designed. Finally, achieved accuracy is 98.21% with precision, recall, and F1 score of 96.42%, 95.62 and 96.02, respectively.
Identification of fan dynamics in a spray booth using genetic algorithms

An important element in the design of automation systems is the identification of the dynamics of the control object. There are many analytical methods for determining object dynamics models. This article presents the results of identifying the dynamics of a fan installed in a spray booth using genetic algorithms. A model of changes in the volume of air stream from the fan was created depending on the frequency of the current supplying the fan motor. The fan with ventilation channels is a system with non-linear and non-stationary dynamics. A linear model of such a system was adopted in the form of a second-order inertial element with coefficients depending on the frequency of the fan supply current. Calculations were carried out based on the results of measurements in a real object.
Geometric Pretraining for Monocular Depth Estimation

ImageNet-pretrained networks have been widely used in transfer learning for monocular depth estimation. These pretrained networks are trained with classification losses for which only semantic information is exploited while spatial information is ignored. However, both semantic and spatial information is important for per-pixel depth estimation. In this paper, we design a novel self-supervised geometric pretraining task that is tailored for monocular depth estimation using uncalibrated videos. The designed task decouples the structure information from input videos by a simple yet effective conditional autoencoder-decoder structure. Using almost unlimited videos from the internet, networks are pretrained to capture a variety of structures of the scene and can be easily transferred to depth estimation tasks using calibrated images. Extensive experiments are used to demonstrate that the proposed geometric-pretrained networks perform better than ImageNet-pretrained networks in terms of accuracy, few-shot learning and generalization ability. Using existing learning methods, geometric-transferred networks achieve new state-of-the-art results by a large margin. The pretrained networks will be open source soon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Optimized Random Forest Classifier for Drone Pilot Identification

Random forest is a powerful machine learning scheme which finds applications in real-time systems such as unmanned aerial vehicles. In such applications not only the classification performance is relevant but also several non-functional requirements including the classification time, the memory usage and the power consumption. This paper proposes a new approach to improve the real-time behavior of a random forest classifier. This is accomplished by reducing the number of evaluated nodes and branches as well as by reducing the branch length in the underlying binary decision trees with numerical split values. A hardware architecture is presented for the improved tree-based classification method. A proof-of-concept implementation on an FPGA platform and some preliminary results show the advantage of this approach compared to related work.