Reviewerly/exHarmony huggingface dataset:
HELP: a sparse error locator polynomial for BCH codes

In 1990 Cooper suggested to use Groebner bases’ computations to decode cyclic codes and his idea gave rise to many research papers. In particular, as proved by Sala-Orsini, once defined the polynomial ring whose variables are the syndromes, the locations and the error values and considered the syndrome ideal, only one polynomial of a lexicographical Groebner basis for such ideal is necessary to decode (the general error locator polynomial, a.k.a. GELP). The decoding procedure only consists in evaluating this polynomial in the syndromes and computing its roots: the roots are indeed the error locations. A possible bottleneck in this procedure may be the evaluation part, since a priori the GELP may be dense. In this paper, focusing on binary cyclic codes with length $$n=2^m-1$$, correcting up to two errors, we give a Groebner-free, sparse analog of the GELP, the half error locator polynomial (HELP). In particular, we show that it is not necessary to compute the whole Groebner basis for getting such kind of locator polynomial and we construct the HELP, studying the quotient algebra of the polynomial ring modulo the syndrome ideal by a combinatorial point of view. The HELP turns out to be computable with quadratic complexity and it has linear growth in the length n of the code: $${\mathcal {O}}\left( \frac{n+1}{2}\right)$$.
Using Augmented Reality and Machine Learning in Radiology

Surgeries are one of the main cost factors of health care systems. To reduce the costs related to diagnoses and surgeries, we propose a system for automated segmentation of medical images in order to segment body parts like liver or lesions. The model is based on convolutional neural networks, for which we show promising results on real computed tomography scans. The deep learning algorithm is part of a larger system that aims to support doctors by visualizing the segments in a Microsoft HoloLens, an augmented reality device. Our approach allows doctors to intuitively look at and interact with the holographic data rather than using 2D screens, enabling them to provide better health care. Both the machine learning algorithm and the visualization utilize high-performance GPUs in order to enable doctors to interact efficiently with our system.
CascNet: No-reference saliency quality assessment with cascaded applicability sorting and comparing network

In recent years, saliency detection is playing vital role in many computer vision tasks. Due to the diversity of visual pattern in natural images, a specific saliency approach is hardly suitable for any type of images. The optimal selection of no-reference saliency map without the pixel-wise ground-truth mask as reference is an important problem, which directly influences the performance of the higher-level tasks. This is essentially a saliency quality assessment problem. Unlike other existing solutions, we propose CascNet for both highly efficient and accurate quality assessment. CascNet is consisted of Applicability Sorting Net (ASNet) and Comparing Net (CompNet), where ASNet serves as an efficient filter to only keep suitable saliency algorithms for the given image with no need of generating saliency maps beforehand, while CompNet is used for further fine assessment with a deep Siamese structure. Comprehensive experiments conducted on the commonly used salient object detection datasets THUS10000, DUT-OMRON and the currently most challenging dataset SOC demonstrate the good performance of the proposed method.
Embedded Face Recognition System Based on Multi-task Convolutional Neural Network and LBP Features

Based on neural network and local binary pattern algorithm, this paper builds a lightweight artificial face recognition system on chip Firefly-RK3399, with high speed, strong robustness and high recognition accuracy. Our embedded artificial intelligent face recognition system mainly consists of face detection, feature extraction and recognition. Multi-task convolutional neural network (MTCNN) under the CaffeOnACL framework is utilized for face detection, and the local binary pattern (LBP) is applied as face recognition algorithm. Experiments illustrate that our artificial intelligent embedded face recognition system has high speed and accuracy, which is easy-carrying and of high commercial value as well.
An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

High-utility pattern mining is an effective technique that extracts significant information from varied types of databases. However, the analysis of data with sensitive private information may cause privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, privacy-preserving utility mining (PPUM) has become an important research topic in recent years. The MSICF algorithm is a sanitization algorithm for PPUM. It selects the item based on the conflict count and identifies the victim transaction based on the concept of utility. Although MSICF is effective, the heuristic selection strategy can be improved to obtain a lower ratio of side effects. In our paper, we propose an improved sanitization approach named the Improved Maximum Sensitive Itemsets Conflict First Algorithm (IMSICF) to address this issue. It dynamically calculates conflict counts of sensitive items in the sanitization process. In addition, IMSICF chooses the transaction with the minimum number of nonsensitive itemsets and the maximum utility in a sensitive itemset for modification. Extensive experiments have been conducted on various datasets to evaluate the effectiveness of our proposed algorithm. The results show that IMSICF outperforms other state-of-the-art algorithms in terms of minimizing side effects on nonsensitive information. Moreover, the influence of correlation among itemsets on various sanitization algorithms’ performance is observed.
Construction of mutually unbiased maximally entangled bases in $${\mathbb {C}}^{2^s}\otimes {\mathbb {C}}^{2^s}$$ by using Galois rings

Mutually unbiased bases plays a central role in quantum mechanics and quantum information processing. As an important class of mutually unbiased bases, mutually unbiased maximally entangled bases (MUMEBs) in bipartite systems have attracted much attention in recent years. In the paper, we try to construct MUMEBs in $${\mathbb {C}}^{2^s}\otimes {\mathbb {C}}^{2^s}$$ by using Galois rings, which is different from the work in [17], where finite fields are used. As applications, we obtain several new types of MUMEBs in $${\mathbb {C}}^{2^s}\otimes {\mathbb {C}}^{2^s}$$ and prove that $$M(2^s,2^s)\ge 3(2^s-1)$$, which raises the lower bound of $$M(2^s,2^s)$$ given in [16].
Poisoning Attack on Show and Tell Model and Defense Using Autoencoder in Electric Factory

Recently, deep neural network technology has been developed and used in various fields. The image recognition model can be used for automatic safety checks at the electric factory. However, as the deep neural network develops, the importance of security increases. A poisoning attack is one of security problems. It is an attack that breaks down by entering malicious data into the training data set of the model. This paper generates adversarial data that modulates feature values to different targets by manipulating less RGB values. Then, poisoning attacks in one of the image recognition models, the show and tell model. Then use autoencoder to defend adversarial data.
Generalization-Driven Semantic Clone Detection in CLP

In this work we provide an algorithm capable of searching for semantic clones in CLP program code. Two code fragments are considered semantically cloned (at least to some extent) when they can both be transformed into a single code fragment thus representing the functionality that is shared between the fragments. While the framework of what constitutes such semantic clones has been established before, it is parametrized by a set of admissible program transformations and no algorithm exists that effectively performs the search with a concrete set of allowed transformations. In this work we use the well-known unfolding and slicing transformations to establish such an algorithm, and we show how the generalization of CLP goals can be a driving factor both for controlling the search process (i.e. keeping it finite) as for guiding the search (i.e. choosing what transformation(s) to apply at what moment).
Seeking frequent episodes in baseline data of In-Situ Decommissioning (ISD) Sensor Network Test Bed with temporal data mining tools

Savannah River National Laboratory (SRNL) has established an In-Situ Decommissioning (ISD) Sensor Network Test Bed—a unique, small scale, and configurable environment— for the assessment of prospective sensors on actual ISD system at minimal cost. The temporal data mining (TDM) technique can be employed to process the extensive data collected by the ISD sensors well because these data are time-specific, age-specific, and development stage-specific. This paper analyzed the baseline data collected by ISD Sensor Network Test Bed in recent years with the assistant of TDM algorithms to work out frequency episodes in the event stream. The results have confirmed that TDM techniques are effective tools to validate ISD performance, and the frequent episodes found in the data stream not only showed the daily cycle of the sensor responses, but also established the response sequences of different types of sensors, which was verified by the actual experimental setup. Some abnormal patterns may have the potential for prediction of system failures.
Phishing Attacks in Qatar: A Literature Review of the Problems and Solutions

This paper outlines the literature on phishing attacks in Qatar and techniques for countering them. It begins with the background of phishing, specifically spearphishing, in Qatar and the urgency of the issue. Then, it presents some of the literature and researches on anti-phishing techniques. There are several technical solutions proposed in the literature to detect and defend email phishing attacks, however, these techniques cannot detect and stop genuinely looking phishing emails. Email phishing does not attack devices but it attacks human perception which is hard to defend using technical solutions alone. However, awareness training programs are essential to effectively reduce the success of email phishing attacks. In this systematic review paper, we have tried to classify the existing applications and limitations to figure out the open issues and propose potential solutions.
Learning channel-wise spatio-temporal representations for video salient object detection

Video salient object detection aims at extracting most attention-grabbing objects in videos, which tends to greatly enhance many vision based tasks such as video understanding. In this work we explore this research issue from a novel perspective, i.e., learning the spatio-temporal representations associated with salient regions in separated feature channels. We propose a Channel-wise Spatio-Temporal Representation learning block (CSTR), which is trained to discriminate between salient spatio-temporal patterns and non-salient spatio-temporal patterns in separated channels. A whole CNN architecture based on this block is constructed for video salient object detection. This architecture combines dynamic saliency learned from CSTR and static saliency learned from a constructed Multi-scale Dilated Convolution block (MDC), deriving the final saliency detection results. This intuitive combination improves feature representation capability which contributes to more precise detection results. Compared with previous works that leverage optical flow or RNNs (LSTM, GRU etc.) to utilize temporal cues, the proposed method is simple to implement and offers an intuitive way to understand how spatio-temporal patterns are correlated with salient regions. Extensive experimental evaluations verify the effectiveness of the insight of the proposed method and confirm that our proposed model outperforms other outstanding methods on four popular benchmarks.
Big Data and its Analyzing Tools : A Perspective

Data are generated and stored in databases at a very high speed and hence it need to be handled and analyzed properly. Nowadays industries are extensively using Hadoop and Spark to analyze the datasets. Both the frameworks are used for increasing processing speeds in computing huge complex datasets. Many researchers are comparing both of them. Now, the big questions arising are, Is Spark a substitute for Hadoop? Is hadoop going to be replaced by spark in mere future?. Spark is “built on top of” Hadoop and it extends the model to deploy more types of computations which incorporates Stream Processing and Interactive Queries. No doubt, Spark's execution speed is much faster than Hadoop, but talking in terms of fault tolerance, hadoop is slightly more fault tolerant than spark. In this article comparison of various bigdata analytics tools are done and Hadoop and Spark are discussed in detail. This article further gives an overview of bigdata, spark and hadoop issues. In this survey paper, the approaches to resolve the issues of spark and hadoop are discussed elaborately.
Improved Box Particle CPHD Algorithm for Group Target Tracking

The existing box particle CPHD algorithm for group target tracking has large computation and poor estimation performance in strong clutter environment, an improved box particle CPHD algorithm for group target tracking is proposed to solve this problem. This algorithm utilizes the characteristic of likelihood function in box particle filter to generate an adaptive rectangular tracking threshold, which eliminates a large number of clutter measurements and reduces greatly the computational burden. In addition, the estimation performance is improved by modifying the way of box particles supplementation and k-means clustering algorithm in the state extraction. The simulation experiments show that the proposed algorithm has a higher real-time performance and better estimation performance in strong clutter environment.
Variance analysis of noisy LFM signal in linear canonical Cohen's class

Exact expression for the discrete-time linear canonical Cohen's class's average variance of signals in additive stationary white circular Gaussian noise processes is derived. It is shown that the average variance depends on signal form, kernel type and linear canonical transform (LCT) free parameters. For a kind of special analytic signal form, linear frequency-modulated signals, the effect of kernels and LCT free parameters on the average variance is observed. By focusing on two kinds of minimum variance kernels, the frequency marginal constrained kernel and the time marginal, frequency marginal and time support constrained kernel, a correlation analysis between the average variance and LCT free parameters is further conducted.
An Insight into the Relevance of Word Ordering for Text Data Analysis

Sentence ordering and word ordering is always remaining as a critical task for natural language processing applications. It is expected that introduction of word order information will lead to improvements in document related tasks like keyword extraction, context identification, topic analysis, intent identification, summary generation, document classification, sentiment analysis, clustering etc. In this paper, we are maintaining the structure of the document data by using various deep learning techniques. Most of the techniques can be compared on the basis of vector similarity. The proposed research work helps to improve the accuracy on the basis of the order of word occurrence. We also compare different types of word ordering techniques to maintain the structure of the document. The obtained results indicate that Doc2Vec model outperforms Tfidf model in terms of word order similarity.
Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm

In order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevention measures (drilling cuttings indices, residual gas content in coal seam) during roadway advancing in Yuecheng coal mine were first analyzed. Then, the correlation between drilling cuttings indices and residual gas content was established, as well as the neural network prediction model based on BA-ELM. Finally, the prediction result of the proposed method was compared with that of back-propagation (BP), support vector machine (SVM), and extreme learning machine (ELM) to verify the accuracy. The results show that the average absolute error, the average absolute percentage error, and the determination coefficient of the proposed prediction method of residual gas content in coal seam are 0.069, 0.012, and 0.981, respectively. This method has higher accuracy than other methods and can effectively reveal the nonlinear relationship between drilling cuttings indices and residual gas content. It has prospective application in the prediction of residual gas content in coal seam.
High-resolution measurement based on the combination of multi-vision system and synthetic aperture imaging

A new system that combines multi-vision system and synthetic aperture system is proposed to improve the measurement of small-scale or longer-range object. In the system, the parallax information is recorded by multi-vision system, meanwhile, a higher optical resolution image is obtained by synthesizing several optical apertures of multi-vision system to increase the effective aperture. By fusing the higher resolution image into the parallax images, we can acquire higher resolution images with parallax information from which the measurement can be improved greatly. As an example, a trinocular vision system improved by synthetic aperture has been studied experimentally. Results show that the method can improve the resolution in detection about two times and perform more accurate measurement than traditional multi-vision system.
Layer-Assisted Video Quality Adaptation for Improving QoE in Wireless Networks

HTTP adaptive streaming (HAS) is a state-of-the-art technology for video streaming. Optimal adaptive streaming schemes should be designed to maximize the quality of experience (QoE) in network environments with unstable bandwidths such as wireless networks. QoE is degraded for Advanced Video Coding (AVC)-based HAS due to frequent quality switching and rebuffering in wireless networks. Meanwhile, Scalable Video Coding (SVC)-based HAS can maximize the QoE by flexibly requesting the base layer (BL) and enhancement layer (EL) based on variable network conditions. However, in SVC-based HAS, the required bandwidth is significantly high due to the encoding overhead. In this paper, we propose a layer-assisted video quality adaptation for improving QoE in wireless networks. The proposed scheme employs a video encoding method, composed of multiple BLs and ELs, using both AVC and SVC. The quality of the BL is determined using the buffer occupancy level and measured bandwidth to minimize rebuffering. The quality of the EL is determined using the buffer region and segment quality differences to minimize instability. enhancement dummy (ED) consists of the selected ELs. The layer scheduler decides the segment that is to be requested based on the QoE model according to the BL and ED to maximize the QoE. Experimental results show that the proposed scheme achieves high QoE compared to the existing schemes because the instability is low and no rebuffering occurs.
Gesture Recognition of Basketball Referee Violation Signal by Applying Dynamic Time Warping Algorithm Using a Wearable Device

This research develops a sensor-based wearable device for gesture recognition in basketball violation. It gears to improve and enhance the ways of identifying signals performed by the referee. Moreover, it aims to lessen the workload of the sports officials or committees. The research presents a system wherein it is capable of recognizing basketball referee violation signals using an algorithm called dynamic time warping. The proponents used LabVIEW software for extracting data from the device that is considered as the input data. These data were transferred through Wi -Fi module and were processed in the system where dynamic time warping algorithm is applied. The system was able to recognize or identify which basketball violation is performed through the provided data set of the user. The proponents concluded that the accelerometer sensor was able to recognize the basketball referee violation signals by using dynamic time warping algorithm.
Clusters Analyzer Algorithm for Informative Acquaintances - Quantum Clustering Algorithm

In this internet and digitalization age, information in any form is very important to perform a digital task. The processing of any information to obtain the desired results requires a specific medium and sometimes to accomplish various tasks related to that set of information like browsing, searching, sorting, retrieval and management. In order to perform those tasks on the information, which are present on various acquaintances, we need to analyze the information by performing an unsupervised clustering in the realm of quantum computation. Quantum clustering is the core technique used in quantum computation to perform clustering of information with several algorithms that have been introduced and studied till date to analyze the cluster for increasing the efficiency of information exploration, information retrieval, information management and browsing system. Hence, introducing a quantum clustering technique to form clusters which would include sentences from a set of informative data set and the formation of clusters would be carried out by performing Semantic Analysis.
Machine Learning Applications Using Waikato Environment for Knowledge Analysis

Data is the most important word of the present world. As nowadays' data is growing rapidly in every second, handling of data has become a great challenge. Data mining basically extracts knowledge from large amount of data and is used to obtain rules or patterns from the existing data. As Machine Learning (ML) is introduced, it applies new algorithms on the pattern of data and from past experience. ML is used so that the machine can handle the data more efficiently. Many algorithms are used for this purpose. WEKA-knowledge analysis based on the Waikato environment is introduced as a data mining platform and it has the facility to use machine learning algorithms with reference to data mining.