Reviewerly/exHarmony huggingface dataset:
p3Enum: A New Parameterizable and Shared-Memory Parallelized Shortest Vector Problem Solver

Due to the advent of quantum computers, quantum-safe cryptographic alternatives are required. Promising candidates are based on lattices. The hardness of the underlying problems must also be assessed on classical hardware. In this paper, we present the open source framework p3Enum for solving the important lattice problem of finding the shortest non-zero vector in a lattice, based on enumeration with extreme pruning. Our parallelized enumeration routine scales very well on SMP systems with an extremely high parallel efficiency up to 0.91 with 60 threads on a single node. A novel parameter $$\nu $$ within the pruning function increases the probability of success and the workload of the enumeration. This enables p3Enum to achieve runtimes for parallel enumerations which are comparable to single-threaded cases but with higher success rate. We compare the performance of p3Enum to publicly available libraries and results in the literature. For lattice dimensions 66 to 88, p3Enum performs the best which makes it a good building block in lattice reduction frameworks.
Implementation of Fire Image Processing for Land Fire Detection Using Color Filtering Method

Peat swamp forest fires are the process of burning widely dispersed organic matter, which can be caused by burning waste, peat, grass, twigs, weeds, shrubs, leaves and fresh trees. Forest fire monitoring can be done using satellite patterns or unmanned aerial vehicles (UAVs) with pattern modeling applications. The image is interpreted by a computer so that it can be understood by humans. The purpose of this study is to implement the color filtering method by determining the value (HSV) and thresholding value of the image of the fire on peatland. the image will be classified to obtain environmental pattern recognition data. The results obtained are a value of fire images in HSV. HSV value for HEU is in the range 0-18, saturation value: 75-166 and value: 200-230. While the error test results for every 46 HSV pixel values are 4, so the percentage of errors is 8.89%.
Designing and Implementation of a Wireless Gesture Controlled Robot for Disabled and Elderly People

Gesture controlled robot should perform its job with the embedded system, combination of hardware and software that is designed for performing dedicated tasks for specific users which can be controlled by hand gestures. In this paper, we propose a gesture controlled robot for physically challenged and elderly people. The robot uses motion sensors to recognize five gestures of hand. These five hand gestures are employed to control five directions such as stop or steady, forward, backward, left and right. In this paper, the gesture has been recognized by motion sensors and without any image processing to keep the system simple & efficient. The prototype device can be modified into a wheelchair, trolley bed or any physical platform consisting of more than two wheels. An Arduino Nano v3 embedded system with accelerometer gyroscope held by the user and the signals will be transferred wirelessly to the motors of the platform intended to move it as per the users hand gestures in order to carry themselves in their surrounding environment. Machine learning is used to classify hand gesture accurately. The result shows that the classification accuracy is near to 94% (93.8%).
First Identification of Periodic Degassing Rhythms in Three Mineral Springs of the East Eifel Volcanic Field (EEVF, Germany)

We present a geochemical dataset acquired during continual sampling over 7 months (bi-weekly) and 4 weeks (every 8 h) in the Neuwied Basin, a part of the East Eifel Volcanic Field (EEVF, Germany). We used a combination of geochemical, geophysical, and statistical methods to describe and identify potential causal processes underlying the correlations of degassing patterns of CO2, He, Rn, and tectonic processes in three investigated mineral springs (Nette, Kärlich and Kobern). We provide for the first time, temporal analyses of periodic degassing patterns (1 day and 2–6 days) in springs. The temporal fluctuations in cyclic behavior of 4–5 days that we recorded had not been observed previously but may be attributed to a fundamental change in either gas source processes, subsequent gas transport to the surface, or the influence of volcano–tectonic earthquakes. Periods observed at 10 and 15 days may be related to discharge pulses of magma in the same periodic rhythm. We report the potential hint that deep low-frequency (DLF) earthquakes might actively modulate degassing. Temporal analyses of the CO2–He and CO2–Rn couples indicate that all springs are interlinked by previously unknown fault systems. The volcanic activity in the EEVF is dormant but not extinct. To understand and monitor its magmatic and degassing systems in relation to new developments in DLF-earthquakes and magmatic recharging processes and to identify seasonal variation in gas flux, we recommend continual monitoring of geogenic gases in all available springs taken at short temporal intervals.
Multiple foci modulation with controllable positions and intensity ratios through decomposed optimization

In this Letter, multiple foci modulation with controllable positions and intensity ratios is presented with a multi-belt binary phase mask in a tightly focusing system. Different from previous methods, the diffractive optical element (DOE) in our model is first virtually decomposed into two or more simple sub-DOEs. Then, after optimization, the sub-DOEs are combined to form the desired DOE through superposition. By such a decomposed optimization, the optimization complexities are greatly reduced, and the calculation becomes simpler and more effective. Furthermore, since the quantities and structures of sub-DOEs can be adjusted according to the original DOE, the method is very flexible and adaptable. As a demonstration, up to nine foci on the optical axis are studied, and the simulation results show that multiple foci can be well modulated. The proposed method may provide a universal strategy for complex light field modulations in a simple way.
Predicting failures in hard drivers based on isolation forest algorithm using sliding window

With the surge in the development of cloud computing and big data, the scale of the data center is constantly expanding. The data missing caused by the damage of large data center disk has become a problem that cannot be ignored. Predicting the failures of the hard drivers can effectively improve the reliability and reduce the maintenance cost of the data center. The SMART data is volume and multi-dimensional, as well as the high dynamic characteristics of data center hard disk accessing, so we propose an algorithm based on isolation forest using sliding window. The experiments show that this algorithm can effectively save the time of modeling and ensure high prediction accuracy.
Improvement of the Dynamic Priority Scheduling Algorithm Based on a Heapsort

The earliest deadline first (EDF) scheduling algorithm is a typical representative of the dynamic priority scheduling algorithm. However, once the system is overloaded, the deadline miss rate increases and the scheduling performance deteriorates sharply, which causes a reduction in system resource utilization. To overcome this problem, we proposed an improved dynamic priority scheduling algorithm based on heap sorting. The task deadline, task value, energy consumption, and other parameters were introduced. The fuzzy analytic hierarchy process (FAHP) and the value density method were then used to determine the comprehensive priority of tasks. A heapsort algorithm with a lower time complexity was used to sort the comprehensive priority index so as to reduce the sorting overhead of the system. The system sorting overhead was then introduced to improve the decision condition of the priority scheduling subset and expand the schedulable range of the algorithm. The experimental results showed that the improved method reduced the deadline miss rate by an average of 0.1789, which improved the scheduling performance of the algorithm. The optimized scheduling algorithm can be applied to industrial control to improve system efficiency, and reasonable resource scheduling can reduce data center costs.
Analyzing Documents with TF-IDF

This lesson focuses on a foundational natural language processing and information retrieval method called Term Frequency - Inverse Document Frequency (tf-idf). This lesson explores the foundations of tf-idf, and will also introduce you to some of the questions and concepts of computationally oriented text analysis.
Tutorial: Sequence-Aware Recommender Systems

Recommender systems are widely used in online applications to help users find items of interest and help them deal with information overload. In this tutorial, we discuss the class of sequence-aware recommender systems. Differently from the traditional problem formulation based on a user-item rating matrix, the input to such systems is a sequence of logged user interactions. Likewise, sequence-aware recommender systems implement alternative computational tasks, such as predicting the next items a user will be interested in an ongoing session or creating entire sequences of items to present to the user. We propose a problem formulation, sketch a number of computational tasks, review existing algorithmic approaches, and finally discuss evaluation aspects of sequence-aware recommender systems.
Limitations of relative abundance indices calculated from camera-trapping data

Abundance is an important parameter used to estimate the population size of various wildlife species.With the growing application of camera-traps (movement or heat activated) to monitoring wildlife, the relative abundance index (RAI) has become one of the most popular indicators of population abundance for inventories and assessment.Despite a simple and convenient indicator of population size, RAI obtained from camera-trapping data can be greatly affected by many factors such as species traits, detection rates and environmental factors.Therefore, we need verify the correlation between RAI and population density prior to its general application.So far, several types of RAIs have been developed based on camera-trapping data, and it is critical to compare these RAI indices and their applications.In this paper, we summarized the methods calculating RAI with camera-trapping data and reviewed their applications in wildlife monitoring and inventories in China.Four main types of RAIs were identified including (1) the number of days when one animal is photographed, (2) the number of photographs of focal species per 100 trap days, (3) the number of photographs of focal species per trap day, and (4) the proportion of photos from the focal species compared to all photos of all animals.Among them, the second RAI type is the most widely used (72.5%) in wildlife monitoring and inventories in China, and the fourth RAI type is used to compare species components in •综述•
Enhancing Information Accessibility and Digital Literacy for Minorities Using Language Technology—the Example of Sámi and Other National Minority Languages in Sweden

"Enhancing Information Accessibility and Digital Literacy for Minorities Using Language Technology—the Example of Sámi and Other National Minority Languages in Sweden" published on 10 Dec 2019 by Brill.
Automatic Detection of Network Traffic Anomalies and Changes

Accurately predicting network behavior is beneficial for TCP congestion control, and can help improve routing, allocating network resources, and optimizing network designs.This task is challenging because many factors could affect network traffic, such as the number of network sessions and synthetic reordering. There are also many ways to measure the network state, such as the number of retransmissions per flow and packet duplication. For this work, we use a set of passive TCP flow measurements collected at a major computer center on multiple data transfer nodes (DTN). To assist the operations of the computer network, we propose to detect abnormally slow network transfers in real-time. The proposed system breaks the network monitoring logs into fixed-size chunks and employs a state of art classifier to identify the slow time windows. This method will be validated on real large datasets collected from several DTNs. The proposed method is able to generate models to quickly detect large intervals of low performing network transfers, which require attention from network engineers.
Towards a semantics representation framework for narrative images

Purpose The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process. Design/methodology/approach This paper explores the essential features of semantics evolution in the process of narrative images interpretation. It proposes a novel semantics representation framework, ESImage (evolution semantics of image) for narrative images. ESImage adopts a hierarchical architecture to progressively organize the semantic information in images, enabling the evolutionary interpretation under the support of a graph-based semantics data model. Also, the study shows the feasibility of this framework by addressing the issues of typical semantics representation with the scenario of the Dunhuang fresco. Findings The process of image interpretation mainly concerns three issues: bottom-up description, the multi-faceted semantics representation and the top-down semantics complementation. ESImage can provide a comprehensive solution for narrative image semantics representation by addressing the major issues based on the semantics evolution mechanisms of the graph-based semantics data model. Research limitations/implications ESImage needs to be combined with machine learning to meet the requirements of automatic annotation and semantics interpretation of large-scale image resources. Originality/value This paper sorts out the characteristics of the gradual interpretation of narrative images and has discussed the major issues in its semantics representation. Also, it proposes the semantic framework ESImage which deploys a flexible and sound mechanism to represent the semantic information of narrative images.
Efficient component-hypertree construction based on hierarchy of partitions

The component-hypertree is a data structure that generalizes the concept of component-tree to multiple (increasing) neighborhoods. However, construction of a component-hypertree is costly because it needs to process a high number of neighbors. In this article, we present some properties used to obtain optimized neighborhoods for component-hypertree computation. Using these properties, we explore a new strategy to obtain neighboring elements based on hierarchy of partitions, leading to a more efficient algorithm with the drawback of a slight loss of precision on the distance of merged nodes.
Hunting Brand Domain Forgery: A Scalable Classification for Homograph Attack

Visual homograph attack is a way that the attackers deceive victims about what domain they are communicating with by exploiting the fact that many characters look alike. The attack is growing into a serious problem and raising broad attention in reality when recently many brand domains have been attacked such as apple.com (Apple Inc.), adobe.com (Adobe Systems Incorporated), lloydsbank.co.uk (Lloyds Bank), etc. Therefore, how to detect visual homograph becomes a hot topic both in industry and research community. Several existing papers and tools have been proposed to find some homographs of a given domain based on different subsets of certain look-alike characters, or based on an analysis on the registered International Domain Name (IDN) database. However, we still lack a scalable and systematic approach that can detect sufficient homographs registered by attackers with a high accuracy and low false positive rate. In this paper, we construct a classification model to detect homographs and potential homographs registered by attackers using machine learning on feasible and novel features which are the visual similarity on each character and some selected information from Whois. The implementation results show that our approach can bring up to 95.90% of accuracy with merely 3.27% of false positive rate. Furthermore, we also make an empirical analysis on the collected homographs and found some interesting statistics along with concrete misbehaviors and purposes of the attackers.
Deep Learning Under Scrutiny: Performance Against Health Care Professionals in Detecting Diseases from Medical Imaging - Systematic Review and Meta-Analysis

Background: Deep learning offers considerable promise for medical diagnostics. In this review, we evaluated the diagnostic accuracy of deep learning (DL) algorithms versus health care professionals (HCPs) in classifying diseases from medical imaging. Methods: We searched (Pre-)Medline, Embase, Science Citation Index, Conference Proceedings Citation Index, and arXiv from 01 January 2012 until 31 May 2018. Studies comparing the diagnostic performance of DL models and HCPs, for any pre-specified condition based on medical imaging material, were included. We extracted binary diagnostic accuracy data and constructed contingency tables at the reported thresholds to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample validation were included in a meta-analysis. Results: 24 studies, from a starting number of 19889, compared DL models with HCPs. 22 studies provided enough data to construct contingency tables, enabling calculation of test accuracy. The mean sensitivity for DL models was 78% (range 13 - 100%), and mean specificity was 86% (range 51 - 100%). An out-of-sample external validation was performed by 5 studies and were therefore included in the meta-analysis. We found a pooled sensitivity of 86% (95% CI: 84 - 88%) for DL models and 93% (95% CI: 87 - 97%) for HCPs, and a pooled specificity of 88% (95% CI: 84 - 92%) for DL models and 87% (95% CI: 84 - 89%) for HCPs. Conclusion: Our review found the diagnostic performance of deep learning models to be similar to health care professionals. A major finding was the poor reporting and potential biases arising from study design that limited reliable interpretation of the reported diagnostic accuracy. New reporting standards which address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology.Funding Statement: The authors state: "None"Declaration of Interests: All authors have completed the ICMJE uniform disclosure form online (available on request from the corresponding author) and declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.Ethics Approval Statement: The authors state: "Not required." The authors utilized PRISMA and MOOSE protocols.
Abstractive Multi-Document Text Summarization Using a Genetic Algorithm

Multi-Document Text Summarization (MDTS) consists of generating an abstract from a group of two or more number of documents that represent only the most important information of all documents. Generally, the objective is to obtain the main idea of several documents on the same topic. In this paper, we propose a new MDTS method based on a Genetic Algorithm (GA). The fitness function is calculated considering two text features: sentence position and coverage. We propose the binary coding representation, selection, crossover and mutation operators to improve the state-of-the-art results. We test the proposed method on DUC02 data set, specifically, on Abstractive Multi-Document Text Summarization (AMDST) task demonstrating the improvement over the state-of-art methods. Four different tasks for each of the 59 collection of documents (in total 567 documents) are tested. In addition, we test different configurations of the most used methodology to generate AMDST summaries. Moreover, different heuristics such as topline, baseline, baseline-random and lead baseline are calculated. The proposed method for AMDTS demonstrates the improvement over the state-of-art methods and heuristics.
A Fast Multi-layer Approximation to Semi-discrete Optimal Transport

The optimal transport (OT) framework has been largely used in inverse imaging and computer vision problems, as an interesting way to incorporate statistical constraints or priors. In recent years, OT has also been used in machine learning, mostly as a metric to compare probability distributions. This work addresses the semi-discrete OT problem where a continuous source distribution is matched to a discrete target distribution. We introduce a fast stochastic algorithm to approximate such a semi-discrete OT problem using a hierarchical multi-layer transport plan. This method allows for tractable computation in high-dimensional case and for large point-clouds, both during training and synthesis time. Experiments demonstrate its numerical advantage over multi-scale (or multi-level) methods. Applications to fast exemplar-based texture synthesis based on patch matching with two layers, also show stunning improvements over previous single layer approaches. This shallow model achieves comparable results with state-of-the-art deep learning methods, while being very compact, faster to train, and using a single image during training instead of a large dataset.
On the Scalar Complexity of Chudnovsky $$^2$$ Multiplication Algorithm in Finite Fields

We propose a new construction for the multiplication algorithm of D.V. and G.V. Chudnovsky in order to improve scalar algebraic complexity. In particular, we improve the Baum-Shokrollahi construction for multiplication in $$\mathbb F_{256}/\mathbb F_4$$ based on the elliptic Fermat curve $$x^3+y^3=1$$ .
Mining Frequent Itemsets Using Improved Apriori on Spark

Finding the frequent itemset is one of the most investigated extents of data mining. The Apriori algorithm is the most established algorithm for frequent itemset mining, but it has issues regarding scanning frequent databases and generating a large amount of candidate sets. To solve these issues, an Improved Apriori algorithm was proposed. We examined the data structure, implementation, and algorithmic features that mainly focus on frequent itemset mining. We are representing an Improved Apriori algorithm on Spark in which simple and scalable implementation is done to achieve a faster process with lower support thresholds. We examined the improved Apriori algorithm on Extended Bakery Dataset and Retail Dataset. The results show execution time was reduced by 40% and 57% compared with the original Apriori algorithm.
Aspect Extraction from Reviews Using Convolutional Neural Networks and Embeddings

Aspect-based sentiment analysis is an important natural language processing task that allows to extract the sentiment expressed in a review for parts or aspects of a product or service. Extracting all aspects for a domain without manual rules or annotations is a major challenge. In this paper, we propose a method for this task based on a Convolutional Neural Network (CNN) and two embedding layers. We address shortcomings of state-of-the-art methods by combining a CNN with an embedding layer trained on the general domain and one trained the specific domain of the reviews to be analysed. We evaluated our system on two SemEval datasets and compared against state-of-the-art methods that have been evaluated on the same data. The results indicate that our system performs comparably well or better than more complex systems that may take longer to train.