Reviewerly/exHarmony huggingface dataset:
Visual attention system based on Fuzzy Classifier to define priority of traffic signs for intelligent robotic vehicle navigation purposes

In this paper we propose the use of Multiple Decision Attributes and Fuzzy Sets so that it is possible to classify the importance and priority of the detected traffic signs. The Analytic Hierarchy Process (AHP) was applied to calculate attribute weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to classify the traffic signs into their importance levels. The main objective is to contribute with a new system of perception and, through a knowledge base rules set, to be able to semantically relate the scene and to define which traffic sign is more important in a certain moment of navigation of the autonomous vehicle. The system of vision with 2D and 3D images must provide the a priori data of detection and classification of traffic signs for the fuzzy visual attention system, being able to detect the use of auxiliary signs (cones and emergency signs) and relates. Then, relate then to the detection of the navigable area in cases of road blocking (road at work, with a traffic accident, etc.) and give priority to the most important signs for the decision making of the vehicle. The results are promising and very satisfactory, we obtained an accuracy of 98.9% in the 2D classification task and 88% accuracy in the single frame 3D detection task.
Electric power saving optimization simulation for the comfort of classroom lighting

Abstract Classroom lighting is one of the essential factors in learning process for students in a university. There are two types of lighting available to apply inside a classroom, i.e., natural and artificial lightings. This research aims to simulate classroom lighting management technology concerning Indonesia National Standard SNI 6197:2001 using Dialux software. The simulation result shows that the applied artificial lighting inside E11-210 room had not followed the standard yet, in 350 lux. The existence of both artificial and natural lightings has made E11-210 room were excessive in light intensity, despite successfully obeying the standard. This paper uses lamp power modification to optimize the light intensity efficiently without to infringe the standard. This paper also presents the potential benefit of implementing power modification in saving electricity usage costs for 56.94%.
Image Enhancement by using Triple Filter and Histogram Equalization for Organ Segmentation

Convolution filters are used for blurring and sharpening to modifying image when combined with the Histogram equalization for enhancing medical image by adjusting the contrast of the image. Then, the image will be optimized. So, use morphological for segmentation. This research has been designed the filtering in a part of the convolution filter by using the triple filter to adjust the threshold of morphological and normalization value. The results of this experiment are presented that the accuracy of segmentation is increasing and detecting the multiple organs. Nevertheless, we also found that each algorithm can be used for each organ.
Online Product Review Classification

Reviews are the most important part that people look upon while purchasing a product online. The problem in existing system is sometimes the user review and the rating mismatch each other. This happens when the user forgets to update either review or rating when the user updates the review or while providing a new rating for the product, the user might randomly put some wrong rating or undesired rating.The main aim of this project is to tell the user whether a product is good or bad based on the reviews provided by other users and to provide a better rating for the product by analyzing sentiment. To classify the reviews into good or bad, the system uses two machine learning algorithms KNN and Naïve Bayes classification algorithms and to stem the review porter stemmer algorithm is used and to compute new rating system uses rule-based extraction method. K Nearest Neighbor will select the nearest neighbor class to the test review and classify the review into two classes that is either class = good or class = bad, whereas the Naïve Bayes algorithm uses a probabilistic approach to classify the product into good or bad by selecting the highest probability class label.
Estimating Uncertainty Intervals from Collaborating Networks.

Effective decision making requires understanding the uncertainty inherent in a prediction. In regression, this uncertainty can be estimated by a variety of methods; however, many of these methods are laborious to tune, generate overconfident uncertainty intervals, or lack sharpness (give imprecise intervals). We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions. Specifically, one network approximates the cumulative distribution function, and the second network approximates its inverse. We refer to this method as Collaborating Networks (CN). Theoretical analysis demonstrates that a fixed point of the optimization is at the idealized solution, and that the method is asymptotically consistent to the ground truth distribution. Empirically, learning is straightforward and robust. We benchmark CN against several common approaches on two synthetic and six real-world datasets, including forecasting A1c values in diabetic patients from electronic health records, where uncertainty is critical. In the synthetic data, the proposed approach essentially matches ground truth. In the real-world datasets, CN improves results on many performance metrics, including log-likelihood estimates, mean absolute errors, coverage estimates, and prediction interval widths.
Disparities selection controlled by the compensated image quality for a given bitrate

A stereoscopic image consists of two views rendering a depth sense. Indeed each eye is constrained to look at one view, and the small objects displacements across the two views are interpreted as an indication of depth. These displacements are exploited as specific inter-view redundancies from a compression viewpoint. The classical still compression scheme, called disparity-compensated compression scheme, compresses one view independently of the second view, and a block-based disparity map modeling the displacements is losslessly compressed. The difference between the original view and its disparity predicted view is then compressed and used by the decoder to compute the compensated view to improve the disparity predicted view. However, a proof of concept work has already shown that selecting disparities according to the compensated view, instead of the predicted view, yields increased rate-distortion performance. This paper derives from the JPEG-coder, a disparity-dependent analytic expression of the distortion induced by the compensated view. This expression is embedded into an algorithm with a reasonable numerical complexity approaching the performance obtained with the proof of concept work. The proposed algorithm, called fast disparity-compensated block matching algorithm, provides at the same bitrate an average performance increase as compared to the classical stereoscopic image coding schemes.
EEG-Based Recognition Method of Intentional and Unintentional Hand Movement

We present an electroencephalogram (EEG) signals-based method to distinguish intentional and unintentional hand movements by using the optimized linear discriminant analysis (LDA) algorithm by combining the particle swarm optimization (PSO) with the traditional LDA. We design a novel experimental paradigm to validate the proposed method. Experimental results of seven participants show that the proposed method perform well. Furthermore, the performance of the PSO-LDA algorithm which is 86.4% are better than that of the traditional LDA algorithm. This work advances the field of hand movement recognition and gives some insights into the study on human-robot interaction.
BENCHMARKING: ESTIMATING EFFICIENCY AND ENSURING DEVELOPMENT TOOL OF NATIONAL HIGH EDUCATION SYSTEMS

Establishing efficient system of high education at present circumstances is the most important state task, because of this solution depends not only economic and social welfare of society, but the national competitiveness of the country. Nevertheless today is still the open question what is should be understood as effectiveness of universities and the national system of high education in general. Current approach of estimating university efficiency is based on indicators ratings on national and international levels, which have market discourse, estimate individual achievements of certain universities, and is the reason to refuse the concept of social state and social oriented market which were prevalent in XX century. In this article is proposed to make the transition from estimating the efficiency of certain universities and to review the efficiency of the national high education system in general, based on benchmarking — intercountry suppositions, mutual education and support processes of developing, related to assessment the best managing methods of whole university groups of the particular country.
Modelling large parallel corpora. The Zurich Parallel Corpus Collection

Text corpora come in many different shapes and sizes and carry heterogeneous annotations, depending on their purpose and design. The true benefit of corpora is rooted in their annotation and the method by which this data is encoded is an important factor in their interoperability. We have accumulated a large collection of multilingual and parallel corpora and encoded it in a unified format which is compatible with a broad range of NLP tools and corpus linguistic applications. In this paper, we present our corpus collection and describe a data model and the extensions to the popular CoNLL-U format that enable us to encode it.
Multi-objective ACO Algorithm for WSN Layout: InterCriteria Analisys

One of the key objectives during wireless sensor networks deployment is full coverage of the monitoring region with a minimal number of sensors and minimized energy consumption of the network. In this paper we apply multi-objective Ant Colony Optimization (ACO) to solve this hard, from the computational point of view telecommunication problem. The number of ants is one of the key algorithm parameters in the ACO and it is important to find the optimal number of ants needed to achieve good solutions with minimal computational resources. The InterCriteria Analisys is applied in order to study the influence of ants number on the algorithm performance.
Traffic Analysis of WhatsApp Calls

There has been an increase in usage of applications for the ordinance of instant messages and voice and video calls known as Voice over Internet Protocol (VoIP). WhatsApp being one of such applications which uses messaging protocols and Voice over Internet Protocol (VoIP) which has, over the years, became a very popular functionality. The advent of VoIP necessitates the analysis of the forensics of WhatsApp. The possibility of differentiation of the multiple calls in a given traffic using blind traffic detection is carried out in this project.
Dangerous gas traceability route planning for Four- rotor UAV based on the weighted centroid method

In the face of gas leakage, this paper proposes a positioning algorithm for mobile detectors, turning the three-dimensional gas traceability problem into a two-dimensional problem. The detector is fixed on mobile drones, and the immersion sensor is used to get the dangerous gas concentration value. Based on the weighted centroid method, the four-rotor UAV, immersion sensor and Gaussian plume model, the theoretical derivation and simulation of the positioning algorithm are carried out in this paper.
HDFS Efficiency Storage Strategy for Big Data in Smart City

With the rapid development of smart city and artificial intelligence technology, massive city-related information has been increased. The traditional storage strategy for big data can easily lead to hot nodes, which makes it difficult to meet requirement of big data storage efficiency. A Hadoop Distributed File System (HDFS) storage strategy for data based on the response time of data nodes (DNs) is proposed. Four parameters of memory utilization, network distance, bandwidth utilization and CPU utilization of DNs are token as evaluation indicators of the strategy. Then the master node evaluates the response time of each data node (DN) based on BP neural network. Finally, the DNs with shorter response time would be chosen for data storage. The simulation results show that the proposed strategy can realize the distributed storage for big data and avoid the emergence of hot nodes. Furthermore, the strategy can effectively improve the response time of DNs and the load balancing of cluster.
Revisiting linpack algorithm on large-scale CPU-GPU heterogeneous systems

As the widening gap between GPU computing capability and other components (CPU, PCIe bus and communication network), it's increasingly challenging to design high performance parallel algorithms for large CPU-GPU heterogeneous systems. There are mainly two reasons. Firstly, simply offloading the kernel library to GPU incurs large volume data transfer through low-speed PCIe bus. Secondly, communication overheads through network severely affects scalability. To solve the above issues, we advocate a paradigm shift to CPU-centric and fine-grained pipelining algorithm design. By taking Linpack benchmark as a case study, the new algorithm design paradigm shows its effectiveness. Our optimized Linpack program achieves 63.79PFlops on 16384 GPUs. Its floating-point efficiency outperforms the NVIDIA proprietary counterparts by 5% on average.
A Metric to Measure Contribution of Nodes in Neural Networks

We propose a metric that can measure the influence of a node in a layer of a neural network to a node in subsequent layers. The metric, called influential scores, enables us to interpret the effect of elements in the input layer on the output layer. Furthermore, it provides us a way to inspect hidden layers of a neural network. When the former usage is applied to an image classification task, one can see which parts of an image contribute positively and which parts work negatively to the classification result. That is, the metric provides an interpretable explanation about the input image on the classification result. For the latter, we could explain how hidden layers control the output layer values.
A Graph Pattern Based Approach for Automatic Decomposition of IoT Aware Business Processes

The context information that business process can get from the Internet of Things (IoT) can be used as a competitive advantage in terms of optimisation and agility. However, the exchange of messages between central systems and IoT devices come with a price, battery consumption, a scarcely resource of such devices. Despite the literature offers many technical proposals to tackle this problem, we take an approach driven by the process definition perspective. We propose to reduce the number of exchanged messages by decentralising process execution, moving parts of the business processes to IoT devices, and taking advantage of their computational capabilities. The first step for decentralisation is decomposition, i.e., the division of processes into parts and identify those that IoT devices can execute. In this paper, we present an automatic decomposition solution for IoT aware business processes, described using the Business Process Model and Notation (BPMN). We start from a BPMN definition that follows a centralised approach and apply our decomposition method to transfer to the IoT devices the operations that can be performed there. We use a graph based approach and transform a BPMN definition into a directed graph. Thereafter, we identify cuts that define the parts to be transferred to the IoT devices. This decomposition preserves the control and the data dependencies of the original process, reduces the number of exchanged messages as well as the central processing. The code that IoT devices execute is automatically generated from the BPMN process being decentralised.
An Automatic Summarizer for a Low-Resourced Language

In the current scenario with the availability of huge volumes of information has given rise to the quench for auto summarizers. Our paper proposes a simple auto summarizer for text document in Odia language, a language that is computationally impoverished. It is a statistical-based extractive text summarizer and does a shallow approach. The summarizer also considers some linguistic features in the process. The sentences for the summary are extracted on the basis of their significant values. The program-generated summary is evaluated against human-generated summaries on the basis of F score values and has got a performance score of 66.92% giving a clear gist of the input text.
Distributed Feature Extraction on Apache Spark for Human Action Recognition

Local feature extraction is one of the most important tasks to build robust video representation in human action recognition. Recent advances in computing visual features, especially deep-learned features, have achieved excellent performance on a variety of action datasets. However, the extraction process is computing-intensive and extremely time-consuming when conducting it on large-scale video data. Consequently, to extract video features over big data, most of the existing methods that run on single machine become inefficient due to the limit of computation power and memory capacity. In this paper, we propose the elastic solutions for feature extraction based on the Spark framework. Particularly, exploiting the in-memory computing capability of Spark, the process of computing features are parallelized by partitioning video data into videos or frames and place them into resilient distributed datasets (RDDs) for the subsequent processing. Then, we present the parallel algorithms to extract the state-of-the-art deep-learned features on the Spark cluster. Subsequently, using the distributed encoding, the extracted features are aggregated into the global representation which is fed into the learned classifier to recognize actions in videos. Experimental results on a benchmark dataset demonstrate that our proposed methods can significantly speed up the extraction process and achieve the promising scalability performance.
Guiding Quadrotor Landing with Pointing Gestures

We present a system which allows an operator to land a quadrotor on a precise spot in its proximity by only using pointing gestures; the system has very limited requirements in terms of robot capabilities, relies on an unobtrusive bracelet-like device worn by the operator, and depends on proven, field-ready technologies. During the interaction, the robot continuously provides feedback by controlling its position in real time: such feedback has a fundamental role in mitigating sensing inaccuracies and improving user experience. We report a user study where our approach compares well with a standard joystick-based controller in terms of intuitiveness (amount of training required), landing spot accuracy, and efficiency.
Aligning Sentences Between Comparable Texts of Different Styles

Monolingual parallel corpus is crucial for training and evaluating text rewriting or paraphrasing models. Aligning parallel sentences between two large body of texts is a key step toward automatic construction of such parallel corpora. We propose a greedy alignment algorithm that makes use of strong unsupervised similarity measures. The algorithm aligns sentences with state-of-the-art accuracy while being more robust on corpora with special linguistic features. Using this alignment algorithm, we automatically constructed a large English parallel corpus from various translated works of classic literature.
Educational Professionally Oriented Tasks As a Mean of Stimulating Professional Self-Development of Future Teachers

Teaching and research competence, as a complex multicomponent structure, is both the goal and the means of stimulating the professional self-development of students. The inclusion of students in the process of solving professionally oriented educational problems is a necessary condition for the implementation of a competency-based learning model. The article discusses examples of tasks related to the implementation of methodological and mathematical training of students. On the example of the assignment related to the design of the mathematical calendar, the capabilities of this assignment are demonstrated, reflecting its focus on the formation of educational and research competence of students. The consistent inclusion of future school teachers in the work on the described tasks contributes to the formation and control of the formation of their methodological and mathematical competence as a whole.
CopyCat: Controlled Instruction-Level Attacks on Enclaves for Maximal Key Extraction.

The adversarial model presented by trusted execution environments (TEEs) has prompted researchers to investigate unusual attack vectors. One particularly powerful class of controlled-channel attacks abuses page-table modifications to reliably track enclave memory accesses at a page-level granularity. In contrast to noisy microarchitectural timing leakage, this line of deterministic controlled-channel attacks abuses indispensable architectural interfaces and hence cannot be mitigated by tweaking microarchitectural resources. We propose an innovative controlled-channel attack, named CopyCat, that deterministically counts the number of instructions executed within a single enclave code page. We show that combining the instruction counts harvested by CopyCat with traditional, coarse-grained page-level leakage allows the accurate reconstruction of enclave control flow at a maximal instruction-level granularity. CopyCat can identify intra-page and intra-cache line branch decisions that ultimately may only differ in a single instruction, underscoring that even extremely subtle control flow deviations can be deterministically leaked from secure enclaves. We demonstrate the improved resolution and practicality of CopyCat on Intel SGX in an extensive study of single-trace and deterministic attacks against cryptographic implementations, and give novel algorithmic attacks to perform single-trace key extraction that exploit subtle vulnerabilities in the latest versions of widely-used cryptographic libraries. Our findings highlight the importance of stricter verification of cryptographic implementations, especially in the context of TEEs.
A Semantic Approach of Building Dynamic Learner Profile Model Using WordNet

The learners’ interest forms the essential characteristics of the learner profile in various applications, such as information retrieval, classification, and recommender systems. This paper proposes a method to improve learner interest extraction from the frequently used documents of the learner by exploring the concept of WordNet. Initially, the web log files of each learner are obtained from the learning management system, and then the frequently visited documents of each learner are downloaded and processed to identify domain-related words. The learner’s interest is then extracted initially using the standard vector space model and then improved using the semantic-based representation of WordNet. The WordNet identifies a set of semantic concepts related to the document words. To select the appropriate meaning of a word from a set of concepts, “Word Sense Disambiguation (WSD)” semantic similarity algorithm is used. The experiments were performed in NetBeans IDE using Java language and WordNet 2.1. The effect of the proposed method is examined with classification experiments, and the result proved that the use of WordNet concepts in learner interest retrieval shows better classification performance than compared to the existing method of term representation, thereby obtaining a classification accuracy of 89%.