Reviewerly/exHarmony huggingface dataset:
Demo: Learning to Perceive Long-Range Obstacles Using Self-Supervision from Short-Range Sensors

We demonstrate a self-supervised approach which learns to detect long-range obstacles from video: it automatically obtains training labels by associating the camera frames acquired at a given pose to short-range sensor readings acquired at a different pose.
Descrizione e spiegazione nella linguistica storica

Descriptions and explanations in historical linguistics. The principles underlying language change involve structural, sociocultural and neurocognitive factors, as languages are complex and coherent systems governed by algorithms, at the intersection of nature and nurture. Therefore, in order to understand these principles it is necessary to adopt an interdisciplinary approach in which linguistics could benefit from philology, sociohistorical sciences, physiology and neurosciences. Moreover, there is a need for disentangling the actuation problem from the transition problem, to explain the patterns of language change and its effect on the systems involved. KEYWORDS: Actuation and transition of language change, markedness, frequency, memory system
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization

Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation. We treat this task as a factor decomposition problem: we aim to separate what appears to be healthy and where disease is (as a map). The two factors are then recombined (by a network) to reconstruct the input disease image. We train our models in an adversarial way using either paired or unpaired settings, where we pair disease images and maps (as segmentation masks) when available. We quantitatively evaluate the quality of pseudo healthy images. We show in a series of experiments, performed in ISLES and BraTS datasets, that our method is better than conditional GAN and CycleGAN, highlighting challenges in using adversarial methods in the image translation task of pseudo healthy image generation.
Writing Script Files

The MATLAB® command line is suitable for checking simple calculations and exploring how individual functions work. However, for most tasks it is more efficient to write your code in script files. In this chapter you will learn how to create, edit and save script files, add descriptive text that automatically generates a documentation file for your script, split your code into manageable sections, use the MATLAB Editor to identify coding errors and use it to help debug code and determine how much each command in your code contributes to the overall run time. The chapter will cover writing scripts in both the standard editor and the Live Editor, a relatively recent feature of MATLAB that enables live scripts to be created which can include formatted text, images, equations and user-controlled input.
Research on the influence of node deployment in cluster for modeling efficiency

With the rapid development of oblique photography (OP) in recent years, the accuracy of reality modeling has increased, which has led to a surge in computational complexity. To solve the problem, a lot of reality modeling software adopts the strategy of cluster parallel computing for modeling. In this paper, the regression analysis method is used to study the influence of the configuration of the compute nodes in the cluster, which aims at improving the computational efficiency of the cluster for the 3D reconstruction task. Furthermore, the M/M/S queuing model in queuing theory is used to model the multi-task assignment of the cluster, and the mathematical model between compute nodes and performance of the cluster is established, which achieves the effective quantitative evaluation of the cluster computing efficiency. Experiments show that the CPU performance of the compute nodes is the most critical hardware factor affecting the efficiency of the cluster.
Recurrent attention networks for medical concept prediction

This paper presents the working notes for the CRADLE group's participation in the ImageCLEF2019 medical competition. Our group focused on the concept detection task which challenged participants to approximate the mapping from radiology images to concept labels. Traditionally, such a task is often mod-elled as an image tagging or image retrieval problem. However, we empirically discovered that many concept labels had weak visual connotations; hence, image features alone are insufficient for this task. To this end, we utilize a recurrent neural network architecture which enables our model to capture the relational dependencies among concepts in a label set to supplement visual grounding when their association to image features is weak or unclear. We also exploit soft attention and visual gating mechanisms to enable our network to dynamically regulate “where” and “when” to extract visual data for concept generation.
Modeling and Finite Element Analysis of Frame Type High Precision Adjustment Structure

This paper mainly studies the finite element modeling problem of frame type high precision adjustment structure. According to the various parameters provided in the simulation analysis software, three different finite element models of the focusing mechanism are established. The first two models are to set the contact mode to bonded contact and no separation contact respectively, and the third model set the contact as a cylindrical pair. Firstly, the static analysis of the three models is carried out to obtain the deformation of the guide rail and the support bar, and then the modal analysis is performed to obtain the first six natural frequencies of the structure. Then the model is simplified, and the maximum deformation calculated by the mechanical method is compared with the deformation value obtained by the finite element. It is verified that the third model is more accurate. Finally, the modal experiment is carried out on the structure to obtain the experimental modal parameters. Finally, the theoretical modal parameters and the experimental modal parameters are compared. It is verified that the model analysis result by processing the contact surface into the cylindrical pair is more accurate and can well reflect the actual situation of the structure.
Implementation of RSA and CRT-RSA with MIST to Resist Power Analysis Attacks

Security has transformed out into a fundamental range of research in space of computer science. With the advancement of side channel attacks, all the private and public key cryptosystems accessible worldwide has been discovered defenceless. Accordingly there has broughtdireness up in planning resisting systems against such unpredictable attacks. Power analysis attacks, which is the most famous side channel attacks has turned out to be a test for the majority of the normal cryptosystems like advanced encryption standard (AES), data encryption standard (DES), Rivets-Shamir-Adleman (RSA), ECC and so forth. MIST is an algorithm which was intended for processing exponentiation. As the most critical operation of RSA is the modular exponentiation for encryption and decryption, MIST can assume an imperative part in planning modified RSA to counteract power analysis attacks. We have extended our work CRT-RSA (Chinese Remainder Theorem–Rivets-Shamir-Adleman) which is more widely used in computation. The analysis results shows that modified RSA can perform better in computing RSAespecially when CRT is has been used. The results have also been verified using VHDL (very high speed integrated circuits) . Keywords: Power analysis attacks, cryptosystems, RSA, RSA-CRT, MIST, VHDL
Evaluating the availability of open citation data

4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019), co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019). Paris, France, July 25, 2019.
Trace-Share: Towards Provable Network Traffic Measurement andAnalysis

Research in network traffic measurement and analysis is along-lasting field with growing interest from both scientistsand the industry. However, even after so many years, resultsreplication, criticism, and review are still rare. The aim ofour research is to overcome the mentioned controversy withfocus on the whole issue covering all areas of dataanonymization, authenticity, recency, publicity, and theirusage for research provability. We believe that thesechallenges can be solved by utilization of semi-labeleddatasets composed of real-world network traffic and annotatedunits with interest-related packet traces only.
Data Sonification: A Prehistory

The idea that sound can reliably convey information predates the modern era. The term data sonification has evolved along with its applications and usefulness in various disciplines. It can be broadly described as the creation and study of the aural representation of information, or the use of sound to convey non-linguistic information. As a field of contemporary enquiry and design practice, it is young, interdisciplinary and evolving; existing in parallel to the field of data visualization, which is concerned with the creation and study of the visual representation of information. Sonification and visualization techniques have many applications in “humanizing” information, particularly when applied to large and complex sets of data. Drawing on ancient practices such as auditing, and the use of information messaging in music, this chapter provides an historical understanding of how sound and its representational deployment in communicating information has changed. In doing so, it aims to encourage critical awareness of some of the socio-cultural as well as technical assumptions often adopted in sonifying data, especially those that have been developed in the context of Western music of the last half-century or so. Whilst acknowledging the Eurocentricity of the enquiry, there is no suggestion that the ideas discussed do not have wider applicability.
Quantum Technology Overview

This paper dealts with many qubits technologies being explored; NISQ era today, moving toward FTQC; early demosntration in NISQ algorithms; some practical problem with advantage 3-5 years away; FTQC era with advantage >20 years away.
Special Section from the International Conference on Global Software Engineering, 2018

Information Quality (IQ) is particularly important for organizations: they depend on information for managing their daily tasks and relying on low-quality information may negatively influence their overall performance. Despite this, the literature shows that most software development approaches do not consider IQ requirements during the system design, which leaves the system open to different kinds of vulnerabilities.The main objective of this research is proposing a framework for modeling and analyzing IQ requirements for Socio-Technical Systems (STS).We propose STS-IQ, a goal-oriented framework for modeling and analyzing IQ requirements in their social and organizational context since the early phases of the system design. The framework extends and refines our previous work, and it consists of: (i) a modeling language that provides concepts and constructs for modeling IQ requirements; (ii) a set of analysis techniques that support the verification of the correctness and consistency of the IQ requirements model; (iii) a mechanism for deriving the final IQ specifications in terms of IQ policies; (iv) a methodology to assist software engineers during the system design; and (v) a CASE tool, namely STS-IQ Tool.We demonstrated the applicability, usefulness, and scalability of the modeling and reasoning techniques within a stock market case study, and we also evaluated the usability and utility of the framework with end-users.We conclude that the STS-IQ framework supports the modeling and analysis of IQ requirements, and also the derivation of precise IQ specifications in terms of IQ policies. Therefore, we believe it has potential in practice.
Sistem Pendukung Keputusan Pemilihan Induk Ikan Lele yang Berkualitas Untuk Meningkatkan Produksi Benih Ikan Lele Menggunakan Metode MOORA (Multi-Objective Optimization On The Basis Of Ratio Analysis)

The demand for national catfish seeds continues to increase. Therefore, catfish hatchery is a very prospective business for the future. The quality of the catfish parent in Indonesia which has greatly decreased makes the cultivation time longer and the quality of the seeds less good. To get good catfish seeds, researchers must look for quality catfish first. Before spawning, the mother catfish must feed a lot and also vitamins first in order to increase their production. In this case, the Decision Support System for Determining quality catfish is made using the MOORA (Multi-Objective Optimization Based on Analysis of Ratio) method, a method that will determine the choice to look for quality ones. Results Conclusions with this system indicate a system that determines predetermined criteria.
Two-Dimensional Discriminative Feature Selection for Image Recognition

In many computer vision tasks, the available original data is in matrix form. Traditional methods often convert a matrix into a vector before processing them. This kind of methods not only ignore the location information of matrix elements, but have to deal with the high-dimensional vectors. Two-dimensional linear discriminant analysis (2DLDA) is a widely used approach in image recognition which works with data matrix directly and computes efficiently. When mapping the original data onto low-dimensional space, however, the two projection matrices of 2DLDA cannot remove features with little or no information, resulting in redundant features in the projected space. To address the problem, in this paper we propose an algorithm named two-dimensional discriminative feature selection (2DDFS) for bidirectional direct feature selection on matrix data directly. Based on 2DLDA, it employs the l2,1 norm to regularize the two transformation matrices when learning them. To obtain the optimal solutions, we design an effective optimization algorithm. Then we conduct experiments on two image databases to evaluate the performance of the proposed method, by comparing with other related methods. The promising results demonstrate the effectiveness of our method.
Autonomous Coverage Path Planning and Navigation Control System for Search Operations using a UAV

This paper introduces an autonomous control system to search for a designated target by the use of UAV. The system calculates a coverage path plan for a designated area around a provided location, then navigates through it until the specified target has been found or the entire area has been covered. We combined NED (North-East-Down) coordinates, the relative FRU (Front-Right-Up) position of the UAV and GPS location to plan and execute movements. Using the motion algorithm, we calculate and generate the minimum required way-points to completely cover the desired area. A image processing technique for object detection and a camera facing downwards continuously searches for the designated target. When the image detection system has found the target, the navigation is halted and the target location is reported. We conducted experiments in real environment to test our object detection model. A 3d simulation environment using Gazebo was also implemented for extensive testing in similar conditions to the real world and in multiple scenarios.
Research on Equipment Fault Diagnosis Classification Model Based on Integrated Incremental Dynamic Weight Combination

This study proposes a classification model of equipment fault diagnosis based on integrated incremental learning mechanism on the basis of characteristics of industrial equipment status data. The model first proposes a dynamic weight combination classification model based on long short-term memory (LSTM) and support vector machine (SVM). It solved the problem of fault feature extraction and classification in high noise equipment state data. Then, in this model, integrated incremental learning mechanism and unbalanced data processing technology were introduced to solve problems of massive unbalanced new data feature extraction and classification and sample category imbalance under equipment status data. Finally, an equipment fault diagnosis classification model based on integrated incremental dynamic weight combination is formed. Experiments prove that the model can effectively overcome the problems of excessive data volume, unbalanced, high noise, and inability to correlate data samples in the process of equipment fault diagnosis.
Why Machines Cannot Learn Mathematics, Yet.

Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods. However, while mathematics is a precise and accurate science, it is usually expressed by less accurate and imprecise descriptions, contributing to the relative dearth of machine learning applications for IR in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in ML, it seems canonical to apply ML techniques to represent and retrieve mathematics semantically. In this work, we apply popular text embedding techniques to the arXiv collection of STEM documents and explore how these are unable to properly understand mathematics from that corpus. In addition, we also investigate the missing aspects that would allow mathematics to be learned by computers.
A design framework for adaptive e-learning environment

This study proposes an adaptive e-learning environment design framework with a focus on learner personas. Following the TPACK model, this study puts forward a framework of Persona-based Technological Pedagogical Content Design (PTPCD). The framework of PTPCD guides designers of adaptive e-learning environment through suggested and recommended Technological Pedagogical Content to the target learners with matching persona intelligently or semiintelligently. Designers are suggested to select the indicators consciously based on the pedagogy, technology, and specific content. Using data mining techniques to label personalized classification, which leads to learner personas. The PTPCD has theoretical and practical implications for designers and researchers of adaptive e-learning environment. Future studies are suggested to demonstrate and complement the framework in practice.
ACIRS

Recommender system is that which collect users preferences and suggest the user some similar item that the user might like. It increases our interest in e-commerce sites by suggesting items. Recommender system suggests the appropriate items to the users based on his previous inclination. In clustering techniques, similar users or products are clustered together that can be used for a product recommendation. But it is difficult for an individual clustering algorithm to find a correlation that can help in recommendation. An item-based recommendation system has been proposed here based on clustering algorithms that can find the similarity between items. Item-Item based clustering algorithm first analyzes the user-item rating matrix to find out the relationship between different items and then use these relationships to compute recommendation for users. Finally, our experimental result provides good performance in terms of mean absolute error, precision, recall, and f-measures.
Breast Cancer Digital Patient Model to Capture and Visualize Real World Data

Digital revolution in health enables clinicians to access huge amount of data that can be exploited for decision making. However, the lack of integration of the various data sources, the existence of data sources not directly exploitable (e.g. free text, image, signals, genomic sequences) and the lack of digital data models (i.e. digital representation of the data) make such exploitation difficult. The development of effective Decision Support Systems (DSS) in concrete clinical contexts involves the development of appropriate and integrated representations of them, together with new paradigms for the exploitation, modeling and visualization of data oriented to decision-making. The European project DESIREE aims to contribute to the development of a system with these characteristics that has application to decision making by the Breast Committee. In particular, the visual analytics tool can contribute to the exploitation of clinical data in Breast Cancer.
Representing music with prefix trees

Tonal music contains repeating or varying patterns that occur at various scales, exist at multiple locations, and embody diverse properties of musical notes. We define a language for representing music that expresses such patterns as musical transformations applied to multiple locations in a score. To concisely represent collections of patterns with shared structure, we organize them into prefix trees. We demonstrate the effectiveness of this approach by using it to recreate a complete piece of tonal music.
Adaptive High Beam Control in Vehicles using Image Processing

The numbers of accidents occurring in night are increasing day by day because of the improper illumination on the bended streets and also because of the blindness caused to the driver by high intensity beam coming from the front coming vehicle.With the end goal to give improved evening time security measures, this work means to plan and assemble a headlight by adjusting a traditional static headlamp by keeping in mindthe expenses and unwavering quality. Also, to switch the headlight to dipper when there’s a vehicle approaching from front reaches within a defined range.
A Hybrid Metaheuristic for the Task Assignment Problem in Parallel and Distributed Systems

This paper proposes a hybrid metaheuristic, namely, the molecular dynamics optimization (MDO), to solve the task assignment problem (TAP). The main ideas of MDO stem from the molecular dynamics simulation (MDS) of the statistical physics, which is used to exploring the macroscopic trend of the thermodynamic system according to the interaction of microcosmic particles. TAP is concerned with assigning a set of tasks to suitable agents, which has been widely used for modeling parallel and distributed systems. The objective of TAP is to minimize the sum of the assignment cost and the communication cost of tasks assigned to different agents. Intuitively, TAP desires to find the best clustering and interaction modes of tasks to improve the overall benefit of the considered system. Thus, the decision making of TAP is quite similar to the mechanism of MDS, which motivates the MDO-based metaheuristic. In MDO, first, a basic search framework consisting of the attraction and collision operators is proposed to perform the global exploration. Second, a local search based on the task uncertainty degree (TUD) is embedded into MDO, so that the local exploitation can be enhanced. Finally, computational results and comparisons on 27 benchmark instances demonstrate the effectiveness of the proposed MDO.
Splicing Detection Technique for Images

The authenticity of digital image find out by using the Image Forgery Detection technique. In the area of digital forensics and multimedia security it is more important. Photo-editing software, powerful computers, and a device the high resolution images are used for the manipulation. So, it is very difficult for finding the transformation in the digital image by the human eyes. The proposed method to identify splicing forgery for images which are blurred, resized and angular transformed. These images need a special kind of feature extraction namely SWT in order to perform the task. MS-LBP is applied to the SWT for finding key-points.