Reviewerly/exHarmony huggingface dataset:
Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping

With the recent boost in autonomous driving, increased attention has been paid on radars as an input for occupancy mapping. Besides their many benefits, the inference of occupied space based on radar detections is notoriously difficult because of the data sparsity and the environment dependent noise (e.g. multipath reflections). Recently, deep learning-based inverse sensor models, from here on called deep ISMs, have been shown to improve over their geometric counterparts in retrieving occupancy information [1], [2], [3]. Nevertheless, these methods perform a data-driven interpolation which has to be verified later on in the presence of measurements. In this work, we describe a novel approach to integrate deep ISMs together with geometric ISMs into the evidential occupancy mapping framework. Our method leverages both the capabilities of the data-driven approach to initialize cells not yet observable for the geometric model effectively enhancing the perception field and convergence speed, while at the same time use the precision of the geometric ISM to converge to sharp boundaries. We further define a lower limit on the deep ISM estimate's certainty together with analytical proofs of convergence which we use to distinguish cells that are solely allocated by the deep ISM from cells already verified using the geometric approach.
PillarFlowNet: A Real-time Deep Multitask Network for LiDAR-based 3D Object Detection and Scene Flow Estimation

Mobile robotic platforms require a precise understanding about other agents in their surroundings as well as their respective motion in order to operate safely. Scene flow in combination with object detection can be used to achieve this understanding. Together, they provide valuable cues for behavior prediction of other agents and thus ultimately are a good basis for the ego-vehicle's behavior planning algorithms. Traditionally, scene flow estimation and object detection are handled by separate deep networks requiring immense computational resources. In this work, we propose PillarFlowNet, a novel method for simultaneous LiDAR scene flow estimation and object detection with low latency and high precision based on a single network. In our experiments on the KITTI dataset, PillarFlowNet achieves a 16.3 percentage points higher average precision score as well as a 21.4 % reduction in average endpoint error for scene flow compared to the state-of-the-art in multitask LiDAR object detection and scene flow estimation. Furthermore, our method is significantly faster than previous methods, making it the first to be applicable for real-time systems.
Extending Registration and Authentication Processes of FIDO2 External Authenticator with QR Codes

FIDO2, the newest set of FIDO specifications, enables the user to leverage an external authenticator for the authentication in both mobile and desktop environments (referred to as user agent). For the secure registration and authentication, FIDO2 requires the external authenticator and user agent to establish a confidential and mutually authenticated data transport channel through either USB interfaces, Near Field Communication (NFC) or Bluetooth. However, the external authenticator and host may not be equipped with one of the above physical media simultaneously, for example, a desktop may only have USB interfaces while an external authenticator (e.g., a smartphone) may have no USB inferfaces. This affects the wide adoption of FIDO2. In this paper, we extend the registration and authentication processes of FIDO2 external authenticator with QR code, which enables the external authenticator being equipped with a camera to be used for the authentication at any user agent. During the registration process, our scheme requires the user to provide the original credential and a one-time password displayed on the authenticator, and therefore ensures the correct user will only be bound with the expected authenticator. The security of our scheme has been formally analyzed based on the Dolev-Yao style model, a widely adopted model for the analysis of web systems. We have implemented the prototype, and the performance evaluation demonstrated the efficiency of our scheme, which needs 373 ms for registration and 141 ms for authentication in our environment.
Multipopulation artificial bee colony algorithm based on a modified probability selection model

Abstract Artificial bee colony (ABC) performs excellently over many problems, but it has some shortcomings, such as weak exploitation as well as slow convergence. For the sake of dealing with these issues, a modified ABC known as MPABC is presented. Firstly, the entire population is partitioned into two different subpopulations at the stage of employed bees, and they use different search strategies. Then, a new probability selection strategy is designed on the basis of the principle of Soft Maximum function. Finally, a novel search method is constructed for improving the intensity of exploitation by gradually increasing the ratio of the current optimal solutions. In order to comprehensively validate the capability of MPABC, 12 benchmark problems are employed. Computational results clearly demonstrate MPABC surpasses the basic ABC and some other famous ABCs.
Virtual Reality–Enhanced Soft and Hard Skills Development Environment for Higher Education

Indeed, the 21st-century dynamic and competitive employment and labor market require Higher Education (HE) graduates to be equipped with a substantial degree of knowledge and skills. Comparable to knowledge, skills can be domain-specific or domain-general. Both types of skills are crucial for a well-crafted HE program graduate. To provide students with key skills and the chance to test, experience, and integrate them, institutions should not merely rely on short work placements and industrial training programs. Students should be given the opportunity to sharpen their competencies, acquire, and develop the right skills, technical and non-technical, throughout their study life. Virtual reality, as a revolutionary technology, can support achieving that. In this paper, a web-based environment enhanced with VR framework is proposed to enable students to increase their knowledge, experiences, and gain employability skills progressively for the duration of their study journey. The environment, accessible starting from the first year, provides the students with the opportunity to be engaged with a professional setting and immersed in interactive experiences to gradually, effectively, and affordably experiment concepts, enhance their skills and develop new ones.
A Hybrid Edge-Cloud Computing Method for Short-Term Electric Load Forecasting Based on Smart Metering Terminal

As the edge node in electric Internet of Things, the application of smart metering terminal (SMT) enables the massive electric big data to be widely collected and processed on the edge. This creates a positive condition for short-term electric load forecasting, which is very important for electricity sales company under the back ground of electricity spot market. In this study, the structure of a novel hybrid edge-cloud computing framework for electric load forecasting is proposed, and the forecasting model based on extreme learning machine (ELM) is also developed. In the proposed framework, the distributed SMTs are regarded as the edge nodes and widely collect electric data inner electricity customers. Then, these original data are preprocessed in the regional SMT, and then sent to the cloud server as standard time series data. Finally, the proposed ELM forecasting model runs in the cloud server, and outputs the forecasting load demand for all the customers of electricity sales company. Experimental results show the efficiency of the proposed framework and ELM forecasting model.
An Enhanced Anti-phishing Scheme to Detect Phishing Website

Abstract Phishing is a network attack in which attacker generates a replica of current legitimate site to contrivance the users into submitting their personal and sensitive information. A good anti-phishing solution should have rapid access time and rapid detection rate, but most of the existing solutions fail to satisfy it. A new method is proposed to prevent phishing attack which evaluates the URL Characteristics and cross check with the white-list. The proposed method has higher access time and higher detection rate than other methods. Whenever the user opens a website for the first time which is not available in the white-list, the algorithm proposed here examines the webpage using URL characteristics. The experimental results depict the accuracy of proposed method having accuracy of 93.8% with less negative rate.
An Ecological Approach to Data Governance

This article offers critical investigation of data and how it should be redefined and governed to produce more transparency and mitigate possible harms to individuals and communities because of its use in AI systems. In essence, this article argues that data should be viewed as a networked representation or observation. This definition recognizes that data is not singular, but always comes attached with labels, contexts, and biases fastened from its inception, if not collection, and that attachments increase depending on its place in the ecosystem. This view also requires a different strategy for governance – one that acknowledges data’s nature and networked existence, and moves beyond the individualistic, consent-based current models. Such an approach allows for the creation of better frameworks for collection, use, storage, access, and security of data. At the same time, this writing lays out a research agenda for further exploration of frameworks for harm reduction.
Składnica: a constituency treebank of Polish harmonised with the Walenty valency dictionary

Abstract This paper reports on the developments in three interrelated linguistic resources for Polish. The first is Świgra 2—a rule based constituency parser for Polish. The second is Składnica—a treebank built using Świgra 2. The third resource is valency dictionary Walenty, which became available when the work on the first two was already advanced. However, since the dictionary is much more comprehensive than the ad-hoc dictionary used previously with Świgra, a decision was made to switch the parser and the treebank to the new dictionary. The switch required several modifications to the Świgra 2 parser, including implementation of unlike coordination, introducing semantically motivated phrases, and non-standard case values. A semi-automated procedure to upgrade previously disambiguated trees in Składnica was required as well. Modifications introduced in the treebank during the upgrade included systematic changes of notation and resolving newly introduced ambiguities resulting from the use of the more detailed distinctions made in the dictionary. The procedure for confronting Składnica with the trees generated with the new version of the Świgra 2 parser using the Walenty dictionary allowed us to check all of these resources for consistency. This resulted in several corrections being introduced in both the treebank and the valency dictionary.
New Features of Learners in Education

Throughout the recent decades there have been rapid developments and changes in terms of science and technology. From now on, individuals want a flexible education format in which time and place are changeable in line with their desires and concerns. Particularly from the perspective of lifelong learning, digitalization can be seen as an important element which forces individuals to change. Some of these are digital awareness, digital competence, and digital fluency. As one of the digital terms, digital competence includes the effective, efficient, critical, flexible, and reflective structuring of information for business, learning, and socializing, while the latter digital fluency is the ability to know when and where to use technology. The main problem that stands out is adapting education programs according to the new developments. The restructuring of the program development activities, so as to raise individuals with changing and developing knowledge, skills, and attitudes, acts like a spotlight which highlights the critical role of program development in the education system.
Automatic Identification of Braille Blocks by Neural Network Using Multi-Channel Pressure Sensor Array

In recent years, the number of visually impaired people in Japan has exceeded 300,000 including those with low vision, and accidental falls on the station platform involving them have not been eliminated. Persons having acquired visual impairment make up one third of all cases of blindness in Japan. It is known that they often cannot walk alone with only a white cane or guide dog. The main cause of platform accidents was misidentification of braille blocks. Therefore, it was necessary to develop an auxiliary device for accurately identifying braille blocks that the acquired visually impaired could also use easily. In this research, we developed an automatic identification system for braille blocks using foot pressure data acquired by a multi-channel pressure sensor array. First, we devised a new foot pressure data acquisition device using a multi-channel pressure sensor array. Our proposed device had excellent features such as being light weight, low cost, and easy to extend to multi-channel. Second, in order to accurately identify the braille blocks, the foot pressure data acquired under various conditions was learned by neural network, and identification performance evaluated. As a result of the experiment, the braille blocks could be identified with a high rate of at least 98% accuracy by neural network, with a very simple structure of an input layer (16 neurons), a hidden layer (5 neurons), and an output layer (4 neurons).
Nonparametric Active Learning on Bearing Fault Diagnosis

Bearing plays decisive roles in modern industrial and electrical foundations. For authentic situation, immensely streaming and distributed data are congregated by Prognostics and Health Management (PHM) systems. The massive rigid data conduces the following puzzle: comparable huge excesses for PHM system, which is bounded on the whole huge sets. For this task, we employ active learning framework. In this paper, we firstly propose a novel nonparametric active learning (NAL) method and prove that NAL acquisition function is a tightly upper-bound of naive form. We validate our method on TCN (Temporal Convolutional Network) and achieve the state of the art performance on CWRU benchmark, providing mighty data effectiveness enhancement on industrial field.
WITHDRAWN: A novel approach to CAD for the detection of small cell and non-small cell lung cancers

The Uncontrolled development of cells in the Lung is called Lung Cancer. Early detection will increase the recovery rate of cancer patients, and immediate treatment will raise the patients' life span expectancy. Manually it’s tough to analyze the large number of images generated by medical imaging techniques for the radiologists, and it’s challenging to understand and diagnose the carcinoma from Computer Tomography scans. So use a Computer-Aided Diagnosis (CAD), which helps the doctors diagnose medical images and detect tumors from Computer Tomography scan images. Most of the CAD systems concentrate on Non-Small Cell Lung Cancers. This research aims to assist radiologists by proposing a CAD system which uses an image subtraction technique to diagnose both small cell lung cancers and non-small cell lung cancer from the Computer Tomography images, which can overcome the limitations of earlier CAD systems. In this work, Median Filter and Gaussian Filters are used for filtering the image and image binarization is performed and Particle Swarm Optimization based thresholding and the Artificial Bee Colony based thresholding algorithms are used to segment the lung nodules, image subtraction is used to extract ROI, later nodule features are extracted these features are used to train multilayer feed forward neural network as 80% training and 20% testing, which will classify the lung cancer images. The proposed system is achieving the accuracy of 95%.
Android-based Disaster Management Application for After-Disaster Rapid Mobile Assessment

As the most disaster-prone countries in the world, Indonesia is facing multiple hazards. However, due to some technical problems such as condition of the hazard level, the performances of disaster management have not yet run optimally. In contributing to the disaster management, this paper proposes the development of after-disaster rapid mobile assessment application using android platform. The application which is a client-server-based system is deployed on a web server for the server side and implemented on a mobile device for the client side. By using the proposed application which then it is named as Mobile Indonesia Damage Data Assessor (MIDDAS), the assessors are allowed to inventory disaster-damaged buildings by uploading the damage data into the rapid damage assessment server to be displayable on the web pages. Based on the data, the decision maker can assess rapidly the essential damage information and calculate the required recovery cost.
Intelligent Regulation System of Central Heating Pipe Network based on Internet of Things

Abstract The level of heating quality determines the comfort level of residents’ indoor life in winter. In order to effectively improve the hydraulic imbalance of the pipe network, improve the self-adjusting ability of the pipe network and reduce manual operations. This paper proposes an Intelligent Regulation System for Centralized Heating Pipe Network based on the Internet of Things, and takes the No.3 building of Guangxian Jiayuan Community in Tianjin as a pilot project to select the flow rate and return water temperature in front of the representative users in the low, middle and high floors Measure the date and analyze it. The results show that the flow of the indoor pipe network of the user runs smoothly, and the change range is within 0.05 m 3 /h, and the change range of the return water temperature of the pipe network is maintained within 3 °C. The Intelligent Regulation System of the Pipe Network has a good self-regulation capability.
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples

We address the problem of adversarial examples in machine learning where an adversary tries to misguide a classifier by making functionality-preserving modifications to original samples. We assume a black-box scenario where the adversary has access to only the feature set, and the final hard-decision output of the classifier. We propose a method to generate adversarial examples using the minimum description length (MDL) principle. Our final aim is to improve the robustness of the classifier by considering generated examples in rebuilding the classifier. We evaluate our method for the application of static malware detection in portable executable (PE) files. We consider API calls of PE files as their distinguishing features where the feature vector is a binary vector representing the presence-absence of API calls. In our method, we first create a dataset of benign samples by querying the target classifier. We next construct a code table of frequent patterns for the compression of this dataset using the MDL principle. We finally generate an adversarial example corresponding to a malware sample by selecting and adding a pattern from the benign code table to the malware sample. The selected pattern is the one that minimizes the length of the compressed adversarial example given the benign code table. This modification preserves the functionalities of the original malware sample as all original API calls are kept, and only some new API calls are added. Considering a neural network, we show that the evasion rate is 78.24 percent for adversarial examples compared to 8.16 percent for original malware samples. This shows the effectiveness of our method in generating examples that need to be considered in rebuilding the classifier.
Fuzzy C-Means Clustering with Minkowski and Euclidean Distance for Cerebral Infarction Classification

Abstract Cerebral infarction is a condition in which the death of neuronal cells, glial cells and blood vessel system is caused by a lack of oxygen and nutrients. The cause of nerve damage is hypoxia, which is a decrease in oxygen pressure in the alveoli which can cause hypoxemia in brain tissue. Cerebral infarction can also be caused by obstruction of blood flow to the brain so that the brain does not get enough oxygen. This situation is called ischemia. The initial stage of ischemic neurons is characterized by the formation of micro vacuolization, which is characterized by cell size that is still normal or slightly reduced, vacuoles occur in the perikaryon area, which can be found in neurons in the hippocampus and cortical 5-15 minutes after hypoxia. The final sign of cell damage due to ischemia is the nucleus which becomes pyknotic and fragmented. To diagnose the presence or absence of cerebral infarction in the brain it is not enough just to use a CT scan, therefore machine learning will also be used to diagnose the presence or absence of cerebral infarction in the brain. For this reason, the authors propose Fuzzy C-Means Clustering with Minkowski and Euclidean Distance as a classification method that has good accuracy, good precision, good memory, and a good F1-score in calcifying patients whose brains experience infarction or not. In this proposed method, Fuzzy C-Means Clustering with Minkowski and Euclidean Distance is a modification of the Fuzzy C-Means Clustering Algorithm. This modification is proposed to increase the detection capacity of Fuzzy C-Means Clustering. The parameterized Minkowski distance metric is adjusted for implementation with FCM with various settings. The experimental results show that this method can improve the results of the FCM grouping with an accuracy of around 88%.
Data deduplication applications in cognitive science and computer vision research

Computer vision (CV) makes computers mimic as human and thus utilizes the concept of human vision. The human vision capabilities are brought in computers and huge number of operations can be performed by the computers. Image processing is an integral part of any CV system that involves acquisition of images and their processing for specific applications. Medical image processing is researched in modern times and brain research is ongoing all across the world. The study of cognitive science (CS) involves huge amount of data especially images and videos; and in various stages of CS implementations, lot of redundant data are also processed. This limits the computational speed of the system and also affects the utilization of data storage. This chapter highlights the difficulties in CS- and CV-based research, especially related to dimensionality problem and data duplicates. The applications of data deduplication methods will be discussed in context with CS and CV research.
CB-Path2Vec: A Cross Block Path Based Representation for Software Defect Prediction

Software defects are common in software projects and threaten the security of software. Software defect prediction has been proven to be an effective method to solve software security problems, assisting developers in finding potential errors and effectively allocating test resources, in the early stage of the software life cycle. Traditional defect prediction models are designed based on hand-designed metric features, but these features usually cannot capture the semantic and structural information of code. In order to solve this problem, this paper proposes a novel software defect prediction model called CB-Path2Vec, which can automatically learn features composed of Cross Block path for representing source code and use them for software defect prediction. An evaluation on the public PROMISE dataset shows that on average CB-Path2Vec improvements over the state-of-the-art method both on within-project defect prediction (WPDP) and cross-project defect prediction (CPDP).