Reviewerly/exHarmony huggingface dataset:
PD-Bug: A Novel Motion Planning Algorithm for Nonholonomic Car-Like Robots in Unknown Environments

In this work, we propose a novel motion planning algorithm for nonholonomic car-like robots in unknown environments. The proposed algorithm termed Polynomial spiral and Dubins path Bug (PD-Bug) is developed as a variant of TangentBug. Considering the errors of range measurements and the shape of a robot, an environment model termed local target set (LTS) is established to provide an optimal local target during each planning period. For localization errors, a more robust motion mode switching mechanism is integrated into PD-Bug. A lookup table for cubic polynomial spirals is generated offline, providing a smooth and optimized trajectory with curvature constraints. The Dubins path is generated online as the sub-optimal solution and utilized to meet the orientation requirement of the target. We validate the applicability of PD-Bug in outdoor scenarios with real-time requirements. Compared with the traditional Bug algorithms, PD-Bug has a significant improvement in the robustness of the motion mode and the smoothness of the trajectory.
Extraction of Laryngeal Cancer Informative Frames from Narrow Band Endoscopic Videos

Laryngeal cancer is one of the most common types of throat cancer. One of the most powerful technologies is a Narrow-band imaging (NBI) endoscope, which helps in diagnosing the early stage of cancer and reducing the biopsy risks. However, reviewing an endoscopic video is a labor-intensive process, as it contains a large number of uninformative frames due to the illumination or reflection effect and the appearance of saliva. This paper aims at designing and implementing an enhanced automated model to select the informative Laryngoscope video frames to reduce the computational time of scanning all the frames. Also, the selection of the informative frame will help the specialist in the diagnosis process. The proposed model uses a set of quality assessment features including texture and color features. Texture features have been used to detect the sharpness of the image as it is an important measure of image clarity. Moreover, the color features will help in identifying the images with saliva or specular reflections. Then, the extracted features are fed to different classifiers such as Support Vector Machine (SVM) and different ensemble classifiers. The classifiers have been used to classify the video frame into one of four types (informative (I), blurred (B), saliva or specular reflections (S), and underexposed (U)). The experimental results show that the Random Forest (RF) classifier produced a very promising classification result, with an average classification recall equals to 95.8&#x0025;. The proposed model obtained better classification recall by 2.2&#x0025; compared to the existing state-of-the-art method.
On the boundary of services and research collaborations in Japanese state-of-the-art academic research infrastructures

Abstract Recently, research infrastructures (RIs) are recognized to take on a crucial role in the national innovation system in driving open innovation via shared use that include offering measurement service to external users. Generally, these services are performed by staff in line with the social division of labor as scientists and technicians in research institutes. However, services rarely lead to research collaborations because research collaborations generally do not stem from services per se and require researchers to engage more with partners in initiating collaboration. This article focuses on its boundary of services and research collaborations by conducting an ethnographic survey of four years since 2016 to analyze epistemic cultures in eight Japanese state-of-the-art RIs. It first examines what is occurring at the boundaries and offers two frameworks, i.e. the subject-driven approach and the user-driven approach, for finding potential solutions to the issues that impedes service from extending into research collaboration.
Detection and Automatic Correction of Bengali Misspelled Words Using Minimum Edit Distance

In this paper, a work is presented on Detection and Automatic Correction of misspelled words in Bengali language. In Indian languages, very less numbers of works have been developed in this particular domain due to its complex linguistic behaviors. Among those works, major portion is on misspelled word detection only. The proposed work represents the design, implementation and evaluation of Detection and Auto-correction strategy of misspelled words in Bengali language based on Minimum Edit Distance technique, Trie data structure and depth first search strategy. The test data are collected from the Bengali text corpus, developed under the TDIL (Technology Development for Indian Languages) project of the Government of India. The pro-posed model is tested on 200 misspelled words from different parts of speech in Bengali and the overall accuracy in Misspelled Word Detection is 92% and auto-Correction task is 73%. The challenges and the pitfalls of this work are explained at the end of this paper.
Research on dynamic and secure storage of financial data based on cloud platform

Traditional financial data storage methods are prone to data leakage and narrow data coverage. Therefore, this paper proposes a dynamic and secure storage method of financial data based on cloud platform.In order to improve the ability of enterprise data management, the paper constructs a financial cloud computing platform, mining financial data by rough set theory, and analyzing the results of frequent pattern mining of financial data by fuzzy attribute characteristics.According to the granularity theory, the financial data is classified and processed, and the CSA cloud risk model is established to realize the dynamic and secure storage of financial data.The experimental results show that. The maximum data storage delay of this method is no more than 4.1 s, the maximum data leakage risk coefficient is no more than 0.5, the number of data types can reach 30, and the data storage coverage is improved.
Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms

The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for further improving the performance of the AOS methods. AOS aims at delivering high performance in solving a given problem through combining the strengths of multiple operators. Although the AOS methods are expected to outperform running each operator separately, there is no one AOS method can consistently perform the best. Thus, there is still room for improvement which can be provided by using the best AOS method for each problem instance being solved. For this purpose, the AS problem on AOS is investigated. The underlying AOS methods are applied to choose the crossover operator for a Genetic Algorithm (GA). The Quadratic Assignment Problem (QAP) is used as the target problem domain. For carrying out AS, a suite of simple and easy-to-calculate features characterizing the QAP instances is introduced. The corresponding empirical analysis revealed that AS offers improved performance and robustness by utilizing the strenghts of different AOS approaches.
Analysis and Detection of Breast Cancer Using Various Machine Learning Strategies

Identification of Breast Cancer cells as Benign or Malignant is an important task for the early stages of Cancer to reduce fatality caused by it. Using Machine learning, we can predict the type of cancer, which allows us to provide better treatment. To classify the tumors, we use 7 different models such as Logistic Regression, Decision Tree Classification, K-Nearest Neighbors, Stochastic Gradient Descent, Support Vector Machine, Gaussian Naive Bayes and Random Forest Classification. These models are then compared on various evaluation metrics such as Accuracy, Precision, Recall and F1-Score to choose the best model for the prediction. This way the data is analyzed and misdiagnosis in the future can be prevented.
WORKFLOWFOR TRAINING AND SERVING DEEP LEARNING MODELS FOR IMAGE CLASSIFICATION AND OBJECT DETECTION - APPLICATION TO FAULT DETECTION ON ELECTRIC POLES

This work presents the development of a workflow that allows for training, testing and serving deep learning models that can use any image coming from the image repository and can enrich these images with metadata derived from the results provided by the deep learning (DL) vision models. Using this workflow, an electric pole image classification model and a missing top cap object detection model were trained and served on millions on helicopter inspection images. In addition to obtaining very accurate models, we observe that such a modular workflow have reduced both the time from idea to prototype and the time from prototype to product.
HGATs

For decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-to-N, N-to-1, and N-to-N relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topic-sensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users' comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.
F3DsCNN: A Fast Two-Branch 3D Separable CNN for Moving Object Detection

Deep learning methods have been actively applied in moving object detection and achieved great performance. However, many existing models render superior detection accuracy at the cost of high computational complexity and slow inference speed, which hindered the application on mobile and embedded devices with limited computing resources. In this paper, we propose a two-branch 3D separable convolutional neural network named “F3DsCNN” for moving object detection. The network extracts both high-level global features and low-level detailed features. It achieves a fast inference speed of 120 frames per second, suitable for tasks that need to be carried out in a timely manner on a computationally limited platform with high accuracy.
Enabling Sensemaking and Trust in Communities: An Organizational Perspective

The large volume of information being produced in organizations today poses new challenges to the accuracy and effectiveness of any organizations’ decision-making processes. These challenges, namely sensemaking and trust, can critically impact the decision-making processes, even if the organizations are relying on business intelligence (BI) strategies. Given the critical impact an organizations’ BI can have on its sustainability and thus its success, in this work, we attempt to draw insights from the literature on collective intelligence and, based on these, present a novel artifact that aims to empower organizations’ BI by supporting the organizations’ employees in establishing trust and sense when working up with new ideas and solutions. The proposed artifact utilizes a novel reputation model, which calculates reputation based on an individual’s area of expertise and reputation score, in order to assist in establishing trust among system users, and thus helps improve decision-making processes.
Fuzzy Noisy Network for Stable Exploration

Noisy network is a typical method for the exploration of reinforcement learning by adding noises in parameter domain. However, the slow reduction of noises will lead to an unstable policy. In this paper, fuzziness is introduced to the noise network to achieve the stable policy without extra hyper-parameters added. Experiments on gym Atari games show that the proposed fuzzy noisy DQN (FNDQN) algorithm can achieve stable policy, as well as the higher scores for the agent.
Domain specific ontologies from Linked Open Data (LOD)

Logical and probabilistic reasoning tasks that require a deeper knowledge of semantics are increasingly relying on general purpose ontologies such as Wikidata and DBpedia. However, tasks such as entity disambiguation and linking may benefit from domain-specific knowledge graphs, which make it more efficient to consume the knowledge and easier to extend with proprietary content. We discuss our experience bootstrapping one such ontology for IT with a domain-agnostic pipeline, and extending it using domain-specific glossaries.
Automated Reinforcement Learning Based on Parameter Sharing Network Architecture Search

The performance of machine learning depends on the choice of hyperparameters to a great extent. Only by choosing the appropriate hyperparameters can we learn the desired learning results. At present, the end-to-end learning algorithm is widely concerned in the academic circles, and realizes the agile design from the demand end to the execution end at the design task level, which can dramatically reduce the complexity of the design. However, there are still a large number of hyperparameters, which need to be tuned manually, increasing the difficulty of machine learning application. Thus, with the continuous development of high-performance parallel computing, automated machine learning method arises. In this paper, aiming at the automatic design of the hyperparameter, the neural network architecture of deep reinforcement learning in the field of motion control, LSTM recurrent neural network topology generation algorithm, parameter sharing based fast reinforcement learning and evaluation mechanism, and graph generator parameter learning algorithm based on policy gradient are combined. An automated search and optimization framework of neural network architecture in the deep reinforcement learning is proposed, realizing the automated generation of network architecture. Finally, the effectiveness of the proposed approach is verified by taking the lunar lander landing control problem as an example.
Impact of Load Allocation and High Penetration PV Modeling on QSTS-Based Curtailment Studies

The rising penetration levels of photovoltaic (PV) systems within distribution networks has driven considerable interest in the implementation of advanced inverter functions, like autonomous Volt- Var, to provide grid support in response to adverse conditions. Quasi-static time-series (QSTS) analyses are increasingly being utilized to evaluate advanced inverter functions on their potential benefits to the grid and to quantify the magnitude of PV power curtailment they may induce. However, these analyses require additional modeling efforts to appropriately capture the time-varying behavior of circuit elements like loads and PV systems. The contribution of this paper is to study QSTS-based curtailment evaluations with different load allocation and PV modeling practices under a variety of assumptions and data limitations. A total of 24 combinations of PV and load modeling scenarios were tested on a realistic test circuit with 1,379 loads and 701 PV systems. The results revealed that the average annual curtailment varied from the baseline value of 0.47% by an absolute difference of +0.55% to -0.43 % based on the modeling scenario.
A Heuristic Method to Approximate the Closest Vector Problem

The closest vector problem, or CVP for short, is a fundamental lattice problem. The purpose of this challenge is to identify a lattice point in its ambient space that is closest to a given point. This is a provably hard problem to solve, as it is an NP-hard problem. It is considered to be more difficult than the shortest vector problem (SVP), in which the shortest nonzero lattice point is required. There are three types of algorithms that can be used to solve CVP: Enumeration algorithms, Voronoi cell computation and seiving algorithms. Many algorithms for solving the relaxed variant, APPROX-CVP, have been proposed: The Babai nearest algorithm or the embedding technique. In this work we will give a heuristic method to approximate the closest vector problem to a given vector using the embeding technique and the reduced centered law.
DepthNet: A Monocular Depth Estimation Framework

Depth estimation is an important technique in computer vision for applications such as pose modelling, activity recognition, etc. Common methods of depth estimation include stereo vision which aims to trace the corresponding feature displacement to reconstruct a geometry as a depth map. To reduce the reliance on stereo vision systems, training-based deep learning methods have been utilized to generate depth maps using RGB images from a single camera. In this paper we propose a DepthNet framework to predict the relative depth of objects placed in the scene of an RGB image with respect to each other, with the help of multiple resizing upskip connections and up-convolutional layers, which further enhance its depth estimation abilities. The results specialize on the edges and the gradients of the objects existing within the scene by introduced as an Edge Loss. The single layered model with limited number of parameters which enables it to be implemented on any platform which has certain limiting factors of space occupancy and computational power. To validate the performance of the proposed architecture, the experiments are conducted on three publically available datasets: NYU depth dataset V2 [1], KITTI depth dataset [2], SUN-RGBD dataset. The projected work exhibits superior depth estimation results victimizing single RGB images, from the opposite state-of-the-arts. The performance of proposed work is evaluated on the following victimizing parameters: REL, RMS, Squared REL, and RMS_log10.
Generation of Structured Query Language from Natural Language Using Recurrent Neural Networks

With the rise of the digital era, data has become a prominent resource. A humongous amount of data is generated every single day. This data, typically raw, is said to be in an unstructured format and is converted into a structured format to derive meaning and value and often stored in huge databases. The data is then queried upon using querying languages like SQL. This is where the generation of SQL from natural language comes into the picture. Natural language processing is a technology used to help computers understand the natural language of humans. While it is easier for humans to communicate in natural language, it is almost impossible for a computer to master. By providing an efficient method to convert NL to SQL, this paper provides an overview of the various approaches in existence, and further goes on to develop a mechanism for the same using recurrent neural networks. The proposed approach makes use of the Spider dataset and has been differentiated to account for the accuracy for various clauses including JOIN operations in an SQL query.
Sliding Window and Parallel LSTM with Attention and CNN for Sentence Alignment on Low-Resource Languages

A parallel text corpus is an important resource for building a machine translation (MT) system. Existing resources such as translated documents, bilingual dictionaries, and translated subtitles are excellent resources for constructing parallel text corpus. A sentence alignment algorithm automatically aligns source sentences and target sentences because manual sentence alignment is resource-intensive. Over the years, sentence alignment approaches have improved from sentence length heuristics to statistical lexical models to deep neural networks. Solving the alignment problem as a classification problem is interesting as classification is the core of machine learning. This paper proposes a parallel long-short-term memory with attention and convolutional neural network (parallel LSTM+Attention+CNN) for classifying two sentences as parallel or non-parallel sentences. A sliding window approach is also proposed with the classifier to align sentences in the source and target languages. The proposed approach was compared with three classifiers, namely the feedforward neural network, CNN, and bi-directional LSTM. It is also compared with the BleuAlign sentence alignment system. The classification accuracy of these models was evaluated using Malay-English parallel text corpus and UN French-English parallel text corpus. The Malay-English sentence alignment performance was then evaluated using research documents and the very challenging Classical Malay-English document. The proposed classifier obtained more than 80% accuracy in categorizing parallel/non-parallel sentences with a model built using only five thousand training parallel sentences. It has a higher sentence alignment accuracy than other baseline systems.
Cyber crime as a security risk on the internet

The development of the Internet affects human society in two ways: it enables society to progress and endangers security.Online access platforms facilitate business, education and contacts.On the other hand, individuals who access virtual platforms expose their privacy and their assets to the risk of cyber attacks.Unfortunately, one gets the impression that legislation cannot keep pace with technological advances so that many crimes committed on the internet remain unsanctioned. This paper points out the characteristics of cybercrime, gives an overview of international documents aimed at establishing cyber security.A list of domestic legislative regulations by which the Republic of Serbia seeks to solve the problems caused by cybercrime is also given.It also points out the characteristics of this type of crime that directly affect the impossibility of global regulation and protection of Internet users.
Smart Cities as Identities

In 2008 bitcoin was born, based on blockchain technology. Soon after various blockchain frameworks came out, offering unparallel security and supporting rule-based logic. At their core two concepts are prominent, identity and rule-based transactions. These two inherent characteristics are cornerstone in any process. Cities can benefit from programmatically facilitating their own internal processes to achieve efficiencies at scale, securing their citizens' personal data, and eventually creating their own individual identities. It is a long road ahead, where calculated steps must be taken in integrating the promise of blockchain technology in the quest of building the smart cities of tomorrow.