Reviewerly/exHarmony huggingface dataset:
Rapidly-exploring Random Trees Based on Cylindrical Sampling Space

In this paper, the defect of traditional fast-expanding random tree (RRT) is optimized for the path planning in three-dimensional environment. Due to the randomness generated by its own nodes, the traditional RRT algorithm has the defects of the planned path twist and easy to cross the target point, and greatly differs from the ideal path, so that the time required for the robot to implement path tracking is greatly increased. In this paper, an RRT algorithm based on cylindrical sampling space is proposed. The algorithm uses the line connecting the starting point and the ending point of the path as the central axis. The maximum distance from all nodes on the path to the cylinder axis is taken as the radius value of the first cylinder. The cylinder space is used as the new node sampling space in the next path planning. Then, a new cylinder is generated by reducing the radius of the previous cylinder as the current sample sampling space. Repeat the above steps until find the last optimized path. The path generated by the traditional RRT algorithm and the path generated by the RRT optimization algorithm based on cylindrical sampling space are compared and analyzed under the given obstacle and obstacle-free environment. The simulation results show that the path generated by the RRT optimization algorithm based on the cylindrical sampling space is smoother and shorter, almost no redundant path. The path meets the actual motion requirements of the robot.
An Image Processing Method Reducing Road Marking Line Edge Features for Patrol Robots Identifying Road Boundaries

When automatic patrol robots trying to identify road boundaries through edge detection, road marking lines on the road may cause errors. In this regard, this paper proposes a method to process images base on edge detection results, and builds a system based on the method for experiments. The experimental results show that the proposed method can effectively reduce the edge features of road markings lines while preserving the edge features of real boundaries, so that robots can identify real road boundaries.
An Optimal Coverage Model for the Deployment of IoT Devices in Feature-based Video Transmission Systems

With the development of visual devices and artificial intelligence technologies, many researchers focus on how to deploy a massive of IoT devices efficiently. The visual devices in smart cities produce large amount of real-time visual data, which challenges the ability of current transmission architecture in Cloud computing. We address how to improve the utilization of both computation and transmission resources for visual data processing through introducing the Fog computing technologies. We propose to use deep learning models and salient zones extraction method to decrease the data amount as well as maintaining important features of visual data. Furthermore, we develop how to arrange Fog nodes reasonably according to the location of the end devices and the data produced from the devices. Simulation results on public video dataset demonstrate that the proposed method achieves 48.85% in term of SSIM higher than that of HEVC. The proposed approach decreases the delay by around 75.93% than the traditional transmission without Fog nodes. The proposed method achieves a high compression ratio and guarantees the quality of the video.
Privacy-Preserving Fine-Grained Outsourcing PHR with Efficient Policy Updating

Personal Health Record (PHR) is a novel way of managing individual health. With the help of cloud computing and Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the fine-grained access control, as well as authenticated sharing, can be realized. However, the importance of policy preserving has emerged to be a major security risk in the PHR cloud sharing scenario. Besides, cloud-assist policy updating, as a practical functionality of the PHR system, should also be taken into consideration. In this paper, we present an integrated policy preserving PHR scheme with efficient policy updating. The scheme is proved to be selectively secure under q-BDHE assumption. The evaluation result indicates that our scheme can reach a balanced trade-off in terms of privacy and efficiency.
Extending Liquid Types to Arrays

A liquid type is an ordinary Hindley-Milner type annotated with a logical predicate that states the properties satisfied by the elements of that type. Liquid types are a powerful tool for program verification, as programmers can use them to specify pre- and post conditions of their programs, whereas the predicates of intermediate variables and auxiliary functions are inferred automatically. Type inference is feasible in this context, as the logical predicates within liquid types are constrained to a quantifier-free logic to maintain decidability. In this article, we extend liquid types by allowing them to contain quantified properties on arrays so that they can be used to infer invariants on array-related programs (e.g., implementations of sorting algorithms). Although quantified logic is, in general, undecidable, we restrict properties on arrays to a decidable subset introduced by Bradley et al. We describe in detail the extended type system, the verification condition generator, and the iterative weakening algorithm for inferring invariants. After proving the correctness and completeness of these two algorithms, we apply them to find invariants on a set of algorithms involving array manipulations.
Real-Time Environment Description Application for Visually Challenged People

In real world, visually challenged people face the great challenge of understanding nearby objects and movements going on in their vicinity. They mainly depend upon their auditory or physical abilities of touch to recognize things that are happening around them. Being able to describe the surrounding environment and objects that are present around them using properly formed sentences could only be done if a normal person is present and can describe it to them. We plan on creating an application that can solve this very challenging task by generating description of a real-time video captured from a mobile phone camera, which will aid the visually challenged in their day to day activities. In this paper, we are using concepts of Object detection and Caption generation and present our approach for the same, this which will enable us to run the model on smart phone devices in real time. The description pertaining to the objects, as seen in real time video generated will be converted to audio as the output. We train our proposed model on various datasets so that the generated descriptions are correct and up to the mark. Using the combinations of Convolutional Neural-Network and Recurrent Neural-Network and our own modifications, we tend to create a new model. We also are implementing an Android application for the visually challenged people to show the real-life applicability and usefulness of the Neural Network.
A Two-level Adaptive Target Recognition and Tracking Method Based on Vision for Multi-robot System

The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multi-robot collaborative perception provides a potential solution to deal with the limitation of single-view observation, however, the challenging of environmental adaptability for multi-robot collaborative decision still remains unsolved. To solve this problem, this paper proposes a two-level adaptive target recognition and tracking method based on vision for multi-robot system. The problem of multi-robot target recognition and tracking is solved under a two-level framework, which contains the features fusion level of individual robot and the cooperation level of multi-robot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multi-robot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multi-robot system in target recognition and tracking with an effective performance.
Applying the document vector model to tour recommendation

Today many tourists read online tour reviews before planning their trips. However, it is stressful to thoroughly check online reviews because of a huge amount of information. In this paper, we propose a tour recommendation system based on online reviews. The system recommends tour reviews written by other tourists who have similar interests in tours to the user. The user’s interest in tours is extracted from a review and it is modelled as a document vector based on the Word2Vec model. Interest matching between the user and other travellers is performed based on the vector space model. The proposed system is tested on a data set including 259,084 tour reviews and compared with a conventional recommendation system which is based on the BoW (bag-of-words) model. The experimental results show that the proposed system is effective and promising.
Detection of Fall Event with Extreme Learning Machine and One Dimensional Local Binary Pattern

We can use developing technology to make people's lives easier and to respond to their needs accurately and effectively. Therefore, the focus of most of the work is on people and facilitating people's lives. Many studies focusing on people have examined human movements; human movements were detected and identified. The act of falling between different human movements can cause serious vital problems for people, especially for the elderly. When the fall occurs, first aid must be delivered to the falling individual quickly. We can use technology to accurately and effectively detect falls and provide emergency first aid to falling individuals. In this study, we examined human movements for effective and efficient detection of fall action, and we performed fall detection using one-dimensional LBP (1D-Local Binary Pattern) and ELM (Extreme Leaning Machine) method. Although only acceleration data is used in our application, these methods have achieved very good results and these results are presented at the end of the article.
A Security Architecture for Internet of Things Based on Blockchain

Internet-of-Things (IoT) began to be widely used in smart city, smart agriculture, and smart factories. However, security issues have always threatened the development of the IoT. Blockchain is the current promising technologies, which uses several security mechanisms to protect system. In this paper, we propose a security architecture for IoT based on blockchain. Sensor data in IoT are collected by border-router, and is sent to blockchain network. The communication of IoT system is encrypted by AES algorithm. Smart contracts are used to build the access control mechanisms. Finally, theoretical analysis shows that the proposed architecture can secure against different attacks, as well as low latency.
Intelligent Surveillance Camera using PCA

Face detection, recognition and tracking of the moving face have been progressive areas of research. Intelligent surveillance camera points out a process that figure out a human face from different objects, recognize the person. This paper presents the methods for detecting and recognizing the person. Viola Jones algorithm, a method is used all around the world for face detection as well as object detection has been used for face detection. Principal component analysis, an analytical approach that is utilized to analyze correlations between a variety of variables and also explains these variables in terms of a lesser variables known as principal components that have very low information loss, has been utilized for face recognition. The efficiency of face recognition has been recorded up to 81%.
An OACF-Preserving Operation Based on Parker’s Transformation

Binary sequences with low odd-periodic correlation magnitudes have found important applications in communication systems. It is well known that the three operations, negacyclic shift, decimation, and negation, preserve the odd-periodic autocorrelation function (OACF) in general. In this paper, we define a new operation based on Parker’s transformation, which also preserves the OACF of binary sequences. This enables us to classify Parker’s 16 cases into 8 ones, and may possibly further allow to classify all constructions based on Parker’s transformation.
Distributed Embedding Approach for Max-Plus Algebra-Based Morphological Wavelet Transform Watermarking

This paper presents distributed embedding approach for the max-plus algebra-based morphological wavelet transform watermarking. Distributed embedding approach uses low- and high-frequency-included signal groups after decomposition process for embedding information bits. During experiments, benchmark images processed by the distributed approach using the MMT watermarking are improved with image quality up to 1.18 and 1.14 on PSNR and SSIM, respectively. Furthermore, this approach obtains better results in comparison with related works.
On the concurrent computational content of intermediate logics

We provide a proofs-as-concurrent-programs interpretation for a large class of intermediate logics that can be formalized by cut-free hypersequent calculi. Obtained by adding classical disjunctive tautologies to intuitionistic logic, these logics are used to type concurrent λ-calculi by Curry–Howard correspondence; each of the calculi features a specific communication mechanism, enhanced expressive power when compared to the λ-calculus, and implements forms of code mobility. We thus confirm Avron's 1991 thesis that intermediate logics formalizable by hypersequent calculi can serve as basis for concurrent λ-calculi.
A Computationally Light Pruning Strategy for Single Layer Neural Networks based on Threshold Function

Embedded machine learning relies on inference functions that can fit resource-constrained, low-power computing devices. The literature proves that single layer neural networks using threshold functions can provide a suitable trade off between classification accuracy and computational cost. In this regard, the number of neurons directly impacts both on computational complexity and on resources allocation. Thus, the present research aims at designing an efficient pruning technique that can take into account the peculiarities of the threshold function. The paper shows that feature selection criteria based on filter models can effectively be applied to neuron selection. In particular, valuable outcomes can be obtained by designing ad-hoc objective functions for the selection process. An extensive experimental campaign confirms that the proposed objective function compares favourably with state-of-the-art pruning techniques.
An Extraction-Abstraction Hybrid Approach for Long Document Summarization

In this paper, we propose a hybrid model of extractive and abstractive methods to tackle the long document automatic summarization task. The model first trains an extractor to extract salient sentences from the original text. Next, these salient sentences are put together to get a condensed version of the original text. Then we use the abstractive model to rewrite the extracted sentences to get the final summary. In order to avoid the exposure bias, reinforcement training is used to optimize the proposed model. Experiments in NLPCC2017 Shared Task 3 show that our models achieve competitive performance. Additionally, the ROUGE score of our model exceeds the score of the state-of-the-art model in the original NLPCC2017 Shared Task 3, where a sentence summary is generated from each Chinese news article.
Weighted Mean Deviation Similarity Index for Objective Omnidirectional Video Quality Assessment

Objective quality assessment for omnidirectional videos is essential for the watching experience and optimization processes of Virtual Reality (VR) technologies. Currently, the most used objective video quality assessment (VQA) metrics for omnidirectional videos are based on PSNR. Because PSNR usually shows weak consistency with human perception, we adapt the mean deviation similarity index (MDSI) for omnidirectional VQA, and present a weighted mean deviation similarity index (WS-MDSI) for objective omnidirectional VQA. Because an omnidirectional video usually has a large frame resolution and a large number of frames, we also investigate the influence of temporal sampling on VQA. The proposed index WS-MDSI is evaluated on an omnidirectional VQA database. Experimental results show that, compared with the state-of-the-art methods based on PSNR, WS-MDSI increases the performance by 23.5% and 23.25% on the indicators of SROCC and PLCC, respectively. We also found that a 1/4 reduction in temporal resolution does not significantly affect the performance of the objective VQA for omnidirectional video.
Improving the accuracy of old and young face detection in the template matching method with Fuzzy Associative Memory (FAM)

Abstract Every human has a face pattern and certain characteristics even though identical twins, but the human face pattern still has its own distinctiveness as well as old face patterns and young face patterns even though the human face pattern is very diverse but for young and old face patterns will be a difference between one face and the other face. Face detection (face detection) is one of the initial stages that very important in face recognition that is used in biometric identification. Face detection can also be used to search or index face data from images or videos that contain faces of various sizes, positions, and backgrounds. Face detection (face detection) automatically with the help of a computer is a problem that is not easy because the human face has a high level of variability both intra-personal and extra-personal variability. This study shows that systems with template matching methods combined with FAM can successfully detect differences in human faces, 80% accuracy, 10% better by using ordinary template matching.
Automatic Conversion of Electronic Medical Record Text for OpenEHR Based on Semantic Analysis

The OpenEHR specification standard utilizes the two-layer modeling principle which is in line with the electronic medical record specification to ensure the high scalability of the electronic medical record model and the high applicability of the medical field knowledge, and to solve the problem of poor accessibility and low availability. In order to realize the automatic conversion of any format-text electronic medical records to OpenEHR, this paper proposes a wide & deep network to match the electronic records of free texts with OpenEHR templates, and proposes a labeling model which use conditional random fields and cyclic neural networks for the extraction of clinical test items.
Deep Crowd Counting In Congested Scenes Through Refine Modules

Crowd counting, which aims to predict the number of persons in a highly congested scene, has been widely explored and can be used in many applications like video surveillance, pedestrian flow, etc. The severe mutual occlusion among person, the large perspective distortion and the scale variations always hinder an accurate estimation. Although existing approaches have made much progress, there still has room for improvement. The drawbacks of existing methods are 2-fold: (1)the scale information, which is an important factor for crowd counting, is always insufficiently explored and thus cannot bring well-estimated results; (2)using a unified framework for the whole image may result to a rough estimation in subregions, and thus leads to inaccurate estimation. Motivated by this, we propose a new method to address these problems. We first construct a crowd-specific and scale-aware convolutional neural network, which considers crowd scale variations and integrates multi-scale feature representations in the Cross Scale Module (CSM), to produce the initial predicted density map. Then the proposed Local Refine Modules (LRMs) are performed to gradually re-estimate predictions of subregions. We conduct experiments on three crowd counting datasets (the ShanghaiTech dataset, the UCF_CC_50 dataset and the UCSD dataset). Experiments show that our proposed method achieves superior performance compared with the state-of-the-arts. Besides, we conduct experiments on counting vehicles in the TRANCOS dataset and get better results, which proves the generalization ability of the proposed method.
Content Based Sentiment Analysis for Turkish Twitter Feeds Through Vector Space Model

Sentiment analysis on social media is the process of determining the sentimental values contained in social media expressions about a specific subject. With the help of sentimental analysis performed by people, institutions or organizations on social media, sentiments in a society related to a certain subject can be determined. These positive, negative, or neutral sentimental values can be used when making decisions about the future that are relevant to the subject. In this study, it is aimed to proportionally determine positive, negative or neutral sentimental values of the tweets written in Turkish on a specific topic on Twitter with the help of Vector Space Model. In order to determine the performance ratio of the work performed, a dataset collected from various social media platforms was created. As a result, classification and sentiment analysis were carried out with 84% accuracy using Vector Space Model.
Arithmetic Coding Based on Reflected Binary Codes

Arithmetic coding is a lossless data compression technology on a sequence of symbols in independent and identically distributed (i.i.d.) distributions. In this paper, we pointed out that the conventional arithmetic coding has the possibility of burst outputs between the encoder and the output buffer, and this brings up a number of potential risks. To solve this issue, we present a new arithmetic coding which encodes the output bitstream in reflected binary codes. The analysis shows that the proposed coding method can avoid the burst outputs during the encoding. The comparisons with Huffman codes, conventional arithmetic codes and Asymmetric numeral system are discussed. Simulations show that the proposed approach has better throughput performance than the conventional approach.
Checklist‐based techniques with gamification and traditional approaches for inspection of interaction models

Interaction models specify the structure and content of the user interface, the allowed user actions, and the corresponding system responses. There is a need to inspect interaction models, as it avoids the propagation of defects to other artefacts. We created two inspection techniques for interaction models, called MoLVERIC Cards (MCards) and MoLVERIC Check (MCheck). MCards employs a gamification mechanism to motivate practitioners during the inspection. MCheck is a simple technique to be used by practitioners in a traditional way. Both techniques have questions, whose answers assist in identifying defects. We performed three studies to verify whether these techniques support the inspection of interaction models. In the first and second studies, we evaluated MCards and MCheck in comparison with an ad-hoc technique supported by the conventional inspection approach based on defect types. The results of these studies showed that both techniques support the inspection of interaction models. In the third study, we evaluated MCard in comparison with MCheck to understand the participants'; perceptions of both techniques. The study results showed that MCards was considered more suitable for practitioners interested in using dynamic activities, while MCheck was considered more suitable for practitioners who want to use a more traditional technique.