Reviewerly/exHarmony huggingface dataset:
FORMI: A Fast Holonomic Path Planning and Obstacle Representation Method Based on Interval Analysis

This paper presents a path planning approach for mobile robots in 2D spaces. The algorithm uses a quadtree decomposition where the discretization precision is improved until a path to the goal is found if one exists. The algorithm uses interval analysis-based methods to categorize the quadtree decomposition to occupied, free and partly occupied cells. The proposed algorithm is compared against other concurrent path planning algorithms, A* on an ordinary quadtree, A* for shortest path on a binary occupancy grid, and a Dijkstra's algorithm for lowest collision probability in a continuous-valued occupancy grid, in five different scenarios.Compared to the other methods, the main advantage of our method is achieving a compromise between driving distance, safety, and computation time. The proposed algorithm was found to require significantly fewer collision checks in all scenarios while providing sub-optimum results, based on the obstacle distance and path length criteria. The algorithm is suitable for further extension to include non-euclidean measures and for higher dimensions of configuration spaces. The proposed algorithm will be publicly available on GitHub repository.
A Monte Carlo localization Algorithm based on Ranging

To overcome the limitations of the traditional Monte Carlo localization (MCL) algorithm, such as complex sampling processes and excessive energy consumption of nodes, this paper proposed an MCL algorithm based on ranging. The MCL-R introduces Received Signal Strength(RSS)values to modify the weights between the anchor nodes and the unknown nodes, and controls the sampling environment dynamically. The simulation results show that the proposed algorithm has a lower sampling time and a higher sampling rate, which reduces the energy consumption required for positioning computations in sensor networks.
Smart Viewer Counter for Digital Billboards

This paper presents an approach for counting the number of people that are watching their advertisements on signals, near some specific area of interest. This would give the advertising company/advertisers that whether or not to show this particular advertise in that particular area. This will be helpful for organizations to make decision on spending money on advertising or to change the theme or strategy of advertise or to advertise in that area or not.
An information theoretic approach to post randomization methods under differential privacy

Abstract Post randomization methods are among the most popular disclosure limitation techniques for both categorical and continuous data. In the categorical case, given a stochastic matrix M and a specified variable, an individual belonging to category i is changed to category j with probability $$M_{i,j}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>M</mml:mi> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>,</mml:mo> <mml:mi>j</mml:mi> </mml:mrow> </mml:msub> </mml:math> . Every approach to choose the randomization matrix M has to balance between two desiderata: (1) preserving as much statistical information from the raw data as possible; (2) guaranteeing the privacy of individuals in the dataset. This trade-off has generally been shown to be very challenging to solve. In this work, we use recent tools from the computer science literature and propose to choose M as the solution of a constrained maximization problems. Specifically, M is chosen as the solution of a constrained maximization problem, where we maximize the mutual information between raw and transformed data, given the constraint that the transformation satisfies the notion of differential privacy. For the general categorical model, it is shown how this maximization problem reduces to a convex linear programming and can be therefore solved with known optimization algorithms.
A Study on Sign Language Recognition-A Literature Survey

This survey primarily focuses on the recent development of technologies being used for hearing loss and speech loss people to converse easily and frequently with usual people. The work so far gone for the development introduced in various technologies and techniques such as smart gloves, android application, techniques such as Convolution Neural Networks, Gaussian filtering, HMM, speech to text, video to Text then to Speech etc. The system was tested with SSVM classifier to check the accuracy with 10 sample gestures and found that the average recognition rate was 95.4% accuracy. Hence I conclude based on the accuracy obtained in test results my future work will continue on SSVM classifier.
Structured feature for multi-label learning

Multi-label learning tackles the problem in which each instance is composed of a single sample and associated with multiple labels simultaneously. In the past decades, many algorithms have been proposed for this emerging machine learning paradigm. However, these methods often focus on designing new classification strategies. If the original input data with a high dimensionality, it will suffer from the curse of dimensionality and get highly biased estimates. To address the issue, this paper proposes a novel feature construction approach named Structured Feature (SF), which can achieve a discriminant representation for multi-label data by exploiting the local and global geometric structures. These structures are characterized by the reconstruction coefficients of data in manifold and sparse subspaces. Since reconstruction coefficients reflect the data similarity relationships, and the similar data usually has the common class labels, SF incorporates the similarity relationships and supervised information into the feature representation. The SF contains supervised information, thus, even though classifier design ignores the correlations and interdependences between labels, it is able to obtain satisfactory classification performance.SF combines manifold and sparse structures in the feature representation for multi-label learning. Extensive experiments validate that SF can achieve comparable to or even better results than other state-of-the-art algorithms.
Face Recognition Using Open CV with Deep Learning

Face recognition is one of the most biometrics authentication techniques from the past few years. It is mainly divided into two different tasks. One is face identification and the other is face recognition. The verification is in the ratio of 1:N and the identification is in the ratio of 1:1 ratio. The image can be captured by using digital cameras and the captured image can detect all the facial expressions. The image can also be captured by the videos and can identify the person. The captured images are compared with the database images and the trained data sets. Face recognition is recognized as an authentication to the user interaction to the system. Artificial neural networks algorithms and techniques has been proposed by many researches and the developers.
From Goals to Organisations: Automated Organisation Generator for MAS

An explicit organisational structure helps entrants in open multi-agent systems (MAS) to reason about their positions in the organisation for cooperating to achieve mutual goals. In spite of its importance, there are few studies on automatic organisation generators that create explicit organisational structures. This paper introduces GoOrg, a proposal for automatic design of organisations. Our approach considers as inputs a goal decomposition tree (gdt) and user preferences. From the gdt with annotations such as necessary skills to achieve organisational goals, predicted workload and throughput, GoOrg creates roles in the form of an organisational chart. The main challenge is to define strategies to search the space of all organisational structures for those that can achieve the goals respecting constraints and taking into account user preferences. We can, for instance, prefer a flatter or a taller structure, more specialist or more generalist roles, and we can accept matrix connections or not.
Gradual Image Outpainting with Pixel to Pixel Mapping

As a computer vision problem, image inpainting aiming at restoring the missing portions within an image has been widely studied. Motivated by inpainting, we begin to research outpainting, which extrapolates beyond image boundaries. Then an image can be expanded arbitrarily by recursive outpainting. Image outpainting based on deep learning can be regarded as an information generation problem from the input image to the corresponding output image. With deep convolutional network and conditional generative adversarial structure, our trained model generates the plausible outpainting contents of a specific image. We adopt two subnetworks based on residual net and U-net to replace the single encoder-decoder structure in the most existing image inpainting networks. The first half of the generator network is used to generate rough outline images, while the second half is used to enhance outline images to clear outpainted images with pixel to pixel mapping. Experimental results show that the generated contents are smoothly integrated with the pixels around the boundaries and express real semantics together with the input image.
Structurally Comparative Hinge Loss for Dependency-Based Neural Text Representation

Dependency-based graph convolutional networks (DepGCNs) are proven helpful for text representation to handle many natural language tasks. Almost all previous models are trained with cross-entropy (CE) loss, which maximizes the posterior likelihood directly. However, the contribution of dependency structures is not well considered by CE loss. As a result, the performance improvement gained by using the structure information can be narrow due to the failure in learning to rely on this structure information. To face the challenge, we propose the novel structurally comparative hinge (SCH) loss function for DepGCNs. SCH loss aims at enlarging the margin gained by structural representations over non-structural ones. From the perspective of information theory, this is equivalent to improving the conditional mutual information of model decision and structure information given text. Our experimental results on both English and Chinese datasets show that by substituting SCH loss for CE loss on various tasks, for both induced structures and structures from an external parser, performance is improved without additional learnable parameters. Furthermore, the extent to which certain types of examples rely on the dependency structure can be measured directly by the learned margin, which results in better interpretability. In addition, through detailed analysis, we show that this structure margin has a positive correlation with task performance and structure induction of DepGCNs, and SCH loss can help model focus more on the shortest dependency path between entities. We achieve the new state-of-the-art results on TACRED, IMDB, and Zh. Literature datasets, even compared with ensemble and BERT baselines.
Efficient data encryption technique using quaternions for wireless sensor network

Security in a wireless sensor network is an essential requirement to safeguard the system from misuse of the collected data. In a wireless sensor network, the sensed data is to be transmitted to a base station securely and safely. In this process, data packets may route through multiple sensor nodes and reached the base station at the end. The encryption is the basic tool to protect data from vulnerable attacks in a wireless sensor network. Among the two basic encryption classes namely symmetric key encryption and asymmetric key encryption, the prior is most suitable for wireless sensor network for its simplicity and low computation cost. In this article, we propose a quaternion based encryption system applicable to the wireless sensor network with a key distribution scheme. We use the IEEE standard half-precision number system in the arrangement of special quaternions and secret keys. The quaternion encryption mainly rotates one quaternion w.r.t. an axis with a given angle and thereby produce a new quaternion. Experimental results for the encryption and decryption process and its efficiency is discussed in this article.
BCGV: Blockchain Enabled Certificate Generation, Verification and Storage

Citizens all over the world require certificates to prove their identity, qualifications, and achievements. Issuing, verifying and maintaining these certificates remained a problem, as most certificates were available only as hard copy, until the advent of computers. Digitization of important certificates and documents can lead to potential security risks. Firstly, digitization makes it possible to easily forge counterfeit certificates, and further risks the privacy of documents. Moreover, individuals still have to be physically present at issuing offices to get hold of new certificates. This paper proposes a solution to these problems by employing cryptography and blockchain technology to securely issue, store and verify important certificates.
Human Tracking in Cultural Places Using Multi-agent Systems and Face Recognition

Hassan, Adel Sawan, Aktham Rattrout, Amjad Sabha, MuathHeritage places are considered among the most valuable places to any nation for maintaining their history. Computer Vision (CV) and Multi-Agent Systems (MAS) are used for preserving, studying, and analyzing cultural heritage places. This paper introduces a new technique that combines both CV and MAS to track visitors' faces in cultural places. The model consists of four layers of MAS architecture. The proposed system shows its ability to tackle the human face tracking problem and its flexibility to solve the problem with different tracking parameters. This paper also describes the ability of the agent-based system to deploy a computer vision system to execute different algorithms that fit in solving the human face recognition and tracking problem. The proposed system can be used in any similar place with real-time agent-based human face tracking system.
Power Lines Detection from a Stereo Vision System

Technological equipment for industrial applications requires to have different types of functionalities for control and communication, it is accomplished by having several modules working together, this is also a programming challenge because these modules can come from different sources and therefore technologies. Unmanned Aerial Vehicles (UAV) are excellent platforms for validation of new applications. This research presents the development of a stereo vision system for power line detection from UAV images. This technique allows generating a three-dimension point cloud with which is possible to identify shapes and objects in order to avoid collisions, guaranteeing the safety of surrounded people and goods, and vehicle integrity itself. A simulation environment is used for pre-flight planning and testing. Implementation requires a specific set up to detect power lines and an algorithm for online data processing to finally obtain a parametric equation that is used to guide the vehicle on the fly, 3D analysis is a post-operation process.
Unsupervised Person Re-identification via Discriminative Exemplar-level and Patch-level Feature Fusion

Abstract The majority of existing person re-identification(re-ID) approaches adopt supervised learning pattern, which require large amount of labeled data to train models. However, due to the high cost of marking by hand, they are limited to be widely used in reality. On the other hand, due to the difference of the camera angle, there are many variations in pedestrian postures and illumination. It is known that Extracting discriminative features is pretty effective to solve the problem of person re-ID. Therefore, we propose to fuse exemplar-level features and patch-level features to obtain more distinguishing pedestrian image features for unsupervised person re-ID. Firstly, We carefully design exemplar-level and patch-level feature learning framework(EPFL). The skeleton frame adopts bicomponent branch, one branch is used to learn the global features of pedestrian images, the other is used to learn local features. Then, the global features at the example level and local features at the patch level are fused, thus the discriminative pedestrian image features can be obtained. Furthermore, feature memory bank (FMB) is introduced to facilitate the calculation of the similarity between pedestrian images on unlabeled dataset. We carry on our proposed method on two frequently-used datasets, namely, Market-1501 and DukeMTMC-reID dateset. Experimental results clearly demonstrate the advantage of the proposed approach for unsupervised person re-ID.
Sentence Sentiment Classification Using Convolutional Neural Network in Myanmar Texts

There are still few works on application of deep learning for Myanmar language. This paper presents an approach to use a convolutional neural network (CNN) model to classify sentence sentiment in Myanmar texts. A CNN model is constructed on the top of a word embedding model (i.e., Word2Vec), which converts words into vectors. The model classifies the input sentences and labels each sentence with positive, negative, neutral, unrelated and unreadable sentiments. The model is learnt from 1,152 sentences taken from the customers' reviews of products, which is provided by a telecommunication company. Then, the model is tested on 495 unseen sentences with the result of 86.26% accuracy and 82.58% average f-measure in prediction. The model is compared with the traditional machine learning (ML) classifiers, especially support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR). The model outperforms these classifiers since SVM results in 64.44% accuracy, NB obtains 60.20% accuracy and LR gets 55.15% accuracy.
Capacitive-sensing module with dynamic gesture recognition for automotive applications

Capacitive sensing offers new possibilities for HMI product development. Its short range of interaction entails robustness against environmental noise and its flexibility for integration makes it a genuine technology for embedded systems. In the automotive context, capacitive sensing is explicitly devoted to driver interaction with car functionalities. However, the increasing complexity of captured signals and related interaction procedures impose severe difficulties for a classic modelling approach. Neural networks have demonstrated unbeatable performance in tasks with abundant data. Specifically, recurrent neural networks (RNNs) show excellent performance for tasks with inherent temporal structure. In this article, we develop a capacitive-sensing module that includes RNN-based dynamic gesture recognition, which has a suitable implementation size for embedded automotive applications.
The 3-D animation of radiation concept using augmented reality technology

Abstract Radiation concept in Nuclear subject in Physics Learning include some devices, such as Geiger Muller counter, Wilson cloud chamber, scintillation detector. This research aims to visualize the radiation concept from that device using augmented reality technology. The result shows the 3-D animation can: describe the mechanism of the Geiger Muller counter detector to detect radiation; describe the mechanism of Wilsons fog chamber detects radiation; and describe the mechanism of the scintillation detector tool to detect radiation. All the markers developed to get a decent value to be used as a medium for learning physics about radiation concept.
Detection of level crossing barriers using the histogram of oriented gradients method and support vector machine

Abstract Railroad crossing is a place where the railroad lines intersect with other roads, such as a highway. Referring to the Regulation of Minister of Transportation No. 36/2011, level crossing must be equipped with signs, markers and traffic signaling devices and crossing gate guards. However, 4600 of the 5800 level crossing points are without railroad keeper so that they are prone to traffic accidents. In addition, hazard information (danger signs) from the railroad keeper to the PUSDALOP and machinists sometimes cannot be seen at night and in a foggy situation. Therefore, this research aims to detect obstacles (cars) at a railroad crossing using the Histogram of Oriented gradient (HOG) method and the Support Vector Machine (SVM) classifier. HOG functions to extract object features (cars), while SVM is responsible for classifying car objects whether they fit the criteria of car features or not. The results show that an accuracy rate of car objects was 85%, 73% for empty train tracks and 91% for detection of passing trains.
Feature Weighted Linguistics Classifier for Predicting Learning Difficulty Using Eye Tracking

This article presents a new approach to predict learning difficulty in applications such as e-learning using eye movement and pupil response. We have developed 12 eye response features based on psycholinguistics, contextual information processing, anticipatory behavior analysis, recurrence fixation analysis, and pupillary response. A key aspect of the proposed approach is the temporal analysis of the feature response to the same concept. Results show that variations in eye response to the same concept over time are indicative of learning difficulty. A Feature Weighted Linguistics Classifier (FWLC) was developed to predict learning difficulty in real time. The proposed approach predicts learning difficulty with an accuracy of 90%.
Awareness as inference in a higher-order state space

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Motivating Web and Blockchain Application Modeling

Blockchain technology was developed initially for the creation of a crypto-currency. Now it is being exploited for several other applications, such as health, agriculture, IoT and education. Adding blockchain technology to existing systems requires a careful planning of integration between the different environments and technologies. Building blockchain-based applications enables a software which is executed in a decentralized, trustless, transparent and tamper-proof environment, generally called decentralized applications. This article aims to motivate systems modeling activity, specifically between web and blockchain applications. This is an initial study, and as preliminary results we suggest the use of sequence diagrams for modeling the integration between technologies.
EcoPatches

Year-round ultraviolet exposure silently causes skin damage that goes unnoticed until sunburn. Current personal wearables for monitoring UV exposure have not seen significant uptake, which may be attributed to their one-size-fits-all aesthetic or inapplicability to people with different skin tones. We present EcoPatches, inkjet-printable chemical patches that mediate a person's relationship with their environment by allowing them to create designs and formulations that resonate with them. Supporting human- and machine-interpretability for EcoPatches' visual changes means that users can glance at their EcoPatch during the day to see large exposure changes or take a picture of their EcoPatch with a smartphone app for more accurate and precise readings. We conducted an online survey to elicit visual design recommendations that support these features. We also evaluated both interpretation methods, finding that they achieved strong Pearson correlation coefficients with the \projectnames' known exposure levels (human: 0.79, app: 0.90).