Reviewerly/exHarmony huggingface dataset:
A.H.R.D - Autonomous Human Recognition Device

During disasters like earthquakes, several people lose their lives in the debris, as they cannot be rescued spontaneously. Situations, where the rescue team cannot act immediately due to lack of the information pertaining to the existence of lives trapped in remains, urge an expeditious need of some technology/tool/device triggering an alarm and thus revealing the existence of living beings ambushed in the affected area. Certain devices already exist, but the extremely high cost of the devices refrain many countries to buy it and use during the catastrophe. As per NDMA (national disaster management authority), there is not a single registered technology in the country that can easily be implemented in small towns and villages to deal with such critical situations. So as a step forward, this paper is aimed at constructing a cost effective handy autonomous robot for sensing and recognizing human beings caught in calamitous situations, having ability to steer itself on uneven terrains independently of user control and avoiding obstacles coming its way, by building a predictive model to recognize images by imparting training using open source machine learning framework.
Robust Visible Digital Stamp for Instant Documents Authentication and Verification

Confidential documents, especially the ones issued by educational institutions, large companies, government departments, military organizations, and intelligence agencies are facing great challenges of being used, copied, and redistributed without authorization such as being used for forgery cases. This puts great threats to businesses and any legislature, executive, and judiciary forms in any community. In this paper, a novel model called robust visible digital stamp, RVDS, is introduced to secure and protect important / sensitive documents with the aid of low-power computers that do not require any type of network connections. The proposed model in this paper converts the document's information into a coded form of a customized quick response code, QR code. This code is then bound to the document for the verification process. This model uses a combination of keys for encryption and the authentication check process. The model does not need network connection, which makes it an off-line process that guarding users from the risk of losing their privacy.
CoDR: Correlation-Based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data

A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot.
Using Statistical Data Processing in the Identification of Individuals by Principal Component Analysis Method

The system of identification of persons at the entrance of a checkpoint is considered. A frontal faces database of participants of the experiment has been previously created: "owns" who are allowed to enter a checkpoint, and "aliens" with a ban on entry. Viola-Jones algorithm was used to detect the area of each person's face for comparison and decision-making was performed by using the principal component analysis method. Two control options are considered: an instant photo and video stream at the entrance. In this article, the Matlab application was used to implement all experiments by utilizing statistical data processing based on the principal component analysis method. As a result, the threshold value for the identification of persons was found and the comparison results of experiments with different number of test images are shown in this article.
Object Tracking System on Ultrawide Video from Double Camera using Motion Detector

Abstract This research aims to design, test, and analyze the object tracking application system using a motion detector method that is able to take the output image of several cameras by combining the output images of the two cameras into a wider field of view (FOV). This research uses 2 cameras, i3-6006U laptop camera and Logitech C270 HD webcam using OpenCV and python programming language. The stitching results from the initialization of the two cameras are able to detect the motion of objects performed on panoramatic videos in realtime by showing time in detail starting from days, dates, hours, minutes and seconds. This object tracking application system has been tested in Adi Sucipto airport exactly in the area of arrival and departure of passengers. The system can detected the motion in the stitching images by exploring it in red rectanguler in the real time video.
Transparency by Design in eScience Research

Both the landscape of eScience research and the environment in which the research is conducted are undergoing change. Transparency by design in eScience is proposed as a term to describe transparency in eScience practices, processes, methodologies, and research results. We break down different aspects of transparency and urge the eScience community towards a renewed commitment to scientific rigor because of the important role that we as scientists have to improve society and protect the good will that society has bestowed on science.
Feedback-Based Keyphrase Extraction from Unstructured Text Documents

Machine Learning experts use classification and tagging algorithms considering the black box nature of these algorithms. These algorithms, primarily key-tags extraction from unstructured text documents are meant to capture key concepts in a document. With increasing amount of data, size and complexity of the data, this problem is key in industrial setup. Different possible use cases being in IT support, conversational systems/ chatbots and financial domains, this problem is important as shown in [1], [2]. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a key-tags extraction framework in the form of natural language. We focus on the problem of key-tags extraction in which the quality of the output can easily be judged by non-experts. Our system automatically reads natural language documents, extracts key concepts and presents an interactive information exploration user interface for analysing these documents.
A comparative Analysis of Feature Extraction Algorithms and Deep Learning Techniques for Detection from Natural Images

Text detection from natural scene images have major role in image content analysis. It is considered as complex issue because of the variability in text size, fonts, complex background, line orientation in image with non-uniform illumination. For overcoming the issue, efficient text image recognition features are being utilized. This research has proposed the comparative analysis of various text detection technique based on pixel dependable feature extraction and deep learning dependable text region classification. The feature extraction technique includes extraction of feature sets from the text region which divides input natural image into several regions. The text regions are identified using morphological operations like binarization, thinning, segmentation etc. based on the color, consistency and numerical features. In this paper, the effectiveness of feature extraction algorithms and deep learning techniques are compared on the basis of various performance parameters to detect the text from natural images.
Advance Recommendation System for the Formation of More Prolific and Dynamic Software Project Teams

For the successful software project management and its success, many studies have been done but still many of the software projects cannot end up well. Formation of software teams has always given less priority in the entire project development process, but we have identified it as a strong determinant in project success. We have developed a methodology that will allow the formation of software project teams through a recommendation system. In this system, the team members which are best compatible with each other as a team will be recommended. This system gives weightage to both technical and non-technical skills of individual team member. We have used social network analysis techniques to dig out information and connections that are helpful in formation of best compatible teams. The results show a remarkable difference in the performance of the team that is formed using our recommendation system as compared to the traditional team selection criteria i.e. based on technical skills. Hence, selection of right project team in right way increases project productivity. Moreover, non-technical and social factors can boost the team performance remarkably.
Clustering Web Users Based on K-means Algorithm for Reducing Time Access Cost

Numerous organizations are providing web-based services due to the consistent increase in web development and number of available web searching tools. However, the advancements in web-based services are associated with increasing difficulties in information retrieval. Efforts are now toward reducing the Internet traffic load and the cost of user access to important information. Web clustering as an important web usage mining (WUM) task groups web users based on their browsing patterns to ensure the provision of a useful knowledge of personalized web services. Based on the web structure, each Uniform Resource Locator (URL) in the web log data is parsed into tokens which are uniquely identified for URLs classification. The collective sequence of URLs a user navigated over a period of 30 minutes is considered as a session and the session is a representation of the users' navigation pattern. In this paper, K-Means algorithm was used to cluster web users based on their similarity in a vector matrix and K-means algorithm implemented several times when k=2,3,4 till k=8 and the results showed the best similarity was when k=8 and the Residual Sum of Squares (RSS) evaluation measure achieved a high intra-cluster similarity value (3.049) when k=8.
Portable Hand Gesture Recognition System for Generalized Sign Language

Gesture recognition is a technical innovation that has numerous applications and has been continually evolving over time to achieve higher accuracy and lower latency. This paper presents the design of a two hand gesture recognition system which is fast as well as portable. Contrary to the existing designs based on image processing, a sensor based approach is followed, in which both the gloves work in synchronization to recognize static gestures corresponding to user customized sign language. The hardware framework consists of an array of sensors for both the gloves. A highly efficient method has been implemented for data collection on which predictions are made using various machine learning algorithms. A novel technique has been adopted for recording data, achieving inter-glove communication and ensuring synchronization to reduce latency. The predicted gesture is displayed on a mobile application.
Intelligent Pathfinding Algorithm in Web Games

In recent years, with the hardware development of the web game is getting better and better, the game graphics computing ratio decreased a lot, the traditional scene and picture effect obtained great development at the same time, players began to pay attention to the content of a video game, hope the game role behavior can more close to real people, which makes the game developers in the artificial intelligence to enhance investment in the game, and in game ai, the role of intelligent pathfinding system is always the key point of the research. Intelligent pathfinding algorithm plays A leading role in intelligent pathfinding system. At present, the most classic intelligent pathfinding algorithm used in video games is A* (A-Star) algorithm. Many other excellent pathfinding algorithms are also developed from this algorithm. The purpose of this paper is to get rid of the limitation of having to use standard A* algorithm functions to guide pathfinding so as to quickly find A shortest path in the map. In this paper, through A detailed study of the standard A* algorithm, several optimization methods are proposed to improve it. Firstly, heuristic coefficients are added to the heuristic function of A* algorithm to optimize the search for redundant paths in pathfinding process. Binary heap is used to maintain the open list in A* algorithm. Then, the path weight in the map is optimized. The generated path is smoothed by “take the median insertion method”, and the A* algorithm is further optimized by the zonal search method, so that it can be applied to A wider range of game map types. Finally, through the test of a simulation experiment program produced by Unity3D engine, the above methods are verified. The results of this paper show that after improving the standard intelligent pathfinding algorithm, pathfinding efficiency is higher, the range of use is wider, the player experience is better, and it can be applied in the game.
Using Transfer Learning and BPDFHE to Improve Ocular Image Recognition Accuracy

We used image enhancement algorithms along with transfer learning to fine-tune a deep convolutional neural network to perform ocular image recognition. To enhance the input images, we used a novel color image histogram equalization technique called Brightness Preserving Dynamic Fuzzy Histogram Equalization, which showed significant accuracy improvements: on the test data, using AlexNet, the ROC Area Under the Curve (AUC) increased to over 0.99, Equal Error Rate (EER) decreased 4-fold and dropped below 4%, and decidability (a measure of class separability) increased from 1.89 to 4.17.
Netspeak in asynchronous student-student discussion among different faculty, gender and language groups

In this Research Full Paper, the amount of Netspeak elements in the discussion student-student among the students at the Faculty of Technical Sciences (FTS, Novi Sad, Serbia), and among students at Zagreb School of Economics and Management (ZSEM, Croatia) is compared. Asynchronous discussions are an important part of each e-learning system since they allow students to choose when they will be included in the discussion. Numerous studies specifically encourage student-student discussion in which students are freer to express their opinion than in a professor-student discussion moderated by a professor. The sample consists of 135 students, 75 of whom are students of ZSEM, and 60 of them are from FTS. All of them are in the first year of their college education. The analyzed discussions are conducted in the Learning Management System (LMS) Moodle on courses English Language (FTS) and Information and Communication Technologies (ZSEM). Student-student discussions are in English (FTS) and in Croatian (ZSEM). The methodological approach taken was to measure the amount of Netspeak elements in the discussion. Netspeak is a language form that generates itself from a spoken and a symbolic language. It is developing very rapidly and through the use of new technologies such as Skype, Facebook, Viber, WhatsApp, etc. Its main feature is the extensive use of abbreviations, emoticons, and punctuation. In order to conduct quantitative analysis, the methodology for measuring the frequency in using Netspeak elements through ten standards was used. These standards are divided into four following groups: standard related to information and communication technology (ICT), Grammar and syntax (G), Prosody (P) and Others (O). The difference in the use of Netspeak elements will be analyzed between both groups, particularly differences between the type of standards used, between different languages and genders. Also, the correlation between using Netspeak elements and the final grade on the course will be calculated. It is expected that both groups of students will have Netspeak elements in their discussions, but that there will be some differences in the standards they use.
An improved Evolutionary Strategy with dynamic mutation rate applied to Kakuro puzzles

In this paper we apply a modified Evolutionary Strategy (ES) algorithm to the discrete optimization problem of solving Kakuro puzzles. Our ES modifies the mutation rate each generation based on the best fitness of the population. If the fitness improves then the mutation rate declines; if the fitness is stagnant for multiple generations then the mutation rate increases. We show that the dynamic mutation rate change leads to increased performance relative to a standard (μ,λ) ES, a standard (μ+λ) ES and a standard Genetic Algorithm. We compared the performance of the ES with dynamic mutation to the other algorithms with multiple puzzles of different sizes and the dynamic mutation ES, in general, found the correct solution more often and with less fitness function evaluations.
Chinese NER by Span-Level Self-Attention

In this paper, we investigate how to improve Chinese named entity recognition (NER) by applying self-attention mechanism on span-level semantic representations. Specifically, we propose a model which acquires character representations through pre-trained BERT, then extracts features of each possible character-span through LSTM, estimates the semantic reference value of each span, then explicitly leverages span-level information by performing self-attention calculation among span representations. Experiments on OntoNotes 4.0 dataset have demonstrated that the proposed model achieves 79.97% F1-score, outperforming our baseline methods.
Correlational Neural Network Based Feature Adaptation in L2 Mispronunciation Detection

Due to the difficulties of collecting and annotating second language (L2) learner's speech corpus in Computer-Assisted Pronunciation Training (CAPT), traditional mispronunciation detection framework is similar to ASR, it uses speech corpus of native speaker to train neural networks and then the framework is used to evaluate non-native speaker's pronunciation. Therefore there is a mismatch between them in channels, reading style, and speakers. In order to reduce this influence, this paper proposes a feature adaptation method using Correlational Neural Network (CorrNet). Before training the acoustic model, we use a few unannotated non-native data to adapt the native acoustic feature. The mispronunciation detection accuracy of CorrNet based method has improved 3.19% over un-normalized Fbank feature and 1.74% over bottleneck feature in Japanese speaking Chinese corpus. The results show the effectiveness of the method.
Healthcare Yelp: Health Care Services Prediction

Patients need to find a trustworthy physician to receive medical treatment from. Although healthcare review websites currently exist, the ratings on those websites are determined subjectively by reviewers. These reviewers rate services based on various criteria, so this does not give an accurate description of a physician's service. We propose a method to create an overall rating for a doctor by combining all of their existing reviews on the Internet. Although we will take the original numerical rating (generally 1-5) into account, we will be putting more emphasis on the review's text and will use a sentiment analysis tool to analyze the reviews for their positivity or negativity. We will take this information and use the mixture model framework to generate an overall numerical rating of a physician. In this way, an individual would only need to go to one platform to find a physician's reviews rather than scourge through a plethora of reviews. Our research focuses on creating a space where users can seek medical care and easily discover numerous options for physicians that share similar review levels. These are determined by taking the doctor's reviews and analyzing it in a way that can categorize the doctors on the sites based on these reviews.
Employing Gated Attention and Multi-similarities to Resolve Document-level Chinese Event Coreference

Event coreference resolution is a challenging task. To address the issues of the influence on event-independent information in event mentions and the flexible and diverse sentence structure in Chinese language, this paper introduces a GANN (Gated Attention Neural Networks) model to document-level Chinese event coreference resolution. GANN introduces a gated attention mechanism to select eventrelated information from event mentions and then filter noisy information. Moreover, GANN not only uses a single Cosine distance to calculate the linear distance between two event mentions, but also introduces multi-mechanisms, i.e., Bilinear distance and Single Layer Network, to further calculate the linear and nonlinear distances. The experimental results on the ACE 2005 Chinese corpus illustrate that our model GANN outperforms the state-of-the-art baselines.
Deep Pixel-Level Matching via Attention for Video Co-Segmentation

In video object co-segmentation, methods based on patch-level matching are widely leveraged to extract the similarity between video frames. However, these methods can easily lead to pixel misclassification because they reduce the precision of pixel localization; thus, the accuracies of the segmentation results of these methods are deducted. To address this problem, we propose a framework based on deep neural networks and equipped with a new attention module, which is designed for pixel-level matching to segment the object across video frames in this paper. In this attention module, the pixel-level matching step is able to compare the feature value of each pixel from one input frame with that of each pixel from another input frame for computing the similarity between two frames. Then a features fusion step is applied to efficiently fuse the feature maps of each frame with the similarity information for generating dense attention features. Finally, an up-sampling step refines the feature maps for obtaining high quality segmentation results by using these dense attention features. The ObMiC and DAVIS 2016 datasets were utilized to train and test our framework. Experimental results show that our framework achieves higher accuracy than those of other video segmentation methods that perform well in common information extraction.