Reviewerly/exHarmony huggingface dataset:
POST

Organizing and assigning papers into sessions within a large conference is a formidable challenge. Some conference organizers, who are typically volunteers, have utilized event planning software to ensure simple constraints, such as two people can not be scheduled to talk at the same time. In this work, we proposed utilizing natural language processing to find the topics within a corpus of conference submissions and then cluster them together into sessions. As a preliminary evaluation of this technique, we compare session assignments from previous conferences to ones generated with our proposed techniques.
Application of the Motion Detector and the Artificial Neural Network to Detect Vehicle Collisions: A Case Study

Motor vehicle collisions are a common cause of deaths or/and injuries. The key to lowering the death rate and damages to the health of collision accident victims is a timely arrival of Emergency Services to the accident scene. In the paper, we present and discuss the first results of the design and implementation of the vehicles collision detection system, which is based on a motion detector (MD) and Artificial Neural Network (ANN). To test MD and ANN separately, a small set of video records from traffic cameras that was not a part of a training dataset, were used. We found that while MD demonstrates reasonable performance, Haar Cascades-based pre-trained ANN requires significant improvements. Possible solutions to the aforementioned problem were proposed and discussed.
Robust CBFR for Forensic Application using CNN

Face recognition for forensic applications is a very challenging task. The challenges arise due to the unavailability of the mug shot of the culprit from a crime scene. The available face is either occluded or blurred. In both the cases, Component-based face recognition is capable of recognizing an individual or the culprit through a part or component of the face, which is the major focus of the paper. Convolutional Neural Network is the base of the proposed framework for face recognition via components: nose, mouth and, eyes. The novelty of this framework is that it can single-handedly recognize an individual by full face or by anyone or all the components of the face using Deep Learning with high recognition rate. In a practical scenario, the collected images are corrupted with different noises that reduce the recognition rate. Robustness of the system is tested for different noises viz. White Gaussian noise, Speckle noise, and Salt Pepper noise. Results show the descending trend in the accuracy of the recognition as the intensity of the noise is increased. Faces94, which is the standard database of face, is used for the experiment.
Evidence Based Feature Selection and Collaborative Representation Towards Learning Based PSF Estimation for Motion Deblurring

The motion blur in an image is due to the relative motion between the camera and the scene being captured. Due to the degraded quality of the motion-blurred images, it is challenging to use them in different applications such as text detection, scene understanding, content-based image retrieval, etc. Typically, a motion-blurred image is modeled as a convolution between the un-blurred image and a Point Spread Function (PSF). Motion de-blurring is sensitive to the estimated PSF. In this paper, we propose to address the problem of motion deblurring by estimating PSF using a learning-based approach. We model motion blur as a function of length and angle and propose to estimate these parameters using a learning-based framework. It is challenging to find distinct features to precisely learn the extent of motion blur through deep learning. To address this, we model an evidence-based technique to select the relevant features for learning from a set of features, based on the confidence generated by combining the evidences using Dempster Shafer Combination Rule (DSCR). We propose to use Clustering and Collaborative Representation (CCR) of feature spaces to learn length and angle. We model the deblurred image as an MRF (Markov Random Field) and use MAP (maximum a posteriori) estimate as the final solution. We demonstrate the results on real and synthetic datasets and compare the results with different state of art methods using various quality metrics and vision tools.
Association Rule Mining on the BIST100 Stock Exchange

Data mining is the process of finding valuable information in data. Association mining is one of the fields of the data mining. It is used to discover all associations between items in a dataset. It also discovers hidden patterns in the large dataset. Patterns are discovered using predefined constraints (such as minimum support and minimum confidence values). Data mining on stock market is an important research area. This research field and its results are valuable information for all of the investors. There are many studies on this field in the world. But to the best of our knowledge, there is not any study on BIST100 stock exchange that finds associations between stocks. In this study, we focused on BIST100 stock exchange, used 87 different BIST100 stocks daily data for dates between 10/21/2013 and 10/19/2018 from "finance.yahoo.com". We figured out association rules on BIST100 stock exchange using historical data, Apriori algorithm and two different condensation methods.
Shift and Accumulate Convolution Processing Unit

Convolutional Neural Network (CNN) is the state-of-the-art learning technique for image understanding in several artificial vision systems. Extensive uses of memory storage and bandwidth with high computation capacity boost up the performance of a CNN. Convolution is the fundamental operation in CNN models and hence a large number of multiplier-accumulator (MAC) unit is required to compute the convolution operations adequately. MAC processing incurs extreme computational complexity by consuming significant amounts of time, energy and area. These limitations in computation have led to the exploration of different architecture of MAC to meet the demand of CNN processing. To increase the overall speed of a CNN model, an architecture called SAC (shift-accumulator) is proposed that reduces the number of overall convolution computations. Further, the SAC architecture is especially designed for convolution operations used in image sharpening, edge detection, blurring etc. The proposed SAC architecture facilitates faster convolution computations and reduces the overhead in terms of area and power than the conventional MAC architecture. Although the accuracy is slightly reduced, it does not significantly affect the efficacy of computational unit used in CNN processing.
Using Multicultural Herbal Information to Create Multi-pattern Herb Name Retrieval System

In Thailand, many botanical gardens are attracted by local and foreign visitors. Searching herbal information in these gardens usually applies plant names as keywords. However, various patterns of names can be used, e.g., common names, local names for each language, and scientific names. Moreover, misspelling and different spelling terms are usually found. Information of plants in gardens is often given in Thai and/or English. Searching information by inputting Thai plant names may be unfamiliar to foreign visitors. To help visitors retrieve herbal information by herb names, a multi-pattern herb name retrieval system was implemented. Using a multicultural herbal information system (HerbINFO), an approximate string matching technique, and a machine translator, visitors can easily search herbal information by any patterns of herb names. Using herb names contributed from members of the HerbINFO, feedbacks from a mobile phone and a voting system in the HerbINFO, efficiency of searching will be dynamically improved. A set of herb names in several patterns and languages was used to demonstrate the system via a mobile application.
Nummatus: A Privacy Preserving Proof of Reserves Protocol for Quisquis

Quisquis is a recently proposed design for a privacy-focused cryptocurrency. We present Nummatus, a privacy preserving proof of reserves protocol for Quisquis. Nummatus enables exchanges to create a Pedersen commitment to the amount of Quisquis coins they own, without revealing the exact accounts they own. These commitments can be combined with a commitment to the total liabilities of an exchange to generate a proof of solvency. The Nummatus protocol also facilitates detection of account sharing collusion between exchanges. Our simulations show that the cost of using Nummatus instead of a non-private proof of reserves protocol is not prohibitive.
Research on the Relevancy of Scientific Literature Based on the Citation-Mention Frequency

This is because references have different citation-mention frequencies.Some references are mentioned only once, while some are mentioned several times.Papers are cited by others when they are relevant to the citing paper.The fact that a reference has a high citation-mention frequency may mean that its content is more closely related to the citing paper.From this point of view, we examined the relevancy of a cited paper to a citing paper on the basis of citation-mention frequency.Two aspects of relevancy are considered: citation linkage and content.We construct a highly mentioned class of references and a rarely mentioned class of references.We introduce the concepts of ''reference-similarity'' and ''content-similarity.'' First, we count the number of co-cited references and calculate the reference-similarity. Second, we extract the abstracts of papers and calculate the content-similarity using the bag-of-words model.The results show that references from the highly mentioned class are more relevant to the citing papers than those from the rarely mentioned class.
Edge-Embedded Multi-Dropout Framework for Real-Time Face Alignment

We propose the Edge-Embedded Multi-Dropout (EEMD) framework for real-time face alignment. The EEMD framework extracts facial edge features and explores multiple dropout architecture for locating facial landmarks. It consists of two major component networks, namely the Contour Detection Network (CDN) and the Multi-Dropout Network (MDN); and two supplementary networks, one for face detection and the other for pose regression. When a face is detected by the face detector, its pose will be classified by the pose classifier, then the associated facial edges be detected by the CDN, and then the landmarks be located by the MDN. The embedding of the CDN into the EEMD framework describes the observation that most landmarks are located on the contours/edges of the facial components and of the whole face. We revise a state-of-the-art edge detector as part of the base network for the CDN. The MDN is proposed to better design the regression architecture with appropriate dropout settings for better preventing overfitting and enhancing regression accuracy. Unlike most of the 2D approaches unable to locate landmarks in extreme poses, the proposed framework can detect landmarks on profile faces, i.e., ±90° in yaw, in real time. Evaluated on benchmark databases, the EEMD demonstrates a competitive performance to other state-of-the-art approaches with a satisfying runtime speed.
IMDB-Attire: A Novel Dataset for Attire Detection and Localization

People's attire, or their way of dressing defines not only their social status and personality but also affects the way people meet and greet them. Attire detection has many useful applications such as clothing preferences in diverse regions of the world could be monitored and quantified. This information is very valuable for fashion designers. Real-time clothing recognition can be useful for security surveillance, where information about an individual's clothes can be used to identify crime suspects. Recently, deep learning algorithms have shown promise in the field of object detection and recognition. These algorithms are data hungry and are only as good as the data they are trained on. In this work, we have focused on three tasks to address this problem. We created a unique dataset of ~8000 images from IMDBb.com (movie rating website) to address the challenge of real-world application of the algorithm training for attire detection. The dataset contains pictures from movies, making the dataset a good source of images from the wild. We manually labelled 60 different classes of attire. Then we focused on multiclass classification and attire object detection using customized deep learning architectures including YOLO and SSD. We achieved a mean Average Precision (mAP) of 64.14% and an Average Precision (AP) of 91.14% for top 5 classes on YOLO. Available at https://github.com/saadyousuf45/ .
Electronic health cloud as service to improve collaboration in healthcare organizations

Abstract There are many factors contributing to lack of collaboration which relates to teamwork and partnership in healthcare organization such as low awareness level in technology use, computer literacy, absence of e-health cloud, poor health information systems, decentralized systems, language barriers, difficulty to manage and control large amounts of data, work independently and time factor. Among medical staffs it is important to be able to share information and knowledge in patient treatments by having a better implementation of the electronic health cloud as service can improve and enhance the collaboration This research was conducted using mixed method approach combining observations, questionnaires and interviews used to determine the level of collaboration among medical staff in e-health cloud environment.
Universal Participatory Design: Achievements and Challenges

According to the principles of participatory design, a genuine democratic process requires effective participation of all affected people in the design process; this must include affected disabled users. However, user participation entails complex problems, which are aggravated by conditions of illiteracy and/or aging. This article presents the concept of Universal Participatory Design, a design philosophy and practice that aims to be inclusive during the design process, and which has a positive result for all. We first conducted a review of the literature to understand the limits of the relationships between participatory design and universal design. This paper then addresses some of the challenges to achieve Universal Participatory Design (UPD) by juxtaposing deficits observed in the literature with issues we experienced during two research projects. We discuss the key components of Participatory Design and its relationship to UPD, and establish a research agenda that aims to conceptualize and investigate participatory design with universal access. Our findings indicate the need for flexible design methods, adaptable artifacts, and positive designers’ attitudes when encountering unexpected situations.
A New Non-smart Water Meter Digital Region Localization and Digital Character Segmentation Method

The efficient and accurate meter reading method can help us manage water resources more reasonably. Aiming at the image of the non-smart water meter taken by mobile phone photographing, a new method for locating and fragmenting digital area of water meter is proposed in this paper. Firstly, locate the position of the measuring unit in the water meter. Obtain the binary image of the measurement unit and correct the image according to its vertical projection feature, and then locate the number area of the water meter again. The adaptive threshold is used to binarize the digital region, then the median filtering and connected-domain method are used for denoising. Finally, the digital segmentation is completed by finding the circumscribed rectangle of the character. The experimental results show that the method can effectively locate the digital area and segmentation numeric characters for the water meter image with rotation problem and has strong adaptability to the water meter of different water meter companies.
Knowledge Systems and Risk Management

Knowledge is the most important asset that a company can have. Thus, it is imperative that this asset is safeguarded just like generic information assets. However, knowledge management (KM) and knowledge systems are different than traditional information systems with different threats and different operational requirements. Information security professionals recognize that risk assessment is the cornerstone to information security. The authors build on this perspective and propose that risk assessment techniques need to be applied to KM too to properly safeguard this asset. They discuss risk assessment frameworks and build on a KM/knowledge system specific risk assessment framework with a step-by-step guideline for KM/knowledge system specific threat assessment.
Study on real-time correction of site amplification factor

Abstract. The site amplification factor was usually considered to be scalar values, such as amplification of peak ground acceleration or peak ground velocity, or increments of seismic intensity in the earthquake early warning (EEW) system or seismic-intensity repaid report system. This paper focuses on evaluating an infinite impulse recursive filter method that could produce frequency-dependent site amplification and compare the performance of the scalar-value method with the infinite impulse recursive filter method. A large number of strong motion data of IBRH10 and IBRH19 of the Kiban Kyoshin network (KiK-net) triggered in more than 1000 earthquakes from 2004 to 2012 were selected carefully and used to obtain the relative site amplification ratio; we model the relative site amplification factor with a casual filter. Then we make a simulation from the borehole to the surface and also simulate from the front-detection station to the far-field station. Compared to different simulation cases, it can easily be found that this method could produce different amplification factors for different earthquakes and could reflect the frequency-dependent nature of site amplification. Through these simulations between two stations, we can find that the frequency-dependent correction for site amplification shows better performance than the amplification factor relative to velocity (ARV) method and station correction method. It also shows better performance than the average level and the highest level of the Japan Meteorological Agency (JMA) earthquake early warning system in ground motion prediction. Some cases in which simulation did not work very well were also found; possible reasons and problems were analyzed and addressed. This method pays attention to the amplitude and ignores the phase characteristics; this problem may be improved by the seismic-interferometry method. Frequency-dependent correction for site amplification in the time domain highly improves the accuracy of predicting ground motion in real time.
Rat Grooming Behavior Detection with Two-stream Convolutional Networks

Rat grooming behavior can be used to reflect its states of physiology and psychology. Behavioral studies in rats are often based on human observations involving the viewing of segments of long video recordings. In the case of grooming, the number of subjectively identified grooming movements is manually counted, typically over long video sessions lasting for days. Therefore, an intelligent approach is needed to help analyze such datasets automatically with high precision. Here, we develop a grooming detection method using deep learning algorithms that combine a Convolutional Neural Network (ConvNets) and a Long Sort-Term Memory network (LSTM). Experimental results demonstrate that the proposed method produces a satisfactory and higher detection rate for grooming behavior of rats.
New bivariate Hensel lifting algorithm for <i>n</i> factors

We present a new algorithm for performing linear Hensel lifting of bivariate polynomials over a finite field F p with p elements. It lifts n monic, univariate polynomials to recover the factors of a polynomial A ( x , y ) ∈ F p [ x , y ] which is monic in x , and bounded by degrees d x = deg( A , x ) and d y = deg( A , y ). Our algorithm improves upon Bernardin's algorithm in [1] and reduces the cost from [EQUATION] to [EQUATION]. Experimental results show that our algorithm works efficiently for large degree polynomials.
Quantum Resistant Public Key Encryption Scheme polarRLCE

In order to reduce the key size of the original McEliece cryptosystem, several variants exploited the particular structure in the public key. Unfortunately, most of these variants are vulnerable to structural attacks because of the algebraic structure of the underlying codes. In this work, we propose the first efficient secure scheme based on polar codes (i.e., polarRLCE), which is inspired by RLCE scheme, a candidate for the NIST post-quantum cryptography standardization. We show that, with the proper choice of parameters, using polar codes, it is possible to design an encryption scheme to achieve the intended security level while keeping a reasonably small key size. In addition, possible attacks are outlined and the key size of several choices of parameters is compared to those of known schemes with the same security level. It is shown that our proposal has the apparent advantage to decrease the key size, especially on the high-security level.
A Natural Scene Text Extraction Approach Based on Generative Adversarial Learning

Extracting textual information embodied in natural scenes is a very challenge task, and has a great influence on the performance of the following text recognition and understanding. It can be seen as an image-to-image conversion task, in which we transform the front text in each natural image into a specified color and the background into black. After that, we use the connected component algorithm to extract text from the two-color image. Based on such motivation, we proposed an approach based on generative adversarial learning to deal with the image-to-image conversion. The neural network in our approach consists of a generator sub-network and a discriminator sub-network, which are trained with paired images (scene images and their corresponding two-color images) in an adversarial way. After the training stage, the generator network is used to perform image conversion. Experiments on standard datasets including KAIST scene text database and MSRA text detection 500 database demonstrate that the proposed algorithm achieves a very competitive performance.
Contact Assist Control Barrier Function for Human Assist Control

In a modern aging society, electric wheelchairs are an essential means of transportation for the elderly. The methods for passing over a step safely with an electric wheelchair have attracted much attention. When an electric wheelchair contacts a step with speed 0, the vehicle does not receive a great shock. In this study, we propose a contact assist CBF based on a relaxed CBF. Moreover, we propose a method of human assist control using the contact assist CBF. We aim to design a control system such that the speed of a vehicle converges to 0 at the moment when the vehicle contacts the step. The effectiveness of the method is confirmed by two computer simulations.