Reviewerly/exHarmony huggingface dataset:
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation

Remarkable progress has been made in 3D human pose estimation from a monocular RGB camera. However, only a few studies explored 3D multi-person cases. In this paper, we attempt to address the lack of a global perspective of the top-down approaches by introducing a novel form of supervision - Hierarchical Multi-person Ordinal Relations (HMOR). The HMOR encodes interaction information as the ordinal relations of depths and angles hierarchically, which captures the body-part and joint level semantic and maintains global consistency at the same time. In our approach, an integrated top-down model is designed to leverage these ordinal relations in the learning process. The integrated model estimates human bounding boxes, human depths, and root-relative 3D poses simultaneously, with a coarse-to-fine architecture to improve the accuracy of depth estimation. The proposed method significantly outperforms state-of-the-art methods on publicly available multi-person 3D pose datasets. In addition to superior performance, our method costs lower computation complexity and fewer model parameters.
A Genetic Algorithm for Travel Itinerary Recommendation with Mandatory Points-of-Interest

Traveling as a very popular leisure activity enjoyed by many people all over the world. Typically, people would visit the POIs that are popular or special in a city and also have desired starting POIs (e.g., POIs that are close to their hotels) and destination POIs (e.g., POIs that are near train stations or airports). However, travelers often have limited travel time and are also unfamiliar with the wide range of Points-of-Interest (POIs) in a city, so that the itinerary planning is time-consuming and challenging. In this paper, we view this kind of itinerary planning as MandatoryTour problem, which is tourists have to construct an itinerary comprising a series of POIs of a city and including as many popular or special POIs as possible within their travel time budget. We term the most popular and special POIs as mandatory POIs in our paper. For solving the presented MandatoryTour problem, we propose a genetic algorithm GAM. We compare our approach against several baselines GA, MaxM, and GreedyM by using real-world datasets from the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), which include POI visits of seven touristic cities. The experimental results show that GAM achieves better recommendation performance in terms of the mandatory POIs, POIs visited, time budget (travel time and visit duration), and profit (POI popularity).
Gabor Filter Algorithm for medical image processing: evolution in Big Data context

In the health field, several thousand images are generated every day in medical imaging establishments. On the one hand, the volume of information involved is still far from being fully controlled. On the other hand, the development of machine learning tools today opens the way to a new generation of image analysis in this context of “BigData”. Moreover, our approach is a part towards this dynamic research using Gabor Filter Algorithm in image processing, analysis and diagnosis. In order to test the robustness of our algorithm and its degree of application in the context of BigData, we tested, in a first analysis phase, our algorithm on an image-database containing 320 mammograms. The precision obtained is estimated at 75 percent for a recall of 33 percent. In a second analysis phase, we performed the test on an image data-base containing 1000 medical images. The precision obtained is estimated at nearest 70 percent for a recall of 33 percent. Although the precision obtained in this first step is far from perfect, our processing algorithm remains promising and shows a good adaptation in contest of “Big Data”. The purpose of this research work is to contribute to machine learning. Therefore, the analysis process based on Gabor Filter can distinguish, more and concisely, tumor mammograms from healthy mammograms in Big Data context.
Scientific Paper Recommendation Using Author’s Dual Role Citation Relationship

Vector representations learning (also known as embeddings) for users (items) are at the core of modern recommendation systems. Existing works usually map users and items to low-dimensional space to predict user preferences for items and describe pre-existing features (such as ID) of users (or items) to obtain the embedding of the user (or item). However, we argue that such methods neglect the dual role of users, side information of users and items (e.g., dual citation relationship of authors, authoritativeness of authors and papers) when recommendation is performed for scientific paper. As such, the resulting representations may be insufficient to predict optimal author citations. In this paper, we contribute a new model named scientific paper recommendation using Author’s Dual Role Citation Relationship (ADRCR) to capture authors’ citation relationship. Our model incorporates the reference relation between author and author, the citation relationship between author and paper, and the authoritativeness of authors and papers into a unified framework. In particular, our model predicts author citation relationship in each specific class. Experiments on the DBLP dataset demonstrate that ADRCR outperforms state-of-the-art recommendation methods. Further analysis shows that modeling the author’s dual role is particularly helpful for providing recommendation for sparse users that have very few interactions.
A Topical Category-Aware Neural Text Summarizer

The advent of the sequence-to-sequence model and the attention mechanism has increased the comprehension and readability of automatically generated summaries. However, most previous studies on text summarization have focused on generating or extracting sentences only from an original text, even though every text has a latent topic category. That is, even if a topic category helps improve the summarization quality, there have been no efforts to utilize such information in text summarization. Therefore, this paper proposes a novel topical category-aware neural text summarizer which is differentiated from legacy neural summarizers in that it reflects the topic category of an original text into generating a summary. The proposed summarizer adopts the class activation map (CAM) as topical influence of the words in the original text. Since the CAM excerpts the words relevant to a specific category from the text, it allows the attention mechanism to be influenced by the topic category. As a result, the proposed neural summarizer reflects the topical information of a text as well as the content information into a summary by combining the attention mechanism and CAM. The experiments on The New York Times Annotated Corpus show that the proposed model outperforms the legacy attention-based sequence-to-sequence model, which proves that it is effective at reflecting a topic category into automatic summarization.
Novel DWT/IDWT Architecture for 3D with Nine Stage 2D Parallel Processing using Split Distributed Arithmetic

Novel high-speed memory optimized distributed arithmetic (DA)-based architecture is developed and modeled for 3D discrete wavelet transform (DWT). The memory requirement for the proposed architecture is designed to [Formula: see text] pixel dynamic memory space and [Formula: see text] ROM. The proposed 3D-DWT architecture implements 9/7 Daubechies wavelet filters, synthesizes 7127 bytes of memory for temporary storage and uses 758 adders, 36 multiplexers of 16:1 and 36 up counter to realize the 3D-DWT hardware. The 3D-DWT engine is implemented and tested in a Xilinx FPGA Vertex5 XC5VLX155T with high area and power efficiency. The maximum delay in the timing path is 2.676[Formula: see text]ns and the 3D-DWT works at maximum frequency of 381[Formula: see text]MHz clock.
VEHICLE DETECTION IN REMOTE SENSING IMAGES USING DEEP NEURAL NETWORKS AND MULTI-TASK LEARNING

Abstract. Vehicle detection in remote sensing image has been attracting remarkable attention over past years for its applications in traffic, security, military, and surveillance fields. Due to the stunning success of deep learning techniques in object detection community, we consider to utilize CNNs for vehicle detection task in remote sensing image. Specifically, we take advantage of deep residual network, multi-scale feature fusion, hard example mining and homography augmentation to realize vehicle detection, which almost integrates all the advanced techniques in deep learning community. Furthermore, we simultaneously address super-resolution (SR) and detection problems of low-resolution (LR) image in an end-to-end manner. In consideration of the absence of paired low-/highresolution data which are generally time-consuming and cumbersome to collect, we leverage generative adversarial network (GAN) for unsupervised SR. Detection loss is back-propagated to SR generator to boost detection performance. We conduct experiments on representative benchmark datasets and demonstrate that our model yields significant improvements over state-of-the-art methods in deep learning and remote sensing areas.
Deep Self-Supervised Hierarchical Clustering for Speaker Diarization

The state-of-the-art speaker diarization systems use agglomerative hierarchical clustering (AHC) which performs the clustering of previously learned neural embeddings. While the clustering approach attempts to identify speaker clusters, the AHC algorithm does not involve any further learning. In this paper, we propose a novel algorithm for hierarchical clustering which combines the speaker clustering along with a representation learning framework. The proposed approach is based on principles of self-supervised learning where the self-supervision is derived from the clustering algorithm. The representation learning network is trained with a regularized triplet loss using the clustering solution at the current step while the clustering algorithm uses the deep embeddings from the representation learning step. By combining the self-supervision based representation learning along with the clustering algorithm, we show that the proposed algorithm improves significantly 29% relative improvement) over the AHC algorithm with cosine similarity for a speaker diarization task on CALLHOME dataset. In addition, the proposed approach also improves over the state-of-the-art system with PLDA affinity matrix with 10% relative improvement in DER.
Words, clauses, sentences, and T-units in learner language: Precise and objective units of measure?

In research on learner language complexity, accuracy and fluency (CAF), syntactic complexity is often studied with quantitative measures based on words, clauses, sentences, and T-units. The findings have been mixed, but segmenting learner language into these units of measure has seldom been problematised, even if the need for accurate coding is well known. The present study explores words, clauses, sentences, and T-units as production units in written learner language using a corpus of 352 L2 Finnish texts (28,813 words). The results illustrate how written learner language can be hard to fit into the production unit categories, which are essential for the most frequently used quantitative measures of syntactic complexity. On the one hand, the results support calls to include explicit definitions of the units of measure when reporting findings obtained with these quantitative measures. On the other hand, they align with calls to introduce new measures to better gauge the changes in learner language syntax as it develops with increasing language proficiency.
Detection and Forensics of Encryption Behavior of Storage File and Network Transmission Data

In recent years, with the widespread application of encryption technology, criminals can hide malicious data without being discovered by security regulatory authorities, which has brought serious challenges to computer forensic investigation.Therefore, it is urgent to study the technology of detection and forensics of encrypted data.This paper proposes a method for encryption detection based on a deep convolutional neural network.The method first converts the raw data into two-dimensional matrixes as the input of the convolutional neural network.Then, the multiscale feature extraction mechanism with multiple activation functions is utilized to provide representative features as the input of subsequent layers.Next, the residual learning operation can further enhance the discrimination of features.By this mean, a network which can automatically extract and learn global contextual information of encrypted data is constructed.The experiment results show that the proposed method achieves high accuracy in the detection of storage file and network transmission data compare to the competitive methods and the detection accuracy on different types of mixed data is higher than 99%.Moreover, the proposed method can accurately detect data encrypted with different algorithms.The average detection rate of DES-encrypted data is higher than that of competitors by more than 5%.
Demosaicing of RGBW Color Filter Array Based on Rank Minimization with Colorization Constraint

Recently, the white (w) channel has been incorporated in various forms into color filter arrays (CFAs). The advantage of using the W channel is that W pixels have less noise than RGB pixels; therefore, under low-light conditions, pixels with high fidelity can be obtained. However, RGBW CFAs normally suffer from spatial resolution degradation due to a smaller number of color pixels than in RGB CFAs. Therefore, even though the reconstructed colors have higher sensitivity, which results in larger CPSNR values, there are some color aliasing artifacts due to a low resolution. In this paper, we propose a rank minimization-based color interpolation method with a colorization constraint for the RGBW format with a large number of W pixels. The rank minimization can achieve a broad interpolation and preserve the structure in the image, and it thereby eliminates the color artifacts. However, the colors fade from this global process. Therefore, we further incorporate a colorization constraint into the rank minimization process for better reproduction of the colors. Experimental results show that the images can be reconstructed well even from noisy pattern images obtained under low-light conditions.
Adapting Usability Heuristics to the Context of Mobile Augmented Reality

Augmented reality (AR) is an emerging technology in mobile app design during recent years. However, usability challenges in these apps are prominent. There are currently no established guidelines for designing and evaluating interactions in AR as there are in traditional user interfaces. In this work, we aimed to examine the usability of current mobile AR applications and interpreting classic usability heuristics in the context of mobile AR. Particularly, we focused on AR home design apps because of their popularity and ability to incorporate important mobile AR interaction schemas. Our findings indicated that it is important for the designers to consider the unfamiliarity of AR technology to the vast users and to take technological limitations into consideration when designing mobile AR apps. Our work serves as a first step for establishing more general heuristics and guidelines for mobile AR.
A NEW CAD SYSTEM FOR BREAST CANCER CLASSIFICATION USING DISCRIMINATION POWER ANALYSIS OF WAVELET’S COEFFICIENTS AND SUPPORT VECTOR MACHINE

The Computer-Aided Diagnostic (CAD) system is an important tool that helps radiologists to provide a second opinion for the early detection of breast cancer and therefore, aids to reduce the mortality rates. In this work, we try to develop a new (CAD) system to classify mammograms into benign or malignant. The proposed system consists of three main steps. The preprocessing stage consists of noise filtering, elimination of unwanted objects and suppressing the pectoral muscle. The Seeded Region Growing (SRG) segmentation technique is applied in a triangular region that contains the pectoral muscle to localize it and extract the region of interest (ROI). The features extraction step is performed by applying the discrete wavelet transform (DWT) to each obtained ROI, and the most discriminating coefficients are selected using the discrimination power analysis (DPA) method. Finally, the classification is carried out by the support vector machine (SVM), artificial neural networks (ANN), random forest (RF) and Naive Bayes (NB) classifiers. The evaluation of the proposed system on the mini-MIAS database shows its effectiveness compared to other recently published CAD systems, and a classification accuracy of about 99.41% with the SVM classifier was obtained.
Contribution on Arabic Handwriting Recognition Using Deep Neural Network

Arabic handwriting recognition is considered among the most important and challenging recognition research subjects due to the cursive nature of writing and the similarities between different characters shapes. In this paper, we investigate the problem of handwritten Arabic recognition. We propose a new architecture combining CNN and BLSTM based on character model approach with CTC decoder. The handwriting Arabic database KHATT is used for experiments. The results demonstrate a net advantage of performance for the CNN-BLSTM combining approach compared to the approaches used in the literature.
A New ECDLP-Based PoW Model

We lay the foundations for a blockchain scheme, whose consensus is reached via a proof of work algorithm based on the solution of consecutive discrete logarithm problems over the point group of elliptic curves. In the considered architecture, the curves are pseudorandomly determined by block creators, chosen to be cryptographically secure and changed every epoch. Given the current state of the chain and a prescribed set of transactions, the curve selection is fully rigid, therefore trust is needed neither in miners nor in the scheme proposers.
The effect of transformational leadership behavior on organizational performance in gold industry of Saudi Arabia

This paper mainly aims at investigating the effect of transformational leadership behaviour on organizational performance, as well as other factors such as psychological ownership, working environment, employee involvement, incentives, and employee motivation take into account their effect on organizational performance.To achieve the objectives of the study, quantitative research approach was applied through a questionnaire specifically designed to collect primary information from the samples of the study, which consisted of (250) managers in gold shops in Jaddah.The collected data was analysed using SPSS 20.0.The results showed that transformational leadership behaviour, psychological ownership, working environment, employee involvement, incentives, and employee motivation significantly affect organizational performance in the Saudi Arabian gold industry.Finally, the researcher recommended ways to promote the concept of transformational leadership behaviour in the gold sector in Saudi Arabia at various administrative levels by holding courses and seminars.
Multilayer Perceptron Back propagation Algorithm for Predicting Breast Cancer

Machine learning applications are growing rapidly in the present world due to its learning capabilities and improved performance. Supervised learning is a concept of machine learning. In supervised learning, target values are known. Classification problem is addressed by various techniques, and in this paper, we are focusing on perceptron-based learning. In perceptron-based learning, they are single layer perceptron, multilayer perceptron, and RBF network. The work is based on the prediction of breast cancer in women. For prediction task, we designed an artificial neural network (ANN) and the trained the model with back propagation algorithm. The weight optimization in back propagation algorithm is done using stochastic gradient decent algorithm. The proposed model predicts the accuracy of the classifier using performance metric. The proposed algorithm is compared with decision tree algorithm.
COMIC STRIPS AND BOARD GAME AS A MEDIA IN LEARNING ORAL LANGUAGE SKILLS FOR STUDENTS

This research aims to find out the improvement of the students’ oral language skills using comic strips and board game as an instructional media in terms of its physical feasibility, design, color, material, images and text by using role-playing. The subject was the 1 st grade students of the English department of Unimed, consisted of 30 students for the field trial. This research was use classroom action research by Kemmis and Taggart. It was accomplished in two cycles. Each cycles consisted of four steps: planning, action, observation and reflection. The first cycle consisted of one meeting including giving material and pretest. The second cycle consisted of one meeting, given material and post-test. Based on the students' assessment, it was showed that the mean of pre-test was 52 and the mean of post-test was 88. And the higher score was 92. From the data, it indicated that using Comic Strips and Board Game in learning Oral Language Skills was effective, and the data concluded that the students’ have the improvement in speaking. The results showed that comic strips and board game were very good and feasible to be used as a media to learn oral language skills.
ZiMM: A deep learning model for long term and blurry relapses with non-clinical claims data

This paper considers the problems of modeling and predicting a long-term and “blurry” relapse that occurs after a medical act, such as a surgery. We do not consider a short-term complication related to the act itself, but a long-term relapse that clinicians cannot explain easily, since it depends on unknown sets or sequences of past events that occurred before the act. The relapse is observed only indirectly, in a “blurry” fashion, through longitudinal prescriptions of drugs over a long period of time after the medical act. We introduce a new model, called ZiMM (Zero-inflated Mixture of Multinomial distributions) in order to capture long-term and blurry relapses. On top of it, we build an end-to-end deep-learning architecture called ZiMM Encoder-Decoder (ZiMM ED) that can learn from the complex, irregular, highly heterogeneous and sparse patterns of health events that are observed through a claims-only database. ZiMM ED is applied on a “non-clinical” claims database, that contains only timestamped reimbursement codes for drug purchases, medical procedures and hospital diagnoses, the only available clinical feature being the age of the patient. This setting is more challenging than a setting where bedside clinical signals are available. Our motivation for using such a non-clinical claims database is its exhaustivity population-wise, compared to clinical electronic health records coming from a single or a small set of hospitals. Indeed, we consider a dataset containing the claims of almost all French citizens who had surgery for prostatic problems, with a history between 1.5 and 5 years. We consider a long-term (18 months) relapse (urination problems still occur despite surgery), which is blurry since it is observed only through the reimbursement of a specific set of drugs for urination problems. Our experiments show that ZiMM ED improves several baselines, including non-deep learning and deep-learning approaches, and that it allows working on such a dataset with minimal preprocessing work.