Reviewerly/exHarmony huggingface dataset:
The Functionalities of Cognitive Technology in Management Control System

Cognitive technologies are an important factor accelerating the development of business and society. They are an artificial intelligence tool with cognitive skills enabling learning through empirical experience acquired through direct interaction with the environment. The application of these technologies in the management control system enriches it with the functions of automatic analysis of real data mapping business processes that are implemented at all levels of management. The aim of this paper is to analyze the functionalities of cognitive technology for the intelligent knowledge processing process by the management control system. To achieve this goal, the following research methods were used: literature analysis, observation of phenomena, deduction, and induction. The main results of researches are conclusions that cognitive technology should be used, among other, for realize following functions: automatic creation of a new strategic and operating budget plan, automatic real-time conversion of budgets in case of a change in the organizational structure of the enterprise, automatic deviations control, interpretation and identification the causes of deviations.
Secure Big Data Deduplication with Dynamic Ownership Management in Cloud Computing

Due to the huge size of data, traditional database systems cannot handle the big data. Cloud is the suitable platform for storing and processing the big data, but moving data into cloud brings privacy problem. Privacy of the data can be protected by storing encrypted data in the cloud. However, encrypted data introduces a new challenge for cloud that is data duplication, which is most important for big data storage, because storing redundant copies waste the lot of storage space and complicates the data management. Recently, several schemes were proposed to address the secure data deduplication. However, existing schemes have security weakness and do not support data access controls. In this paper, we propose a secure big data deduplication with dynamic ownership management in cloud based on identity-based encryption and proxy re-encryption. Through the security and performance analysis, we prove that our scheme is secure and efficient.
Genre-ous: The Movie Genre Detector

The advent of Natural Language Processing (NLP) and deep learning allows us to achieve tasks that sounded impossible 10 years ago. One of those tasks is text genre classification such as movies, books, novels, and various other texts, which, more often than not, belong to one or more genres. The purpose of this research is to classify those texts into their genres while also calculating the weighted presence of this genre in the aforementioned texts. Movies in particular are classified into genres mostly for marketing purposes, and with no indication on which genre predominates. In this paper, we explore the possibility of using deep neural networks and NLP to classify movies using the contents of the movie script. We follow the philosophy that scenes make movies and we generate the final result based on the classification of each individual scene.
Adoption of Integrated Voice Assistants in Health Care – Requirements and Design Guidelines

Integrated voice assistants (IVA) receive more and more attention and are widespread for entertainment use cases, such as radio hearing or web searches. At the same time, the health care segment suffers in process inefficiency and missing staff, whereas the usage of IVA has the potential to improve caring processes and patient satisfaction. By applying a design science approach and based on a qualitative study, we identify IVA requirements, barriers and design guidelines for the health care sector. The results reveal three important IVA functions: the ability to set appointments with care service staff, the documentation of health history and the communication with service staff. Integration, system stability and volume control are the most important nonfunctional requirements. Based on the interview results and project experiences, six design and implementation guidelines are derived.
PAC-Bayesian Meta-learning with Implicit Prior.

We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a prior distribution of the model of interest. Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot learning setting to upper-bound generalisation errors on unseen tasks and samples. We also propose a generative-based approach to model the shared prior and the posterior of task-specific model parameters more expressively compared to the usual diagonal Gaussian assumption. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on few-shot classification (mini-ImageNet and tiered-ImageNet) and regression (multi-modal task-distribution regression) benchmarks.
Calcul exact de faible complexité des décompositions conjonctive et disjonctive pour la fusion d'information.

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been done to reduce the complexity of computations used in information fusion with Dempster’s rule. Yet, few research had been conducted to reduce the complexity of computations for the conjunctive and disjunctive decompositions of evidence, which are at the core of other important methods of information fusion. In this paper, we propose a method designed to exploit the actual evidence (information) contained in these decompositions in order to compute them. It is based on a new notion that we call focal point, derived from the notion of focal set. With it, we are able to reduce these computations up to a linear complexity in the number of focal sets in some cases. In a broader perspective, our formulae have the potential to be tractable when the size of the frame of discernment exceeds a dozen possible states, contrary to the existing litterature. In addition, we have developed a complete open-source C++ framework containing an implementation of this method.
Design of a Tourism Recommendation System Based on User’s Profile

Recommender systems have emerged as a powerful tool in addressing the issue of information overload, they allow to filter and select relevant content that fit users’ preferences and needs. In this paper we describe the general architecture of a recommender system which uses users’ profiles to provide personalized and tailored journey to visitors of the Darâa-Tafilalet region. User profile contains various attributes that characterize the user and reflect his behavior. It helps to recommend the best offers for users and therefore to improve touristic experiences in the region Draâ-Tafilalet.
Improvement of Text Segmentation TextTiling Algorithm

Abstract Text segmentation has a wide range of applications in the fields of information extraction and abstract generation. The method uses local information to determine a specific segmentation, and cannot fully consider the information of the remaining paragraphs. In response to this problem, this paper improves the segmentation point selection of the algorithm. After the similarity values of the respective interval points are obtained by the similarity calculation, the sliding window is used to slide on the interval points, and the similarity value of the interval points at the center of the window is replaced with the average of all the similarity values in the window. The experimental results show that the text segmentation based on the improved method can suppress small local changes on the curve, thus highlighting the large similarity value changes, and effectively improving the performance of text segmentation.
Saving Energy and Increasing Density in Information Processing Using Photonics

We argue energy and interconnect density in information processing can be improved by orders of magnitude using parallel free-space optical channels inside and between racks, enabled by integrated waveguide photonics, and run synchronously without time-multiplexing.
Automatic Segmentation of U-Zone Area on Facial Images using Fuzzy Edge Detection

Each person has different types of facial skin.Knowing the type of skin that we have is very important for skin care because different skin types require different treatments.The face area commonly used for examining skin types includes the right cheek, left cheek, and chin, commonly referred as the U-zone area.However, determining the U-zone area manually gives subjective results and requires a long time.Currently there is very few research on determining the Uzone area using the aid of computer.In this research a new scheme for detecting the U-zone area automatically was proposed by combining Fuzzy edge detection, Hough transformation, and Viola Jones algorithm.Fuzzy edge detection and Hough transformation are used to detect face locations, while the Viola-Jones algorithm is used to detect the location of eyes and mouth which is the basis for detecting cheeks and chin.Based on the research that has been done, it is proven that Fuzzy edge detection and Hough transformation can be used to detect the location of the elliptical face even though the range of facial rotation values must be between 80-100 degrees.The method of determining the U-zone area proposed by combining with the Viola-Jones Algorithm can provide a fairly accurate U-area result with the accuracy of 89.90%.
Approach the Interval Type-2 Fuzzy System and PSO Technique in Landcover Classification

In fuzzy classification systems, the estimation of the optimal number of clusters and building base-rules are very important and greatly affects the accuracy of the fuzzy system. Base-rules are often built on the experience of experts, but this is not always good and the results are often unstable. Particle swarm optimization (PSO) techniques have many advantages in finding optimal solutions and have been used successfully in many practical problems. This paper proposes a method using the PSO technique to build base-rules for the interval type-2 fuzzy system (IT2FS). Experiments performed on satellite image data for the landcover classification problem have shown that the proposed method works more stably and effectively than the non-PSO technique.
An Efficient Web Server Log Analysis Using Genetic Algorithm-Based Preprocessing

Popularity of web increases day by day, everything associated with daily life of human being all are connected to web and most of the people spending their time over the web through social networking web apps for their purchasing. Web server stores every activity of users in the form of logs, which contain very useful patterns, henceforth web server log analysis is the vital research area. Web log data analysis has primary step is preprocessing which is meant for dimensionality reduction because web log data is hefty in size and need to normalize the data for further cognitive analysis or other data analysis. Bulky data size degrades the performance of data analytic algorithm so there is necessity of an efficient algorithm for preprocessing over web server log data. In this paper, we put emphasis on data preprocessing. We have proposed the use of genetic algorithm for dimension reduction and normalization of input web server log data. Experimental result shows the data preprocessed data produces higher precision value, precision calculated using MATLAB 2016 classification learner tool.
Show, Edit and Tell: A Framework for Editing Image Captions

Most image captioning frameworks generate captions directly from images, learning a mapping from visual features to natural language. However, editing existing captions can be easier than generating new ones from scratch. Intuitively, when editing captions, a model is not required to learn information that is already present in the caption (i.e. sentence structure), enabling it to focus on fixing details (e.g. replacing repetitive words). This paper proposes a novel approach to image captioning based on iterative adaptive refinement of an existing caption. Specifically, our caption-editing model consisting of two sub-modules: (1) EditNet, a language module with an adaptive copy mechanism (Copy-LSTM) and a Selective Copy Memory Attention mechanism (SCMA), and (2) DCNet, an LSTM-based denoising auto-encoder. These components enable our model to directly copy from and modify existing captions. Experiments demonstrate that our new approach achieves state-of-art performance on the MS COCO dataset both with and without sequence-level training.
DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. However, real-time recognition of dynamic micro hand gestures from video streams is challenging for in-vehicle scenarios since (i) the gestures should be performed naturally without distracting the driver, (ii) micro hand gestures occur within very short time intervals at spatially constrained areas, (iii) the performed gesture should be recognized only once, and (iv) the entire architecture should be designed lightweight as it will be deployed to an embedded system. In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues. For this purpose, we initially collected a dataset named Driver Micro Hand Gestures (DriverMHG), which consists of RGB, depth and infrared modalities. The challenges for dynamic recognition of micro hand gestures have been addressed by proposing a lightweight convolutional neural network (CNN) based architecture which operates online efficiently with a sliding window approach. For the CNN model, several 3-dimensional resource efficient networks are applied and their performances are analyzed. Online recognition of gestures has been performed with 3D-MobileNetV2, which provided the best offline accuracy among the applied networks with similar computational complexities. The final architecture is deployed on a driver simulator operating in real-time. We make DriverMHG dataset and our source code publicly available.
Adaptive Object Tracking Using Algorithms Employing Machine Learning

Object tracking is considered as one of the most crucial areas of research. Real time applications of object tracking include video surveillance, vehicle tracking and so on. In this paper, solutions for adaptive tracking of moving objects using algorithms that employ machine learning are discussed. This paper discusses the following machine learning tracking algorithms; Multiple Instance Learning Track (MIL Track), Tracking-Learning-Detecting (TLD), Multi Domain Network (MDNet) and Circulant Structure Kernel tracker (CSK). MIL Track algorithm uses a special classifier to generate an adaptive appearance model. TLD is a tracking framework that is based on the processes of tracking learning and detection. MDNet uses Convolutional Neural Network for training the tracker. The depth analysis method employs CSK to handle occlusions. These tracking algorithms use techniques that have proven to be successful in overcoming the adaptive object tracking problems. These problems and their corresponding solutions are discussed further in this paper.
SpokeSense

SpokeSense is an easy-to-install sensing solution intended for wheelchair basketball players. Our aim with SpokeSense is to provide support for both long-term and real-time analysis of performance information for wheelchair court sport athletes. We developed and tested SpokeSense in the context of wheelchair basketball. We extend prior work quantifying wheelchair court-sport activity by enabling real-time review of performance data and other important events as well as supporting long-term logging. Our secondary focus was on creating a means of tracking activity that would be accurate, safe to use, and practical. In this article, we discuss the design of our sensor to support practical use during wheelchair basketball activities, provide access to relevant accurate data, and support real-time and post-hoc review. We discuss the performance of our sensor and the initial interface to support data review. Finally, we discuss broader implications for the design of assistive technologies for physical activity.
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations. However, vanilla GP methods scale cubically in the amount of observations. In this work, we propose a novel approach for scaling GP regression with derivatives based on quadrature Fourier features. We then prove deterministic, non-asymptotic and exponentially fast decaying error bounds which apply for both the approximated kernel as well as the approximated posterior. To furthermore illustrate the practical applicability of our method, we then apply it to ODIN, a recently developed algorithm for ODE parameter inference. In an extensive experiments section, all results are empirically validated, demonstrating the speed, accuracy, and practical applicability of this approach.
An Improved YOLOv3 Algorithm for Pedestrian Detection on UAV Imagery

Pedestrian detection algorithms based on deep learning often rely on high-performance image processing platforms, and it does not adapt to high portability requirements of practical engineering. In order to solve the problem, an improved approach is proposed, which can be applied to embedded platforms. Firstly, reinforcing dense connectivity is adopted to connect the multiple layer features. Then the k-means++ algorithm is used to cluster the pedestrian targets, and the method of eliminating duplicate detection is optimized. Finally, the detection model is obtained through multi-scale training. Experiment results show that Dense-YOLOv3-tiny can detect pedestrian on UAV imagery in real time and exhibit higher precision and recall rate than the YOLOv3-tiny model in the embedded platforms.
Deep learning in precision medicine

This chapter introduces deep learning and its application in precision medicine. It provides the readers the knowledge about the prerequisites for working with deep learning and the potential that this technique to better understand human diseases, the fundamental biology underlying it, and guide contribute its diagnosis and treatment. Deep learning has a wide variety of models and we have focused our chapter on the most popular one: convolutional neural network (CNN). In this chapter, we have reviewed key concepts and relevant recent work in medicine applying this methodology and, as an example; we have explained deep learning application for segmentation purpose in breast cancer hematoxylin and eosin (H&E)-stained pathological images. Thus, this provides the essential knowledge for researchers who are going to work with deep learning and are new in this field.
An Improved Retinex low-illumination image enhancement algorithm

Low-illumination images are generally low-quality images. The retinex algorithm can cause halo artifacts and loss of details in processing. Therefore, an improved Retinex algorithm is proposed. Firstly, the HSI color space which is more in line with the human visual characteristics is selected instead of the RGB image, that is, the luminance component I is processed. Then, the illuminance image is estimated by using a guided filter that fuses the edge detection operator, and the edge detection operator can be better positioned. At the edge, an illuminance image with rich edge information can be obtained; after obtaining the illuminance image, the reflected image can be obtained by the Retinex principle, the obtained reflected image is subjected to low-rank decomposition, and the low-rank property of the image is used to suppress the enlarged halo and the enhancement process. Noise; finally, the visual effect is further improved by local contrast enhancement. Experiments show that the algorithm can effectively improve the brightness and contrast of the image, preserve the details of the image, and also suppress the noise interference in the enhancement process. The subjective visual effect and objective evaluation results of the image have also been greatly improved.
Concentration of the Matrix-Valued Minimum Mean-Square Error in Optimal Bayesian Inference

We consider Bayesian inference of signals with vector-valued entries. Extending concentration techniques from the mathematical physics of spin glasses, we show that the matrix-valued minimum mean-square error concentrates when the size of the problem increases. Such results are often crucial for proving single-letter formulas for the mutual information when they exist. Our proof is valid in the optimal Bayesian inference setting, meaning that it relies on the assumption that the model and all its hyper-parameters are known. Examples of inference and learning problems covered by our results are spiked matrix and tensor models, the committee machine neural network with few hidden neurons in the teacher-student scenario, or multi-layers generalized linear models.
Prevention of Medication Loss through a Marketplace and Blockchain

The emergence of a digital marketplace in the world and especially in Brazil around 2012 has innovated the way merchants and consumers use technology to commercialize products and services. Consumers are able to search for products and services from many merchants at once while merchants can reach consumers in many different platforms as well as creating a network of business and relationships between the two.
On inharmonicity in bass guitar strings with application to tapered and lumped constructions

Abstract In this study, the inharmonicity of bass guitar strings with and without areas of lowered and raised mass near the saddle is studied. Using a very high sample rate of over 900 kHz enabled finite difference time domain simulation to be applied for strings that simultaneously have nonzero stiffness and linear density which varies along the length of the string. Results are compared to experiments on specially constructed strings. Perturbation theory is demonstrated to be sufficiently accurate (and much more computationally efficient) for practical design purposes in reducing inharmonicity. The subject of inharmonicity is well known in pianos but has not been studied extensively in bass guitar strings. Here, the inharmonicity is found to be low in the lowest (open string) pitch on the five string bass guitar ( $${B}_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>B</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math> ) given typical standard construction. Conversely, the inharmonicity is high (around 100 cents at the 10th partial) when that string is sounded when stopped at the 12th fret and very high (around 100 cents at the 6th partial) when that string is stopped at the 21st fret. Bass guitar strings were constructed with three different constructions (standard, tapered and lumped) in order to demonstrate how incorporating a lump of raised mass near the saddle can achieve close to zero inharmonicity for the lower frequency partials. This also has potential in terms of improving the use of higher fret numbers for musical harmony (reducing beating) and also in controlling pitch glide that has, with some exceptions, often been attributed solely to nonlinear behaviour.