Reviewerly/exHarmony huggingface dataset:
Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach

Sentiment analysis of user-generated online content is crucial for smart city analytics and relevant social services. Researchers have relied mainly on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their feelings and share emotions. Sentiment analysis of such large-scale visual content, such as those in image tweets, helps to obtain user sentiments toward events or topics and therefore complement textual sentiment analysis. Motivated by the need to leverage large scale yet noisy training data to solve the extremely challenging problem of face sentiment analysis, we employ Convolutional Neural Networks (CNN). We designed a suitable CNN architecture to classify facial emotions and analyze sentiments. We have conducted extensive experiments on labeled images. The results show that the proposed CNN achieved a very good performance in face sentiment analysis with 89.9% of F1-measure.
Domain adaptation based on incremental adversarial learning

Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and target domain with related but different distributions. Unsupervised domain adaptation could be a scenario wherever we've labeled unlabeled target data and source data. In this paper, an incremental adversarial learning method is proposed for unsupervised domain adaptation. In this work, the unknown target labels are predicted and according to these estimated labels, some target data with more similarity to the source data are added to the source data to improve the adaptation between two domains. We use the adversarial discriminative approach as the base unsupervised domain adaptation technique. We do this to handle the large domain shift between the source and target domain distributions. Experimental reports prove that our approach performs much better on several visual domain adaptation tasks.
Görüntü İyileştirme için Sıralı Modifiyeli Yerçekimi Arama Algoritması

Gravitational Search Algorithm (GSA) is based on the acceleration trend feature of objects with a mass towards each other and includes many interdependent parameters. The gravitational constant among these parameters influences the speeds and positions of the agents, meaning that the search capability depends on the largescale gravitational constant. The proposed new algorithm, which was obtained with the use of two operators at different times of the call and sequentially doing works, was named as Sequentially Modified ‎ Gravitational Search Algorithm (SMGSA). SMGSA is applied to 10 basic and 6 composite benchmark functions. Each function is run 30 times and the best, mean and median values are obtained. The achieved results are compared with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSA among the heuristic optimization algorithms. Between GSA and the operator for each function convergence speed, standard deviation and graphical comparisons are included. Beside this, by using the Wilcoxon signed rank test, the comparison of the averages of the data as two dependent groups of GSA and the new operators is performed. It is seen that the obtained results provided better results than the other methods. Additionally, in this study, SMGSA was applied to the transformation function among image enhancement techniques which are engineering applications. The success of this method has been increased by optimizing the parameters of the transformation function used. Effective improvement has been achieved in terms of both visual and information quality.
Symbol recognition system based on 3D stereo vision

With the continuous development of computer science and technology, symbol recognition systems may be converted from two-dimensional space to three-dimensional space. Therefore, this article mainly introduces the symbol recognition system based on 3D stereo vision. The three-dimensional image is taken by the visual coordinate measuring machine in two places on the left and right. Perform binocular stereo matching on the edge of the feature points of the two images. A corner detection algorithm combining SUSAN and Harris is used to detect the left and right camera calibration templates. The two-dimensional coordinate points of the object are determined by the image stereo matching module, and the three-dimensional discrete coordinate points of the object space can be obtained according to the transformation relationship between the image coordinates and the actual object coordinates. Then draw the three-dimensional model of the object through the three-dimensional drawing software. Experimental data shows that the logic resources and memory resources occupied by image preprocessing account for 30.4% and 27.4% of the entire system, respectively. The results show that the system can calibrate the internal and external parameters of the camera. In this way, the camera calibration result will be more accurate and the range will be wider. At the same time, it can effectively make up for the shortcomings of traditional modeling techniques to ensure the measurement accuracy of the detection system.
Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data

Online nonnegative matrix factorization (ONMF) is a matrix factorization technique in the online setting where data are acquired in a streaming fashion and the matrix factors are updated each time. This enables factor analysis to be performed concurrently with the arrival of new data samples. In this article, we demonstrate how one can use online nonnegative matrix factorization algorithms to learn joint dictionary atoms from an ensemble of correlated data sets. We propose a temporal dictionary learning scheme for time-series data sets, based on ONMF algorithms. We demonstrate our dictionary learning technique in the application contexts of historical temperature data, video frames, and color images.
Towards Unranked Fuzzy Theory

One of the main tools in knowledge representation is ontology, which is a collection of logic-based formal language sentences. These sentences are used by automated reasoning programs to extract new knowledge and answer to the given questions. Although ontology languages are standardized by W3C, there are still many problems remaining. One of the most important problems is related to, so called, fuzzy ontologies. These are ontologies, where information is vague and imprecise. Fuzzy ontologies are obtained by integrating fuzzy logic with ontologies. Such kind of ontologies have applications in many different fields, such as medicine, biology, e-commerce and the like. In this paper, we develop an unranked fuzzy logic and study some of its properties. The novelty of our approach is that we will extend many-valued logics with sequence variables and flexiblearity function and predicate symbols. To the best of our knowledge, such formalisms are not yet studied in the literature. The unranked fuzzy language will broaden the knowledge engineering capabilities in different fields.
Using Accelerometers in Mobile Phones to Estimate Blood Alcohol Levels

This paper studies methods for determining the blood alcohol content of individuals by using data from commodity accelerometers in mobile phones carried on person. A significant challenge is that such data is very noisy and often irregular (many large gaps) as well. This paper provides a detailed analysis of a recently released dataset of accelerometer traces and associated readings of transdermal alcohol content (TAC). It describes a set of features extracted from the raw accelerometer traces that are effective for the task of determining TAC levels. It presents results of an experimental study of regression methods that use these features to predict TAC levels from accelerometer traces as well as of classification methods that predict whether the person carrying the mobile phone has TAC levels above given thresholds.
Nimble: Mobile Interface for a Visual Question Answering Augmented by Gestures

Virtual Assistants are becoming increasingly popular. However voice-only systems providing limited functionality and minimal variability are often unusable because the exposed behavior is either fully deterministic or essentially ambiguous for supporting human-like dialogues. This paper introduces a Nimble, solution that allows users to ask short questions and to get answers about objects selected from a scene by natural pointing gestures. With a modified Visual Question Answering model we have shown how the integration of gestures to the attention mechanism can reduce questions? ambiguity while sustaining the same accuracy level of the system. We performed this by modifying the model's attention scores using gestures fused with linguistic information.
Measuring efficiency levels in Qatari organizations and causes of inefficiencies

Organizational efficiency is considered as one of the important factors affecting the sustainability and viability of an organization and of the larger society that such organizations are part of. Scholars in the interdisciplinary studies of engineering, business, and economics have concluded that the socioeconomic parameters of society are deeply linked with organizational efficiency. Organizational efficiency is generally defined as a measure of the relation between the input resources and output generated within a system. Organizational efficiency in public and private sector enterprises plays a crucial role in ensuring value creation and sustaining the economic and social development of countries. The importance of a sustainable economy and social well-being requires many countries around the world to design and implement tailored pathways to achieve their target of sustainable development, which requires assessing and continuously improving the efficiency of their organizations. Measuring efficiency and implementing periodic course corrections are imperative for maintaining high levels of organizational efficiency. The literature on organizational efficiency measurement is limited and qualitatively inferior, especially when it comes to sustainability considerations and for organizations operating in the Arabian Gulf. The research approach developed in this study to measure efficiency in Qatari organizations is crucial in understanding and improving the transformation journey of Qatari, and similar organizations. This paper focuses on the research approach adopted to measure organizational efficiency based on international best practices and conducts a comparative analysis on the local organizations from a sustainability perspective considering all dimensions of economics, environment and social impacts. The methodology involved in measuring and benchmarking organizational sustainability identifies the “as is” state of organizational sustainability and efficiency in the organization. Identifying the causes of the efficiency gap and improving organizational efficiency from the local perspective are the objectives. The survey result ranked the main areas for improvement within local organizations as 23% Overall organization culture and behavior, 21% Human Resources, 19% Leadership and Governance, 18% Operation, 11% Finances and 8% Quality Assurance.
Artificial intelligence and data analytics for geosciences and remote sensing

To address the limitation of conventional statistics in dealing with hyperspectral data of satellite and airborne images, two contextual analyses are introduced in this chapter. The first case study presents the development of an artificial intelligence (AI) and data analytics algorithm capable of classifying hyperspectral data to support remote sensing and geographic information systems researchers in understanding and predicting changes in natural earth processes. The classification algorithm is based on a fuzzy approach combining a decision tree classifier with a fuzzy multiple-criteria decision analysis classifier. The second case study presents the development of an AI tool that extracts features from the hyperspectral data to transform a two-dimensional (2D) satellite and airborne picture to a pseudo-3D picture to improve complexity and produce multidirectional sun-shaded pictures and their edges. Such 3D images are useful in supporting the discovery of prospective ground for mineral exploration, extraction from the earth of precious minerals or other geological materials, usually from deposits of ore, veins, lodes, seams, reefs, or placer deposits, and overall to improve the efficiency and effectiveness of mineral exploration.
An Improved Conditional Generative Adversarial Network for Microarray Data

Microarray data has the characteristics of high dimension and few samples, which brings much difficulty to its processing. It is necessary to expand microarray data to increase the data sample size. Although the traditional generative adversarial network (GAN) could expand the sample of microarray data set, it could not get the corresponding label value of the generated sample and may generate “dirty” samples. Although the conditional generative adversarial network could get the labels of the generated samples, it is difficult to make the algorithm converge, and there are also “dirty” samples in the generated samples. To ensure that the algorithm could converge and the generated samples have corresponding labels and eliminate the “dirty” samples in the generated samples, an improved conditional generative adversarial network based on feature matching penalty and probability model setting threshold is proposed. On one hand, to improve the convergence probability of the CGAN, a feature matching penalty strategy is proposed in this study, which consists in finding a Nash equilibrium to a two-player non-cooperative game. On the other hand, to overcome the problem of the “dirty” samples from the generated samples, a strategy of the probability model is proposed to set the threshold, which could screen high-quality samples and discard “dirty” samples. The proposed CGAN could improve the classification accuracy as well as data generation ability, which is conducive to the diagnosis of diseases and the development of functional genomics. Experimental results on several public microarray data sets verifies the effectiveness of the proposed CGAN.
Improved Fuzzy Hierarchical Clustering Algorithm and its Application in Software Re-architecting

Abstract Computer technology changes with each passing day. As a member of software system, the architecture of legacy system becomes complex and difficult to adapt to complex software functional requirements in the process of evolution. It has not only affected the late legacy system maintenance work, but also to the enterprise has brought serious losses. In order to solve the above problems, this paper presents a Fuzzy Hierarchical Clustering algorithm Based on Information Loss, FHCBIL. There are two improvements in FHCBIL algorithm: On the one hand, this algorithm extends the weights allocation of entity features, which solves the problem that the two element relation can not reflect the influence degree of entity feature on entity. On the other hand, FHCBIL algorithm uses the information loss as a similarity calculation method, avoiding the problem of sensitive data set to the irregular data set in the clustering process. In this paper, we further apply FHCBIL algorithm in software re-architecting, give the model of software re-architecting, and implement new nested software architecture. In order to evaluate the quality of the new architecture, we evaluate the new architecture from two aspects: the rationality of the new architecture hierarchy and the accuracy of entity division. The experimental results show that the new architecture based on FHCBIL algorithm has high cohesion and low coupling. And the accuracy of the new architecture is high.
Narrowly avoided cusp catastrophes in Least Squares Orthogonal Distance Fitting

Computer animation often requires that complex objects need to be modeled by fitting them with simpler shapes such as splines. It is necessary that these simpler shapes respond continuously as the object changes shape. This response can become complex if the topology of the error function is changing. The topological events which can occur when a spline is fit to a changing object are classified and analyzed, with a view towards defining conditions which make them possible. The Least Squares Orthogonal Distance Fitting (ODF) method is used to fit a continuous parametric function whose shape is changing. The fit is performed using a closed-shape Beta2-spline. Three types of topological events can occur: self-annihilation of two solutions, simultaneous merge of three solutions into one, and crossover of two solutions in which a local minimum and saddle point interchange roles. The events can be detected and classified by inspecting the determinants of two ​second-order response matrices. The last two types of event occur only within sub-manifolds defined by additional constraints not normally present in the ODF solution. The nature of these constraints is defined. The constrained events are interpreted in the context of catastrophe theory, which describes dynamical systems that can exist in multiple states with the possibility of discontinuous transitions between them. Within this theory they appear as narrowly avoided cusp catastrophes.
Can a Social Robot Learn to Gesticulate Just by Observing Humans?

The goal of the system presented in this paper is to develop a natural talking gesture generation behavior for a humanoid robot. With that aim, human talking gestures are recorded by a human pose detector and the motion data captured is afterwards used to feed a Generative Adversarial Network (GAN). The motion capture system is capable of properly estimating the limbs/joints involved in human expressive talking behavior without any kind of wearable. Tested in a Pepper robot, the developed system is able to generate natural gestures without becoming repetitive in large talking periods. The approach is compared with a previous work, in order to evaluate the improvements introduced by a computationally more demanding approach. This comparison is made by calculating the end effectors’ trajectories in terms of jerk and path lengths. Results show that the described system is able to learn natural gestures just by observation.
Design of Binocular Stereo Vision System Via CNN-based Stereo Matching Algorithm

In this paper, we design a binocular stereo vision system based on an adjustable narrow-baseline stereo camera for extracting depth information from a rectified stereo pair. The camera calibration and rectification are performed to get a rectified stereo pair serving as the input to the stereo matching algorithm. This algorithm searches the corresponding points between the left and right images and produces a disparity map that is used to obtain the depths via the triangulation principle. We focus on the first stage of the algorithm and propose a CNN-based approach to calculating the matching cost. Fast and slow networks are presented and trained on standard stereo datasets. The output of either network is regarded as the initial matching cost, followed by a series of post-processing methods for generating qualified disparity maps. The contrast tests have demonstrated that the CNN-based methods outperform census transformation on the mentioned datasets. Finally, we advance two error criteria to acquire the range of system working distance under diverse baseline lengths.
CHASM: Security Evaluation of Cache Mapping Schemes

Cache side-channel attacks have become a significant security threat across a variety of hardware architectures. By observing which sets of a cache are accessed by the victim, the attacker gleans critical information about the address bits in the victim’s access, thereby revealing portions of secret keys used by encryption algorithms (or other sensitive information). Fundamentally, this ability to deduce information about addresses given the accessed sets depends on knowing (or discovering) how addresses are mapped to cache sets by hardware.In this work, we evaluate the security of the various cache mapping functions. Using an information-theoretic formulation, our framework (denoted CHASM) estimates the number of address bits that are likely leaked by different mapping schemes. Our analysis leads to several new insights. One, all one-to-one schemes that map n set-index address bits to \(2^n\) set-indices leak all n bits. Two, based on memory footprint, programs often leak several additional (viz., tag) bits (e.g., AES leaks 39 bits out of 42 at L2). Three, tag bits leak even with the use of address space layout randomization (16–33 bits). Four, the use of huge pages in order to reduce pressure on TLBs increases leakage (5 additional bits on average). Since many of these techniques have opposing impact on performance and security, we use a new security-delay ratio metric to jointly evaluate mapping schemes for both performance and security.
Human Behavior Recognition Based on the Genetic Algorithm Feature Selection and Sensor Data

In multi-sensor behavior recognition, considering that human behavior is the result of multi-node collaboration, sensors in various parts are related under the same behavior. In this study, three acceleration sensors were deployed to collect data. Besides acceleration data, RSS data between different sensors is also collected to avoid the impact of the single sensor data on the recognition results and improve the accuracy of behavior recognition. In order to avoid the subjective effect of manual feature selection on multi-sensor behavior recognition, genetic algorithm is used to select the optimal feature subset from all features as input of the classifier to establish a classification model. The experimental results show the feasibility and effectiveness of using RSS data and genetic algorithm to select features, and the recognition accuracy rate reaches 95.63%.
Supply Chain Management for Selling Farm Produce Using Blockchain

The blockchain technology holds an innovative and secured stand for a novel distributed and translucent business mechanism in businesses. The key goal of this research is to develop a framework for supplying farm produce in a system which monitors the profit of each node and maintains transparency using this technology. It helps the farmers to get the expected fair price of his product. If the price of the commodity of the particular product is increased beyond his/hers registered commission value, then fraud is detected easily in the system and the transaction will be terminated with an error message. The farmer can view the commission as well as the product price at each node (viz. packager, carrier, retailer) of supply chain. In addition to maintain transparency in the system, all these nodes can see the status of their products and transactions entered by every other node in the supply chain.
Fast Similar Patient Retrieval from Large Scale Healthcare Data: A Deep Learning-Based Binary Hashing Approach

Patient similarity plays an important role in precision evidence-based medicine. While great efforts have been made to derive clinically meaningful similarity measures, how to accurately and efficiently retrieve similar patients from large scale healthcare data remains less explored. Similar patient retrieval has become increasingly important and challenging as the volume of healthcare data grows rapidly. To address the challenge, we propose a coarse-to-fine approach using binary hash codes and embedding vectors derived from an artificial neural network. Experimental results demonstrated that this approach can reduce the time for retrieval by up to over \(50.6\%\) without sacrificing the retrieval accuracy. The time reduction became more evident as the data size increased. The retrieval efficiency increased as the number of bits in binary hash codes increased. Descriptive analysis revealed distinct profiles between similar patients and the overall patient cohort.