Reviewerly/exHarmony huggingface dataset:
Multi-feature fusion of convolutional neural networks for Fine-Grained ship classification

Fine-Grained ship classification is quite challenging because the visual differences between the subcategories are small. Due to the large intra-class similarity, it is very difficult to classify the ship objects without bounding box/part annotations. In this paper, we propose a model that combine s multiple deep CNN features and use fusion strategies to explore of multi-scale features relationship. Because different levels/depths CNN features have different properties, so we combine multiple low-level local CNN features with high-level global CNN feature for object classification. The model shows a good way of tailoring pre-trained CNN models to fine-grained ship classification, which have lower cost in computation and storage compared with some state-of-the-art CNN methods and achieves the significant classification performances in FGVC-Aircraft and Stanford Cars datasets.
A parallel refined probabilistic approach for QoS-aware service composition

Service composition integrates existing online services to provide a value-added service. With the rapid growth of web services with similar functionalities, Quality of Service (QoS) has emerged as an important quantitative criterion on non-functional aspects. The optimization of QoS-aware service composition, depending on different aggregated QoS attributes has attracted significant attention. The dynamic nature of QoS-aware service composition adds further challenges to the optimization problem. Most existing approaches ignore the diversity of solutions, which have the potential to provide alternative compositions when changes occur. A few works only partially explore the search space and do not consider the optimality of solutions and the computational cost concurrently. To address these issues, we propose a novel reactive approach, called MrEDA, which integrates the estimation of distribution algorithm (EDA), restricted boltzmann machine (RBM), and multi-agent technology. It constructs a refined probabilistic model to diversify alternative solutions and guide the search by adaptively capturing the promising information of a service composition. Meanwhile, multiple agents make use of a flexible parallelism with distinct explorations and adaptive sampling to improve the global optimization and speed up the optimization. The effectiveness and efficiency of our approach for adaptive service composition is validated through an extensive experimental evaluation.
Are you emoji savvy? Exploring nonverbal communication through emojis

Courses: Undergraduate courses in interpersonal communication, nonverbal communication, relational communication, computer-mediated communication (CMC), and intercultural communication, as well as basic communication courses.Objectives: The purpose of this activity is twofold: (1) to demonstrate how nonverbal communication shapes content and relational meaning in interpersonal communication; and (2) to demonstrate how nonverbal communication is rule-guided and influenced by culture.
Human Robot Interaction Using Diver Hand Signals

Current methods for human robot interaction in the underwater domain seem antiquated in comparison to their terrestrial counterparts. Visual tags and custom built wired remotes are commonplace underwater, but such approaches have numerous drawbacks. Here we describe a method for human robot interaction underwater that borrows from the long standing history of diver communication using hand signals; a three stage approach for diver-robot communication using a series of neural networks.
A rewrite-based optimizer for Spark

Spark is the leading platform for distributed large-scale data processing. Spark’s Application Programming Interface (API) has a powerful easy-to-use distributed abstractions similarly related to functional programming (e.g., map, filter, reduce) in several different languages. However, writing an efficient Spark applications is still error-prone, time-consuming, and requires a clear and deep understanding of the inner-workings of Spark. For instance, the same task can be implemented in several different ways, yet the execution time can vary drastically between them. For this, we introduce TaBOS, a rewrite-based optimizer for Spark programs. TaBOS takes a Spark job and automatically generates a state-space of equivalent optimized jobs using a set of semantics-preserving rewrite rules. Then, from the generated state-space, it selects one optimal program based on a predefined strategy. We introduce several selection strategies (e.g., job with maximum number of applied rewrite rules, job with minimum number of heavy operations) for identifying an optimal job from the generated state-space. We evaluate the effectiveness, robustness and speedup gain of our solutions using several case studies.
Class of tight bounds on the <i>Q</i>‐function with closed‐form upper bound on relative error

In this paper, we propose a novel class of parametric bounds on the Q ‐function, which are lower bounds for 1 ≤ a &lt; 3 and x &gt; x t = ( a ( a ‐1) / (3‐ a )) 1/2 , and upper bound for a = 3. We prove that the lower and upper bounds on the Q ‐function can have the same analytical form that is asymptotically equal, which is a unique feature of our class of tight bounds. For the novel class of bounds and for each particular bound from this class, we derive the beneficial closed‐form expression for the upper bound on the relative error. By comparing the bound tightness for moderate and large argument values not only numerically, but also analytically, we demonstrate that our bounds are tighter compared with the previously reported bounds of similar analytical form complexity.
A PARAMETRIC, RESOURCE-BOUNDED GENERALIZATION OF LÖB’S THEOREM, AND A ROBUST COOPERATION CRITERION FOR OPEN-SOURCE GAME THEORY

Abstract This article presents two theorems: (1) a generalization of Löb’s Theorem that applies to formal proof systems operating with bounded computational resources, such as formal verification software or theorem provers, and (2) a theorem on the robust cooperation of agents that employ proofs about one another’s source code as unexploitable criteria for cooperation. The latter illustrates a capacity for outperforming classical Nash equilibria and correlated equilibria, attaining mutually cooperative program equilibrium in the Prisoner’s Dilemma while remaining unexploitable, i.e., sometimes achieving the outcome (Cooperate, Cooperate), and never receiving the outcome (Cooperate, Defect) as player 1.
Investigating Quantum Coherence by Negative Excursions of the Wigner Quasi-Distribution

Quantum information and quantum communication are both strongly based on concepts of quantum superposition and entanglement. Entanglement allows distinct bodies, that share a common origin or that have interacted in the past, to continue to be described by the same wave function until evolution is coherent. So, there is an equivalence between coherence and entanglement. In this paper, we show the relation between quantum coherence and quantum interference and the negative parts of the Wigner quasi-distribution, using the Wigner signed-particle formulation. A simple physical problem consisting of electrons in a nanowire interacting with the potential of a repulsive dopant placed in the center of it creates a quasi two-slit electron system that separates the wave function into two entangled branches. The analysis of the Wigner quasi-distribution of this problem establishes that its negative part is principally concentrated in the region after the dopant between the two entangled branches, maintaining the coherence between them. Moreover, quantum interference is shown in this region both in the positive and in the negative part of the Wigner function and is produced by the superposition of Wigner functions evaluated at points of the momentum space that are symmetric with respect to the initial momentum of the injected electrons.
Design and Implementation of an Intelligent Dust Cleaner Robot for Uneven and Nonstructural Environment

With the advancement of automobile industry the control for robotics have much attention just because of reliability and time optimization capability with performance. There is a need of an automatic robot which is capable for the cleaning of the room, office, mall etc and this paper is focusing on such prototype. This paper represents the room cleaning robot which has map storage and wall following functionality and all this implemented using Arduino Uno. With the combination of sensor assembly, algorithm and the intelligent dust cleaner shape make him feasible and effective for all type of environments. It has much more benefits over conventional household vacuum cleaners such as there is no need of personal to clean the room, office etc and fulfilling the main purpose for cleaning the room automatically. In the last results show that this robot is saving much money and time for cleaning the rooms, offices, etc.
Complaint Classification Using Hybrid-Attention GRU Neural Network

Recently, a growing number of customers tend to complain about the services of different enterprises on the Internet to express their dissatisfaction. The correct classification of complaint texts is fairly important for enterprises to improve the efficiency of transaction processing. However, the existing literature lacks research on complaint texts. Most previous approaches of text classification fail to take advantage of the information of specific characters and negative emotions in complaint texts. Besides, some grammatical and semantic errors caused by violent mood swings of customers are another challenge. To address the problems, a novel model based on hybrid-attention GRU neural network (HATT-GRU) is proposed for complaint classification. The model constructs text vectors at character level, and it is able to extract sentiment features in complaint texts. Then a hybrid-attention mechanism is proposed to learn the importance of each character and sentiment feature, so that the model can focus on the features that contribute more to text classification. Finally, experiments are conducted on two complaint datasets from different industries. Experiments show that our model can achieve state-of-the-art results on both Chinese and English datasets compared to several text classification baselines.
Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization

The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learning algorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.
Introduction to Nature-Inspired Algorithms

This chapter is an introduction to nature-inspired algorithms. It first discusses the reason why such algorithms have been very popular in the last decade. Then, different classifications of such methods are given. The chapter also includes the algorithms and problems investigated in this book.
A Method for Detecting and Analyzing the Sentiment of Tweets Containing Conditional Sentences

Society is developing daily, and consequently, the population is more interested in public opinion. Surveys are frequently organized for detecting the attitude as well as the belief of the community in situations and their opinion about the measures or products. Users particularly express their feelings through comments posted on social networks, such as Twitter. Tweet sentiment analysis is a process that automatically detects personal information from the public emotion of the users about the events or products related to them from published tweets. Many studies have solved the sentiment analysis problem with high accuracy for the general tweets. However, these previous studies did not consider or dealt with low performance in case of tweets containing conditional sentences. In this study, we focus on solving the detection and sentiment analysis problem of a specific tweet type that includes conditional sentences. The results show that the proposed method achieves high performance in both the tasks.
Age and Gender Identification in Unbalanced Social Media

Nowadays, people share a lot of information in social media in the form of videos, news, photos, posts, likes, etc. This large amount of generated information reflects the opinions, emotions and preferences of the users. As an example of the previous, Pinterest is a popular social network where the users show their interests in the form of pins, which are information units formed by a short text comment and an image. In this research, we study the problem of building a model to characterize users of Pinterest with two demographic variables, age and gender, using their textual information post in the network. To do that, we introduce a dataset formed by the texts in English from 548,761 pins corresponding to 264 users. This dataset is imbalanced and reflects the actual distribution of the social network for gender and age, with a dominant presence of women over men, and of middle age persons over young persons. With this dataset, we conducted experiments with a diversity of machine learning models, a variety of features and considering a set of performance metrics. Our results produce interesting insights about the problem.
A Framework for Large-Scale Analysis of Video \"in the Wild\" to Assist Digital Forensic Examination

The European Union-funded project LASIE, aims to assist forensics investigators through innovative tools for image and video analysis, object detection and tracking, and event detection. These tools exploit the latest advances in machine learning to handle the challenges in processing content from real-world data sources.
Group K-SVD for the classification of gene expression data

In this paper, we propose a new sparse learning model for the classification of gene expression data, named Group K-Singular Value Decomposition (Group K-SVD). It learns the optimal dictionary and sparse representation from the training data, and then assigns the out-of-sample data to the class with the nearest centroid. Group K-SVD reduces the redundancy of over-complete dictionary by using a group update strategy during the dictionary update stage. To solve the optimization problem, we also develop a Multivariate Orthogonal Matching Pursuit (MOMP) algorithm for the sparse coefficient update stage. In the evaluation of the performance of Group K-SVD model, we implement three Group K-SVD algorithms by incorporating MOMP as well as another two popular optimization algorithms into the sparse coefficient update stage. Sufficient experimental results demonstrate that our Group K-SVD algorithms are both effective and efficient in comparison with the state-of-the-methods on real-world gene expression datasets.
Information flow in logic for distributed systems: Extending graded consequence

In this paper we have proposed a variation of Barwise and Seligman’s proposal for information flow among a distributed network of agents by bringing in a notion of belief structure and a notion of graded inference. In their proposal, belief profile of an agent has played an important role as it is explicitly mentioned in the principles of information flow. In contrary, while developing the formal counterpart of information flow they did not address any connection with the belief profile of an agent. Besides, they have accommodated a non-deterministic notion of derivation to design the local logic of an agent keeping a room open for unsound inferences. Our proposal here is to bring in the notion of belief structure of an agent in the development of the local logic of an agent, and convert the non-deterministic nature of consequence to a deterministic graded (four-valued) notion of consequence. For the time being, we have focused on a specific context of decision making by aggregating opinions of agents based on an approach, known as the theory of graded consequence.
Real-Time Anomaly Detection Method for Space Imager Streaming Data Based on HTM Algorithm

Healthy operational status of space imager is important for the successful completion of space exploration tasks, and any abnormal event may lead to serious faults or disasters. Real-time anomaly detection is an important technique for finding abnormal parameters and potential faults of space equipment. The downlink data of space imager is streaming data that presents technique challenges and opportunities. The fundamental capability of anomaly detection techniques for space imager streaming data is to model each stream in an unsupervised fashion and detect unusual. Under the influence of operating instructions, environmental conditions and equipment performance, the streaming times series fluctuates acutely and indicates obvious concept drifting. Besides, application constraints require the method to process data in real-time, not batches. However, most anomaly detection methods need offline training of amount of historical data, and it is difficult to realize the online learning and detecting continuously. In this paper, a novel method is proposed that meets the constraints. The method is based on online time series memory and learning algorithm called Hierarchical Temporal Memory (HTM). We also present the comparison results of the proposed method and other algorithms, and the experiments show the proposed method could realize the real-time anomaly detection effectively.
Automatic Power Line Detection for Low-Altitude Aircraft Safety Based on Deep Learning

Power line detection (PLD) is of vital importance for the flight security of low-altitude aircraft, such as helicopters and unmanned aerial vehicles (UAVs). This paper firstly summarises past studies on PLD based on image processing technique extensively. Secondly, different from the traditional PLD methods, we propose an approach to detect power lines based on deep learning which has been demonstrated having unparalleled performance in the field of image processing and computer vision. Specifically, the convolutional neural network (CNN) is employed in this study to extract features and thus detecting power lines from images. By utilising CNN, the feature extraction and object detection process are completed jointly unlike traditional PLD methods. A public dataset is used to demonstrate the performance of the proposed approach. This paper also gives recommendations for the future development of PLD.
A miniaturized biaxial tensile apparatus based on torsional loading design

A miniaturized biaxial tensile apparatus is presented. Based on a torsional loading design, the total dimensions and weight of the apparatus have been reduced, while the output force has been increased. Owing to the use of four independent stepping motors, the apparatus can realize arbitrary displacement ratios during the loading processes. The apparatus is designed to be applied with a laser scanning confocal microscope realizing in situ mechanical tests. Through a proof-of-principle experiment with an optimized cruciform shaped specimen, the developed biaxial tensile apparatus is proved to be suitable for in situ tests of biaxial stress analysis.
Measuring Vulnerabilities of Bangladeshi Websites

No web application is without the risk of exploits and fully infallible. Even so, web services have been integrated into our daily lives in such a way that we have been accustomed to sharing huge personal information on the web. Within this setting, the aim of this study is to show a method to detect maximum vulnerabilities of web-applications at minimum cost and effort. Using the penetration testing and source code analysis mechanisms, representing the black box and white box testing respectively, we have evaluated the vulnerabilities of selected websites of Bangladesh against a set of most common and prevalent attack vectors. Then, we have presented the vulnerability status of the websites in different graphical formats. The result shows that 64% of the selected web applications in Bangladesh are running with the vulnerabilities and specifically, government websites are in a critical state.
Why Is Auto-Encoding Difficult for Genetic Programming?

Unsupervised learning is an important component in many recent successes in machine learning. The autoencoder neural network is one of the most prominent approaches to unsupervised learning. Here, we use the genetic programming paradigm to create autoencoders and find that the task is difficult for genetic programming, even on small datasets which are easy for neural networks. We investigate which aspects of the autoencoding task are difficult for genetic programming.
Password-Less Authentication

Over the years, passwords have been our safeguards by acting to prevent one's data from unauthorized access. With the advancement of technologies, the way we have been using passwords has changed and transformed into much secure yet more user friendly than they were ever been in the past. However, the vulnerabilities identified and observed in this traditional system has motivated industry and researchers to find some alternate where there is no threat like stealing, hacking, and cracking of password. This chapter discusses the major developed password-less authentication techniques in detail and also puts an effort to explain the in-depth details along with the working principle of each of the technique through a use-case diagram. It would be of great benefit and contribution to the callow trying to explore research opportunities in this area.
Quality and Competitiveness in the System of Higher Education: Aspects of Interrelation

The article examines the relationship between the theoretical categories “quality of education” and “competitiveness of education”, as applied to educational activities of higher educational institutions. Based on a review of the scientific literature, the author adduces the examples of the opposition and mixing of the quality and competitiveness of education. The quality of education and its competitiveness are characterized as fundamental values that are the objects of analysis and evaluation in the university management system. The author’s definition of the competitiveness of educational organization as a feature reflecting the relative superiority over rivals at the pre-active stage is proposed. The author considers the relationship of competitiveness and quality of education by highlighting the methods and indicators of their evaluation and analysis in the intra-university management system. The mutual dependence of the quality and competitiveness of education is manifested in the formulation of indicators and methods for their evaluation. Assessment of the competitiveness of education and evaluation of its quality have a methodological commonality – using assessment approaches based on the “resource” and the “resultant” methodology. It has been proved that when evaluating the competitiveness of education, the results of educational activities should be taken into account, and using only an approach focusing on the resources of educational activities to assess the competitiveness of education will make such an assessment incomplete. The “quality” – “competitiveness” matrix built by the author allows to reveal four types of universities.