Reviewerly/exHarmony huggingface dataset:
An Algorithm for Finding Similar Objects in an Image

In this paper, a combined algorithm for counting objects in images is proposed. The pattern-matching algorithm is used to identify the patterns that are present in an image. Proposed algorithm consists of two parts. Pattern matching subroutine is based on normalized cross correlation technique which is widely used in image processing application. Pattern matching can be used to recognize and/or locate specific objects in an image. It is one of the emerging areas in computational object counting. Neural network subroutine is needed to filter out false positives that may occur during cross correlation function evaluation.Furthermore, an experimental evaluation is carried out to estimate the performance of the proposed efficient pattern matching algorithm for images of blood microscopy and chamomile field image. In the first case, the task is to count erythrocytes in the blood sample. In the second case, it is needed to count the flowers in the field.
Multi-Oriented Real-Time Arabic Scene Text Detection with Deep Fully Convolutional Networks

Scene text detection is one of the raising aspects of Information and Communications Technology (ICT) field used by individuals in our daily life. Therefore, textual information detection aim to determinate text line coordinates in two fields: printed documents and real-world scenes images. One of the key points to the success of detecting text on both accuracy and precision values for printed documents is deep learning. However, real-world scenes images still face Challenges from recognizing text from the remaining shapes. In this paper, we propose a deep Fully Convolutional Networks (FCN) multi-oriented system to real-time localized text through an end-to-end trainable single network. For training and evaluating stages, we have used the freely available Arabic-Text-in-Video (AcTiV) dataset.
Learning Options from Demonstration using Skill Segmentation

We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills using nonparametric Bayesian clustering and a reward function for each segment is then learned using inverse reinforcement learning. From this, a set of inferred trajectories for the demonstration are generated. Option initiation sets and termination conditions are learned from these trajectories using the one-class support vector machine clustering algorithm. We demonstrate our method in the four rooms domain, where an agent is able to autonomously discover usable options from human demonstration. Our results show that these inferred options can then be used to improve learning and planning.
A System for Analyzing Diversified Software Binaries

In recent years, software diversification has become a viable security assurance measure to prevent the proliferation of reusable exploits. Lacking, however, are tools that focus on comparative analysis of diversification techniques on memory usage and CPU usage, as well as energy consumption, along with metrics associated with the persistence of return oriented programming ROP gadgets across diversified binaries. In this paper, we present an open source tool-set system to streamline binary diversification. In addition, we use our system to diversify several Linux binaries, and present an analysis of the impacts of several diversification algorithms on CPU clock cycles and on the total number of ROP gadgets between variants.
Ethics and Digital Innovation in Finance

Digital technologies are a powerful innovative and disruptive factor in finance. They have several dimensions, including an ethical dimension, that call upon the people and organizations that take part in financial activity to take into account the consequences of their actions on others, first of all, their users and customers, but also the stakeholders of the financial or technological organization in question, other organizations, sometimes in distant places, local communities and society in general, including when their decisions are supported, facilitated, or even replaced by programs and machines. This chapter explains the ethical dimension of digital technologies applied to finance and offers ideas for guiding the work of the agents who design or use these technologies so that they can contribute positively to the good of the people and society.
Texture feature based oblivious watermarking with slant transform using fuzzy logic

An improved method of robust gray scale water marking is proposed in this paper. The proposed method employs slant transform using Quantization Index Modulation (QIM).The threshold used in QIM of each 8x8 block is computed using fuzzy logic, based on texture parameters namely variance, uniformity and entropy. The experimental results confirm that the proposed method is highly transparent and the watermark is recovered from seriously distorted images with attacks such as Gaussian Noise (GN), Salt and Pepper noise, vertical flipping, low quality JPEG compression and cropping the image to 25 %.
Quicksort Algorithm—An Empirical Study

Quick sort is one of the most sought after algorithms, because if implemented properly quick sort could be much faster than its counterparts, merge sort and heapsort. The main crux of quick sort algorithm is the implementation of the partitioning operation. Nico Lomuto and C. A. R Hoare have put forth partitioning algorithms that have gained prominent significance. Despite this, one can always shed more light on this partially understood operation of partition. Sorting algorithms have been further developed to enhance its performance in terms of computational complexity, memory and other factors. The proposed method is the use of randomized pivot in the implementation of Lomuto partition and Hoare partition algorithms, respectively. It is analysed with and without a randomized pivot element. This paper presents a comparison of the performance of a quick sort algorithm in different cases using contrasting partition approaches with and without randomized pivot. The results provide a theoretical explanation for the observed behaviour and give new insights on behaviour of quick sort algorithm for different cases. The Hoare partition approach with randomized pivot gives best time complexity in comparison to the other cases.
Case-Based Reasoning for Mortality Prediction of Catfish Farming Based on Water Quality

Abstract Catfish (Clarias gariepinus) is a species of freshwater fish that has good prospects for cultivation. This type of fish has a fast growth rate, is able to adapt to a bad environment and is easily cultivated. Water quality is the main element that influences the level of fish life. However, catfish deaths often occur when water quality is in certain conditions. Fish mortality can be predicted using an appropriate system so that it does not greatly affect the cultivation process. This research has succeeded in developing a prediction system for catfish mortality as a result of the water quality used for catfish farming. This system is based on case-based reasoning. The matching process starts by entering water quality condition variables such as Temperature, pH, Ammonia, Nitrite, and Dissolved Oxygen. Features entered by the user will be referred to the knowledge base. The system will look for a base case that has been indexed before. The similarity calculation with the highest value is used as a prediction of the effect of water quality on the mortality rate of catfish farming. The similarity of the test cases shows that the new case has a similarity level of up to 80%.
About features of management of mobile robots with stepping movers of a rope type in a water environment

Abstract The methods of moving robotic platforms having in their arsenal either an anchor-rope or an anchor-rope-tracked mover are analyzed. The algorithms of operation of caterpillar-anchor-cable movers of various designs and gait algorithms for a platform with an anchor-cable mover are determined.
Mobile Learning of Mathematics Games to Enhance Problem-Solving Skill

This research focuses on the mobile learning of mathematics games (MLM games) to enhance problem-solving skills and adopts the structure of a closed-ended and a five-point Likert scale questionnaire. The fifteen experts agreed with the eight factors that influence to enhance problem-solving skill (Mean = 4.59, S.D. = 0.56). The questionnaires are randomly assigned to forty students. The results indicate: (1) most of the students are evaluated with MLM games in terms of quality game content (Mean = 4.70, S.D. = 0.65) (2) the students have benefited from the use of MLM games in terms of the sound aspect, especially the appropriateness of the song used in the lesson at a high level, and (3) the students are satisfied at a high level that the lesson could be reviewed at any time. The fifth time achieved the learning achievement at 82 percent, so teachers and schools should put more effort into developing mobile learning games.
Parallel Intent and Slot Prediction using MLB Fusion

Intent and Slot Identification are two important tasks in Spoken Language Understanding (SLU). For a natural language utterance, there is a high correlation between these two tasks. A lot of work has been done on each of these using Recurrent-Neural-Networks (RNN), Convolution Neural Networks (CNN) and Attention based models. Most of the past work used two separate models for intent and slot prediction. Some of them also used sequence-to-sequence type models where slots are predicted after evaluating the utterance-level intent. In this work, we propose a parallel Intent and Slot Prediction technique where separate Bidirectional Gated Recurrent Units (GRU) are used for each task. We posit the usage of MLB (Multimodal Low-rank Bilinear Pooling) fusion for improvement in performance of intent and slot learning. To the best of our knowledge, this is the first attempt of using such a technique on text based problems. Also, our proposed methods outperform the existing state-of-the-art results for both intent and slot prediction on two benchmark datasets.
Attribute-Based Data Security with Obfuscated Access Policy for Smart Grid Applications

Smart grid employs intelligent transmission and distribution networks for effective and reliable delivery of electricity. It uses fine-grained electrical measurements to attain optimized reliability and stability by sharing these measurements among different entities of energy management systems of the grid. There are many stakeholders like users, phasor measurement units (PMU), and other entities, with changing requirements involved in the sharing of the data. Therefore, data security plays a vital role in the correct functioning of a power grid network. In this paper, we propose an attribute-based encryption (ABE) for secure data sharing in Smart Grid architectures as ABE enables efficient and secure access control. Also, the access policy is obfuscated to preserve privacy. We use Linear Secret Sharing (LSS) Scheme for supporting any monotone access structures, thereby enhancing the expressiveness of access policies. Finally, we also analyze the security, access policy privacy and collusion resistance properties along with efficiency analysis of our cryptosystem.
Mixed-Model Text Classification Framework Considering the Practical Constraints

Text classification is one of the valuable tasks in the industry. Most of the classification tasks are accomplished by supervised learning which has a high demand for labeled data, especially for employing deep learning models. This demand has become the key development bottleneck for industry, especially for domain-specific data. In this paper, we propose a label generation process combining topic modeling and active learning. We review the state-of-art classification models and create a mixed-model approach that leverages the hierarchical structure of labels to maximize the information gained from limited labeled data. We integrate the approach into a text classification framework to help subject matter experts to label data and to train accurate classification models effectively.
Deep Decentralized Reinforcement Learning for Cooperative Control

In order to collaborate efficiently with unknown partners in cooperative control settings, adaptation of the partners based on online experience is required. The rather general and widely applicable control setting, where each cooperation partner might strive for individual goals while the control laws and objectives of the partners are unknown, entails various challenges such as the non-stationarity of the environment, the multi-agent credit assignment problem, the alter-exploration problem and the coordination problem. We propose new, modular deep decentralized Multi-Agent Reinforcement Learning mechanisms to account for these challenges. Therefore, our method uses a time-dependent prioritization of samples, incorporates a model of the system dynamics and utilizes variable, accountability-driven learning rates and simulated, artificial experiences in order to guide the learning process. The effectiveness of our method is demonstrated by means of a simulated, nonlinear cooperative control task.
Histogram shape based Gaussian sub-histogram specification for contrast enhancement

Contrast enhancement is an essential primary part of computer vision in all fields of engineering including aerospace, agriculture, electrical, mechanical, medical, surveillance, etc. Annoying side effect in the enhanced images due to variation of gray levels is a key concern in the existing contra st enhancement techniques. In this paper, a new method for contrast enhancement is proposed, which captures the variation in the gray level distribution using skewness of the pixels distribution of the input image to avoid annoying side effects in order to produce contrast enhanced image with entropy close to that of the input image. In the proposed method, the given image is decomposed into two sub-images using a bisecting gray scale value which is determined based on the shape of the histogram of the input image. The desired Gaussian based histograms for sub-images are determined dynamically by controlling the parameters (mean and standard deviation). The performance of the proposed Histogram shape based Gaussian Sub-Histogram Specification technique (HGSHS) is evaluated on the images taken from standard databases and NASA database using the quality metrics: contrast, entropy and gradient. The performance of the proposed technique is found to be better than that of the existing contrast enhancement techniques.
Open-Domain Dialogue Generation

Dialogue systems have received more and more attention from researchers, especially open-domain dialogue generation system. The application scenario of it, also chat-bot, is very extensive, from website customer service to dialogue robot such as Siri and Microsoft Xiaoice. More and more studies have been done on open-domain dialogue generation. In recent years, deep learning is gradually emerging which can leverage a massive amount of data to learn meaningful feature representations. In this paper, we first give the development of deep learning in Natural Language Processing(NLP) especially in dialogue generation. Next we analyze the current problems and introduce several improvements which are the current research hotspot. The purpose of this paper is to sort out the research results of recent years and make contributions to the research and development of the dialogue system in the future.
Human Activity Recognition in Imbalanced Big Data Using Fuzzy Rule-Based Classification System

Human activities can be recognized by application of several sensors on different body parts. This leads to generation of big data which is also imbalance in nature. Classification of such imbalanced big data is a tedious task because performance of traditional machine learning algorithms becomes limited in this scenario. To deal with imbalanced classification problem for human activity recognition, fuzzy logic along with MapReduce architecture to handle big data has been used in this paper. Fuzzy rule-based classification system techniques FRBCS.CHI and FRBCS.W have been used for learning in imbalanced big data. Results show that fuzzy algorithms have performed well in prediction as imbalance ratio of dataset is increased.
Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communications

Recent trends in computer-mediated communications (CMC) have seen messaging with richer media not only in images and videos, but in visual communication markers (VCM) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Furthermore, conventional means of accessing VCMs continue to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. In this work, we aim to address this issue, by facilitating the use of an alternative form of VCM entry: hand gestures. To that end, we propose a user-defined hand gesture set that is highly representative of a number of VCMs and a two-stage hand gesture recognition system (trajectory-based, shape-based) that can identify these user-defined hand gestures with an accuracy of 82%. By developing such a system, we aim to allow people using low-bandwidth forms of CMCs to still enjoy their convenient and discreet properties, while also allowing them to experience more of the intimacy and expressiveness of higher-bandwidth online communication.
Disaster mitigation tutorial based on android

Abstract A natural disaster is a situation where an unexpected arrival. In that condition damage and even death can occur for humans, animals and plants. The occurrence of natural disasters cannot be prevented, but the community can minimize the impact that occurs by seeking knowledge about how to reduce the impact of disaster risk or what is called disaster mitigation. The development of android-based technology is very popular with the community as a tool for communication and other work. Therefore this research develops a natural disaster mitigation application that can be run on an android phone. This tutorial application is built using Adobe Flash and uses the programming language Actionscript 3.0. The application development method uses the Multimedia Development Life Cycle (MDLC) model with six stages namely concept, design, material collecting, assembly, testing, and distribution. The seven types of natural disasters conveyed in this application are natural disasters that often occur in Indonesia, namely earthquakes, landslides, floods, volcanic eruptions, tornadoes, fires, and tsunamis. This tutorial application provides an explanation of the understanding of each type of natural disaster, how to reduce the impact of disaster risk, and a scientific video on the process of disaster occurrence. This application can be run does not have to use internet access to access information in it, so it can be used at any time.
Decision Sequencing and Distributed Data Association

Multi-sensor multi-target tracking requires the solution to a challenging data association problem. While the processing architecture may be dictated by sensor network characteristics, distributed processing often provides benefits in both performance and robustness with respect to centralized processing. We cast the selection of fusion architecture as one of decision sequencing and study the benefits of competing architectures with respect to a probability of correct association criterion. We use a similar approach to study decision sequencing in forensic surveillance problems. Additionally, we propose a generalization to the Mori-Chang-Chong model to account for multiple-hypothesis association and discuss the impact of measurement latency.
Probability Distribution for Association of Maneuvering Vehicles

The usual multivariate Gaussian probability may not give a good representation of the real measurement to track association probability if the time period of the prediction is long compared to the time scales of vehicle turning maneuvers. This paper proposes a fast procedure for calculating a more correct measurement to track association probability in such cases.
Target Classification and Tracking Based on Aerodynamic Properties and RCS Information Using Rao-Blackwellized Particle Filter

During the stage separation phase of ballistic missile (BM), in addition to the warhead, different types of confusing objects such as fuel tanks and boards occur. At this point, classifying warhead and other confusing objects is very important for missile defense. Therefore, we propose tracking and classification algorithm in the BM stage separation environment through Rao-Blackwellized particle filter. The proposed algorithm utilizes the known physical characteristics, aerodynamic forces, moments of the targets in advance for dynamic update and RCS information for measurement update. The performance of the proposed framework is analyzed by several simulation results.
Enhanced Multiple Model GPB2 Filtering Using Variational Inference

Multiple model filtering has been widely used to handle uncertainties in system dynamics and noise characteristics in state estimation problems. The generalized pseudo-Bayesian filter of order 2 (GPB2) is a suboptimal multiple model state estimator. It achieves computational tractability via approximating each model-matched state posterior, which is a Gaussian mixture, with a single Gaussian density. This paper illustrates from the viewpoint of variational inference that this approximation affects the performance of GPB2 through the model probability update stage. An enhanced GPB2 algorithm is proposed. It takes into account the above approximation by applying a correction factor that is dependent on the Kullback-Leibler divergence (KLD) of the Gaussian mixture and single Gaussian density. A control variate-based Monte Carlo method for evaluating the KLD is developed. The upper and lower bounds for the desired KLD are derived to correct the Monte Carlo KLD result if it falls out of bounds. Simulations show that the enhanced GPB2 algorithm outperforms the original GPB2 and interacting multiple model (IMM) methods in maneuvering target tracking tasks.
Correlation-based Iterative Estimation of Distortion Kernel for Image Restoration

The main goal of the work is to develop an algorithm for automated image restoration. The method of analyzing image quality is based on the analysis of line-to-line and column-to-column correlation coefficients. Using such quality measures the refinement of the distortion operator is performed iteratively based on the Lucy-Richardson method for image reconstruction. The estimate of the distorting operator is found using gradient descent. The experiments showed that applying the proposed method to the images having smear and blur distortion gives correct restoration results. Since the approach uses optimization methods of directional search, it is also possible to reduce the number of necessary calculations.
Real-time IaaS Cost Model and Estimation Results for Cloud PC Laboratory Service

IaaS is well known as a very cost effective computing service which enables required infrastructures to be rented on demand without ownership of real hardware. It is very suitable for price sensitive services due to pay-per-use style. Operators of such services would want to adjust utilization policy promptly by estimating costs for cloud infrastructures as soon as possible. However, instantaneous response is not possible due to that cloud service providers offer at least several hours delayed billing information. Our work proposes a real-time IaaS cost estimation model based on resource usages monitored by virtual machine instance. We had operated PC laboratory service on a public cloud during full semester to validate our suggested model. From our experiments, averaged disparity between estimation and actual cost is less than 5.2%.