Reviewerly/exHarmony huggingface dataset:
Deep Learning Methods for Image Segmentation Containing Translucent Overlapped Objects

Convolutional neural networks(CNN) are a subset of deep learning methods recently used widely for image segmentation. SegNet network [4] has shown interesting results for semantic segmentation, but it is designed to segment images with non-overlapped objects. However in some data translucent regions partially overlap. Having overlapped regions will cause methods not designed for overlapped objects to perform poorly or not work at all. To our knowledge no CNN has been designed yet to segment partially overlapped translucent objects.In this paper, we have designed a CNN to segment partially overlapped translucent regions. We used SegNet [4] as transfer learning for our overlapped image segmentation method. We also designed a new CNN with a simpler network for our data. Results on synthetic images give more than 95% segmentation accuracy for both methods.
Axiomatizing first order consequences in inclusion logic

Abstract Inclusion logic is a variant of dependence logic that was shown to have the same expressive power as positive greatest fixed‐point logic. Inclusion logic is not axiomatisable in full, but its first order consequences can be axiomatized. In this paper, we provide such an explicit partial axiomatization by introducing a system of natural deduction for inclusion logic that is sound and complete for first order consequences in inclusion logic.
An Automatic Requirement Transformation Approach for Code Generation in Industrial Cyber-Physical Systems

Requirement Engineering (RE) is a crucial step prior to building their control program for industries. Traditional requirement engineering is time-consuming and error-prone that an optimized way is necessary to build an accurate model within fractions of a second. In this paper, an automatic software requirement modeling method is proposed to achieve fast and accurate requirement modeling. A new requirement model is designed to prove the concept. The newly designed model is designed based on the flexible object-oriented structure with process flow and constraints. Furthermore, to be compatible with the UML model format, the transformation rules and algorithm are provided. The requirement input system uses a general defined model, which is also the same format as the newly designed model, to deal with information from any input form like drawings, semiformal text and uploaded UML model files. Requirement model is further transformed into control code as illustrated in this paper.
A Survey on IntelligentTraffic Control System Using Image Processing

The present traffic control systems depends upon the manual operation which leads to unnecessary wastage of fuel and time, which again leads to increase in the traffic congestion. More time is wasted at the signals even if there are no vehicles on the lane because the signals have fixed timing for all roads which is not dependent of traffic queue. Some nonuniform illuminations that come from artificial light sources affect the night-time surveillance. Artificial light sources result in glow that the objects or the vehicles present near the light sources will not be visible. Web camera is fixed and the images of all the four lanes will be captured. The method used in this paper removes the effect of glow through image gradient decomposition. The enhanced image is obtained through Poisson solver. The real time vehicle density is measured to control the traffic system using canny edge detection algorithm and the vehicle count is calculated for each path or road and comparison is made with the other roads. The green signal and red signal are received by the roads with the maximum vehicles and minimum vehicles respectively.
A random-valued impulse noise removal algorithm via just noticeable difference threshold detector and weighted variation method

The paper presents a random-valued impulse noise (RVIN) removal algorithm by using distinct impulse noise detection and correction stages. In the noise detection phase, the local luminance adaption of an image is used to detect the obvious noise pixels firstly, and the just noticeable difference (JND) is introduced to detect the noise pixels hiding in image details afterward. In the noise candidate correction phase, the obvious noise pixels, almost distributing in the smooth region, are restored by a weighted mean filter. For the noise candidates hiding in image details, we adopt a weighted detail-preserving variation method to restore them. Since human visual perception and detail-preserving are taken into account during the course of the noises detection and restoration, our method outperforms some existing methods, both in visual and quantitative measurements.
Closed Form Word Embedding Alignment

We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith et al. (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.
Artificial Intelligence Applications in Defense

Artificial intelligence (AI) is a rapidly growing field of computer science, which involves computer programming and offers tremendous advantages in the military expert system, human intelligence development, and support. This paper describes the use of artificial intelligence in the military field. It shows how the natural language of processing, ontology, and a system based on knowledge is used to create a unified military system. It also shares an insight into the use of AI in the military field for autonomous weapons, land analysis, and aircraft carrier landing.
Vehicle Speed Detection System using Motion Vector Interpolation

Vehicle speed detection system is used to estimate the speed of a moving vehicle at a given instant of time. The current technology using radar gun is more expensive and it can detect only one object in an instance, with lesser accuracy. Hence, an alternative vehicle speed detection system based on video frame extraction and motion detection is proposed. The moving vehicles are detected and tracked using Motion Vector Interpolation technique. The speed is calculated and displayed for each vehicle in the input video which is for every 0.6 seconds. The simulation is performed using MATLAB and system proved to be an efficient one producing a result with minimum error of less than 3%.
A 3D Convolutional Neural Network for Light Field Depth Estimation

Depth estimation has always been a great challenge in the field of computer vision and machine learning. There is a rich literature focusing on depth estimation in stereo vision or in monocular imaging, while the domain of depth estimation in light field images is still in its infancy. The paper proposes a fully convolutional 3D neural network that estimates the disparity in light field images. The proposed method is parametric as it is able to adapt to input images of arbitrary size and it is lightweight and less prone to overfitting thanks to its fully convolutional nature. The experiments reveal competitive results against the state of the art, demonstrating the potential offered by deep learning solutions for disparity estimation in light field images.
Modified Grey Wolf Randomized Optimization in Dementia Classification Using MRI Images

In this research work, a novel Modified Grey Wolf Randomized Optimization technique is used to classify the brain MRI images into two classes i.e. Non-Dementia or Dementia class. The swarm intelligence algorithms like Grey Wolf Optimization technique is predominantly used in solving optimization and feature selection problems. But using it to categorize brain MRI images for dementia classification will be incisive. Cross-sectional MRI of 65 Non-Dementia and 52 Dementia subjects collected from OASIS are used in the work. The original grey wolf optimization offers very poor performance due to its randomness and so randomness is removed and control parameters are inserted in modified grey wolf optimization. But the accuracy of this technique is not satisfactory due to the local optima problem. Hence the novel modified grey wolf randomized optimization with controlled randomness at an appropriate position of the original algorithm. Further Principal Component Analysis, Detrended fluctuation analysis, Hilbert Transform, K-Means clustering are added to the modified Grey Wolf Optimization versions for accuracy improvement and evaluated with and without statistical features. The highest accuracy of 93.16% is provided by Hilbert Transform-Modified Grey Wolf Randomized Optimization technique without statistical features while 51.28% and 52.99% accuracy is achieved by the original Grey Wolf Optimization with and without statistical features respectively.
Flexible Graph Matching and Graph Edit Distance Using Answer Set Programming

The graph isomorphism, subgraph isomorphism, and graph edit distance problems are combinatorial problems with many applications. Heuristic exact and approximate algorithms for each of these problems have been developed for different kinds of graphs: directed, undirected, labeled, etc. However, additional work is often needed to adapt such algorithms to different classes of graphs, for example to accommodate both labels and property annotations on nodes and edges. In this paper, we propose an approach based on answer set programming. We show how each of these problems can be defined for a general class of property graphs with directed edges, and labels and key-value properties annotating both nodes and edges. We evaluate this approach on a variety of synthetic and realistic graphs, demonstrating that it is feasible as a rapid prototyping approach.
Graph-Based Design of Hierarchical Reinforcement Learning Agents

There is an increasing interest in Reinforcement Learning to solve new and more challenging problems, as those emerging in robotics and unmanned autonomous vehicles. To face these complex systems, a hierarchical and multi-scale representation is crucial. This has brought the interest on Hierarchical Deep Reinforcement learning systems. Despite their successful application, Deep Reinforcement Learning systems suffer from a variety of drawbacks: they are data hungry, they lack of interpretability, and it is difficult to derive theoretical properties about their behavior. Classical Hierarchical Reinforcement Learning approaches, while not suffering from these drawbacks, are often suited for finite actions, and finite states, only. Furthermore, in most of the works, there is no systematic way to represent domain knowledge, which is often only embedded in the reward function. We present a novel Hierarchical Reinforcement Learning framework based on the hierarchical design approach typical of control theory. We developed our framework extending the block diagram representation of control systems to fit the needs of a Hierarchical Reinforcement Learning scenario, thus giving the possibility to integrate domain knowledge in an effective hierarchical architecture.
Identification of Colombian coffee price dynamics

The daily Colombian coffee price is a chaotic signal that has emerged from a complex economic system. This work proposes to identify its dynamics by means of two models: a single multiscroll Chua system and the coupling of two of these systems. Models are fine-tuned through an artificial bee colony optimization algorithm. Results show that this approach can reconstruct the price signal in terms of several statistics and points out a way for its long-term forecasting.
Comparative Study of BP Neural Network and RBF Neural Network in Surface Reconstruction

Aiming at the surface reconstruction problem, the surface reconstruction methods based on BP and RBF neural networks are studied respectively, and the two neural network models are compared. The results show that both network models can effectively reconstruct the surface. The BP model has a short running time, but the randomness of the weights and thresholds make the model stable. The RBF model has high reconstruction stability, but the fitting time is longer than the BP model. Both have their own advantages. The specific application should be determined according to the specific research object.
Design and Development of Wearable Sensor System using MQTT Protocol for Real Time Kinematics Analysis

Wearable sensors data fusion is the most advanced and robust technology to study the motion of a body and perform kinematics analysis in real time. The main application of such sensor systems is in sports training and performance measurement. Advancements in wearable technology permit continuous observation and analysis of the sports person without causing any discomfort during training sessions. Multiple sensors are required to be mounted on the subject and synchronous data is needed from all the sensors to get a complete picture of the athlete's performance. The presented design of wearable sensor system involves a wireless sensor network of smart Inertial Measurement Units and implementation of MQTT Protocol for communication. Pi Zero W has been installed with Mosquitto broker. The transmitted data is published in real time on two different MQTT clients - open source software MQTT SPY and a custom android mobile app. This data can be further stored in a data-base to explore various data fusion algorithms or techniques for kinematics analysis of the subject.
iMCircle: Automatic Mining of Indicators of Compromise from the Web

With the rapidly evolving landscape of cyber threats, Indicators of Compromise (IOCs) are aggressively exchanged as forensic artifacts to help security professionals quickly identify and response cyber threats. Previous related studies mostly focus on extracting and generating IOCs from some fixed-point monitoring data sources, which are passive and time-consuming. In this paper, we present iMCircle, an innovation system that automatically mines IOCs from the Web by checking suspicious indicators with the help of open-source threat information. Based on the initial input of several suspicious indicators, iMCircle first collects their relevant public threat information from the Web and generates IOCs by checking whether those indicators are threat indicators in the target threat field. Second, it actively extracts new indicators from the search results as new inputs and checks them as described above. In that way, the system works in a circle and generates IOCs continuously. Running this system for almost two months in the real world, it has the appreciable performances on the active checking of suspicious indicators and the automatic generation of IOCs.
CNN-BLSTM-CRF Network for Semantic Labeling of Students' Online Handwritten Assignments

Automatic semantic labeling of strokes in online handwritten documents is a crucial task for many applications such as diagram interpretation, text recognition, and search. We formulate this task as a stroke classification problem in which each stroke is classified as a cross-out, free body diagram, or text. Separating free body diagram and text in this work is different than the traditional text/non-text separation problem because these two classes contain both text and graphics. The text class includes textual notes, mathematical symbols/equations, and graphics such as arrows that connect other elements. The free body diagram class also contains graphics and various alphanumeric characters and symbols that mark or explain the graphical objects. In this work, we present a novel deep neural network model for classification of strokes in online handwritten documents. There are two input sequences to the network. The first sequence contains the trajectories of the pen strokes while the second contains features of the strokes. Each of these sequences is fed to its own CNN-BLSTM channel to extract features and encode relationships between nearby strokes. The output of the two channels is concatenated and used as the input to a CRF layer that predicts the best sequence of labels for given input sequences. We evaluated our model on a dataset of 1,060 pages written by 132 students in an undergraduate statics course. Our model achieved an overall classification accuracy of 94.70% on this dataset.
Data Security and Privacy-Preserving in Cloud Computing Paradigm

Cloud computing is a new technique that has been widely spread recently due to the services provided to users according to their need. Being a pay-for-what-you-use service, it provides a much-encapsulated set of services. Cloud computing acts as a main attraction for the business owners. Whether they are big or small, they can choose from the required services. Management of heavy flow data is very likely to be managed under this with the confidentiality and security of the data attached. Having all these features may attract everyone, but every technique always comes with some issues. The main aim of this survey chapter is to gain a better understanding of security issues that can occur in cloud computing.
Data Augmentation via Adversarial Networks for Optical Character Recognition/Conference Submissions

With the ongoing digitalization of ressources across the industry, robust OCR solutions (Optical Character Recognition) are highly valuable. In this work, we aim at designing models to read typical damaged faxes and PDF files and training them with unlabeled data. State-of-art deep learning architectures require scalable tagged datasets that are often difficult and costly to collect. To ensure compliance standards or to provide reproducible cheap and fast solutions for training OCR systems, producing datasets that mimic the quality of the data that will be passed to the model is paramount. In this paper we discuss using unsupervised image-to-image translation methods to learn transformations that aim to map clean images of words to damaged images of words. The quality of the transformation is evaluated through the OCR brick and these results are compared to the Inception Score (IS) of the GANs we used. That way we are able to generate an arbitrary large realistic dataset without labeling a single observation. As a result, we propose an end-to-end OCR training solution to provide competitive models.
Comparison of Models of Parallelized Genetic Algorithms

The aim of the paper is to describe the most widely used methods of parallelization of Genetic Algorithms (GA) and subsequently to use the outputs of the theoretical part for the design of implementation. Python was chosen as the implementation language, so the design is implemented with this language in mind. Selected problems of sequential GA are described in the theoretical part of the paper. Optimization problems and parallel models are described. They are the Global One - Population Master-Slave Model, the One-Population Fine-Grained Model, the Multi-Population Coarse-Grained Model, and the Hierarchical Model. The practical part deals with the design and implementation of parallelized GA.
Designing Toys and Play Activities for the Development of Social Skills in Childhood

Although the influence of toys and play activities on child development is a consistent research topic in psychological research, designing artefacts explicitly for social growth can be considered a novel topic in design research. This paper focuses on identifying design opportunities to develop toys and play activities to stimulate social skills in school age children. Twenty-two participatory observations were carried out with children attending the first two years of school. The analysis revealed 15 ways in which social skills can be stimulated by toys and play activities. Design insights are given to each of the 15 categories.
Prediction Of Students Academic Success Using Case Based Reasoning

Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.
Toward Migration of SGX-Enabled Containers

Containers are becoming the de facto platform for cloud computing. While cloud security has been a major concern, Intel SGX provisions powerful protection guarantees that can be used for containers. However, this technology does not come for free. For example, limited Enclave Page Cache (EPC) challenges the migration design of SGX-enabled containers.We note that previous security protocols are problematic concerning migration of SGX-enabled containers, which will lead to the failure of measures to prevent fork/fallback attacks. In this paper, we propose the migration of SGX-enabled containers and explore the challenges of deploying and migrating SGX-enabled containers considering both EPC resources and persistent storage. To our best knowledge, we are the first to design and implement such a framework for the SGX-enabled container migration that is easy, flexible and lightweight to deploy. We evaluate the proposed framework by migrating SGX-enabled Sqlite3 container and the experimental result shows that the proposed framework has about 15% overhead, which is acceptable due to its security advantage.
Machine Learning in Text Analysis

This chapter provides a basic understanding of processes and models needed to investigate the data posted by users on social networking sites like Facebook, Twitter, Instagram, etc. Often the databases of social networking sites are large and can't be handled using traditional methodology for analysis. Moreover, the data is posted in such a random manner that can't be used directly for the analysis purpose; therefore, a considerable preprocessing is needed to use that data and generate important results that can help in decision making for various areas like sentiment analysis, customer feedback, customer reviews for brand and product, prevention management, risk management, etc. Therefore, this chapter is discussing various aspects of text and its structure, various machine learning algorithms and their types, why machine learning is better for text analysis, the process of text analysis with the help of examples, issues associated with text analysis, and major application areas of text analysis.