Reviewerly/exHarmony huggingface dataset:
Using OpenMP to Optimize Model Training Process in Machine Learning Algorithms

It is obvious the digital world is continuing to expand while the completion of Big Data solutions frequently relies on the timely extraction of valuable insights from data. This will continue to become more challenging due to the rise in data volume without a corresponding increase in velocity. Therefore, it has the potential to optimize storage space, decrease the amount of processing required for further information extraction, and save I/O and network communications. In Artificial intelligence specifically, implementing machine learning algorithms on big data, such as social networks and web graph, is challenging. Even though many types of researches have focused on making sequential algorithms more scalable, however, their running times remain to be prohibitively long. It becomes necessary to divide the work among multiple threads. In this paper, we have used the power of parallel computing particularly OpenMP implementation in supervised machine learning processes to reduce computation time. An experiment was done on a binary classification problem, that was trained on a set of realistic data and it proved a significant reduction in calculation time compared to classical algorithms. In the end, we have described possible directions for further researches concerning parallel optimization of time calculation in the supervised perceptron learning processes.
Learning to hash based on angularly discriminative embedding

Hashing, a widely-studied tool to the approximate nearest neighbor search, aims to embed samples as compact binary representations. Current approaches to this issue generally seek a low-dimensional Hamming Space where representations are discrete and have smaller intra-class distance and larger inter-class distance. As a result, the performance is often limited by the discrete constraint. In this work, we propose to seek an angularly discriminative Embedding Space where representations are continuous and have smaller intra-class angular margin and larger inter-class angular margin. For our goal is to learn continuous representations rather than discrete hash codes, the problems caused by discrete constraint can be avoided. Besides, in order to further reduce the gap between Embedding Space and Hamming Space, we introduce an additional coordinate-constraint for representations. Our method is simple yet effective. Extensive experiments on the image retrieval task show that it achieves encouraging results on four benchmark datasets. Furthermore, the success of our proposed method demonstrates that leveraging the progress made in representation learning to improve hashing is promising in future.
Multi-hypothesis Track Initialization With The Use Of Multiple Trajectory Models

One of the biggest challenges for a tactical ballistic missile (TBM) tracking system is taking into account many possible missile trajectories during the track initialization process. The correct missile type classification of the track hypothesis determines the correctness of further tracking. This paper presents a multi-hypothesis track initialization algorithm (MHTI), as well as an analysis of the initialization efficiency in terms of the track initialization rule.
Text Classification Models for CRM Support Tickets

With this study, a system will be implemented for the use of Kariyer.net Customer Solution Center, which estimates the category of the mails that comes from customers and job seekers. These e-mails sent to Customer Solution Center employees via an internal demand system will be automatically estimated by the system to which category they belong before the action is taken. And guidance will be provided for proper action to be taken. Technological expertise, such as natural language processing, classification algorithms, machine learning, user interface will be studied in the system.
Particle Swarms Reformulated Towards a Unified and Flexible Framework

The Particle Swarm Optimisation (PSO) algorithm has undergone countless modifications and adaptations since its original formulation in 1995. Some of these have become mainstream whereas others have faded away. A myriad of alternative formulations have been proposed raising the question of what the basic features of an algorithm must be to belong in the PSO family. The aim of this paper is to establish what defines a PSO algorithm and to attempt to formulate it in such a way that it encompasses many existing variants. Therefore, different versions of the method may be posed as settings within the proposed unified framework. In addition, the proposed formulation generalises, decouples and incorporates features to the method providing more flexibility to the behaviour of each particle. The closed forms of the trajectory difference equation are obtained, different types of behaviour are identified, stochasticity is decoupled, and traditionally global features such as sociometries and constraint-handling are re-defined as particle’s attributes.
Game of Drones - Detecting Spying Drones Using Time Domain Analysis

Drones have created a new threat to people’s privacy. We are now in an era in which anyone with a drone equipped with a video camera can use it to invade a subject privacy by filming the subject in his/her private space using encrypted First Person View (FPV) channel. Although many methods have been suggested to detect a nearby drone, they all suffer from the same shortcoming: they cannot detect what specifically is being captured and therefore they fail to distinguish between the legitimate use of a drone that does not invade a subject’s privacy (for example, neighbor’s drone flying and shoot his garden) and illegitimate use (same drone shooting the subject’s property), where in many cases depends on the orientation of the drone’s video camera rather than on the drone’s location. In this paper we present a method that utilizes a flicker in order to detect whether the drone’s camera is directed towards the private space by analyzing the encrypted video stream sent from the drone in real time. We investigate the influence of changing pixels on the transmitted traffic (in a lab setup). We leverage our conclusions and demonstrate how an interceptor can apply a side-channel attack to detect that a subject is video streamed by DJI Mavic drone from its encrypted FPV channel when the subject is located inside a private house.
Recommender system: An analytical report on decision making for large scale online social networks

Social networking playing a very important role in communication, business, and sharing of information. During these activities, a huge amount of data it generates. Organizations need to analyze, process, and then find patterns to take the decision. A recommender system can analyze Online Social Network (OSN) and recommends that leads to help the organization to take the decision. Ever growing Size of OSNs required a more efficient recommender system that provides more accurate results. The study reviews the different design issues of the recommender system and compares them with the real data set. This study analyses the different recommender systems and compares them to identify challenges and future scope for the design of the recommender system.
Identification of taxon through classification with partial reject options

Abstract Identification of taxa can significantly be assisted by statistical classification based on trait measurements either individually or by phylogenetic (clustering) methods. In this article, we present a general Bayesian approach for classifying species individually based on measurements of a mixture of continuous and ordinal traits, and any type of covariates. The trait vector is derived from a latent variable with a multivariate Gaussian distribution. Decision rules based on supervised learning are presented that estimate model parameters through blocked Gibbs sampling. These decision regions allow for uncertainty (partial rejection), so that not necessarily one specific category (taxon) is output when new subjects are classified, but rather a set of categories including the most probable taxa. This type of discriminant analysis employs reward functions with a set-valued input argument, so that an optimal Bayes classifier can be defined. We also present a way of safeguarding against outlying new observations, using an analogue of a p-value within our Bayesian setting. We refer to our Bayesian set-valued classifier as the Karlsson–Hössjer method, and it is illustrated on an original ornithological data set of birds. We also incorporate model selection through cross-validation, exemplified on another original data set of birds.
Learning Modal and Spatial Features With Lightweight 3D Convolution for RGB Guided Depth Completion

RGB guided depth completion aims to recover a complete depth map from a sparse set of depth measurements and one corresponding RGB image, which is efficient for 3D applications to generate high-quality depth maps. Most prevailing approaches feed the sparse depth data and RGB image collected by consumer devices into a 2D convolutional network performed only at the spatial level. We argue that there is a correlation in registered multi-modal data between different modalities, which is ignored in 2D convolutional operations, resulting in loss of accuracy. In order to acquire the extra modal information between different modalities, we adopt 3D convolution for the depth completion task. Meanwhile, to decrease the significantly increased parameter size 3D convolutions, we propose a simple and effective method to reduce this increase while retaining their modal information. We verified the effectiveness of our proposed method for modal and spatial features learning on NYUv2 and KITTI depth completion datasets. Our lightweight 3D convolution achieved approximate accuracy as the standard 3D convolution, but with the same parameter size of 2D convolution. Our proposed modal features and lightweight 3D convolution are helpful to inform the development of depth sensors for consumer devices.
Runtime verification for trustworthy secure shell deployment

Incorrect cryptographic protocol implementation and malware attacks targeting its runtime may lead to insecure execution even if the protocol design has been proven safe. This research focuses on adapting a runtime-verification-centric trusted execution environment (RV-TEE) solution to a cryptographic protocol deployment --- particularly that of the Secure Shell Protocol (SSH). We aim to show that our approach, which does not require any specific security hardware or operating system modifications, is feasible through the design of a framework and work-in-progress empirical evaluation. We provide: (i) The design of the setup involving SSH, (ii) The provision of the RV-TEE setup with SSH implementation, including (iii) An overview of the property extraction process through a methodical analysis of the SSH protocol specifications.
Wearable Mapping Suit: Body Mapping for Identification Wearables

This research describes a new design method – the Wearable Mapping Suit – which combines bodystorming and prototyping techniques in a human-centred development process for wearable technologies. The method derives from visually prepared body maps that are used in designerly decision-making processes. The idea is illustrated with an exemplified workshop where four visual Identification (ID) Wearables used for authentication interactions get co-created by six participants and two facilitators.
Time complexity analysis of the deductive sort in the best case

Non-dominated sorting is one of the important step in multiobjective evolutionary algorithms (MOEAs) which are based on Pareto dominance concept. Though non-dominated sorting can be performed in polynomial time, it remains an asymptotical bottleneck in many of these MOEAs. Here we show that an algorithm, Deductive Sort from the paper "Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting" by McClymont et al., has the best-case time complexity of [EQUATION].
Region Selection with Discrete Fireworks Algorithm for Person Re-identification

Various hand-crafted features with metric learning methods have improved the person re-identification (Re-ID) accuracy. Metric learning methods for person Re-ID mean to match the features acquired from different persons. However, not all information of the features is valid for metric learning. Compared to these metric learning methods, the region selection with discrete fireworks algorithm (RS-DFWA) is proposed in this paper for hand-crafted feature designing. RS-DFWA uses the fireworks algorithm after discretization to select the effective regions of the feature maps at the metric learning stage. RS-DFWA has a faster convergence speed and a better optimization accuracy so that the noise regions such as background features would be ignored. RS-DFWA optimizes the fitness of the discrete fireworks algorithm while training the deep networks for person feature learning. The method we proposed is validated on the CUHK03 dataset, region selection with discrete fireworks algorithm for the deep features achieve favorable accuracy. For example, on the CUHK03 dataset in single query mode, an improvement of mAP = +4.6% is obtained by RS-DFWA compared to the Baseline model.
Modeling of Convolutional Neural Networks for Detection and Classification of Three Vehicle Classes

Abstract The increasing number of vehicles causes traffic density on each road segment, especially in urban areas. Planning and regulating vehicle traffic, requires traffic flow parameter data, which currently is still largely done manually by assigning several people to count vehicles passing divided by a certain time period. The development of artificial intelligence in the field of computer vision enables the observation of traffic data using smart cameras. This study aims to produce a Convolutional Neural Netwoork (CNN) model for the detection and classification of three vehicle classes, namely bus, car and motorcycle. The architecture used to build CNN uses smallVGGNet with 3x3 convolution layers and one fully connected layer. The first three convolution layers are considered as feature extraction layers while the last one is for classification where there are three output types of vehicles. The model is trained using 3, 000 different images according to the class. In the training phase, it was determined 75 epochs where the consumption time of each epoch was ± 30 minutes so that the total time needed was 37.5 hours. The results of tests that have been done show that CNN models that have been trained can classify vehicle types with an accuracy of between 63-100%.
Machine Reading Comprehension (LSTM) Review (State of Art)

The ability of a machine to read and understand unstructured text and after read it be able to answer questions related with the information given in the text file is also known as Machine Reading Comprehension, and it is aiming to teach machines to understand text as a human does, and is a new challenging direction in Artificial Intelligence due to its potential in various enterprise applications. This is because many natural language processing tasks like information extraction or machine translation depend on the ability of the machine to understand natural language which can be set with Question- Answers model, in which with a given text the machine will be able to answer a question related with that text in order to demonstrate its understanding of the subject. This major goal has tremendous value not only for Artificial Intelligence field, but also for the industry, more specific for personal assistants like Cortana, Siri, Alexa, and Google Assistant web, as well as this will improve the search process of the search engines and online helpdesk services. This chapter presents three methods of the most used techniques implemented in machine reading comprehension and a basic explanation of its functionality along with a brief history of the origin of these methods and some practical examples in the industry which apply these techniques.
Remote Intelligent Teaching Platform of Automobile Experiment Course Based on Agent

To improve the teaching level of automobile specialty, this paper proposes and implements a multi-agent intelligent network teaching experiment system model based on XML extended Agent communication language. The scheme uses the cooperation mechanism to realize the knowledge exchange and sharing between them, and forms a multi-level system. Then, the intelligent Agent server is used to collect the most interesting information for the learners and establish a comprehensive teaching system. Finally, during the process of system implementation, the specific curriculum case is taken as the practical application, and the case adopts ASP.NET, MSSQL and other technologies to provide the overall structure, the design of each function module and so on. The test results show that the method is in line with the knowledge base and cognitive characteristics of the learners themselves, and effectively solves the shortcomings of low intelligence of the online experimental teaching system.
Feasibility Study for Computer-Aided Diagnosis System with Navigation Function of Clear Region for Real-Time Endoscopic Video Image on Customizable Embedded DSP Cores

This paper presents examination result of possibility for automatic unclear region detection in the CAD system for colorectal tumor with real time endoscopic video image. We confirmed that it is possible to realize the CAD system with navigation function of clear region which consists of unclear region detection by YOLO2 and classification by AlexNet and SVMs on customizable embedded DSP cores. Moreover, we confirmed the real time CAD system can be constructed by a low power ASIC using customizable embedded DSP cores.
Model Fragmentation, Shuffle and Aggregation to Mitigate Model Inversion in Federated Learning

Federated learning is a privacy-preserving learning system where participants locally update a shared model with their own training data. Despite the advantage that training data are not sent to a server, there is still a risk that a state-of-the-art model inversion attack, which may be conducted by the server, infers training data from the models updated by the participants, referred to as individual models. A solution to prevent such attacks is differential privacy, where each participant adds noise to the individual model before sending it to the server. Differential privacy, however, sacrifices the quality of the shared model in compensation for the fact that participants' training data are not leaked. This paper proposes a federated learning system that is resistant to model inversion attacks without sacrificing the quality of the shared model. The core idea is that each participant divides the individual model into model fragments, shuffles, and aggregates them to prevent adversaries from inferring training data. The other benefit of the proposed system is that the resulting shared model is identical to the shared model generated with the naive federated learning.
Learning Languages with Decidable Hypotheses

In language learning in the limit, the most common type of hypothesis is to give an enumerator for a language, a W-index. These hypotheses have the drawback that even the membership problem is undecidable. In this paper, we use a different system which allows for naming arbitrary decidable languages, namely programs for characteristic functions (called C-indices). These indices have the drawback that it is now not decidable whether a given hypothesis is even a legal C-index. In this first analysis of learning with C-indices, we give a structured account of the learning power of various restrictions employing C-indices, also when compared with W-indices. We establish a hierarchy of learning power depending on whether C-indices are required (a) on all outputs; (b) only on outputs relevant for the class to be learned or (c) only in the limit as final, correct hypotheses. We analyze all these questions also in relation to the mode of data presentation. Finally, we also ask about the relation of semantic versus syntactic convergence and derive the map of pairwise relations for these two kinds of convergence coupled with various forms of data presentation.
Accelerated Smoke Simulation by Super-Resolution With Deep Learning on Downscaled and Binarized Space

In this paper, we propose a highly efficient method for synthesizing high-resolution(HR) smoke simulations based on deep learning. A major issue for physics-based HR fluid simulations is that they require large amounts of physical memory and long execution times. In recent years, this issue has been addressed by developing deep-learning-based super-resolution(SR) methods that convert low-resolution(LR) fluid simulation results to HR(High-resolution) versions. However, these methods were not very efficient because they performed operations even in areas with low density or no density. In this paper, we propose a method that can maximize its efficiency by introducing a downscaled and binarized adaptive octree. However, even if it is divided by octree, because the number of nodes increases when the resolution of the simulation space is large, we reduce the size of the space by multiscaling and at the same time perform binarization to preserve the density that may be lost in this process. The octree calculated in this process has a structure similar to that of a multigrid solver, and the octree calculated at coarse resolution is restored to its original size and used for HR expression. Finally, we apply the SR process only to those areas having significant density values. Using the proposed method, the SR process is significantly faster and the memory efficiency is improved. The performance of our method is compared with that of an existing SR method to demonstrate its efficiency.
Graph-Based Modeling for Adaptive Control in Assistance Systems

The topic of this contribution is characterization and analysis of assistance systems in order to enable adaptivity, i.e., as personalized adaptive systems. The research question of this article is how to facilitate the modeling efforts in adaptive e-learning assistance systems. Adaptivity here means to personalize the usage experience to the individual needs of the users and their current working context. For that, adaptive systems need usage models and user models. The problem statement is that expert knowledge and recurrent efforts are needed to create and update these types of models. Data driven and graph analytics approaches can help here, in particular when looking at standardized interaction data and models which encode sequences such as interaction paths or learning paths. This article studies how to make use of interaction usage data to create sequence-typed domain and user models for their use in adaptive assistance systems. The main contribution of this work is an innovative concept and implementation framework to dynamically create Ideal Paths Models (IPM) as reference models for adaptive control in adaptive assistance systems.
Vision Based Real Time Gesture Recognition

With the advent of image processing and computer vision techniques there is a huge advancement going on in the field of bio-metric analysis. Bio-metric analysis is the measurement and statistical analysis of peopleâ€™s unique physical and behavioral characteristics. A wide range of applications ranging from face recognition to finger print scanning are being developed. One of the applications of bio-metrics is in gesture recognition for human-machine communication, since it leads to a more intuitive interaction and control. This paper describes a vision based real-time gesture recognition system, designed for operating in an automotive environment. We have implemented a system in which the user can change the direction of movement of a bot using gestures. These gesture are converted to commands using image processing algorithms. Commands are transmitted wireless to a robot to move it in a specific direction.