Reviewerly/exHarmony huggingface dataset:
Inferring Dynamic Group Leadership Using Sequential Bayesian Methods

In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for inferring dominant entities in tracked groups from observations. Unlike traditional leader-follower models, here we develop a new rotated leadership model that can capture the dynamic evolution of the interaction patterns in groups over time. Two methods, an online Gibbs sampler and deterministic particle filter, are then designed to infer sequentially the leader in group object tracking scenarios. Synthetic and real pigeon flocking data are used to demonstrate the effectiveness of the proposed techniques in terms of identifying the group leader under complex dynamics.
Robust Multimedia Watermarking

The data transmission on network channel has increased tremendously in the past few decades. Watermarking is a process to add watermark as a digital signal, label, or tag into a cover media. The primary requirements of multimedia watermarking are robustness and embedding capability. The robustness is defined as the strength of an algorithm to repel the noise. However, it is challenging to achieve both at the same time. The numerous characteristics of watermarking are very imperative in the multimedia watermarking system. The researchers are using watermarking schemes in various applications such as military, digital forensics, medical, and so on. Attacks of these watermarking harm or uncover the secret information carried in the watermark. Potential researchers have been presented various techniques for balancing or improving these concerns. This chapter reviews the recent multimedia watermarking techniques on the basis of robustness and embedding capability. The characteristics, applications, and attacks on multimedia watermarking techniques are introduced.
The impact of artificial intelligence on corporate control

This paper investigates the relationship between artificial intelligence (AI) and corporate control in the United Arab Emirates (UAE) emerging market. An exploratory study was conducted to derive the research questions. The nonprobability purposive sampling technique was implemented to select 10 highly experienced interviewees. In-depth primary data was collected through semi-structured interviews during 2019. Qualitative content analysis was used to answer the research questions. The results show a positive impact of AI on firm productivity and the auditing process, but uncertain influence on accounting information systems. More specifically, AI intervention increases firm productivity, creates new jobs and speeds up work processes. However, current AI technology is less likely to redefine auditing roles and still insufficient for developing accounting information systems. Human integration with AI systems will lead to more efficient results. This paper increases our understanding of how AI techniques can improve corporate control practices and the importance of selecting appropriate accounting professionals to decrease AI operation risks.
Fuzzy-Based Fine-Grained Human Activity Recognition within Smart Environments

With the increasing ageing population, Smart Home (SH) has been under vigorous investigation to enable Ambient Assisted Living (AAL) and foster independent living. Human Activity Recognition (HAR) is the backbone of AAL systems in order to detect Activities of Daily Living (ADL) and provide timely, context-aware assistance. Existing SH based AAL systems primarily focus on coarse-grained activity recognition (AR) and assume successful usage of everyday objects using binary sensors. Limited attention is given to fined-grained AR by verifying the intended object interactions with evidence from multiple heterogeneous sensor data. This paper proposes a fine-grained AR approach which fuses multimodal data from single objects and handles the imprecise nature of non-binary sensor measurements. This approach leverages the fuzzy ontology to model fine-grained actions with imprecise membership states of the sensors in relation to object and fuzzyDL reasoning tool to classify action completion. In addition, a microservice architecture is proposed with a non-intrusive heterogeneous ambient and embedded object based sensing method. The sensing method integrates both off-the-shelf and bespoke devices to collect fine-grained object level interactions. A case study is provided to illustrate the use of the fine-grained AR approach to recognize kitchen-based activities.
Whosecough: In-the-Wild Cougher Verification Using Multitask Learning

Current automatic cough counting systems can determine how many coughs are present in an audio recording. However, they cannot determine who produced the cough. This limits their usefulness as most systems are deployed in locations with multiple people (i.e., a smart home device in a four-person home). Previous models trained solely on speech performed reasonably well on forced coughs [1]. By incorporating coughs into the training data, the model performance should improve. However, since limited natural cough data exists, training on coughs can lead to model overfitting. In this work, we overcome this problem by using multitask learning, where the second task is speaker verification. Our model achieves 82.15% classification accuracy amongst four users on a natural, in-the-wild cough dataset, outperforming human evaluators on average by 9.82%.
A Novel Saliency-Driven Oil Tank Detection Method for Synthetic Aperture Radar Images

Synthetic Aperture Radar (SAR) imaging system plays an important role in earth observation research. This leads to the significance of target detection in SAR image. In this paper, we propose a novel saliency-driven oil tank detection method (SDD) for SAR images. First, we use the enhanced directional smoothing (EDS) to remove speckle noise from SAR images; in the step of saliency analysis, the integer wavelet transforms (IWT) and the DoG filter are used to obtain orientation and intensity features, respectively. Then, the orientation feature map and the intensity feature map resulting from these two kinds of features are utilized to compute the final saliency map; after segmenting the saliency map, the obtained connected domain guide the Active Contour Model (ACM) to acquire accurate contours of tops of oil tanks, and the bottoms of the oil tanks can be detected by the strong scattering points around the tops. Experimental results show that the proposed model outperforms the classical/state-of-the-art models in maintaining complete targets and accurate boundaries.
Toward Better Speaker Embeddings: Automated Collection of Speech Samples From Unknown Distinct Speakers

The accuracy of speaker verification and diarization models depends on the quality of the speaker embeddings used to separate audio samples from different speakers. With the goal of training better embedding models, we devise an automatic pipeline for large-scale collection of speech samples from unique speakers that is significantly more automated than previous approaches. With this pipeline, we collect and publish the BookTubeSpeech dataset, containing 8,450 YouTube videos (7.74 min per video on average) that each contains a single unique speaker. Using this dataset combined with VoxCeleb2, we show a substantial improvement in the quality of embeddings when tested on LibriSpeech compared to a model trained on only VoxCeleb2.
Security Technique for Authentication Process in the Cloud Environment

Cloud technology is a prospective field of information technology. Moreover, this development is always accompanied by a variety of vulnerabilities that are exploited by malicious people to access data stored in cloud environment. Data protection is one of the most challenging issues of cybersecurity. Since many companies have decided to store their data in cloud services, it is extremely important to provide information security in general. Thus, the most vulnerable point is the authentication process. In this paper the requirements for information technology protection based on SRP protocol were defined. Also the architecture of next generation data center based on cloud computing was developed.
A Method for Detecting Lines on Soccer Field by Color of Grass Variation

The line detection algorithm in a soccer field image is an important step in order to detect an offside situation. In this paper, a method of line detection in a soccer field is proposed by using color of grass variation. This method consists of three steps. Firstly, the soccer ground is segmented by a constraint color in RGB channel. Second, the edge in the image is detected. Finally, a line is detected by a line hough transform technique. To evaluate the performance of the proposed method, 50,000 image form 22 matches are used to detecting lines. Results is compared with ground truth data. The proposed method has shown a satisfactory result.
Internet Traffic Detection using Naïve Bayes and K-Nearest Neighbors (KNN) algorithm

Growth of internet has led to rise in number of users and its usage. Despite its advantages, exponential rise in internet usage has resulted in excess data flow over the system flooding the internet. To maintain quality of service and speed of internet along with ensuring data security as well as preventing data misuse, analysis of the internet data becomes essential. Analysis of the dataflow involves characterizing it into different types. This can be done by inspecting the packets either on basis of port numbers, payload information or statistical features. This paper aims to discuss the analysis of internet traffic using statistical features such as interpacket arrival time, time to live and number of packets helping us prevent invasion of packet information. This helps us protect user's privacy. To automate the process of categorizing internet traffic, machine learning based supervised classification techniques namely Naive Bayes and K Nearest Neighbors are implemented. Experiments to obtain highest accuracy in classifying internet traffic on basis of transaction protocol were performed. The dataset used is UNSW-NB. The results show that classification using K-Nearest Neighbors algorithm gives accuracy of 85% whereas maximum accuracy achieved using Naïve Bayes algorithm is 54%.
Multi-view routing visualization for the identification of BGP issues

The Internet is constantly evolving and changing over time. Outages, attacks, upgrades, censorships, and policy changes modify the routing very frequently everywhere in the world. To give the possibility to the Internet Service Providers and to the network operators of monitoring such an evolving scenario, many organizations collect information on the routing changes and give free access to them. We propose, prototype, and evaluate a visual interface, called Upstream Visibility, to display such data. It is based on three views: (1) a Global View that, based on stacked area charts, gives the high level trend of the visibility of an IP prefix; (2) a Local View that allows the user to check the effects over time of the visibility of an IP prefix on specific locations of the network; and (3) a traditional graph Animation View. Also, we propose heuristics for producing such type of drawings, evaluating their effectiveness and performance against scenarios describing well-known Internet incidents. Further, we classify Internet issues and study how they are visualized with our interface. We then identify and describe visual patterns which can be used to spot networking issues at a glance.
PPMC Training Algorithm: A Deep Learning Based Path Planner and Motion Controller

In the pursuit of a fully autonomous learning agent able to interact, move, and be useful in the real world, two fundamental problems are path planning and motion control, and user-agent interaction. We address these through reinforcement learning using our Path Planning and Motion Controller (PPMC) Training Algorithm, which uses a combination of observable goals and randomization of goals during training, with a customized reward function, to teach a simulated quadruped agent to respond to user commands and to travel to designated areas. In this regard, we identified two critical components of path planning and motion control: the first is region enabled travel, or the ability to travel towards any location within a prescribed area; the second is multi-point travel, or the ability to travel to multiple points in succession. An important open ended question is how many tasks should be handled by a single policy and if a single policy can even learn to manage several tasks. We demonstrate that it is possible to contain both a maples path planner and motion controller on a single neural network, which could prove promising in future work due to their interlinked and synergistic nature. Using control group policies and various test cases and using ACKTR and PPO, we empirically validate our algorithm teaches the agent to respond to user commands as well as path planning and motion control.
Object Detection Using Vision and LiDAR Sensor Fusion for Multi-channel V2X System

In this paper, we propose an algorithm that recognizes the environment around the vehicle using Vision and LiDAR sensor fusion, and extracts the object affecting the multichannel V2X communication system and transmits information to the TCU. A general object detection algorithm for autonomous vehicle detects pedestrians, signs, vehicles. However, in this paper, we detect the surroundings that affect the communication performance. For example, the detection targets are not only a bulky vehicle such as a large truck or bus but also a bulky structure such as a building, a tunnel or a soundproof wall. The proposed method has the three steps. The first is receiving cameras and LiDAR sensors and to preprocess the data from each sensor. In this step, the distortion of image data is calibrated, and the LiDAR sensor separates the ground point group and the non-ground point group. The second step is sensor fusion. The coordinate system is integrated into a transformation matrix between two predetermined sensors, and the results of the two data are fused. The last step is object classification using CNN. The proposed method can improve the reliability and stability of communication by detecting the objects affecting the communication performance in advance and changing the channel parameters of the multichannel TCU.
Psychological Characteristics of Cadets from the Military Schools as Subjects of Educational Activity

The goal of the present study is to reveal the essence of the reasons that lead to cadets’ dropout of the All-Army Faculty of National Military University and to propose actions for their impact reduction.
Setting up a neural machine translation system for English to Indian languages

Natural language translation is one of the most difficult tasks being handled by computer scientist community. This is certainly one task in which machine is definitely lagging behind the cognitive powers of human beings. From the initial ages of computer science a lot of approaches have been proposed for solving this task. Statistical machine translation (SMT) is one of the conventional and matured ways of solving the problem of MT. This approach is based on the Bayes rule and requires huge datasets to train the system. SMT performs well on similar grammar structured language pairs. In recent years, neural MT (NMT) has emerged as an alternate way of addressing the same issue. In this approach, we train a neural network on the source and target language pairs and train the system to develop the translation rules. In this work, we explore different configurations for setting up an NMT system for six different Indian languages. We have focused more on Hindi, which is the most widely spoken language, and more datasets are available for this language. We have also deployed the same system on Bangla, Tamil, Telugu, Urdu, and Malayalam as well. We have experimented with eight different architecture combinations of NMT for English to Indian languages and compared our results with conventional MT techniques. We have also observed in this work that NMT requires very less amount of data size for training and thus exhibits satisfactory translation for few thousands of training sentences as well.
Design of an Indicative Featured and Speed Controlled Obstacle Avoiding Robot

The paper represents the design of an Obstacle Avoiding Robot with the capability of detecting objects in its course and navigating around those objects by making a proper decision. It demonstrates a robotic unit based on Arduino UNO and Adafruit Motor Shield where the code is written in Arduino IDE Software. Obstacle avoidance is one of the considerable key factors for developing mobile robots. The implementation of the Ultrasonic Distance Sensor placed on the Micro-Servo Motor yielded more precision for detecting the surrounding objects. This designed robot is different than other available robots because of the integration of a Magnetic Buzzer and a CC RGB Diffused LED by which it has achieved the ability to indicate object detecting procedure and control the speed of DC Gear Motors in 4 types of circumstances. As an autonomous robot, the potentiality to maneuver through unknown conditions without creating any impact was executed. Furthermore, this designed technology can be deployed for military operations and humanitarian assistance by improving the capacity of object detection in diverse environments.
Finite Mixtures of ERGMs for Modeling Ensembles of Networks

Ensembles of networks arise in many scientific fields, but there are relatively few statistical tools for inferring their generative processes, particularly in the presence of both dyadic dependence and cross-graph heterogeneity. To address this gap, we propose characterizing network ensembles via finite mixtures of exponential family random graph models (ERGMs), a class of parametric statistical models that has been successful in explicitly modeling the complex stochastic processes that govern the structure of edges in a network. Our proposed modeling framework can also be used for applications such as model-based clustering of ensembles of networks and density estimation for complex graph distributions. We develop a joint approach to estimate the number of mixture components and identify cluster-specific parameters simultaneously as well as to obtain an identified model under the Bayesian paradigm. Specifically, we develop a Metropolis-within-Gibbs algorithm to perform Bayesian inference, and estimate the number of mixture components using a strategy of deliberate overfitting with sparse priors that removes excess components during MCMC. As the true ERGM likelihood is generally intractable for model specifications with dyadic dependence terms, we consider two tractable approximations (pseudolikelihood and adjusted pseudolikelihood) to facilitate efficient statistical inference. We run simulation studies to compare the performance of these two approximations with respect to multiple metrics, showing conditions under which both are useful. We demonstrate the utility of the proposed approach using an ensemble of political co-voting networks among U.S. Senators and an ensemble of brain functional connectivity networks.
Sentiment analysis in Turkish: Supervised, semi-supervised, and unsupervised techniques

Abstract Although many studies on sentiment analysis have been carried out for widely spoken languages, this topic is still immature for Turkish. Most of the works in this language focus on supervised models, which necessitate comprehensive annotated corpora. There are a few unsupervised methods, and they utilize sentiment lexicons either built by translating from English lexicons or created based on corpora. This results in improper word polarities as the language and domain characteristics are ignored. In this paper, we develop unsupervised (domain-independent) and semi-supervised (domain-specific) methods for Turkish, which are based on a set of antonym word pairs as seeds. We make a comprehensive analysis of supervised methods under several feature weighting schemes. We then form ensemble of supervised classifiers and also combine the unsupervised and supervised methods. Since Turkish is an agglutinative language, we perform morphological analysis and use different word forms. The methods developed were tested on two datasets having different styles in Turkish and also on datasets in English to show the portability of the approaches across languages. We observed that the combination of the unsupervised and supervised approaches outperforms the other methods, and we obtained a significant improvement over the state-of-the-art results for both Turkish and English.
Application of SSD framework model in detection of logs end

Abstract External factors in the natural environment interfere with the detection of logs end. A method for detecting the logsend using an SSD model in a natural scene is proposed. The use of default frame in SSD can realize the extraction and utilization of target features with different scales and improve the detection accuracy. By manually marking the foreground area of the logs end, the interference of the features of the background area is reduced, and the convolution learning of the feature of the target area is enhanced. The performance evaluation and comparison test of the method showed that the accuracy rate was 94.87% and the recall rate was 91.34%. Compared with the traditional method, it reduces the occlusion effect caused by light and shooting angle, and improves the detection accuracy.
Vision-based recognition of pedestrian crossing intention in an urban environment

Autonomous moving platforms (like self-driving cars and mobile robots) need to understand the behaviors of pedestrians for better performance. Recognizing pedestrians' crossing intention is one of the most critical capabilities of self-driving cars to guarantee safe operations in an urban environment. Some researchers utilize pedestrians' trajectories to identify their crossing intention, whereas ignoring the informative appearance. This work studies two vision cues which reveal pedestrians' crossing intention. A looking/not looking classifier is built to capture eye contacts between pedestrians and self-driving cars. Such eye contacts are considered to be highly related to pedestrians' crossing intention. A crossing/not crossing classifier is designed to catch early actions before pedestrians starting to cross, like limb movements. Data augmentation is used before training both classifiers due to limited training samples. Meanwhile, both classifiers are optimized with focal loss, which can relieve the issue of easy/hard samples. Experimental evaluations verify the effectiveness of the proposed vision cues in recognizing pedestrians' crossing intention. Comparisons with the baseline indicate the superiorities of the proposed strategies in training the two classifiers.
Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) for Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with Pigeon Inspired Optimization, Simulated Annealing, Greedy Search, Hybrid Bee with Simulated Annealing, and Hybrid Bee with Greedy Search using BDeu score function as a metric for all algorithms. They investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of evaluations, the proposed algorithm achieves better performance than the other algorithms and produces better scores as well as the better values.