Reviewerly/exHarmony huggingface dataset:
Firearm Detection Using Deep Learning

Surveillance Cameras (SCs) are great support in crime investigation and proximity alarms which play a critical role in public safety and peace. The major drawback is their limited use in producing evidences in judicial court but they were not used in providing early firearms detection to stop attacks in crime scenes. Traditional firearm detection techniques use X-Rays for detecting weapons in limited coverage area that fail in detecting non-metallic weapons. The main motivation of this research is developing a deep learning object detection model to early detect firearms in crime scenes and alert the corresponding authorities. Designing an efficient and accurate object detection model that localize and classify the firearm classes with overcoming variations in shape, size, appearance and occlusions is a real challenge. We collected a dataset of different firearm classes from Kaggle and Google Open Images and manually annotated about 2300 samples. The dataset consists of variety of firearms classes handgun, revolver, rifle along with knife and person classes. YOLOv5 (You Only Look Once) is a unified object detector which detects objects without losing their precision and accuracy. All the YOLOv5 models are built from scratch and generate final models that achieved 89.4%, 70.1%, 80.5% of precision, recall and mAP@0.5 respectively and achieved the highest 94.1% precision per individual class.
Eigen Value Decomposition Utilizing Method for Data Hiding Based on Wavelet Multi-resolution Analysis

Eigen value decomposition utilizing method for data hiding based on wavelet Multi-Resolution Analysis (MRA) is proposed. It is possible to improve an invisibility of the hidden information by using eigen value decomposition as a preprocessing of the conventional wavelet Multi-Resolution Analysis (MRA) based method. In the proposed method, the information of the key image is protected by the existence of the eigenvector. That is, the key image information can be restored only if the true original image information is known. The coefficient of eigen value decomposition differs for each original image and is composed of the eigenvectors of the original image. In a 3-band color image, a method involving Hue Saturation Intensity (HIS) conversion, or the like can be considered for the purpose of protecting the information of the key image, but the conversion coefficient such as HSI conversion is a well-known coefficient. Since the conversion coefficient of the method involving HSI conversion is well known, there is a possibility that a third party may obtain the information of the key image. The effectiveness of the proposed method is confirmed from the viewpoint of protecting the information of the key image.
BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks

In the time past, virtually all the contemporary communication systems depend on distinct source and channel encoding schemes for data transmission.Irrespective of the recorded success of the distinct schemes, the new developed scheme known as joint source channel coding technique has proven to have technically outperformed the conventional schemes.The aim of the study is centered in developing an enhanced joint source-channel coding scheme that could mitigate some of the limitations observed in the contemporary joint source channel coding schemes.The study tends to leverage on recent developments in machine learning known as deep learning techniques for robust and enhanced scheme, devoid of explicit code dependence for the signal compression and as well in error correction but learn automatically on end-to-end mapping structure for the source signals.It primarily aimed at providing an improved channel performance approach for wireless communication network.A deep learning algorithm was implemented in the study, the scheme focused on improving the Bit Error Rate (BER) performance while reducing latency and the processing complexity in Joint Source Channel Coding systems.The deep learning autoencoder model was deployed to compare with the hamming code, convolution code, and uncoded systems.JSCC using neural networks were simulated based on BER performance over a range of energy per symbol to noise ratio (Eb/No).Training and test error for the fully connected neural network autoencoder models on channels with 0.0dB and 8.0dB were carried out.The results obtained showed that the autoencoder model had a better BER performance when compared with the convolution code and uncoded systems, it also outperformed the uncoded BFSK with an approximately equal BER performance when compared with the hamming code (soft decision) decoding system.
Body Feature Filter for Occluded Person Re-Identification

Occluded person re-identification attracted widespread attention in recent years since it is more consistent with the real-world scenarios compared with other tasks of person re-identification. Researchers proposed a lot of creative works to tackle this task and achieved inspiring progress. However, most of these works severely relied on extra cues from some pre-trained models, such as pose estimator, which made the result of their models sensitive to the estimation error in the extra cues. Moreover, they utilized pose estimator in both training and inference stage, which slowed down the inference speed a lot. In this paper, we propose a novel method named body feature filter (BFF), which is robust to inaccurate pose information from pose estimator by simply transferring score maps of key-points to body part label. Our BFF helps the model filter out noisy information from the occluded regions or the background and focus on the body features of person images. Our method consists of three steps in the training stage. In the first step, we utilize score maps of key-points from pose estimator to get local feature vectors. Then we calculate similarity map in which the value of each location is the similarity score between the local feature vector and the global feature of this location. Finally, we add similarity maps related to the same body part together to generate body part label. In the second step, we design a simple network, BFF, to learn the information from body part label. It generates body part masks to guide the model in knowing where is noisy feature to suppress and where is body part feature to enhance. In the third step, we add a module named body feature refiner (BFR) to further refine the body feature. In the inference stage, we only use body feature filter and body feature refiner without any cues from pose estimator to get final features for person retrieval. The simple architecture of our model makes inference speed faster than that of most other works. Besides, our BFF can be utilized in many person re-identification models, which uses pose estimator. Experiments show the effectiveness of our proposed method.
Concept of Operations of Next-Generation Traffic Control Utilizing Infrastructure-Based Cooperative Perception

This paper provides a system architecture for an infrastructure-based cooperative perception fusion engine for next-generation traffic control. This engine will provide a complete state-space digital representation with measurable accuracy to support a wide-range of applications. The architecture includes inputs, functional flow, data standardization recommendations, outputs, and supported applications. The cooperative perception engine addresses critical needs with respect to accelerating the benefits of automation through intelligent roadway infrastructure, which complements and accelerates connected and automated vehicle (CAV) technology. The cooperative perception acquires and fuses information from sensors (radar, LiDAR, and cameras) and CAVs to perceive roadway traffic states of moving objects, creates a complete 3D digital representation of that state-space, and communicates it to downstream application such as intelligent signal control, safety and energy applications, and cooperate driving applications. The intelligent roadway infrastructure approach, as opposed to a vehicle-centric approach, is more scalable because it can be deployed to the roughly 300,000 signalized intersections more readily than over 300 million vehicles in the United States, and accrues early-stage benefits equitable to all roadway users addressing safety, equity, fuel efficiency, and greenhouse gas reduction.
Image-Based Rain Detection with Local Binary Pattern-Based Features Using Machine Learning

The main objective of this paper is to develop a low-cost rain detection system using an in-vehicle video camera capable of providing trajectory-level road surface weather information in real-time. The study utilized the Second Strategic Highway Research Program’s (SHRP2) naturalistic driving study (NDS) video data and based on image-based classification techniques. k-means clustering analysis was applied to annotate the extracted images automatically from the video data. To train the detection models local binary pattern (LBP) based images’ features were utilized as classification parameters, and artificial neural network (ANN), random forest (RF), and support vector machine (SVM) were used as classification learners. The overall detection accuracy of the trained RF, ANN, and SVM model was found to be around 99.3%, 98.8%, and 99.8%, respectively. In a connected vehicle (CV) environment, the proposed weather detection system could significantly improve the VSL algorithms, especially on roadways with no weather stations.
Identity and belonging

In a global and digital world, the binding power of old institutions is declining while new identities and ways of belonging are emerging. Education can help support the needs of diverse learners and their communities while cultivating global competences. This chapter explores these issues through five themes: You can go your own way: examines the decline of traditional institutions and rise of individualism. It’s a small world after all: looks at diversification within nations and multilateral cooperation. Speak your mind: highlights changing patterns of civic and political engagement. All for one and one for all: shows how LGBTI+ and disability rights and opportunities are evolving. The many profiles of us: examines the role of digital platforms in identity and community formation. These trends are linked to education and its future through a series of questions and scenarios. Potential implications of COVID-19, both immediate- and longer-term, are discussed.
Learning Bayesian Networks from the Knowledge of a Never-Ending Learning System

Never-Ending Learning (NEL) is a new paradigm of machine learning in which computer systems can learn continuously and incrementally. The first never-ending learning system reported in literature is named NELL (Never-Ending Language Learning). NELL has built a Knowledge Base (KB) containing many types of knowledge. In this paper, we propose to learn Bayesian networks from the knowledge stored in the NELLs KB aiming to assist in the development of expert systems in the future. In addition, Bayesian networks have shown to be promising to solve the problem of representing semantic relations and extending the NELLs initial ontology. In the initial experiments, we have built a dataset on the domain of diseases from relations existing in NELL and applied two learning algorithms of Bayesian networks named K2 and DMBC. The empirical results have shown that this proposal is promising.
Resource Savings Associated With Use of an Automated Symptom Monitoring Tool for COVID-19 Public Health Response, Summer 2020-Summer 2021

Context: Active symptom monitoring is a key component of the public health response to COVID-19, but these activities are resource-intensive. Digital tools can help reduce the burden of staff time required for active symptom monitoring by automating routine outreach activities. Program: Sara Alert is an open-source, Web-based automated symptom monitoring tool launched in April 2020 to support state, tribal, local, and territorial jurisdictions in their symptom monitoring efforts. Implementation: As of October 2021, a total of 23 public health jurisdictions in the United States had used Sara Alert to perform daily symptom monitoring for more than 6.1 million individuals. This analysis estimates staff time and cost saved in 3 jurisdictions that used Sara Alert as part of their COVID-19 response, across 2 use cases: monitoring of close contacts exposed to COVID-19 (Arkansas; Fairfax County, Virginia), and traveler monitoring (Puerto Rico). Evaluation: A model-based approach was used to estimate the additional staff resources that would have been required to perform the active symptom monitoring automated by Sara Alert, if monitoring instead relied on traditional methods such as telephone outreach. Arkansas monitored 283 705 individuals over a 10-month study period, generating estimated savings of 61.9 to 100.6 full-time equivalent (FTE) staff, or $2 798 922 to $4 548 249. Fairfax County monitored 63 989 individuals over a 13-month study period, for an estimated savings of 24.8 to 41.4 FTEs, or $2 826 939 to $4 711 566. In Puerto Rico, where Sara Alert was used to monitor 2 631 306 travelers over the 11-month study period, estimated resource savings were 849 to 1698 FTEs, or $26 243 161 to $52 486 322. Discussion: Automated symptom monitoring helped reduce the staff time required for active symptom monitoring activities. Jurisdictions reported that this efficiency supported a rapid and comprehensive COVID-19 response even when experiencing challenges with quickly scaling up their public health workforce.
An integrity-preserving technique for range queries over data streams in two-tier sensor networks

Two-tier sensor networks enable energy and computation saving due to the introduction of non-constrained storage nodes acting as intermediates between the sensors, which provide data, and the sink, which needs to access these data by submitting queries to the storage nodes. However, the storage nodes maintain a lot of data coming from different sensors and represent a single point of failure targeted by the attackers. In this scenario, robust guarantees about the integrity of the query result (i.e., completeness, freshness, and correctness) should be provided. This is a very well-known problem, traditionally faced through Merkle-Hash-Tree-based data structures. This paper presents a technique more efficient than the state-of-the-art methods for data insertions and deletions, supporting range queries over even non-temporal dimensions. Thus, the proposed solution appears suitable to the considered scenario, in which data can be updated with high frequency and insertions and deletions are executed by constrained devices.
Empathetic Connection

This chapter explores the increased interest in consumers’ emotional private life. Algorithmic systems allow marketing organisations to create new in-depth profiles of consumers’ psychology, measure sentiment in the digital marketplace, and decipher emotional cues during the offline-online customer journey. At the same time, the expansion of language-based interfaces enables marketers to engage in new human-like interactions with consumers, generating a sense of social attachment in consumers while auctioning-o intimate spaces of their lives. This development drives consumers’ surveillance and predictive personalisation into the depths of human emotions and affective lives, which trigger new dynamics of hidden persuasion and social affection in algorithmic marketing.
Camera calibration based on improved differential evolution particle swarm

Aiming at the problem of camera calibration with multiple parameters, this paper proposes the fusion optimization algorithm of improved differential evolution and particle swarm in the calibration of the camera. Adaptive judgment factors is Introduced to control the improvement of differential evolution during each iteration (IDE) algorithm and particle swarm optimization (PSO) algorithm solve the multiple parameters of traditional camera calibration algorithm. The proposed algorithm can ensure the diversity and effectiveness of individual evolution of the population. Experimental results show that the algorithm has excellent global search capabilities and local optimizations ability. It can accurately calibrate the camera. The convergence precision, speed and robustness performance significantly is superior to PSO (Particle swarm optimization algorithm), DE (differential evolution algorithm), GA (Genetic algorithm) and Zhang’s method. It improves the precision and speed of the proposed calibration method. The root mean square error of the calibration algorithm proposed in this paper is only 0.182, the calibration error result is smaller than other several algorithms. The reprojection error of our method is 0.05938 (Ex/pixel) and 0.02988 (Ey/pixel). It is smaller than GA, PSO, DE, and Zhang’s method. So, the algorithm performance is excellent.
Assessment of the Technological Maturity of Marine Autonomous Surface Ships

Abstract Marine Autonomous Surface Ships (MASS) are gaining increasing attention due to the potential benefits of improving safety and efficiency. The paper presents the trends in the development of autonomous ships and reliability modeling carried out to assess the maturity of Guidance, Navigation, Control, Power and Propulsion (GNCPP) subsystems that are critical for safe and reliable operation of MASS. Based on the simulations with field-failure data as inputs, it is identified that a typical vessel GNCPP system can have a mean time to fail period of 1.2 years. The emergency shore support systems shall require a proof test interval of 22 days to comply with IEC61508 HSE Safety Integrity Level 3 of on-demand availability. The results could help in efficient maintenance planning and integrity management of the autonomous ship systems. The efforts required to realize situation-aware autonomous navigation technologies complying with stringent operational regulatory frameworks are also discussed.
CIRCUMSTANTIAL ELEMENTS ANALYSIS OF LYDIA MACHOVA’S SPEECH IN THE SECRETS OF LEARNING A NEW LANGUAGE TED TALKS: A SYSTEMIC FUNCTIONAL LINGUISTICS STUDY

Systematic Functional Linguistic (SFL) views language as a sign system that can be analysed based on language structure and language use. The research is about circumstantial elements of Lydia Machova’s speech at The Secret of Learning a New Language TED Talks. This study aims to find out the types of circumstantial elements and the most dominant circumstantial element in Lydia Machova’s utterances. This research used the theory proposed by Halliday &amp; Mattiessen (2004). This research used descriptive quantitative research. The source of the data was taken from the utterances of Lydia Machova signified as the circumstantial elements. The research result uncovered all types of circumstances occurring in Lydia Machova’s utterances. The highest usages of circumstance are location due to the function of location. It is not the main verb but has to be a participle phrase or in a form of adjective clause. the information about the absolute place where the experience, methods and tips occurred and also giving the details of the time when the moment actually happened for how long and how often.
INVESTIGATIONS OF AUTOENCODER HYPER-PARAMETERS ON ANOMALY DETECTION

Most of anomaly detection techniques, such as density-based methods, often perform inefficiently on the high dimension of network data because the curse of dimensionality phenomenon. Our previous work presented a novel approach of using the feature space of AutoEncoders (AEs) as a new feature representation for addressing this problem. In this study, we attempt to investigate the characteristics of the latent representation of AEs. Thus, we first discuss the hypothesis of using the latent representation in more details, and extend several experiments showed in the previous work. Following this, we design three intensive examinations (an investigation on the middle hidden layer size, an evaluation on the performance of the hybrid and an exploration on latent data). These aim to get insight into the latent representations of AEs, which is fundamental for designing good latent representations in the future work. This paper closes with analysis and discussion on the experimental results.
Adaptive Education System for Drama Education in College Education System Based on Human-Computer

Drama has key skills to develop creativity, speech, trust, communication, teamwork, and language. Regular access to the classroom develops capabilities, intelligence, emotional, critical thinking, and higher-order abstraction skills. A serious problem is the lack of confidence and administration. However, building student societies are now taking the initiative to change teaching methods in colleges and independent organizations. Several physical action human-computer interaction (HCI) competitions designed to improve interest, sentiment, and visual coordination will be presented in this article. The lack of professional teachers within the area is a further problem in teaching a particular course. In this article, the artificial intelligence-based dramatic social organization material (AI-DSOM) framework has been proposed to solve the challenges faced in drama education. Developing children's first language is fundamental to cognitive development because it provides new language opportunities. Data analysis technique can be applied to evaluate, understand, and analyze the problem to provide a cognitive awareness object considered inanimate. Furthermore, grammatical evaluations using deep learning are an effective and efficient technological solution with A.I. technology used to regulate and automate resources. The adaptive difficulty-based system aims to keep players interested in their games. Children with hearing impairments are being used as part of an ongoing project to develop adaptive educational systems based on HCI. These combined chores of method eliminate loss of time and energy in teaching by drama method. Simulation results show that proposed method enhances performance, knowledge, vocabulary, and efficiency compared to existing models.
An Efficient Identification Scheme Based on Bivariate Function Hard Problem

Symmetric cryptography allows faster and more secure communication between two entities using the identical pre-established secret key. However, identifying the honest entity with the same secret key before initiating symmetric encryption is vital since the communication may be impersonated. Tea and Ariffin, in 2014, proposed a new identification (ID) scheme based on the Bivariate Function Hard Problem (BFHP) that proved secure against impersonation under passive, active and concurrent attacks via the BFHP-hardness assumption. In this paper, we upgrade the ID scheme and improve some of its settings. Next, we provide the security proof against impersonation under active and concurrent attacks in the random oracle model via the hardness assumption of the One-More BFHP. Finally, we include an additional discussion about the computational efficiency of the upgraded ID scheme based on BFHP and present its comparison with other selected ID schemes.