Reviewerly/exHarmony huggingface dataset:
Deep-Reinforcement-Learning-Based Latency Minimization in Edge Intelligence Over Vehicular Networks

A novel paradigm that combines federated learning with blockchain to empower edge intelligence over vehicular networks (FBVN) can enable latency-sensitive deep neural network-based applications to be executed in a distributed pattern. However, the complex environments in FBVN make the system latency much harder to minimize by traditional methods. In this article, we model the training and transmission latency of each autonomous vehicle (AV) and consensus latency of the blockchain in-edge side in FBVN. Considering the dynamic and time-varying wireless channel conditions, unpredictable packet error rate, and unstable data sets quality, we adopt duel deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning (DDQL) as the solving approach. We propose a federated DDQL algorithm, in which the learning agent is deployed on each AV side, and the sensing states on each AV do not need to be shared so that it increases scalability and flexibility for practical implementation. Simulation results show that the proposed algorithm has better performance in reducing system latency compared with the other schemes.
AI Support Marketing: Understanding the Customer Journey towards the Business Development

Artificial Intelligence (AI) fundamentally works on the antecedents of business management's exploration, systematical incorporation of businesses details, and focus on business extension. The main purpose of this study is to know the morals of services related to AI in order to develop the marketing and businesses. This study shows the opinions of marketers to know the value of Artificial Intelligence. We propose the research model so that study can be seen in one frame. Study indicates the observations of marketers regarding AI which is composed of 12 services of marketing, the 4Ps (Product, Price, Place, Promotion), the 4Cs (Consumer, Cost, Convenience, Communication), and the 4Es (Experience, Exchange, Everyplace, Evangelism). Using Cronbach's alpha to analyze the data collected from 508 samples. We showed the reliability and validity of the data so that it can be used for further analysis. We proposed the hypothesis which showed the relationship of each marketing service with AI for developing the business. Consequently, results indicates that all the services, except Evangelism, have positive relationship with AI. Additionally, study also showed that AI highly works on the business development. And marketing also shows significant effect on Business development. Study also offers some important implications for business development that further research should be done on different services, area and audience.
A new data augmentation method to use in machine learning algorithms using statistical measurements

Cancer disease is among the leading causes of death in the world today. It has been scientifically proven that if the disease is detected in the first stages, the success rate in treatment can be close to 100%. Accordingly, it can be said that the problems caused by breast cancer can be solved to a great extent thanks to early diagnosis. Data sets that can be processed in early diagnosis are required. Researchers use artificial intelligence techniques to develop systems to assist specialist doctors. It is of great importance to have a data set on which researchers can work. The more parameters of these data sets and the number of these parameters, the more artificial learning process takes place. In this study, both a new data augmentation method for two different classes in the database of breast cancer was presented and breast cancer was diagnosed with this method. First, the database was analyzed statistically with the proposed data augmentation method. Then, the new values obtained from the data augmentation process were added to the database with a 5-times and 10-times augmentation. Experimental studies were conducted between raw database and databases with augmentatied data. The proposed model with 5 different Machine Learning Algorithms (MLA) (k Nearest Neighbors (k-NN), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF)) and Support Vector Machine (SVM)) were tested. The accuracy rates in train and test operations were increased in the following order; for k-NN, same − 15%, for DT, 0.1–13.01%, for RF, same − 14.03%, for NB, 14.7%−19.7%, for SVM, 15.9%−14.06%. The rates obtained from the experimental results proved to be very promising. The proposed method can be used as an alternative data augmentation method for researchers to get more accurate results in their studies.
Temporal Head Pose Estimation From Point Cloud in Naturalistic Driving Conditions

Head pose estimation is an important problem as it facilitates tasks such as gaze estimation and attention modeling. In the automotive context, head pose provides crucial information about the driver’s mental state, including drowsiness, distraction and attention. It can also be used for interaction with in-vehicle infotainment systems. While computer vision algorithms using RGB cameras are reliable in controlled environments, head pose estimation is a challenging problem in the car due to sudden illumination changes, occlusions and large head rotations that are common in a vehicle. These issues can be partially alleviated by using depth cameras. Head rotation trajectories are continuous with important temporal dependencies. Our study leverages this observation, proposing a novel temporal deep learning model for head pose estimation from point cloud. The approach extracts discriminative feature representation directly from point cloud data, leveraging the 3D spatial structure of the face. The frame-based representations are then combined with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bidirectional long short term memory</i> (BLSTM) layers. We train this model on the newly collected <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multimodal driver monitoring</i> (MDM) dataset, achieving better results compared to non-temporal algorithms using point cloud data, and state-of-the-art models using RGB images. We further show quantitatively and qualitatively that incorporating temporal information provides large improvements not only in accuracy, but also in the smoothness of the predictions.
Multi-threshold token-based code clone detection

Clone detection plays an important role in software engineering. Finding clones within a single project introduces possible refactoring opportunities, and between different projects it could be used for detecting code reuse or possible licensing violations.In this paper, we propose a modification to bag-of-tokens based clone detection that allows detecting more clone pairs of greater diversity without losing precision by implementing a multi-threshold search, i.e. conducting the search several times, aimed at different groups of clones. To combat the increase in operation time that this approach brings about, we propose an optimization that allows to significantly decrease the overlap in detected clones between the searches.We evaluate the method for two different popular clone detection tools on two datasets of different sizes. The implementation of the technique allows to increase the number of detected clones by 40.5-56.6% for different datasets. BigCloneBench evaluation also shows that the recall of detecting Strongly Type-3 clones increases from 37.5% to 59.6%.
Handwriting Recognition for Medical Prescriptions using a CNN-Bi-LSTM Model

It is commonly seen that it is tough to read the handwritten text from medical prescriptions. It is mostly due to the different style of handwriting and the use of Latin abbreviations for medical terms which is usually unknown to the general public. This can make it difficult for both patients and even pharmacists to read the prescription, which can have negative or even fatal consequences if read incorrectly. This paper demonstrates the use of a CNN-Bi-LSTM model along with Connectionist Temporal Classification. The prescribed model consists of three components, the convolutional layers for feature extraction, the Bi-LSTM network for making predictions for each frame of the context vector and the final decoding to translate each character in the recognized sequence by LSTM layers into an alphabetic character using the CTC loss function. A linear layer is added after the bi-LSTM layer to compute the final probabilities, which will be decoded. We also built a corpus manually containing the terms widely used in the medical domain, commonly used in prescriptions. We then use string matching algorithms, and string distance functions to find the nearest word in the corpus, so that bias is given to medical terms for increasing accuracy of the predicted output.
Deep Learning Architectural Designs for Super-Resolution Of Noisy Images

Recent advances in deep learning have led to significant improvements in single image super-resolution (SR) research. However, due to the amplification of noise during the upsampling steps, state-of-the-art methods often fail at reconstructing high-resolution images from noisy versions of their low-resolution counterparts. However, this is especially important for images from unknown cameras with unseen types of image degradation. In this work, we propose to jointly perform denoising and super-resolution. To this end, we investigate two architectural designs: "in-network" combines both tasks at feature level, while "pre-network" first performs denoising and then super-resolution. Our experiments show that both variants have specific advantages: The in-network design obtains the strongest results when the type of image corruption is aligned in the training and testing dataset, for any choice of denoiser. The pre-network design exhibits superior performance on unseen types of image corruption, which is a pathological failure case of existing super-resolution models. We hope that these findings help to enable super-resolution also in less constrained scenarios where source camera or imaging conditions are not well controlled. Source code and pretrained models are available at https://github.com/angelvillar96/super-resolution-noisy-images.
Importance sampling in reinforcement learning with an estimated behavior policy

Abstract In reinforcement learning, importance sampling is a widely used method for evaluating an expectation under the distribution of data of one policy when the data has in fact been generated by a different policy. Importance sampling requires computing the likelihood ratio between the action probabilities of a target policy and those of the data-producing behavior policy. In this article, we study importance sampling where the behavior policy action probabilities are replaced by their maximum likelihood estimate of these probabilities under the observed data. We show this general technique reduces variance due to sampling error in Monte Carlo style estimators. We introduce two novel estimators that use this technique to estimate expected values that arise in the RL literature. We find that these general estimators reduce the variance of Monte Carlo sampling methods, leading to faster learning for policy gradient algorithms and more accurate off-policy policy evaluation. We also provide theoretical analysis showing that our new estimators are consistent and have asymptotically lower variance than Monte Carlo estimators.
Neighbourhood Wattch: Using Speculative Design to Explore Values Around Curtailment and Consent in Household Energy Interactions

Smart energy technologies provide new opportunities for network demand management, while generating data that can provide inference into household activities and which is increasingly valuable for user profiling and targeted marketing. This paper explores the human values implicated by smart energy technologies as energy consumption emerges as a potentially sensitive and attributable data trail. Using a values-based approach involving semi-structured interviews with 39 Australians, we use speculative design to engage participants with the implications of high frequency household energy data and understand users' values and attitudes towards curtailment, surveillance, accountability and consent. The principal contributions are (1) a nuanced understanding of user values around privacy, responsibility, trust, collectivism and curtailment in relation to interpersonal household energy use data and considerations for speculative designs in this space, and (2) reflections on the design of mechanisms for consumer participation in demand response for energy networks.
Key-Based Enhancement of Data Encryption Standard For Text Security

Securing various data types such as text, image, and video is needed in real-time communications. The transmission of data over an insecure channel is a permanent challenge, especially in mass Internet applications. Preserving confidentiality and integrity of data toward malicious attacks, accidental devastation, change during transfer, or while in storage must be improved. Data Encryption Standard (DES) is considered as a symmetric-key algorithm that is most widely used for various security purposes. In this work, a Key-based Enhancement of the DES (KE-DES) technique for securing text is proposed. The KEDES is implemented by the application of two steps: the first is merging the Odd/Even bit transformation of every key bit in the DES algorithm. The second step is replacing the right-side expansion of the original DES by using Key-Distribution (K-D) function. The K-D allocation consists of 8-bits from Permutation Choice-1 (PC-1) key outcome. The next 32-bits outcomes from the right-side of data, there is also 8-bits outcome from Permutation Choice-2 (PC-2) in each round. The key and data created randomly, in this case, provide adequate security and the KEDES model is considered more efficient for text encryption.
Solar Tracking System with New Hybrid Control in Energy Production Optimization from Photovoltaic Conversion for Polish Climatic Conditions

This paper presents a comparison of the 24-month production of electricity and momentary power by two photovoltaic systems with a nominal power of 1000 Wp each. The analyzed systems are connected to the power grid and operate independently: a fixed system with optimal all-year-round angular positioning and an astronomical-sensor-controlled dual-axis tracking system. The systems under consideration consist of PV modules with a unit electrical power of 200 Wp, and the production technology of those modules is the same. The tested systems were located on the building rooftop of the Faculty of Control, Robotics and Electrical Engineering of the Poznan University of Technology in Poland (Central Poland, 52°24.4152′ N, 16°55.7958′ E) at a height of 30 m above ground level. The measurements take into account the consumption of electricity by the actuators of the dual-axis positioning system. Increases in the produced electricity by 35.6% and 44.7% were observed for local climatic conditions during the first and second measuring years, respectively, when comparing the use of the dual-axis tracking system with the use of fixed units. Results show that the analyzed region of central Poland, represented by the city of Poznań, creates favorable climatic conditions for the use of astronomical-sensor spatial positioning systems for photovoltaic modules, also in the case of a significant share of diffuse solar radiation in the global value. These results may make the planning of solar tracking investments easier and help with the estimate calculations of the total investment return period and operational costs.
Speech-Based Depression Prediction Using Encoder-Weight-Only Transfer Learning and a Large Corpus

Speech-based algorithms have gained interest for the management of behavioral health conditions such as depression. We explore a speech-based transfer learning approach that uses a lightweight encoder and that transfers only the encoder weights, enabling a simplified run-time model. Our study uses a large data set containing roughly two orders of magnitude more speakers and sessions than used in prior work. The large data set enables reliable estimation of improvement from transfer learning. Results for the prediction of PHQ-8 labels show up to 27% relative performance gains for binary classification; these gains are statistically significant with a p-value close to zero. Improvements were also found for regression. Additionally, the gain from transfer learning does not appear to require strong source task performance. Results suggest that this approach is flexible and offers promise for efficient implementation.
Distinguishing Between Smartphones and IoT Devices via Network Traffic

Internet of Things (IoT) devices are increasingly growing in mobile networks with the ubiquity of various IoT services. They share the same infrastructure with smartphones while having different requirements for communication resources and security defense mechanisms. Distinguishing IoT devices from smartphones has far-reaching implications on effective network design, resource allocation scheme, pricing scheme, etc. In this article, we distinguish between 12 107 IoT devices and 12 693 smartphones in the real world via characterizing their network traffic. The IoT devices fall into five categories, namely, locating, monitoring, portable, point of sale (POS), and vehicle. We analyze the device behaviors from the network domain, physical domain, and time domain, make comparisons between each kind of IoT devices and smartphones, and design effective features based on the distinguishable network behavior characteristics at packet level, traffic level, and mobility level. Then, we train several classifiers based on our feature set to identify different kinds of mobile devices. Specifically, the accuracy of identifying IoT devices from smartphones achieves 95.86%, and the accuracies of distinguishing IoT devices in each category from smartphones are all over 95%. In the trained classifiers, feature importance verifies the discriminability of different network traffic characteristics observed in our multidomain measurement. Our study reveals the network traffic behavior characteristics for IoT devices, and successfully distinguishes them from smartphones, which paves the way for better network design, resource allocation, pricing scheme, and security defense mechanisms.
Training a Seismogram Discriminator Based on ResNet

Selecting appropriate data of good quality is the primary precondition for conducting meaningful analysis in seismological research. The data set used in a study is usually selected by setting a threshold for searching the earthquake catalog to ensure data quality, such as parameters of magnitude or hypocentral distance. While the threshold approach is useful, it cannot guarantee the consistently good quality of the selected seismograms. For that reason, a manual checking process is often required for quality control, which is inefficient and fallible for large data sets. In this study, we develop an automated seismogram discriminator that is capable of selecting quality seismograms from massive events. The discriminator is created based on a machine learning technique using a residual neural network (ResNet), a well-designed architecture in computer image recognition. Three-component seismic records of an earthquake from the catalog are considered as images by the ResNet. Using the images of seismic records, the ResNet can be trained to distinguish between good and poor seismic records. This discriminatory ability is evaluated using a blind testing data set of approximately 20 000 three-component seismic records related to the events that occurred in Sichuan between January 2014 and May 2018. The results show that our seismogram discriminator achieves an accuracy of greater than 95%.
Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving

Motion estimation is crucial to predict where other traffic participants will be at a certain period of time, and accordingly plan the route of the ego-vehicle. This paper presents a novel approach to estimate the motion state by using region-level instance segmentation and extended Kalman filter (EKF). Motion estimation involves three stages of object detection, tracking and parameter estimate. We first use a region-level segmentation to accurately locate the object region for the latter two stages. The region-level segmentation combines color, temporal (optical flow), and spatial (depth) information as the basis for segmentation by using super-pixels and Conditional Random Field. The optical flow is then employed to track the feature points within the object area. In the stage of parameter estimate, we develop a relative motion model of the ego-vehicle and the object, and accordingly establish an EKF model for point tracking and parameter estimate. The EKF model integrates the ego-motion, optical flow, and disparity to generate optimized motion parameters. During tracking and parameter estimate, we apply edge point constraint and consistency constraint to eliminate outliers of tracking points so that the feature points used for tracking are ensured within the object body and the parameter estimates are refined by inner points. Experiments have been conducted on the KITTI dataset, and the results demonstrate that our method presents excellent performance and outperforms the other state-of-the-art methods either in object segmentation and parameter estimate.
Classification-enhancement deep hashing for large-scale video retrieval

With the explosive growth of video data on the Internet, retrieving and detecting similar video contents effectively has become a challenging problem. Whereas hashing is a mature technique for dealing with this problem, especially in image retrieval, when hashing techniques are applied to videos, a large gap in performance is always encountered compared with that of image hashing methods because of the complicated structures of videos. Generally, existing video hashing methods just directly apply image hashing approaches into video frames without considering temporal structure, leading to low performance in video retrieval. In this study, we proposed a video hashing method, called classification-enhancement deep hashing (CEDH), for large-scale video searches. The proposed CEDH first fuses the spatial–temporal information of videos in a deep end-to-end hashing network, and then leverages both neighborhood structure of semantics and triple similarity information to learn video hash codes. Subsequently, to enhance the precision of the hash codes during hash learning, a classification module is added after the fully connected layer of the deep network. We also use an additional code constraint to make the hash codes more suitable for containing sufficient information. Extensive experiments on three real-world large-scale video datasets show that our proposed method significantly outperforms state-of-the-art algorithms. • Triplet-wise loss is applied into video hashing for similarity preserving. • Add constraints to make hash codes more informative. • Utilize classification term to enhance the hash learning.