Reviewerly/exHarmony huggingface dataset:
Small Group Interactions with Voice-User Interfaces

More and more voice-user interfaces (VUIs), such as smart speakers like Amazon Alexa or social robots like Jibo or Cozmo, are entering multi-user environments including homes. VUIs can utilize multi-modal cues such as graphics, expressive sounds, and movement to convey social engagement, affecting how users perceive agents as social others. Reciprocal relationships with VUIs, i.e., relationships with give-and-take between the VUI and user, are of key interest as they are more likely to foster rapport and emotional engagement, and lead to successful collaboration. Through an elicitation study with three commercially available VUIs, we explore small group interactions (n = 33 participants) focused on the behaviors participants display to various VUIs to understand (1) reciprocal interactions between VUIs and participants and among small groups and (2) how participants engage with VUIs as the interface's embodiment becomes more socially capable. The discussion explores (1) theories of sociability applied to the users' behaviors seen with the VUIs, and (2) the group contexts where VUIs that build reciprocal relationships with users can become a powerful persuasive technology and a collaborative companion. We conclude the discussion with recommendations for promoting reciprocity from participants and, therefore, fostering rapport and emotional engagement in VUI interactions.
Survey: Convolution Neural networks in Object Detection

Abstract In latest years, deep neural networks were observed to be the most influential among all innovations in the computer vision field, generating remarkable performance on image classification. Convolution neural networks (CNNs) are considering as an interesting tool for studying vision of biological because this category of artificial vision systems shows the capabilities of visual recognition similar to those of human observers. By improving the recognition performance of these models, it appears that they become more effective in prediction. Recent benchmarks have shown that deep CNNs are excellent approaches for object recognition and detection. In this paper, we are focusing on the core building blocks of convolution neural networks architecture. Different object detection methods that utilize convolution neural networks are discussed and compared. On the other hand, there is a simple summary of the common CNNs architectures.
RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance

An essential component for the autonomous flight or air-to-ground surveillance of a UAV is an object detection device. It must possess a high detection accuracy and requires real-time data processing to be employed for various tasks such as search and rescue, object tracking and disaster analysis. With the recent advancements in multimodal data-based object detection architectures, autonomous driving technology has significantly improved, and the latest algorithm has achieved an average precision of up to 96%. However, these remarkable advances may be unsuitable for the image processing of UAV aerial data directly onboard for object detection because of the following major problems: (1) Objects in aerial views generally have a smaller size than in an image and they are uneven and sparsely distributed throughout an image; (2) Objects are exposed to various environmental changes, such as occlusion and background interference; and (3) The payload weight of a UAV is limited. Thus, we propose employing a new real-time onboard object detection architecture, an RGB aerial image and a point cloud data (PCD) depth map image network (RGDiNet). A faster region-based convolutional neural network was used as the baseline detection network and an RGD, an integration of the RGB aerial image and the depth map reconstructed by the light detection and ranging PCD, was utilized as an input for computational efficiency. Performance tests and evaluation of the proposed RGDiNet were conducted under various operating conditions using hand-labeled aerial datasets. Consequently, it was shown that the proposed method has a superior performance for the detection of vehicles and pedestrians than conventional vision-based methods.
Chaotic Harris Hawks Optimization for Unconstrained Function Optimization

Swarm-based techniques, a form of meta-heuristic techniques, are derived from the swarm system's social conduct in nature. A newly brought optimization algorithm is Harris Hawks Optimization (HHO) that is stimulated through looking conduct of Harris Hawks (agents) of finding food (optimal solution). Balancing between exploitative and exploratory processes of the original HHO algorithm is desirable for achieving better performance to many optimization problems. An algorithm, Chaotic Harris Hawks Optimization (CHHO), is proposed in this work for the unconstrained function optimization. The CHHO algorithm is presented based on adjusting the exploration mechanism of the original HHO algorithm. Ten chaotic maps are used to control this mechanism instead of random adjusting. The unimodal, multimodal, and multimodal based fixed-dimension benchmark functions are used to compare the CHHO algorithm with the HHO algorithm. Visibility of the CHHO algorithm for optimizing the unconstrained benchmark function, with the intelligible effect of the Gauss and Logistic chaotic maps over other maps, is shown in the experiments.
Deep Learning Approach for Detecting Malicious Activities Over Encrypted Secure Channels

Nowadays, most cyber attackers exploit secure communication channels to hide malicious activities and imitate the behaviors of a legitimate user.These attacks over a secure channel make networked systems more vulnerable to new threats and increase the possibility of significant damage to other end users.Traditional TCP/IP-level traffic inspections do not suffice in investigating a secure sockets layer (SSL) conversation because the SSL conversation data is encrypted by a public key system and the SSL uses its own data unit of an SSL record.In this paper, we propose a novel malicious SSL traffic detection method, which reassembles SSL records from captured IP packets and inspects the characteristics of SSL records using a deep learning method.After an SSL record is reassembled from a single or multiple IP packets, the proposed method extracts unencrypted contents of the reassembled record and generates a sequence of unencrypted data from successive SSL records for deep learning-based classification.The sequences of SSL records are encoded using a long short-term memory autoencoder, and then an encoded feature map is generated for each SSL flow.These feature maps are forwarded to the convolutional neural network-based classifier to determine whether the SSL flow is malicious or not.The experiment shows that our proposed approach has a great separability between benign and malicious traffic flows on an encrypted SSL channel.
Fast Coding of Irregular Binary Binomial Numbers with a Set Number of Units Series

The variant of new generation coding technology concept realization on the methods of quantum binomial-polyadic coding in binary space basis is considered. This coding technology allowing to provide additional increase of efficiency of syntactic representation of two-dimensional (multidimensional) binary data structures. The proposed concept allows to increase the energy efficiency of quantum binary binomial-polyadic coding. For this purpose, technological mechanisms of fast quantum coding are proposed.
Tiny moving vehicle detection in satellite video with constraints of multiple prior information

With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information this paper, a tiny vehicle detection method based on spatio-temporal information is proposed to constrain the significance of the image. Firstly, the background modelling method is used to obtain the motion heat map of the image and constrain the motion region. A significance detection method for small targets was used to obtain the significance mapping of these regions. Finally, the detection results were optimized by combining the significance neighbourhood information and the time information between frames to output the binary target detection map. Finally, taking different urban road scenes in 'Jilin-1'satellite video as examples and compares a variety of existing algorithms. Experiments prove that the proposed algorithm can maintain false alarm rate of less than 10% when the detection accuracy and recall rate reach 85% and has certain anti-interference ability in the image environment with satellite Angle deviation.
A Hybrid Multi-phased Particle Swarm Optimization with Sub Swarms

As is known, multi-phase particle swarm optimization (MPPSO) outperforms standard PSO and it is fitful for searching with small swarm size but does not converge as quickly as expected. A hybrid MPPSO with sub swarms called HMPPSO is developed in this paper. In HMPPSO, following the MPPSO updating rules, the sub swarms work cooperatively and alternatively with the optimal swarm, which is formed by the global best particle of each sub swarm, to enhance the global exploration and local exploration. The reinitiating operator is applied to maintain the diversity of the swarms; the feedback operator is used to improve the information exchange among the sub swarms and the optimal swarm. In addition, the size of each sub swarm and the size of the optimal swarm are kept small to save the computation time of fitness function. Numerical simulation results on some benchmarks show that the HMPPSO outperforms MPPSO in both the optimization ability and computation efficiency.
Mask-guided GAN for robust text editing in the scene

Text editing in the scene aims to replace the source text with target text while maintaining the original text style and background, which involves text detection, style transfer and image inpainting techniques. Due to diversified text styles in the real scene, such as colorful outlines and shadows, it is quite challenging to edit complicated text. To address this problem, a mask-guided GAN method is proposed to adequately use the body, outline, and shadow of text to guide the task. First, a mask generating module is designed to detect the body, outline, and shadow regions in the text image. The generated mask in the source image is then used for the proposed text inpainting module and background inpainting module to extract source stylish text and restore the background, respectively. Next, a shape transfer module is designed to infer the target mask that depicts the text structural style from the source mask, which guides a style transfer module to transfer the text style onto the target image. Finally, a fusion module is developed to fuse the target text and background. Each module fulfills its own functional role and collaborates with each other, thus decomposing the complex task into easy-to-learn ones. Experiments and comparisons demonstrate the effectiveness of the proposed method.
Large-scale analysis of grooming in modern social networks

Social networks are evolving to engage their users more by providing them with more functionalities. One of the most attracting ones is streaming. Users may broadcast part of their daily lives to thousands of others world-wide and interact with them in real-time. Unfortunately, this feature is reportedly exploited for grooming. In this work, we provide the first in-depth analysis of this problem for social live streaming services. More precisely, using a dataset that we collected, we identify predatory behaviours and grooming on chats that bypassed the moderation mechanisms of the LiveMe, the service under investigation. Beyond the traditional text approaches, we also investigate the relevance of emojis in this context, as well as the user interactions through the gift mechanisms of LiveMe. Finally, our analysis indicates the possibility of grooming towards minors, showing the extent of the problem in such platforms.
Examining the Relationship of Code and Architectural Smells with Software Vulnerabilities

Context: Security is vital to software developed for commercial or personal use. Although more organizations are realizing the importance of applying secure coding practices, in many of them, security concerns are not known or addressed until a security failure occurs. The root cause of security failures is vulnerable code. While metrics have been used to predict software vulnerabilities, we explore the relationship between code and architectural smells with security weaknesses. As smells are surface indicators of a deeper problem in software, determining the relationship between smells and software vulnerabilities can play a significant role in vulnerability prediction models. Objective: This study explores the relationship between smells and software vulnerabilities to identify the smells. Method: We extracted the class, method, file, and package level smells for three systems: Apache Tomcat, Apache CXF, and Android. We then compared their occurrences in the vulnerable classes which were reported to contain vulnerable code and in the neutral classes (non-vulnerable classes where no vulnerability had yet been reported). Results: We found that a vulnerable class is more likely to have certain smells compared to a non-vulnerable class. God Class, Complex Class, Large Class, Data Class, Feature Envy, Brain Class have a statistically significant relationship with software vulnerabilities. We found no significant relationship between architectural smells and software vulnerabilities. Conclusion: We can conclude that for all the systems examined, there is a statistically significant correlation between software vulnerabilities and some smells.
RNN-CNN MODEL:A Bi-directional Long Short-Term Memory Deep Learning Network For Story Point Estimation

In recent years, an increased interest in the adaption of agile software development by companies. Using iterative methodology enables them to do issue-based estimation and respond quickly to changes in the requirements. Agile methodology adopts Story Point Approach to estimate the effort that involves a user story or a resolving issue. Unlike traditional estimation, Agile Methodology focuses on individual programming task estimation instead of whole project estimation. In this work, we approach story point estimation using the RNN-CNN model. We consider the contextual information in a user story in both forward and backward directions to build the RNN-CNN model. The proposed model adopts a Bi-directional Long Short-Term Memory (BiLSTM), a tree-structured Recurrent Neural Network (RNN) with Convolutional Neural Network (CNN), tries to predict a story point for a user story description. Here, BiLSTM forward and backward feature learning will make network preserve the sequence data and CNN makes feature extraction accurate. The experimental results show the improvement in estimating the story points with a user story as an input using the proposed RNN-CNN. Furthermore, the analysis shows that the proposed RNN-CNN model outperforms the existing model and gives 74.2 % R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score on the Bamboo data set.
XOR-Net: An Efficient Computation Pipeline for Binary Neural Network Inference on Edge Devices

Accelerating the inference of Convolution Neural Networks (CNNs) on edge devices is essential due to the small memory size and poor computation capability of these devices. Network quantization methods such as XNOR-Net, Bi-Real-Net, and XNOR-Net++ reduce the memory usage of CNNs by binarizing the CNNs. They also simplify the multiplication operations to bit-wise operations and obtain good speedup on edge devices. However, there are hidden redundancies in the computation pipeline of these methods, constraining the speedup of those binarized CNNs. In this paper, we propose XOR-Net as an optimized computation pipeline for binary networks both without and with scaling factors. As XNOR is realized by two instructions XOR and NOT on CPU/GPU platforms, XOR-Net avoids NOT operations by using XOR instead of XNOR, thus reduces bit-wise operations in both aforementioned kinds of binary convolution layers. For the binary convolution with scaling factors, our XOR-Net further rearranges the computation sequence of calculating and multiplying the scaling factors to reduce full-precision operations. Theoretical analysis shows that XOR-Net reduces one-third of the bit-wise operations compared with traditional binary convolution, and up to 40% of the full-precision operations compared with XNOR-Net. Experimental results show that our XOR-Net binary convolution without scaling factors achieves up to 135× speedup and consumes no more than 0.8% energy compared with parallel full-precision convolution. For the binary convolution with scaling factors, XOR-Net is up to 17% faster and 19% more energy-efficient than XNOR-Net.
Efficient Hierarchical and Time-Sensitive Data Sharing with User Revocation in Mobile Crowdsensing

Recently, cloud-based mobile crowdsensing (MCS) has developed into a promising paradigm which can provide convenient data sensing, collection, storage, and sharing services for resource-constrained terminates. Nevertheless, it also inflicts many security concerns such as illegal access toward user secret and privacy. To protect shared data against unauthorized accesses, many studies on Ciphertext-Policy Attribute-Based Encryption (CP-ABE) have been proposed to achieve data sharing granularity. However, providing a scalable and time-sensitive data-sharing scheme across hierarchical users with compound attribute sets and revocability remains a big issue. In this paper, we investigate this challenge and propose a hierarchical and time-sensitive CP-ABE scheme, named HTR-DAC, which is characteristics of time-sensitive data access control with scalability, revocability, and high efficiency. Particularly, we propose a time-sensitive CP-ABE for hierarchical structured users with recursive attribute sets. Moreover, we design a robust revocable mechanism to achieve direct user revocation in our scheme. We also integrate verifiable outsourced decryption to improve efficiency and guarantee correctness in decryption procedure. Extensive security and performance analysis is presented to demonstrate the security requirement satisfaction and high efficiency for our data-sharing scheme in MCS.
Assessment of selected major and trace elements in groundwater of Lagos based on land use and implication on human health

Abstract Sixty-seven (67) groundwater samples from different land use areas such as dumpsites, industrial, markets and residential all within Lagos, southwest Nigeria, were sampled and sent to ACME Laboratories, Ontario, Canada, for geochemical analysis to determine some essential and toxic elements using inductively coupled plasma-mass spectrometer method. The purpose was to establish baseline values for the geochemical composition of groundwater in some parts of Lagos as well as the health risk assessment. Based on essential elements, 83%, 80% and 100% of groundwater samples were deficient in Ca, Mg and Se, respectively, while 75%, 10%, 7% and 8% of samples can be categorised as soft, moderately hard, hard and very hard water, respectively. The soft water, as well as different deficiencies in water, had been linked to heart problems, impeded growth, fertility disorders and liver diseases. Concentrations of As, Cd, Cr, Pb, Ni and Fe were higher than recommended standards in 2.94%, 1.5%, 1.5%, 29.4%, 4.4% and 4.7% of groundwater samples, respectively. Groundwater in the study area can be classified between not contaminated (&lt; 1) to very highly contaminated (CI &gt; 5) based on the contamination index. Highly contaminated samples were within dumpsites and industrial areas. The land-use activities have a direct or indirect impact on groundwater quality. The apparent sources of groundwater pollutants include industrial discharge, effluents, leachates and municipal wastes. The result of total health risk index for both ingestion and dermal exposure to water showed values far &gt; 1 for all the toxic elements and generally pose non-carcinogenic health risk hazards for child and adult population in the study area. THI for samples from residential, market, industrial and dumpsites showed mean values of (9.60 and 3.75), (14.17 and 5.37), (9.8 and 3.4) and 95.44 and 37.48) for child and adult population, respectively. The degree of contamination in groundwater showed the following trends: dumpsite &gt; industrial &gt; markets &gt; residential in the study area.
Practical security of wavelength-multiplexed decoy-state quantum key distribution

Wavelength division multiplexing (WDM) of quantum key distribution (QKD) with classical optical channels has been proven to be feasible experimentally. In this paper, we will analyze the practical security of WDM-QKD theoretically. Specifically, we first establish the noise model of classical signals. Then, the influence of classical noises on the performance of decoy-state WDM-QKD is studied with a finite-key security analysis. For numerical simulations, the effect of classical noise-reduction methods for different experimental settings is given. The secret key rate and maximum transmission distance of WDM-QKD is simulated with various sent number of quantum pulses or transmitted power of classical signals. Furthermore, the performance of two kinds of decoy-state WDM-QKD is compared, which shows that the optimal decoy-state protocol for WDM-QKD is related to the sent number of quantum pulses. Since WDM-QKD is desired to reduce the cost of QKD networks and since experiments have already been carried out, our work can not only close the gap between theory and practice, but also be used to optimize the experimental parameters and improve its performance.