Reviewerly/exHarmony huggingface dataset:
The effect of fin height on forced convection heat transfer from rectangular fin array

In this research, experimental investigation of the coefficient of the heat transfer has been conducted for a set of rectangular fins with different fin height, 0.27, 0.47 and 0.67 m at air velocity test points of 1.1, 1.3, 1.5, 1.7 and 2 m/s, to meet the Reynolds number (Re). Thirty samples from the two cases with changing fin positions were studied to verify the thermal performance of the system. The study was conducted based on the cross-sectional diameter of 21167, 25016, 28864, 32713, and 38486. Total heat flux in all tubes has been settled as 26.3, 58, and 88.7 W/m2. The experimental results showed that the temperature difference between the surface of the block and flowing air increases with increasing Reynolds number for all heat flow states.
Cut, Distil and Encode (CDE): Split Cloud-Edge Deep Inference

In cloud-edge environments, running all Deep Neural Network (DNN) models on the cloud causes significant network congestion and high latency, whereas the exclusive use of the edge device for execution limits the size and structure of the DNN, impacting accuracy. This paper introduces a novel partitioning approach for DNN inference between the edge and the cloud. This is the first work to consider simultaneous optimization of both the memory usage at the edge and the size of the data to be transferred over the wireless link. The experiments were performed on two different network architectures, MobileNetV1 and VGG16. The proposed approach makes it possible to execute part of the network on very constrained devices (e.g., microcontrollers), and under poor network conditions (e.g., LoRa) whilst retaining reasonable accuracies. Moreover, the results show that the choice of the optimal layer to split the network depends on the bandwidth and memory constraints, whereas prior work suggests that the best choice is always to split the network at higher layers. We demonstrate superior performance compared to existing techniques.
Deep Neural Network Hardware Deployment Optimization via Advanced Active Learning

Recent years have witnessed the great successes of deep neural network (DNN) models while deploying DNN models on hardware platforms is still challenging and widely discussed. Some works proposed dedicatedly designed accelerators for some specific DNN models, while some others proposed general-purpose deployment frameworks that can optimize the hardware configurations on various hardware platforms automatically. However, the extremely large design space and the very time-consuming on-chip tests bring great challenges to the hardware configuration optimization process. In this paper, to optimize the hardware deployment, we propose an advanced active learning framework which is composed of batch transductive experiment design (BTED) and Bootstrap-guided adaptive optimization (BAO). The BTED method generates a diverse initial configuration set filled with representative configurations. Based on the Bootstrap method and adaptive sampling, the BAO method guides the selection of hardware configurations during the searching process. To the best of our knowledge, these two methods are both introduced into general DNN deployment frameworks for the first time. We embed our advanced framework into AutoTVM, and the experimental results show that our methods reduce the model inference latency by up to 28.08% and decrease the variance of inference latency by up to 92.74%.
Cryptanalysis and improvement of a semi-quantum private comparison protocol based on Bell states

Semi-quantum private comparison (SQPC) allows two participants with limited quantum ability to securely compare the equality of their secrets with the help of a semi-dishonest third party (TP). Recently, Jiang (Quantum Inf Process 19(6): 180, 2020) proposed a SQPC protocol based on Bell states and claimed it is secure. In this paper, we present two types of attack on Jiang's SQPC protocol. In the first type of attack, an outside eavesdropper can make participants accept a wrong result. In the second type of attack, a malicious participant not only can make the other participant accept an incorrect result, but also can learn the secret of the honest participant. Neither type of attack is possible to be detected. In addition, we propose an improved SQPC protocol that can resist these two types of attack.
Modeling and Tracking of Maneuvering Extended Object With Random Hypersurface

Extended object tracking, an important and emerging research field, has attracted attention for applications in civilian and military fields. However, the modeling and tracking of maneuvering extended objects still face challenges that need to be solved explicitly. Specifically, it is usually difficult to describe the irregular or complex extension dynamics (e.g., the object extension changes in shape and orientation over time) of a maneuvering extended object with random hypersurface. Further, it is tightly coupled to the centroid kinematics during the object maneuvers. Hence, this paper proposes a general approach for modeling a maneuvering extended object with random hypersurface. In this approach, both the object centroid and the extension dynamics for describing the turn maneuvers with different turn rates can be characterized accurately, in a concise linear form. This facilitates the design of an effective tracking algorithm to deal with different turn maneuvers of a maneuvering extended object with random hypersurface. The merits of this proposed model are illustrated by simulation results, where the kinematic state and object extension are estimated jointly and effectively.
Evaluation of end user requirements for Smart Home applications and services based on a decision support system

Smart Home (SH) systems are considered one of the prominent applications in the era of Internet of Things (IoT), where it is possible to control home devices to achieve a better usage in terms of cost and convenience. IoT offers the internet connection and remote management of home automation, integrated with numerous sensors. SHs constitute to be an innovative and popular form of residence in the modern cities, therefore designers need to comply with users preferences and requirements. The current paper defines requirements by chosen features, describing the either non-functional or functional technical, social and financial aspects of a SH. PairWise Comparison (PWC) framework, a fundamental part of many decision making problems, is applied and ranks the users requirements and also presents and prioritizes SHs services, such as e-health and entertainment.
A Comparative Analysis of Arabic Text Steganography

Protecting sensitive information transmitted via public channels is a significant issue faced by governments, militaries, organizations, and individuals. Steganography protects the secret information by concealing it in a transferred object such as video, audio, image, text, network, or DNA. As text uses low bandwidth, it is commonly used by Internet users in their daily activities, resulting a vast amount of text messages sent daily as social media posts and documents. Accordingly, text is the ideal object to be used in steganography, since hiding a secret message in a text makes it difficult for the attacker to detect the hidden message among the massive text content on the Internet. Language’s characteristics are utilized in text steganography. Despite the richness of the Arabic language in linguistic characteristics, only a few studies have been conducted in Arabic text steganography. To draw further attention to Arabic text steganography prospects, this paper reviews the classifications of these methods from its inception. For analysis, this paper presents a comprehensive study based on the key evaluation criteria (i.e., capacity, invisibility, robustness, and security). It opens new areas for further research based on the trends in this field.
A Novel Rule-Based Online Judge Recommender System to Promote Computer Programming Education

Reducing students’ high dropout rates in the computer programming courses is a challenging problem of great concern in computer science education. Online Judge (OJ) systems were recently being investigated to address this problem and promote computer programming education. Most of the existing OJ systems have been confined only for evaluation purposes, and do not provide any personalized recommendations to enhance the productivity of a student. With this motivation, this paper proposes a novel rule-based OJ recommender system to promote computer programming education. The proposed system involves the following five steps: (i) scoring the programs submitted by a student automatically, (ii) generation of a transactional database, (iii) clustering the database with respect to their scores and other evaluation parameters, (iv) discovering interesting association rules that exist in each of the cluster’s data, and (v) providing appropriate recommendations to the users. Experimental results on the data generated by a real-world OJ system demonstrate that the proposed system is efficient.
Towards Robust Representations of Spatial Networks Using Graph Neural Networks

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.
Recipient Design for Conversational Agents: Tailoring Agent’s Utterance to User’s Knowledge

While interest in AI conversational agents has grown rapidly in recent years, creating agents capable of holding recipient-specific conversations remains a challenge. In other words, an automated agent should be able to engage in recipient design or tailoring the form of its utterances and selection of utterances based on the user's knowledge, or assumptions about what the user knows. Without recipient design, the agent formulates its utterances the same way for every user. While an agent might still fall back on conversational repair practices, such as paraphrase or definition requests, recipient design can minimize the necessity for repair in the first place. In this work, we leverage recipient design methods from natural conversation, as identified in the field of Conversation Analysis (CA) and implement three of them (self-reports of knowledge, prior difficulty in understanding, and prior exposure to a reference) as part of our conversational agent, the Alma Assistant.
Random Fourier feature-based fuzzy clustering with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2456" altimg="si348.svg"><mml:mi>p</mml:mi></mml:math>-Laplacian regularization

Random feature is one successful technique to approximate traditional kernel functions, and the random feature-based fuzzy clustering has been proved to be effective and efficient for handling non-linear data. However, the existing random feature-based fuzzy clustering methods fail to consider the locality information hidden in original input data. From some perspective, the membership obtained by fuzzy clustering can be seen as the encoding results of data. Thus, constraining the relationships between membership degrees to be consistent with that of data is beneficial to improve clustering performance. To this end, we propose a novel random Fourier feature-based fuzzy clustering method (pLRFCM) in this paper. The random Fourier feature is used to approximate Gaussian kernels in this method, and the fuzzy clustering is performed in the feature space. More importantly, the p-Laplacian regularization is conducted on the membership matrix to preserve the local structures of original data into the clustering results, to guarantee good partition of data. The maximum-entropy technique is also utilized to fine-tune the weights of features automatically during the process of clustering, so as to further promote the performance of clustering. In the experiments on four synthetic non-linear datasets and eight real-world datasets, pLRFCM outperforms several classical and state-of-the-art fuzzy clustering methods.
Ambipolar Self-Driving Polarized Photodetection

The polarized photodetectors based on the anisotropy of low-dimensional materials have drawn extensive attention; however, the polarities of the detected photocurrents are normally independent of the light polarization, and thus, the calibration on the incident power is necessary. Here, introducing the Dember effect modulated by the photonic mechanism, we propose a core–shell nanowire structure, which can identify the photocurrent polarities under various incident light polarizations and simultaneously operate in self-driving mode. Taking the Ag@CH3NH3PbI3@ITO core–shell nanowire as an example, we perform a strict optoelectronic simulation based on finite-element method. It shows that the ambipolar signals under transverse magnetic and electric (TM and TE) incidences can indeed be achieved, with the unbiased current densities of 24.9 and −10.4 mA/cm2, respectively (light wavelength: 500 nm). We further identify that the enhanced photodetection capability comes from the Dember effect together with the specific electric field distributions controlled by the whispering-gallery mode and plasmonic resonance under TE and TM incidences. The proposed architecture is expected to realize a more accurate and calibration-free polarized photodetection with a simplified system but a higher performance, multifunction capability, and integration capability with existing electronic systems.
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search

In this paper, we present a novel multi-objective hardware-aware neural architecture search (NAS) framework, namely HSCoNAS, to automate the design of deep neural networks (DNNs) with high accuracy but low latency upon target hardware. To accomplish this goal, we first propose an effective hardware performance modeling method to approximate the runtime latency of DNNs on target hardware, which will be integrated into HSCoNAS to avoid the tedious on-device measurements. Besides, we propose two novel techniques, i.e., dynamic channel scaling to maximize the accuracy under the specified latency and progressive space shrinking to refine the search space towards target hardware as well as alleviate the search overheads. These two techniques jointly work to allow HSCoNAS to perform fine-grained and efficient explorations. Finally, an evolutionary algorithm (EA) is incorporated to conduct the architecture search. Extensive experiments on ImageNet are conducted upon diverse target hardware, i.e., GPU, CPU, and edge device to demonstrate the superiority of HSCoNAS over recent state-of-the-art approaches.
Cybersecurity in the Quantum Era-A Study of Perceived Risks in Conventional Cryptography and Discussion on Post Quantum Methods

Abstract Information security in communication networks is a persistent problem and essentially requires the usage of encryption methods. Quantum computing was first used to break encryption codes in the latter half of the 20 th century with the introduction of the SHOR algorithm. Though the recent developments in QC capabilities have increased confidentiality, integrity, and availability of networks by protecting them against passive attacks like eavesdropping yet the transformation of classical to quantum computation can bear catastrophic implications as it has the potential to put the currently secure methods of transactions in jeopardy. This paper aims at the evaluation and comparison of traditional cryptographic techniques by the application of a SWOT framework. It takes up an exploratory study of the advanced quantum computing capabilities that can pose a massive risk to network security. The various security enhancements that can be adopted in data transmission to curtail these risks post-quantum are also discussed.
Gradient Descent Optimization in Deep Learning Model Training Based on Multistage and Method Combination Strategy

Gradient descent is the core and foundation of neural networks, and gradient descent optimization heuristics have greatly accelerated progress in deep learning. Although these methods are simple and effective, how they work remains unknown. Gradient descent optimization in deep learning has become a hot research topic. Some research efforts have tried to combine multiple methods to assist network training, but these methods seem to be more empirical, without theoretical guides. In this paper, a framework is proposed to illustrate the principle of combining different gradient descent optimization methods by analyzing several adaptive methods and other learning rate methods. Furthermore, inspired by the principle of warmup, CLR, and SGDR, the concept of multistage is introduced into the field of gradient descent optimization, and a gradient descent optimization strategy in deep learning model training based on multistage and method combination strategy is presented. The effectiveness of the proposed strategy is verified on the massive deep learning network training experiments.
MINet: Meta-Learning Instance Identifiers for Video Object Detection

Recent advances in video object detection have characterized the exploration of temporal coherence across frames to enhance object detector. Nevertheless, previous solutions either rely on additional inputs (e.g., optical flow) to guide feature aggregation, or complex post-processing to associate bounding boxes. In this paper, we introduce a simple but effective design that learns instance identifiers for instance association in a meta-learning paradigm, which requires no auxiliary inputs or post-processing. Specifically, we present Meta-Learnt Instance Identifier Networks (namely MINet) that novelly meta-learns instance identifiers to recognize identical instances across frames in a single forward-pass, leading to the robust online linking of instances. Technically, depending on the detection results of previous frames, we teach MINet to learn the weights of an instance identifier on the fly, which can be well applied to up-coming frames. Such meta-learning paradigm enables instance identifiers to be flexibly adapted to novel frames at inference. Furthermore, MINet writes/updates the detection results of previous instances into memory and reads from memory when performing inference to encourage temporal consistency for video object detection. Our MINet is appealing in the sense that it is pluggable to any object detection model. Extensive experiments on ImageNet VID dataset demonstrate the superiority of MINet. More remarkably, by integrating MINet into Faster R-CNN, we achieve 80.2% mAP on ImageNet VID dataset.
The Role of Chatbots in Foreign Language Learning: The Present Situation and the Future Outlook

While crucial for all language learners, conversation practice can be very expensive and challenging to get enough of. Chatbots are computer programs developed to engage in conversations with humans. They are designed as software avatars with limited, but growing, conversational capability. The most natural and potentially powerful application of chatbots is related to their fundamental nature—language practice. However, their role and outcomes within (in)formal language learning are currently tangential at best. Existing research has generally focused on chatbots' comprehensibility and the motivation they inspire in their users. In this paper, we provide an overview of chatbots for learning languages, critically analyze existing approaches, and discuss the major challenges for future research.
Robust semi-supervised classification based on data augmented online ELMs with deep features

One important strategy in semi-supervised learning is to utilize the predicted pseudo labels of unlabeled data to relieve the overdependence on the ground truth of supervised learning algorithms. However, the performance of such kinds of semi-supervised methods heavily relies on the quality of pseudo labels. To address this issue, a robust semi-supervised classification method, named data augmented online extreme learning machines (ELMs) with deep features (DF-DAELM) is proposed. This method firstly extracts features and infers labels for unlabeled data through self-training. Then, with the learned features and inferred labels, two noise-robust shallow classifiers based on data augmentation (i.e., SLI-OELM and CR-OELM) are proposed to eliminate the adverse effects of noises on classifier training. Specifically, inspired by label smoothing, a data augmented method, SLI-OELM is designed based on stochastic linear interpolation to improve the robustness of classifiers based on ELMs. Furthermore, based on the smoothing assumption, the proposed CR-OELM utilizes an ℓ2-norm consistency regularization term to implicitly weight noisy samples. Comprehensive experiments demonstrate that DF-DAELM achieves competitive or even better performance on CIFAR-10/100 and SVHN over the related state-of-the-art methods. Meanwhile, for the proposed classifiers, experimental results on the MNIST dataset with different noise levels and sample scales demonstrate their superior performance, especially when the sample scale is small (≤20K) and the noise is strong (40%∼80%).