Reviewerly/exHarmony huggingface dataset:
Real-time Detection of Tiny Objects Based on a Weighted Bi-directional FPN

Tiny object detection is an important and challenging object detection subfield. However, many of its numerous applications (e.g., human tracking and marine rescue) have tight detection time constraints. Namely, two-stage object detectors are too slow to fulfill the real-time detection needs, whereas one-stage object detectors have an insufficient detection accuracy. Consequently, enhancing the detection accuracy of one-stage object detectors has become an essential aspect of real-time tiny objects detection. This work presents a novel model for real-time tiny objects detection based on a one-stage object detector YOLOv5. The proposed YOLO-P4 model contains a module for detecting tiny objects and a new output prediction branch. Next, a weighted bi-directional feature pyramid network (BiFPN) is introduced in YOLO-P4, yielding an improved model named YOLO-BiP4 that enhances the YOLO-P4 feature input branches. The proposed models were tested on the Tiny-Person dataset, demonstrating that the YOLO-BiP4 model outperforms the original model in detecting tiny objects. The model satisfies the real-time detection needs while obtaining the highest accuracy compared to existing one-stage object detectors.
Software Functional and Non-function Requirement Classification Using Word-Embedding

The classification of software requirements is an essential task in software engineering. Manual classification requires a large amount of efforts, time and cost. Hence, automated techniques are required to classify software requirements. This work aims to develop requirement classification models based on extraction of relevant features from requirement documents and thereafter classifying requirement into functional and non-function requirements. In this paper, different word- embedding techniques to extract numerical features, feature selection to remove irrelevant feature, SMOTE to balance data, and six different classifiers for models training. The experiments have been conducted on PROMISE software engineering dataset. The experimental finding indicate that Word2vec is best way to extracting numerical features from requirement documents, RANK-SUM test is best way to find important features, and SVM-R was found as the best classifier.
Backpropagation Neural Network (BPNN) Algorithm for Predicting Wind Speed Patterns in East Nusa Tenggara

The Paris agreement compels all countries to make major contributions to the zero-emission scheme, a legally binding international treaty on climate change. This fulfilment must be supported by technological developments towards Society 5.0, forcing every country to develop renewable energy (clean energy) on a large scale. One of the renewable energies with the highest efficiency is wind power generation. Its construction requires a large cost, and the best location must consider the high wind speed. East Nusa Tenggara Province is one of the locations in the border area with insufficient electricity. The choice of location was supported by military operations in guarding the border which required a lot of energy. Therefore, it is necessary to predict wind speed patterns based on historical data from the database so that wind power plants can be realized. One of the best methods for long-term prediction of wind speed is the backpropagation neural network (BPPN) method. Wind speed data was used from January 2003 to December 2020 with a total of 216 data sets obtained from NASA. It should be noted that January 2003 to December 2010 data is positioned as input data, while training target data is from January 2011-December 2015. Validation data is determined from January 2016-December 2020. The best predictive architecture model is 8-11-5- 5, learning rate is 0.4 and epoch is 20,000. Prediction accuracy is very good with a mean square error (MSE) value of 0.007634 and a mean absolute percentage error (MAPE) of 11.62783. The highest wind speed was shown in February 2018 as 10.75 m/s.
Packet-Level Open-World App Fingerprinting on Wireless Traffic

Mobile apps have profoundly reshaped modern lifestyles in different aspects.Several concerns are naturally raised about the privacy risk of mobile apps.Despite the prevalence of encrypted communication, app fingerprinting (AF) attacks still pose a serious threat to users' online privacy.However, existing AF attacks are usually hampered by four challenging issues, namely i) hidden destination, ii) invisible boundary, iii) app multiplexing, and iv) open-world recognition, when they are applied to wireless traffic.None of existing AF attacks can address all these challenges.In this paper, we advance a novel AF attack, dubbed PACKETPRINT, to recognize user activities associated with the app of interest from encrypted wireless traffic and tackle the above challenges by proposing two novel models, i.e., sequential XGBoost and hierarchical bag-ofwords model.We conduct extensive experiments to evaluate the proposed attack in a series of challenging scenarios, including i) open-world setting, ii) packet loss and network congestion, iii) simultaneous use of different apps, and iv) cross-dataset recognition.The experimental results show that PACKETPRINT can accurately recognize user activities associated with the apps of interest.It achieves the average F1-score 0.884 for open-world app recognition and the average F1-score 0.959 for in-app user action recognition. I. INTRODUCTIONMobile devices, such as smartphones and tablets, are ubiquitous in modern life.A myriad of mobile apps empower them with the capabilities that profoundly reshape people's lifestyle, ranging from information retrieval to instant messaging, and from shopping to entertainment.The recent prevalence of online to offline apps, e.g., food delivery, further signalizes this trend by bridging the gap between the online information and physical businesses [1], [2].Every coin has a flip side.Mobile apps offer users highquality services, accompanied by the collection, transmission, storage, and even sharing of user data, raising serious privacy concerns.For example, compromising apps' cloud servers may cause disastrous privacy leakage [3]- [5].Such attacks are basically due to software flaws.While threatening, these flaws are scarce and, once found, will be immediately fixed to avoid severe consequences.Data transmission channel can be
Active Classification With Uncertainty Comparison Queries

Noisy pairwise comparison feedback has been incorporated to improve the overall query complexity of interactively learning binary classifiers. The positivity comparison oracle is extensively used to provide feedback on which is more likely to be positive in a pair of data points. Because it is impossible to determine accurate labels using this oracle alone without knowing the classification threshold, existing methods still rely on the traditional explicit labeling oracle, which explicitly answers the label given a data point. The current method conducts sorting on all data points and uses explicit labeling oracle to find the classification threshold. However, it has two drawbacks: (1) it needs unnecessary sorting for label inference and (2) it naively adapts quick sort to noisy feedback. In order to avoid these inefficiencies and acquire information of the classification threshold at the same time, we propose a new pairwise comparison oracle concerning uncertainties. This oracle answers which one has higher uncertainty given a pair of data points. We then propose an efficient adaptive labeling algorithm to take advantage of the proposed oracle. In addition, we address the situation where the labeling budget is insufficient compared to the data set size. Furthermore, we confirm the feasibility of the proposed oracle and the performance of the proposed algorithm theoretically and empirically.
Information Leakage in Code-Based Masking: A Systematic Evaluation by Higher-Order Attacks

Code-based masking is a recent line of research on masking schemes aiming at provably counteracting side-channel attacks. It generalizes and unifies many masking schemes within a coding-theoretic formalization. In code-based masking schemes, the tuning parameters are the underlying linear codes, whose choice significantly affects the side-channel resilience. In this paper, we investigate the exploitability of the information leakage in code-based masking and present attack-based evaluation results of higher-order optimal distinguisher (HOOD). Particularly, we consider two representative instances of code-based masking, namely inner product masking (IPM) and Shamir’s secret sharing (SSS) based masking. Our results do confirm the state-of-the-art theoretical derivatives in an empirical manner with numerically simulated measurements. Specifically, theoretical results are based on quantifying information leakage; we further complete the panorama with attack-based evaluations by investigating the exploitability of the leakage. Moreover, we classify all possible candidates of linear codes in IPM with 2 and 3 shares and (3, 1)-SSS based masking, and highlight both optimal and worst codes for them. Relying on our empirical evaluations, we therefore recommend investigating the coding-theoretic properties to find the best linear codes in strengthening instances of code-based masking. As for applications, our attack-based evaluation directly empowers designers, by employing optimal linear codes, to enhance the protection of code-based masking. Our framework leverages simulated leakage traces, hence allowing for source code validation or patching in case it is found to be attackable.
Emotionally augmented mental models, connectivity and beaver reintroduction in Southwest England

Understanding the psychology of human-wildlife interactions has grown beyond cognitive frameworks to include appreciation of roles played by emotion in human responses to wildlife.From its beginnings as an essentially cognitively framed proposition, mental modeling has been shown readily applicable to representing and interpreting stakeholder perspectives on combined social and natural systems, but lacks an integrated approach to emotion.This is an important knowledge gap.To commence an investigation into the relationship focused on the requirements of wildlife conservation, we carried out a case study of perspectives toward a free-living Eurasian beaver (Castor fiber) reintroduction in Southwest England, ecologically significant as a generator of highvalue wetland habitat while interacting strongly with local human interests.Using fuzzy cognitive mapping techniques, we report predictive relationships between model measurements and subjective emotional valence elicited in relation to stakeholder conceptual content.Significant interactions were identified between three measures of concept connective influence within mental models and associated emotional valence intensity: single model concept connective salience, aggregated model concept connective salience, and aggregated model predictive inferences made by dynamic analysis.A possible explanation for these findings is outlined in which we propose that criteria-based evaluations suggested by appraisal theory of emotion are sensitive to the strength and distribution of connective influences within mental models.Apart from its theoretical significance, the evidence presented in this paper highlights the importance of attending to conservation stakeholder emotional experiences, and may assist in new approaches to mitigation where conservation objectives require human adjustment.
SimEmotion: A Simple Knowledgeable Prompt Tuning Method for Image Emotion Classification

Image emotion classification is an important computer vision task to extract emotions from images. The state-of-the-art methods for image emotion classification are primarily based on proposing new architectures and fine-tuning them on pre-trained Convolutional Neural Networks. Recently, learning transferable visual models from natural language supervision has shown great success in zero-shot settings due to the easily accessible web-scale training data, i.e., CLIP. In this paper, we present a conceptually simple while empirically powerful framework for supervised image emotion classification, SimEmotion, to effectively leverage the rich image and text semantics entailed in CLIP. Specifically, we propose a prompt-based fine-tuning strategy to learn task-specific representations while preserving knowledge contained in CLIP. As image emotion classification tasks lack text descriptions, sentiment-level concept and entity-level information are introduced to enrich text semantics, forming knowledgeable prompts and avoiding considerable bias introduced by fixed designed prompts, further improving the model’s ability to distinguish emotion categories. Evaluations on four widely-used affective datasets, namely, Flickr and Instagram (FI), EmotionROI, Twitter I, and Twitter II, demonstrate that the proposed algorithm outperforms the state-of-the-art methods to a large margin (i.e., 5.27% absolute accuracy gain on FI) on image emotion classification tasks.
Auxiliary Graph based QKD Key Provisioning for End-to-End Security Service in Optical Networks

We propose a quantum-key-distribution (QKD) key provisioning scheme by applying auxiliary graph for end-to-end security service in optical networks. Simulation demonstrates the good performance in terms of security level and key provisioning latency.
Adjacency relations induced by some Alexandroff topologies on $ {\mathbb Z}^n $

&lt;abstract&gt;&lt;p&gt;Let $ (X, T) $ be an Alexandroff space. We define the adjacency relation $ AR_T $ on $ X $ induced by $ T $ as the irreflexive relation defined for $ x \neq y $ in $ X $ by:&lt;/p&gt; &lt;p&gt;&lt;disp-formula&gt; &lt;label/&gt; &lt;tex-math id="FE1"&gt; \begin{document}$ (x,y) \in AR_T\,\,{\rm{if \;and\; only\; if}}\,\, x \in SN_T(y)\,\,{\rm{or}}\,\, y \in SN_T(x), $\end{document} &lt;/tex-math&gt;&lt;/disp-formula&gt;&lt;/p&gt; &lt;p&gt;where $ SN_T(z) $ is the smallest open set containing $ z $ in $ (X, T) $ and $ z \in \{x, y\} $. Two families of Alexandroff topologies $ (T_k, k \in {\mathbb Z}) $ and $ (T_k^\prime, k \in {\mathbb Z}) $ have been recently introduced on $ {\mathbb Z} $. The aim of this paper is to show that for each nonzero integers $ k $, the topologies $ T_k, T_k^\prime $, $ T_{-k} $, and $ T_{-k}^\prime $ are homeomorphic. The adjacency relations induced by the product topologies $ (T_k)^n $ and $ (T_k^\prime)^n $ are studied and compared with classical ones. We also show that the adjacency relations induced by $ T_k, T_k^\prime $, $ T_{-k} $, and $ T_{-k}^\prime $ are isomorphic. Then, note that the adjacency relations on $ {\mathbb Z} $ induced by these topologies, $ k \neq 0 $, are different from each other.&lt;/p&gt;&lt;/abstract&gt;
Parameter Differentiation Based Multilingual Neural Machine Translation

Multilingual neural machine translation (MNMT) aims to translate multiple languages with a single model and has been proved successful thanks to effective knowledge transfer among different languages with shared parameters. However, it is still an open question which parameters should be shared and which ones need to be task-specific. Currently, the common practice is to heuristically design or search language-specific modules, which is difficult to find the optimal configuration. In this paper, we propose a novel parameter differentiation based method that allows the model to determine which parameters should be language-speciﬁc during training. Inspired by cellular differentiation, each shared parameter in our method can dynamically differentiate into more specialized types. We further deﬁne the differentiation criterion as inter-task gradient similarity. Therefore, parameters with conﬂicting inter-task gradients are more likely to be language-specific. Extensive experiments on multilingual datasets have demonstrated that our method signiﬁcantly outperforms various strong baselines with different parameter sharing conﬁgurations. Further analysis reveals that the parameter sharing configuration obtained by our method correlates well with the linguistic proximities.
Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection.The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention.However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter.To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states.Then, The likelihood weight in the tracking process is deduced again.Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance.The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.
Hierarchical Coded Matrix Multiplication in Heterogeneous Multihop Networks

The performance of distributed computing is restricted by the slowest worker nodes, known as stragglers, in the system. Coded computation has emerged as an efficient technique to mitigate the straggler effects in distributed computing. Most existing works only considered the computation straggler for single-hop networks. However, in multi-hop networks, the straggler effects will occur not only on worker nodes but also on relay nodes. In this paper, we consider a heterogeneous multi-hop network. The nodes in the network are heterogeneous, i.e., their computation capacities and transmission capacities are different. We propose a hierarchical coding scheme for such a network. Firstly, we reorganize it into a hierarchical network containing multiple layers. Each layer in the network consists of several groups. Then, a new hierarchical coding scheme is proposed, where coding is applied to each group to mitigate the stragglers. By taking both the computation time and transmission time into consideration, the overall task completion time is derived. To improve the performance of the network, heterogeneous hierarchical coded computation (HHCC) algorithm is proposed to provide an asymptotically optimal task allocation strategy. Compared with existing uniform uncoded, load balanced uncoded, and heterogeneous coded matrix multiplication schemes, HHCC has significant improvement.
Continuous Operator Authentication for Teleoperated Systems Using Hidden Markov Models

In this article, we present a novel approach for continuous operator authentication in teleoperated robotic processes based on Hidden Markov Models (HMM). While HMMs were originally developed and widely used in speech recognition, they have shown great performance in human motion and activity modeling. We make an analogy between human language and teleoperated robotic processes (i.e., words are analogous to a teleoperator’s gestures, sentences are analogous to the entire teleoperated task or process) and implement HMMs to model the teleoperated task. To test the continuous authentication performance of the proposed method, we conducted two sets of analyses. We built a virtual reality (VR) experimental environment using a commodity VR headset (HTC Vive) and haptic feedback enabled controller (Sensable PHANToM Omni) to simulate a real teleoperated task. An experimental study with 10 subjects was then conducted. We also performed simulated continuous operator authentication by using the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The performance of the model was evaluated based on the continuous (real-time) operator authentication accuracy as well as resistance to a simulated impersonation attack. The results suggest that the proposed method is able to achieve 70% (VR experiment) and 81% (JIGSAWS dataset) continuous classification accuracy with as short as a 1-second sample window. It is also capable of detecting an impersonation attack in real-time.
Control of Robotic Manta Based on T-S Fuzzy Neural Network

PurposeThe purpose of this paper is to study the control method of the Robotic Manta and complete its depth control using a T-S fuzzy neural network controller.MethodsThe Robotic Manta adopts Median and/or Paired Fin propulsion mode, which has good stability. By adjusting the caudal fin angle, the Robotic Manta can swim upward or downward. Due to the complex hydrodynamics and the uncertainty existing in the environment, it is difficult to establish an accurate mathematical model. In this paper, data acquisition is carried out for the depth control of the Robotic Manta and a T-S fuzzy neural network is designed and trained. Finally, this paper implements the automatic depth control of the Robotic Manta with the T-S fuzzy neural network controller.FindingsThe experimental results show that the depth error of the Robotic Manta is less than 60 mm, with good rapidity and small overshoot, which verifies the effectiveness of the T-S fuzzy neural network controller in design and implementation.KeywordsT-S fuzzy neural networkRobotic mantaDepth control
Elliptical convolution kernel: More real visual field

Most common convolutional neural networks (CNNs) adopt square convolution kernels because the computer can quickly process the square matrix data. However, the shape of the visual field observed by the human visual system is not a square or a rectangle but rather a low-curvature quasi-corrected ellipse. Based on this view, we consider using elliptical convolution kernels to perform convolution operations. According to the natural characteristics of the human visual system, in this oval receptive field, the imaging sharpness of the central area is higher than that of the marginal area of the ellipse. We gradually convert the rectangular convolution kernels into elliptical convolution kernels by adopting an ellipse correction matrix, of which the weights and shape are fixed and pre-calculated. The ellipse correction matrix performs matrix multiplication with the original rectangular convolution kernels without increasing extra computational cost and parameters. The square convolution kernels in the existing CNNs can be easily replaced with elliptical convolution kernels. We have conducted lots of image classification and object detection experiments on some standard datasets, and the experiment results show that the elliptical convolution kernel we proposed is more effective than the currently popular square convolution kernel.
SAR-to-Optical Image Translating Through Generate-Validate Adversarial Networks

Synthetic aperture radar (SAR) has the advantages of high resolution in all-weather and all-day. However, SAR images are hard to be understood, due to their unique imaging mechanism. The SAR to optical image translation can assist in interpreting and has become a topic of growing interest in the field of remote sensing. In this letter, a SAR to optical image translation network is proposed, called generate-validate adversarial networks (GVANs). More specifically, there are two Pix2Pix networks form the cyclic structure. The validate module is employed to increase the training process and improve the edge retention ability. In order to improve multidomain images adaptability, the embedded layer is proposed. Additionally, the dilation convolution layer is employed in the generator, which is more suitable for the characteristics of SAR images. The proposed method has experimented on the SEN1-2 dataset. The result demonstrates the superiority of the proposed method over state-of-the-art methods.