Reviewerly/exHarmony huggingface dataset:
Code-Viz: Data Structure Specific Visualization and Animation Tool For User-Provided Code

Data structures are used by programmers to help in the arrangement of data for a particular purpose. The use of the right data structure improves the performance of algorithms and increases time and space efficiency.Data structures involve the usage of pointers and references for representing user’s data. It is very difficult for new users/learners to see how these pointers and references change. It is also very difficult to find a correlation between the program being executed and the data structure being used in the code.Immersive understanding and involved learning are enabled by visualisation of these data structures. As a result, the user can appreciate the way the pointers and references are used in data structures.The aim of Code-Viz is to provide data structure specific visualisations and animations that are both intuitive and accurate. The tool is language agnostic, supporting major programming languages (currently C and Python). Code-Viz is implemented as a browser-based tool with a code editor where users can visualize code without the need to install any software, thus facilitating ease of access.
Tripod: Use Data Augmentation to Enhance Website Fingerprinting

Website Fingerprinting (WF) enables a passive adversary to identify the website a user is visiting, even when the web access adopts security or privacy technologies. WF attacks based on deep learning are highly effective when feeding sufficient training traces, for example, hundreds of traffic traces of accessing each website. However, collecting extensive traffic consumes much time and resources, degenerating WF attacks' timeliness and invisibility. Nevertheless, decreasing training traces dramatically drops the WF accuracy. This paper proposes Tripod, a novel data augmentation method to enhance WF attacks, making them effective with a small training set. It applies three packet manipulations (Injecting, Removing, and Losing) on one collected traffic trace to generate several augmented traces. WF attacks then use the website classifier trained by the augmented set of all traces. In the closed-world scenario, the Var-CNN attack with 20 training traces per website only correctly identifies 56.1% of websites, while Tripod significantly increases this accuracy to 95.9%. Furthermore, Tripod increases the true positive rate of Var-CNN from 26.9% to 91.4% in the more realistic open-world scenario.
Stealth Maneuvering Multi-target Tracking With IMM-LMB Filter

Aiming at the tracking problem of stealth maneuvering multi-target, a labeled multi-Bernoulli (LMB) filter combined with interacting multiple models (IMM) approach is proposed, which is denoted by IMM-LMB filter. In the prediction step of IMM-LMB filter, the mixed prediction of multi-target state is executed. Then, the Gibbs sampling method is used to get the result of measurement partition. Finally, the posterior probability density of multi-target and the model probability are updated interactively by using the quasi-partition measurement subsets. Moreover, the Gaussian mixture implementation of IMM-LMB filter is given. Simulation results show that the proposed IMM-LMB filter is able to track stealth maneuvering multi-target stably. Compared with the LMB filter, the OSPA distance of the proposed filter is smaller and the cardinality estimation is more current.
Neural Network Models for Russian Language Speaker Recognition

Speaker recognition is a form of biometrics that treats a person's voice as a unique biological characteristic for identification, verification or speakers diarisation. Speech recognition usually used acoustic features of human speech. These acoustic patterns reflect both the anatomy and learned behavioral patterns of the individual. Present work has shown a performance of state of art approaches for speaker recognition on dataset of Russian voices. We have compared a performance of Time Delay Neural Network model based on Emphasized Channel Attention, Propagation, and Aggregation (ECAPA-TDNN) pre-trained on English data, model trained directly on Russian data and pre-trained model additionally trained on Russian data and model with some improvements in TDNN architecture.
Indonesian Continuous Speech Recognition Using CNN and Bidirectional LSTM

Speech recognition is the machine&#x0027;s ability to recognize words based on speech. Utilization of speech recognition technology into a real form of human-machine interaction. This allows humans to control smart devices more easily. However, the variability of speech such as dialect or accent, vocabulary size, type of recognition, speech speed, environmental noise, and type of microphone will affect the speech recognition rate. In recent years, deep learning approaches such as CNN and BLSTM have been widely used and have provided significant recognition improvements. Inspired by the advantages of CNN in exploiting local interspectral correlations and capturing frequency changes in speech signals and BLSTM in learning the temporal context, this study uses a hybrid CNN and BLSTM models for speech recognition with CTC as decoder. This study uses continuous speech data in Indonesian with five different dialects, namely, Balinese, Bataknese, Javanese, Minangese and Sundanese. There are four test scenarios that carried out sequentially to improve speech recognition performance including layer structure, use of dropouts, number of filters and units, and types of input features. The first three scenarios only use 13 coefficients MFCC without deltas features as the input feature. The results showed that the combination of 2 CNN layers with 64 filters and 2 BLSTM layers with 128 units and the application of a dropout with rate of 0.2 on all hidden layers achieve WER of 37.31&#x0025;. And the addition of delta and double delta features can reduce the recognition error and achieve WER 10.80&#x0025;.
PF_RING-TA: A Traffic-adaptive Method of Packet Capturing by Enhancing PF_Ring

This paper works on the ubiquitous situation in network packet capturing. The packet capture tool randomly loses packages due to burst network traffic, especially when handling massive network flow in cloud computing data centers. Using the high-performance packet capture tool PF_RING, we propose an adaptive version with a scalable ring structure named PF_RING-TA. When the network traffic changes significantly, PF_RING-TA can automatically scale the size of the buffer space in the kernel to ensure that the random packet loss rate is reduced to an acceptable level. Experiments show that in various scenarios, PF_RING-TA has a notable improvement in the packet loss rate compared that of the original PF_RING.
A proposal for using active contour parametrical models in Cobb angle determination

research-article A proposal for using active contour parametrical models in Cobb angle determination Share on Authors: Jorge Cerqueiro-Pequeño Universidad de Vigo, Spain Universidad de Vigo, SpainView Profile , Alberto Comesaña-Campos Universidad de Vigo, Spain Universidad de Vigo, SpainView Profile , Manuel Casal-Guisande Universidad de Vigo, Spain Universidad de Vigo, SpainView Profile , José-Benito Bouza-Rodríguez Universidad de Vigo, Spain Universidad de Vigo, SpainView Profile Authors Info & Claims TEEM'21: Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21)October 2021 Pages 297–304https://doi.org/10.1145/3486011.3486465Online:20 December 2021Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Enabler Development Infrastructure Design for Hyper-connected Common Networking Services

The key to the 4th industrial revolution is a digitization platform research infrastructure that can immediately realize, test, and verify ideas based on hyper-connection where vast amounts of data are connected. In this paper, we intend to propose the R&D infrastructure that can develop, manage, and provide enablers to support a convergence service development platform and empirical research infrastructure in preparation for the 4th industrial revolution.
Consensus Mechanism for Peer-to-Peer Energy Trading

The rapid growth of distributed energy systems requires a change from traditional centralized approach to a distributed one, for energy trading in peer-to-peer manner. The distributed ledger technology of blockchain can be used to build such peer-to-peer system without any third party involvement. It will provide to customers, a secure platform to participate in energy trading. The blockchain uses a mechanism to reach a certain point of consensus to distributively record transactions between peers. This paper discusses three types of consensus mechanism, viz. proof of work (PoW), proof of stake (PoS), and delegated Byzantine fault tolerance (DBFT). All these algorithms can be used in the peer-to-peer energy trading system. Based on type (permissioned or permission-less), fault tolerance, power consumption, and application considerations, we found that DBFT is most suitable consensus mechanism for the energy trading. The paper presents a novel implementation of DBFT-based peer to energy trading system and its comparison with all the other existing system. A case study based on DBFT is also presented to verify our hypothesis.
Multi-modal Scene-compliant User Intention Estimation in Navigation

A multi-modal framework to generate user intention distributions when operating a mobile vehicle is proposed in this work. The model learns from past observed trajectories and leverages traversability information derived from the visual surroundings to produce a set of future trajectories, suitable to be directly embedded into a perception-action shared control strategy on a mobile agent, or as a safety layer to supervise the prudent operation of the vehicle. We base our solution on a conditional Generative Adversarial Network with Long-Short Term Memory cells to capture trajectory distributions conditioned on past trajectories, further fused with traversability probabilities derived from visual segmentation with a Convolutional Neural Network. The proposed data-driven framework results in a significant reduction in error of the predicted trajectories (versus the ground truth) from comparable strategies in the literature (e.g. Social-GAN) that fail to account for information other than the agent's past history. Experiments were conducted on a dataset collected with a custom wheelchair model built onto the open-source urban driving simulator CARLA, proving also that the proposed framework can be used with a small, un-annotated dataset.
The Secret Lives of Pets: Trends from the 2020-2021 Year in Review Reports of the Academic Business Library Directors

A summary of Year in Review reports of members of the Academic Business Library Directors group (ABLD). Themes include new and ongoing initiatives in libraries, library collections, organizational change in libraries and member business schools and changes in library spaces.
Constructive and Mechanised Meta-Theory of Intuitionistic Epistemic Logic

Artemov and Protopopescu proposed intuitionistic epistemic logic (IEL) to capture an intuitionistic conception of knowledge. By establishing completeness, they provided the base for a meta-theoretic investigation of IEL, which was continued by Krupski with a proof of cut-elimination, and Su and Sano establishing semantic cut-elimination and the finite model property. However, to the best of our knowledge, no analysis of these results in a constructive meta-logic has been conducted. We aim to close this gap and investigate IEL in the constructive type theory of the Coq proof assistant. Concretely, we present a constructive and mechanised completeness proof for IEL, employing a syntactic decidability proof based on cut-elimination to constructivise the ideas from the literature. Following Su and Sano, we then also give constructive versions of semantic cut-elimination and the finite model property. Given our constructive and mechanised setting, all these results now bear executable algorithms. We expect that our methods used for mechanising cut-elimination and decidability also extend to other modal logics (and have verified this observation for the classical modal logic K).
Performance Analysis of Deep Learning Models for Network Traffic Identification

This paper analyses deep learning (DL) techniques for network traffic identification. Various DL models have been presented, implemented, and compared, including 1D convolutional, stacked autoencoder, multi-layer perceptron, and a combination of the aforementioned. The real dataset including both encrypted and unencrypted traffic has been collected, pre-processed in a way that ensures non-biased results, and used to train and test DL models. The models have been compared in terms of precision, recall, F1 score and accuracy for different levels of complexity. The obtained results suggest that the best performance is achieved with neural networks using a combination of 1D convolutional layers and fully-connected layers.
Effect of N-I-R LED Power in Enhancing the Vein Acquisition

The image acquisition procedure is one of the critical areas that need to be explored in depth to enhance capturing the raw image of the human vein. Many factors that contributed to the failure rate of locating the accurate vien location such as obesity, skin pigmentation, etc. To increase the success of extraction of the vein features acquired from the digital camera, the image quality obtained in the acquisition process needs to be enhanced. Thus, the variation of power dissipation level of the N-I-R LED as the illumination source for the vein image acquisition process was investigated in this paper. The investigation was conducted by using three power levels of N-I-R LED consisting of 2000mW, 3000mW, and 10000mW. From the overall result obtained, it was discovered that there was a significant improvement in the quality of the vein image acquisition by increasing the N-I-R LED power level. In conclusion, the vein image captured in which illuminated by the 10000mW N-I-R LED power had shown the highest image quality amongst the three sets of illumination power levels.
Deep Multi-level Feature Fusion Network for Age Estimation

In computer vision, estimating face age from face images is an important but challenging task. In this paper, we put forward a general framework called deep multilevel feature fusion network. This deep convolution neural network framework can combine the features extracted from convolution layers of different depths, which can not only ensure the heterogeneity of features, but also ensure the complementarity of features, and can estimate age attributes from the face image. We finetune the deep convolution neural network to a deep level feature fusion network, and use the age classifier to classify the age-related features of the face extracted by the new feature extraction method. This strategy can incorporate the information extracted from face image more effectively, and further improve the ability of age estimation. The highly discriminative and compact classification framework enables the classification accuracy to reach the advanced level on several face image datasets.
The Development of a Self-Study System for Language Learning - Overview of the Project and Related Work-

In Japan, the importance of teaching English and programming languages has been pointed out. However, no studies have analyzed both languages at the same time. In addition, the level of understanding of learners should differ from learner to learner, but the current e-learning system only presents pre-prepared tasks to all learners. We aim to develop a self-study system fortified with an artificial teacher that advises students by understanding their learning conditions. Such a system can act as a unified framework for evaluating language learning. In this study, we give an overview of previous related work and describe our research and development.
HARL-A: Hardware Agnostic Reinforcement Learning Through Adversarial Selection

The use of reinforcement learning (RL) has led to huge advancements in the field of robotics. However data scarcity, brittle convergence and the gap between simulation & real world environments, mean that most common RL approaches are subject to over fitting and fail to generalise to unseen environments. Hardware agnostic policies would mitigate this by allowing a single network to operate in a variety of test domains, where dynamics vary due to changes in robotic morphologies or internal parameters. We utilise the idea that learning to adapt a known and successful control policy is easier and more flexible than jointly learning numerous control policies for different morphologies.This paper presents the idea of Hardware Agnostic Reinforcement Learning using Adversarial selection (HARL-A). In this approach training examples are sampled using a novel adversarial loss function. This is designed to self regulate morphologies based on their learning potential. Simply applying our learning potential based loss function to current state-of-the-art already provides ~ 30% improvement in performance. Meanwhile experiments using the full implementation of HARL-A report an average increase of 70% to a standard RL baseline and 55% compared with current state-of-the-art.
Polarmask-Tracker: Lightweight Multi-Object Tracking and Segmentation Model for Edge Device

The image or video input from the camera is one of the important data sources for unmanned vehicles to perceive the environment. However, the 2D/3D bounding box can only provide a very coarse approximation because one box often contains other targets and background. In order to solve the problem of precise target tracking and computing limitations of edge devices, this paper proposes Polarmask-Tracker, a lightweight segmentation-based multi-object tracking network for vehicular edge devices. Polarmask-Tracker extended the lightweight Polarmask segmentation head with tracking vector. The polar mask replaces the traditional mask prediction by regression of a group of fixed edge points in polar coordinate system, which can greatly optimize the computational complexity and regression difficulty of the mask. With an additional tracking vector branch generated based on mask, the model can learn tracking tasks in an end-to-end manner. Finally, we further accelerated the entire model based on TensorRT and achieve real-time tracking on mobile edge computing platform. Different from previous evaluations on the ImageNet and COCO datasets, this study uses the KITTI tracking dataset to extend the instance segmentation task to segmentation tracking, also called MOTS. At the same time, the target scales captured from the autonomous vehicle camera are usually smaller, which also brings additional challenges. Evaluations on NVidia Jetson AGX show that the final Polarmask-Tracker can achieve 122.55 FPS, 46.57 mAP for mask segmentation, 56.418 HOTA for tracking.
Smart Bracelet Base on Internet of Things for Cashless Payment Transaction at Sunan Bonang Smart Tourism Supported by WLAN Network as a Communication System

In this paper, we use a location to be used as a research with the concept of Smart Tourism, the location used is Sunan Bonang Tuban. at the Sunan Bonang Tourism Complex, there is something identical, namely the existence of a tricycle as transportation to surround the Tourism Complex. When traveling with many visitors, the cash payment system is not very effective, RFID technology will help with its advantages that can be detected by radio frequency. Coupled with the android application that is used to order food, to reduce a queue. RFID is presented in the form of a Smart Bracelet that is supported by the QR Code, which is the QR Code used to login to the application. Smart bracelets are used as a means of payment, where payment transactions will be stored in the android application. With this concept, a network connection is needed to facilitate the transaction system, as a communication supporter, the Tourist Complex is equipped with a communication network, namely WLAN which is realized by several access points placed at several location points. So the systems built will run exact and suitable. Thus the Sunan Bonang Tourist Complex will form a Smart Tourist town based IoT.
A Robust MP3 Steganographic Method against Multiple Compressions Based on Modified Discrete Cosine Transform

MP3 appears in various social networking sites wildly, and it is very suitable to be applied for covert communication indeed. However, almost all social networking sites recompress the uploaded MP3 files, which leads to the ineffectiveness of the existing MP3 steganographic methods. In this paper, a robust MP3 steganographic algorithm is proposed with the ability of multiple compressions resistance. First, we discover a new embedding domain with strong robustness. The scalefactor bands of higher energy are applied as the embedding bands. The message bits are embedded by adjusting the position of the MDCT coefficients with the largest magnitude in the embedding bands. Besides, the embedding and extraction operations are realized in the process of MP3 decoding at the same time. Experimental results illustrate that our proposed method is of strong robustness against multiple MP3 compressions. The bit error rate is less than 1% at the MP3 bitrate of 320 kbps. It is worth mentioning that the proposed method is proved to be applicable to social networking sites, such as SoundCloud, for covert communication. Our method achieves a satisfactory level of embedding capacity, imperceptibility, and undetectability.
Optimization algorithm of digital media sharing platform based on blockchain technology

Abstract In today's big data era, the number of daily data visits on the platform is an incalculable number, which poses a challenge for users' data security. This article aims to study the decentralized blockchain technology, take advantage of the immutable advantages of the blockchain and integrate it into the digital media sharing platform, so as to achieve the optimization and improvement of the algorithm. This paper proposes an algorithm optimization plan for building a digital media sharing platform, adding a user data security backstage on the platform side, combining blockchain technology to design data security maintenance algorithms, and setting relevant parameters to maximize the immutability of data and ensure user data security. The experimental results of this article show that blockchain technology can be fully applied to digital media sharing platforms, and data security can be increased to more than 90%.