Reviewerly/exHarmony huggingface dataset:
Preliminary Design and Evaluation of an Interfacing Mechanism for Maneuvering Virtual Minimally Invasive Surgical Instruments

Augmenting the motion of virtual surgical instruments onto a minimally invasive surgical field acts as a visual cue for the operating surgeon. In this work we propose an interfacing mechanism to provide input for maneuvering such virtual surgical instruments. Specifically, an interface in the form of a 3D-printed dodecahedron pen with attached binary squared planar markers is employed. The proposed tracking mechanism computes the pose of the interface from a realtime video feed acquired from a camera. The system provides accurate pose estimation with mean errors of $0.27 \pm 0.06$ mm in translation and $0.37 \pm 0.04$ degrees in rotation. The object pose estimation takes ~6 ms. Utilized Azure Kinect camera with frame rate of 30 FPS and 1280 x 720 image resolution video, the tracking speed of the proposed system is ~25 FPS. The easy to integrate, cost effective setup makes the interfacing mechanism particularly suitable for remote surgical tele-mentoring applications.
Framework for Estimating Distance and Detecting Object on Mono-camera

Autonomous driving is one of the hottest topics in recent years, receiving extensive attention from the automotive industry and research institutes. In order to ensure the safety of drivers and pedestrians, it is necessary to predict possible hazards during driving based on the information collected by sensors. The two most important sensors used on the vehicles are Lidar and cameras. They are used to measure the distance and detect objects on the front, respectively. However, Lidar is very expensive, which limits its use in autonomous driving. This paper presents a new framework, which uses the mono-camera to realize both object detection and relative distance estimation. The framework is tested on the benchmark of the KITTI dataset, its performance depends on the algorithms used in the framework.
Protoformer: Embedding Prototypes for Transformers

Transformers have been widely applied in text classification. Unfortunately, real-world data contain anomalies and noisy labels that cause challenges for state-of-art Transformers. This paper proposes Protoformer, a novel self-learning framework for Transformers that can leverage problematic samples for text classification. Protoformer features a selection mechanism for embedding samples that allows us to efficiently extract and utilize anomalies prototypes and difficult class prototypes. We demonstrated such capabilities on datasets with diverse textual structures (e.g., Twitter, IMDB, ArXiv). We also applied the framework to several models. The results indicate that Protoformer can improve current Transformers in various empirical settings.
Network Traffic Impact on Cloud Usage at Different Providers

The research question focuses on finding an optimal cost-less and efficient solution, analyzing the differences between uploading and downloading data between different cloud solutions. The conducted experiments compare AWS cloud platform offered by Amazon and use two of its products: computational cloud platform EC2 and storage cloud platform S3. We explore three different versions of S3 storage and S3 Standard, each of them with its own purposes, characteristics and costs. Additionally we test the data transfer and computational capabilities of a t2.micro instance of the EC2 virtual machine. Then, we compare the experiment on the European Cloud provider - Scaleway Elements and use two of its products - Virtual Instances based on the Stardust instances for computations and Object Storage a S3 compatible storage platform.We conclude that the network connection between Amazon nodes is far superior than any other, both in performance and costs. Fetching results from an AWS computational service (EC2) is much cheaper and faster when using an AWS platform as an intermediary (S3) compared to a direct transfer to the local machine. We observe a similar superior network performance in the comparison of Scaleway Cloud Infrastructure.
A Three-Step One-Time Password, Textual and Recall-Based Graphical Password for an Online Authentication

Text passwords are the most extensively used technique of computer authentication. This approach has been found to have several flaws. Users, for example, typically select passwords that are simple to guess. A difficult-to-guess password, on the other hand, is also difficult-to-remember. Textual passwords are vulnerable to brute-force and keylogger attacks. Graphic passwords have been proposed in the literature as a possible replacement for alphanumerical passwords, based on the assumption that people remember pictures better than text. Existing graphical passwords, on the other hand, are vulnerable to a shoulder surfing assault. To solve these security flaws, this paper proposes an authentication method for online applications that uses a combination of one-time passwords, textual, and graphical passwords. The efficacy of the recommended solution was confirmed by usability testing and security analysis procedures. A total of thirty participants took part in the system evaluation. The security assessment found that the proposed system meets all its primary security requirements. The proposed system was found to be simple to use, friendly, and secure throughout the usability test. When compared to traditional authentication solutions, this study exhibited greater usability and security.
Stealthy Attack on Algorithmic-Protected DNNs via Smart Bit Flipping

Recently, deep neural networks (DNNs) have been deployed in safety-critical systems such as autonomous vehicles and medical devices. Shortly after that, the vulnerability of DNNs were revealed by stealthy adversarial examples where crafted inputs—by adding tiny perturbations to original inputs—can lead a DNN to generate misclassification outputs. To improve the robustness of DNNs, some algorithmic-based countermeasures against adversarial examples have been introduced thereafter.In this paper, we propose a new type of stealthy attack on protected DNNs to circumvent the algorithmic defenses: via smart bit flipping in DNN weights, we can reserve the classification accuracy for clean inputs but misclassify crafted inputs even with algorithmic countermeasures. To fool protected DNNs in a stealthy way, we introduce a novel method to efficiently find their most vulnerable weights and flip those bits in hardware. Experimental results show that we can successfully apply our stealthy attack against state-of-the-art algorithmic-protected DNNs.
SV-FPN: Small Object Feature Enhancement and Variance-Guided RoI Fusion for Feature Pyramid Networks

Small object detection is one of the research difficulties in object detection, and Feature Pyramid Networks (FPN) is a common feature extractor in deep learning; thus, improving the results of small object detection based on FPN is of great significance in this field. In this paper, SV-FPN is proposed for a small object detection task, which consists of Small Object Feature Enhancement (SOFE) and Variance-guided Region of Interest Fusion (VRoIF). When using FPN as a feature extractor, an SOFE module is designed to enhance the finer-resolution level feature maps from which the small object features are extracted. VRoIF takes the variance of RoI features as the data driver to learn the completeness of several RoI features from different feature layers, which avoids wasting information and introducing noise. Ablation experiments on three public datasets (KITTI, PASCAL VOC 07+12 and MS COCO 2017) demonstrate the effectiveness of SV-FPN, and the mean Average Precision (mAP) of SV-FPN in the three datasets achieves 41.5%, 53.9% and 38.3%, respectively.
The Pentest Method for Business Intelligence

Information transactions and data retention comprise critical inputs to Business Intelligence processes. However, despite ongoing data-driven Business Intelligence process improvements, many companies only discover they are vulnerable to a cyber-attack after a breach materializes the risk. In this study, we propose that compliance regimes such as the global Payment Card Industry Data Security Standard (PCI-DSS), the federal Gramm-Leach Bliley Act (GLBA), and the regional 23-NYCRR-500 standard provide externally-imposed risk discovery opportunities that should be part of managerial decision-making. This paper describes the penetration test (pentest) method relative to those regulatory regimes. We then consider the potential for the pentest method to yield predictive Business Intelligence data sources in five historical cases: the 2017 Equifax Breach, the 2014 J.P. Morgan Chase Breach, the 2012 Global Payments Breach, the 2010 Nasdaq Hack, and the 2009 Heartland Payments Breach. Our findings suggest that the pentest method–especially relative to PCI-DSS compliance–is a promising inclusion in Business Intelligence processes.
VMM: Viewpoint-based Memory Mechanism for Object Detection of Moving Sensors

Video object detection is a promising technology to enhance perception capability of mobile intelligent devices in practical scenes. Recent algorithms continously achieve remarkable detection performance on popular datasets. However, the performance is still unsatisfactory on practical scene videos from a moving sensor. In this paper, we propose a viewpoint-based memory framework to improve detection performance by exploiting viewpoint information of the sensors, which obviously enhances the detection accuracy at a minor cost of execution time. Videos and the sensors’ corresponding angle information are collected as test dataset by a camera-IMU module, and the memory framework is actualized as an extension module of a mainstream image detector. Experiments are designed to compare performance of the image detector and the framework extended detector. The experiment results indicate the extended detector achieves a 25.0% average localization margin and costs extra 12.36 ms/frame in average compared with the image detector.
Fusion Network for Super Resolution of UAVs Visible and Thermal Images

Object identification is a fundamental and important problem in computer vision. Although impressive results have been obtained for medium to large objects in visible images, the performance on small objects, including mini/micro-UAVs (Unmanned Air Vehicle) in visible and thermal images, has received less attention. Therefore, due to the different appearances of these small flying objects, the identification of these objects, especially under poor early illumination conditions is currently a challenge. To overcome this challenge, we propose an increase in the resolution of the captured images by adding high frequency details which allows to produce better visual quality. In this work, we propose a Fusion Network for Super Resolution of UAVs Visible and Thermal Images (FNSR), which aims to produce a Hight Resolution image (HR) using visible and infrared (IR) reference images (Ref). FNSR is made up of five related modules, including a multi-scale texture extractor for feature extraction from visible and thermal images, a spatial fusion module, a relevance embedding module, a features transfer block and a spatial decoder module. We introduce channel attention mechanisms to highlight the most promising features, as well as a Spatial Total Variation (STV) loss to preserve spatial information. Experimental results on image pairs (IR/Visible) show that the FNSR is capable of producing high quality super-resolved images. Compared to the State-Of-The-Art (SOTA), it presents better performances.
Using Twitter Sentiment Analysis for Sustainable Improvement of Business Intelligence in Nigerian Small and Medium-Scale Enterprises

For most Small and Medium-scale Enterprises (SMEs) and startups in Nigeria, conducting market research and evaluation is solely based on customers’ verbal feedback and demands, which is often flawed and highly limited. Social media (Twitter), as a business intelligence tool, can help SMEs gain insight into customers’ perspectives of their products and services through Sentiment Analysis. This research presents a Twitter Sentiment Analysis for business intelligence using three machine learning algorithms; Bernoulli Naive Bayes (BNB), Linear Support Vector Classification (SVC), and Logistic Regression (LR) algorithm, to get the text polarity of tweets (Negative/positive). The dataset used for the research was gotten from Kaggle; the tweets were processed and analyzed using Python programming libraries on Kaggle’s Jupyter Notebook cloud environment. Our result showed that Logistic Regression achieved better performance with an accuracy of 83% based on Precision-Recall evaluation metrics.
Transfer Learning for Event-Type Differentiation on Power Systems

Machine Learning (ML) models are continuously introduced to power systems in domains like state estimation and event identification. However, training an ML model usually requires a lot of data. For data-limited grids, we propose a transfer learning framework to transfer knowledge from a source grid with rich Phasor Measurement Unit (PMU) data for the event-type differentiation problem. The goal is challenging due to (1) different dimensionalities of the source and the target measurement spaces, (2) dissimilar data distributions, and (3) redundant PMU&#x2019;s information. Thus, we project the source and the target measurement space into a latent feature space, which reduces and aligns the dimensionality of input measurements, maintains close data distributions in the latent space, and enables the transferability from the source domain to the target domain. Then, we introduce transfer learning in supervised learning by vectorizing each PMU&#x2019;s measurement window as one training sample, forming the latent space. We theoretically show that our approach minimizes the upper bound of misclassification rate and experimentally demonstrates the high performance on various synthetic datasets.
Visualization of Blockchain Consensus Degradation

In this paper we present a prototype system for easy testing and visualization of the Bitcoin consensus protocol. The system consists of a modified version of the Bitcoin Core client which is intended to run on Raspberry PI machines, modified so that the latency in the block production and propagation can be manually introduced, which simulates network communication issues in the real Bitcoin network. Built-in LCD displays on each machine visualize current state of their local blockchains which allows easy visual observation of the consensus in the network. As machines are running fully capable Bitcoin Core clients, as well as mining clients for production of new blocks in the Proof of Work (PoW) consensus protocol, it is possible to investigate different configuration scenarios and when these lead to the break of the consensus.
Robustness enhancement against adversarial steganography via steganalyzer outputs

Recently, CNN (convolutional neural network) steganalyzers have significantly outperformed handcrafted features in detecting steganography. However, adversarial steganography has challenged the applications of them in the real world. Adversarial steganography can easily deceive the target CNN steganalyzer while sending secret messages. In this paper, a general framework is proposed. It that can improve the robustness of CNN steganalyzers against adversarial steganography while keeping detecting cover and conventional stego images accurately. Specifically, a rough filter that filters adversarial stego images out of the input data is set. It exploits the differences between cover and adversarial stego images on probabilistic outputs of the target CNN steganalyzer and a handcrafted steganalyzer. Extensive experiments show that the proposed framework can significantly improve the robustness of CNN steganalyzers. In the real-world scenario where cover, conventional stego and adversarial stego images are mixed, the robustness enhanced CNN steganalyzers can achieve the optimal overall performance.
A robust target tracking algorithm based on spatial regularization and adaptive updating model

Abstract The correlation filtering-based target tracking method has impressive tracking performance and computational efficiency. Nevertheless, a few issues limit the accuracy of the correlation filter-based tracking methods including the object deformation, boundary effects, scale variations, and the target occlusion. This article proposes a robust target tracking algorithm to solve these issues. First, a feature fusion method is used to enhance feature response discrimination between the target and others. Second, a spatial weight function is introduced to penalize the magnitude of filter coefficients and an ADMM algorithm is employed to reduce the iteration of filter coefficients when tracking. Third, an adaptive scale filter is designed to make the algorithm adaptable to the scale variations. Finally, the correlation peak average difference ratio is applied to realize the adaptive updating and improve the stability. The experiment’s result demonstrates the proposed algorithm improved tracking results compared to the state-of-the-art correlation filtering-based target tracking method.
Collaborative Knowledge Platform: when the learning route provides data for the Knowledge-based System

The Digital Age has brought not only new tools but also several new methods. A Collaborative Knowledge Platform with a hybrid intelligent system may be the appropriate base of a knowledge management system to ensure inspiration and new knowledge for a professional group of individuals. The introduced concept contributes to Knowledge Collaboration and Knowledge Engineering. The method is a special form of Knowledge Engineering which involves combining machine learning algorithms with cased-based reasoning and the result is the transformation of personal knowledge to widely adaptable explicit knowledge. Individuals can learn informally while their learning route automatically generates data for reductive reasoning process, which finally leads to the opportunity of experience mining. A concept and an approach are suggested to improve the knowledge collaboration in innovative communities, and a creative problem solving process delivers the outcome in the development of a Knowledge Management System. Finally, some partial results of the design phase of an application are presented.
Digital media innovations through participatory action research: Interventions for digital place-based experiences

Abstract This article presents an action-research study investigating a spatially sensitive innovation process of place-based experiences in a rural area of Sweden. Lately, there have been a growing number of initiatives focused on developing location-aware mobile media – geomedia technologies – to offer place-based digital experiences within tourism. Drawing on contemporary critical studies on geomedia technologies, we stress the importance of reflecting upon the implications of place-based technologies to minimise both the negative impacts on a place and the neglect of local perspectives. We conducted action-research interventions to unpack the complexity of developing place-based mediated experiences. The study makes an illustrative case of how interventions lead to more nuanced development processes of geomedia technologies while simultaneously fostering creativity. We argue that as action research allows researchers to intervene in media innovations, it identifies models for more nuanced place-based development processes, including local spatial and sociocultural perspectives.
Multi-probe attention neural network for COVID-19 semantic indexing

The COVID-19 pandemic has increasingly accelerated the publication pace of scientific literature. How to efficiently curate and index this large amount of biomedical literature under the current crisis is of great importance. Previous literature indexing is mainly performed by human experts using Medical Subject Headings (MeSH), which is labor-intensive and time-consuming. Therefore, to alleviate the expensive time consumption and monetary cost, there is an urgent need for automatic semantic indexing technologies for the emerging COVID-19 domain.In this research, to investigate the semantic indexing problem for COVID-19, we first construct the new COVID-19 Semantic Indexing dataset, which consists of more than 80 thousand biomedical articles. We then propose a novel semantic indexing framework based on the multi-probe attention neural network (MPANN) to address the COVID-19 semantic indexing problem. Specifically, we employ a k-nearest neighbour based MeSH masking approach to generate candidate topic terms for each input article. We encode and feed the selected candidate terms as well as other contextual information as probes into the downstream attention-based neural network. Each semantic probe carries specific aspects of biomedical knowledge and provides informatively discriminative features for the input article. After extracting the semantic features at both term-level and document-level through the attention-based neural network, MPANN adopts a linear multi-view classifier to conduct the final topic prediction for COVID-19 semantic indexing.The experimental results suggest that MPANN promises to represent the semantic features of biomedical texts and is effective in predicting semantic topics for COVID-19 related biomedical articles.
ARBench: Augmented Reality Benchmark For Mobile Devices

This paper takes an important step towards the improvement of the AR mobile experience by designing and developing ARBench, the first Augmented Reality (AR) benchmark for mobile devices. ARBench incorporates different AR workloads that stress multiple hardware units of the SoC (CPU, GPU, DSP, etc), and measures the individual score for each AR workload. The proposed benchmark suite is then used to evaluate the AR performance of various commercial mobile devices, and their ability to support various functions of AR workloads.
POLYBRIDGE: A Crosschain Bridge For Heterogeneous Blockchains

While the Bitcoin and Ethereum are still leading the world of permissionless blockchains, we are increasingly seeing a multipolar ecosystem where new blockchains keep emerging instead of migrating to the two big players. As a result, it is highly desired to enable multiple blockchains to interoperate, e.g., move assets from one blockchain to the other. The crosschain bridge service, as a solution to the blockchain interoperability problem, has been offered by a wide range of service providers. However, the existing bridges either rely on centralised notaries or require complicated preparations, therefore are far from sufficient in practice. In this demo proposal, we highlight the Poly Bridge for heterogeneous blockchains to interoperate with cryptocurrencies. In specific, Poly Bridge is based on an un-derlying Poly Chain and a pair of relays to confirm crosschain transactions and form consensus among relevant parties. More importantly, Poly Bridge delivers extensibility to flexibly inter-face to blockchains with different consensus models and atomicity as well in a way that a sequence of crosschain operations are either all confirmed or all rejected. Poly Bridge is now available as a web application to support crosschain requests with over 200 types of cryptocurrencies on 18 blockchains.