Reviewerly/exHarmony huggingface dataset:
CADUE: Content-Agnostic Detection of Unwanted Emails for Enterprise Security

End-to-end email encryption (E2EE) ensures that an email could only be decrypted and read by its intended recipients. E2EE’s strong security guarantee is particularly desirable for the enterprises in the event of breaches: even if attackers break into an email server, under E2EE no contents of emails are leaked. Meanwhile, E2EE brings significant challenges for an enterprise to detect and filter unwanted emails (spams and phishing emails). Most existing solutions rely heavily on email contents (i.e., email body and attachments), which would be difficult when email contents are encrypted. In this paper, we investigate how to detect unwanted emails in a content-agnostic manner, that is, without access to the contents of emails at all.
Collaborative MSER and Faster R-CNN Model for Retrieval of Objects in Images

Object detection and retrieval are one of the fast-growing fields of computer vision. Retrieval of objects in images can be performed through either traditional approaches or deep learning-based approaches. A method known as Faster Region-based Convolutional Neural Network (R-CNN) was used for retrieving the same object in different scenes. Faster R-CNN was introduced to overcome the problems with the fast R-CNN framework. For enhancing feature extraction in the same object retrieval using faster R-CNN, we are imposing an existing algorithm called Maximally Stable Extremal Regions (MSER) which will enhance the features of low-resolution images. MSER can extract several regions from an image. The collaborative proposed model uses the prominent features of both faster R-CNN and MSER algorithm to retrieve a queried object from a given dataset with better accuracy. Experimental results have shown that our collaborative model achieved a retrieval efficacy in terms of mean Average Precision (mAP) 0.79 in the Vehicle dataset and 0.632 in the Oxford Building dataset. The Collaborative MSER and faster R-CNN model outperform with a mAP of 0.86 in the Vehicle dataset and 0.714 in the Oxford Building dataset.
Towards a Taxonomy of Trustworthy Digital Repository Impacts

Abstract Measuring the societal impact of digital repositories is a wicked problem. To capture information about the impacts of digital repositories that become certified as Trustworthy Digital Repositories (TDRs), a taxonomy describing activities cited as evidence of compliance with TDR standards is needed to relate those activities to socially beneficial outcomes. This paper presents a Minimum Viable Prototype (MVP) for a TDR Impacts taxonomy to enable the expression of their activities in a structured way. Our MVP provides a proof‐of‐concept that by formalizing concepts about TDRs in a taxonomy, we can investigate ways to measure whether the impacts associated with becoming and remaining a certified TDR are also impacts that generate societal value. Implications of the work described include potential strategies to identify, extract, and/or author machine‐readable descriptions of measurable facets of TDR activities and the resulting impacts on communities.
Streaming Dictionary Matching with Mismatches

In the $k$-mismatch problem we are given a pattern of length $n$ and a text and must find all locations where the Hamming distance between the pattern and the text is at most $k$. A series of recent breakthroughs have resulted in an ultra-efficient streaming algorithm for this problem that requires only $O(k \log \frac{n}{k})$ space and $O(\log \frac{n}{k} (\sqrt{k \log k} + \log^3 n))$ time per letter [Clifford, Kociumaka, Porat, SODA 2019]. In this work, we consider a strictly harder problem called dictionary matching with $k$ mismatches. In this problem, we are given a dictionary of $d$ patterns, where the length of each pattern is at most $n$, and must find all substrings of the text that are within Hamming distance $k$ from one of the patterns. We develop a streaming algorithm for this problem with $O(k d \log^k d \mathrm{polylog}(n))$ space and $O(k \log^{k} d \mathrm{polylog}(n) + |\mathrm{occ}|)$ time per position of the text. The algorithm is randomised and outputs correct answers with high probability. On the lower bound side, we show that any streaming algorithm for dictionary matching with $k$ mismatches requires $\Omega(k d)$ bits of space.
Segmentation and Quantification of Bi-Ventricles and Myocardium Using 3D SERes-U-Net

Automatic cardiac MRI segmentation, including left and right ventricular endocardium and epicardium, has an essential role in clinical diagnosis by providing crucial information about cardiac function. Determining heart chamber properties, such as volume or ejection fraction, directly relies on their accurate segmentation. In this work, we propose a new automatic method for the segmentation of myocardium, left, and right ventricles from MRI images. We introduce a new architecture that incorporates SERes blocks into 3D U-net architecture (3D SERes-U-Net). The SERes blocks incorporate squeeze-and-excitation operations into residual learning. The adaptive feature recalibration ability of squeeze-and-excitation operations boosts the network’s representational power while feature reuse utilizes effective learning of the features, which improves segmentation performance. We evaluate the proposed method on the testing dataset of the MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset and obtain highly comparable results to the state-of-the-art methods.
Text to Scene

In this work, we show the Text to Scene system, which can configure 3D indoor scene from natural language. Given a text, the system will organize inclusive semantic message to a graph template, complete the graph with a novel graph-based contextual completion method Contextual ConvE(CConvE) and visulize the graph by arranging 3D models under an object location protocol. In the experiments, qualitative results obtained by the Text to Scene(T2S) system and quantitative evaluation of CConvE compared with other state-of-the-art approaches are reported.
Offset-Symmetric Gaussians for Differential Privacy

The Gaussian distribution is widely used in mechanism design for differential privacy (DP). Thanks to its sub-Gaussian tail, it significantly reduces the chance of outliers when responding to queries. However, it can only provide approximate (&#x03B5;,&#x03B4;(&#x03B5;))-DP. In practice, &#x03B4;(&#x03B5;) must be much smaller than the size of the dataset, which may limit the use of the Gaussian mechanism for large datasets with strong privacy requirements. In this paper, we introduce and analyze a new distribution for use in DP that is based on the Gaussian distribution, but has improved privacy performance. The so-called offset-symmetric Gaussian tail (OSGT) distribution is obtained through using the normalized tails of two symmetric Gaussians around zero. Consequently, it can still have sub-Gaussian tail and lend itself to analytical derivations. We analytically derive the variance of the OSGT random variable and &#x03B4;(&#x03B5;) of the single-dimensional OSGT mechanism. We extend the OSGT mechanism to <i>k</i>-dimensional queries, iteratively compute its &#x03B4;<sub><i>k</i></sub>(&#x03B5;), derive its R&#x00E9;nyi differential privacy, and study its composition. Numerical results show the OSGT mechanism can offer better privacy-utility performance compared to the Gaussian and Laplace mechanisms. We also derive a method for post processing the output of the OSGT mechanism to approximate the query based on the minimum mean square error (MMSE) estimation technique. The simulation results of such processing confirm the efficacy of the OSGT mechanism over the Gaussian mechanism.
Fuzzy Integration of kernel-based Gaussian Processes applied to Anomaly Detection in Nuclear Security

Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.
Delving into Deep Image Prior for Adversarial Defense

Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images. To defend against adversarial attacks in a training-free and attack-agnostic manner, this work proposes a novel and effective reconstruction-based defense framework by delving into deep image prior (DIP). Fundamentally different from existing reconstruction-based defenses, the proposed method analyzes and explicitly incorporates the model decision process into our defense. Given an adversarial image, firstly we map its reconstructed images during DIP optimization to the model decision space, where cross-boundary images can be detected and on-boundary images can be further localized. Then, adversarial noise is purified by perturbing on-boundary images along the reverse direction to the adversarial image. Finally, on-manifold images are stitched to construct an image that can be correctly predicted by the victim classifier. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art reconstruction-based methods both in defending white-box attacks and defense-aware attacks. Moreover, the proposed method can maintain a high visual quality during adversarial image reconstruction.
TDI TextSpotter: Taking Data Imbalance into Account in Scene Text Spotting

Recent scene text spotters that integrate text detection module and recognition module have made significant progress. However, existing methods encounter two problems. 1). The data imbalance issue between text detection module and text recognition module limits the performance of text spotters. 2). The default left-to-right reading direction leads to errors in unconventional text spotting. In this paper, we propose a novel scene text spotter TDI to solve these problems. Firstly, in order to solve the data imbalance problem, a sample generation algorithm is proposed to generate plenty of samples online for training the text recognition module by using character features and character labels. Secondly, a weakly supervised character generation algorithm is designed to generate character-level labels from word-level labels for the sample generation algorithm and the training of the text detection module. Finally, in order to spot arbitrarily arranged text correctly, a direction perception module is proposed to perceive the reading direction of text instance. Experiments on several benchmarks show that these designs can significantly improve the performance of text spotter. Specifically, our method outperforms state-of-the-art methods on three public datasets in both text detection and end-to-end text recognition, which fully proves the effectiveness and robustness of our method.
A Tool for Monitoring and Controlling Standalone Immersive HCI Experiments

We present an open source tool which provides commonly required functionality in HCI experimental research targeted at Immersive technologies. Using the tool, researchers can monitor and control a Unity scene from a controller panel running in the web-browser. Researchers can also use the tool to track the user status (e.g. see what the user sees) and trigger the execution of remote functions (e.g. alter the Unity Scene, the objects appearing, etc.). Moreover, we show a use case to exemplify how the tool is being used to support our research regarding Immersive Multisensory Interaction in a room-sized environment.
Image quality assessment based on the image contents visual perception

Image quality assessment (IQA) is widely used in image transmission, processing, and storage, which has important application value. To obtain an IQA model with the excellent performance, combining with the image content features and human visual system characteristics, based on the contrast definition in visual psychology, an IQA method and its mathematical model are proposed. In this method, first based on the contrast definition, combining with the visual characteristics, the contrast between the distorted image and the reference image is used to describe the difference between them; further, based on the difference, a definition method for image quality is proposed. Second, based on the gray and gradient co-occurrence matrix, a concept, which is called as the image gray-gradient expectation (GGE), is proposed, and its calculation method is also illustrated. And based on the GGE values and local contrast of image, a method for describing the image content and its visual perception is proposed. Finally, based on the image content features and the proposed definition method of image quality, an IQA method and its mathematical model are proposed. Further, they were tested using the 142 reference images and 7220 distorted images in the LIVE, CSIQ, TID2008, TID2013, CIDIQ, and IVC databases. And the results were compared with those of seven existing typical IQA models in terms of accuracy, complexity, and generalization performance. These experimental results show that, in the six databases, the IQA accuracy Pearson linear correlation coefficient of the proposed model can all be more than 0.7892 and reach 0.9638 highest, whose comprehensive efficiency is better than ones of the seven existing IQA models. These analysis and comparison show that the proposed model is an excellent IQA model on performance.
Vision-based Path Following of Snake-like Robots

Due to the head swinging and the body winding, the self-localization and path following for snake-like robots based on vision are very challenging. In this paper, a novel pantilt compensation method and curve parameter compensation path following controller are proposed to solve these problems, which can achieve high-precision path following. More specifically, to realize real-time positioning of the snakelike robot, a camera-mounted pan-tilt is equipped on the head of the snake-like robot, and the Apritag detection is used after the angle between the Apriltag plane and the camera plane being compensated. Then, due to the modeling approximation, the snake-like robot with a simplified controller usually deviates from the desired path, so a curve parameter compensation path following controller is proposed to eliminate the deviation and improve the following accuracy. Finally, the experiments show that with proposed methods, the snake-like robot can converge to the desired path stably and accurately.
Breast Cancer Histopathological Image Classification Using Convolutional Neural Networks

Nowadays, the classification of medical images has become an essential part of identifying the disease. Among various existing critical diseases, identification of breast cancer has now come up with the topic of investigation. To identify the affected regions of the images, a deep learning-based approach has got wide attention for decades. Convolutional neural networks (CNN) among all deep learning techniques proved their best efficiency in this field. In this paper, one improved CNN-based approach has been proposed to classify the breast cancer images obtainable from the standard PatchCamelyon (PCam) benchmark dataset. It is available for free from the website link https://www.kaggle.com/c/histopathologic-cancer-detection/data . In the improved model, various existing layers like convolutional, ReLU, pooling, fully connected have been added as well as modified for better efficiency and efficacy of the algorithm. Further to be added that Adam optimizer has been used here with cross entropy as a loss function. This improved model has been compared with two recent CNN-based approaches applied to medical datasets. The comparative outcomes suggest strong improvements in terms of classification accuracy (probably 2–3%) and computational time of validation loss for both training and testing data over existing models.
Targeted Aspect-Based Sentiment Analysis for Ugandan Telecom Reviews from Twitter

In this paper we present SentiTel, a fine-grained opinion mining dataset that is human annotated for the task of targeted aspect-based sentiment analysis (TABSA). SentiTel contains Twitter reviews about three telecoms in Uganda posted in the period between February 2019 and September 2019. The reviews in the dataset have a code-mix of English and Luganda a language that is commonly spoken in Uganda. The dataset in this paper consists of 5973 human annotated reviews with the target entity which is the target telecom, aspect and sentiment towards the aspect of the target telecom. Each review contains at least one target telecom. Two models are trained for the TABSA task that is random forest which is the baseline model and BERT. The best results are obtained using BERT with an Area Under the ROC Curve (AUC) of 0.950 and 0.965 on aspect category detection and sentiment classification respectively. The results show that even though tweets are written without the intention of writing a formal review, they are rich in information and can be used for fine-grained opinion mining. Finally, the results show that fine-tuning the pre-trained BERT model on a downstream task generates better results compared to the baseline models.
Construction of Lightweight Authenticated Joint Arithmetic Computation for 5G IoT Networks

Abstract The next generation of Internet of Things (IoT) networks and mobile communications (5G IoT networks) has the particularity of being heterogeneous, therefore, it has very strong ability to compute, store, etc. Group-oriented applications demonstrate its potential ability in 5G IoT networks. One of the main challenges for secure group-oriented applications (SGA) in 5G IoT networks is how to secure communication and computation among these heterogeneous devices. Conventional protocols are not suitable for SGA in 5G IoT networks since multiparty joint computation in this environment requires lightweight communication and computation overhead. Furthermore, the primary task of SGA is to securely transmit various types of jointly computing data. Hence, membership authentication and secure multiparty joint arithmetic computation become two fundamental security services in SGA for 5G IoT networks. The membership authentication allows communication entities to authenticate their communication partners and the multiparty joint computations allow a secret output to be shared among all communication entities. The multiparty joint computation result can be used to protect exchange information in the communication or be used as a result that all users jointly compute by using their secret inputs. A novel construction of computation/communications-efficient membership authenticated joint arithmetic computation is proposed in this paper for 5G IoT networks, which not only integrates the function of membership authentication and joint arithmetic computation but also realizes both computation and communication efficiency on each group member side. Our protocol is secure against inside attackers and outside attackers, and also meets all the described security goals. Meanwhile, in this construction the privacy of tokens can be well protected so tokens can be reused multiple times. This proposal is noninteractive and can be easily extended to joint arithmetic computation with any number of inputs. Hence, our design has more attraction for lightweight membership authenticated joint arithmetic computation in 5G IoT networks.
Virtual Viewpoint Film and Television Synthesis Based on the Intelligent Algorithm of Wireless Network Communication for Image Repair

With the development of the computer vision field, the acquisition of scene depth information is one of the important topics in the three-dimensional reconstruction of the computer vision field, and its significance is particularly important. The purpose of this paper is to study the virtual viewpoint video synthesis for image restoration based on the intelligent algorithm of wireless network communication. Aiming at the hole problem caused by the change of occlusion relationship, this paper proposes a hole-filling method based on background recognition. A threshold segmentation algorithm is used to reduce the filling priority of foreground pixels at the boundary of the hole and fully solve the hole problem. This paper also proposes a wireless sensor network node positioning model with swarm intelligence algorithm, which combines swarm intelligence algorithm with some key issues of wireless sensor network, speeds up the convergence, and improves the traditional intelligence algorithm. According to the experimental data in this paper, the algorithm in this paper is about 20% higher than the traditional algorithm in PSNR. On SSIM, the performance of the algorithm in this paper is 4.6% higher than the traditional algorithm at most, and the lowest is 2.2%.
Wa/ondering with data - or, Responsibly measuring socio-technical serendipity in the urban environment

Current trends in serendipity research and collaborative ethics point to the importance of cultivating bottom-up approaches to designing for datafication in urban centers. The focus on pattern recognition in big scale data analysis, combined with an exponential growth in and infrastructural support of ubiquitous information and communication technologies (ICIs), has led to concerns about whether smart cities will turn urban environments into sites that leave little space for diverse and unplanned encounters. We take the position that smart cities need to take citizen agency into account and explain how to conceive the smart city in terms of serendipitous opportunity and community engagement. We do this by elaborating on the idea of situated serendipity, and how this kind of serendipity is co-constructed by technologies, citizens, and the urban setting. We subsequently present a methodology in line with recent work with sensory ethnography, to better understand the meaning and value of serendipity in the smart city. Ultimately, we propose a new way to imagine the ‘living lab’ as a cultivator of serendipity, through techniques developed in the fields of design, innovation, improvisation, citizen science and participatory ethics.
Weaving Schematics and Code: Interactive Visual Editing for Hardware Description Languages

In many engineering disciplines such as circuit board, chip, and mechanical design, a hardware description language (HDL) approach provides important benefits over direct manipulation interfaces by supporting concepts like abstraction and generator meta-programming. While several such HDLs have emerged recently and promised power and flexibility, they also present challenges – especially to designers familiar with current graphical workflows. In this work, we investigate an IDE approach to provide a graphical editor for a board-level circuit design HDL. Unlike GUI builders which convert an entire diagram to code, we instead propose generating equivalent HDL from individual graphical edit actions. By keeping code as the primary design input, we preserve the full power of the underlying HDL, while remaining useful even to advanced users. We discuss our concept, design considerations such as performance, system implementation, and report on the results of an exploratory remote user study with four experienced hardware designers.