Reviewerly/exHarmony huggingface dataset:
Scalable and Efficient Hardware Architectures for Authenticated Encryption in IoT Applications

Internet of Things (IoT) is a key enabling technology, wherein sensors are placed ubiquitously to collect and exchange information with their surrounding nodes. Due to the inherent interconnectivity, IoT devices are vulnerable to cybersecurity attacks. To mitigate these vulnerabilities, cryptographic primitives can be employed, but they require significant computation, which restricts their adoption in IoT. Moreover, IoT systems have diverse requirements, ranging from high-throughput (TP) to the area constrained. This makes it hard to deploy appropriate security measures in a systematic manner. To address these issues, three generic implementation strategies (unrolled, round-based, and serialized) are proposed for developing highly efficient hardware architectures. They are applicable to all authenticated encryption schemes and are lightweight and fast, compared to conventional public key encryption. In this article, Ascon is implemented as an example based on those three strategies: 1) the unrolled architecture achieves TP of 766.9 Mb/s (Ascon-128) and 1389.2 Mb/s (Ascon-128a), which are suitable for high-throughput IoT applications; 2) the round-based architecture achieves 0.153 (Ascon-128) and 0.244 (Ascon-128a) TP-to-area ratio, which are, respectively, 73.8% and 40.2% better than state-of-the-art results; and 3) a novel serialized implementation technique is proposed wherein the substitution-box (S-box) is processed in multiple-bit-per-cycle, in contrast to the conventional one-bit-per-cycle approach. The TP of the two-bits-per-clock-cycle implementation is increased by 230.8% with only 36.8% additional hardware area. The proposed strategies allow us to scale the number of rounds (round-based) and bits-per-clock-cycle (serialized) to meet differing requirements in TP and area which are demonstrated for smart city IoT applications.
Real-Time Weakly Supervised Object Detection Using Center-of-Features Localization

We propose a high-speed convolutional neural network approach for weakly supervised localization (WSL) and weakly supervised object detection (WSOD). The proposed method, called center-of-features localization (COFL), performs localization of objects in a visual scene by combining both multi-label classification and regression for the number of instances of each class object. A modified Xception network architecture is used as the main feature extractor, and a classification-plus-regression loss function is used to perform the detection task. The method does not require bounding box annotations but only image labels and counts of the objects of each class in the image. This combination can produce a clear localization of objects in the scene through a masking technique between class activation maps (CAMs) and regression activation maps (RAMs). The proposed method was trained and tested on the PASCAL VOC2007 and VOC2012 datasets; it attained a mean average precision (mAP) of 47.0% and a correct localization CorLoc of 64.1% on PASCAL VOC2007 and a mAP of 42.3% and a CorLoc of 65.5% on PASCAL VOC2012 while performing object detection at a speed of ~50 fps. These results demonstrate that the network can perform object detection accurately in real-time using only image labels and object counts, which are inexpensive to annotate compared with the bounding box annotations typically employed in fully supervised object detection methods. The network far outperforms other weakly supervised methods and some fully supervised methods in terms of processing time while achieving fair accuracy.
Active contour model with adaptive weighted function for robust image segmentation under biased conditions

The segmentation of images under biased conditions such as low contrast, high-intensity inhomogeneity, and noise is a challenge for any image segmentation model. The ideal image segmentation model must be capable of segmenting images with maximum accuracy and a minimum false-positive rate under biased conditions. In this paper, we propose a region-based active contour model (ACM), called global signed pressure and K-means clustering based on local correntropy with the exponential family (GSLCE), to address segmentation challenges under biased conditions. An adaptive weighted function is formulated based on the global and local image differences such that a single weighted function can drive both the global and local intensities. Further, the Riemannian steepest descent method is used for convergence of the proposed GSLCE energy function, and a Gaussian kernel is applied for spatial smoothing to obviate the computationally expensive level-set re-initialization. The experimental results show that, compared with state-of-the-art ACMs, the proposed GSLCE model obtained the best visual image segmentation results for synthetic and real images under biased conditions. Further, the qualitative and quantitative experimental results validate that the proposed model outperforms the state-of-the-art ACMs by yielding higher values of performance metrics. Moreover, the proposed GSLCE model requires substantially less processing time compared to the state-of-the-art ACMs.
SMS Spam Detection Using Machine Learning

Abstract In the modern world where digitization is everywhere, SMS has become one of the most vital forms of communications, unlike other chatting-based messaging systems like Facebook, WhatsApp etc, SMS does not require active inter n et conne ct ion a t all. As we all know that Hackers / Spammer tries to intrude in Mobile Computing Device, and SMS support for mobile devices had become vulnerable, as attacker tries to intrude to the system by sending unwanted link, with which on clicking those link the attacker can gain remote access over the mobile computing device. So, to identify those messages Authors have developed a system which will identify such malicious messages and will identify whether or not the message is SPAM or HAM (malicious or not malicious). Authors have created a dictionary using the TF-IDF Vectorizer algorithm, which will include all the features of words a SPAM SMS possess, based on content of message and referring to this dictionary the system will be classifying the SMS as spam or ham.
Chinese Public’s Support for COVID-19 Surveillance in Relation to the West

Surveillance is never only about surveillance but is embedded in the broader social context, both domestically and internationally. During the COVID-19 pandemic, the surveillance practices of non-Western countries have often been analyzed from the perspective of the West, impacting domestic surveillance policymaking and public perception. However, we rarely know how Western societies’ surveillance practices and discourses may impact how people in non-Western societies understand their own domestic surveillance. Combining data from varied sources, this article examines domestic surveillance during COVID-19 in China and explores how Chinese residents perceive it, with a focus on perceptions that are in relation to the West.
Segmenting Beyond the Bounding Box for Instance Segmentation

Instance segmentation needs to locate all instances in an image correctly and segment each instance precisely. Currently, the most dominant methods for instance segmentation take object detection as a pre-task. However, they rely on the accuracy of object detection incredibly. If the pre-task cannot predict an accurate bounding box, the performance of instance segmentation will degenerate. In this paper, we present a novel method for instance segmentation to solve this problem, which is called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> egmenting <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> eyond the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ounding <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ox ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S3B-Net</b> ). Our S3B-Net designs a sub-network to help instance segmentation methods based on object detection to segment the part of an instance beyond the bounding box. Specifically, the sub-network first predicts a two-dimensional pixel embedding for each pixel. Then, the Gaussian function is employed to calculate a pixel’s probability belongs to a corresponding instance according to the two-dimensional pixel embedding. Finally, the output of the sub-network combines with the output of instance segmentation based on object detection to generate a more precise instance mask. Our sub-network can easily extend on the existing instance segmentation method based on object detection to segment instance beyond the bounding box. We do our experiments on dominant instance segmentation datasets, such as the COCO dataset and Cityscapes dataset. The results show that our method can achieve 6.8 points gain compared with the baseline Mask R-CNN with ResNet-50-FPN in Cityscapes datasets, and 1.7 points gain with ResNet-101-FPN-DCN in COCO datasets. Our S3B-Net outperforms the previous state-of-the-art instance segmentation method, which proves our method is competitive. The source code of our method will be made available.
Adaptive interval type-2 fuzzy recurrent RBFNN control design using ellipsoidal membership functions with application to MEMS gyroscope

In this article, a hybrid control approach is provided to control the micro-electro-mechanical system (MEMS) triaxial gyroscope as a multi-input multi-output (MIMO) system. Control design includes a fast non-singular terminal sliding mode control (FNTSMC) as a main part of the proposed hybrid control method and since the MEMS gyroscope performance is affected by parameter variations, quadrature errors, and external disturbances in the core of the main controller, adaptive interval type-2 recurrent fuzzy radial basis function neural network (IT2-RFRBF-NN) is employed to estimate the lumped uncertainties. The proposed hybrid approach has four main attributes: (1) it lies in the category of model-free control structures; (2) There is no negative power involved. Hence, the suggested method does not have the singularity problem; (3) to enhance the capability of the proposed method in the present of noise the ellipsoidal membership functions are employed to design adaptive IT2-RFRBF-NN; (4) the Fourier series expansion as a function approximation technique is efficiently used to online estimate the discontinuous component by establishing a soft switching in the proposed controller. With the help of the Lyapunov stability theory, guaranteeing the closed-loop control system stability by the suggested control design is confirmed. The findings of the simulations and comparison with other approaches confirm the superiority of the suggested hybrid approach.
Kernel Probabilistic K-Means Clustering

Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.
Scalable federated machine learning with FEDn

Federated machine learning promises to overcome the input privacy challenge in machine learning. By iteratively updating a model on private clients and aggregating these local model updates into a global federated model, private data is incorporated in the federated model without needing to share and expose that data. Several open software projects for federated learning have appeared. Most of them focuses on supporting flexible experimentation with different model aggregation schemes and with different privacy-enhancing technologies. However, there is a lack of open frameworks that focuses on critical distributed computing aspects of the problem such as scalability and resilience. It is a big step to take for a data scientist to go from an experimental sandbox to testing their federated schemes at scale in real-world geographically distributed settings. To bridge this gap we have designed and developed a production-grade hierarchical federated learning framework, FEDn. The framework is specifically designed to make it easy to go from local development in pseudo-distributed mode to horizontally scalable distributed deployments. FEDn both aims to be production grade for industrial applications and a flexible research tool to explore real-world performance of novel federated algorithms and the framework has been used in number of industrial and academic R&D projects. In this paper we present the architecture and implementation of FEDn. We demonstrate the framework's scalability and efficiency in evaluations based on two case-studies representative for a cross-silo and a cross-device use-case respectively.
Quantum DevOps: Towards Reliable and Applicable NISQ Quantum Computing

Quantum Computing is emerging as one of the great hopes for boosting current computational resources and enabling the application of ICT for optimizing processes and solving complex and challenging domain specific problems.However, the Quantum Computing technology has not matured to a level where it can provide a clear advantage over high performance computing yet.Towards achieving this "quantum advantage", a larger number of Qubits is required, leading inevitably to a more complex topology of the computing Qubits.This raises additional difficulties with decoherence times and implies higher Qubit error rates.Nevertheless, the current Noisy Intermediate-Scale Quantum (NISQ) computers can prove useful despite the intrinsic uncertainties on the quantum hardware layer.In order to utilize such error-prone computing resources, various concepts are required to address Qubit errors and to deliver successful computations.In this paper describe and motivate the need for the novel concept of Quantum DevOps.which entails regular checking of the reliability of NISQ Quantum Computing (QC) instances.By means of testing the computational reliability of basic quantum gates and computations (C-NOT, Hadamard, etc.)it consequently estimates the likelihood for a large scale critical computation (e.g.calculating hourly traffic flow models for a city) to provide results of sufficient quality.Following this approach to select the best matching (cloud) QC instance and having it integrated directly with the processes of development, testing and finally the operations of quantum based algorithms and systems enables the Quantum DevOps concept.
LiDAR–camera calibration method based on ranging statistical characteristics and improved RANSAC algorithm

For sensory data fusion, a calibration method between 3D light detection and ranging (LiDAR) and color camera based on ranging statistical characteristics and improved RANSAC algorithm is proposed. The multi-frame LiDAR point cloud data of the calibration triangular board are recorded. The scanned points with close angles are defined a cluster within same degrees. Furthermore, accurate points are preserved using statistical filtering based on Gaussian distribution. Afterwards, the plane and edge parameters of the triangular board are estimated by the reserved point cloud using improved the random sample consensus (RANSAC) algorithm to obtain the 3D locations of the vertices. Meanwhile, corner points in the image can be extracted manually. Finally, the projection matrix between the camera and the LiDAR is estimated by using the 2D–3D​ correspondences in different positions. The projection errors of different frames and corresponding points are calculated. The results demonstrate that the average error with 300 frames is reduced by 23.05% compared to 1 frame. Moreover, the standard deviation diminishes with the increasing of corresponding points. The reliability and advantage of the method are verified compared with other state-of-art methods. It provides theoretical and technical support for low resolution LiDAR.
A two‐branch network with pyramid‐based local and spatial attention global feature learning for vehicle re‐identification

In recent years, vehicle re-identification has attracted more and more attention. How to learn the discriminative information from multi-view vehicle images becomes one of the challenging problems in vehicle re-identification field. For example, when the viewpoint of the image changes, the features extracted from one image may be lost in another image. A two-branch network with pyramid-based local and spatial attention global feature learning (PSA) is proposed for vehicle re-identification to solve this issue. Specifically, one branch learns local features at different scales by building pyramid from coarse to fine and the other branch learns attentive global features by using spatial attention module. Subsequently, pooling operation by using global maximum pooling (GMP) for local features and global average pooling (GAP) for global feature is performed. Finally, local feature vectors and global feature vector extracted from the last pooling layer, respectively, are employed for identity re-identification. The experimental results demonstrate that the proposed method achieves state-of-the-art results on the VeRi-776 dataset and VehicleID dataset.
Automated lexical and time series modelling for critical discourse research: A case study of Hong Kong protest editorials

This paper advances a novel approach to critical synchronic and diachronic discourse analysis using automated lexical and time series modelling. It is illustrated by a case study of near-daily editorials (N = 201; 300,081 words) from 9 June to 2 October 2019 on the Hong Kong protest movement in three ideologically contrasting sources – China Daily (CD), South China Morning Post (SCMP), and Hong Kong Free Press (HKFP). Lexical analysis with Linguistic Inquiry and Word Count (LIWC) first revealed four predominant socio-psychological word categories - relativity, drive, cognitive, and affect. Overall, HKFP expresses anger at the government, CD lays blame on protestors' violent actions, and SCMP occupies a middle position to focus on less political aspects. Time series modelling is then applied to redirect attention from these aggregated differences to how they unfold day-to-day. It was found that while positive affect words are characterized by short-term consistencies and fluctuations, most variables exhibit random variation across time. The approach allows precise description of how linguistic variables in neighbouring time periods inter-relate, offering rich interpretative possibilities for different linguistic/discourse contexts. Furthermore, determining whether a variable is 'modelable' offers a systematic and replicable way to interrogate the assumption that discourse inevitably serves to construe social reality.
Neural partial differential equations for chaotic systems

Abstract When predicting complex systems one typically relies on differential equation which can often be incomplete, missing unknown influences or higher order effects. By augmenting the equations with artificial neural networks we can compensate these deficiencies. We show that this can be used to predict paradigmatic, high-dimensional chaotic partial differential equations even when only short and incomplete datasets are available. The forecast horizon for these high dimensional systems is about an order of magnitude larger than the length of the training data.
The interactional principle in digital punctuation

While early CMC research already mentioned the repetition and omission of certain punctuation signs as salient features of digital interactional writing (cf. Crystal, 2001; Runkehl et al., 1998; Werry, 1996), the theoretical perspective on punctuation was mostly limited to noting down these phenomena in their deviations from an orthographic norm and interpreting them as emulation of spoken language features. In contrast, this paper adopts a graphocentric approach and argues for investigating digital punctuation with regard to its emergent interactional principle: While the rhetorical principle (marking intonational structures) and the grammatical principle (marking syntactical structures) of punctuation are well known in the history of writing (cf. Parkes, 1992), digital punctuation appears as an innovative extension to these functional realms in that it is used by co-participants to shape and organize their mediated interactional order. When punctuation is deployed in an interactional mode, it structures primarily neither intonational patterns nor grammatical patterns, but interactional patterns such as shaping sequential organization and stance-taking. Drawing on a data set of 47 text-messaging threads by German adolescents, the paper investigates the interactional principle of punctuation by frequency analyses as well as by in-depth sequential analyses of <.>, <:>, <!>, <?>, and <…>. The findings suggest that even punctuation signs whose codification in descriptive and prescriptive grammars is based on pure syntactic criteria are utilized to achieve interactional goals. It shows that by following the interactional principle, punctuation establishes collaborative interactional management and serves participants as a graphic means of communicative and social contextualization in digital interactions.
Multiauthority Attribute-Based Encryption with Traceable and Dynamic Policy Updating

Ciphertext policy attribute-based encryption (CP-ABE) is an encryption mechanism that can provide fine-grained access control and adequate cloud storage security for Internet of Things (IoTs). In this field, the original CP-ABE scheme usually has only a single trusted authority, which will become a bottleneck in IoTs. In addition, different users may illegally share their private keys to obtain improper benefits. Besides, the data owners also require the flexibility to change their access policy. In this paper, we construct a multiauthority CP-ABE scheme on prime order groups over a large attribute universe. Our scheme can support white-box traceability along with policy updates to solve the abovementioned three problems and, thus, can fix the potential requirements of IoTs. More precisely, the proposed scheme supports multiple authority, white box traceability, large attribute domains, access policy updates, and high expressiveness. We prove that our designed scheme is static secure and traceable secure based on the state-of-the-art security models. Moreover, by theoretical comparison, our scheme has better performance than other schemes. Finally, extensive experimental comparisons show that our proposed algorithm can be better than the baseline algorithms.