Reviewerly/exHarmony huggingface dataset:
Securing Parallel-chain Protocols under Variable Mining Power

Several emerging proof-of-work (PoW) blockchain protocols rely on a ''parallel-chain'' architecture for scaling, where instead of a single chain, multiple chains are run in parallel and aggregated. A key requirement of practical PoW blockchains is to adapt to mining power variations over time (Bitcoin's total mining power has increased by a 1014 factor over the decade). In this paper, we consider the design of provably secure parallel-chain protocols which can adapt to such mining power variations.
Evaluation of Visual Requirements and Software-Design for Immersive Visibility in Industrial Applications

Currently, many sources predict increasing use of AR technology in the industrial environment. The task of immersive productive assistance systems is to provide information contextually to the industrial user. Therefore, it is essential to explore the factors and effects that influence the visibility and the corresponding quality of this information. Caused by the technical limitations of additive display technology and application conditions, this new approach has evaluated the immersive visibility of Landolt Rings in various greyscales against ambient illuminance levels on different industrial-like surfaces, coupled with and without a white virtual background. For this purpose, an empirical study in a within-subjects-design with full factorial experimental design (n=23) was conducted on Microsoft HoloLens 2 hardware. The mean values of the main effects indicate that visibility is significantly affected by ambient illuminance (best results at lower level), greyscale (best results at middle level) and virtual background (best results with background). In contrast, the choice of surface is shown to have no statistically significant effect on visibility, however it affects the response time. Additionally, cross-interactions of variables were analyzed and lead to a design recommendation for immersive industrial applications.
Towards In-situ Authoring of AR Visualizations with Mobile Devices

Augmented Reality (AR) has been shown to enhance the data visualization and analysis process by supporting users in their immersive exploration of data in a real-world context. However, authoring such visualizations still heavily relies on traditional, stationary desktop setups, which inevitably separates users from the actual working space. To better support the authoring process in immersive environments, we propose the integration of spatially-aware mobile devices. Such devices also enable precise touch interaction for data configuration while lowering the entry barriers of novel immersive technologies. We therefore contribute an initial set of concepts within a scenario for authoring AR visualizations. We implemented an early prototype for configuring visualizations in-situ on the mobile device without programming and report our first impressions.
Learning edges and adaptive surroundings for discriminant segmentation tracking

Segmentation based discriminant trackers have currently achieved astonishing performance in terms of scale estimation, but are not robust to significant surroundings changes and ignore the completeness of object shape. In this paper, we propose a conceptually concise yet efficient segmentation based tracking algorithm which can adaptively learn the surroundings and the edges of the tracked object. The proposed approach, named Learning Edges and Adaptive Surroundings Tracking (LEAST), can effectively leverage the backdrop information by updating the background features online. As a consequence, it can adapt to the changes in the background of the object and suppress the background clutters or distractors to obtain a more precise object state estimation. Besides, a novel loss function is introduced to emphasize on the edges of the object to provide a more accurate mask prediction for object localization. Furthermore, we introduce a new mask initialization network and a modified refinement module to make the segmentation network more suitable for tracking tasks. And the extensive qualitative and quantitative experimental results on several common visual object tracking benchmarks show that the proposed tracker outperforms all advanced trackers and sets a new state-of-the-art performance on GOT-10K dataset.
Practical Settlement Bounds for Proof-of-Work Blockchains

Nakamoto proof-of-work ledger consensus currently underlies the majority of deployed cryptocurrencies and smart-contract blockchains. While a long and fruitful line of work has succeeded to identify its exact security region---that is, the set of parametrizations under which it possesses asymptotic security---the existing theory does not provide concrete settlement time guarantees that are tight enough to inform practice.
Fuzzy Message Detection

Many privacy-preserving protocols employ a primitive that allows a sender to "flag" a message to a recipient's public key, such that only the recipient (who possesses the corresponding secret key) can detect that the message is intended for their use. Examples of such protocols include anonymous messaging, privacy-preserving payments, and anonymous tracing. A limitation of the existing techniques is that recipients cannot easily outsource the detection of messages to a remote server, without revealing to the server the exact set of matching messages. In this work we propose a new class of cryptographic primitives called \em fuzzy message detection schemes. These schemes allow a recipient to derive a specialized message detection key that can identify correct messages, while also incorrectly identifying non-matching messages with a specific and chosen false positive rate p. This allows recipients to outsource detection work to an untrustworthy server, without revealing precisely which messages belong to the receiver. We show how to construct these schemes under a variety of assumptions; describe several applications of the new technique; and show that our schemes are efficient enough to use in real applications.
Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.
Deep Learnt Features and Machine Learning Classifier for Texture classification

Abstract Texture classification plays a vital role in the emerging research field of image classification. This paper approaches the texture classification problem using significant features extracted from pre-trained Convolutional Neural Network (CNN) like Alexnet, VGG16, Resnet18, Googlenet, MobilenetV2, and Darknet19. These features are classified by machine learning classifiers such as Support Vector Machine (SVM), Ensemble, K Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), and Discriminant Analysis (DA). The performance of the work is evaluated with the texture databases namely KTH-TIPS, FMD, UMD-HR, and DTD. Among these CNN features derived from VGG16 classify by SVM provides better classification accuracy rather than using VGG16 with a softmax classifier.
Thirty years of Epistemic Specifications

Abstract The language of epistemic specifications and epistemic logic programs extends disjunctive logic programs under the stable model semantics with modal constructs called subjective literals. Using subjective literals, it is possible to check whether a regular literal is true in every or some stable models of the program, those models, in this context also called belief sets , being collected in a set called world view . This allows for representing, within the language, whether some proposition should be understood accordingly to the open or the closed world assumption. Several attempts for capturing the intuitions underlying the language by means of a formal semantics were given, resulting in a multitude of proposals that makes it difficult to understand the current state of the art. In this article, we provide an overview of the inception of the field and the knowledge representation and reasoning tasks it is suitable for. We also provide a detailed analysis of properties of proposed semantics, and an outlook of challenges to be tackled by future research in the area.
Blockchains Enable Non-interactive MPC

We propose to use blockchains to achieve MPC which does not require the participating parties to be online simultaneously or interact with each other. Parties who contribute inputs but do not wish to receive outputs can go offline after submitting a single message. In addition to our main result, we study combined communication- and state-complexity in MPC, as it has implications for the communication complexity of our main construction. Finally, we provide a variation of our main protocol which additionally provides guaranteed output delivery.
Dual-Stream Structured Graph Convolution Network for Skeleton-Based Action Recognition

In this work, we propose a dual-stream structured graph convolution network ( DS-SGCN ) to solve the skeleton-based action recognition problem. The spatio-temporal coordinates and appearance contexts of the skeletal joints are jointly integrated into the graph convolution learning process on both the video and skeleton modalities. To effectively represent the skeletal graph of discrete joints, we create a structured graph convolution module specifically designed to encode partitioned body parts along with their dynamic interactions in the spatio-temporal sequence. In more detail, we build a set of structured intra-part graphs, each of which can be adopted to represent a distinctive body part (e.g., left arm, right leg, head). The inter-part graph is then constructed to model the dynamic interactions across different body parts; here each node corresponds to an intra-part graph built above, while an edge between two nodes is used to express these internal relationships of human movement. We implement the graph convolution learning on both intra- and inter-part graphs in order to obtain the inherent characteristics and dynamic interactions, respectively, of human action. After integrating the intra- and inter-levels of spatial context/coordinate cues, a convolution filtering process is conducted on time slices to capture these temporal dynamics of human motion. Finally, we fuse two streams of graph convolution responses in order to predict the category information of human action in an end-to-end fashion. Comprehensive experiments on five single/multi-modal benchmark datasets (including NTU RGB+D 60, NTU RGB+D 120, MSR-Daily 3D, N-UCLA, and HDM05) demonstrate that the proposed DS-SGCN framework achieves encouraging performance on the skeleton-based action recognition task.
An Empirical Study of Machine Learning-based Bangla News Classification Methods

Technological furtherance has enabled the attainability of enormous digitized texts. However vast unorganized text is futile. Text classification in this regard can generate momentary solutions in terms of knowledge mining in texts. Though the improvement in NLP has advocated these categorizing tasks in recent times, their availability in Bangla text is a concern. Bangla is a globally spoken language and that hints at the extraneous availability of texts that are being used over various sectors. TF-IDF has shown preeminent feature maps calculation in terms of texts as the expression of the significance of words in a corpus. Again supervised machine learning algorithms have done wonders in terms of text classification. Keeping that in mind, an empirical study to categorize Bangla news articles using the TF-IDF features with the assistance of machine learning algorithms namely SVM, LR (Logistic Regression), DT (Decision Tree), RF (Random Forest), and KNN has been espoused. Two approaches have been engendered for that purpose, one the baseline approach with unbalanced data and the other is the SMOTE (Synthetic Minority Oversampling Technique) approach, achieving the best performance with Random Forest in SMOTE approach with an accuracy of 95% and an Fl-Score of 95%.
Oriole: Thwarting Privacy Against Trustworthy Deep Learning Models

Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their explicit permission for the purpose of training high-precision face recognition models, creating a serious violation of privacy. Recently, a well-known system named Fawkes [] (published in USENIX Security 2020) claimed this privacy threat can be neutralized by uploading cloaked user images instead of their original images. In this paper, we present Oriole, a system that combines the advantages of data poisoning attacks and evasion attacks, to thwart the protection offered by Fawkes, by training the attacker face recognition model with multi-cloaked images generated by Oriole. Consequently, the face recognition accuracy of the attack model is maintained and the weaknesses of Fawkes are revealed. Experimental results show that our proposed Oriole system is able to effectively interfere with the performance of the Fawkes system to achieve promising attacking results. Our ablation study highlights multiple principal factors that affect the performance of the Oriole system, including the DSSIM perturbation budget, the ratio of leaked clean user images, and the numbers of multi-cloaks for each uncloaked image. We also identify and discuss at length the vulnerabilities of Fawkes. We hope that the new methodology presented in this paper will inform the security community of a need to design more robust privacy-preserving deep learning models.
Smart Irrigation System using Internet of Things (IoT) and Machine Learning

Despite the world being in 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century most of the developing and under-developed nation use traditional method for farming we requires tremendous energy and hectic schedule from a small scale farmer with a very measly return in terms of profit moreover the water wastage and continuous monitoring required to keep check in plants condition is just unjustified but with 58% of population having agriculture as primary income source most of the Indian farmers having extremely low income it seems impossible for them to hop over costly machineries. But now with the cost-effective processors out there in the market can provide a solution to all these issues faced by Indian farmers. With exponential progress of Internet of Things (IoT) devices in the market smart irrigation systems are becoming a new trend. This paper proposes design and theory of one such smart irrigation system using NodeMCU to wirelessly operate a network of irrigation modules by irrigating the field when required by measuring the water content of soil and keep checking condition of plant using a camera this paper also provide insight of how to keep safe integrity of data which travels from NodeMCU to user smartphone using ciphering methods and by keeping proposed system reliable and cost effective.
iFetch

Most of the interaction between large organizations and their users will be mediated by AI agents in the near future. This perception is becoming undisputed as online shopping dominates entire market segments, and the new "digitally-native" generations become consumers. iFetch is a new generation of task-oriented conversational agents that interact with users seamlessly using verbal and visual information. Through the conversation, iFetch provides targeted advice and a "physical store-like" experience while maintaining user engagement. This context entails the following vital components: 1) highly complex memory models that keep track of the conversation, 2) extraction of key semantic features from language and images that reveal user intent, 3) generation of multimodal responses that will keep users engaged in the conversation and 4) an interrelated knowledge base of products from which to extract relevant product lists.
Fine-Grained Named Entity Recognition Using a Multi-Stacked Feature Fusion and Dual-Stacked Output in Korean

Named entity recognition (NER) is a natural language processing task to identify spans that mention named entities and to annotate them with predefined named entity classes. Although many NER models based on machine learning have been proposed, their performance in terms of processing fine-grained NER tasks was less than acceptable. This is because the training data of a fine-grained NER task is much more unbalanced than those of a coarse-grained NER task. To overcome the problem presented by unbalanced data, we propose a fine-grained NER model that compensates for the sparseness of fine-grained NEs by using the contextual information of coarse-grained NEs. From another viewpoint, many NER models have used different levels of features, such as part-of-speech tags and gazetteer look-up results, in a nonhierarchical manner. Unfortunately, these models experience the feature interference problem. Our solution to this problem is to adopt a multi-stacked feature fusion scheme, which accepts different levels of features as its input. The proposed model is based on multi-stacked long short-term memories (LSTMs) with a multi-stacked feature fusion layer for acquiring multilevel embeddings and a dual-stacked output layer for predicting fine-grained NEs based on the categorical information of coarse-grained NEs. Our experiments indicate that the proposed model is capable of state-of-the-art performance. The results show that the proposed model can effectively alleviate the unbalanced data problem that frequently occurs in a fine-grained NER task. In addition, the multi-stacked feature fusion layer contributes to the improvement of NER performance, confirming that the proposed model can alleviate the feature interference problem. Based on this experimental result, we conclude that the proposed model is well-designed to effectively perform NER tasks.
Deep-learning framework based on a large ultrasound image database to realize computer-aided diagnosis for liver and breast tumors

The quality and quantity of training data is vital for computer-aided diagnosis (CADx) based on deep learning. However, the biomedical industry lacks large database of ultrasound images. Therefore, The Japan Society of Ultrasonics in Medicine (JSUM) is currently constructing an ultrasound image database for liver tumors, breast tumors, and heart diseases. As of August 2021, the project has collected more than 140,000 ultrasound images and videos. This database contains ultrasound images, their corresponding labels, and annotation information. That is, the ultrasound image data contains information related to the size and location of the tumor. In this study, we developed a CADx to classify liver tumors and breast tumors by utilizing approximately 71,000 liver tumor and 14,000 breast tumor ultrasound images from the abovementioned database. We classified liver tumors into four classes: cysts, hemangiomas, hepatocellular carcinomas, and metastatic liver cancers. Similarly, we classified breast tumors into four classes: breast cancer, fibroadenoma, cysts, and others. We used a convolutional neural network based on VGG19 for these classifications, and evaluated the accuracy of each case unit by k-fold cross-validation, thereby achieving an accuracy of 91.1% and 85.2% for four-class classification of liver tumor and breast tumor, respectively. In addition, the accuracy, sensitivity, and specificity of the benign/malignant classification based on this result was, respectively, 94.3%, 82.8%, and 96.7% for liver tumors and 89.9%, 92.6%, and 86.6% for breast tumors. Furthermore, when compared with the results obtained in a previous study that utilized a small database, using a large database provided a higher accuracy for both liver and breast tumors.
Biomedical image segmentation based on full-Resolution network

Convolutional neural networks (CNN) has been widely used in biomedical image segmentation (BIS) tasks for its remarkable feature representation capability, and most of existing CNN-based segmentation networks leverage a down-sampling operation to achieve larger acceptance domain. However, down-sampling operations could inevitably loss the detailed information of images which is very important for the BIS task. In this paper, we propose a full-resolution biomedical image segmentation network(FRNet) that could maintain the integrated detailed information of image while keeping sufficient semantic information and large receptive field. Specifically, the basic semantic feature and non-destructive feature are employed to represent the semantic and detailed information of images, respectively. A backbone network and a new full-feature extraction branch are conducted to extract those two kinds of complementary features. Furthermore, a novel feature fusion module is designed to integrate those complementary features to achieve non-destructive description of images. Finally, in order to further improve the description ability of the integrated feature, a Densely connected Atrous Spatial Pyramid Pooling(DenseASPP) module is arranged at the end of our proposed FRNet to extract the multiscale information of images. Thorough experimental results on several available databases demonstrate the effectiveness and advancement of FRNet.