Reviewerly/exHarmony huggingface dataset:
InSpectre: Breaking and Fixing Microarchitectural Vulnerabilities by Formal Analysis

The recent Spectre attacks have demonstrated the fundamental insecurity of current computer microarchitecture. The attacks use features like pipelining, out-of-order and speculation to extract arbitrary information about the memory contents of a process. A comprehensive formal microarchitectural model capable of representing the forms of out-of-order and speculative behavior that can meaningfully be implemented in a high performance pipelined architecture has not yet emerged. Such a model would be very useful, as it would allow the existence and non-existence of vulnerabilities, and soundness of countermeasures to be formally established. This paper presents such a model targeting single core processors. The model is intentionally very general and provides an infrastructure to define models of real CPUs. It incorporates microarchitectural features that underpin all known Spectre vulnerabilities. We use the model to elucidate the security of existing and new vulnerabilities, as well as to formally analyze the effectiveness of proposed countermeasures. Specifically, we discover three new (potential) vulnerabilities, including a new variant of Spectre v4, a vulnerability on speculative fetching, and a vulnerability on out-of-order execution, and analyze the effectiveness of existing countermeasures including constant time and serializing instructions.
Evaluating KNN Performance on WESAD Dataset

In this paper performance of KNN models are evaluated by changing K-fold cross validation parameter and total number of nearest neighbors while classifying WESAD dataset using sklearn library of python programming language, in order to finalize best possible number of nearest neighbors. Performance of KNN models drastically change when total number of nearest neighbors are modified irrespective of the dataset. Consequently for KNN based machine learning applications, tradeoff between optimum performance and computational cost is achieved by limiting total number of neighbors and hence controlling complexity of the model. Thus less computationally expensive KNN models can be directly implemented on raspberry pi, multicore microcontrollers, and low power IoT devices for classifying sensor data on portable embedded systems.
Achieving Real-Time Path Planning in Unknown Environments Through Deep Neural Networks

Real-time path planning is crucial for intelligent vehicles to achieve autonomous navigation. In this paper, we propose a novel deep neural network (DNN) based method for real-time online path planning in unknown cluttered environments. Firstly, an end-to-end DNN architecture named online three-dimensional path planning network (OTDPP-Net) is designed to learn 3D local path planning policies. It determines actions in 3D space based on multiple value iteration computations approximated by recurrent 2D convolutional neural networks. Moreover, a path planning framework is also developed to realize near-optimal real-time online path planning. The effectiveness of the proposed planner is further improved by a switching scheme, and the path quality is optimized by line-of-sight checks. Both virtual and real-world experimental results demonstrate the remarkable performance of the proposed DNN-based path planner in terms of efficiency, success rate and path quality. Different from existing methods, the computational time and effectiveness of the developed DNN-based path planner are both independent of environmental conditions, which reveals its superiority in large-scale complex environments. A video of our experiments can be found at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/gb4nSG4hd6s</uri> .
Secure Collaborative Training and Inference for XGBoost

In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has the potential to greatly benefit all parties involved, but organizations have also encountered obstacles in sharing sensitive data due to business, regulatory, and liability concerns.
Model-Driven Chatbot Development

Chatbots are software services accessed via conversation in natural language. They are increasingly used to help in all kinds of procedures like booking flights, querying visa information or assigning tasks to developers. They can be embedded in webs and social networks, and be used from mobile devices without installing dedicated apps. While many frameworks and platforms have emerged for their development, identifying the most appropriate one for building a particular chatbot requires a high investment of time. Moreover, some of them are closed – resulting in customer lock-in – or require deep technical knowledge. To tackle these issues, we propose a model-driven engineering approach to chatbot development. It comprises a neutral meta-model and a domain-specific language (DSL) for chatbot description; code generators and parsers for several chatbot platforms; and a platform recommender. Our approach supports forward and reverse engineering, and model-based analysis. We demonstrate its feasibility presenting a prototype tool and an evaluation based on migrating third party Dialogflow bots to Rasa.
Reward Sharing Schemes for Stake Pools

We introduce and study reward sharing schemes (RSS) that promote the fair formation of stake pools in collaborative projects that involve a large number of stakeholders such as the maintenance of a proof-of-stake (PoS) blockchain. Our mechanisms are parameterized by a target value for the desired number of pools. We show that by properly incentivizing participants, the desired number of stake pools is a Nash equilibrium arising from rational play. Our equilibria also exhibit an efficiency / security tradeoff via a parameter that calibrates between including pools with the smallest cost and providing protection against Sybil attacks, the setting where a single stakeholder creates a large number of pools in the hopes to dominate the collaborative project. We then describe how RSS can be deployed in the PoS setting, mitigating a number of potential deployment attacks and protocol deviations that include censoring transactions, performing Sybil attacks with the objective to control the majority of stake, lying about the actual cost and others. Finally, we experimentally demonstrate fast convergence to equilibria in dynamic environments where players react to each other's strategic moves over an indefinite period of interactive play. We also show how simple reward sharing schemes that are seemingly more “fair”, perhaps counterin-tuitively, converge to centralized equilibria.
Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils

The application of multivariate geostatistical and statistical methods remain valuable tools for environmental pollution assessment. In particular, stochastic simulation techniques like sequential Gaussian simulation (SGS) and the self-organizing map artificial neural networks (SeOM-ANNs) have facilitated the understanding of the spatial distribution of potentially toxic elements (PTEs) in polluted soils. However, there is a dearth of literature on the application of SGS and SeOM-ANN in mapping potentially toxic elements (PTE) in heavily polluted mining and smelter affected floodplain soils. This study shows the applicability SGS and SeOM–ANN which is a powerful visualization tool for the categorization of PTEs [Cadmium (Cd), Arsenic (As), Antimony (Sb), Lead (Pb) and Zinc (Zn)] levels together with selected soil properties [oxidizable carbon (Cox) and soil reaction (pH_H2O)] in one of the most polluted mining floodplain soils in Europe. A k-means algorithm was used to classify distinct clusters which were visually unclear based on the SeOM–ANN Neighbor distance plot (U-Matrix). The k-means resulted in 5 distinct clusters. Cluster 1 to 5 based on SeOM–ANN for all PTEs revealed an increase in concentration levels in the same order (1–5) while for soil properties the trend was not clear. The soils were successfully assessed based on different intensity level combinations and k-means clustering results efficiently mapped into a spatial distribution map. High concentration levels of the PTEs were noticed in the northern parts of the study area based on the conditional Gaussian simulations (CGSs) generated through SGS, while low levels were prominent in the southwestern parts. The hotspot areas were comparable with the k-means spatial distribution maps. It is recommended that special attention be paid to the identified hotspots for possible remediation. This study further demonstrates the usefulness of geostatistics and advanced statistical methods in site-specific planning and implementation of remediation measures for polluted mining floodplain soils.
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 Challenge

Training a deep neural network is an optimization problem with four main ingredients: the design of the deep neural network, the per-sample loss function, the population loss function, and the optimizer. However, methods developed to compete in recent BraTS challenges tend to focus only on the design of deep neural network architectures, while paying less attention to the three other aspects. In this paper, we experimented with adopting the opposite approach. We stuck to a generic and state-of-the-art 3D U-Net architecture and experimented with a non-standard per-sample loss function, the generalized Wasserstein Dice loss, a non-standard population loss function, corresponding to distributionally robust optimization, and a non-standard optimizer, Ranger. Those variations were selected specifically for the problem of multi-class brain tumor segmentation. The generalized Wasserstein Dice loss is a per-sample loss function that allows taking advantage of the hierarchical structure of the tumor regions labeled in BraTS. Distributionally robust optimization is a generalization of empirical risk minimization that accounts for the presence of underrepresented subdomains in the training dataset. Ranger is a generalization of the widely used Adam optimizer that is more stable with small batch size and noisy labels. We found that each of those variations of the optimization of deep neural networks for brain tumor segmentation leads to improvements in terms of Dice scores and Hausdorff distances. With an ensemble of three deep neural networks trained with various optimization procedures, we achieved promising results on the validation dataset of the BraTS 2020 challenge. Our ensemble ranked fourth out of the 693 registered teams for the segmentation task of the BraTS 2020 challenge.
Impact of Temporary Fork on the Evolution of Mining Pools in Blockchain Networks: An Evolutionary Game Analysis

Temporary fork is a fundamental phenomenon in many blockchains with proof of work, and the analysis of temporary fork has recently drawn great attention. Different from existing efforts that focus on the blockchain system factors such as block size, network propagation delay or block generation speed, in this paper we explore a new key dimension of computing power from the miners’ perspective. Specifically, we first propose a detailed mathematical model to characterize the impact of computing power competition of the mining pools on the temporary fork. We also derive closed-form formula of the probability of temporary fork and the expected mining reward of a mining pool. To reveal the long-term trends on the computing power distributions over the competing mining pools, we then develop an evolutionary game framework based on the temporary fork modeling and accordingly characterize the set of stable evolution equilibriums. Both extensive numerical simulations and realistic blockchain data based evaluation provide evidence to support our theoretical models and discoveries.
Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.
Understanding build issue resolution in practice: symptoms and fix patterns

Build systems are essential for modern software maintenance and development, while build failures occur frequently across software systems, inducing non-negligible costs in development activities. Build failure resolution is a challenging problem and multiple studies have demonstrated that developers spend non-trivial time in resolving encountered build failures; to relieve manual efforts, automated resolution techniques are emerging recently, which are promising but still limitedly effective. Understanding how build failures are resolved in practice can provide guidelines for both developers and researchers on build issue resolution. Therefore, this work presents a comprehensive study of fix patterns in practical build failures. Specifically, we study 1,080 build issues of three popular build systems Maven, Ant, and Gradle from Stack Overflow, construct a fine-granularity taxonomy of 50 categories regarding to the failure symptoms, and summarize the fix patterns for different failure types. Our key findings reveal that build issues stretch over a wide spectrum of symptoms; 67.96% of the build issues are fixed by modifying the build script code related to plugins and dependencies; and there are 20 symptom categories, more than half of whose build issues can be fixed by specific patterns. Furthermore, we also address the challenges in applying non-intuitive or simplistic fix patterns for developers.
Two novel color local descriptors for face recognition

This research paper introduces two novel color descriptors for Face Recognition (FR) called as Color ZigZag Binary Pattern (CZZBP) and Color Median Block ZigZag Binary Pattern (CMBZZBP). The pixels aligned in ZigZag manner are utilized for producing the feature size of both the proposed descriptors. For first proposed descriptor, the ZigZag features are extracted from 3 color components of the RGB color space format, which are concatenated to produce the entire feature size of the CZZBP descriptor. Specifically, on 3 color components i.e. R, G and B, the features are extracted by utilizing three different type of ZigZag binary patterns called as ZZBP1, ZZBP2 and ZZBP3. The ZZBP1 is applied on the R component, ZZBP2 is applied on the G component and ZZBP3 is applied on the B component. In the second proposed descriptor, the same concept is employed on medians computed from 9 different blocks of 9 × 9 pixel window, where each block size is 3 × 3. To produce compact feature size, PCA is employed and classification is performed by SVMs and NN. The 2 databases used for performance evaluation are GT and Faces94. Experiments reveals effectiveness of the proposed descriptors than compared descriptors. Among all descriptors, CMBZZBP is most impressive.
DeepCommenter: a deep code comment generation tool with hybrid lexical and syntactical information

As the scale of software projects increases, the code comments are more and more important for program comprehension. Unfortunately, many code comments are missing, mismatched or outdated due to tight development schedule or other reasons. Automatic code comment generation is of great help for developers to comprehend source code and reduce their workload. Thus, we propose a code comment generation tool (DeepCommenter) to generate descriptive comments for Java methods. DeepCommenter formulates the comment generation task as a machine translation problem and exploits a deep neural network that combines the lexical and structural information of Java methods. We implement DeepCommenter in the form of an Integrated Development Environment (i.e., Intellij IDEA) plug-in. Such plug-in is built upon a Client/Server architecture. The client formats the code selected by the user, sends request to the server and inserts the comment generated by the server above the selected code. The server listens for client’s request, analyzes the requested code using the pre-trained model and sends back the generated comment to the client. The pre-trained model learns both the lexical and syntactical information from source code tokens and Abstract Syntax Trees (AST) respectively and combines these two types of information together to generate comments. To evaluate DeepCommenter, we conduct experiments on a large corpus built from a large number of open source Java projects on GitHub. The experimental results on different metrics show that DeepCommenter outperforms the state-of-the-art approaches by a substantial margin.
Deep Stereoscopic Image Super-Resolution via Interaction Module

Deep learning-based methods have achieved remarkable performance in single image super-resolution. However, these methods cannot be effectively applied in stereoscopic image super-resolution without considering the characteristics of stereoscopic images. In this article, an interaction module-based stereoscopic image super-resolution network (IMSSRnet) is proposed to effectively utilize the correlation information in stereoscopic images. The key insight of the network lies with how to explore the complementary information of one view to help the reconstruction of another view. Thus, an interaction module is designed to acquire the enhanced features by utilizing complementary information between different views. Specifically, the interaction module is composed of a series of interaction units with a residual structure. In addition, the single image features of left and right views are obtained by a spatial feature extraction module, which can be realized by any existing single image super-resolution models. In order to obtain high-quality stereoscopic images, a gradient loss is introduced to preserve the texture details in a view, and a disparity loss is developed to constrain the disparity relationship between different views. Experimental results demonstrate that the proposed method achieves a promising performance and outperforms the state-of-the-art methods.
Continual Learning for Natural Language Generation in Task-oriented Dialog Systems

Natural language generation (NLG) is an essential component of task-oriented dialog systems.Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains.To better fit real-life applications where new data come in a stream, we study NLG in a "continual learning" setting to expand its knowledge to new domains or functionalities incrementally.The major challenge towards this goal is catastrophic forgetting, meaning that a continually trained model tends to forget the knowledge it has learned before.To this end, we propose a method called ARPER (Adaptively Regularized Prioritized Exemplar Replay) by replaying prioritized historical exemplars, together with an adaptive regularization technique based on Elastic Weight Consolidation.Extensive experiments to continually learn new domains and intents are conducted on MultiWoZ-2.0 to benchmark ARPER with a wide range of techniques.Empirical results demonstrate that ARPER significantly outperforms other methods by effectively mitigating the detrimental catastrophic forgetting issue.
Quantum random number generator based on single-photon emitter in gallium nitride

We experimentally demonstrate a real-time quantum random number generator by using a room-temperature single-photon emitter from the defect in a commercial gallium nitride wafer. Due to the brightness of our single-photon emitter, the raw bit generation rate is about 1.8 MHz, and the unbiased bit generation rate is about 420 kHz after the von Neumann's randomness extraction procedure. Our results show that the commercial gallium nitride wafer has great potential for the development of integrated high-speed quantum random number generator devices.
Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sentiment classification

Sentiment analysis has long been suffering from inaccuracies using either machine learning methods that mostly benefit from text features or sentiment lexicon-based methods that are prone to domain-dependent problems. Furthermore, since labeling is a time-consuming and an expensive task, supervised machine learning methods suffer from the drawback of insufficient labeled samples. To tackle the mentioned issues, this paper proposes a novel approach with a hybrid of a neural network and a sentiment lexicon. This combination can simultaneously adapt word polarities to the target domain and leverage the polarity of whole document in order to alleviate the need for large labeled corpora in an unsupervised manner. In this respect, a sentiment lexicon is constructed from the source domain in the preprocessing phase using the labeled data. In the Next phase, having a Multilayer Perceptron (MLP), the weights of the first hidden layer are set to the corresponding polarity of each word from the retrieved sentiment lexicon and the network is trained. Finally, a Domain-Independent Lexicon (DIL) is introduced which contains words (mostly adjectives) with static positive or negative scores independent from a specific domain. After feeding the target domain to the pre-trained model, the total accuracy of the framework is enhanced by estimating the sentiment polarity of each sentence using the summation of the scores of the constitutive domain independent words. The experiments on Amazon multi-domain sentiment dataset illustrate that our approach significantly outperforms several alternative previous approaches of unsupervised domain adaptation.