Reviewerly/exHarmony huggingface dataset:
Block Chain Based Cloud Computing Model on EVM Transactions for Secure Voting

Voting is a tedious task in this nation. The highest amount of controversies and corruptions are involved along this path. A nation needs a fair and unbiased election for the masses to redeem their right to elect the leader of their choice. The latest trends of EVM (Electronic Voting Machine) hacking has taken over the ability to hold a clear and transparent election. In this paper, we discuss an innovative approach by imbibing the latest technologies with the traditional voting methods. The technology involves a secure data storage technique named as block chain which is used for cryptocurrencies and is proven [7] for its security. The votes which are recorded by the EVM will be updated with the cloud-based storage (SAAS). Any changes made to the voting panel or tampering with the votes will cause the hash to break the link and by detecting the manipulated votes, any discrepancy can be removed by marking them as NOTA, hence not affecting the polling by any means. Blockchain technology has a significant feature of Proof-of-Work which does not allow the continuous creation of data blocks hence protecting the rapid manipulation of data. Also, this system only requires uploading the hashes created along with the blocks which are stored in a hash table [12] on the cloud. The data of the EVM, when tallied with the hash table, can help in identifying the point of manipulation. Moreover, the areas with no broadband internet connection can also implement these techniques as it required only a few kilobytes of upload data rate to update the hash values. The already existing UIDAI based biometric system already adds an extra layer of security to the voting system. Each vote will be treated as a single block of data. Also, as added features of block chain technology, the P2P (Peer to Peer) network allows only communication between two already connected peers. Man-in-Middle attacks are impossible in this case as the blockchain proof-of-work does not allow mass updating of data at a time, hence reducing the rate at which votes can be manipulated, hence securing a country’s political future.
Detection of Emergency Vehicles Using Modified Yolo Algorithm

This paper is a brief overview of the topic of object recognition and how it can be used to detect different objects present on a busy road. Using this technology, we aim to detect emergency vehicles present on the road so that a free path can be created for them, which would be able to save precious time in time-sensitive situations. The introduction to object recognition includes what is the work done and how it can be executed in the best way possible in an error-free manner. The best results are depicted in our research methodology. In this paper, we compared different methodologies available for object recognition such as YOLO algorithm, Region-based convolutional networks (R-CNN), and Single-Shot Detector (SDD) and ultimately chose YOLO algorithm, which from our results would work optimally in a situation where emergency vehicles need to be detected on a busy road.
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks

Recent efforts to improve the interpretability of deep neural networks use saliency to characterize the importance of input features to predictions made by models. Work on interpretability using saliency-based methods on Recurrent Neural Networks (RNNs) has mostly targeted language tasks, and their applicability to time series data is less understood. In this work we analyze saliency-based methods for RNNs, both classical and gated cell architectures. We show that RNN saliency vanishes over time, biasing detection of salient features only to later time steps and are, therefore, incapable of reliably detecting important features at arbitrary time intervals. To address this vanishing saliency problem, we propose a novel RNN cell structure (input-cell attention), which can extend any RNN cell architecture. At each time step, instead of only looking at the current input vector, input-cell attention uses a fixed-size matrix embedding, each row of the matrix attending to different inputs from current or previous time steps. Using synthetic data, we show that the saliency map produced by the input-cell attention RNN is able to faithfully detect important features regardless of their occurrence in time. We also apply the input-cell attention RNN on a neuroscience task analyzing functional Magnetic Resonance Imaging (fMRI) data for human subjects performing a variety of tasks. In this case, we use saliency to characterize brain regions (input features) for which activity is important to distinguish between tasks. We show that standard RNN architectures are only capable of detecting important brain regions in the last few time steps of the fMRI data, while the input-cell attention model is able to detect important brain region activity across time without latter time step biases.
Trust Region-Guided Proximal Policy Optimization

Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, as a model-free RL method, the success of PPO relies heavily on the effectiveness of its exploratory policy search. In this paper, we give an in-depth analysis on the exploration behavior of PPO, and show that PPO is prone to suffer from the risk of lack of exploration especially under the case of bad initialization, which may lead to the failure of training or being trapped in bad local optima. To address these issues, we proposed a novel policy optimization method, named Trust Region-Guided PPO (TRGPPO), which adaptively adjusts the clipping range within the trust region. We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well. Extensive experiments verify the advantage of the proposed method.
Comparison of the contribution of differently depth geological processes in the formation of a trace elements characteristic of caustobiolites

The article analyzes the correlation dependences between the logarithms of the concentrations of trace elements (TE) in various geochemical environments (oil, coal, fuel and black shales, as well as in clays, organic matter (OM) of various types and biota) in comparison with the average chemical composition of the upper, middle and lower continental crust. At the same time, along with the TE content of oils of the main oil and gas basins (OGB) – the Volga-Ural and West Siberian ones, the data on the TE content in the so-called young oils were summarized; as such, data on the oil fields of the West Kamchatka oil and gas field and oil manifestations in the area of ​​the caldera of the Uzon volcano were used. Particular attention was also paid to the results of the analysis of the TE composition of the oils of the Romashkino group of fields, as it is possible that they are subject to the influence of deep-seated processes. The correlation coefficients between the studied parameters for the various studied oil- and gas-generating basins, including for the Romashkino group of fields, turned out to be close. For all oils, except for the young oils of Kamchatka and the caldera of the Uzon volcano, a closer connection of their TE composition with the TE composition of the lower crust was revealed. For young oils of the Uzon caldera in Kamchatka, this trend is absent, and a slightly closer relationship is revealed with the average composition of the upper but not lower crust, while for statistically more reliable data on the TE composition of the hydrothermal waters of the caldera of the Uzon volcano, a significantly closer relationship is observed with the average chemical composition of the middle and upper crust. Based on the identified correlations between the TE compositions of oil, caustobioliths and the crust of different levels, conclusions are made about the likely relationship between biogenic and deep processes in the formation of oil and gas fields. According to the authors, the obtained results support the crucially important role in the processes of naphthidogenesis of the upward flows of the low crustal fluids with the dominant source of hydrocarbons from the initial OM of sedimentary basins.
Human computer interaction based eye controlled mouse

With advanced technologies in this digital era, there is always scope for development in the field of computing. Hands free computing is in demand as of today it addresses the needs of quadriplegics. This paper presents a Human computer interaction (HCI) system that is of great importance to amputees and those who have issues with using their hands. The system built is an eye based interface that acts as a computer mouse to translate eye movements such as blinking, gazing and squinting towards the mouse cursor actions. The system in discussion makes use of a simple webcam and its software requirements are Python(3.6), OpenCv, numpy and a few other packages which are necessary for face recognition. The face detector can be built using the HOG (Histogram of oriented Gradients) feature along with a linear classifier, and the sliding window technique. It is hands free and no external hardware or sensors are required.
Fooling AI with AI: An Accelerator for Adversarial Attacks on Deep Learning Visual Classification

Recent studies identify that Deep learning Neural Networks (DNNs) are vulnerable to subtle perturbations, which are not perceptible to the human visual system but can fool the DNN models and lead to wrong outputs. These algorithms are the first efforts to move forward to secure deep learning by providing an avenue to train future defense networks. We propose the first hardware accelerator for adversarial attacks based on memristor crossbar arrays. Our design significantly improves the throughput of a visual adversarial perturbation system, which can further improve the robustness and security of future deep learning systems. Based on the algorithm uniqueness, we propose four implementations for the adversarial attack accelerator (A^3) to improve the throughput, energy efficiency, and computational efficiency.
Predicting Image Emotion Distribution by Learning Labels’ Correlation

Image emotion analysis attracts considerable attention with the increasing demanding of opinion mining in social networks. Emotion evoked by an image is always ambiguous for emotion's subjectivity. Different from previous researches on image emotion classification, Label Distribution Learning framework which assigns a set of labels with degree value to an instance, describes emotions more explicitly. However, in our study, we find that some labels have co-occurrence relation with others and all the labels together appear some structural forms. To make use of these relations as complementary information to the holistic distribution of labels, we analysis the correlations among emotion labels and then propose a method based on Structural Learning framework, which learns a mapping from images to the distribution labels with correlations. On the other hand, images usually contain some emotion-unrelated contents, to extract features that can represent image emotion at utmost, we propose a cropping method to select the emotional region from the images with the help of Fully Convolutional Networks. Extensive experiments on two widely used datasets show the advantages of our methods.
A First Look into Privacy Leakage in 3D Mixed Reality Data

We have seen a rise in mixed (MR) and augmented reality (AR) applications and devices in recent years. Subsequently, we have become familiar with the sensing power of these applications and devices, and we are only starting to realize the nascent risks that these technology puts over our privacy and security. Current privacy protection measures are primarily aimed towards known and well-utilised data types (i.e. location, on-line activity, biometric, and so on) while a few works have focused on looking into the security and privacy risks of and providing protection on MR data, particularly on 3D MR data. In this work, we primarily reveal the privacy leakage from released 3D MR data and how the leakage persist even after implementing spatial generalizations and abstractions. Firstly, we formalize the spatial privacy problem in 3D mixed reality data as well as the adversary model. Then, we demonstrate through an inference model how adversaries can identify 3D spaces and, potentially, infer more spatial information. Moreover, we also demonstrate how compact 3D MR Data can be in terms of memory usage which allows adversaries to create lightweight 3D inference models of user spaces.
Towards Universal Dialogue Act Tagging for Task-Oriented Dialogues

Machine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented dialogue systems from human-human conversations, which may be available in ample amounts in existing customer care center logs or can be collected from crowd workers. Annotating these datasets can be prohibitively expensive. Recently multiple annotated task-oriented human-machine dialogue datasets have been released, however their annotation schema varies across different collections, even for well-defined categories such as dialogue acts (DAs). We propose a Universal DA schema for task-oriented dialogues and align existing annotated datasets with our schema. Our aim is to train a Universal DA tagger (U-DAT) for task-oriented dialogues and use it for tagging human-human conversations. We investigate multiple datasets, propose manual and automated approaches for aligning the different schema, and present results on a target corpus of human-human dialogues. In unsupervised learning experiments we achieve an F1 score of 54.1% on system turns in human-human dialogues. In a semi-supervised setup, the F1 score increases to 57.7% which would otherwise require at least 1.7K manually annotated turns. For new domains, we show further improvements when unlabeled or labeled target domain data is available.
Semantic Representation of Scientific Publications

In this work, we tackle the problem of generating comprehensive overviews of research findings in a structured and comparable way. To bring structure to such information and thus to enable researchers to, e.g., explore domain overviews, we present an approach for automatic unveiling of realm overviews for research artifacts (Aurora), an approach to generate overviews of research domains and their relevant artifacts. Aurora is a semi-automatic crowd-sourcing workflow that captures such information into the OpenResearch.org semantic wiki. Our evaluation confirms that Aurora, when compared to the current manual approach, reduces the effort for researchers to compile and read survey papers.
On Self‐dual and LCD Double Circulant Codes over a Non‐chain Ring*

In this paper, we study double circulant codes of length 2n over the non-chain ring R = 𝔽q + v𝔽q + v2𝔽q; where q is an odd prime power and v3 = v. Exact enumerations of self-dual and LCD double circulant codes of length 2n over R are derived. When n is an odd prime, using random coding, we obtain families of asymptotically good self-dual and LCD codes of length 6n over 𝔽q.
A Modified LIME and Its Application to Explain Service Supply Chain Forecasting

Recently, researchers are focusing more on the study of explainable artificial intelligence due to its usefulness on various scenarios that request trust, such as deciding if one should trust a prediction, choosing between models, improving an untrustworthy model and identifying why a model should be trusted. One main research issues is how to improve the interpretability, while preventing any deterioration of accuracy of the model. For this issues the model-agnostic explanation method is a kind of solution. In the paper we propose a modified LIME algorithm based on locally fitted by decision tree regression called tree-LIME which is a model-agnostic method. Further, we clarify the fidelity measure definition in regression explanation problem by using mean absolute error (MAE). The experiments on real service supply chain forecasting application show that (1) our proposed approach can improve the fidelity of the explainer which lead to a more accurate explanations for individual instances and (2) our approach gives a more intuitive and visualized tree expression for explanation. (3) The approach also works well when applied to service supply chain forecasting.
Improvement of unidimensional continuous-variable quantum key distribution systems by using a phase-sensitive amplifier

Inherent defects of the detectors in a practical system cause deterioration of the performance of the unidimensional coherent-state continuous-variable quantum key distribution protocol. Aiming to achieve optimal performance, we propose a method of using a phase-sensitive amplifier to overcome the limitation that the detectors introduce. The modified protocol, corresponding to a higher key rate, provides the practical system with more robustness. To conduct a thorough investigation into the effects that the phase-sensitive amplifier creates, the expressions of the secret key rates are derived, under the conditions of coherent states, homodyne detection against collective attacks, and a phase-sensitive optical amplifier added at the output of the channel. Then, we carry out simulations to observe the performance in the cases of different gains. The simulation results demonstrate that the method we propose can improve the performance of the unidimensional protocol using coherent states and make up for the imperfections of the detector in a practical system.
Combined no-reference IQA metric and its performance analysis

The problem of increasing efficiency of blind image quality assessment is considered. No-reference image quality metrics both independently and as components of complex image processing systems are employed in various application areas where images are the main carriers of information. Meanwhile, existing noreference metrics have a significant drawback characterized by a low adequacy to image perception by human visual system (HVS). Many well-known no-reference metrics are analyzed in our paper for several image databases. A method of combining several noreference metrics based on artificial neural networks is proposed based on multi-database verification approach. The effectiveness of the proposed approach is confirmed by extensive experiments.
Co-Locating Online Workload and Offline Workload in the Cloud: An Interference Analysis

Workload characterization plays an important role in resource allocation and performance optimization. In order to improve the resource utilization, cloud providers like Google and Alibaba usually co-locate online and offline jobs on the same cluster. However, the characteristics of co-located services running in containers is still not clear. In this paper, we explore various characteristics of micro-architecture and application level of co-located workloads running in containers on the same server via two popular monitor tools: Perf and Prometheus. Our study focuses on quantifying interference of workloads by analyzing the characteristics of workloads running alone and co-located. Based on the interference analysis, we can design more efficient container deployment or scheduling algorithms in the future.
On the Prediction of Smart Contracts’ Behaviours

Smart contracts are pieces of software stored on the blockchain that control the transfer of assets between parties under certain conditions. In this paper we analyze the bahaviour of smart contracts and the interaction with external actors in order to maximize objective functions. We define a core language of programs with a minimal set of smart contract primitives and we describe the whole system as a parallel composition of smart contracts and users. We therefore express the system behaviour as a first logic formula in Presburger arithmetics and study the maximum profit for each actor by solving arithmetic constraints.
FREGEX: A Feature Extraction Method for Biomedical Text Classification using Regular Expressions

In this work, we present FREGEX a method for automatically extracting features from biomedical texts based on regular expressions. Using Smith-Waterman and Needleman-Wunsch sequence alignment algorithms, tokens were extracted from biomedical texts and represented by common patterns. Three manually annotated datasets with information on obesity, obesity types, and smoking habits were used to evaluate the effectiveness of the proposed method. Features extracted using consecutive sequences of tokens (n-grams) were used for comparison, and both types of features were mathematically represented using the TF-IDF vector model. Support Vector Machine and Naïve Bayes classifiers were trained, and their performances were ultimately used to assess the ability of the feature extraction methods. Results indicate that features based on regular expressions not only improved the performance of both classifiers in all datasets but also use fewer features than n-grams, especially in those datasets containing information related to anthropometric measures (obesity and obesity types).
Field Testing of GNSS User Protection Techniques

GNSS is a key element for a wide range of applications in our daily lives. Mass-market applications such as sports tracking or user guidance, liability-critical applications such as banking and telecommunication time synchronization, and safety critical services such as aviation and automotive-related solutions, all rely on GNSS. The huge growth experimented during the last decade puts GNSS in the target of attackers. The Galileo program is complementing the Galileo Open Service with Navigation Message Authentication (OSNMA) and providing signal authentication through the Commercial Service signals. These new services will be able to provide added protection to the current GNSS applications. Nevertheless, these features will require the users to implement new algorithms to exploit them. In this context, the European Commission launched the Navigation Authentication through Commercial Service-Enhanced Terminal (NACSET) project aiming at researching and implementing different techniques to detect and mitigate thus improving the resilience at user-level. In the frame of the NACSET project, a user terminal has been developed based on a high-end multi-GNSS receiver that is able to track E1/L1 and E6-B/C signals for data and signal protection. The terminal is equipped with a set of resilience techniques. Among these techniques, this paper focuses on an anti-replay technique protecting against zero-delay Secure Code Estimate-Replay (SCER) attacks based on the analysis of the unpredictable symbols from OSNMA cryptographic data. This paper firstly describes the NACSET project and its aim. Secondly, the theory of Anti-replay protection is explained from the point of view of a receiver, and anti-replay techniques based on OSNMA are introduced. Then, we describe the SCER simulator developed to assess the performances of the technique. To conclude, an attack is defined and performed with the SCER simulator over a real receiver. The results with and without OSNMA replay protection are presented and explained, and some conclusions of the experiment are derived.