Reviewerly/exHarmony huggingface dataset:
Real Life Application of a Question Answering System Using BERT Language Model

Real life scenarios are often left untouched by the newest advances in research.They usually require the resolution of some specific task applied to a restricted domain, all the while providing small amounts of data to begin with.In this study we apply one of the newest innovations in Deep Learning to a task of text classification.The goal is to create a question answering system in Italian that provides information about a specific subject, e-invoicing and digital billing.Italy recently introduced a new legislation about e-invoicing and people have some legit doubts, therefore a large share of professionals could benefit from this tool.We gathered few pairs of question and answers; afterwards, we expanded the data, using it as a training corpus for BERT language model.Through a separate test corpus we evaluated the accuracy of the answer provided.Values show that the automatic system alone performs surprisingly well.The demo interface is hosted on Telegram, which makes the system immediately available to test.
Simplified Revocable Hierarchical Identity-Based Encryption from Lattices

As an extension of identity-based encryption (IBE), revocable hierarchical IBE (RHIBE) supports both key revocation and key delegation simultaneously, which are two important functionalities for cryptographic use in practice. Recently in PKC 2019, Katsumata et al. constructed the first lattice-based RHIBE scheme with decryption key exposure resistance (DKER). Such constructions are all based on bilinear or multilinear maps before their work. In this paper, we simplify the construction of RHIBE scheme with DKER provided by Katsumata et al. With our new treatment of the identity spaces and the time period space, there is only one short trapdoor base in the master secret key and in the secret key of each identity. In addition, we claim that some items in the keys can also be removed due to the DKER setting. Our first RHIBE scheme in the standard model is presented as a result of the above simplification. Furthermore, based on the technique for lattice basis delegation in fixed dimension, we construct our second RHIBE scheme in the random oracle model. It has much shorter items in keys and ciphertexts than before, and also achieves the adaptive-identity security under the learning with errors (LWE) assumption.
Space-efficient quantum multiplication polynomials for binary finite fields with sub-quadratoc Toffoli gate count

Multiplication is an essential step in a lot of calculations. In this paper we look at multiplication of 2 binary polynomials of degree at most n-1, modulo an irreducible polynomial of degree n with 2n input and n output qubits, without ancillary qubits, assuming no errors. With straightforward schoolbook methods this would result in a quadratic number of Toffoli gates and a linear number of CNOT gates. This paper introduces a new algorithm that uses the same space, but by utilizing space-efficient variants of Karatsuba multiplication methods it requires only O(n^{\log_2(3)}) Toffoli gates at the cost of a higher CNOT gate count: theoretically up to O(n^2) but in examples the CNOT gate count looks a lot better.
A Random Forest-based Approach for Hand Gesture Recognition with Wireless Wearable Motion Capture Sensors

Gesture Recognition has a prominent importance in smart environment and home automation. Thanks to the availability of Machine Learning approaches it is possible for users to define gestures that can be associated with commands for the smart environment. In this paper we propose a Random Forest-based approach for Gesture Recognition of hand movements starting from wireless wearable motion capture data. In the presented approach, we evaluate different feature extraction procedures to handle gestures and data with different duration. To enhance reproducibility of our results and to foster research in the Gesture Recognition area, we share the dataset that we have collected and exploited for the present work.
A Hierarchical Approach for Visual Storytelling Using Image Description

One of the primary challenges of visual storytelling is developing techniques that can maintain the context of the story over long event sequences to generate human-like stories. In this paper, we propose a hierarchical deep learning architecture based on encoder-decoder networks to address this problem. To better help our network maintain this context while also generating long and diverse sentences, we incorporate natural language image descriptions along with the images themselves to generate each story sentence. We evaluate our system on the Visual Storytelling (VIST) dataset [7] and show that our method outperforms state-of-the-art techniques on a suite of different automatic evaluation metrics. The empirical results from this evaluation demonstrate the necessities of different components of our proposed architecture and shows the effectiveness of the architecture for visual storytelling.
Contemporary Technologies and Methods for Cross-Platform Application Development

This paper reviews the existing technologies for the development of cross-platform applications. Both Server-side as well as client-side applications for web, desktop and mobile devices are considered which mainly consists of smart phones and tablets. Further, based on the review process and research issues a web-based development approach is recommended for the development of truly cross-platform applications across devices and operating systems.
Deep Learning Approaches Outperform Conventional Strategies in De-Identification of German Medical Reports.

One of the major obstacles for research on German medical reports is the lack of de-identified medical corpora. Previous de-identification tasks focused on non-German medical texts, which raised the demand for an in-depth evaluation of de-identification methods on German medical texts. Because of remarkable advancements in natural language processing using supervised machine learning methods on limited training data, we evaluated them for the first time on German medical reports using our annotated data set consisting of 113 medical reports from the cardiology domain. We applied state-of-the-art deep learning methods using pre-trained models as input to a bidirectional LSTM network and well-established conditional random fields for de-identification of German medical reports. We performed an extensive evaluation for de-identification and multiclass named entity recognition. Using rule based and out of domain machine learning methods as a baseline, the conditional random field improved F2-score from 70 to 93% for de-identification, the neural approach reached 96% in F2-score while keeping balanced precision and recall rates. These results show, that state-of-the-art machine learning methods can play a crucial role in de-identification of German medical reports.
Foreign aid volatility and lifelong learning

This paper has put a demand-side empirical structure to the hypothesis that foreign aid volatility adversely affects choices to lifelong learning in recipient countries. Lifelong learning is measured as the combined knowledge acquired during primary, secondary and tertiary educational enrolments. Three types of aggregate foreign aid volatilities are computed in a twofold manner: baseline standard deviations and standard errors (standard deviations of residuals after first-order autoregressive processes). An endogeneity robust system GMM empirical strategy is employed. The findings broadly show that foreign aid volatility does not adversely affect the demand-side choices of lifelong learning in Africa. As a policy implication, when faced with aid uncertainty, the demand for education would increase. This may be explained by the need for more self-reliance in order to mitigate income risks or/and the use of education as means of coping with uncertainty. More policy implications are discussed.
Radial basis function neural network for 2 satisfiability programming

&lt;span&gt;Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.&lt;/span&gt;
Analysis and research on intelligent manufacturing medical product design and intelligent hospital system dynamics based on machine learning under big data

In order to promote the application of machine learning in intelligent manufacturing, especially in medical products and intelligent hospitals, based on the theoretical research of machine learning under big data, intelligent medical disease diagnosis and classification products are designed. Two algorithms, MLP and SVM, are used to compare the accuracy, recall and F1. It is concluded that the effect of medical diagnosis classification of both methods is maintained above and below 90%. At the same time, the research proves that the corresponding accuracy can be improved only when the fuzzy matching is suitable for more positive examples.
SVM Model Selection Using PSO for Learning Handwritten Arabic Characters

Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is applied to handwritten Arabic characters learning, with a database containing 4840 Arabic characters in their different positions (isolated, beginning, middle and end). Some very promising results have been achieved.
Hypermodels for Exploration

We study the use of hypermodels to represent epistemic uncertainty and guide exploration. This generalizes and extends the use of ensembles to approximate Thompson sampling. The computational cost of training an ensemble grows with its size, and as such, prior work has typically been limited to ensembles with tens of elements. We show that alternative hypermodels can enjoy dramatic efficiency gains, enabling behavior that would otherwise require hundreds or thousands of elements, and even succeed in situations where ensemble methods fail to learn regardless of size. This allows more accurate approximation of Thompson sampling as well as use of more sophisticated exploration schemes. In particular, we consider an approximate form of information-directed sampling and demonstrate performance gains relative to Thompson sampling. As alternatives to ensembles, we consider linear and neural network hypermodels, also known as hypernetworks. We prove that, with neural network base models, a linear hypermodel can represent essentially any distribution over functions, and as such, hypernetworks do not extend what can be represented.
Multihop nondestructive teleportation via different nonmaximally entangled channels

We propose a teleportation scheme for nondestructively transmitting an unknown qudit state between two remote communication parties that are linked by sequential Bell pairs. With independent and simultaneous entanglement swapping among the intermediate nodes, a direct entangled channel between the source node and the destination node is established. Our scheme preserves the initial unknown state even if the teleportation fails. Different nonmaximally entangled channels are distributed among the participants so that the quantum channel requirement is reduced. In addition, the communication delay is reduced significantly since the measuring and the transmitting are conducted simultaneously by the intermediate nodes.
The Combinatorics of the Longest-Chain Rule: Linear Consistency for Proof-of-Stake Blockchains

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)The Combinatorics of the Longest-Chain Rule: Linear Consistency for Proof-of-Stake BlockchainsErica Blum, Aggelos Kiayias, Cristopher Moore, Saad Quader, and Alexander RussellErica Blum, Aggelos Kiayias, Cristopher Moore, Saad Quader, and Alexander Russellpp.1135 - 1154Chapter DOI:https://doi.org/10.1137/1.9781611975994.69PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The blockchain data structure maintained via the longest-chain rule—popularized by Bitcoin—is a powerful algorithmic tool for consensus algorithms. Such algorithms achieve consistency for blocks in the chain as a function of their depth from the end of the chain. While the analysis of Bitcoin guarantees consistency with error 2−k for blocks of depth O(k), the state-of-the-art of proof-of-stake (PoS) blockchains suffers from a quadratic dependence on k: these protocols, exemplified by Ouroboros (Crypto 2017), Ouroboros Praos (Eurocrypt 2018) and Sleepy Consensus (Asiacrypt 2017), can only establish that depth Θ(k2) is sufficient. Whether this quadratic gap is an intrinsic limitation of PoS—due to issues such as the nothing-at-stake problem—has been an urgent open question, as deployed PoS blockchains further rely on consistency for protocol correctnes. We give an axiomatic theory of blockchain dynamics that permits rigorous reasoning about the longest-chain rule and achieve, in broad generality, Θ(k) dependence on depth in order to achieve consistency error 2−k In particular, for the first time we show that PoS protocols can match proof-of-work protocols for linear consistency. We analyze the associated stochastic process, give a recursive relation for the critical functionals of this process, and derive tail bounds in both i.i.d. and martingale settings via associated generating functions. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
WMFP-Outlier: An Efficient Maximal Frequent-Pattern-Based Outlier Detection Approach for Weighted Data Streams

Since outliers are the major factors that affect accuracy in data science, many outlier detection approaches have been proposed for effectively identifying the implicit outliers from static datasets, thereby improving the reliability of the data. In recent years, data streams have been the main form of data, and the data elements in a data stream are not always of equal importance. However, the existing outlier detection approaches do not consider the weight conditions; hence, these methods are not suitable for processing weighted data streams. In addition, the traditional pattern-based outlier detection approaches incur a high time cost in the outlier detection phase. Aiming at overcoming these problems, this paper proposes a two-phase pattern-based outlier detection approach, namely, WMFP-Outlier, for effectively detecting the implicit outliers from a weighted data stream, in which the maximal frequent patterns are used instead of the frequent patterns to accelerate the process of outlier detection. In the process of maximal frequent-pattern mining, the anti-monotonicity property and MFP-array structure are used to accelerate the mining operation. In the process of outlier detection, three deviation indices are designed for measuring the degree of abnormality of each transaction, and the transactions with the highest degrees of abnormality are judged as outliers. Last, several experimental studies are conducted on a synthetic dataset to evaluate the performance of the proposed WMFP-Outlier approach. The results demonstrate that the accuracy of the WMFP-Outlier approach is higher compared to the existing pattern-based outlier detection approaches, and the time cost of the outlier detection phase of WMFP-Outlier is lower than those of the other four compared pattern-based outlier detection approaches.
A Digital Watermarking Encryption Technique Based on FPGA Cloud Accelerator

Digital watermarking has the properties such as invisibility and anti-aggression, so the digital watermarking technology has been widely used in copyright protection, information hiding.The watermarking technology takes into account the invisibility and robustness of the watermark by controlling the embedding intensity and position of the watermark mainly in the transformation domain.In this paper, discrete cosine transform (DCT) is adopted to transform the given image from spatial domain to frequency domain for adding watermark information.In order to meet the demands of image watermarking batch processing and cloud processing in the future, this paper optimized the DCT algorithm and the data precision, and successfully deployed the designed accelerator kernel on the FPGA cloud platform to speed up the processing of watermarking.The implementation of data processing based on cloud platform is the development trend of big data era.The cloud platform adopted in this paper is based on the OpenCL heterogeneous architecture combining CPU and FPGA.The cloud-based implementation makes digital watermarking application highly extensible, widely shareable, and more secure.The whole system implements a series of complete cloud processes including image decoding, image preprocessing, watermark embedding, and watermarked image encoding.The watermarking algorithm is accelerated by the efficient parallel computing capabilities of FPGA.It can be seen that the result of acceleration is remarkable, providing the state-of-the-art throughput of 1.676 GBps and the highest processing speed of 937 FPS for 800 × 800 sized colorimage.
Identity-Based Public Auditing Scheme for Cloud Storage with Strong Key-Exposure Resilience

Secured storage system is a critical component in cloud computing. Cloud clients use cloud auditing schemes to verify the integrity of data stored in the cloud. But with the exposure of the auditing secret key to the Cloud Service Provider, cloud auditing becomes unsuccessful, however strong the auditing schemes may be. Therefore, it is essential to prevent the exposure of auditing secret keys, and even if it happens, it is necessary to minimize the damage caused. The existing cloud auditing schemes that are strongly resilient to key exposure are based on Public Key Infrastructure and so have challenges of certificate management/verification. These schemes also incur high computation time during integrity verification of the data blocks. The Identity-based schemes eliminate the usage of certificates but limit the damage due to key exposure, only in time periods earlier to the time period of the exposed key. Some of the key exposure resilient schemes do not provide support for batch auditing. In this paper, an Identity-based Provable Data Possession scheme is proposed. It protects the security of Identity-based cloud storage auditing in time periods both earlier and later to the time period of the exposed key. It also provides support for batch auditing. Analysis shows that the proposed scheme is resistant to the replace attack of the Cloud Service Provider, preserves the data privacy against the Third Party Auditor, and can efficiently verify the correctness of data.
Sequence-to-sequence prediction of spatiotemporal systems

We propose a novel type of neural networks known as “attention-based sequence-to-sequence architecture” for a model-free prediction of spatiotemporal systems. This architecture is composed of an encoder and a decoder in which the encoder acts upon a given input sequence and then the decoder yields another output sequence to make a multistep prediction at a time. In order to demonstrate the potential of this approach, we train the neural network using data numerically sampled from the Korteweg–de Vries equation—which describes the interaction between solitary waves—and then predict its future evolution. Furthermore, we validate the applicability of the approach on datasets sampled from the chaotic Lorenz system and three other partial differential equations. The results show that the proposed method can achieve good performance in predicting the evolutionary behavior of studied spatiotemporal dynamics. To the best of our knowledge, this work is the first attempt at applying attention-based sequence-to-sequence architecture to the prediction task of solitary waves.
Evolutionary computing approach to optimize superframe scheduling on industrial wireless sensor networks

The industrial wireless sensor domain has undergone a shift in paradigm as a consequence of Internet of Things (IoT), a thriving technology that has been leading the way in short-range and fixed wireless sensing. One of the problems associated with industrial wireless sensor networks (IWSNs) is finding the optimal solution for minimizing defect time in superframe scheduling. This paper proposed a method based on the use of evolutionary algorithms, namely particle swarm optimization (PSO), orthogonal learning PSO, genetic algorithm (GA), and modified GA for optimizing the superframe scheduling. Additionally, we evaluated a contemporary method, deadline monotonic scheduling, on the ISA 100.11a protocol. The use of this standard as a case study means that the presented 72 simulations are object-oriented, with numerous variations in the number of timeslots and wireless sensor nodes. The simulation results show that the use of GA and modified GA can improve the performance in terms of idle, missed deadlines, memory consumption, and processing time comparing to other metaheuristic algorithms. A comprehensive analysis and detailed performance evaluation are provided in the paper.
Development of Smart Home Using Gesture Recognition for Elderly and Disabled

Smart home has gained popularity not only as a luxury but also due to the numerous advantages. It is especially useful for senior citizens and children with disabilities. In this work, home automation is achieved using gesture for controlling appliances. Gesture recognition is an area in which lot of research and innovations are blooming. This paper discusses the development of a wearable device which captures hand gestures. The wearable device uses accelerometer and gyroscopes to sense and capture tilting, rotation and acceleration of the hand movement. Four different hand gestures are captured using this wearable device and machine learning algorithm namely Support Vector Machine has been used for classification of gestures to control ON/OFF of appliances.