Reviewerly/exHarmony huggingface dataset:
Es-Tacotron2: Multi-Task Tacotron 2 with Pre-Trained Estimated Network for Reducing the Over-Smoothness Problem

Text-to-speech synthesis is a computational technique for producing synthetic, human-like speech by a computer. In recent years, speech synthesis techniques have developed, and have been employed in many applications, such as automatic translation applications and car navigation systems. End-to-end text-to-speech synthesis has gained considerable research interest, because compared to traditional models the end-to-end model is easier to design and more robust. Tacotron 2 is an integrated state-of-the-art end-to-end speech synthesis system that can directly predict closed-to-natural human speech from raw text. However, there remains a gap between synthesized speech and natural speech. Suffering from an over-smoothness problem, Tacotron 2 produced ’averaged’ speech, making the synthesized speech sounds unnatural and inflexible. In this work, we first propose an estimated network (Es-Network), which captures general features from a raw mel spectrogram in an unsupervised manner. Then, we design Es-Tacotron2 by employing the Es-Network to calculate the estimated mel spectrogram residual, and setting it as an additional prediction task of Tacotron 2, to allow the model focus more on predicting the individual features of mel spectrogram. The experience shows that compared to the original Tacotron 2 model, Es-Tacotron2 can produce more variable decoder output and synthesize more natural and expressive speech.
Hybrid Homomorphic Encryption Scheme for Secure Cloud Data Storage

Cloud computing is the budding paradigm nowadays in the macrocosm of the information processing system. It offers a variety of services for the users through the Internet and is highly cost-efficient and flexible. Information storage in the cloud is showing great attention. Yet, despite all its advantages, security and privacy has evolved to be of significant apprehension in cloud computing and is discouraging factor for potential adopters. Consumers and businesses prefer online computing only if their data are guaranteed to remain secret and safe. Hence, the focal point is to discover techniques in the direction of offering more confidentiality. Homomorphic encryption is one such technique. This paper targets to study several key concepts of cloud computing, namely characteristics, delivery models, deployment models and cloud computing platforms. The theme includes the security challenges/issues and their associated work in cloud computing. A generic flow of data and the operations to be performed in the proposed scheme for multi-clouds are presented. The report explains the details and effects related to different parameters of Homomorphic properties of some cryptosystems. In this paper, our main work is to ensure the protection of information, thus we offered a method to amend the Paillier Homomorphic algorithm without compromising the protection of existing technique. In our prospect work, we plan to propose an efficient Multicloud architecture so that information is stored, maintained and retrieved efficiently by employing a modified Paillier approach.
Improving Natural Language Interaction with Robots Using Advice

Nikhil Mehta, Dan Goldwasser. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Partially Occluded Hands:

Recognizing the pose of hands matters most when hands are interacting with other objects. To understand how well both machines and humans perform on single-image 2D hand-pose reconstruction from RGB images, we collected a challenging dataset of hands interacting with 148 objects. We used a novel methodology that provides the same hand in the same pose both with the object being present and occluding the hand and without the object occluding the hand. Additionally, we collected a wide range of grasps for each object designing the data collection methodology to ensure this diversity. Using this dataset we measured the performance of two state-of-the-art hand-pose recognition methods showing that both are extremely brittle when faced with even light occlusion from an object. This is not evident in previous datasets because they often avoid hand-object occlusions and because they are collected from videos where hands are often between objects and mostly unoccluded. We annotated a subset of the dataset and used that to show that humans are robust with respect to occlusion, and also to characterize human hand perception, the space of grasps that seem to be considered, and the accuracy of reconstructing occluded portions of hands. We expect that such data will be of interest to both the vision community for developing more robust hand-pose algorithms and to the robotic grasp planning community for learning such grasps. The dataset is available at occludedhands.com .
Memorized sparse backpropagation

Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers. Despite its success of existing works in accelerating propagation through sparseness, the relevant theoretical characteristics remain under-researched and empirical studies found that they suffer from the loss of information contained in unpropagated gradients. To tackle these problems, this paper presents a unified sparse backpropagation framework and provides a detailed analysis of its theoretical characteristics. Analysis reveals that when applied to a multilayer perceptron, our framework essentially performs gradient descent using an estimated gradient similar enough to the true gradient, resulting in convergence in probability under certain conditions. Furthermore, a simple yet effective algorithm named memorized sparse backpropagation (MSBP) is proposed to remedy the problem of information loss by storing unpropagated gradients in memory for learning in the next steps. Experimental results demonstrate that the proposed MSBP is effective to alleviate the information loss in traditional sparse backpropagation while achieving comparable acceleration.
Remote Sensing Contribution to The Identification of Potential Geothermal Deposits: A Case Study of The Moroccan Sahara

Abstract Geothermal energy refers to any heat stored, naturally, in the earth's crust and is considered as renewable, ecological, universal and sustainable energy. In Morocco, geothermal energy is not yet exploited, neither considered, to support the institutional energy mainly focused on solar and wind energy. However, modest researches and some interventions related to geothermal resources were led and conducted by few Moroccan university. Remote sensing is a preliminary prospection tool for geothermal research allowing to face the lack of valuable data constraint and also the absence of officially credible data which will allow us to successfully complete our study. The main purpose of this research is to locate potential geothermal resources in the Moroccan Sahara using remote sensing and geospatial technologies. A set of satellite images (MODIS, ASTER and Landsat) were used and were completely processed and analyzed. Furthermore, significant indices were calculated from satellite images to prospect for potential deposits, namely diurnal and nocturnal surface temperature, hydrothermal alteration indices and lineaments density layer. This study allowed us to detect nocturnal thermal anomalies in the locality of Tichla. The statistical analysis showed a strong correlation between the intensities of the different indices in the prospected area of Tichla. Therefore, this significant concordance leads us to conclude that this locality has a strong potential for geothermal reservoirs. important tool for the preliminary identification of potential geothermal energy deposits.
Human Posture Recognition for Estimation of Human Body Condition

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.
Using Blockchain for Traceability in the Drug Supply Chain

The illegal trade of medication is a problem which has claimed lives all over the world. In spite of the efforts from different international institutions aiming to stop this situation, illegal sales continue to grow. The following article proposes the use of Blockchain technology as a solution to the traceability problems and lack of control in the drug trade, as well as the way different entities would participate in it.
On-device Structured and Context Partitioned Projection Networks

A challenging problem in on-device text classification is to build highly accurate neural models that can fit in small memory footprint and have low latency.To address this challenge, we propose an on-device neural network SGNN++ which dynamically learns compact projection vectors from raw text using structured and context-dependent partition projections.We show that this results in accelerated inference and performance improvements.We conduct extensive evaluation on multiple conversational tasks and languages such as English, Japanese, Spanish and French.Our SGNN++ model significantly outperforms all baselines, improves upon existing on-device neural models and even surpasses RNN, CNN and BiLSTM models on dialog act and intent prediction.Through a series of ablation studies we show the impact of the partitioned projections and structured information leading to 10% improvement.We study the impact of the model size on accuracy and introduce quantization-aware training for SGNN++ to further reduce the model size while preserving the same quality.Finally, we show fast inference on mobile phones.
A model of the optimal selection of crypto assets

We propose a modelling framework for the optimal selection of crypto assets. We assume that crypto assets can be described according to two features: security (technological) and stability (governance). We simulate optimal selection decisions of investors, being driven by (i) their attitudes towards assets' features, (ii) information about the adoption trends, and (iii) expected future economic benefits of adoption. Under a variety of modelling scenarios-e.g. in terms of composition of the crypto assets landscape and investors' preferences-we are able to predict the features of the assets that will be most likely adopted, which can be mapped to macro-classes of existing crypto assets (stablecoins, crypto tokens, central bank digital currencies and cryptocurrencies).
Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

Modern distributed machine learning (ML) training workloads benefit significantly from leveraging GPUs. However, significant contention ensues when multiple such workloads are run atop a shared cluster of GPUs. A key question is how to fairly apportion GPUs across workloads while ensuring overall cluster efficiency. We find that established cluster scheduling disciplines that provide instantaneous fair share of resources are a poor fit because of ML workloads' unique attributes. ML jobs are typically long running, have coarse grained tasks that need to be gang-scheduled, and their performance is sensitive to tasks' relative placement. These properties cannot be captured by existing fair sharing schemes. We propose Themis, a new scheduling framework for ML training workloads. It's GPU allocation policy enforces that ML workloads complete in a finish-time fair manner, a new notion we introduce. To capture placement sensitivity and ensure efficiency, Themis uses a two-level scheduling architecture where ML workloads bid on available resources that are offered in an auction run by a central arbiter. Our auction design allocates GPUs to winning bids by trading off efficiency for fairness in the short term but compensating for finish-time fairness in the long term. Our evaluation on a number of machine learning models shows that Themis can ensure greater fairness while providing more efficient allocations compared to state-of-the-art schedulers.
Towards Parameter-Optimized Vessel Re-identification Based on IORnet

Reliable vessel re-identification would enable maritime surveillance systems to analyze the behavior of vessels by drawing their accurate trajectories, when they pass along different camera locations. However, challenging outdoor conditions and varying viewpoint appearances combined with the large size of vessels limit conventional methods to obtain robust re-identification performance. This paper employs CNNs to address these challenges. In this paper, we propose an Identity Oriented Re-identification network (IORnet), which improves the triplet method with a new identity-oriented loss function. The resulting method increases the feature vector similarities between vessel samples belonging to the same vessel identity. Our experimental results reveal that the proposed method achieves 81.5% and 91.2% on mAP and Rank1 scores, respectively. Additionally, we report experimental results with data augmentation and hyper-parameters optimization to facilitate reliable ship re-identification. Finally, we provide our real-world vessel re-identification dataset with various annotated multi-class features to public access.
Vidgets

We present Vidgets , a family of mechanical widgets, specifically push buttons and rotary knobs that augment mobile devices with tangible user interfaces. When these widgets are attached to a mobile device and a user interacts with them, the widgets' nonlinear mechanical response shifts the device slightly and quickly, and this subtle motion can be detected by the accelerometer commonly equipped on mobile devices. We propose a physics-based model to understand the nonlinear mechanical response of widgets. This understanding enables us to design tactile force profiles of these widgets so that the resulting accelerometer signals become easy to recognize. We then develop a lightweight signal processing algorithm that analyzes the accelerometer signals and recognizes how the user interacts with the widgets in real time. Vidgets widgets are low-cost, compact, reconfigurable, and power efficient. They can form a diverse set of physical interfaces that enrich users' interactions with mobile devices in various practical scenarios. We demonstrate their use in three applications: photo capture with single-handed zoom, control of mobile games, and making a playable mobile music instrument.
Effective Super-Resolution Methods for Paired Electron Microscopic Images

This paper is concerned with investigating super-resolution algorithms and solutions for handling electron microscopic images. We note two main aspects differentiating the problem discussed here from those considered in the literature. The first difference is that in the electron imaging setting. We have a pair of physical high-resolution and low-resolution images, rather than a physical image with its downsampled counterpart. The high-resolution image covers about 25% of the view field of the low-resolution image, and the objective is to enhance the area of the low-resolution image where there is no high-resolution counterpart. The second difference is that the physics behind electron imaging is different from that of optical (visible light) photos. The implication is that super-resolution models trained by optical photos are not effective when applied to electron images. Focusing on the unique properties, we devise a global and local registration method to match the high- and low-resolution image patches and explore training strategies for applying deep learning super-resolution methods to the paired electron images. We also present a simple, non-local-mean approach as an alternative. This alternative performs as a close runner-up to the deep learning approaches, but it takes less time to train and entertains a simpler model structure.
A fast algorithm for solving linearly recurrent sequences

We present an algorithm which computes the D th term of a sequence satisfying a linear recurrence relation of order d over a field K in O (M(d̄) log( D ) + M( d ) log( d )) operations in K, where d̄ ≤ d is the degree of the squarefree part of the annihilating polynomial of the recurrence and M is the cost of polynomial multiplication in K. This is a refinement of the previously optimal result of O (M( d ) log( D )) operations, due to Fiduccia.
QPPDs: Querying Property Paths Over Distributed RDF Datasets

A key property of linked data, i.e., the web-based representation and publication of data as interconnected labeled graphs, is that it enables querying and navigating through datasets distributed across the network.SPARQL1.1, the current standard query language for RDF-based linked data, defines a construct-called property paths (PP)-to navigate between the entities of a graph.This is potentially very useful in a number of use cases, e.g., in the biomedical domain, where large datasets are available as linked data graphs.However, the use of PP in SPARQL 1.1.is possible only on a single local graph, requiring us to merge all distributed datasets into one large, centrally stored graph, therefore reducing the value of using linked data in the first place.We propose an index-based approach-called QPPDs-for answering queries for paths distributed across multiple, distributed datasets.We provide a heuristic-based source selection mechanism to select the relevant datasets (also called data sources) for a given path query, and a technique that federates queries to selected sources, and assembles (merges) the paths (i.e., partial or complete) retrieved from those remote datasets.We demonstrate our approach on a genomics use-case, where the description of biological entities (e.g., genes, diseases, and drugs) is scattered across multiple datasets.In our preliminary investigation, we evaluate the QPPDs approach with real-world path queries-on biological data that are very heterogeneous in nature-in terms of performance (overall path retrieval time) and result completeness, i.e., the number of paths retrieved.
Geolocating Political Events in Text

This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event–location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.
&lt;p&gt;A reliability and content analysis of Italian language anorexia nervosa-related websites&lt;/p&gt;

Internet represents a major source of information related to health issues, increasingly used by providers. Indeed, there are numerous websites concerning eating and weight disorders, such as anorexia nervosa. The aim of the current investigation was to systematically perform a reliability and content analysis of Italian language anorexia nervosa-related websites.Reliability of the anorexia nervosa website pages was assessed using the Health on the Net Foundation Code of Conduct Standards (HonCode®) certification mark. A comprehensive content analysis (thematic cluster analysis, correspondence analysis of emerging themes and topics, density and degree-centrality analysis, and co-word analysis) was performed using the commercial software T-Lab.402 unique website pages were included and analyzed in the current study. Statistically significant differences related to the accomplishment or less to the items of the HonCode® certificate mark were found for all items, except for confidentiality and webmaster/additional contact. From the thematic cluster analysis, four clusters were identified: namely, treatment (26.6%), vomiting (24.7%), weight (24.4%) and onset-age (24.3%). Treatment (in particular, cognitive-behavioral psychotherapy) was the most represented theme (with a frequency of 97.76%, recurring in 393 websites). Pro-ana content characterized 147 website pages (with a frequency of 36.57%).The quality of Italian language anorexia nervosa-related websites was rather moderate-poor, being generally inconsistent with the principles of the HonCode® certification mark. Therefore, physicians and health authorities should be aware of such findings to provide their patients with proper answers and education.
Statistical Zeroizing Attack: Cryptanalysis of Candidates of BP Obfuscation over GGH15 Multilinear Map

We present a new cryptanalytic algorithm on obfuscations based on GGH15 multilinear map. Our algorithm, statistical zeroizing attack, directly distinguishes two distributions from obfuscation while it follows the zeroizing attack paradigm, that is, it uses evaluations of zeros of obfuscated programs. Our attack breaks the recent indistinguishability obfuscation candidate suggested by Chen et al. (CRYPTO’18) for the optimal parameter settings. More precisely, we show that there are two functionally equivalent branching programs whose CVW obfuscations can be efficiently distinguished by computing the sample variance of evaluations. This statistical attack gives a new perspective on the security of the indistinguishability obfuscations: we should consider the shape of the distributions of evaluation of obfuscation to ensure security. In other words, while most of the previous (weak) security proofs have been studied with respect to algebraic attack model or ideal model, our attack shows that this algebraic security is not enough to achieve indistinguishability obfuscation. In particular, we show that the obfuscation scheme suggested by Bartusek et al. (TCC’18) does not achieve the desired security in a certain parameter regime, in which their algebraic security proof still holds. The correctness of statistical zeroizing attacks holds under a mild assumption on the preimage sampling algorithm with a lattice trapdoor. We experimentally verify this assumption for implemented obfuscation by Halevi et al. (ACM CCS’17).
Software Defect Prediction Model Based on Improved Deep Forest and AutoEncoder by Forest

Software defect prediction is an important way to make full use of software test resources and improve software performance.To deal with the problem that of the shallow machine learning based software defect prediction model can not deeply mine the software tool data, we propose software defect prediction model based on improved deep forest and autoencoder by forest.Firstly, the original input features are transformed by the data augmentation method to enhance the ability of feature expression, and the autoencoder by forest performs the data of dimensionality reduction on the features.Then, we use the improved deep forest algorithm and autoencoder by forest to build software defect prediction model.The experimental results show that the proposed algorithm has higher performance than the original deep forest (gcForest) algorithm and other existing start-of-art algorithms, and has higher performance and efficiency than other deep learning algorithms.