Reviewerly/exHarmony huggingface dataset:
Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine

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Language and Dialect Identification of Cuneiform Texts

This article introduces a corpus of cuneiform texts from which the dataset for the use of the Cuneiform Language Identification (CLI) 2019 shared task was derived as well as some preliminary language identification experiments conducted using that corpus. We also describe the CLI dataset and how it was derived from the corpus. In addition, we provide some baseline language identification results using the CLI dataset. To the best of our knowledge, the experiments detailed here represent the first time that automatic language identification methods have been used on cuneiform data.
Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Federated learning has been a hot research area in enabling the collaborativetraining of machine learning models among different organizations under theprivacy restrictions. As researchers try to support more machine learningmodels with different privacy-preserving approaches, there is a requirement indeveloping systems and infrastructures to ease the development of variousfederated learning algorithms. Just like deep learning systems such as Caffe,PyTorch, and Tensorflow that boost the development of deep learning algorithms,federated learning systems are equivalently important, and face challenges fromvarious issues such as unpractical system assumptions, scalability andefficiency. Inspired by federated systems in other fields such as databases andcloud computing, we investigate the existing characteristics of federatedlearning systems. We find that two important features for federated systems inother fields, i.e., heterogeneity and autonomy, are rarely considered in theexisting federated learning systems. Moreover, we provide a thoroughcategorization for federated learning systems according to six differentaspects, including data partition, machine learning model, privacy mechanism,communication architecture, scale of federation and motivation of federation.The categorization can help the design of federated learning systems as shownin our case studies. Lastly, we take a systematic comparison among the existingfederated learning systems and present future research opportunities anddirections.
A Bitcoin-inspired infinite-server model with a random fluid limit

The synchronization process inherent to the Bitcoin network gives rise to an infinite-server model with the unusual feature that customers interact. Among the closed-form characteristics that we derive for this model is the busy period distribution which, counterintuitively, does not depend on the arrival rate. We explain this by exploiting the equivalence between two specific service disciplines, which is also used to derive the model’s stationary distribution. Next to these closed-form results, the second major contribution concerns an asymptotic result: a fluid limit in the presence of service delays. Since fluid limits arise under scalings of the law-of-large-numbers type, they are usually deterministic, but in the setting of the model discussed in this paper the fluid limit is random (more specifically, of growth-collapse type).
Multi-Domain Sentiment Classification Based on Domain-Aware Embedding and Attention

Sentiment classification is a fundamental task in NLP. However, as revealed by many researches, sentiment classification models are highly domain-dependent. It is worth investigating to leverage data from different domains to improve the classification performance in each domain. In this work, we propose a novel completely-shared multi-domain neural sentiment classification model to learn domain-aware word embeddings and make use of domain-aware attention mechanism. Our model first utilizes BiLSTM for domain classification and extracts domain-specific features for words, which are then combined with general word embeddings to form domain-aware word embeddings. Domain-aware word embeddings are fed into another BiLSTM to extract sentence features. The domain-aware attention mechanism is used for selecting significant features, by using the domain-aware sentence representation as the query vector. Evaluation results on public datasets with 16 different domains demonstrate the efficacy of our proposed model. Further experiments show the generalization ability and the transferability of our model.
Combating Counterfeit Drugs: A quantitative analysis on cracking down the fake drug industry by using Blockchain technology

India, a global leader in manufacturing and the largest supplier of cost-effective generic drugs around the world, especially to the African countries under the United Nations aid, is facing a widespread fake drug epidemic. One of the major reasons behind this is the unregulated and complex drug supply chain. When a drug consignment change ownership from manufacturer to distributor and then to the wholesaler, no information is exchanged between the parties. We lack adequate auditability and transparency of the original source of drugs. Consequences include counterfeit drug problem, inefficient recall process of the drugs and lack of trust. By putting the entire drug supply chain of India on Blockchain, we propose to perform a quantitative analysis on leveraging unique blockchain properties like authenticity, accountability, and ability to handle sensitive information to solve India’s counterfeit drugs problem permanently. It will strengthen the supply chain and will introduce the much-needed transparency of transactions and quality assurance by tracking a drug through its entire lifecycle from the extraction of raw materials to the patient taking the drug. Along with this, smart contracts can be deployed which will provide automation, trust, and security to the system. Smart contracts are digital protocols that can be programmed to perform a task when a particular goal is reached without any intervention of a third party and can facilitate services like automatic payments, quality control, and trust between stakeholders.
On the Complexity of String Matching for Graphs

Exact string matching in labeled graphs is the problem of searching paths of a graph G=(V,E) such that the concatenation of their node labels is equal to the given pattern string P[1..m]. This basic problem can be found at the heart of more complex operations on variation graphs in computational biology, of query operations in graph databases, and of analysis operations in heterogeneous networks.We prove a conditional lower bound stating that, for any constant epsilon>0, an O(|E|^{1 - epsilon} m)-time, or an O(|E| m^{1 - epsilon})-time algorithm for exact string matching in graphs, with node labels and patterns drawn from a binary alphabet, cannot be achieved unless the Strong Exponential Time Hypothesis (SETH) is false. This holds even if restricted to undirected graphs with maximum node degree two, i.e. to zig-zag matching in bidirectional strings, or to deterministic directed acyclic graphs whose nodes have maximum sum of indegree and outdegree three. These restricted cases make the lower bound stricter than what can be directly derived from related bounds on regular expression matching (Backurs and Indyk, FOCS'16). In fact, our bounds are tight in the sense that lowering the degree or the alphabet size yields linear-time solvable problems. An interesting corollary is that exact and approximate matching are equally hard (quadratic time) in graphs under SETH. In comparison, the same problems restricted to strings have linear-time vs quadratic-time solutions, respectively (approximate pattern matching having also a matching SETH lower bound (Backurs and Indyk, STOC'15)).
Semi-Supervised Semantic Role Labeling with Cross-View Training

The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering.Recent approaches rely on high-quality annotations which are costly to obtain, and mostly unavailable in low resource scenarios (e.g., rare languages or domains).Our work aims to reduce the annotation effort involved via semisupervised learning.We propose an end-toend SRL model and demonstrate it can effectively leverage unlabeled data under the crossview training modeling paradigm.Our LSTMbased semantic role labeler is jointly trained with a sentence learner, which performs POS tagging, dependency parsing, and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools.Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.
Biases for Emergent Communication in Multi-agent Reinforcement Learning

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks. In temporally extended reinforcement learning domains, it has proved hard to learn such communication without centralized training of agents, due in part to a difficult joint exploration problem. We introduce inductive biases for positive signalling and positive listening, which ease this problem. In a simple one-step environment, we demonstrate how these biases ease the learning problem. We also apply our methods to a more extended environment, showing that agents with these inductive biases achieve better performance, and analyse the resulting communications protocols.
Human hand kinematic data during feeding and cooking tasks

This work presents a database of human hand kinematics containing data collected during the performance of a wide variety of activities of daily living involving feeding and cooking. The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each. A total of 20 subjects participated in each part of the experiment, and the objects and their arrangement were the same across subjects, although they performed the tasks in a natural non-directed way. This dataset contains a total of 1160 continuous calibrated recordings taken at 100 Hz during the performance of the tasks, with filtered signal. Statistical descriptive analyses from these data are presented. This database can be useful for machine learning purposes and prostheses control, as well as for the characterization of healthy human hand kinematics.
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme (G2P) conversion is an important task in automatic speech recognition and text-to-speech systems. Recently, G2P conversion is viewed as a sequence to sequence task and modeled by RNN or CNN based encoder-decoder framework. However, previous works do not consider the practical issues when deploying G2P model in the production system, such as how to leverage additional unlabeled data to boost the accuracy, as well as reduce model size for online deployment. In this work, we propose token-level ensemble distillation for G2P conversion, which can (1) boost the accuracy by distilling the knowledge from additional unlabeled data, and (2) reduce the model size but maintain the high accuracy, both of which are very practical and helpful in the online production system. We use token-level knowledge distillation, which results in better accuracy than the sequence-level counterpart. What is more, we adopt the Transformer instead of RNN or CNN based models to further boost the accuracy of G2P conversion. Experiments on the publicly available CMUDict dataset and an internal English dataset demonstrate the effectiveness of our proposed method. Particularly, our method achieves 19.88% WER on CMUDict dataset, outperforming the previous works by more than 4.22% WER, and setting the new state-of-the-art results.
Next Generation Generative Neural Networks for HEP

Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulations within HEP. These studies, while promising, have been insufficiently precise and also, like GANs in general, suffer from stability issues.We apply GANs to to generate full particle physics events (not individual physics objects), explore conditioning of generated events based on physics theory parameters and evaluate the precision and generalization of the produced datasets. We apply this to SUSY mass parameter interpolation and pileup generation. We also discuss recent developments in convergence and representations that match the structure of the detector better than images.In addition we describe on-going work making use of large-scale distributed resources on the Cori supercomputer at NERSC, and developments to control distributed training via interactive jupyter notebook sessions. This will allow tackling high-resolution detector data; model selection and hyper-parameter tuning in a productive yet scalable deep learning environment.
Kubeflow and Kubeflow Pipelines

Machine learning is often and rightly viewed as the use of mathematical algorithms to teach the computer to learn tasks that are computationally infeasible to program as a set of specified instructions. However, it turns out that these algorithms constitute only a small fraction of the overall learning pipeline from an engineering perspective. Building high-performant and dynamic learning models includes a number of other critical components. These components actually dominate the space of concerns for delivering an end-to-end machine learning product.
Tunnel contour detection during construction based on digital image correlation

During the tunnel construction process, contours of primary supports and secondary linings of the tunnel need to be tested to ensure that their quality meets the designed requirements. Traditionally, one total station is used to measure the three-dimensional (3D) coordinates of the measuring points of a cross section of the tunnel to check whether there are any intrusions. This traditional method has some disadvantages, such as few measuring points, low precision, heavy workload and long working time. In order to avoid these shortcomings, a method combining three-dimensional digital image correlation (3D-DIC) and angle feedback scanning mechanism that measures the 3D topography of tunnels during construction is proposed in this paper. In order to reduce the systematic deviation caused by the nonstandard positional relationship between the camera and the rotational axis, a calibration method is proposed and implemented to calibrate the positional relationship between the camera and the rotational axis. The proposed device is fabricated, and a tunnel is measured at the construction stage. The scanner only takes about 30 s to operate. When measuring the tunnel with an inner diameter of about 14 m, the overall 3D reconstruction accuracy of the device is better than 10 mm.
Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU

We present a new neural architecture for widecoverage Natural Language Understanding in Spoken Dialogue Systems.We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures).The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers.We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics.Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domainspecific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson.For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.
An Image Encryption Scheme Based on Hybrid Electro-Optic Chaotic Sources and Compressive Sensing

In this paper, a novel image encryption scheme based on analog-digital hybrid electro-optic chaotic sources and compressive sensing is proposed.Distinguished from other existing chaos-based encryption schemes, analog-digital hybrid electro-optic chaotic sources are utilized, in which the synchronization of chaotic systems can be achieved by transmitting binary digital bits.The robustness of the encryption scheme can be significantly improved and the theoretical transmission distance will be much longer because of digital chaos synchronization.Moreover, owing to the compression of compressive sensing and the sensitivity to initial values of chaotic sources, the proposed scheme can greatly reduce the amount of data and strengthen safety.Exhaustive simulations and analysis demonstrate that the proposed scheme is of good peculiarities such as high efficiency, good robustness and security.
Fine-Grained Argument Unit Recognition and Classification

Prior work has commonly defined argument retrieval from heterogeneous document collections as a sentence-level classification task. Consequently, argument retrieval suffers both from low recall and from sentence segmentation errors making it difficult for humans and machines to consume the arguments. In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling. For this, we define the task as Argument Unit Recognition and Classification (AURC). We present a dataset of arguments from heterogeneous sources annotated as spans of tokens within a sentence, as well as with a corresponding stance. We show that and how such difficult argument annotations can be effectively collected through crowdsourcing with high inter-annotator agreement. The new benchmark, AURC-8, contains up to 15% more arguments per topic as compared to annotations on the sentence level. We identify a number of methods targeted at AURC sequence labeling, achieving close to human performance on known domains. Further analysis also reveals that, contrary to previous approaches, our methods are more robust against sentence segmentation errors. We publicly release our code and the AURC-8 dataset.1
The Doing Business ranking and the GDP. A qualitative study

The topics of economic development and the importance of the business environment are repeatedly being studied by academia. Funding issues focus the debate, in particular on SME financing and the importance of the financial sector (especially the banking sector). Some institutions have developed specific indicators that seek to synthesize the greater or lesser ease of doing business and/or the resulting competitiveness. This research specifically focuses on one of them, namely, the ease of Doing Business index. The survey compares the bulk of African countries over two years, 2008 and 2017. This research uses fsQCA methodology to conclude that the factors affecting the capacity for doing business change over time. In 2008, only one set of Doing Business indicators was related to high wealth, but by 2017 there were four. We also conclude that credit is not only the determining factor for the ease of doing business and paperwork, as difficulties in dealing with the authorities are also highly significant.
Towards Criteria Characterizing the Metrological Performance of Full-field Measurement Techniques

Users of full-field measurement methods like Digital Image Correlation (DIC) often aim to perform measurements with the best trade-off between spatial resolution, bias and measurement resolution. Whenever two full-field methods are compared, it is essential that these criteria are taken into consideration. Recently a metrological efficiency indicator for full-field measurements has been proposed and discussed. This indicator combines measurement resolution and spatial resolution. It has been shown to be invariant to the subset size in the case of Local DIC. The goal of this article is to discuss a method, which determines both the spatial and the measurement resolutions for a given bias for two different DIC methods, in order to obtain the metrological efficiency indicator for each of these methods. The benefit of this indicator is that it does not depend on setting parameters such as the subset size, which are chosen by the user. As such, it can be considered as intrinsic to each technique, thus enabling fair comparison. Local DIC and triangular finite element based Global DIC will be the subject of this investigation. With this setting, their respective subset and triangular element sizes will be related to the spatial resolution of both methods for a given acceptable bias. By using the metrological efficiency indicator, the performance of the two methods will be compared and discussed to a new level of detail. Generally speaking, the indicator shows that the metrological performance of both methods is similar, confirming their popularity. However, it will be shown that, depending on the choice of what an acceptable bias is, one of the method may be preferred to another. The results show that for the specific DIC versions used in the study, for cases for which a significant bias is acceptable, Local DIC outperforms Global DIC, while the opposite is true in the case for which the bias requirements are more stringent. Finally, the quadratic versions of both DIC versions are shown to significantly outperform their respective linear versions.
Studies of a Lacustrine‐Volcanic Mars Analog Field Site With Mars‐2020‐Like Instruments

Abstract On the upcoming Mars‐2020 rover two remote sensing instruments, Mastcam‐Z and SuperCam, and two microscopic proximity science instruments, Scanning for Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) and Planetary Instrument for X‐ray Lithochemistry (PIXL), will collect compositional (mineralogy, chemistry, and organics) data essential for paleoenvironmental reconstruction. The synergies between and limitations of these instruments were evaluated via study of a Mars analog field site in the Mojave desert, using instruments approximating the data that will be returned by Mars‐2020. A ground truth data set was generated for comparison to validate the results. The site consists of a succession of clay‐rich mudstones of lacustrine origin, interbedded tuffs, a carbonate‐silica travertine deposit, and gypsiferous mudstone strata. The major geological units were mapped successfully using simulated Mars‐2020 data. Simulated Mastcam‐Z data identified unit boundaries and Fe‐bearing weathering products. Simulated SuperCam passive shortwave infrared and green Raman data were essential in identifying major mineralogical composition and changes in lacustrine facies at distance; this was possible even with spectrally downsampled passive IR data. Laser‐induced breakdown spectroscopy and simulated PIXL data discriminated and mapped major element chemistry. Simulated PIXL revealed millimeter‐scale zones enriched in zirconium, of interest for age dating. Scanning for SHERLOC‐like data mapped sulfate and carbonate at submillimeter scale; silicates were identified with increased laser pulses/spot or by averaging of hundreds of spectra. Fluorescence scans detected and mapped varied classes of organics in all samples, characterized further with follow‐on spatially targeted deep‐UV Raman spectra. Development of dedicated organics spectral libraries is needed to aid interpretation. Given these observations, the important units in the outcrop would be sampled and cached for sample return.