Reviewerly/exHarmony huggingface dataset:
An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures

Earlier approaches indirectly studied the information captured by the hidden states of recurrent and non-recurrent neural machine translation models by feeding them into different classifiers. In this paper, we look at the encoder hidden states of both transformer and recurrent machine translation models from the nearest neighbors perspective. We investigate to what extent the nearest neighbors share information with the underlying word embeddings as well as related WordNet entries. Additionally, we study the underlying syntactic structure of the nearest neighbors to shed light on the role of syntactic similarities in bringing the neighbors together. We compare transformer and recurrent models in a more intrinsic way in terms of capturing lexical semantics and syntactic structures, in contrast to extrinsic approaches used by previous works. In agreement with the extrinsic evaluations in the earlier works, our experimental results show that transformers are superior in capturing lexical semantics, but not necessarily better in capturing the underlying syntax. Additionally, we show that the backward recurrent layer in a recurrent model learns more about the semantics of words, whereas the forward recurrent layer encodes more context.
Knowledge-Aware Conversational Semantic Parsing over Web Tables

Conversational semantic parsing over tables requires knowledge acquiring and reasoning abilities, which have not been well explored by current state-of-the-art approaches. Motivated by this fact, we propose a knowledge-aware semantic parser to improve parsing performance by integrating various types of knowledge. In this paper, we consider three types of knowledge, including grammar knowledge, expert knowledge, and external resource knowledge. First, grammar knowledge empowers the model to effectively replicate previously generated logical form, which effectively handles the co-reference and ellipsis phenomena in conversation Second, based on expert knowledge, we propose a decomposable model, which is more controllable compared with traditional end-to-end models that put all the burdens of learning on trial-and-error in an end-to-end way. Third, external resource knowledge, i.e., provided by a pre-trained language model or an entity typing model, is used to improve the representation of question and table for a better semantic understanding. We conduct experiments on the SequentialQA dataset. Results show that our knowledge-aware model outperforms the state-of-the-art approaches. Incremental experimental results also prove the usefulness of various knowledge. Further analysis shows that our approach has the ability to derive the meaning representation of a context-dependent utterance by leveraging previously generated outcomes.
Short Paper: The Proof is in the Pudding

We propose a proof of work protocol that computes the discrete logarithm of an element in a cyclic group. Individual provers generating proofs of work perform a distributed version of the Pollard rho algorithm. Such a protocol could capture the computational power expended to construct proof-of-work-based blockchains for a more useful purpose, as well as incentivize advances in hardware, software, or algorithms for an important cryptographic problem. We describe our proposed construction and elaborate on challenges and potential trade-offs that arise in designing a practical proof of work.
Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction

In just a few years, online dating has become the dominant way that young people meet to date, making the deceptively error-prone task of picking good dating profile photos vital to a generation’s ability to form romantic connections. Until now, artificial intelligence approaches to Dating Photo Impression Prediction (DPIP) have been very inaccurate, unadaptable to real-world application, and have only taken into account a subject’s physical attractiveness. To that effect, we propose Photofeeler-D3 - the first convolutional neural network as accurate as 10 human votes for how smart, trustworthy, and attractive the subject appears in highly variable dating photos. Our “attractive” output is also applicable to Facial Beauty Prediction (FBP), making Photofeeler-D3 state-of-the-art for both DPIP and FBP. We achieve this by leveraging Photofeeler’s Dating Dataset (PDD) with over 1 million images and tens of millions of votes, our novel technique of voter modeling, and cutting-edge computer vision techniques.
Hierarchical authority based weighted attribute encryption scheme

With the development of cloud storage technology, data storage security has become increasingly serious. Aiming at the problem that existing attribute-based encryption schemes do not consider hierarchical authorities and the weight of attribute. A hierarchical authority based weighted attribute encryption scheme is proposed. This scheme will introduce hierarchical authorities and the weight of attribute into the encryption scheme, so that the authorities have a hierarchical relationship and different attributes have different importance. At the same time, the introduction of the concept of weight makes this scheme more flexible in the cloud storage environment and enables fine-grained access control. In addition, this scheme implements an online/offline encryption mechanism to improve the security of stored data. Security proof and performance analysis show that the scheme is safe and effective, and it can resist collusion attacks by many malicious users and authorization centers. It is more suitable for cloud storage environments than other schemes.
Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation

Background Since its inception, artificial intelligence has aimed to use computers to help make clinical diagnoses. Evidence-based medical reasoning is important for patient care. Inferring clinical diagnoses is a crucial step during the patient encounter. Previous works mainly used expert systems or machine learning–based methods to predict the International Classification of Diseases - Clinical Modification codes based on electronic health records. We report an alternative approach: inference of clinical diagnoses from patients’ reported symptoms and physicians’ clinical observations. Objective We aimed to report a natural language processing system for generating medical assessments based on patient information described in the electronic health record (EHR) notes. Methods We processed EHR notes into the Subjective, Objective, Assessment, and Plan sections. We trained a neural network model for medical assessment generation (N2MAG). Our N2MAG is an innovative deep neural model that uses the Subjective and Objective sections of an EHR note to automatically generate an “expert-like” assessment of the patient. N2MAG can be trained in an end-to-end fashion and does not require feature engineering and external knowledge resources. Results We evaluated N2MAG and the baseline models both quantitatively and qualitatively. Evaluated by both the Recall-Oriented Understudy for Gisting Evaluation metrics and domain experts, our results show that N2MAG outperformed the existing state-of-the-art baseline models. Conclusions N2MAG could generate a medical assessment from the Subject and Objective section descriptions in EHR notes. Future work will assess its potential for providing clinical decision support.
Attention Network Robustification for Person ReID

The task of person re-identification (ReID) has attracted growing attention in recent years leading to improved performance, albeit with little focus on real-world applications. Most SotA methods are based on heavy pre-trained models, e.g. ResNet50 (~25M parameters), which makes them less practical and more tedious to explore architecture modifications. In this study, we focus on a small-sized randomly initialized model that enables us to easily introduce architecture and training modifications suitable for person ReID. The outcomes of our study are a compact network and a fitting training regime. We show the robustness of the network by outperforming the SotA on both Market1501 and DukeMTMC. Furthermore, we show the representation power of our ReID network via SotA results on a different task of multi-object tracking.
Deep Reinforcement Learning in HOL4.

The paper describes a deep reinforcement learning framework based on self-supervised learning within the proof assistant HOL4. A close interaction between the machine learning modules and the HOL4 library is achieved by the choice of tree neural networks (TNNs) as machine learning models and the internal use of HOL4 terms to represent tree structures of TNNs. Recursive improvement is possible when a task is expressed as a search problem. In this case, a Monte Carlo Tree Search (MCTS) algorithm guided by a TNN can be used to explore the search space and produce better examples for training the next TNN. As an illustration, term synthesis tasks on combinators and Diophantine equations are specified and learned. We achieve a success rate of 65% on combinator synthesis problems outperforming state-of-the-art ATPs run with their best general set of strategies. We set a precedent for statistically guided synthesis of Diophantine equations by solving 78.5% of the generated test problems.
Short-term PV power forecasting based on sky-conditions using intelligent modelling techniques

The work in this paper involves intelligent modelling techniques i.e. fuzzy logic, Artificial Neural Network (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) methodologies for estimating the power in a solar photovoltaic (SPV) system. Since, the generation of power is subjective to environmental factors such as ambient temperature, variation in sky- conditions and solar insolation, therefore, an intelligent modelling techniques have been proposed for forecasting the power of a solar photovoltaic system employing 210 W Heterojunction with Intrinsic Thin layer (HIT) photovoltaic modules for different sky-conditions such as clear sky, hazy sky, partially foggy/cloudy sky and fully foggy/cloudy sky conditions respectively for composite climate zone and performance has been evaluated using statistical indicators.Keywords: ANFIS (adaptive neural-fuzzy inference system); ANN (artificial neural network); forecasting; fuzzy logic; sky- condition; SPV system.
Towards the Design of a Covert Channel by Using Web Tracking Technologies

Browser Fingerprinting is the process in which the device and browser-related properties (or attributes) are collected through the browser for various reasons, especially, for user identification. The user is monitored through the tracking and collection of technical information, also detecting intrinsic properties of the device being analyzed. In particular, the collected results provide, if properly combined, sufficient information to profile and even identify a device. Those attributes include system information, such as screen dimensions, software versions and plugins, user-installed system fonts list, time zone, language and browser configuration. Browser profiling techniques are activities that typically invade user privacy. The objective of this work is to use those technologies underlying profiling systems for a purpose opposite to the one just indicated, i.e., to provide a mechanism for protecting user privacy by creating hidden communication channels. Usually, privacy protection is achieved by using cryptographic techniques. The main limitation of those techniques consists in exposing not the content of the communication but the communication itself. In this paper, the use of Steganography is motivated by this. Considering the wide use of the web technologies, in addition to the increased attention to the privacy of users connected to the Network, the aim is to analyze and design a steganographic system in order to create a covert channel between two communicating peers through the HTTP protocol.
Supervised Morphological Segmentation Using Rich Annotated Lexicon

Morphological segmentation of words is the process of dividing a word into smaller units called morphemes; it is tricky especially when a morphologically rich or polysynthetic language is under question.In this work, we designed and evaluated several Recurrent Neural Network (RNN) based models as well as various other machine learning based approaches for the morphological segmentation task.We trained our models using annotated segmentation lexicons.To evaluate the effect of the training data size on our models, we decided to create a large hand-annotated morphologically segmented corpus of Persian words, which is, to the best of our knowledge, the first and the only segmentation lexicon for the Persian language.In the experimental phase, using the hand-annotated Persian lexicon and two smaller similar lexicons for Czech and Finnish languages, we evaluated the effect of the training data size, different hyperparameters settings as well as different RNN-based models.
Smart Wallets on Blockchain—Attacks and Their Costs

Smart wallets are the future of e-payments and digital payments but to utilize them to their full potential, we need to secure them from malicious actors who have already started exploiting various vulnerabilities in the existing wallets. In this work, we study the existing attacks and vulnerabilities and present possible hypothetical attack scenarios which may get executed in future by these particular vulnerabilities. We have surveyed on different attacks with comparison of attack cost and benefits of the attacker and comparison of mitigation cost and damage cost of each attack. We focus on the different attacks and usecases on the blockchain smart wallets which would help developers to secure the smart wallets. We describe each attack with its mechanism, usecase, benefits and requirements of attacker for successful attack with the possible damage scenarios and consequences, comparison of attack cost and benefits, comparison of mitigation cost and damage cost, possible mitigation and some security measures for each attack.
Compensation of nonlinear errors of phase-shifted fringes in phase measuring deflectometry

In the phase measuring deflectometry, the phase error caused by the nonlinear intensity response, called the gamma distortion, can negatively affect the measurement quality of specular surfaces. Based on the generic exponential four-step phase-shifting fringe modal, this paper proposes a flexible and simple phase retrieval method to eliminate the phase errors without complex calibration or additional fringe patterns. The experimental results illustrate that the proposed method can accurately retrieve the phases from the distorted fringe patterns with the Gamma distortion, and the measurement precision can henceforth be improved.
Towards a technocratic governance system?

The Blockchain technology: Towards utopia or dystopia? In his book, Campbell-Verduyn (2018) brings scholars of anthropology, economics, science, technology and sociology together with global political economic scholars to assess the actual implications posed by Bitcoin and blockchain for contemporary global governance. Its interdisciplinary contributions provide better understandings of technological change in the evolving character of governance.
On a Class of Objective Priors from Scoring Rules (with Discussion)

Objective prior distributions represent an important tool that allows one to have the advantages of using a Bayesian framework even when information about the parameters of a model is not available.The usual objective approaches work off the chosen statistical model and in the majority of cases the resulting prior is improper, which can pose limitations to a practical implementation, even when the complexity of the model is moderate.In this paper we propose to take a novel look at the construction of objective prior distributions, where the connection with a chosen sampling distribution model is removed.We explore the notion of defining objective prior distributions which allow one to have some degree of flexibility, in particular in exhibiting some desirable features, such as being proper, or log-concave, convex etc.The basic tool we use are proper scoring rules and the main result is a class of objective prior distributions that can be employed in scenarios where the usual model based priors fail, such as mixture models and model selection via Bayes factors.In addition, we show that the proposed class of priors is the result of minimising the information it contains, providing solid interpretation to the method.
Extracting Complex Relations from Banking Documents

In order to automate banking processes (e.g.payments, money transfers, foreign trade), we need to extract banking transactions from different types of mediums such as faxes, e-mails, and scanners.Banking orders may be considered as complex documents since they contain quite complex relations compared to traditional datasets used in relation extraction research.In this paper, we present our method to extract intersentential, nested and complex relations from banking orders, and introduce a relation extraction method based on maximal clique factorization technique.We demonstrate 11% error reduction over previous methods.
Algorithms and programs of suboptimal nonlinear filtering for Markov processes

The problem of optimal recurrence estimation of state vector for a nonlinear stochastic dynamic system, which can be multi-modal (multi-structure, switching, logical-dynamical), according to discrete nonlinear measurements is considered. The problem of obtaining real-time estimates on a low power computer is discussed. The covariance Gaussian and Taylorian approximations are used both to the classical infinite-dimensional filter and to a number of new finite-dimensional optimized structure filters. The software package for modeling these filters by the Monte-Carlo method is described.
Image steganalysis feature selection based on the improved Fisher criterion

In order to improve the detection accuracy of hidden message in images, steganalysis features are selected as inputs for steganalysers. However, the existing Fisher criterion ignores the contribution of steganalysis feature components in dispersion to classification, which causes the useful feature components to be deleted, and decreases the detection accuracy of steganalysis features. By analyzing the separability of steganalysis feature components, we introduce the sigmoid function into Fisher's criterion and propose an improved Fisher criterion (I-Fisher criterion), which can make up for the traditional Fisher criterion in separability measurement of steganalysis feature components. To optimize the steganalysis feature and reduce its dimension, we employ the improved Fisher criterion as the heuristic function of the decision rough set <i>α</i>-positive region reduction, and propose the feature selection method based on the improved Fisher. Experimental results show that the proposed method can reduce the dimension and memory of the GFR high-dimensional feature and the CC-PEV lowdimensional feature while maintaining or improving the detection accuracy.
Question Similarity in Community Question Answering: A Systematic Exploration of Preprocessing Methods and Models

Community Question Answering forums are popular among Internet users, and a basic problem they encounter is trying to find out if their question has already been posed before.To address this issue, NLP researchers have developed methods to automatically detect question-similarity, which was one of the shared tasks in Se-mEval.The best performing systems for this task made use of Syntactic Tree Kernels or the SoftCosine metric.However, it remains unclear why these methods seem to work, whether their performance can be improved by better preprocessing methods and what kinds of errors they (and other methods) make.In this paper, we therefore systematically combine and compare these two approaches with the more traditional BM25 and translation-based models.Moreover, we analyze the impact of preprocessing steps (lowercasing, suppression of punctuation and stop words removal) and word meaning similarity based on different distributions (word translation probability, Word2Vec, fastText and ELMo) on the performance of the task.We conduct an error analysis to gain insight into the differences in performance between the system set-ups.The implementation is made publicly available. 1
VikingDet: A Real-time Person and Face Detector for Surveillance Cameras

In this paper, we propose a novel one-stage detector that can simultaneously detect both pedestrians and their faces. The framework is named as VikingDet for its simple but effective two-headed architecture. To tackle the challenges of person and face detection especially under surveillance cameras (e.g. low data quality, complex environments, requirements for efficiency, etc.), we make contributions in the following several aspects: 1) integrating both person and face detection into one network which current leading object detection algorithms are seldomly able to handle; 2) emphasizing detection in low-quality images. we introduce multiple thresholds for matching different sized positive samples, and set proper hyper-parameters, hence our VikingDet is able to locate small objects in surveillance cameras even of low-quality; 3) introducing a training strategy to utilize datasets on hand. Since most available public datasets annotate only people without their faces or faces without bodies, we use multi-step training and an integrated loss function to train VikingDet with these partly annotated data. As a consequence, our detector achieves satisfactory performances in several relative benchmarks with a speed at more than 60 FPS on NVIDIA TITAN X GPU, and can be further deployed on an embedded device such as NVIDIA Jetson TX1 or TX2 with a real-time speed of over 28 FPS.
DiCoDiLe: Distributed Convolutional Dictionary Learning

Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to represent signals or images. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. Contrarily to standard patch-based dictionary learning, patterns estimated by CDL can be positioned anywhere in signals or images. Optimization techniques consequently face the difficulty of working with extremely large inputs with millions of pixels or time samples. To address this optimization problem, we propose a distributed and asynchronous algorithm, employing locally greedy coordinate descent and a soft-locking mechanism that does not require a central server. Computation can be distributed on a number of workers which scales linearly with the size of the data. The parallel computation accelerates the parameter estimation and the distributed setting allows our algorithm to be used with data that do not fit into a single computer's RAM. Experiments confirm the theoretical scaling properties of the algorithm. This allows to demonstrate an improved pattern recovery as images grow in size, and to learn patterns on images from the Hubble Space Telescope containing tens of millions of pixels.