Reviewerly/exHarmony huggingface dataset:
Integral Matrix Gram Root and Lattice Gaussian Sampling Without Floats

Many advanced lattice based cryptosystems require to sample lattice points from Gaussian distributions. One challenge for this task is that all current algorithms resort to floating-point arithmetic (FPA) at some point, which has numerous drawbacks in practice: it requires numerical stability analysis, extra storage for high-precision, lazy/backtracking techniques for efficiency, and may suffer from weak determinism which can completely break certain schemes. In this paper, we give techniques to implement Gaussian sampling over general lattices without using FPA. To this end, we revisit the approach of Peikert, using perturbation sampling. Peikert's approach uses continuous Gaussian sampling and some decomposition $$\mathbf {\Sigma }= \mathbf {A}\mathbf {A}^t$$ of the target covariance matrix $$\mathbf {\Sigma }$$ . The suggested decomposition, e.g. the Cholesky decomposition, gives rise to a square matrix $$\mathbf {A}$$ with real (not integer) entries. Our idea, in a nutshell, is to replace this decomposition by an integral one. While there is in general no integer solution if we restrict $$\mathbf {A}$$ to being a square matrix, we show that such a decomposition can be efficiently found by allowing $$\mathbf {A}$$ to be wider (say $$n \times 9n$$ ). This can be viewed as an extension of Lagrange's four-square theorem to matrices. In addition, we adapt our integral decomposition algorithm to the ring setting: for power-of-2 cyclotomics, we can exploit the tower of rings structure for improved complexity and compactness.
Forecasting Photovoltaic Power Generation via an IoT Network Using Nonlinear Autoregressive Neural Network

This research work is an attempt to introduce modern computing techniques as a potential decision-making tool in the field of renewable energy supply and management. We aim to demystify and take advantage of the concept of neural networks to predict the conversion of solar energy by a photovoltaic unit. In order to do so, a smart meter will be built and connected to a low power photovoltaic panel, the smart meter once in operation will capture a set of data, send them autonomously to a remote server over a LoRa IoT network which will be then processed to make predictions about the amount of power produced. Using Non Linear Autoregressive Neural Networks (NARX) with Matlab and Thingspeak IoT data capture, the results are favourable for open loop configuration although closed loop can be further improved, recommendations for the same are made at the end.
Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates

Bayesian inference on quantile regression (QR) model with mixed discrete and non-ignorable missing covariates is conducted by reformulating QR model as a hierarchical structure model. A probit regression model is adopted to specify missing covariate mechanism. A hybrid algorithm combining the Gibbs sampler and the Metropolis-Hastings algorithm is developed to simultaneously produce Bayesian estimates of unknown parameters and latent variables as well as their corresponding standard errors. Bayesian variable selection method is proposed to recognize significant covariates. A Bayesian local influence procedure is presented to assess the effect of minor perturbations to the data, priors and sampling distributions on posterior quantities of interest. Several simulation studies and an example are presented to illustrate the proposed methodologies.
A POI-Sensitive Knowledge Graph Based Service Recommendation Method

In the past years, "group psychology" has been fully utilized in service recommendation. The wide use of collaborative filtering recommendation and user's trust in the rating of service have proved its success. Content-based recommendation further optimizes the recommendation accuracy by strengthening the analysis on service contents. However, existing works pay little attention to the content details and their underlying semantic correlations. This paper proposes a knowledge graph based method that comprehensively considers both community effect and details of service contents. We build a knowledge graph for representing service supplydemand networks, and incorporate the community network structures of users and services into it. Further, we give a method of mining POIs (Point of Interests) of users from user reviews based on RAKE text mining algorithm and incorporate POIs into the knowledge graph. User reviews, no matter praise or criticism, always imply user preferences on specific service contents. Finally, we design a recommendation algorithm POIKG RS based on Representation Learning method of Knowledge Graph. A set of experiments demonstrate that the proposed approach can better utilize the characteristics of different group of users and their POIs, resulting in improved recommendation accuracy.
Quantum computing takes flight

A programmable quantum computer has been reported to outperform the most powerful conventional computers in a specific task — a milestone in computing comparable in importance to the Wright brothers' first flights. A 53-qubit processor demonstrates quantum supremacy.
An Evaluation of New Values in Economy and Their Impacts on Future Transformation in Tourism

In this period, when “industrial society” has emerged as an “information society” by experiencing transformation with the effect of a revolution like Industry 4.0, the innovations of digital values in the new economy play an important role. It is inevitable that digital communication networks, bitcoin and blockchain technologies that will change the ways of communication between producer and consumer, will have effects in the transformation of tourism industries as in many industries. The shaping of the elements of change in the economy with the effect of digitalization makes the studies on the relation between economy and technology important in tourism sector as in many other sectors. In this context, a literature study was carried out on the use of blockchain technologies in tourism industries in order to demonstrate the economic policies that can be developed in the direction of transformation in tourism, the place of new technological-economic values in the tourism economy and their use in the field of economics. In this context, a literature study has been carried out on the economic policies that can be developed for the transformation of tourism, the place of new technological-economic values in the tourism economy and the usage of blockchain technologies in tourism industries. This study aims to examine the new values in the economy and the possible effects on the transformation in tourism at the conceptual level and to develop a perspective on the changes in the sector.
Weakly Supervised Segmentation from Extreme Points

Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks. We show that the network's predictions can be refined through several iterations of training and prediction using the same weakly annotated data. Ultimately, our method has the potential to speed up the generation process of new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis.
A C-LSTM with Word Embedding Model for News Text Classification

Traditional text classification methods are based on statistics and feature selection. It does not perform well in processing large - scale corpus. In recent years, with the rapid development of deep learning and artificial neural networks, many scholars use them to solve text classification problems and achieve good results. Common text classification neural network models include textCNN, LSTM, and C-LSTM. Using a specific model can obtain more accurate features but ignore the context information. This paper proposes a C-LSTM with word embedding model to deal with this problem. Experiments show that the model proposed in this paper has great advantages in Chinese news text classification.
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction

Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.
On the Generation of Medical Question-Answer Pairs

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. A series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system. 1
Fast Polynomial Inversion for Post Quantum QC-MDPC Cryptography

The NIST PQC standardization project evaluates multiple new designs for post-quantum Key Encapsulation Mechanisms (KEMs). Some of them present challenging tradeoffs between communication bandwidth and computational overheads. An interesting case is the set of QC-MDPC based KEMs. Here, schemes that use the Niederreiter framework require only half the communication bandwidth compared to schemes that use the McEliece framework. However, this requires costly polynomial inversion during the key generation, which is prohibitive when ephemeral keys are used. One example is BIKE, where the BIKE-1 variant uses McEliece and the BIKE-2 variant uses Niederreiter. This paper shows an optimized constant-time polynomial inversion method that makes the computation costs of BIKE-2 key generation tolerable. We report a speedup of $$11.8{\times }$$ over the commonly used NTL library, and $$55.5{\times }$$ over OpenSSL. We achieve additional speedups by leveraging the latest Intel’s Vector- instructions on a laptop machine, $$14.3{\times }$$ over NTL and $$96.8{\times }$$ over OpenSSL. With this, BIKE-2 becomes a competitive variant of BIKE.
Watch+Strap: Extending Smartwatches with Interactive StrapDisplays

While smartwatches are widely adopted these days, their input and output space remains fairly limited by their screen size. We present StrapDisplays-interactive watchbands with embedded display and touch technologies-that enhance commodity watches and extend their input and output capabilities. After introducing the physical design space of these StrapDisplays, we explore how to combine a smartwatch and straps in a synergistic Watch+Strap system. Specifically, we propose multiple interface concepts that consider promising content distributions, interaction techniques, usage types, and display roles. For example, the straps can enrich watch apps, display visualizations, provide glanceable feedback, or help avoiding occlusion issues. Further, we provide a modular research platform incorporating three StrapDisplay prototypes and a flexible web-based software architecture, demonstrating the feasibility of our approach. Early brainstorming sessions with 15 participants informed our design process, while later interviews with six experts supported our concepts and provided valuable feedback for future developments.
PyTrack: An end-to-end analysis toolkit for eye tracking

Eye tracking is a widely used tool for behavioral research in the field of psychology. With technological advancement, we now have specialized eye-tracking devices that offer high sampling rates, up to 2000 Hz, and allow for measuring eye movements with high accuracy. They also offer high spatial resolution, which enables the recording of very small movements, like drifts and microsaccades. Features and parameters of interest that characterize eye movements need to be algorithmically extracted from raw data as most eye trackers identify only basic parameters, such as blinks, fixations, and saccades. Eye-tracking experiments may investigate eye movement behavior in different groups of participants and in varying stimuli conditions. Hence, the analysis stage of such experiments typically involves two phases, (i) extraction of parameters of interest and (ii) statistical analysis between different participants or stimuli conditions using these parameters. Furthermore, the datasets collected in these experiments are usually very large in size, owing to the high temporal resolution of the eye trackers, and hence would benefit from an automated analysis toolkit. In this work, we present PyTrack, an end-to-end open-source solution for the analysis and visualization of eye-tracking data. It can be used to extract parameters of interest, generate and visualize a variety of gaze plots from raw eye-tracking data, and conduct statistical analysis between stimuli conditions and subject groups.
Human Consensus-Oriented Image Captioning

Image captioning aims to describe an image with a concise, accurate, and interesting sentence. To build such an automatic neural captioner, the traditional models align the generated words with a number of human-annotated sentences to mimic human-like captions. However, the crowd-sourced annotations inevitably come with data quality issues such as grammatical errors, wrong identification of visual objects and sub-optimal sentence focus. During the model training, existing methods treat all the annotations equally regardless of the data quality. In this work, we explicitly engage human consensus to measure the quality of ground truth captions in advance, and directly encourage the model to learn high quality captions with high priority. Therefore, the proposed consensus-oriented method can accelerate the training process and achieve superior performance with only supervised objective without time-consuming reinforcement learning. The novel consensus loss can be implemented into most of the existing state-of-the-art methods, boosting the BLEU-4 performance by maximum relative 12.47% comparing to the conventional cross-entropy loss. Extensive experiments are conducted on MS-COCO Image Captioning dataset demonstrating the proposed human consensus-oriented training method can significantly improve the training efficiency and model effectiveness.
Good and bad events: combining network-based event detection with sentiment analysis

Abstract The huge volume and velocity of media content published on the Web presents a substantial challenge to human analysts. In prior work, we developed a system (network event detection, NED) to assist analysts by detecting events within high-volume news streams in real time. NED can process a heterogeneous stream of news articles or social media user posts, combining text mining and network analysis to detect breaking news stories and generate an easy-to-understand event summary. In this paper, we expand the NED event detection and summarisation approach in two ways. First, we introduce a new approach to named entity disambiguation for tweets, which contain minimal information due to brevity. Second, we apply sentiment analysis techniques to documents associated with a detected event to characterise the event as either broadly ‘positive’ or ‘negative’ based on media portrayal. Our expansion focuses on Twitter streams since Twitter has become an important news dissemination platform and is often the site where emerging events are first seen. To test the extended methodology, we apply it here to three data sets related to political elections in the UK and the USA. The addition of sentiment analysis to the NED event detection methodology improves the insight gained by the user by allowing quick evaluation of the perceived impact of an event. This approach may have potential applications in domains where public sentiment is relevant to decision-making around events, such as financial markets and politics.
System Architecture Modeling for Electronic Systems Using MathWorks System Composer and Simulink

Electronic system architectures have traditionally been documented as static block diagrams in tools such as Microsoft <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Visio <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> or through a richer modeling approach such as Systems Modeling Language (SysML). These approaches did not fully meet the modeling needs for the Gulfstream authors, which led to an alternative approach. This paper introduces the Electronic System Architecture Modeling (eSAM) method, which leverages a new system architecture modeling tool called System Composer™. eSAM was created by the authors to define a standard method for applying the generic System Composer modeling constructs to build functional, physical, and logical architecture models of electronic systems. The eSAM methods are applied to an example avionics architecture to demonstrate capabilities needed for system modeling, collaborative OEM-supplier workflows, data management and ICD generation, systems integration activities, generation of system architecture deliverables for the avionics certification standards governed by SAE ARP4754A, and a Model-Based Design approach that connects a software function to its system-level ICD. System Composer is built on MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> and Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> and leverages the modeling, analysis, and simulation capabilities of these well-established tools. System Composer adds additional capabilities for modeling integration between systems, filtering large models into manageable views, capturing important system and component properties, allocating between different descriptive architecture models, directly connecting system architecture models to software functional models, and flowing data down into specialized design tools. This paper summarizes desirable features in system architecture modeling tools, introduces the features and concepts of System Composer and describes application of the eSAM method.
Miniaturized five fundamental issues about visual knowledge

认知心理学早已指出, 人类知识记忆中的重要部分是视觉知识, 被用来进行形象思维. 因此, 基于视觉的人工智能 (AI) 是AI绕不开的课题, 且具有重要意义. 本文继《论视觉知识》一文, 讨论与之相关的5个基本问题: (1) 视觉知识表达; (2) 视觉识别; (3) 视觉形象思维模拟; (4) 视觉知识的学习; (5) 多重知识表达. 视觉知识的独特优点是具有形象的综合生成能力, 时空演化能力和形象显示能力. 这些正是字符知识和深度神经网络所缺乏的. AI 与计算机辅助设计/图形学/视觉的技术联合将在创造、 预测和人机融合等方面对 AI 新发展提供重要的基础动力. 视觉知识和多重知识表达的研究是发展新的视觉智能的关键, 也是促进 AI 2.0 取得重要突破的关键理论与技术. 这是一块荒芜、 寒湿而肥沃的"北大荒", 也是一块充满希望值得多学科合作勇探的 "无人区".
A Recurrent Vision-and-Language BERT for Navigation

Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language(V&L) BERT. However, its application for the task of vision-and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable Markov decision process present in VLN, requiring history-dependent attention and decision making. In this paper we propose a recurrent BERT model that is time-aware for use in VLN. Specifically, we equip the BERT model with a recurrent function that maintains cross-modal state information for the agent. Through extensive experiments on R2R and REVERIE we demonstrate that our model can replace more complex encoder-decoder models to achieve state-of-the-art results. Moreover, our approach can be generalised to other transformer-based architectures, supports pre-training, and is capable of solving navigation and referring expression tasks simultaneously.
Optimal Selection of Features Using Teaching-Learning-Based Optimization Algorithm for Classification

In the last few decades, there has been an enormous growth and generation of data from various domains. This has presented a big challenge to researchers regarding processing and analyzing data. While dealing with some machine-learning problems, the number of features involved has reached the thousands in many cases. Thus it becomes very difficult to handle them as this consumes a lot of computational power during the feature evaluation and classification processes. A large number of features may also affect the performance of the model and increase the learning time. Hence, it is very important to select the minimum number of relevant features instead of all features. The objective of this chapter is to determine an optimal subset of features among the original features which can effectively represent the underlying data. These significant features increase performance and help the model to learn faster. It is also a very efficient way to reduce the computational cost and increase accuracy. It removes the irrelevant, redundant, and insignificant data that can improve learning accuracy, and reduce the number of features and computation time for the best interpretation of the learning model. In this chapter, teaching-learning-based optimization (TLBO), based on the feature selection (FS) approach called FSTLBO, is proposed for finding the optimal features. This approach updates the weak features with strong features. The experimental results show that the FSTLBO approach has significant improvements on performance as compared to other models for the selection of optimal features. This approach provides an effective method to determine the optimal features for classification problems to support a data-based decision-making process.