Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Bit-Serial Matrix Multiplication for Reconfigurable Computing

Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many matrix multiplication-dependent applications can use reduced-precision integer or fixed-point representations to increase their performance and energy efficiency while still offering adequate quality of results. However, precision requirements may vary between different application phases or depend on input data, rendering constant-precision solutions ineffective. BISMO, a vectorized bit-serial matrix multiplication overlay for reconfigurable computing, previously utilized the excellent binary-operation performance of FPGAs to offer a matrix multiplication performance that scales with required precision and parallelism. We show how BISMO can be scaled up on Xilinx FPGAs using an arithmetic architecture that better utilizes six-input LUTs. The improved BISMO achieves a peak performance of 15.4 binary TOPS on the Ultra96 board with a Xilinx UltraScale+ MPSoC.
SynonymNet: Multi-context Bilateral Matching for Entity Synonyms

Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery. Existing works either require structured annotations, or fail to incorporate context information effectively, which lower the efﬁciency of information usage. In this paper, we propose a framework for synonym discovery from free-text corpus without structured annotation. As one of the key components in synonym discovery, we introduce a novel neural network model S YNONYM N ET to determine whether or not two given entities are synonym with each other. Instead of using entities features, S YNONYM N ET makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema to determine synonymity. Experimental results demonstrate that the proposed model achieves state-of-the-art results on both generic and domain-speciﬁc synonym datasets: Wiki+Freebase, PubMed+UMLS and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve (AUC) and 3.19% in terms of Mean Average Precision (MAP) compare to the best baseline method.
Online Monitoring of Metric Temporal Logic using Sequential Networks

Metric Temporal Logic (MTL) is a popular formalism to specify patterns with timing constraints over the behavior of cyber-physical systems. In this paper, I propose sequential networks for online monitoring applications and construct network-based monitors from the past fragment of MTL over discrete and dense time behaviors. This class of monitors is more compositional, extensible, and easily implementable than other monitors based on rewriting and automata. I first explain the sequential network construction over discrete time behaviors and then extend it towards dense time by adopting a point-free approach. The formulation for dense time behaviors and MTL radically differs from the traditional pointy definitions and in return, we avoid some longstanding complications. I argue that the point-free approach is more natural and practical therefore should be preferred for the dense time. Finally, I present my implementation together with some experimental results that show the performance of the network-based monitors compared to similar existing tools.
Learning Generalizable Physical Dynamics of 3D Rigid Objects

Humans have a remarkable ability to predict the effect of physical interactions on the dynamics of objects. Endowing machines with this ability would allow important applications in areas like robotics and autonomous vehicles. In this work, we focus on predicting the dynamics of 3D rigid objects, in particular an object's final resting position and total rotation when subjected to an impulsive force. Different from previous work, our approach is capable of generalizing to unseen object shapes - an important requirement for real-world applications. To achieve this, we represent object shape as a 3D point cloud that is used as input to a neural network, making our approach agnostic to appearance variation. The design of our network is informed by an understanding of physical laws. We train our model with data from a physics engine that simulates the dynamics of a large number of shapes. Experiments show that we can accurately predict the resting position and total rotation for unseen object geometries.
Learning Humanoid Robot Motions Through Deep Neural Networks

Controlling a high degrees of freedom humanoid robot is acknowledged as one of the hardest problems in Robotics. Due to the lack of mathematical models, an approach frequently employed is to rely on human intuition to design keyframe movements by hand, usually aided by graphical tools. In this paper, we propose a learning framework based on neural networks in order to mimic humanoid robot movements. The developed technique does not make any assumption about the underlying implementation of the movement, therefore both keyframe and model-based motions may be learned. The framework was applied in the RoboCup 3D Soccer Simulation domain and promising results were obtained using the same network architecture for several motions, even when copying motions from another teams.
Prospections archéologiques dans le Hawrân (Syrie)

Voici le rapport de reperages des sites preromains des zones 2 et 3 du Hawan syrien (entre Bosra et Damas). Cette prospection generale a pour but de definir l'occupation de cette region, depuis la periode chalcholithique jusqu'a l'âge du Fer. Les temoignages les plus consequents sont ceux du Bronze moyen (38 sites sur 78 visites). Malgre le choix de cette periode, l'auteur mentionne egalement la decouverte de ceramique et de vestiges romains et postromains sur chaque site concerne.
Approximate computation for big data analytics

Over the past a few years, research and development has made significant progresses on big data analytics. A fundamental issue for big data analytics is the efficiency. If the optimal solution is unable to attain or unnecessary or has a price to high to pay, it is reasonable to sacrifice optimality with a "good" feasible solution that can be computed efficiently. Existing approximation techniques can be in general classified into approximation algorithms, approximate query processing for aggregate SQL queries and approximation computing for multiple layers of the system stack. In this article, we systematically introduce approximate computation, i.e., query approximation and data approximation, for efficient and effective big data analytics. We explain the ideas and rationales behind query and data approximation, and show efficiency can be obtained with high effectiveness, and even without sacrificing for effectiveness, for certain data analytic tasks.
Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation

Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.
Information Extraction from Scientific Literature for Method Recommendation

As a research community grows, more and more papers are published each year. As a result there is increasing demand for improved methods for finding relevant papers, automatically understanding the key ideas and recommending potential methods for a target problem. Despite advances in search engines, it is still hard to identify new technologies according to a researcher's need. Due to the large variety of domains and extremely limited annotated resources, there has been relatively little work on leveraging natural language processing in scientific recommendation. In this proposal, we aim at making scientific recommendations by extracting scientific terms from a large collection of scientific papers and organizing the terms into a knowledge graph. In preliminary work, we trained a scientific term extractor using a small amount of annotated data and obtained state-of-the-art performance by leveraging large amount of unannotated papers through applying multiple semi-supervised approaches. We propose to construct a knowledge graph in a way that can make minimal use of hand annotated data, using only the extracted terms, unsupervised relational signals such as co-occurrence, and structural external resources such as Wikipedia. Latent relations between scientific terms can be learned from the graph. Recommendations will be made through graph inference for both observed and unobserved relational pairs.
Discrete Neural Processes

Many data generating processes involve latent random variables over discrete combinatorial spaces whose size grows factorially with the dataset. In these settings, existing posterior inference methods can be inaccurate and/or very slow. In this work we develop methods for efficient amortized approximate Bayesian inference over discrete combinatorial spaces, with applications to random permutations, probabilistic clustering (such as Dirichlet process mixture models) and random communities (such as stochastic block models). The approach is based on mapping distributed, symmetry-invariant representations of discrete arrangements into conditional probabilities. The resulting algorithms parallelize easily, yield iid samples from the approximate posteriors, and can easily be applied to both conjugate and non-conjugate models, as training only requires samples from the generative model.
Adversarial Risk Bounds via Function Transformation

We derive bounds for a notion of adversarial risk, designed to characterize the robustness of linear and neural network classifiers to adversarial perturbations. Specifically, we introduce a new class of function transformations with the property that the risk of the transformed functions upper-bounds the adversarial risk of the original functions. This reduces the problem of deriving bounds on the adversarial risk to the problem of deriving risk bounds using standard learning-theoretic techniques. We then derive bounds on the Rademacher complexities of the transformed function classes, obtaining error rates on the same order as the generalization error of the original function classes. We also discuss extensions of our theory to multiclass classification and regression. Finally, we provide two algorithms for optimizing the adversarial risk bounds in the linear case, and discuss connections to regularization and distributional robustness.
Hierarchical Fuzzy Opinion Networks: Top–Down for Social Organizations and Bottom–Up for Election

A fuzzy opinion is a Gaussian fuzzy set with the center representing the opinion and the standard deviation representing the uncertainty about the opinion, and a fuzzy opinion network is a connection of a number of fuzzy opinions in a structured way. In this article, we propose a top–down hierarchical fuzzy opinion network to model how the opinion of a top leader is penetrated into the members in social organizations, and a bottom–up fuzzy opinion network to model how the opinions of a large number of agents are agglomerated layer-by-layer into a consensus or a few opinions in the social processes, such as an election. For the top–down hierarchical fuzzy opinion network, we prove that the opinions of all the agents converge to the leader's opinion but the uncertainties of the agents in different groups are generally converge to different values. We demonstrate that the speed of convergence is greatly improved by organizing the agents in a hierarchical structure of small groups. For the bottom–up hierarchical fuzzy opinion network, we simulate how a wide spectrum of opinions are negotiating and summarizing with each other, in a layer-by-layer fashion in some typical situations.
Influence of Digital Coaching on Physical Activity: Motivation and Behaviour of Physically Inactive Individuals

Digital wellness solutions and their use have become increasingly common. Respectively, the need to understand their users and usage has increased. A general problem with wellness technologies is that they typically provide feedback through numbers and graphs instead of providing actual guidance. One potential and novel solution for this is digital coaching. This study is one of the first to examine digital coaching features and their influence. We define digital coaching in the context of sports and wellness technology and investigate its influence on physical activity motivation and behaviour of physically inactive individuals. The study is based on thematic analysis of 20 semi-structured interviews conducted for 10 participants who used a novel digital coaching solution for one month. The digital coaching solution was found to be beneficial for physical activity and exercise motivation and behaviour. It assisted the users in many ways through different elements and mechanisms. Further, the benefits also spurred to other aspect of physical wellness. The results are discussed and implications provided.
Citizen adoption of e-government services - Evidence from Hungary

In a citizen centric approach – which became increasingly popular in the last decade – e-government success begins with citizens starting to use e-government systems, solutions, services. In line with this our paper investigates the factors – presented by the technology acceptance literature – influencing e-government service usage, on a large representative Hungarian sample concerning a wide range of B2C public administration services. Our results imply that the Hungarian government can further increase the usage of e-government services by influencing effort expectancy, trust of internet, facilitating conditions, user experience or habits.
Context dependent vector quantization for continuous speech recognition

The authors present a method for designing a vector quantizer for speech recognition that uses decision networks constructed by examining the phonetic context to obtain models for classes in the quantizer. Diagonal Gaussian models are constructed for the vector quantizer classes at each terminal node of the network and are used to label speech parameter vectors during recognition. Experimental results indicate that this method leads to superior vector quantizers for continuous speech. <<ETX>>
An heuristic-based context-free parsing algorithm

A heuristic-based parsing algorithm for general stochastic context-free grammars is presented. The algorithm is basically a top-down parser that combines an extension of the A* search paradigm with a constraint satisfaction procedure. Heuristics are used to increase the likelihood of making a correct choice while constraint satisfaction eliminates states that cannot lead to a solution. Two simple heuristics are introduced ensuring convergence to the solution and guaranteeing the choice of the most probable parsing in the case of ambiguous grammars. A comparison of the proposed parser with J. Earley's (1986) algorithm on four different grammars is presented. The efficiency of the proposed method relies on three main aspects: guidance of the search through promising paths, pruning of the state space, based on elimination criteria, and restriction of the number of solutions encountered to the best or the n-best. The last two aspects cannot be met by Earley's algorithm. <<ETX>>
The Lincoln large-vocabulary stack-decoder HMM CSR

Recognition of the Wall Street Journal (WSJ) pilot database, a continuous-speech-recognition (CSR) database which supports 5 K, 20 K, and up to 64 K-word CSR tasks, is examined. The original Lincoln tied-mixture hidden Markov model (HMM) CSR was implemented using a time-synchronous beam-pruned search of a static network which does not extend well to this task because the recognition network would be too large. Therefore, the recognizer has been converted to a stack decoder-based search strategy. This decoder has been shown to function effectively on up to 64 K-word recognition of continuous speech. Recognition-time adaptation has also been added to the recognizer. The acoustic modeling techniques and the implementation of the stack decoder used to obtain these results are described. <<ETX>>
Unsupervised embrace pose recognition method for stuffed-toy robot

ABSTRACT This paper presents a new approach to model and recognize embrace interaction based on an embrace-comfortable fabric-based touchpad attached to a stuffed-toy robot with k-mean clustering of location-based features. Evaluation of the method demonstrated its ability to recognize embrace poses in new users with a suitable number of clusters. Consequently, the proposed class assignment method, which assigned classes based on the most common patterns, was used to determine the number of clusters in the model selection experiment. The experimental results showed that the selected model could obtain sufficient recognition performance, which contributed to a guideline for model selection, based directly on variations of embraces without requiring ground truths. This guideline could potentially be applied to different target populations and definitions of the embrace pose. GRAPHICAL ABSTRACT
A perceptual metric for masking

Analytical tools were developed to predict masking of formant frequencies of steady-state vowels in the presence of a noise masker. Two kinds of masking were studied: within-band masking (where the signal frequency was within the bandwidth of a noise masker) and above-band masking (where the signal frequency was above the upper cutoff frequency of the masker). It was found that results of auditory-masking theory can be used to predict accurately within-band masking. Above-band masking, however, could not be predicted well from existing procedures, and an algorithm was developed to predict this kind of masking. <<ETX>>
Waveform-based speech synthesis approach with a formant frequency modification

A novel approach to speech synthesis based on waveform segments is proposed. One novel point of this approach is its new formant frequency modification algorithm which makes it possible to change formant frequency flexibly and so reproduce the desired speech quality. The algorithm characterizes speech formants not only by formant frequencies and formant bandwidths, but also by spectral intensities of formant frequencies. The desirable formant structure, which is specified by the parameters, is obtained by iteratively modifying the formant bandwidths. Using the specified formant structure, the speech signal is synthesized by the FFT (fast Fourier transform). Evaluation by the acoustic distance measure and by listening tests confirms the good performance of the approach. As evaluated by listening tests, the proposed method was found to increase significantly the naturalness of speech and clearly to increase speech quality. <<ETX>>
Source waveform recovery in a reverberant space by cepstrum dereverberation

The minimum-phase components of source waveforms can be recovered by blind dereverberation using the minimum-phase complex cepstrum. This recovery process is robust for changes in the transfer functions (TFs), since the all-pass components of the responses that are highly sensitive to changes in the TFs can be disregarded. Yet it is still necessary to keep all-pass information if the timing of the source event is required. Dereverberation of reverberant speech signals requires the all-pass components to keep the pitch information. <<ETX>>
Optimally integrated adaptive learning

A new self-organized learning algorithm is proposed that is well suited for the problem of image compression. The network consists of a number of modules corresponding to different classes of input data. Each module consists of an orthonormal linear transformation whose weights are calculated during an initial training period. As the network is trained, each input signal x is classified according to a competitive learning scheme based on the maximum norm of the signal's projection under the class transformation. The classification is optimal in the sense that it minimizes the square error. The class transformation weights are updated according to a Hebbian learning rule which converges to the optimal Karhunen-Loeve transformation (KLT) for each class. The performance of the resulting adaptive network is shown to be superior to that of the optimal non-adaptive linear transformation. <<ETX>>
On the dynamic adaptation of stochastic language models

A simple and general scheme for the adaptation of stochastic language models to changing text styles is introduced. For each word in the running text, the adapted model is a linear combination of specific models, the interpolation parameters being estimated on the preceding text passage. Experiments on a 1.1-million English word corpus show the validity of the approach. The adaptation method improves a bigram language model by 10% in terms of test-set perplexity. <<ETX>>