Miracle-dz/UniFAR-FTUs huggingface dataset:
Wide-Coverage Neural A* Parsing for Minimalist Grammars

Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky’s (1995) Minimalist Program. This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing. The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014; Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees. We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies. Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is cubic in the length of the sentence. The parser is publicly available.
Towards a new model of abstraction in software engineering

Existing abstraction techniques lead to implementations which overly constrain their users. This happens because these techniques force the implementor to make all the decisions about how to implement the abstraction including some which would better be made by the user. The author has developed a new model of abstraction, which he calls the two-view approach; the first view is the traditional one, it provides the functionality of the abstraction, the second view allows the user to participate in some implementation decisions. This approach promises to be general enough to handle a wide range of applications. <<ETX>>
Linearizable Quorum Reads in Paxos

Many distributed systems/databases rely on Paxos for providing linearizable reads. Linearizable reads in Paxos are achieved either through running a full read round with followers, or via reading from a stable leader which holds leases on followers. We introduce a third method for performing linearizable reads by eschewing the leader and only reading from a quorum of followers. For guaranteeing linearizability, a bare quorum read is insufficient and it needs to be amended with a rinse phase to account for pending update operations. We present our Paxos Quorum Read (PQR) protocol that implements this. Our evaluations show that PQR significantly improves throughput compared to the other methods. The evaluations also show that PQR achieves comparable latency to the read from stable Paxos leader optimization.
What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.

The ability to engage in goal-oriented conversations has allowed humans to gain knowledge, reduce uncertainty, and perform tasks more efficiently. Artificial agents, however, are still far behind humans in having goal-driven conversations. In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective. This task is challenging since these questions must not only be consistent with a strategy to achieve a goal, but also consider the contextual information in the image. We propose an end-to-end goal-oriented visual dialogue system, that combines reinforcement learning with regularized information gain. Unlike previous approaches that have been proposed for the task, our work is motivated by the Rational Speech Act framework, which models the process of human inquiry to reach a goal. We test the two versions of our model on the GuessWhat? dataset, obtaining significant results that outperform the current state-of-the-art models in the task of generating questions to find an undisclosed object in an image.
Towards an Implementation of Data Analytics for Smart Grid Security

Given the recent increase in frequency, sophistication and success of cyber-attacks against critical IT infrastructure, such as the Smart Grid, the urgent need for advanced cybersecurity solutions is clearly evident. This paper presents a security information analytics (SIA) framework, using various data analytics methods to detect anomalies in metered data, that may indicate attacks. The implementation of the SIA tool has been applied to a live micro-grid test-bed for the modeling of normal behaviour and for performance analysis. Furthermore, the framework is scalable, allowing additional analysis tools and resilient control solutions to be incorporated, further enhancing the reliability of the system. Keywords–Cyber-physical systems; Intrusion detection; Cybersecurity
Exploiting iterative learning control for input shaping, with application to a wafer stage

The objective of an input design technique is to design an input to the system that results in an optimal tracking performance given some knowledge of the system response. Many such techniques focus on eliminating excitation of certain dominant system poles from the trajectory thus reducing any vibrations caused by these system poles. A downside to these methods is that they result in an elongation of the original trajectory. For a point-to-point control setting this means that there will be a trade-off between the elongation of the trajectory and the reduction of the settling time. In this paper will be shown that iterative learning control (ILC) can be used to design the input signal (trajectory) for a point-to-point motion in a way that eliminates all vibrations in the system without any elongation of the trajectory. This result is exactly the objective of classic input shaping techniques. The technique is illustrated with an application to a high precision wafer-stage.
Augmented Post Systems: Syntax, Semantics, and Applications

Augmented Post systems (APS) are string-operating Prolog-like knowledge representation, affiliated with the “ Set of Strings ” Framework (SSF). APS descriptive and logical inference capabilities provide natural integration of Big Data with online analytic processing. This chapter is dedicated to strict formal definition of APS syntax, mathematical and operational semantics, and to its most valuable implementational issues, as well as to APS application to Big Data, Internet of Things, cyberphysical industry, and cybersecurity areas. every activated F-production is interpreted as residual body of S-production (lines 7 – 9). Every variable declaration δ 0 , obtained as a result of this interpretation, is used for the creation of new message by substitution of δ 0 to actor s . This message is used as input value for function F recursive application. So the wave of messages, trig-gered by initial message, is generated, modeling well-known blackboard architecture . This wave propagation of any programs (including DBMS, software/hardware drivers, providing activation and operation of various devices, as well as network-ing middleware) may be applied. Operator terminate (line 11) stops F execution, so no return to the parent call is performed.
From metaphor to theory: the role of resonance in perceptual learning

Unlike dominant cognitivist theories that take perceptual learning to be a process of enriching sensory stimulation with previous knowledge, ecological psychologists take it to be an enhancement in the detection of already rich perceptual information. The difference between beginners and experts is that the latter detect better information to support their task goals. While the study of perceptual learning in terms of perceptual information and perceiver–environment interactions is common in the ecological literature, ecological psychology still lacks a story regarding the way perceptual information is detected by perceptual systems and the plasticity of such detection in learning events. In this article, I propose the ecological notion of resonance—along with biophysical resonance, non-linear resonance, and metastability—as a plausible foundation to account for the process of detection of perceptual information both in perceptual events and in events of perceptual learning.
Limited by Capacity or Blockage? A Millimeter Wave Blockage Analysis.

Millimeter wave (mmWave) communication systems can provide high data rates but the system performance may degrade significantly due to mobile blockers and the user's own body. A high frequency of interruptions and long duration of blockage may degrade the quality of experience. For example, delays of more than about 10ms cause nausea to VR viewers. Macro-diversity of base stations (BSs) has been considered a promising solution where the user equipment (UE) can handover to other available BSs, if the current serving BS gets blocked. However, an analytical model for the frequency and duration of dynamic blockage events in this setting is largely this http URL this paper, we consider an open park-like scenario and obtain closed-form expressions for the blockage probability, expected frequency and duration of blockage events using stochastic geometry. Our results indicate that the minimum density of BS that is required to satisfy the Quality of Service (QoS) requirements of AR/VR and other low latency applications is largely driven by blockage events rather than capacity requirements. Placing the BS at a greater height reduces the likelihood of blockage. We present a closed-form expression for the BS density-height trade-off that can be used for network planning.
A multidisciplinary perspective on multi-agent systems

The theory, principles and practice of multi-agent systems is typically characterised as a computational and engineering discipline, since it is through the medium of computational systems that artificial agent systems are most commonly expressed. However, most definitions of agency draw directly on non-computational disciplines for inspiration. During the 1999 UK workshop on multi-agent systems, UKMAS'99, we invited four speakers to address the conceptualisation of multi-agent systems from their perspective as non-computer scientists. This paper presents their arguments and summarises some of the key points of discussion during the panel.
Semi-supervised Domain Adaptation for Dependency Parsing

During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing. This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-aware datasets. We propose a simple domain embedding approach to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning. In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.
Search strategies for the automatic delineation of the breast area in a multimedia breast self-examination system

Breast cancer is by far the most common cancer in females. Early detection of lumps in the breast is a key to the successful treatment of this possible fatal disease. Breast self-examination (BSE) is considered as one of the early detection methods that is noninvasive, simple and can be carried out by a woman privately and at any time. However, currently, fully interactive systems do not exist and women depend on their subjective feeling while performing BSE procedures. We aim to develop an intelligent interactive multimedia system incorporating pattern recognition and machine vision techniques, and provide real-time feedback and objective data to assist and guide women to perform BSE accurately. One of the important components of the BSE technique is that the entire breast area is examined, where breast tissue may be encountered, in order to detect all possible lumps. This paper presents two search strategies to delineate the entire breast area which represents the potential boundary for the motion of a hand. This will enable the provision for a woman performing BSE with interactive objective feedback about the area of coverage based on the automatic extraction of her personal and volumetric breast information.
Weighted Independent Directed Acyclic Graphs for Resilient and Efficient Multipath Routing

In order to achieve resilient multipath routing with efficient path choosing dynamically, we introduce the concept of Weighted Independent Directed Acyclic Graphs (WIDAGs). In a given network we develop polynomial-time algorithms to compute link-independent and node-independent DAGs. We use Heuristic methods to dynamically choose the maximum profitable (least cost) route upon a single link failure. The algorithm developed in this paper takes the advantage of the resiliency provided by IDAG and in addition, improves efficiency by choosing least cost path dynamically.
Energy Harvesting for Water Quality Monitoring using Floating Sensor Networks: A Generic Framework.

Water is a constrained asset and basic requirement for animals, cultivation and industry that exist on this Earth including mankind. Subsequently, to measure the characteristics like physical, biological and chemical attributes of water becomes indispensable. Floating sensor networks (FSN) with in-situ sensors are now widely used to gather the water quality data. The main goal that deals with FSNs is data reliability, congestion control, optimal node placement and energy. So far, we proposed a generic framework for energy harvesting, reliable data transform congestion detection and deployment strategy for FSN to collect water quality data. Energy harvesting subsystem model will be designed to fulfill the needs for the desired application scenarios in sensor networks and extend the network lifetime. Therefore, this model will help in scheduling the sending/receiving, sleeping and idle time of the node. By incorporating this framework, network lifetime can be prolonged. For reliable data transform and congestion detection; a model will be developed to enhance the data delivery. The objectives of optimal node deployment are full coverage, network connectivity, improve the data fidelity and enhance the network lifetime.
Confusionset-guided Pointer Networks for Chinese Spelling Check

This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary. We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20% F1 score, achieving state-of-the-art performance on three datasets.
Contrastive Antichains in Hierarchies

Concepts are often described in terms of positive integer-valued attributes that are organized in a hierarchy. For example, cities can be described in terms of how many places there are of various types (e.g. nightlife spots, residences, food venues), and these places are organized in a hierarchy (e.g. a Portuguese restaurant is a type of food venue). This hierarchy imposes particular constraints on the values of related attributes---e.g. there cannot be more Portuguese restaurants than food venues. Moreover, knowing that a city has many food venues makes it less surprising that it also has many Portuguese restaurants, and vice versa. In the present paper, we attempt to characterize such concepts in terms of so-called contrastive antichains: particular kinds of subsets of their attributes and their values. We address the question of when a contrastive antichain is interesting, in the sense that it concisely describes the unique aspects of the concept, and this while duly taking into account the known attribute dependencies implied by the hierarchy. Our approach is capable of accounting for previously identified contrastive antichains, making iterative mining possible. Besides the interestingness measure, we also present an algorithm that scales well in practice, and demonstrate the usefulness of the method in an extensive empirical results section.
Hand Gesture Recognition for Human-Computer Interaction Through Infrared Motion Sensing

This paper presents a method of computer interaction using the IS-300Pro Precision Motion Tracker. The motion tracker compares the gestures with a defined pattern of movement so that it can be recognized. The method is developed using infrared light emitting diodes (IR LEDs) attached to the thumb and index fingers of two gloves, and the IS-300Pro motion tracker connected to a computer using the USB port. A microcontroller unit (MCU) supporting USB connection is employed for computer interface. Software developed specifically for the application is installed in the computer. The system is able to recognize gestures for alphanumeric characters, as well as user-defined gestures. The software interprets the gestures captured by the motion tracker and translates them to corresponding response hardcoded in the software. Three software programs were developed to demonstrate the capabilities of the system. Experiments show that distance between the sensor and IR LEDs affects the accuracy of gesture recognition and interpretation.
Software image management for large networks

One of the difficult problems network administrators encounter in large networks consisting of hundreds and thousands of devices is software management - the problems and issues associated with installing, upgrading, and patching the embedded operating system (OS) running in routers, switches, firewalls, VPN devices, and many other types of devices. The upgrade process for each device may be different depending on the type and capabilities of the device and OS. Wide variations in upgrade procedures may exist, even within the same family of devices. Network administrators require a solution that hides such device-specific details and allows them to focus on the overall task of upgrading software on their network devices in a reliable and robust manner, consistent with prevailing practices and policies in their environment. The paper describes an architecture and framework for solving the problem in a large network. A Web-based application based on this architecture was developed successfully in Java and XML at Cisco Systems.
Time-frequency analysis of click-evoked otoacoustic emissions in noise-induced hearing loss

Click-evoked otacoustic emissions (CEOAEs) were recorded from 15 ears with noise-induced hearing loss (NIHL). To identify hearing loss in a frequency-specific manner, CEOAEs were decomposed into narrow band components by means of the wavelet transform. Test-retest reproducibilty of CEOAE components was compared with the audiometric threshold at corresponding frequencies. Agreement between conventional pure-tone audiometry and CEOAEs was found. A general trend of low reproducibility for increasing audiometric thresholds was observed. The presence of a CEOAE component at a given frequency was always associated with audiometric thresholds /spl les/20-25 dB HL.
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions

Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs’ specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.9% on Math23K with 5-fold cross-validation and from 69.2% to 76.1% on MAWPS.
Real-time On-Device Troubleshooting Recommendation for Smartphones

Billions of people are using smartphones everyday and they often face problems and troubles with both the hardware as well as the software. Such problems lead to frustrated users and low customer satisfaction. Developing an automatic machine learning-based solution that would detect that the user has a problem and would engage in troubleshooting has the potential to significantly improve customer satisfaction and retention. Here, we design and implement a system that based on the user's smartphone activity detects that the user has a problem and requires help. Our system automatically detects a user has a problem and then helps with the troubleshooting by recommending possible solutions to the identified problem. We train our system based on large-scale customer support center data and show that it can both detect that a user has a problem as well as predict the category of the problem (89.7% accuracy) and quickly provide a solution (in 10.4ms). Our system has been deployed in commercial service since January, 2019. Online evaluation result showed that machine learning based approach outperforms the existing method by approximately 30% regarding the user problem solving rate.
Scale‐aware camera localization in 3D LiDAR maps with a monocular visual odometry

Localization information is essential for mobile robot systems in navigation tasks. Many visual‐based approaches focus on localizing a robot within prior maps acquired with cameras. It is critical where the Global Positioning System signal is unreliable. In contrast to conventional methods that localize a camera in an image‐based map, we propose a novel approach that localizes a monocular camera within a given three‐dimensional (3D) light detection and ranging (LiDAR) map. We employ visual odometry to reconstruct a semidense set of 3D points from the monocular camera images. These points are continuously matched against the 3D prior LiDAR map by a modified feature‐based point cloud registration method to track a full six‐degree‐of‐freedom camera pose. Since the monocular camera suffers from the scale‐drift problem due to the lack of depth information, the proposed method solves it by adopting updatable scale estimation. Experiments carried out on a publicly large‐scale data set demonstrate that the camera and LiDAR multimodal data matching problem is solved, and the localization accuracy of our method is comparable to state‐of‐the‐art approaches.
Better Exploiting Latent Variables in Text Modeling

We show that sampling latent variables multiple times at a gradient step helps in improving a variational autoencoder and propose a simple and effective method to better exploit these latent variables through hidden state averaging. Consistent gains in performance on two different datasets, Penn Treebank and Yahoo, indicate the generalizability of our method.
Dual-operating-voltage scheme for a single 5-V 16-Mbit DRAM

A dual-operating-voltage scheme (5 V for peripheral circuits and 3.3 V for the memory array) is shown to be the best approach for a single 5-V 16-Mb DRAM (dynamic random-access memory). This is because the conventional scaling rule cannot apply to DRAM design due to the inherent DRAM word-line boosting feature. A novel internal voltage generator to realize this approach is presented. Its features are the switching of two reference voltages, a driver using a PMOS-load differential amplifier, and the word-line boost based on the regulated voltage, which can ensure a wider memory margin than conventional circuits. This approach is applied to an experimental 16-Mb DRAM. A 0.5% supply-voltage dependency and 30-ns recovery time are achieved. >