Reviewerly/exHarmony huggingface dataset:
Efficient Sequence Labeling with Actor-Critic Training

Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies. We set out to establish Recurrent Neural Networks (RNNs) as an efficient alternative to CRFs especially in tasks with large number of output labels. We propose an adjusted actor-critic reinforcement learning algorithm to fine-tune RNN network (AC-RNN). Our comprehensive experiments suggest that AC-RNN efficiently matches the performance of the CRF on NER and CCG tagging, and outperforms it on Machine Transliteration; with an overall faster training time, and smaller memory footprint.
MIMSY: The Micro Inertial Measurement System for the Internet of Things

The Micro Inertial Measurement System (MIMSY) is an open-source wireless sensor node for Internet of Things applications, specifically designed for a small system volume while maintaining functionality and extensibility. MIMSY is a 16mm × 16mm node with an Arm Cortex-M3 microprocessor, 802.15.4 wireless transceiver, and a 9-axis IMU. The system is fully compatible with the OpenWSN wireless sensor networking stack, which enables the straightforward implementation of standards-compliant 6TiSCH mesh networks using MIMSY motes. While the application space of MIMSY is quite vast, we present three sample implementations showcasing the opportunities afforded by a small and relatively low-cost mote with mesh networking and inertial measurement capabilities, including: high granularity areal sensing for sleep monitoring with motes embedded in a foam mattress; high reliability, low latency communication for industrial process automation and control; and long lifetime physical event detection and activity monitoring with minimal setup time.
On the Quantum Complexity of the Continuous Hidden Subgroup Problem

The Hidden Subgroup Problem (HSP) aims at capturing all problems that are susceptible to be solvable in quantum polynomial time following the blueprints of Shor’s celebrated algorithm. Successful solutions to this problems over various commutative groups allow to efficiently perform number-theoretic tasks such as factoring or finding discrete logarithms. The latest successful generalization (Eisenträger et al. STOC 2014) considers the problem of finding a full-rank lattice as the hidden subgroup of the continuous vector space $$\mathbb {R}^m$$ , even for large dimensions m. It unlocked new cryptanalytic algorithms (Biasse-Song SODA 2016, Cramer et al. EUROCRYPT 2016 and 2017), in particular to find mildly short vectors in ideal lattices. The cryptanalytic relevance of such a problem raises the question of a more refined and quantitative complexity analysis. In the light of the increasing physical difficulty of maintaining a large entanglement of qubits, the degree of concern may be different whether the above algorithm requires only linearly many qubits or a much larger polynomial amount of qubits. This is the question we start addressing with this work. We propose a detailed analysis of (a variation of) the aforementioned HSP algorithm, and conclude on its complexity as a function of all the relevant parameters. Our modular analysis is tailored to support the optimization of future specialization to cases of cryptanalytic interests. We suggest a few ideas in this direction.
BI_Apriori Algorithm: Research and Application Based on Battery Production Data

As the problem that the traditional Apriori algorithm has low mining efficiency and generates a large number of irrelevant rules, combined with the characteristics of the process parameters of the battery industry automated production process, an association rule algorithm for battery automation industrial production is proposed--BI_Apriori algorithm. The performance of the algorithm is optimized in the following two aspects: (1) The production process data is stored in the form of a matrix; (2) Delete the item set of the candidate set that does not contain the battery qualification rate parameter (zYsNG) in the connection step. The simulation results show that compared with the traditional Apriori algorithm and Apriori-Tid algorithm, BI_Apriori algorithm has better performance in mining efficiency, generation rule number and generation rule correlation. The algorithm can mine the relationship effectively between process parameters and product qualification rate in the battery industry automation process.
Superradiance in finite quantum systems randomly coupled to continuum

We study the effect of superradiance in open quantum systems, i.e., the separation of short- and long-living eigenstates when a certain subspace of states in the Hilbert space acquires an increasing decay width. We use several Hamiltonian forms of the initial closed system and generate their coupling to continuum by means of the random matrix theory. We average the results over a large number of statistical realizations of an effective non-Hermitian Hamiltonian and relate robust features of the superradiance process to the distribution of its exceptional points. We show that the superradiance effect is enhanced if the initial system is at the point of quantum criticality.
Text analytics for big data using rough–fuzzy soft computing techniques

Abstract Text mining or analytics is important for various applications such as market analysis and biomedical purposes because it enables the efficient retrieval of information from large datasets. During the analysis, increasing the dimensionality of the data reduces the performance of an entire system because doing so may retrieve irrelevant text, which creates errors. Therefore, this paper introduces big data and data mining techniques to analyse large volumes of information while mining texts, emails, blogs, online forums, news, and call centre documents. Initially, the data are collected from various sources that contain noise, which is removed by applying normalization techniques. Data mining techniques eliminate the irrelevant information and noise, and the relevant features are selected using the rough set‐based particle swarm optimization algorithm. The selected features are formed as a cluster using a fuzzy set with the particle swarm optimization algorithm, which improves the efficiency of the mining process. Then, the efficiency of the system is evaluated using the University of California Irvine Machine Learning Repository knowledge process mining database, along with the sum of the intra cluster distances, the mean squared error rate, and the accuracy.
Application of Human-Machine Dialogue in Foreign Language Teaching at Universities

Abstract With the development of internet technology, communication technology and artificial intelligence (AI), the versatile and convenient human-machine dialogue and interaction system has gradually penetrated into the education field and become a new type of teaching method. Focusing on the subject of the application of human-machine dialogue system in foreign language teaching at universities, this paper elaborated on how to apply the human-machine dialogue system in foreign language teaching, what effect can be achieved, and what application strategy can be adopted in human-machine dialogue interactive teaching. Combined with the problems existing in daily teaching, this paper presented suggestions for applying the human-machine dialogue system, which is expected to improve the effects of foreign language teaching.
Data Reliability and Redundancy Optimization of a Secure Multi-cloud Storage Under Uncertainty of Errors and Falsifications

Despite all the benefits a cloud data storages offer to customers, there is a high risk of breach of confidentiality, integrity, and availability related with the uncertainty of errors and falsifications, loss of information, denial of access for a long time, information leakage, conspiracy, and technical failures. In this article, we propose a configurable, reliable, and secure distributed data storage scheme with improved data redundancy, reliability, and encoding/decoding speed. Our system utilizes a Polynomial Residue Number System (PRNS) with a new method of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank (AR) of a polynomial. It reduces the computational complexity of the encoding/decoding and PRNS coefficients size. Based on the properties of the approximate value and PRNS, we introduce the AR-PRNS method for error detection, correction, and controlling computational results with capabilities of scalable parallel computing. We provide a theoretical basis to configure and optimize the redundancy of stored data and encoding/decoding speed to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that, by appropriate selection of AR-PRNS parameters, the proposed scheme increases the safety, reliability, and reduces the overhead of data storage.
Relationship Prior and Adaptive Knowledge Mimic Based Compressed Deep Network for Aerial Scene Classification

The aerial scene classification is one of the major tasks in the remote sensing community that automatically labels the corresponding semantic categories of aerial images. Recently, a lot of methods based on deep neural networks have been proposed, in which hierarchical internal feature are extracted for representations. However, these presented methods often have complex structures and require large volume of memory, and a large number of labeled aerial scene images are difficult to obtain, hindering their implementation in practical applications. In this paper, we present the between-class similarity priori and adaptive knowledge mimic (BPKM) method for aerial scene classification. First, the method extracts the efficient prior relationship information of the scene images from large-scale network. Then, a compressed network is generated through learning the output and the intermediate representations of the large-scale network, and the compressed network achieves better feature description ability; in addition, an improved cross-entropy method with an adaptive threshold is applied to reduce the training time consumption. A largescale data set (AID) and UC-Merced data set are considered for performance evaluation, and the experimental results indicate that the proposed method is about 24× parameters saving compared to popular networks, e.g., AlexNet, and has outstanding classification performance in classes with similar features.
Unsupervised Facial Image Synthesis Using Two-Discriminator Adversarial Autoencoder Network

Recent years have witnessed the unprecedented success in single image synthesis by the means of convolutional neural networks (CNNs). High-level synthesis of facial image such as expression translation and attribute swap is still a challenging task due to high non-linearity. Previous methods suffer from the limitations that being unable to transfer multiple face attributes simultaneously, or incapability of transferring an attribute to another by a continuously changing way. To address this problem, we propose a two-discriminator adversarial autoencoder network (TAAN). The latent-discriminator is trained to disentangle an input image from its original facial attribute, while the pixel-discriminator is trained to make the output image attach to the target facial attribute. By controlling the attribute values, we can choose which and how much a specific attribute can be perceivable in the generated image. Quantitative and qualitative evaluations are conducted on the celebA and KDEF datasets, and the comparison with the state-of-the-art methods shows the competency of our proposed TAAN.
Simulation of intelligent information-measuring systems of thermophysical properties of materials and products

Abstract The article contains a formalized description of functioning of intelligent information measuring system for non-destructive testing of materials and products thermal properties. System simulation has been fulfilled, and as a result of it the mathematical models for transformation of measurement data in the intelligent system in Mathcad software during thermophysical measurements. The mathematical pattern of decision-making in the intelligent system allows to improve the accuracy of parameter determination of thermophysical properties of tested materials and the efficiency of the functioning system.
A simplified method of interferograms matching in the dual-channel phase-shifting interferometry

The dual-channel phase-shifting interferometry (DC-PSI) has been focused widely in the dynamic measurement field because of its outstanding advantages. The interferograms matching between two channels is a key factor to the high precision phase retrieval. Suffered from the relative poses of different channel cameras in six-degree of freedom (S-DOF), the interferograms matching between two channels, including translation, rotation and scaling, is always a difficult problem in the DC-PSI. Aiming at the only translation mismatching is here considered and the iterations are needed to get the translation parameters in the translation method, a simplified matching method based on the transformation matrix is proposed, minding all kinds of interferograms mismatching. In the proposed method, only one shot to the calibration plate is needed to obtain the adjustment parameters and the interferograms is matched quickly and precisely. Both the simulation and experimental results demonstrate that the proposed method can improve the phase retrieval accuracy and interferograms matching efficiency.
Entanglement Loss for Context-Based Still Image Action Recognition

We observed an attribute entanglement phenomenon: samples with similar attributes but from different classes can easily result in recognition errors. This problem is an important cause that results in recognition errors. To address this problem, we propose a new loss function, namely the entanglement loss. It penalizes the compactness between the misclassified entangled samples and their misclassified class centers, such that the features of entangled samples are apart from the misclassified classes. The proposed loss function can effectively enhance the discriminative power of the deeply learned features, thus recognition performance can be significantly improved. Experimental results show that our method outperforms the previous state-of-the-art methods on PASCAL VOC 2012 Action and ASLAN datasets.
Multi-channel expected patch log likelihood for color image denoising

Due to its flexibility and good restoration performance, the Expected Patch Log Likelihood (EPLL) method has attracted extensive attention and has been further developed. However, the basic EPLL method is mainly applied for gray image restoration. For color image denoising with different channel noise levels, concatenating the RGB values into a vector and applying the basic EPLL directly can produce false colors and artifacts. In this paper, a Multi-Channel Expected Patch Log Likelihood (MC-EPLL) method is proposed for color image denoising with different channel noise levels. Considering the within and between channel correlation, the noise model of the concatenated vector of RGB channels can be constructed as a Matrix Normal Distribution. Under the KL divergence framework, the MC-EPLL model can be derived by combining the noise model and Gaussian Mixture Model (GMM) based patch prior. Based on the half quadratic splitting (HQS) strategy, the MC-EPLL model is decomposed into two sub-minimization problems and the closed-form solution of each sub-problem can be obtained. Experiments show the feasibility and superiority of the proposed MC-EPLL over the compared denoising methods.
Defamiliarization, Representation Granularity, and User Experience: A Qualitative Study with Two Situated Visualizations

This work explores the user experience with two situated visualizations that lie on different points of design space. The first visualization - the Activity Clock - displays the aggregate presence of laboratory members into a wall clock. The second - Personal Activities - represents the same persons individually, in a conventional poster media. We interviewed 17 participants and leverage a theoretical lens of Continuous Engagement and Sense-Making to study how design decisions impact the user experience with respect to (1) which design factors attract users, (2) how design features affect users' understanding of the visualization, and (3) what kind of reflections are evoked by design. We discuss how the defamiliarizing effect of the Activity Clock plays a dual role in attracting users while also hindering their understanding of the data. We also consider the evidence that fine representation granularity in the Personal Activities evokes deeper reflections.
A Deep Residual Networks Accelerator on FPGA

Deep residual networks plays an important role in deep learning and is widely used for image classification due to its high recognition rate. Moreover, with the increase of amount of data in the data center and embedded systems, performance and power consumption becomes the key issue. FPGA is an excellent solution, it’s more and more promising to accelerate deep learning inference due to the low latency and low energy consumption. In this paper, we present an OpenCL-based acceleration framework on FPGA for deep residual networks, which shown excellent performance and high energy efficiency ratio. Furthermore, we proposed a new strategy to deal with fully-connected layers, and also proposed an optimization strategy for 1×1 filters. In order to valid our proposal, we evaluate our framework on Intel Arria 10 devices. Evaluation results show that the ResNet50 Network on our framework can achieve a performance of 54img/s or 1.2img/s/W, which is 47% higher than that of the state-of-the- art FPGA-based design on the same device. Moreover, it’s also a competitive result compared to NVidia’s M4 GPUs.
Prediction accuracy analysis with logistic regression and CART decision tree

Classification is one of the most important techniques in machine learning. In classification problems, logistic regression and decision tree are two efficient algorithms in supervised learning. In this paper, we tested logical regression and CART decision tree algorithms on different datasets. The results received from experiments showed that CART decision tree performs much better in data set with more attributes and slight imbalanced data distribution. At the same time logistic regression is more accurate on datasets with fewer attributes and balanced data distribution.
Asymptotically Good Codes Obtained by an Extended Primitive BCH Code Concatenated with Interleaved Codes

How to construct asymptotically good codes is one of the hottest topic in modern coding theory. In this paper, we concatenated an extended primitive BCH outer code with a class of binary inner codes constructed by an interleaved code to get a class of asymptotically good codes. It is shown that Weldon [1] is its special case.
Social Design, Innovation and Ergonomics: Reflections on Education, Transdisciplinarity and New Blurred Models for Sustainable Social Change

Social innovation is not a new fact, but it has grown in importance over the last years as a driver for social change [1]. Although there is no universal definition of it, one widely-used is ‘ideas that are social in their ends and in their means’ [2] and concerns the process of developing and deploying new solutions to often systemic and complex social and environmental issues. On the other hand, social art is seen as any artistic expression that aims at inspiring social impact and change effectively society through emotionally captivating experiences [3]. Also, social ergonomics is understood as responses to social aspects of system use, envisioning, among others, socially proactive design and discussing the possibilities for embedding new paradigms for communication and problem-solving in specialized information systems [4]. Based on these ideas, this paper will elaborate on the above concepts, confronting and correlating them as complementary human actions and domains. Some considerations and challenges on the theme, as outputs of a post-doc research, will be also offered. It will be shown transdisciplinary approaches, methods, social actors, and complementary and blurred practices to an effective and meaningful design and implementation of new products, services, and systems. Its social, cultural and, educational relevance or holistic understanding will be underlined as strategies to achieve the sustainability of social systems, to share social value and promote resilience and wellbeing in a highly complex and changing world.
Decision Process of Autonomous Drones for Environmental Monitoring

Environmental monitoring has a key role to reduce the human effect on nature and wildlife. Due to intense tracking and observation tasks, environmental monitoring is an expensive solution. Autonomous observation is an open discussion to raise the efficiency of environmental monitoring and reduce the cost of operation. Unmanned aerial vehicles (UAVs) are possible candidates with their proven success in monitoring and tracking. Thus, we are offering a decision process for the autonomy of drones in monitoring tasks. Our system is capable to fly autonomously, e.g., covering a given area, and able to perform certain tasks, e.g., identifying bottles. Our simulation results prove that autonomous drones can be used for a large variety of environmental monitoring tasks.
Empty morphemes in Dutch dialect atlases: Reducing morphosyntactic variation by refining emptiness typology

In the literature on Dutch morphosyntactic microvariation, it is sometimes assumed that a subpart of Dutch dialects lack certain morphemes, because they have no direct phonetic exponent. More careful analyses, however, suggest that these dialects display so-called zero morphemes, whose presence is argued for either on paradigmatic or phonological ground. In this contribution, we present some examples of such morphemes in the verbal inflection and adjectival concord systems, and develop an analysis that, by exploiting the formal mechanism relating underlying and surface phonological representations provided by Turbidity Theory, allows for the formalization of various degrees of emptiness: morphosyntactic, phonological and phonetic. This, in turn, allows for the shifting of the burden of (some instances of) microvariation from morphosyntax to PF.
Bitcoin Block-Chain Mining

Among the many cryptocurrencies, the most popular is bitcoin. It is a direct electronic system that deals with money and lets us make payments over network. To verify bitcoin transactions involving payments between users, we need to mine the data involved in these transactions. Data mining keeps a record of the various payments made and the services used in an organized form. Since mining is the bedrock of bitcoin framework, it is very important to make sure that the mined data is unalterable and complete by repeatedly verifying and adding new transactions. For this purpose, we use various algorithms and protocols, along with satisfying proof-of-stake and proof-of-work. These algorithms are studied and compared, and it is proposed that we could switch to proof-of-stake for bitcoin to make it more energy efficient as well as cost effective.
Region of Interest Growing Neural Gas for Real-Time Point Cloud Processing

This paper proposes a real-time topological structure learning method based on concentrated/distributed sensing for a 2D/3D point cloud. First of all, we explain a modified Growing Neural Gas with Utility (GNG-U2) that can learn the topological structure of 3D space environment and color information simultaneously by using a weight vector. Next, we propose a Region Of Interest Growing Neural Gas (ROI-GNG) for realizing concentrated/distributed sensing in real-time. In ROI-GNG, the discount rates of the accumulated error and utility value are variable according to the situation. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.