Reviewerly/exHarmony huggingface dataset:
GAN with Pixel and Perceptual Regularizations for Photo-Realistic Joint Deblurring and Super-Resolution

In this paper, we propose a Generative Adversarial Network with Pixel and Perceptual regularizations, denoted as P2GAN, to restore single motion blurry and low-resolution images jointly into clear and high-resolution images. It is an end-to-end neural network consisting of deblurring module and super-resolution module, which repairs degraded pixels in the motion-blur images firstly, and then outputs the deblurred images and deblurred features for further reconstruction. More specifically, the proposed P2GAN integrates pixel-wise loss in pixel-level, contextual loss and adversarial loss in perceptual level simultaneously, in order to guide on deblurring and super-resolution reconstruction of the raw images that are blurry and in low-resolution, which help obtaining realistic images. Extensive experiments conducted on a real-world dataset manifest the effectiveness of the proposed approaches, outperforming the state-of-the-art models.
Blockchain Enables New Power Markets

Traditional generating stations that produce baseload power measured in hundreds of megawatts are either closing or struggling, unable to compete with inexpensive power produced by solar, wind, or natural gas-burning plants. To handle these multitude of distributed, small-bore sources, a new smart grid is developing, where small buyers and sellers of electricity may find a marketplace. But for that to break into the mainstream, a new system linking disparate generators, buyers, and sellers with fast and secure transactions still needs development. Many industry experts suggest that this new system could well be based on blockchain, a digital technology commonly associated with cryptocurrencies such as Bitcoin. This article takes a closer look at a few pilot projects moving in various market segments.
Towards White-box Benchmarks for Algorithm Control.

The performance of many algorithms in the fields of hard combinatorial problem solving, machine learning or AI in general depends on tuned hyperparameter configurations. Automated methods have been proposed to alleviate users from the tedious and error-prone task of manually searching for performance-optimized configurations across a set of problem instances. However there is still a lot of untapped potential through adjusting an algorithm's hyperparameters online since different hyperparameters are potentially optimal at different stages of the algorithm. We formulate the problem of adjusting an algorithm's hyperparameters for a given instance on the fly as a contextual MDP, making reinforcement learning (RL) the prime candidate to solve the resulting algorithm control problem in a data-driven way. Furthermore, inspired by applications of algorithm configuration, we introduce new white-box benchmarks suitable to study algorithm control. We show that on short sequences, algorithm configuration is a valid choice, but that with increasing sequence length a black-box view on the problem quickly becomes infeasible and RL performs better.
Reading Industrial Inspection Sheets by Inferring Visual Relations

The traditional mode of recording faults in heavy factory equipment has been via handmarked inspection sheets, wherein a machine engineer manually marks the faulty machine regions on a paper outline of the machine. Over the years, millions of such inspection sheets have been recorded and the data within these sheets has remained inaccessible. However, with industries going digital and waking up to the potential value of fault data for machine health monitoring, there is an increased impetus towards digitization of these handmarked inspection records. To target this digitization, we propose a novel visual pipeline combining state of the art deep learning models, with domain knowledge and low level vision techniques, followed by inference of visual relationships. Our framework is robust to the presence of both static and non-static background in the document, variability in the machine template diagrams, unstructured shape of graphical objects to be identified and variability in the strokes of handwritten text. The proposed pipeline incorporates a capsule and spatial transformer network based classifier for accurate text reading, and a customized CTPN [15] network for text detection in addition to hybrid techniques for arrow detection and dialogue cloud removal. We have tested our approach on a real world dataset of 50 inspection sheets for large containers and boilers. The results are visually appealing and the pipeline achieved an accuracy of 87.1% for text detection and 94.6% for text reading.
Community Indicator Data Dashboards as Infrastructures for Data Literacy

Community indicator data dashboards are a class of websites that make community data freely accessible to communities so they can engage with issues of common concern. But this vision of data access and engagement is hindered by numerous barriers. A lack of data literacy is one such barrier that prevents some communities from telling their stories with data. My research is concerned with taking an infrastructural approach to reveal the various socio-technical factors that are critical to the creation, conceptualization and use of data, so I can operationalize a data infrastructure literacy program for such communities. Such a data literacy, which includes not only numeracy, but also knowledge of the human and non-human elements that are implicated in the creation of data infrastructures will, I hope, lead to more critically informed and empowered use of the data for advocacy and civic engagement.
Layered Consensus Mechanism in Consortium Blockchain for Enterprise Services

The concept of blockchain born out of Bitcoin in just a decade ago has greatly attracted the attentions of industry and academia. The third generation of blockchain is believed to be able to support a large number of commercial and social applications with security, scalability, speed and developer friendly. To maintain a distributed ledger in many independent peer nodes without central authority, the consensus mechanism is the key protocol to construct a blockchain system with multiple technologies. However compared to the well-studied consensus protocols in public blockchain, the customized consensus mechanism is still lack of research for the consortium blockchain in both industry and academia, especially in the enterprise scenarios of applications and services. The layered consensus mechanism in the consortium blockchain is discussed after considering and combining the common characteristics of enterprise applications and services. Based on the famous Service-Oriented Architecture (SOA), the new layered and service-oriented consensus mechanism should be more practical to apply the blockchain technology to much diversified enterprise services with decentralization and extensibility in the principle of enterprise-level security.
Application of Blockchain Technology in the Supply Chain Management Process: Case Studies

With the development of technology, new approaches have been used in industry, agriculture and services sector. The remaining sectors in technological factors remain behind in competition. Within the 7R of logistics, each area is disadvantaged. In order to turn these negative conditions into a positive situation in all three sectors, the futuristic approaches should be closely monitored by all sector and corporate managers. In this study, we focus on the use of blockchain technology, which is a relatively new concept and used in limited areas, in the supply chain management process. In the first part, both the supply chain and the blockchain technology are conceptually expressed and introduced. In the last section, examples are included and evaluations are expressed. The study is in the form of case studies and observations review
Query and Resource Optimizations: A Case for Breaking the Wall in Big Data Systems

Modern big data systems run on cloud environments where resources are shared amongst several users and applications. As a result, declarative user queries in these environments need to be optimized and executed over resources that constantly change and are provisioned on demand for each job. This requires us to rethink traditional query optimizers designed for systems that run on dedicated resources. In this paper, we show evidence that the choice of query plans depends heavily on the available resources, and the current practice of choosing query plans before picking the resources could lead to significant performance loss in two popular big data systems, namely Hive and SparkSQL. Therefore, we make a case for Resource and Query Optimization (or RAQO), i.e., choosing both the query plan and the resource configuration at the same time. We describe rule-based RAQO and present alternate decisions trees to make resource-aware query planning in Hive and Spark. We further present cost-based RAQO that integrates resource planning within a query planner, and show techniques to significantly reduce the resource planning overheads. We evaluate cost-based RAQO using state-of-the-art System R query planner as well as a recently proposed multi-objective query planner. Our evaluation on TPC-H and randomly generated schemas show that: (i) we can reduce the resource planning overhead by up to 16x, and (ii) RAQO can scale to schemas as large as 100 table joins as well as clusters as big as 100K containers with 100GB each.
An Algorithmic Approach for Mining Customer Behavior Prediction in Market Basket Analysis

Market basket analysis is the search for data that contain customer purchasing items. Market basket analysis is a process of showing the correlation between the data with respect to support and confidence. Support indicates that how frequently items appear in the database and confidence indicate that rules must be generated based on the frequent items. Data analysis in a supermarket database means to understand each transaction available in the dataset that contains customer purchasing pattern to determine how the product should be located on shelves. Product arrangement is the most important aspect to get supermarket profit. The dataset of the retailer contains transaction of the items which is purchased by the customer and also comment regarding that product whatever they fill regarding that product. Apriori algorithm is used to find frequent items and association rule based on customer transactions. Frequent items calculate with respect to support and Association rule determines with respect to confidence. This paper tells about how customer behavior predicted based on the customer purchase items. This technique generally used in the agricultural field, marketing, and education field.
Exploring the Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on OpenCL FPGA Platform

We choose a random network of Hodgkin-Huxley (HH) neurons with exponential synaptic conductance as a study of accelerating the simulation of networks of spiking neurons on an FPGA. Focused on the conductance-based HH (COBAHH) benchmark, we execute the benchmark on a general-purpose simulator for spiking neural networks, identify a computationally intensive kernel in the generated C++ code, convert the kernel to a portable OpenCL kernel, and describe the kernel optimizations which can reduce the resource utilizations and improve the kernel performance. We evaluate the kernel on an Intel Arria 10 based FPGA platform, an Intel Xeon 16-core CPU, and an NVIDIA Tesla P100 GPU. FPGAs are promising for the simulation of spiking neuron network.
Space lower bounds for linear prediction in the streaming model

We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natural streaming setting. This implies that such problems cannot be solved (at least in this setting) by scalable memory-efficient streaming algorithms. Our results build on a memory lower bound for a simple linear-algebraic problem -- finding orthogonal vectors -- and utilize the estimates on the packing of the Grassmannian, the manifold of all linear subspaces of fixed dimension.
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory (RNN-LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale ($X$), intermediate ($Y$), and fast/small-scale ($Z$) processes. For training or testing, only $X$ is available; $Y$ and $Z$ are never known or used. We show that RC-ESN substantially outperforms ANN and RNN-LSTM for short-term prediction, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps, equivalent to several Lyapunov timescales. The RNN-LSTM and ANN show some prediction skills as well; RNN-LSTM bests ANN. Furthermore, even after losing the trajectory, data predicted by RC-ESN and RNN-LSTM have probability density functions (PDFs) that closely match the true PDF, even at the tails. The PDF of the data predicted using ANN, however, deviates from the true PDF. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems such as weather/climate are discussed.
Solar Tracking Linkage RSSR for all Latitudes

In the paper, a planar 2 DOF RSSR solar tracking linkage applicable for all latitudes is analyzed. The mechanism is contained in the vertical North-South plane of the observer. The coupler angle relative to horizontal plane defines the tilt angle χ of the active conversion surface and depends on the latitude and Earth’s declination. It can be modified by a linear actuator. The coupler passive motion defines the diurnal angle φ3 of the active conversion surface and it is driven by a second linear actuator. The angles χ and φ3 are limited to - 65° … + 65° by constructive requirements of the linkage and pressure angle. Optimal mechanism design and functionality simulation at different latitudes are detailed in the paper. A case study for latitude φ = 50° corresponding to Krakow region is presented in the paper. As result, tracking efficiency is very high, the solar energy lost is below 1% for winter time and under 4% for summer time from the total available solar energy in a clear sky day.
(Un)Discounted Usability

The discounted usability inspections typically employ usability-expert evaluators set to evaluate the target system situated out of the real field. Nonetheless, usability-experts work hard to disguise under an end-user persona, and imitate the context of the original domain and workplace to its maximum, but it hardly is an imitation of the original settings. Hence, these techniques are consistently reported to miss usability problems related to real task scenarios. The problem is especially evident in the particular case of Heuristic Evaluation, where the set of rules used for evaluations weighs domain and task orientation less than other attributes, contributing further to negligence of task and domain related problem. It is thus advised to supplement usability-expert based discounted inspections with expensive end-user based testing---a privilege otherwise unavailable to low-budget projects. The authors in this work, however, propose and test using dual-personae evaluators employed in discounted inspections. The results of the experimentation are promising. The evaluators identify a large number of task and domain related problems, as compared to the number of problems identified in other dimensions during the activity. The authors conclude that such evaluators are especially useful in the case of low-budget projects where expensive testing is not possible.
Decentralized Identity Authentication with Trust Distributed in Blockchain Backbone

To overcome the difficulties in the traditional password-based identity authentication procedure with high security risk and complexity, and to avoid the privacy leaking problems arising from a series of new techniques such as using biometrics as key, this paper proposes a universal solution for decentralized identity authentication by block chain integrated with a trusted execution environment, through a separate hardware to establish a secure area, enabling identity anonymity and avoiding feature disclosure. Smart contract and consensus mechanism are adopted for building trusted identity nodes between decentralized nodes by constructing a traceable identity data structure in blockchain. We conduct formal theoretical and empirical evaluations to show that the proposed can realize user identity registration, cross-platform authentication, and guarantee security in the whole procedure with efficiency and reliability.
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion

Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been challenging due to the disadvantages of the training conditions. Recently, CycleGAN-VC has provided a breakthrough and performed comparably to a parallel VC method without relying on any extra data, modules, or time alignment procedures. However, there is still a large gap between the real target and converted speech, and bridging this gap remains a challenge. To reduce this gap, we propose CycleGAN-VC2, which is an improved version of CycleGAN-VC incorporating three new techniques: an improved objective (two-step adversarial losses), improved generator (2-1-2D CNN), and improved discriminator (PatchGAN). We evaluated our method on a non-parallel VC task and analyzed the effect of each technique in detail. An objective evaluation showed that these techniques help bring the converted feature sequence closer to the target in terms of both global and local structures, which we assess by using Mel-cepstral distortion and modulation spectra distance, respectively. A subjective evaluation showed that CycleGAN-VC2 outperforms CycleGAN-VC in terms of naturalness and similarity for every speaker pair, including intra-gender and inter-gender pairs.
Attention-based Deep Reinforcement Learning for Multi-view Environments

In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies. Since the views may frequently suffer from partial observability, their provided observation can have different levels of importance. In this paper, we present a novel attention-based deep reinforcement learning method in a multi-view environment in which each view can provide various representative information about the environment. Specifically, our method learns a policy to dynamically attend to views of the environment based on their importance in the decision-making process. We evaluate the performance of our method on TORCS racing car simulator and three other complex 3D environments with obstacles.
A Partially Rule-Based Approach to

This paper presents a new approach to generating English text from Abstract Meaning Representation (AMR). In contrast to the neural and statistical MT approaches used in other AMR generation systems, this one is largely rule-based, supplemented only by a language model and simple statistical linearization models, allowing for more control over the output. We also address the difficulties of automatically evaluating AMR generation systems and the problems with BLEU for this task. We compare automatic metrics to human evaluations and show that while METEOR and TER arguably reflect human judgments better than BLEU, further research into suitable evaluation metrics is needed.
Exponential-Time Quantum Algorithms for Graph Coloring Problems

Abstract The fastest known classical algorithm deciding the k -colorability of n -vertex graph requires running time $$\varOmega (2^n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ω</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> for $$k\ge 5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> </mml:math> . In this work, we present an exponential-space quantum algorithm computing the chromatic number with running time $$O(1.9140^n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>.</mml:mo> <mml:msup> <mml:mn>9140</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> using quantum random access memory (QRAM). Our approach is based on Ambainis et al’s quantum dynamic programming with applications of Grover’s search to branching algorithms. We also present a polynomial-space quantum algorithm not using QRAM for the graph 20-coloring problem with running time $$O(1.9575^n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>.</mml:mo> <mml:msup> <mml:mn>9575</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . For the polynomial-space quantum algorithm, we essentially develop $$(4-\epsilon )^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>4</mml:mn> <mml:mo>-</mml:mo> <mml:mi>ϵ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> </mml:math> -time classical algorithms that can be improved quadratically by Grover’s search.
DUTH at SemEval-2019 Task 8: Part-Of-Speech Features for Question Classification

This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums. Our team dealt only with Subtask A: Question Classification. Our approach was based on shallow natural language processing (NLP) pre-processing techniques to reduce the noise in data, feature selection methods, and supervised machine learning algorithms such as NearestCentroid, Perceptron, and LinearSVC. To determine the essential features, we were aided by exploratory data analysis and visualizations. In order to improve classification accuracy, we developed a customized list of stopwords, retaining some opinion- and fact-denoting common function words which would have been removed by standard stoplisting. Furthermore, we examined the usefulness of part-of-speech (POS) categories for the task; by trying to remove nouns and adjectives, we found some evidence that verbs are a valuable POS category for the opinion question class.
Benchmarking Neural Machine Translation for Southern African Languages

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared. This has lead a struggle to reproduce reported results, and few publicly available benchmarks for African machine translation models exist. To start to address these problems, we trained neural machine translation models for 5 Southern African languages on publicly-available datasets. Code is provided for training the models and evaluate the models on a newly released evaluation set, with the aim of spur future research in the field for Southern African languages.