Reviewerly/exHarmony huggingface dataset:
Model Enumeration via Assumption Literals

Modern, efficient Answer Set Programming solvers implement answer set search via non-chronological backtracking algorithms. The extension of these algorithms to answer set enumeration is nontrivial. In fact, adding blocking constraints to discard already computed answer sets is inadequate because t he introduced constraints may not fit in memory or deteriorate the efficiency of the solver. On the other hand, the algorithm implemented by CLASP, which can run in polynomial space, requires to modify the answer set search procedure. The algorithm is revised in this paper so as to make it almost independent from the underlying answer set search procedure, provided that the procedure accepts as input a logic program and a list of assumption literals, and returns an answer set (and associated branching literals). In fact, thanks to an alternative view in terms of transition systems, the revised algorithm is suitable to easily accommodate the enumerate of models of other Boolean languages, among them classical models of propositional theories. On a pragmatic level, the paper presents two implementations of the enumeration algorithm, in WASP for answer set enumeration, and in GLUCOSE for classical models enumeration. The implemented systems are compared empirically to the state of the art solver CLASP.
Predicting Slovene Text Complexity Using Readability Measures

The majority of existing readability measures are designed for English texts. We aim to adapt and test the readability measures on Slovene. We test ten well-known readability formulas and eight additional readability criteria on five types of texts: children’s magazines, general magazines, daily newspapers, technical magazines, and transcriptions of national assembly sessions. As these groups of texts target different audiences, we assume that the differences in writing styles should be reflected in their readability scores. Our analysis shows which readability measures perform well on this task and which fail to distinguish between the groups.
Performance Analysis of LSA for Descriptive Answer Assessment

Latent Semantic Analysis (LSA), in general, can be considered as an excellent information retrieval technique, but for this specific task of Descriptive Answer Assessment (DAA) some more explorations are required and it is still considered as an open problem. This paper discusses and evaluates the performance of LSA for DAA, through experimentation and deep investigations. Several state-of-the-art claim that LSA correlates with the human assessor’s way of assessment. With this as background, we investigated assessment of students’ descriptive answer using Latent Semantic Analysis (LSA). In the course of research, it was discovered that LSA has limitations like: LSA research usually involves heterogeneous text (text from various domains) which may include irrelevant terms that are highly susceptible to noisy, missing and inconsistent data. The experiments revealed that the general behavior of LSA has an adverse impact on DAA. It has also been observed that factors which influence the performance of LSA are corpus preprocessing, the creation of term-document matrix with and without weight function and choice of dimensionality.
Conception of Geomagnetic Data Integrated Space

. As is known, today the problem of geomagnetic field and its variations parameters monitoring is solved mainly by a network of magnetic observatories and variational stations, but a significant obstacle in the processing and analysis of the data thus obtained, along with their spatial anisotropy, are omissions or reliable inconsistency with the established format. Heterogeneity and anomalousness of the data excludes (significantly complicates) the possibility of their automatic integration and the application of frequency analysis tools to them. Known solutions for the integration of heterogeneous geomagnetic data are mainly based on the consolidation model and only partially solve the problem. The resulting data sets, as a rule, do not meet the requirements for real-time information systems, may include outliers, and omissions in the time series of geomagnetic data are eliminated by excluding missing or anomalous values from the final sample, which can obviously lead to both to the loss of relevant information, violation of the discretization step, and to heterogeneity of the time series. The paper proposes an approach to creating an integrated space of geomagnetic data based on a combination of consolidation and federalization models, including preliminary processing of the original time series with an optionally available procedure for their recovery and verification, focused on the use of cloud computing technologies and hierarchical format and processing speed of large amounts of data and, as a result, providing users with better and more homogeneous data.
Spin-Torque-Nano-Oscillator based neuromorphic computing assisted by laser

In spintronic-based neuromorphic computing systems (NCSs), the switching of magnetic moment in a magnetic tunnel junction (MTJ) or magnetic oscillation in spin torque nano-oscillator (STNO) is used to mimic biological neuron firing. Although using STNO reduces the power consumption significantly, still there is a huge gap between the power consumption of spintronic-based NCS and brain due to the high bias current. In this paper, the power consumption of the proposed STNO-based NCS is reduced by thermally assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of STNOs in NCS reduces by 56% by heating them up to 100°C. The total power consumption of the proposed laser assisted oscillation-based NCS (LAO-NCS) is reduced by 46% at 100°C compared with a typical STNO-based NCS at room temperature.
Mining Scholarly Data for Fine-Grained Knowledge Graph Construction

Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain a explicit representation of the knowledge presented in research publications. In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a KG. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships derived from 12, 007 publications in the field of Semantic Web, and iv) discuss how Deep Learning methods can be applied to overcome some limitations of the current techniques.
Profit Maximization and Time Minimization Admission Control and Resource Scheduling for Cloud-Based Big Data Analytics-as-a-Service Platforms

Big data analytics typically requires large amounts of resources to process ever-increasing data volumes. This can be time consuming and result in considerable expenses. Analytics-as-a-Service (AaaS) platforms provide a way to tackle expensive resource costs and lengthy data processing times by leveraging automatic resource management with a pay-per-use service delivery model. This paper explores optimization of resource management algorithms for AaaS platforms to automatically and elastically provision cloud resources to execute queries with Service Level Agreement (SLA) guarantees. We present admission control and cloud resource scheduling algorithms that serve multiple objectives including profit maximization for AaaS platform providers and query time minimization for users. Moreover, to enable queries that require timely responses and/or have constrained budgets, we apply data sampling-based admission control and resource scheduling where accuracy can be traded-off for reduced costs and quicker responses when necessary. We conduct extensive experimental evaluations for the algorithm performances compared to state-of-the-art algorithms. Experiment results show that our proposed algorithms perform significantly better in increasing query admission rates, consuming less resources and hence reducing costs, and ultimately provide a more flexible resource management solution for fast, cost-effective, and reliable big data processing.
Time Discrete Geodesics in Deep Feature Spaces for Image Morphing

Image morphing in computer vision amounts to computing a visually appealing transition of two images. A prominent model for image morphing originally proposed by Trouvé, Younes and coworkers is image metamorphosis. Here, the space of images is endowed with a Riemannian metric that separately quantifies the contributions due to transport and image intensity variations along a transport path. Geodesic curves in this Riemannian space of images give rise to morphing transitions. The classical metamorphosis model considers images as square-integrable functions on some image domain and thus is non-sensitive to image features such as sharp interfaces or fine texture patterns. To resolve this drawback, we treat images not as intensity maps, but rather as maps into some feature space that encode local structure information. In the simplest case, color intensities are such feature vectors. To appropriately treat local structures and semantic information, deep convolutional neural network features are investigated. The resulting model is formulated directly in terms of a variational time discretization developed for the classical metamorphosis model by Berkels, Effland and Rumpf. The key ingredient is a mismatch energy that locally approximates the squared Riemannian distance and consists of a regularization energy of the time discrete flow and a dissimilarity energy that measures the feature vector modulation along discrete transport paths. The spatial discretization is based on a finite difference and a stable spline interpolation. A variety of numerical examples demonstrates the robustness and versatility of the proposed method for real images using a variant of the iPALM algorithm for the minimization of the fully discrete energy functional.
Smart Scheduling Strategy for Lightweight Virtualized Resources Towards Green Computing

Modern cloud orchestrators are generally designed to make efficient use of resources in the data center, by consolidating the servers workload. Recently, energy efficiency has become critical factor to sustain the growth of cloud services; thus, more effective resource allocation and management strategies are required. The situation is exacerbated by introduction of HPC-oriented cloud services, where other aspects of the application execution are critical, such as the minimisation of the makespan. Although a short makespan allows for a rapid application execution, often the overall energy consumption of the whole cluster suffers, growing out of all proportion. Starting from the growing attention paid in recent years to the concept of “green computing” (or ICT sustainability), in this paper we propose a different type of resource scheduler, whose main objective is to maximise the (energy) power efficiency of the computational resources involved, while taking into account the overall application execution time. An artificial intelligence (AI) technique, in the form of population-based evolutionary algorithm, was used to develop the proposed scheduler, in order to find the best possible combination between tasks to be performed and usable nodes able to guarantee lower (energy) power consumption and, at the same time, the fulfilment of possible constraints related to tasks’ execution. This paper focused on the implementation and evaluation of an evolutionary algorithm for efficient task scheduling. Experimental evaluation of such algorithm is discussed.
Learning Strikes Again: The Case of the DRS Signature Scheme

Lattice signature schemes generally require particular care when it comes to preventing secret information from leaking through signature transcript. For example, the Goldreich–Goldwasser–Halevi (GGH) signature scheme and the NTRUSign scheme were completely broken by the parallelepiped-learning attack of Nguyen and Regev (Eurocrypt 2006). Several heuristic countermeasures were also shown vulnerable to similar statistical attacks. At PKC 2008, Plantard, Susilo and Win proposed a new variant of GGH, informally arguing resistance to such attacks. Based on this variant, Plantard, Sipasseuth, Dumondelle and Susilo proposed a concrete signature scheme, called DRS, that is in the round 1 of the NIST post-quantum cryptography project. In this work, we propose yet another statistical attack and demonstrate a weakness of the DRS scheme: one can recover some partial information of the secret key from sufficiently many signatures. One difficulty is that, due to the DRS reduction algorithm, the relation between the statistical leak and the secret seems more intricate. We work around this difficulty by training a statistical model, using a few features that we designed according to a simple heuristic analysis. While we only recover partial secret coefficients, this information is easily exploited by lattice attacks, significantly decreasing their complexity. Concretely, we claim that, provided that $$100\,000$$ signatures are available, the secret key may be recovered using BKZ-138 for the first set of DRS parameters submitted to the NIST. This puts the security level of this parameter set below 80-bits (maybe even 70-bits), to be compared to an original claim of 128-bits. Furthermore, we review the DRS v2 scheme that is proposed to resist above statistical attack. For this countermeasure, while one may not recover partial secret coefficients exactly by learning, it seems feasible to gain some information on the secret key. Exploiting this information, we can still effectively reduce the cost of lattice attacks.
Radar Emitter Identification Based on Weighted Local and Global Consistency

In order to realize radar emitter identification with only a small number of training samples, this paper introduces the Local and Global Consistency (LGC) algorithm into the field of radar emitter identification. Aiming at the problem that the method is greatly affected by the signal measurement error, a radar emitter identification method based on weighted local and global consistency is proposed. The improved method adds the importance weight of the sample in the regularization framework of the original algorithm and reduces the influence of the samples located in the overlapping region, thereby improving the recognition accuracy of the algorithm when the measurement error is large. The simulation results show that the proposed algorithm can effectively identify the operating modes of the radar under the condition of less training samples and reduce the influence of measurement error on the recognition effect.
The Association between Self-Reported Sensory Impairment and Cognitive Function among Korean Older Adults: Results from the 2017 National Survey of the Living Conditions and Welfare Needs of Older Koreans

본 연구는 한국 노인이 주관적으로 인식하는 감각장애와 인지기능 간의 관계 조사이다. 연구방법은 2017년 노인실태조사 자료를 활용하여 65세 이상 10,082명에 대한 주관적 감각장애에 대한 평가 자료와 MMSE-DS를 통한 인지기능 간 관계를 위계적 다중회귀분석과 로지스틱 회귀분석을 실시하였다. 연구결과 시력, 청력, 구강 기능에 대해 주관적 불편감을 보고할수록 인지기능 감소의 위험이 높게 나타났다. 또한 청력, 구강에 대한 불편감은 치매로 이어질 가능성이 높음을 발견하였다. 특히 다른 감각기능에 비해 청력 불편감이 치매의심 집단의 인지기능과 상관관계가 가장 높은 것으로 나타났다. 본 연구의 결과는 노인의 감각기능을 정부 차원에서 관리할 필요성이 있으며, 노인 스스로 감각기능의 신체적 변화를 지각하고 평가하기 위한 도구를 개발할 필요가 있음을 제안한다. 아울러 노인의 건강 증진을 위한 구체적인 방안을 위한 연구가 필요하다.The present study aims to examine the relationship between self-reported sensory impairment and cognitive function among older adults in Korea. Drawn from the 2017 Survey of Living Conditions and Welfare Needs Korean Older Persons, a total of 10,082 adults aged 65 and over were included in analyses. Results from hierarchial multiple regression analysis show that those who reported greater subjective discomfort about vision, hearing, and oral function were more likely to have poor cognitive function. Results from hierarchial logistic regression analysis show. Results from logistic regression analysis show that having hearing and oral discomfort increased the a risk for dementia. Findings suggest that it is necessary to manage sensory function of the elderly at the government level and to develop self-evaluation tools to monitor changes in sensory function for older adults. Future research should target finding effective ways to improve health of older adults.
Demonstration of a Neural Machine Translation System with Online Learning for Translators

We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment.These techniques allow the system to continuously learn from the corrections provided by the translators.We implemented an end-to-end platform integrating our machine translation servers to one of the most common user interfaces for professional translators: SDL Trados Studio.Our objective was to save post-editing effort as the machine is continuously learning from human choices and adapting the models to a specific domain or user style.
Analysis of Energy Consumption in Cloud Center with Tasks Migrations

Reducing the energy consumption of a data center is a major goal in today’s digital world. We consider a cloud system represented by a set of physical servers hosting several Virtual Machines, and each one running tasks. We define a resource management policy that implements task migrations between overused servers to unused servers. The advantage of the policy is to balance the load and reduce the energy consumption. We model the system by a multi-server Jackson network, where each station represents a physical server, and the Virtual Machines are the servers. We derive an analytic formula for the energy consumption of the physical servers. We provide an upper bound of the migration energy for task migrations to reduce energy consumption. Moreover, we optimize the energy consumption, and we compute the migration rate that minimizes energy consumption.
Visible Watermarking Robust against Inpainting Using Seam Carving

Visible watermarking techniques are considered as ideal technique to provide direct copyright protection for images and video. One important requirement of visible watermarking is robustness against removal attacks. However, inpainting methods can be used efficiently to remove visible watermarks. Inpainting techniques need the watermark to perform the removal attack, this watermark can be obtained browsing on the web or applying collusion attacks over many protected images. In this paper we propose a visible watermarking method robust to inpainting attacks. To protect the entire image and increase the robustness against removal attacks, different versions of the watermark pattern generated by seam carving method are embedded into the host image. The proposed system avoids the removal of the watermark by inpainting. The experimental results show that the visible watermark is still recognizable after the visible watermarked image is attacked by inpainting method.
Alfabetização Quantitativa em Alunos da Terceira Série da Primária por meio de Jogo Sério

The last results of national and international evaluations, show that learning math is complex for Mexican students. Different plans and techniques have been implemented to counter this problem, one of them is the use of different technology in the classroom. During the last two decades, the videogame industry in Mexico has gained great traction among children, teenagers and young people, which is why the advantages of these kind of technologic tools must be harnessed. In this paper, we present a serious game to improve quantitative literacy in children studying the third grade of primary school. To design it, an iterative design model that contemplates four stages was use: planification, development, evaluation and improvement; emphasizing the instruction design. Through a quasi-experiment during a two-month period, the game was tested in a class of 33 morning shift third-grade students. The results obtained demonstrated quantitatively an increase in the students’ skills. It was shown, that out of the three subconstructs that constitute quantitative literacy, two of those (natural numbers and mathematical operations) showed significant improvement after treatment. The students enjoyed and engaged with the serious game, which is why it is expected to use this tool in the future in different Mexican communities.
The Ethics of Nonpower

To the question of joining morality to power, Jacques Ellul, professor of the history of law at the University of Bordeaux, expressed again his criticism of modern society first published in his influential book The Technological Society. There he claimed that the old distinction between means and ends no longer has validity, and that technique, formerly regarded as a means, has itself become an end. Our lives and thoughts are completely dominated by the power and scope of present-day technique.
A Simple Attack on CaptchaStar

CaptchaStar is a new type of Captcha, proposed in 2016, based on shape recovery. This paper shows that the security of this Captcha is not as good as intended. More precisely, we present and implement an efficient attack on CaptchaStar with a success rate of 96%. The impact of this attack is also investigated in other scenarios as noise addition, and it continues to be very efficient. This paper is a revised version of the paper entitled How to break CaptchaStar, presented at the conference ICISSP 2018 [29].
Symbol Spotting in Offline Handwritten Mathematical Expressions

Recognition of touching characters in mathematical expressions is a challenging problem in the field of document image analysis. Various approaches for recognizing touching maths symbols have been reported in literature, but they mainly dealt with printed expressions and handwritten numeral strings. In this work, a new segmentation-free approach is proposed which matches convex shape portions of symbols occurring in various layout such as subscript, superscript, fraction etc. and is able to perform spotting of symbols present in a handwritten expression. Our contribution lies in the design of a novel feature which can handle touching symbols effectively in the presence of handwriting variations. This recognition-based approach helps in spotting symbols in an expression even in the presence of clutter created by the presence of other symbols.
Observe Notifications as CoAP Multicast Responses

The Constrained Application Protocol (CoAP) allows clients toobserve resources at a server, and receive notifications as unicastresponses upon changes of the resource state. In some use cases, suchas based on publish-subscribe, it would be convenient for the serverto send a single notification to all the clients observing a sametarget resource. This document defines how a CoAP server sends observenotifications as response messages over multicast, by synchronizingall the observers of a same resource on a same shared Token value.Besides, this document defines how Group OSCORE can be used to protectmulticast notifications end-to-end from the CoAP server to themultiple observer clients.