Reviewerly/exHarmony huggingface dataset:
Selecting texture resolution using a task‐specific visibility metric

Abstract In real‐time rendering, the appearance of scenes is greatly affected by the quality and resolution of the textures used for image synthesis. At the same time, the size of textures determines the performance and the memory requirements of rendering. As a result, finding the optimal texture resolution is critical, but also a non‐trivial task since the visibility of texture imperfections depends on underlying geometry, illumination, interactions between several texture maps, and viewing positions. Ideally, we would like to automate the task with a visibility metric, which could predict the optimal texture resolution. To maximize the performance of such a metric, it should be trained on a given task. This, however, requires sufficient user data which is often difficult to obtain. To address this problem, we develop a procedure for training an image visibility metric for a specific task while reducing the effort required to collect new data. The procedure involves generating a large dataset using an existing visibility metric followed by refining that dataset with the help of an efficient perceptual experiment. Then, such a refined dataset is used to retune the metric. This way, we augment sparse perceptual data to a large number of per‐pixel annotated visibility maps which serve as the training data for application‐specific visibility metrics. While our approach is general and can be potentially applied for different image distortions, we demonstrate an application in a game‐engine where we optimize the resolution of various textures, such as albedo and normal maps.
Linear Time Maximum Segmentation Problems in Column Stream Model

We study a lossy compression scheme linked to the biological problem of founder reconstruction: The goal in founder reconstruction is to replace a set of strings with a smaller set of founders such that the original connections are maintained as well as possible. A general formulation of this problem is NP-hard, but when limiting to reconstructions that form a segmentation of the input strings, polynomial time solutions exist. We proposed in our earlier work (WABI 2018) a linear time solution to a formulation where minimum segment length was bounded, but it was left open if the same running time can be obtained when the targeted compression level (number of founders) is bounded and lossyness is minimized. This optimization is captured by the Maximum Segmentation problem: Given a threshold M and a set $$\mathcal {R} = \{\mathcal {R}_1,\ldots ,\mathcal {R}_m\}$$ of strings of the same length n, find a minimum cost partition P where for each segment $$[i,j] \in P$$ , the compression level $$\vert \{\mathcal {R}_k[i,j]: 1\le k \le m\} \vert $$ is bounded from above by M. We give linear time algorithms to solve the problem for two different (compression quality) measures on P: the average length of the intervals of the partition and the length of the minimal interval of the partition. These algorithms make use of positional Burrows–Wheeler transform and the range maximum queue, an extension of range maximum queries to the case where the input string can be operated as a queue. For the latter, we present a new solution that may be of independent interest. The solutions work in a streaming model where one column of the input strings is introduced at a time.
Three-Dimensional Face Reconstruction from Uncalibrated Photographs: Application to Early Detection of Genetic Syndromes

Facial analysis from photography supports the early identification of genetic syndromes, but clinically-acquired uncalibrated images suffer from image pose and illumination variability. Although 3D photography overcomes some of the challenges of 2D images, 3D scanners are not typically available. We present an optimization method for 3D face reconstruction from uncalibrated 2D photographs of the face using a novel statistical shape model of the infant face. First, our method creates an initial estimation of the camera pose for each 2D photograph using the average shape of the statistical model and a set of 2D facial landmarks. Second, it calculates the camera pose and the parameters of the statistical model by minimizing the distance between the projection of the estimated 3D face in the image plane of each camera and the observed 2D face geometry. Using the reconstructed 3D faces, we automatically extract a set of 3D geometric and appearance descriptors and we use them to train a classifier to identify facial dysmorphology associated with genetic syndromes. We evaluated our face reconstruction method on 3D photographs of 54 subjects (age range 0–3 years), and we obtained a point-to-surface error of 2.01 $$ \pm $$ 0.54%, which was a significant improvement over 2.98 $$ \pm $$ 0.64% using state-of-the-art methods (p < 0.001). Our classifier detected genetic syndromes from the reconstructed 3D faces from the 2D photographs with 100% sensitivity and 92.11% specificity.
How Diverse Are Federated Query Execution Plans Really?

Federated query engines optimize and execute SPARQL queries over the data accessible via SPARQL endpoints; even in the absence of information about which sources provide relevant data. Different query optimization strategies may produce different execution plans, which in many cases explains the huge differences in performance that we encounter with state-of-the-art federated query engines. Related work has so far mainly focused on execution time and number of selected sources, overlooking the importance of the execution plans themselves. In this demonstration paper, we therefore present PIPE, a tool that allows for comparing federated query engines in terms of performance, execution plans, and query answers. Currently, PIPE supports five state-of-the-art federated query engines; we provide a Java library for straightforward integration of additional federated query engines.
Machine translation literacy: Academic libraries' role

ABSTRACT English is the main language of scholarly communication, but most researchers are not native English speakers. Machine translation (MT) technology is advancing, but such tools rarely produce high quality output of specialized text without human intervention. There is an emerging need for MT literacy, and academic librarians are well placed to deliver it. This poster reports the results of an MT literacy workshop delivered at Concordia University Library.
Green Data Center Analysis and Design for Energy Efficiency Using Clustered and Virtualization Method

Data centers are known as a set of servers. Most corporate data and storage are located, operated, and managed on this place. Data center is a backbone of variety internet application and service, growing demand for that has drastically increased the power consumption. The energy use is a central issue in the Data Center. According to the issue, the researchers intend to analyze and design the Green Data Center at Universitas Klabat. Simulation and direct observation using clustered and virtualization are the methods used in this research to reduce power consumption. VM layer metrics on power consumption formula was adopted and calculated from the simulations. Power consumption is calculated by dividing the idle power of the physical host by the number of running VMs. The net physical host power consumption is calculated and multiplied by the CPU utilization of the VM as seen from the physical host. The result is the energy can be utilized and decrease for 286.66 % after virtualization.
Classification of Turkish News Content by Deep Learning Based LSTM Using Fasttext Model

With the increase in the rate of use of the Internet, the number of content produced has increased. Texture classification allows these categorized content to be automatically categorized. In this study, a special type of repetitive artificial neural networks(RNN) using the deep learning based Fasttext model, LSTM (Long-Short Term Memory) was used to classify the news texts. Fasttext, Word2vec and Doc2vec models are used to classify data on the data set and the success rates are compared. LSTM is used to classify the news data on the Fasttext model which gives the most successful result.
A Very Compact Architecture of CLEFIA Block Cipher for Secure IoT Systems

In this paper, a very compact architecture of the CLEFIA block cipher is presented. This architecture has a 128-bit plaintext/ciphertext and a 128-bit key and processes the data using a 4-bit datapath and therefore requires only a small number of hardware resources. The target of this architecture is ultra-low area devices for Internet of Things systems. The design was coded using the Verilog language and the BASYS3 board (Artix 7 XC7A35T) was used for the hardware implementation. The proposed implementation utilizes only 606 FPGA LUTs and 477 FFs and reaches a data throughput of 28 Mbps at 115 MHz clock frequency.
Evaluating the Performance of Machine Learning Sentiment Analysis Algorithms in Software Engineering

In recent years, sentiment analysis has been aware within software engineering domain. While automated sentiment analysis has long been suffering from doubt of accuracy, the tool performance is unstable when being applied on datasets other than the original dataset for evaluation. Researchers also have the disagreements upon if machine learning algorithms perform better than conventional lexicon and rule based approaches. In this paper, we looked into the factors in datasets that may affect the evaluation performance, also evaluated the popular machine learning algorithms in sentiment analysis, then proposed a novel structure for automated sentiment tool combines advantages from both approaches.
Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability are limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching.
“End to End” Towards a Framework for Reducing Biases and Promoting Transparency of Algorithmic Systems

Algorithms play an increasing role in our everyday lives. Recently, the harmful potential of biased algorithms has been recognized by researchers and practitioners. We have also witnessed a growing interest in ensuring the fairness and transparency of algorithmic systems. However, so far there is no agreed upon solution and not even an agreed terminology. The proposed research defines the problem space, solution space and a prototype of comprehensive framework for the detection and reducing biases in algorithmic systems.
Towards Information-centric Collaborative QoE Management using SDN

Recent studies implemented collaboration between Over-The-Top (OTT) service and Internet Service Provider (ISP) concerning information exchange between the providers. This information is used to accurately predict the delivered Quality of Experience (QoE) and decide the correcting network management actions. In this scenario, we aim to investigate the impact of the frequency of information exchange between OTT and ISP on end users' QoE and network resource utilization. Firstly, we propose our information-centric QoE-aware collaborative service management approach by defining the type of information to be acquired and exchanged between OTT and ISP. Secondly, we present our platform based on the Software-Defined Networking (SDN) paradigm, which we used to conduct the experiments. Finally, we conduct experiment results that compare the proposed collaborative approach with the case of no collaboration when the sampling interval of information exchange varies between 2 s and 32 s. The experiment results show that a higher frequency of information exchange may result in better network reliability and delivered QoE, but a frequency higher than 1/4 Hz may not further improve the delivered QoE.
Automatic Semantic Segmentation and Annotation of MOOC Lecture Videos

Locating and searching a desired topic inside a long MOOC lecture video is a rigorous and time consuming task unless video meta data provides annotation information of different topics taught in it. In this work we propose a system that performs topic wise semantic segmentation and annotation of MOOC lecture videos to mitigate user effort. As input, our system takes a lecture video, its associated speech transcript, and list of topic names (available in the form of course syllabus) and produce an annotated information of each semantically coherent topics taught in that video. We have two major contribution in this work. First, we have applied state-of-the art neural network based text segmentation technique on textual information to obtain topic boundaries. Secondly, for annotation we have utilized a combination of visual and textual information, that assigns accurate topic name for each segment. We have tested our method on 100 lecture videos hosted in NPTEL [1] and on an average get $$\sim $$ 72% segmentation accuracy, better than the existing approaches available in literature.
Secure and Effective Implementation of an IOTA Light Node using STM32

A major challenge in networked sensor systems and other IoT environments is addressing security. Vulnerabilities in those systems arise from poor physical security, unauthenticated devices, insecure firmware updates, insecure communication, and data corruption. In recent times Distributed Ledger Technologies (DLTs), of which Blockchain is an instance, have been identified as a possible solution to some of these issues. The blokchain model genetically ensures decentralized security and privacy, and therefore could provide IoT systems with a trusted infrastructure for securely logging data or exchanging tokens without the necessity, and costs, of central servers. Blockchain is no panacea, either. IoT devices that get connected to a blockchain network must still be secured, in particular they must protect the confidentiality of the keys. This requires the embedded microcontroller to execute only authenticated firmware, with protections against software attacks, such as buffer overflows, and resistance against side-channel attacks. In addition, as confirmed from the scarcity of implementations reported in the literature, it is still not clear whether blockchain protocols can be implemented efficiently on resource-constrained IoT devices. In this work, also supported by a Demo, we show an example of secure IoT device that enables the functionalities of IOTA, a DLT specifically designed for the use in the IoT. In particular, we present a Light Node based on STM32 that implements all the cryptographic functions, IOTA specific operations and communication functions required to successfully publish transactions in the IOTA distributed ledger. Our implementations on microcontrollers (ARM Cortex-M) performs up to 22 times faster in terms of cycles and up to 4 times faster in absolute time with respect to the state-of-the-art implementation on a Raspberry PI 3B. Our Light Node also ensures protection of the stored private data and guarantees secure firmware update thanks to a suitable configuration of some security features provided by STM32 microcontrollers.
Nationalism in the Digital Age: Fun as a Metapractice of Extreme Speech

Critical assessments of the recent resurgence of right-wing nationalism have rightly highlighted the role of social media in these troubling times, yet they are constrained by an overemphasis on celebrity leaders defined as populists. This article departs from a leader-centric analysis and the liberal frame that still largely informs assessment of political action, to foreground “fun” as a salient aspect of right-wing mobilization. Building on ethnographic fieldwork among the Hindu nationalists in India, I argue that fun is a metapractice that shapes the interlinked practices of fact-checking, abuse, assembly, and aggression among online volunteers for the right-wing movement. Furthermore, fun remains crucial for an experience of absolute autonomy among online users in ideological battles. Providing the daily drip feed for exclusion, fun as a metapractice bears a formal similarity to objectivity in its performative effects of distance and deniability.
Task performance in a head-mounted display: The impacts of varying latency

Abstract The purpose of this study was to determine how latency in a head-mounted display (HMD) affects human performance. Virtual environments (VEs) are used frequently for training. However, VEs can cause simulator sickness. Prior work in our laboratory has examined the role of varying latency in simulator sickness. However, the effect of varying latency on task performance has not been examined. Subjects participated in a repeated measures study where they were exposed to two different latency conditions in an HMD: constant (70 ms) and varying (70–270 ms). During each HMD exposure, subjects used a laser pointer to repeatedly “shoot” at laser targets while accuracy and time-to-hit were recorded. Subjects scored fewer hits and took longer to hit targets in the varying latency condition. These findings indicate that individuals exposed to varying latency perform worse than individuals exposed to a lower constant latency.
INTELLIGENT CLASSIFICATION OF BIOPHYSICAL SYSTEM STATES USING FUZZY INTERVAL LOGIC

The task of increasing the reliability of the adoption of classification decisions on the state of the biophysical system with fuzzy interval representations about the characteristics or properties of objects is solved. A formalized model for classifying the states of the research object is proposed. The model provides a mechanism for calculating the confidence coefficient for each situation from a defined set of space states of the system and provides an opportunity to present the investigated features of objects based on four types of membership functions, thus ensuring that the inaccuracies, fuzziness or unreliability of the available data and knowledge are eliminated. The proposed modification of the state classification method generates and evaluates several alternatives according to criteria when making classification decisions. Experimental testing of the system has been performed through software simulation, as well as costs, required for the software product development, have been calculated. The increase of the state classification reliability has been confirmed, the versatility of the proposed intellectual methods for the arbitrary set of fuzzy data has been established.
Towards automated computer vision: analysis of the AutoCV challenges 2019

We present the results of recent challenges in Automated Computer Vision (AutoCV, renamed here for clarity AutoCV1 and AutoCV2, 2019), which are part of a series of challenge on Automated Deep Learning (AutoDL). These two competitions aim at searching for fully automated solutions for classification tasks in computer vision, with an emphasis on any-time performance. The first competition was limited to image classification while the second one included both images and videos. Our design imposed to the participants to submit their code on a challenge platform for blind testing on five datasets, both for training and testing, without any human intervention whatsoever. Winning solutions adopted deep learning techniques based on already published architectures, such as AutoAugment, MobileNet and ResNet, to reach state-of-the-art performance in the time budget of the challenge (only 20 minutes of GPU time). The novel contributions include strategies to deliver good preliminary results at any time during the learning process, such that a method can be stopped early and still deliver good performance. This feature is key for the adoption of such techniques by data analysts desiring to obtain rapidly preliminary results on large datasets and to speed up the development process. The soundness of our design was verified in several aspects: (1) Little overfitting of the on-line leaderboard providing feedback on 5 development datasets was observed, compared to the final blind testing on the 5 (separate) final test datasets, suggesting that winning solutions might generalize to other computer vision classification tasks; (2) Error bars on the winners’ performance allow us to say with confident that they performed significantly better than the baseline solutions we provided; (3) The ranking of participants according to the any-time metric we designed, namely the Area under the Learning Curve, was different from that of the fixed-time metric, i.e. AUC at the end of the fixed time budget. We released all winning solutions under open-source licenses. At the end of the AutoDL challenge series, all data of the challenge will be made publicly available, thus providing a collection of uniformly formatted datasets, which can serve to conduct further research, particularly on meta-learning.
Smart education: educational service system for equal quality education

Abstract The problem of unequal quality of high school education in West Java with the viewpoint of Senior High School (SMA) due to the assessment of the quality of education in Indonesia is still focused on Students, Teachers, Principals, and Parents separately. Supposedly the assessors of high school quality are assessed based on an education service system (ESS). Research studies related to high school as an educational service are well suited to a service science approach, and the term educational service system. The ESS study discusses educational sector issues in the perspective of a service system consisting of components, functions, interfaces, component linkages, inputs, processes, and outputs. The purpose of this research is to create educational service system model to improve the quality of high school education services in West Java. Creation of ESS Model with service engineering method with stages: service innovation, service engineering, and service measurement. Output of Educational Service model System in the form of learning management system (LMS) based on Moodle. The prototype measurement result ESS model shows the efficiency of high school education service system. The research output is expected to solve the problem of equalization of high school education quality in West Java.
Computational Intelligence for Life Sciences

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.