Reviewerly/exHarmony huggingface dataset:
Specific Language Impairment Detection Through Voice Analysis

Specific Language Impairment is a communication disorder regarding the mastery of language and conversation that impacts children. The system proposed aims to provide an alternative diagnosis method that does not rely on specific assessment tools. The system will accept a voice sample from the child and then detect indicators that differentiate individuals with specific language impairment from that voice sample. These indicators were based on the timbre and pitch characteristics of sound. Three different feature spaces are calculated, followed by derived features, with three different classifiers to determine the most accurate combination. The three feature spaces are Chroma, Mel-frequency cepstral coefficients (MFCC), and Tonnetz and the three classifiers are Support Vector Machines, Random Forest and a Recurrent Neural Network. MFCC, representing the timbre characteristic, was found to be the most accurate feature vector across all classifiers and Random Forest being the most accurate classifier across all feature spaces. The most accurate combination found was the MFCC feature vector with the Random Forest classifier with an accuracy level of 99%. The MFCC feature vector has the most features that are extracted giving the reason for the high accuracy. However, this accuracy decreases when the recorded word is three syllables or longer. The system proposed has proven to be a valid method that can detect SLI.
Defining Cataloging Ethics: Practitioner Perspectives

Catalogers often need to make ethical decisions about their daily work. What is the starting point for defining cataloging ethics and how do practitioners define the concept? In this paper, the authors explore definitions of ethics and cataloging ethics in the literature, and subsequently analyze responses to a question asking respondents to define cataloging ethics. Set against the existing backdrop of the American Library Association “Code of Ethics,” the authors propose that shared values create a framework for discussing cataloging ethics, rather than a single articulated definition.
Concept and types of digital resources, What are the benefits of consortia approach in collection development?

Sharing resources is nothing more than sharing library resources by certain participating libraries between them on the basis of the principle of cooperation. This is applicable when sharing documents, labor, services, space and equipment. This document mainly highlights what are you sharing resources? Why share resources? Forms, means and methods: definition, Objectives, needs, areas of cooperation and exchange of resources, components, requirements, Concept, advantages, criticism, barrier, steps to promote the exchange of resources through the network, Library Consortium and its types and functions. Advantages and disadvantages. Keywords: Digital resources.
Language Learning: Reading Comprehension, Motivation, Strategies, And Academic Achievement

This study was focused on language learning in terms of reading motivation and comprehension as well as with language learning strategies in relation to academic achievement of college students at the Visayas State University (VSU). The first set of respondents composed of forty-eight (48) Bachelor of Arts in English Language (AB-EL) students took a reading comprehension test and answered a reading motivation survey adopted from Wigfield and Guthrie (1997). The second set of respondents composed of thirty-one (31) Spch 11 (Speech Communication) students answered a survey on language learning strategies adopted from Oxford (1990). The study followed a descriptive survey design. Data were analyzed using descriptive statistics. Spearman's Rho was also used for correlation analyses. With regard to the reading motivation and comprehension of the AB-EL students, results showed that among the 11 aspects of the Motivation for Reading Questionnaire (MRQ), three of them emerged as having a significant relationship with reading comprehension. These are "challenge", "compliance" and "reading work avoidance". "Challenge" is positively correlated while "Compliance" and "Reading Work Avoidance" are negatively correlated to reading comprehension. This result implies that those who feel challenged while engaging in the reading selection have a higher level of reading comprehension. However, those who read because they are required to do so have a lower reading comprehension than those who read because they choose to do so. Also, those who tend to read to escape from work have a lower level of reading comprehension. Therefore, one's reading motivation can in a way affect his or her reading comprehension. Among the three factors that have a significant relationship with reading comprehension, "Compliance" is considered to be the most influential to students' reading comprehension. Therefore, reading to accomplish a requirement has a greater negative effect on the students' reading comprehension than reading to have an excuse to escape from work. It is disadvantageous because reading for the sake of completing or accomplishing a requirement or task is not the ideal expectation when someone reads for comprehension. Reading for comprehension embodies everything from meaning-making to experiential learning. Meanwhile, with regard to the language learning strategies in relation to the academic achievement of Spch 11 students, findings revealed that only Affective Strategies correlate with the students' academic achievement ("good" to "very good"). This implies that all the other language learning strategies covering Memory, Cognitive, Compensation, Metacognitive, and Social are independent to the respondents' academic achievement. Therefore, this study concludes that the affective factors have something to do with one's language learning strategy. Affective strategies tend to have a powerful influence on the techniques to help students control their emotions, attitude, motivation, and values, thus facilitating one's language learning.
Semantic Learning for Image Compression (SLIC)

Image compression is an ever-evolving problem with different approaches coming into prominence at different times. Data analysts are still contemplating about the best approach with compression ratio, visual quality and complexity of the architecture as the main criteria of evaluation. Most recent one is the advancements in machine learning and its applications on image classification. Researchers are currently exploring the possibility of using this advanced computing power to enhance the quality of images with the availability of large datasets. Here, we present a deep learning Convolutional neural network (CNN) implemented and trained for image recognition and image processing tasks, to understand semantics of an image. We achieve higher visual quality in lossy compression by increasing the complexity of an encoder by a slight margin to generate semantic maps of image. The semantic maps are tailored to make the encoder content-aware for a given image. In the proposed work, we present a novel architecture developed specifically for image compression, which generates a semantic map for salient regions so that they can be encoded at higher quality as compared to background regions. Experiments are conducted on the Kodak PhotoCD dataset and the results are compared with other state-of-the-art models. Results of the conducted experiment reveal an increase in compression ratio by more than 17% compared to the referred state-of-the-art model while maintaining the visual quality.
A Design Methodology for Post-Moore’s Law Accelerators: The Case of a Photonic Neuromorphic Processor

Over the past decade alternative technologies have gained momentum as conventional digital electronics continue to approach their limitations, due to the end of Moore’s Law and Dennard Scaling. At the same time, we are facing new application challenges such as those due to the enormous increase in data. The attention, has therefore, shifted from homogeneous computing to specialized heterogeneous solutions. As an example, brain-inspired computing has re-emerged as a viable solution for many applications. Such new processors, however, have widened the abstraction gamut from device level to applications. Therefore, efficient abstractions that can provide vertical design-flow tools for such technologies became critical. Photonics in general, and neuromorphic photonics in particular, are among the promising alternatives to electronics. While the arsenal of device level toolbox for photonics, and high-level neural network platforms are rapidly expanding, there has not been much work to bridge this gap. Here, we present a design methodology to mitigate this problem by extending high-level hardware-agnostic neural network design tools with functional and performance models of photonic components. In this paper we detail this tool and methodology by using design examples and associated results. We show that adopting this approach enables designers to efficiently navigate the design space and devise hardware-aware systems with alternative technologies.
QUALITATIVE EVALUATION OF PERSPECTIVES ON EFFECTIVENESS OF USE OF DIGITAL AND SOCIAL MEDIA IN EDUCATION

QUALITATIVE EVALUATION OF PERSPECTIVES ON EFFECTIVENESS OF USE OF DIGITAL AND SOCIAL MEDIA IN EDUCATION
Design and implementation of high-performance cloud computing system

Cloud computing technology has brought huge changes to modern computing methods and is widely used. However, its security issues cannot be ignored. In order to use cloud services, users must store programs, data, etc. in the cloud. Once the attacker gains control of the server, or the cloud service provider itself is not trustworthy, the user's programs and data face great risks. For the security of cloud computing, SGX (Intel Software Guard Extensions) technology proposed by Intel provides users with a hardware-assisted trusted execution environment, and isolates the program code and data that need to be protected in a container called Enclave to ensure its confidentiality and completeness. However, SGX sacrifices some performance, and applications running in SGX cannot get satisfactory speed. Therefore, in order to improve the availability of the SGX application, it is necessary to optimize the SGX operating mechanism and program logic to increase the speed of the program. This article designs a set of high-performance cloud computing systems, selects a deep learning framework as a case, combines the characteristics of the framework and the SGX operating mechanism, analyzes various factors affecting performance, proposes an optimization scheme, and implements the system. Experiments show that the optimized trusted deep learning framework has reduced the running time by 24.8% compared with that before optimization.
THE ROLE OF BLOCKCHAIN TECHNOLOGIES IN DIGITAL ASSESSMENT

1 Massachusetts Institute of Technology (UNITED STATES)2 Ministry for Education and Employment (MALTA)3 Donau Universität Krems (AUSTRIA)4 Austrian Institute of Technology (AUSTRIA)
LEARNING OF LABORATORY SKILLS VIA AUGMENTED REALITY - CASE STUDY IN PHARMACY

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 7631-7638ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.1934Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
How to Dynamically Incentivize Sufficient Level of IoT Security

This paper propose an incentive mechanism to secure large numbers of devices through the use of insurance based on smart contracts. It consists of the automated security evaluation of enterprise IoT devices and the creation of a dynamic insurance premium. To automate the security evaluation of enterprise IoT devices, we collect and store IoT device status data with privacy preservation on blockchain. Then, we track and assess the risk associated with IoT devices with the use of a smart contract. By monitoring this risk over time, we present a means to incentivize the resolution of vulnerabilities by measuring the latent risk in an environment as well as the vigilance of the devices’ managers in resolving these vulnerabilities. In this way, we produce a dynamic cyber insurance premium that more accurately captures the risk profile associated with an environment than existing cyber insurance. Through the use blockchain and smart contracts, this framework also provides public verification for both insured and insurer and provides a level of risk management for the insurer. We also present regulatory considerations in order for this scheme to meet supervisory requirements.
Space-Efficient Key-Policy Attribute-Based Encryption from Lattices and Two-Dimensional Attributes

Linear secret-sharing scheme (LSSS) is a useful tool for supporting flexible access policy in building attribute-based encryption (ABE) schemes. But in lattice-based ABE constructions, there is a subtle security problem in the sense that careless usage of LSSS-based secret sharing over vectors would lead to the leakage of the master secret key. In this paper, we propose a new method that employs LSSS to build lattice-based key-policy attribute-based encryption (KP-ABE) that resolves this security issue. More specifically, no adversary can reconstruct the master secret key since we introduce a new trapdoor generation algorithm to generate a strong trapdoor (instead of a lattice basis), that is, the master secret key, and remove the dependency of the master secret key on the total number of system attributes. Meanwhile, with the purpose of reducing the storage cost and support dynamic updating on attributes, we extended the traditional 1-dimensional attribute structure to 2-dimensional one. This makes our construction remarkably efficient in space cost, with acceptable time cost. Finally, our scheme is proved to be secure in the standard model.
An investigation of phishing awareness and education over time: When and how to best remind users

Security awareness and education programmes are rolled out in more and more organisations. However, their effectiveness over time and, correspondingly, appropriate intervals to remind users’ awareness and knowledge are an open question. In an attempt to address this open question, we present a field investigation in a German organisation from the public administration sector. With overall 409 employees, we evaluated (a) the effectiveness of their newly deployed security awareness and education programme in the phishing context over time and (b) the effectiveness of four different reminder measures – administered after the initial effect had worn off to a degree that no significant improvement to before its deployment was detected anymore. We find a significantly improved performance of correctly identifying phishing and legitimate emails directly after and four months after the programme’s deployment. This was not the case anymore after six months, indicating that reminding users after half a year is recommended. The investigation of the reminder measures indicates that measures based on videos and interactive examples perform best, lasting for at least another six months.
DeLone and McLean Model Evaluation of Information System Success: A Case Study of Master Program of Civil Engineering Universitas Lambung Mangkurat

This paper examines the adaptation of the DeLone and McLean information system success models that are commonly used in the university's information systems domain. The survey design model was used in this study with 29 structured questionnaire items through Google forms used to collect data from 98 respondents, they were students, lecturers, and administrative staff in the Master Program of Civil Engineering Universitas Lambung Mangkurat Banjarmasin Indonesia. Structural equation modeling is used to test construct models. The results of data analysis showed that the quality of the system affected the use (β = 0.61, p 0.05) but influenced individual impact (β = 0.32, p <0.001) and organizational impact (β = 0.19, p <0.001). User satisfaction affects individual impacts (β = 0.28, p <0.001) and organizational impacts (β = 0.17, p <0.001). Individual impacts affect organizational impacts (β = 0.73, p <0.05). The quality of the system, the quality of information, and the impact of the individual are the determining factors for the success of the information system at the university so it is very important that the information system at the university is designed so that it is easy to use, flexible, and functional to meet its objectives.Keywords: DeLone and McLean Model, system, information, quality, intention, satisfaction, impact.
Multi-task learning with shared random effects and specific sparse effects

In multi-task learning scenarios, random effects may be shared among different tasks while each task can have its own sparse effects. This structure has often been observed in the field of sentiment analysis for movie rating. In this study, we consider a multi-task learning problem in the presence of variables with shared random effects and specific sparse effects. To address this issue, we propose MSS (multi-task learning with shared random effects and specific sparse effects). To build this model, appropriate priors for the shared effects and specific effects under the Bayesian framework are considered. To overcome the computational complexity of Bayesian inference, an efficient algorithm is proposed based on variational inference, which is scalable to large-scale data analysis problems. The effectiveness of MSS in prediction and variable selection is demonstrated through comprehensive simulation studies and real data analysis of movie rating. The results demonstrate that the characterization of shared weak effects and task-specific sparse effects can improve the accuracy of prediction and variable selection.
A method of registering point cloud with CAD model based on line contour matching

Registration of point cloud with its CAD model can be used to detect products. To register point cloud with its CAD model, feature characters are used. In this article, a registration method based on line contour matching is proposed. The registration process includes two parts. The one is to extract line contour points (LCPs) from point cloud. The other is to match LCPs of point cloud with LCPs of CAD model. To extract LCPs from point cloud, a slice set is constructed to extract section line data (SLD) containing LCPs from point cloud. Then LCPs are identified by judging the feature end of SLD and analyzing normal angle sequence (NAS) of SLD. LCPs of CAD model are non-uniformly distributed. So to make the matching process more efficient and robust, two groups of LCPs are uniformly sampled. Then best fitting method is used to match the two groups of sampled LCPs to obtain transformation parameters. Verification experiments are conducted on three line contours. And the calculated transformation parameters are compared with reference values. The results show that the deviations of rotation angle are within (-0.9&deg;~0.3&deg;). And deviations of translation elements are within &plusmn;0.4mm.
Neural Style Difference Transfer and Its Application to Font Generation

Designing fonts requires a great deal of time and effort. It requires professional skills, such as sketching, vectorizing, and image editing. Additionally, each letter has to be designed individually. In this paper, we introduce a method to create fonts automatically. In our proposed method, the difference of font styles between two different fonts is transferred to another font using neural style transfer. Neural style transfer is a method of stylizing the contents of an image with the styles of another image. We proposed a novel neural style difference and content difference loss for the neural style transfer. With these losses, new fonts can be generated by adding or removing font styles from a font. We provided experimental results with various combinations of input fonts and discussed limitations and future development for the proposed method.
Efficiency and Accuracy Improvements of Secure Floating-Point Addition over Secret Sharing

In secure multiparty computation (MPC), floating-point numbers should be handled in many potential applications, but these are basically expensive. In particular, for MPC based on secret sharing (SS), the floating-point addition takes many communication rounds though the addition is the most fundamental operation. In this paper, we propose an SS-based two-party protocol for floating-point addition with 13 rounds (for single/double precision numbers), which is much fewer than the milestone work of Aliasgari et al. in NDSS 2013 (34 and 36 rounds, respectively) and also fewer than the state of the art in the literature. Moreover, in contrast to the existing SS-based protocols which are all based on “roundTowardZero” rounding mode in the IEEE 754 standard, we propose another protocol with 15 rounds which is the first result realizing more accurate “roundTiesToEven” rounding mode. We also discuss possible applications of the latter protocol to secure Validated Numerics (a.k.a. Rigorous Computation) by implementing a simple example.
High-Quality Object Reconstruction From One-Dimensional Compressed Encrypted Signal Based on Multi-Network Mixed Learning

Conventional optical image encryption methods based on phase-shifting interferometry need at least two interferograms, and the storage or transmission of interferograms needs to occupy a lot of resources. At the same time, the low quality of reconstructed complex natural images has always been a main limiting factor in the application of optical image security. In this paper, a high-quality object reconstruction method from one-dimensional compressed encrypted signal based on multi-network mixed learning is proposed. First, an encrypted interferogram can be obtained using the double random phase encoding (DRPE) method. Then, we can obtain the one-dimensional compressed sampling signal of the encrypted hologram on the photodiode using single-pixel compressive holographic imaging method. Finally, the mapping of 1D signal to 2D object image can be learned utilizing multiple neural network models. Numerical simulation results show that the complex natural images can be reconstructed using the proposed method with high quality at lower sampling ratio.
An Answer Set Programming Framework for Reasoning about Agents' Beliefs and Truthfulness of Statements

The paper proposes a framework for capturing how an agent’s beliefs evolve over time in response to observations and for answering the question of whether statements made by a third party can be believed. The basic components of the framework are a formalism for reasoning about actions, changes, and observations and a formalism for default reasoning. The paper describes a concrete implementation that leverages answer set programming for determining the evolution of an agent's ``belief state'', based on observations, knowledge about the effects of actions, and a theory about how these influence an agent's beliefs. The beliefs are then used to assess whether statements made by a third party can be accepted as truthful. The paper investigates an application of the proposed framework in the detection of man-in-the-middle attacks targeting computers and cyber-physical systems. Finally, we briefly discuss related work and possible extensions.