Reviewerly/exHarmony huggingface dataset:
The proprial article and the semantics of names

The proprial article is a functional item that occurs in a number of languages alongside names to the general exclusion of other nouns. In the following I present an account of the semantic function of this article, and how it interacts with names as nouns to form referential expressions in argument position. In particular, I suggest that in line with what a number of researchers have claimed, names generally are count nouns in the lexicon, denoting the property of bearing the name in question. The proprial article then composes with name NPs of a certain sort to yield proprial DPs that rigidly denote individuals, which are presupposed to bear the name in question at their world of use. It follows that ordinary referential names in argument position are not definite descriptions, as related approaches to the semantics of names often suppose, and that the proprial article is a functional element distinct from the definite article. This schema is applied to DP languages with article systems generally: all such languages are taken to have the proprial article, whether it appears overtly or covertly, and to make use of proprial DPs for reference using names. The machinery used further allows for a model-theoretically precise and intuitively compelling characterization of name-bearing. BibTeX info
TRAINEE TEACHERS PERCEIVED ADVANTAGES, BARRIERS AND SUPPORTS OF COMPUTER TECHNOLOGY IN PRIMARY SCHOOLS IN RIFT VALLEY, KENYA.

02Oct 2019 TRAINEE TEACHERS PERCEIVED ADVANTAGES, BARRIERS AND SUPPORTS OF COMPUTER TECHNOLOGY IN PRIMARY SCHOOLS IN RIFT VALLEY, KENYA. Lilian C. Kimaiyo. Department of Educational Psychology, Tom Mboya University College.
Naive Bayes classifier optimization for text classification on e-government using particle swarm optimization

One of the public e-government services is a web-based online complaints portal. Text of complaint needs to be classified so that it can be forwarded to the responsible office quickly and accurately. The standard classification approach commonly used is the Naive Bayes Classifier (NBC) and k-Nearest Neighbor (k-NN), which still classifies one label and needs to be optimized. This research aims to classify the complaint text of more than one label at the same time with NBC, which is optimized using Particle Swarm Optimization (PSO). The data source comes from the Sambat Online portal and is divided into 70 % as training data and 30 % as testing data to be classified into seven labels. NBC and k-NN algorithms are used as a comparison method to find out the performance of PSO optimization. The 10-fold cross-validation shows that NBC optimization using PSO achieves an accuracy of 87.44 % better than k-NN of 75 % and NBC of 64.38 %. The optimization model can be used to increase the effectiveness of services to e-government in society.
Improved supervised classification of bedrock in areas of transported overburden: Applying domain expertise at Kerkasha, Eritrea

A regional bedrock map provides a foundation from which to build geological interpretations. However, rapid and accurate bedrock mapping in an area that lacks outcrop is a common problem, especially in regions with sparse data. A historic bedrock map from an Au and base metal project in the Kerkasha district, Eritrea, is significantly improved by predicting bedrock distribution in areas previously mapped as transported overburden. Publicly-available remote sensing data (DTM and ASTER) were combined with airborne geophysical data (magnetics and radiometrics) to provide features for bedrock prediction. Remote sensing data were pre-processed using Principal Components Analysis (PCA) to yield an equal number of principal components (PC) as input features. Four iterations were trialled, using different combinations of remote sensing PC features. The two initial trials used all available remote sensing data but compared results when feature ranking and selection is applied to reduce the number of PCs used for training and classification. The subsequent two trials used subsets of available remote-sensing data, selected based on domain expertise (i.e., the domain-specific knowledge of a geologist), with all respective PCs were retained. Five-fold cross-validation scores were highest when a DTM, magnetics, and radiometrics data were included as input features. However, qualitative visual appraisal of predicted results across trials, complemented by maps of class membership uncertainty (using a measure of entropy), indicate that geologically-meaningful results are also produced when radiometrics are omitted and only the DTM and magnetics are used. The study concludes with a generalised workflow to assist geologists who are seeking to improve the bedrock interpretation of areas under cover in a single area of interest. Domain expertise is shown to be critical for the selection of appropriate input features and validation of results during predictive lithologic mapping.
Lossless Image Coding Exploiting Local and Non-local Information via Probability Model Optimization

We previously proposed a lossless image coding method based on examples search and probability model optimization. In this paper, we improve coding efficiency of the method by introducing an adaptive prediction technique. Specifically, multiple affine predictors are trained pel-by-pel by using causal neighbor pels, and the predicted values obtained by those predictors are used for generating the probability model. Therefore, both non-local information by the examples search and local information by the adaptive prediction are used together in the probability modeling. Furthermore, an optimization method for the number of examples is also proposed in this paper. Experimental results show that the proposed method achieves better coding rates than the state-of-the-art lossless coding schemes.
Generative Image Translation for Data Augmentation of Bone Lesion Pathology

Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases. In this work, we address the problem of classifying bone lesions from X-ray images by increasing the small number of positive samples in the training set. We propose a generative data augmentation approach based on a cycle-consistent generative adversarial network that synthesizes bone lesions on images without pathology. We pose the generative task as an image-patch translation problem that we optimize specifically for distinct bones (humerus, tibia, femur). In experimental results, we confirm that the described method mitigates the class imbalance problem in the binary classification task of bone lesion detection. We show that the augmented training sets enable the training of superior classifiers achieving better performance on a held-out test set. Additionally, we demonstrate the feasibility of transfer learning and apply a generative model that was trained on one body part to another.
Induction of Fault Trees through Bayesian Networks

Cyber-physical systems have increasingly intricate architectures and failure modes, which is due to an explosion of their complexity, size, and failure criticality. While expert knowledge of individual components exists, their interaction is complex. For these reasons, obtaining accurate system reliability models is a hard task. At the same time, systems tend to be continuously monitored via advanced sensor systems. This data describes the components' failure behavior and can be exploited for failure diagnosis and learning of reliability models. This paper presents an effective algorithm for the learning of Fault Trees from data. Fault trees (FTs) are a widespread formalism in reliability engineering. They capture the failure behavior of components and their propagation through an entire system. To that end, we first use machine learning to compute a Bayesian Network (BN) highlighting probabilistic relationships between the failures of components and root causes. Then, we apply a set of rules to translate a BN into an FT, based on the Conditional Probability Tables to decide, amongst others, the nature of gates in the FT. We evaluate our method on synthetic data and a benchmark set of FTs.
Multi-Target Tracking Based on High-Order Appearance Feature Fusion

Multi-target tracking (MTT) is among the fundamental problems in the field of video analysis and monitoring. In the tracking-by-detection framework, data association is one of the most important and difficult problems. In this paper, we propose a framework to obtain the appearance features of a target in an end-to-end fashion, which fuses high-level and low-level semantic information. The high-order feature map is abstracted using the high-order apparent relationship for each target between the current frame and the previous frames, whereas the similarity matrix is used to describe the high-order features of the target. The best matching relationships between targets are obtained using hierarchical data association and the Hungarian algorithm. This proposed method is called Multi-target tracking Based on High-order Appearance Feature Fusion (MTT-HAFF), which can handle a large number of input sequences, local association failures, and identity exchanges that result from unreliable detections. The results show that the proposed algorithm has a good robustness for long-term occlusion tracking.
From Open Science to Open Innovation

Open science is a powerful way to discover and disseminate new knowledge. But the very norms that animate open science can sometimes inhibit its subsequent commercialization. There exists a Valley of Death between the culmination of a scientific project, and the later commercial use of that science. While a fundamental knowledge of how a technology works may have been achieved, there is far too little known for businesses to invest in developing that knowledge further. And the best use of a new discovery often is unknown to those who first discover it. This is where open science must start to give way to Open Innovation. IMEC and CERN’s ATTRACT project provide two examples wherein a strong culture of open science co-exists with a thriving Open Innovation culture as well to traverse the Valley of Death
The continuum absorption: trust assessment of published graphical information

An approach is presented which allows visual and quantitative trust assessment of composite multi-paper plots, where the results of the graphical representation of calculation or measurement results from different publications are compared. Using plots from the W@DIS IS collection, which characterize the continuum absorption, as an example, we show the procedure of construction of a composite multi-paper plot.
AN INSTRUMENT FOR THE DIGITAL COMPETENCE ACTIONS FRAMEWORK

1 Aristotelion University of Thessaloniki and University of Macedonia (GREECE)2 University of Macedonia (GREECE)
Posting Techniques in Indoor Environments Based on Deep Learning for Intelligent Building Lighting System

Recently, with the rapid development of society, solutions to reduce energy consumption in the world have attracted a lot of attention, especial electric energy. In this regard, a system that can control light on and off by determining the location of the person to reduce the waste of electricity used in public buildings, called intelligent building lighting system. Following the practical requirements of the intelligent building lighting system, a technique for positioning in indoor environments is proposed, supporting the design of a positioning system based on deep learning and the Cerebellar Model Articulation Controller (CMAC), called Y-CMAC.This technique adopts YOLOv3 (the method in the paper of YOLOv3: An Incremental Improvement) for object detections and makes the coordinate of a person in the image. On the other hand, using CMAC to calculate the actual position of the person in the indoor environment. Moreover, massive surveillance video is used to reduce the cost of equipment and facilitate the promotion of applications. The average positioning error is controlled at around 1m in this paper.
Security Analysis of Auctionity: A Blockchain Based E-Auction

Auctions are widely used to sell products between different users. In this paper, we present Auctionity, an English e-auction based on blockchain. We describe the different protocols used in Auctionity. We also define the security models and the associated properties. We formally prove some security properties of this protocol using ProVerif.
Activity Recognition from Smartphones Using Hybrid Classifier PCA-SVM-HMM

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. A various real-life ubiquitous computing applications use smart sensors embedded in smart phones to infer user's human activities. In this work, we proposed a new hybrid classification model to perform recognition of activities using Smartphone data. The proposed method combines SVM learning algorithm with HMM, to classify and identify activity. Principal Component Analysis (PCA) is used to reduce the features set in the dataset. Experiments performed in the real datasets show comparative results between this SVM-HMM, SVM, HMM and the baseline methods in terms of recognition performance, highlighting the advantages of the proposed method.
Planning with Abstract Learned Models While Learning Transferable Subtasks

We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks.
Instantiation of the multi-viewpoints ontology from a resource

We are interested in the acquisition of instances of concepts from a text resource. These concepts are defined in the multi-viewpoints ontology. The multi-viewpoints ontology allows giving several definitions of a single concept. Each definition is guaranteed by a viewpoint. In the multi-viewpoints ontology, a single individual is an instance of several concepts. That is, which we call the multi-instantiations. The acquisition of the multi-instantiations from a resource requires alignment between the resource elements and the multi-viewpoints ontology elements. The structure of the multi-viewpoints ontology and the semantic constraints of the multi-viewpoints ontology concepts differ from the classical ontology’s. For that, the existing processes, which allow aligning the resource element with a classical ontological concept, cannot align the resource element with a multi-viewpoints ontology concept. Thus, we propose a new process, which offers a possibility to align resource elements with multi-viewpoints ontology concepts in order to acquire the multi-instantiations. The obtained multi-instantiations will be represented in the Multi-Viewpoints Ontology Language, in order to be integrated into the multi-viewpoints ontology.
Feature Selection-Based Hierarchical Deep Network for Image Classification

In this paper, a novel hierarchical deep network is proposed to combine the deep convolutional neural network and the feature selection-based tree classifier efficiently for image classification. First, the concept ontology is built for organizing large-scale image classes hierarchically in a coarse-to-fine fashion. Second, a novel selective orthogonal algorithm is proposed to make sure deep features extracted for each level classifiers more in line with the requirements of different classification tasks. Also, the role of useful feature components in multi-level deep features are improved. The experimental results on three datasets show that adding a feature selection module in a hierarchical deep network can perform better performance in large-scale image classification.
Handwritten Arabic Text Recognition using Principal Component Analysis and Support Vector Machines

In this paper, an offline holistic handwritten Arabic text recognition system based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifiers is proposed.The proposed system consists of three primary stages: preliminary processing, feature extraction using PCA, and classification using the polynomial, linear, and Gaussian SVM classifiers.In this proposed system, text skeleton is first extracted and the images of the text are normalized into uniform size for extraction of the global features of the Arabic words using PCA.Recognition performance of this proposed system was evaluated on version 2 of the IFN/ENIT database of handwritten Arabic text using the polynomial, linear, and Gaussian SVM classifiers.The classification results of the proposed system were compared with the results produced by a benchmark.TRS that is depending on the Discrete Cosine Transform (DCT) method using numerous normalization sizes of Arabic text images.The experimental testing results support the effectiveness of the proposed system in holistic recognition of the handwritten Arabic text.
Combining Webqual and Importance Performance Analysis for Assessing A Government Website

Website is a necessity in today's digital era, especially the website of a government that operationalizes electronic government systems. One of the cases is Penyedia Jasa Lainnya Elektronik (e-PJLP), an electronic individual service provider system of the Provincial Government of Special Capital Territory (PG-SCT) Jakarta. The website use has a role to simplify and improve employee performance in processing data and information, but in the implementation found several complaints, namely, frequent errors on the website when logging in and salary calculation, incomplete information, and doubts regarding website security. This research was conducted to determine the quality of e-PJLP websites based on user perceptions. Research instruments used the Webqual 4.0 method with 3 main variables (usability quality, information quality, and service interaction quality) and 2 additional variables (design interface quality and reliability quality). The analysis of the results of the study uses the Importance Performance Analysis (IPA) method. Instrument reliability and validity tests were carried out using SPSS 25 software. Data analysis was performed using a web quality index (WQI), gap analysis, and IPA. The results of the gap analysis in this study presented the negative results in all indicators. It is indicated that the quality of the website does not match the expectations of its users. The biggest gap is in the reliability quality variable with an average value of -0.25 and the smallest gap design interface quality variable with an average value of -0.12. Two indicators need improvement, namely having competency values (UQ7) and a guarantee of personal information security (SQ18). suggestions for the developer is to improve the quality of the website based on the user's perspective to improve the quality.
Specifying the Process Model for Systematic Reviews: An Augmented Proposal

Context: Systematic Literature Review (SLR) is a research methodology intended to obtain evidence from scientific articles stored in digital libraries. SLRs can be performed on primary and secondary studies. Although there are guidelines to the SLR process in Software Engineering, the SLR process is not fully and rigorously specified yet. Moreover, it can often be observed a lack of a clear separation of concerns between what to do (process) and how to do it (methods). Objective: To specify the SLR process in a more detailed and rigorous manner by considering different process modeling perspectives, such as functional, behavioral, organizational and informational. The main objective in this work is specifying the SLR activities rather than their methods. Method: The SPEM (Software &amp; Systems Process Engineering Metamodel) language is used to model the SLR process from different perspectives. In addition, we illustrate aspects of the proposed process by using a recently conducted SLR on software testing ontologies. Results: Our SLR process model specifications favor a clear identification of what task/activities should be performed, in which order, by whom, and which are the consumed and produced artifacts as well as their inner structures. Also, we explicitly specify activities related to the SLR pilot test, analyzing the gains. Conclusion: The proposed SLR process considers with higher rigor the principles and benefits of process modeling backing SLRs to be more systematic, repeatable and auditable for researchers and practitioners. In fact, the rigor provided by process modeling, where several perspectives are combined, but can also be independently detached, provides a greater richness of expressiveness in sequences and decision flows, while representing different levels of granularity in the work definitions, such as activity, sub-activity and task.
The self modifying code (SMC)-aware processor (SAP): a security look on architectural impact and support

Self modifying code (SMC) are code snippets that modify themselves at runtime. Malware use SMC to hide payloads and achieve persistence. Software-based SMC detection solutions impose performance penalties for real-time monitoring and do not benefit from runtime architectural information (cache invalidation or pipeline flush, for instance). We revisit SMC impact on hardware internals and discuss the implementation of an SMC detector at distinct architectural points. We consider three detection approaches: (i) existing hardware counters; (ii) block invalidation by the cache coherence protocol; (iii) the use of Memory Management Unit (MMU) information to control SMC execution. We compare the identified instrumentation points to highlight their strong and weak points. We also compare them to previous SMC detectors’ implementations.
Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems

<p style='text-indent:20px;'>We propose a stochastic gradient descent based optimization algorithm to solve the analytic continuation problem in which we extract real frequency spectra from imaginary time Quantum Monte Carlo data. The procedure of analytic continuation is an ill-posed inverse problem which is usually solved by regularized optimization methods, such like the Maximum Entropy method, or stochastic optimization methods. The main contribution of this work is to improve the performance of stochastic optimization approaches by introducing a supervised stochastic gradient descent algorithm to solve a flipped inverse system which processes the random solutions obtained by a type of Fast and Efficient Stochastic Optimization Method.