Reviewerly/exHarmony huggingface dataset:
Radioactive heat production of Syrian territory

Gamma-ray spectrometry is applied to estimate equivalent uranium (eU), equivalent thorium (eTh), and K% of 748 rock samples collected from Syrian territory. The spectrometry results are used to evaluate and map the radioactive heat production (HP) of Syria. A new approach involving the multifractal technique with the concentration-number model (CN) and log-log plots was originally proposed and hence applied in this paper to map the distribution of uranium concentration and HP of Syria. This approach helps us to differentiate different eU and HP ranges related to different litho-types. The advantages of proposing and applying the fractal technique are that the boundaries of the distinguished radioactive ranges of eU and HP coincide well with the lithological boundaries, which gives this technique superiority over other traditional statistical methods. The fractal CN model with the use of log–log plots proves its efficacy in differentiating between several eU and HP populations that are related directly to the geology of Syrian territory. The fractal model shows four threshold break points corresponding to uranium concentrations of 3.1, 7.38, 16.6, and 28.8 ppm and an HP of 0.715, 1.86, 3.63, and 6.26 μW/m3, respectively. The highest HP ranges are mainly related to the phosphatic deposits, characterized by the highest uranium content.
An Artificial Intelligence Enabled Data Analytics Platform for Digital Advertisement

To offer the most suitable product to the end-users in digital marketing, end-user behaviour needs to be observed in real time while browsing through websites. This enables to profile and define the end-user behaviour with more accurate success rates. However, this analysis requires the development of special methods for processing large data. Within the scope of this demo, we demonstrate a big data analytics platform to increase the effectiveness of digital advertising. During experiments, we demonstrate via the dashboard interface the successful mappings of end-user's various features including URL-category report (Cookie ID - Subscriber ID, URLs-Classified Interactive Advertising Bureau (IAB) category) analysis, non-functional resorts (end-user behaviour report, category score report, device usage report), the most visited websites and new category labeling for classification purposes.
An Automated System for Criminal Police Reports Analysis

Information Extraction (IE) and fusion are complex fields and have been useful in several domains to deal with heterogeneous data sources. Criminal police are challenged in forensics activities with the extraction, processing and interpretation of numerous documents from different types and with distinct formats (templates), such as narrative criminal reports, police databases and the result of OSINT activities, just to mention a few. Such challenges suggest, among others, to cope with and manually connect some hard to interpret meanings, such as license plates, addresses, names, slang and figures of speech. This paper aims to deal with forensic IE and fusion, thus a system was proposed to automatically extract, transform, clean, load and connect police reports that arrived from different sources. The same system aims to help police investigators to identify and correlate interesting extracted entities.
CHILDREN’S WELL-BEING DURING TRANSITION PERIODS IN CROATIA: THE PROPOSAL OF EMPIRICAL VALIDATION OF ECOLOGICAL-DYNAMIC MODEL

CHILDREN’S WELL-BEING DURING TRANSITION PERIODS IN CROATIA: THE PROPOSAL OF EMPIRICAL VALIDATION OF ECOLOGICAL-DYNAMIC MODEL
Refugees and ICTs: Identifying the Key Trends and Gaps in Peer-Reviewed Scholarship

The goal of this paper is to identify existing literature in the field that encompasses one contemporary crisis and one prevailing developmental implement: refugees, and the use of ICT in the development arena. A total of 35 research studies were identified that address these two topics. In the process, several main themes that dominate the field are identified. These themes are the assessment of refugee status and position, education, empowerment and identity, health, and risk of ICT for refugees. Subsequently, a discussion of the missing areas in this research domain is presented. In the authors’ opinion, the missing themes that are urgently needed are ethics of ICT for refugees, longitudinal studies, gendering of ICT, comparative research, and entrepreneurial aspect of refugees. This missing research would help humanity to get a better understanding of this immense challenge and to design appropriate solutions that leverage ICT to alleviate the current refugee crisis.
Knowledge Management in Open Industrial Virtual Reality Applications

This paper presents practical results of a new, open approach to knowledge formalization and management in Virtual Reality applications for use in industry, especially in design and training purposes. Idea of applications with open access to knowledge is presented. Three cases of VR systems are shown – two for the design and one for training, all implemented in industrial companies in Poland. The focus is put on concepts of storing and easy editing knowledge in the applications, in a way to ensure self-sufficiency of applications in a given company after deployment. Dedicated tools for knowledge edition are presented, along with selected results of their implementation in scope of effectiveness of expanding the knowledge base.
How Digital Technology and Standardisation Can Improve Offshore Operations

Abstract This paper explains how digital technology combined with standardisation is used to improve Subsea 7's offshore operations with the aim to avoid incidents, increase efficiency and reduce cost and schedule. A new way to write, use, and complete offshore installation task plans has been developed. Using an electronic database and application-based system, Electronic Task Planner (ETP), hard copies will become redundant. Integration of Standard Task Plans (STP) in the ETP enables re-use of procedures, ensuring best practices are consistently applied during offshore operations, and will significantly reduce time to produce and review documents. The system will auto-generate as-built documentation and input to the vessel daily progress reports. Offshore operations are supported by means of 3D virtualisation technology and animations. VR models are used to visualise the work environment for familiarisation and for design and procedure planning. 3D models can easily be integrated into the VR environment, combined with vessel and ROV models from the 3D model library to quickly analyse a proof of concept for the engineering planning. An emulation room and testing lab has been set-up, and by creating virtual models of the vessels, control systems can be effectively tested under near to "live" conditions. By integrating a physics engine into the emulator system, projects can be verified against expected sea states and can confirm the control system will allow project operations to be completed. Using the emulation techniques means operators can be trained on exact same virtualised control system software in advance of actual construction, or control functions can be tested before delivery to the vessel. The same model can also be used to facilitate efficient planning and familiarisation of deck operations.
Implementation of a two-dimensional quantum walk using cross-Kerr nonlinearity

A quantum walk is the quantum analog of the classical random walk, which provides a powerful tool for quantum computation and quantum simulation. However, the current experimental realization of the quantum walk based on photonic systems has been restricted to evolution in one dimension or small scale in two dimensions so far, and the growing demands cannot be fulfilled for further application of certain quantum computation tasks. Here, we theoretically propose a feasible scheme of a two-dimensional quantum walk with infinite steps by exploiting the orbital angular momentum of the photons and the phase information of the auxiliary coherent state induced by cross-Kerr nonlinearity. The numerical simulation shows that a proper parameter setting guarantees the walking for infinite steps in a two-dimensional grid with nearly $100\%$100% success probability.
How the information use environment influences search activities

Purpose The information use environment (IUE) – the context within which the search activity takes place – is critical to understanding the search process as this will affect how the value of information is determined. The purpose of this paper is to investigate what factors influence search in English primary schools (children aged 4–11) and how information found is subsequently used. Design/methodology/approach Ten teachers, selected using maximal variation sampling, describe search-related activities within the classroom. The resulting interview data were analysed thematically for the influence of the environment on search and different information uses. The findings were then validated against three classroom observations. Findings 12 categories of information use were identified, and 5 aspects of the environment (the national curriculum, best practice, different skills of children and teachers, keeping children safe, and limited time and resource) combine to influence and shape search in this setting. Research limitations/implications Findings support the argument that it is the IUE that is the key influence of search activity. What makes children a distinct user group is linked to the environment within which they use information rather than age, as advocated in previous studies. Practical implications The features of search systems and practical guidance for teachers and children should be designed to support information use within the IUE. Originality/value As far as the authors are aware, this is the first study to consider the influence of the IUE on how search is enacted within primary schools.
Fast Spectral Radius Initialization for Recurrent Neural Networks

In this paper we address the problem of grounded weights initialization for Recurrent Neural Networks. Specifically, we propose a method, rooted in the field of Random Matrix theory, to perform a fast initialization of recurrent weight matrices that meet specific constraints on their spectral radius. Focusing on the Reservoir Computing (RC) framework, the proposed approach allows us to overcome the typical computational bottleneck related to the eigendecomposition of large matrices, enabling to efficiently design large reservoir networks and hence to address time-series tasks characterized by medium/big datasets. Experimental results show that the proposed method enables an accurate control of the spectral radius of randomly initialized recurrent matrices, providing an initialization approach that is extremely more efficient compared to common RC practice.
Off-Surface Tangibles

Tangibles on interactive tabletops are tracked by the surface they are placed on and have been shown to benefit the interaction. However, they are tied to the surface. When picked up, they are no longer recognized and lose any connection to virtual objects shown by the table. We introduce the interaction concept of Off-Surface Tangibles that are tracked by the surface but continue to support meaningful interactions when lifted off the surface. We present a design space for Off-Surface Tangibles, and design considerations when employing them. We populate the design space with prior work and introduce possible interaction designs for further research.
Design and implementation of smart electronic solar tracker based on Arduino

Demand of energy increases in the global and exponential exhaustion is favored of resources by fossil fuel for electricity production with the new systems development. Compared with all other remainder energies, the specialist sun energy is the most bountiful energy and it's typically easy to be changed into electrical energy. The main thing of using solar panel is to produce electrical energy from sun's energy but the optimum energy can be generated by tracking solar panel due to the sun movement from east to west. The problem can be solved by proposed systems where the sun tracking by solar panel that based on high intensity of sun ray. This paper concentrates on tracking the sun by using servo motor coupled with solar panel. So that, the largest quantity of sun light at the incident panel along the day at any time is better than that for method of fixed panel array which is less efficient. The microcontroller Arduino (mode UNO) was programmed by using C++ language while the track of sun light processing was implemented by using light depending resistor (LDR), Chip IC H-bridge and microcontroller Arduino (UNO) circuits have been designed by using Proteus software. By circuit design and sun tracking control process, the cost reduction has been improved and high amount of energy was saved when implemented this system.
Test Selection for Deep Learning Systems

Testing of deep learning models is challenging due to the excessive number and complexity of computations involved. As a result, test data selection is performed manually and in an ad hoc way. This raises the question of how we can automatically select candidate test data to test deep learning models. Recent research has focused on adapting test selection metrics from code-based software testing (such as coverage) to deep learning. However, deep learning models have different attributes from code such as spread of computations across the entire network reflecting training data properties, balance of neuron weights and redundancy (use of many more neurons than needed). Such differences make code-based metrics inappropriate to select data that can challenge the models (can trigger misclassification). We thus propose a set of test selection metrics based on the notion of model uncertainty (model confidence on specific inputs). Intuitively, the more uncertain we are about a candidate sample, the more likely it is that this sample triggers a misclassification. Similarly, the samples for which we are the most uncertain, are the most informative and should be used to improve the model by retraining. We evaluate these metrics on two widely-used image classification problems involving real and artificial (adversarial) data. We show that uncertainty-based metrics have a strong ability to select data that are misclassified and lead to major improvement in classification accuracy during retraining: up to 80% more gain than random selection and other state-of-the-art metrics on one dataset and up to 29% on the other.
Sound Control: Supporting Custom Musical Interface Design for Children with Disabilities

We have built a new software toolkit that enables mu- sic therapists and teachers to create custom digital musical interfaces for children with diverse disabilities. It was designed in collaboration with music therapists, teachers, and children. It uses interactive ma- chine learning to create new sensor- and vision-based musical interfaces using demonstrations of actions and sound, making interface building fast and accessible to people without programming or engineering expertise. Interviews with two music therapy and education professionals who have used the software extensively illustrate how richly customised, sensor-based inter- faces can be used in music therapy contexts; they also reveal how properties of input devices, music-making approaches, and mapping techniques can support a variety of interaction styles and therapy goals.
Predict Breast Tumor Response to Chemotherapy Using a 3D Deep Learning Architecture Applied to DCE-MRI Data

Purpose: Many breast cancer patients receiving chemotherapy cannot achieve positive response unlimitedly. The main objective of this study is to predict the intra tumor breast cancer response to neoadjuvant chemotherapy (NAC). This aims to provide an early prediction to avoid unnecessary treatment sessions for no responders’ patients. Method and material: Three-dimensional Dynamic Contrast Enhanced of Magnetic Resonance Images (DCE-MRI) were collected for 42 patients with local breast cancer. This retrospective study is based on a data provided by our collaborating radiology institute in Brussels. According to the pathological complete response (pCR) ground truth, 14 of these patients responded positively to chemotherapy, and 28 were not responsive positively. In this work, a convolutional neural network (CNN) model were used to classify responsive and non-responsive patients. To make this classification, two CNN branches architecture was used. This architecture takes as inputs three views of two aligned DCE-MRI cropped volumes acquired before and after the first chemotherapy. The data was split into 20% for validation and 80% for training. Cross-validation was used to evaluate the proposed CNN model. To assess the model’s performance, the area under the receiver operating characteristic curve (AUC) and accuracy were used. Results: The proposed CNN architecture was able to predict the breast tumor response to chemotherapy with an accuracy of 91.03%. The Area Under the Curve (AUC) was 0.92. Discussion: Although the number of subjects remains limited, relevant results were obtained by using data augmentation and three-dimensional tumor DCE-MRI. Conclusion: Deep CNNs models can be exploited to solve breast cancer follow-up related problems. Therefore, the obtained model can be used in future clinical data other than breast images.
One-handed Rapid Text Selection and Command Execution Method for Smartphones

We show a one-handed rapid text selection and command execution method for a smartphone; we term this Press & Slide. The user can perform caret navigation or text selection by sliding the finger on a software keyboard after pressing a key. Then, by releasing the key, a command such as "copy the selected text" is executed; the command is specified by the key that is pressed. Therefore, the user needs not touch the text, and thus the fat finger problem does not cause and the user needs not change his/her smartphone grip.
Mining product opinions with most frequent clusters of aspect terms

This paper addresses the problem of more accurately mining product aspect opinions from Twitter posts, in the presence of spam and noisy posts, by proposing an algorithm called Microblog Aspect Miner (MAM). MAM takes a three step approach of classifying the microblog posts into subjective and objective posts using opinion scores of words from SentiWordNet. MAM then represents frequent nouns of subjective posts as vectors in such a way that nouns semantically similar to the products have a similar vector value using the WordVec algorithm. K-Means clustering algorithm is used to obtain the cluster of aspects relevant to the product to separate the noisy aspects so that the most relevant aspects are ranked using proposed Aspect-Product Similarity Threshold based on cosine similarities. Experiments show that this improves accuracy of obtaining relevant aspects of products from microblog posts in comparison to such existing aspect based opinion mining (ABOM) systems as Twitter Aspect Classifier (TAC).
Artificial Intelligence and the Future of War

This article discusses whether the arrival of Artificial Intelligence will fundamnetally change the character of war It argues that until such time as machines gain self-connsciousness will continue to be what Thucydides called ‘the human thing’.
Axiomatising Logics with Separating Conjunction and Modalities

Modal separation logics are formalisms that combine modal operators to reason locally, with separating connectives that allow to perform global updates on the models. In this work, we design Hilbert-style proof systems for the modal separation logics $$\text {MSL} ^{}(*,\langle \ne \rangle )$$ and $$\text {MSL} ^{}(*,\Diamond )$$ , where $$*$$ is the separating conjunction, $$\Diamond $$ is the standard modal operator and $$\langle \ne \rangle $$ is the difference modality. The calculi only use the logical languages at hand (no external features such as labels) and take advantage of new normal forms and of their axiomatisation.
Why do App Reviews get Responded

Recent studies show that providing responses for user reviews of a mobile app can increase its user satisfaction and overall ratings. However, due to large volumes of user reviews, few are actually responded by app developers. In this paper, we conduct a preliminary study on the characteristics of mobile apps' reviews and their human-written responses. We found that an app review often has multiple segments, each has a particular intention. Similarly, a response also has multiple segments, and some are intended to answer the impact parts of reviews (e.g., complaints, requests, or information seeking). Responses often have similar structures and most of their content is consistently written with pre-defined templates. We also found that similar reviews might be answered with the same or similar responses. Based on those findings, we propose to develop a semi-automated tool to help mobile apps' developers write the responses for users reviews more effectively.
On a Class of Constacyclic Codes of Length 4<i>p</i><sup><i>s</i></sup> over Fpm[u]〈 ua 〉

For any odd prime p such that p m ≡ 3 (mod 4), consider all units Λ of the finite commutative chain ring [Formula: see text] that have the form Λ = Λ 0 + uΛ 1 + ⋯ + u a−1 Λ a−1 , where Λ 0 , Λ 1 , …, Λ a−1 ∊ 𝔽 p m, Λ 0 ≠ 0, Λ 1 ≠ 0. The class of Λ-constacyclic codes of length 4p s over ℛ a is investigated. If the unit Λ is a square, each Λ-constacyclic code of length 4p s is expressed as a direct sum of a −λ-constacyclic code and a λ-constacyclic code of length 2p s . In the main case that the unit Λ is not a square, we prove that the polynomial x 4 − λ 0 can be decomposed as a product of two quadratic irreducible and monic coprime factors, where [Formula: see text]. From this, the ambient ring [Formula: see text] is proven to be a principal ideal ring, whose maximal ideals are ⟨x 2 + 2ηx + 2η 2 ⟩ and ⟨x 2 − 2ηx + 2η 2 ⟩, where λ 0 = −4η 4 . We also give the unique self-dual Type 1 Λ-constacyclic codes of length 4p s over ℛ a . Furthermore, conditions for a Type 1 Λ-constacyclic code to be self-orthogonal and dual-containing are provided.
Estimation of a Random Feedback Control System With Unknown Input

A method to estimate/track the state of a high-speed target that can be modeled as a feedback control system in three-dimensional (3-D) space is presented in this paper. The target's lateral acceleration input is not known, but it is known that the target will probably reach a destination position at a future time. The trajectory may involve random maneuvers. If the parameters of the modeled control loop are observable, the proposed model reduces the prediction and estimation errors. If the target's destination is known, then the accuracy can be increased further. The model, based on proportional navigation (PN), is nonlinear and can be used in a multiple-model estimator if the target can also perform random maneuvers along its route to the final destination. It is applied to a 3-D maneuvering aerial target state estimation problem with a target capable of high-magnitude, random lateral accelerations under a PN control policy. It is shown that due to the observability of the feedback control parameters, the filter significantly reduces the error of estimated position/velocity and provides a flexible estimation model for laterally maneuvering point targets in a 3-D fluid.