Reviewerly/exHarmony huggingface dataset:
AuthCODE: A privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning

The authentication field is evolving towards mechanisms able to keep users continuously authenticated without the necessity of remembering or possessing authentication credentials. While relevant limitations of continuous authentication systems -high false positives rates (FPR) and difficulty to detect behaviour changes- have been demonstrated in realistic single-device scenarios, the Internet of Things and next generation of mobile networks (5G) are enabling novel multi-device scenarios, such as Smart Offices, that can help to reduce or address the previous challenges. The paper at hand presents an AI-based, privacy-preserving and multi-device continuous authentication architecture called AuthCODE. AuthCODE seeks to improve single-device solutions limitations by considering additional behavioural data coming from heterogeneous devices. AuthCODE proposes a novel set of features that combine the interactions of users with different devices. The features relevance has been demonstrated in a realistic Smart Office scenario with several users that interact with their mobile devices and personal computers. In this context, a set of single- and multi-device datasets have been generated and published to compare the performance of our multi-device solution against single-device approaches. A pool of experiments with machine and deep learning classifiers measured the impact of time in authentication accuracy and improved the results of single-device approaches by considering multi-device behaviour profiles. Specifically, the multi-device approach using XGBoost with 1-minute window of aggregated features, achieved a 69.33%, 59,65% and 89,35% improvement in the FPR when compared to the single-device approach for computer, mobile applications and mobile sensors respectively. Finally, temporal information classified by a Long-Short Term Memory Network, allowed the identification of additional complex behaviour patterns.
Simultaneous Excitation of Two Noninteracting Atoms with Time-Frequency Correlated Photon Pairs in a Superconducting Circuit

We report the first observation of simultaneous excitation of two noninteracting atoms by a pair of time-frequency correlated photons in a superconducting circuit. The strong coupling regime of this process enables the synthesis of a three-body interaction Hamiltonian, which allows the generation of the tripartite Greenberger-Horne-Zeilinger state in a single step with a fidelity as high as 0.95. We further demonstrate the inhibition of the simultaneous two-atom excitation by continuously measuring whether the first photon is emitted. This work provides a new route in synthesizing many-body interaction Hamiltonian and coherent control of entanglement.
Blockchain-Based Self-Tallying Voting System with Software Updates in Decentralized IoT

IoT revolutionizes academia as well as industry. An increasing number of interconnected smart devices are gradually changing the urban lifestyle, among which, voting machines are one of the most widely used smart devices. However, the existing voting protocols for IoT are barely satisfactory, in the sense that most of them are centralized and subject to fairness issues. Moreover, the embedded softwares in voting machines are susceptible to internal vulnerabilities and external attacks. To address these issues, in this article, we propose a framework of a blockchain-based self-tallying voting system with software updates in decentralized IoT, which is fully decentralized and fair. In the proposed system, everyone can compute the final election results by collecting the ballots on the blockchain with equal privilege. Voting machines achieve self-tallying voting functionality in decentralized IoT systems and each entity in the system can obtain the voting results. Vendors deploy smart contracts to publish new patches securely and reliably. We implement a prototype of the proposed framework on laptops and mobile phones respectively to demonstrate its practicality.
A log-barrier Newton-CG method for bound constrained optimization with complexity guarantees

Abstract We describe an algorithm based on a logarithmic barrier function, Newton’s method and linear conjugate gradients that seeks an approximate minimizer of a smooth function over the non-negative orthant. We develop a bound on the complexity of the approach, stated in terms of the required accuracy and the cost of a single gradient evaluation of the objective function and/or a matrix-vector multiplication involving the Hessian of the objective. The approach can be implemented without explicit calculation or storage of the Hessian.
Identifying the Optimal 3D Display Technology for Hands-On Virtual Experiential Learning: A Comparison Study

The purpose of this study was to test different 3D display technologies in a hands-on virtual experiential learning environment (VELE) and determine the optimal 3D display technology.A conceptual framework was firstly proposed to explain the mechanism of how VR display features affect virtual experiential learning.Then, a single-factor within-subject experimental design was adopted for testing.The within-subject factor was three types of 3D display technology: fully immersive virtual reality (VR) mode [VR head-mounted display (HMD)], partially immersive VR mode (3D projection), and augmented reality (AR) mode (AR HMD).The dependent variables were visual comfort, interaction experience, learning experience, and outcome.A virtual math learning environment was established, and the aforementioned display technologies were tested in two hands-on virtual experiential learning scenarios.Results showed that different display technologies significantly affected users' visual comfort, interaction experience, learning experience, and outcome in experiential learning (ps < 0.05).User ratings on these aspects for the VR HMD were significantly higher than those for the 3D projection and AR HMD.Thus, the VR HMD contributes to a best viewing experience and learning experience in terms of hands-on virtual experiential learning in the scenarios tested.Whether the study results still hold reliably in terms of more complex learning activities and long-term learning needs to be further studied.
Implementation of education for sustainable development principles in the training of future software engineers

The article examines the professional training of future software engineers in higher education institutions in the context of the implementation of the Sustainable Development Goals set by the UN General Assembly, as well as the Education for Sustainable Development (ESD) principles. A number of contradictions that arise in the course of this training are mentioned on the example of training of future object-oriented programming engineers. The UNESCO documents on education for sustainable development, scientific sources on the professional training of future software engineers, as well as the integration of sustainable development into relevant educational programs are analyzed. The methodology and results of experimental work carried out with the aim of overcoming existing contradictions, promoting sustainable development of information technology education and implementation of ESD principles in the process of professional training of future software engineers are presented. In particular, the organizational and methodological conditions, that were implemented into the educational process, and experimental data are presented. The effectiveness of the experimental work was proved by statistical verification of the reliability of the obtained data.
Differences in Interactions with a Conversational Agent

Recent technological advances introduced conversational agents into homes. Many researchers have investigated how people utilize and perceive them. However, only a small number of studies have focused on how older adults interact with these agents. This study presents a 14-day user study of 19 participants who experienced a conversational agent in a real-life environment. We grouped them into two groups by age and compared their experiences. From a log study and semi-structured interviews, we identified several differences between the two groups. Compared to younger adults, older adults used the agent more. They used it primarily for listening to music and reported satisfaction with it. Younger adults mainly used utility skills like weather report checks and setting of alarms, which streamlined their daily lives. Moreover, older adults tended to view the agent as a companion, while younger adults saw it as a tool. Based on these empirical findings, we suggest that conversational agents should be designed with consideration of the different usage patterns and perceptions across age groups.
Statistical verification of learning-based cyber-physical systems

The use of Neural Network (NN)-based controllers has attracted significant attention in recent years. Yet, due to the complexity and non-linearity of such NN-based cyber-physical systems (CPS), existing verification techniques that employ exhaustive state-space search, face significant scalability challenges; this effectively limits their use for analysis of real-world CPS. In this work, we focus on the use of Statistical Model Checking (SMC) for verifying complex NN-controlled CPS. Using an SMC approach based on Clopper-Pearson confidence levels, we verify from samples specifications that are captured by Signal Temporal Logic (STL) formulas. Specifically, we consider three CPS benchmarks with varying levels of plant and controller complexity, as well as the type of considered STL properties - reachability property for a mountain car, safety property for a bipedal robot, and control performance of the closed-loop magnet levitation system. On these benchmarks, we show that SMC methods can be successfully used to provide high-assurance for learning-based CPS.
Tornado: Automatic Generation of Probing-Secure Masked Bitsliced Implementations

Cryptographic implementations deployed in real world devices often aim at (provable) security against the powerful class of side-channel attacks while keeping reasonable performances. Last year at Asiacrypt, a new formal verification tool named tightPROVE was put forward to exactly determine whether a masked implementation is secure in the well-deployed probing security model for any given security order t. Also recently, a compiler named Usuba was proposed to automatically generate bitsliced implementations of cryptographic primitives. This paper goes one step further in the security and performances achievements with a new automatic tool named Tornado. In a nutshell, from the high-level description of a cryptographic primitive, Tornado produces a functionally equivalent bitsliced masked implementation at any desired order proven secure in the probing model, but additionally in the so-called register probing model which much better fits the reality of software implementations. This framework is obtained by the integration of Usuba with $$\mathsf {tightPROVE}^+$$, which extends tightPROVE with the ability to verify the security of implementations in the register probing model and to fix them with inserting refresh gadgets at carefully chosen locations accordingly. We demonstrate Tornado on the lightweight cryptographic primitives selected to the second round of the NIST competition and which somehow claimed to be masking friendly. It advantageously displays performances of the resulting masked implementations for several masking orders and prove their security in the register probing model.
A Diverse Data Augmentation Strategy for Low-Resource Neural Machine Translation

One important issue that affects the performance of neural machine translation is the scale of available parallel data. For low-resource languages, the amount of parallel data is not sufficient, which results in poor translation quality. In this paper, we propose a diversity data augmentation method that does not use extra monolingual data. We expand the training data by generating diversity pseudo parallel data on the source and target sides. To generate diversity data, the restricted sampling strategy is employed at the decoding steps. Finally, we filter and merge origin data and synthetic parallel corpus to train the final model. In the experiment, the proposed approach achieved 1.96 BLEU points in the IWSLT2014 German–English translation tasks, which was used to simulate a low-resource language. Our approach also consistently and substantially obtained 1.0 to 2.0 BLEU improvement in three other low-resource translation tasks, including English–Turkish, Nepali–English, and Sinhala–English translation tasks.
Solar-TK: A Data-Driven Toolkit for Solar PV Performance Modeling and Forecasting

Solar energy capacity is continuing to increase. The key challenge with integrating solar into buildings and the electric grid is its high power generation variability, which is a function of many factors, including a site's location, time, weather, and numerous physical attributes. There has been significant prior work on solar performance modeling and forecasting that infers a site's current and future solar generation based on these factors. Accurate solar performance models and forecasts are also a pre-requisite for conducting a wide range of building and grid energy-efficiency research. Unfortunately, much of the prior work is not accessible to researchers, either because it has not been released as open source, is time-consuming to re-implement, or requires access to proprietary data sources. To address the problem, we present Solar-TK, a data-driven toolkit for solar performance modeling and forecasting that is simple, extensible, and publicly accessible. Solar-TK's simple approach models and forecasts a site's solar output given only its location and a small amount of historical generation data. Solar-TK's extensible design includes a small collection of independent modules that connect together to implement basic modeling and forecasting, while also enabling users to implement new energy analytics. We plan to release Solar-TK as open source to enable research that requires realistic solar models and forecasts, and to serve as a baseline for comparing new solar modeling and forecasting techniques. We compare Solar-TK's simple approach with PVlib and show that it yields comparable accuracy. We present three case studies showing how Solar-TK can advance energy-efficiency research.
Mobile Applications to Improve English Writing Skills Viewed from Critical Thinking Ability for Pre-Service Teachers

This study was aimed at investigating; (1) the pre-service teachers used the MobApps (WhatsApp, U-Dictionary, and Email) to participate in mobile-assisted language learning (MALL) viewed from the critical thinking ability in the teaching of writing skills and; (2) the effectiveness of the Mobile-assisted language learning (MALL) towards pre-service teachers’ writing skills in relation to critical thinking ability. This study was a mixed method using the concurrent embedded strategy. The researchers involve qualitative strategies and quantitative strategies. Both kinds of data are collected simultaneously. This study was conducted in the English language education departments of FPBS IKIP Mataram, Indonesia. 58 pre-service teachers were involved in this study. The qualitative data were attained from pre-service teachers attending the writing classes using observation sheets. Meanwhile, the quantitative data were gained from experimental design through a nonequivalent control-group design. The groups were evaluated by using a writing test and a critical thinking test. The results showed that the pre-service teachers conducted a discussion of what they have to write. The learning activities are done under several stages. The end of this is that the pre-service teachers are able to create a complete descriptive text. Besides, the use of the MALL model was more effective than non-mobile learning to improve pre-service teachers’ writing skills viewed from critical thinking skills.
DAMPAK COVID-19: GURU MENGADOPSI MEDIA SOSIAL SEBAGAI E-LEARNING PADA PEMBELAJARAN JARAK JAUH

The purpose of this study was to find out how teachers as users of social media adopted it into e-learning in distance learning (PJJ) due to the impact of co-19. This research uses a quantitative approach with a survey method. The instruments in this study used a valid instrument from UEQ which was translated into Indonesian, which has 6 scales and 26 items, submitted in digital form via Google form. Participants in this study were 43 PAI teachers chosen randomly. This research describes a descriptively quantitative mean, variance, and standard deviation values. The results found each of the six scales was Attractiveness with a mean value of 1.73 being in the Good benchmark category, Perspicuity 1.56 Above Average, Efficiency (Efficiency) ) 1.87 Excellent, Dependability 1.42 Above average, Stimulation 1.86 Excellent and Novelty 1.34 Good. The conclusion was drawn that teachers as users of social media who were adopted into e-learning at PJJ when the pandemic Covid-19 Indonesia was above good grades and even excellent. It can be said that the PAI teacher has the ability to use good social media even excellent (very well) which he adopted as e-learning in PJJ.
Efficient approach for computing the discrimination ratio-based variant of information entropy for image processing

Information content is an important criterion for many image processing algorithms such as band selection and image fusion.Usually, information content is quantified by using information entropy (i.e., Shannon entropy); however, this is not a suitable measure because information entropy is independent of the spatial distribution of image pixels.Thus, improved information entropies and variants of information entropy have been developed.Among all the entropic measures, the discrimination ratio-based variant of information entropy (hereinafter DVIE) has recently been demonstrated to be the most effective.On the other hand, DVIE is the most inefficient measure in terms of computation time, which severely restricts its applications.To solve this problem, we present a three-strategy approach to efficiently compute the DVIE of an image.The first strategy is to use a simplified equation for DVIE.The second strategy is to selectively compute the two computationally intensive components of DVIE-intra-distance and extra-distance-based on the computational complexity.Only one distance was computed, and the other distance was derived based on the lookup table of average distances.The third strategy was to efficiently construct the lookup table based on geometric symmetry.We performed both validation and evaluation experiments to demonstrate that the proposed approach was not only valid for accurately computing DVIE, but it was also highly efficient.Our proposed approach saved more than 99% of the time taken for the original approach, without compromising the accuracy; therefore, DVIE was made applicable for processing images.
Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation

The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R 2 of 0.8254.
Designing Codes around Interactions: The Case of a Spin

I present a new approach for designing quantum error-correcting codes that guarantees a physically natural implementation of Clifford operations. Inspired by the scheme put forward by Gottesman, Kitaev, and Preskill for encoding a qubit in an oscillator, in which Clifford operations may be performed via Gaussian unitaries, this approach yields new schemes for encoding a qubit in a large spin in which single-qubit Clifford operations may be performed via spatial rotations. I construct all possible examples of such codes, provide universal-gate-set implementations using Hamiltonians that are at most quadratic in angular-momentum operators, and derive criteria for when these codes exactly correct physically relevant noise channels to lowest order, illustrating their performance numerically for specific low-dimensional examples.
Quantum interference device for controlled two-qubit operations

Abstract Universal quantum computing relies on high-fidelity entangling operations. Here, we demonstrate that four coupled qubits can operate as a quantum gate, where two qubits control the operation on two target qubits (a four-qubit gate). This configuration can implement four different controlled two-qubit gates: two different entangling swap and phase operations, a phase operation distinguishing states of different parity, and the identity operation (idle quantum gate), where the choice of gate is set by the state of the control qubits. The device exploits quantum interference to control the operation on the target qubits by coupling them to each other via the control qubits. By connecting several four-qubit devices in a two-dimensional lattice, one can achieve a highly connected quantum computer. We consider an implementation of the four-qubit gate with superconducting qubits, using capacitively coupled qubits arranged in a diamond-shaped architecture.
Interaction Analysis and Cognitive Infocommunications

Cognitive infocommunications encompasses both scientific and engineering oriented approaches to examining extensions of human cognitive capabilities that may be assimilated within the concept of humanity.Necessary (but not sufficient) conditions for the success of any candidate technology include solving problems within private and public spheres of existence, in thought and communication.Exemplar cognitive infocommunication technologies that have been assimilated in to the concept of humanitiy are examined: emotion, gesture, language.Implications for research programmes conducted within the cognitive infocommunications discipline are outlined.