Reviewerly/exHarmony huggingface dataset:
Discharge coefficient estimation for rectangular side weir using GEP and GMDH methods

Flow through the rectangular side weir is a spatially varied type flow with decreasing discharge and used as a flow diversion structure. They are mainly used in the field of hydraulic, irrigation, and environmental engineering for diverting and controlling the flow of water in irrigation–drainage systems, drainage canal systems, and wastewater channels. In this study, gene expression programming and group method of data handling were used to estimate the coefficient of discharge for rectangular side weir under subcritical flow condition. Based on dimensional analysis, the coefficient of the discharge depends on the ratio of the crest height to length, ratio of the width of channel to crest length, ratio of the upstream depth in the channel to crest length and the approach Froude number. The performance of the proposed GMDH and GEP model is based on the coefficient of correlation (0.91), mean absolute percentage error (3.54), average absolute deviation (3.3), root mean square error (0.027) and the coefficient of correlation (0.905), mean absolute percentage error (4.12) average absolute deviation (3.9), root mean square error (0.029), respectively. Finally, the results reveal that GMDH model could provide more satisfactorily estimations as compared to those obtained by traditional regression and GEP models.
Mit Kontaktdaten gegen die Pandemie: Zur Ethik von Corona Warn-Apps

In spring 2020, as much of the world was emerging from widespread "lockdowns" as an emergency measure to combat the spread of SARS-CoV‑2, there was sustained discussion about how to lift measures while preventing further waves of the virus and the need for further lockdowns. One strategy that attracted significant attention was the use of digital contact-tracing apps to quickly alert users of possible exposure to the virus, and to direct them into quarantine. The initially high expectations placed upon this strategy were not met-despite the implementation of a digital contact-tracing app in Germany, further restrictions have been placed on the general population in response to further waves of the virus. We consider how digital contact tracing might have been made more effective.We argue that there is a conflict between collecting as little data as possible, and more effective epidemic control. In contrast to the "Corona-Warn-App" that was implemented in Germany, an app that stored more information on a central server (a so-called "centralized" app) had the potential to significantly decrease viral spread. We then look at the privacy-based arguments against the centralized storage of information, suggesting that "decentralized" systems have privacy problems of their own.The German debate on digital contact tracing apps was quickly dominated by privacy concerns, to the detriment of other ethical factors such as enhancing potential effectiveness. Furthermore, the potential problems with privacy inherent in decentralized apps were obscured in the discussion. Once we recognize these two aspects, we can see that there is an argument to be made for preferring centralized digital contact-tracing apps.
Efficient Ancilla-Free Reversible and Quantum Circuits for the Hidden Weighted Bit Function

The Hidden Weighted Bit function plays an important role in the study of classical models of computation.A common belief is that this function is exponentially hard to implement using reversible ancilla-free circuits, even though introducing a small number of ancillae allows a very efficient implementation.In this paper, we refute the exponential hardness conjecture by developing a polynomial-size reversible ancilla-free circuit computing the Hidden Weighted Bit function.Our circuit has size O(n 6.42 ), where n is the number of input bits.We also show that the Hidden Weighted Bit function can be computed by a quantum ancilla-free circuit of size O(n 2 ).The technical tools employed come from a combination of Theoretical Computer Science (Barrington's theorem) and Physics (simulation of fermionic Hamiltonians) techniques.
3rd INTERNATIONAL BALTIC SYMPOSIUM ON SCIENCE AND TECHNOLOGY EDUCATION “SCIENCE AND TECHNOLOGY EDUCATION: CURRENT CHALLENGES AND POSSIBLE SOLUTIONS (BalticSTE2019)”: SYMPOSIUM REVIEW

Two years after the second international BalticSTE symposium passed very quickly and here the third international science symposium is already a history too. On the 17th -20th of June this year, the 3rd International Baltic Symposium on Science and Technology Education BalticSTE2019 took place in Šiauliai. The topic of the Symposium was “Science and Technology Education: Current Challenges and Possible Solutions”. More than 50 scientists from 20 countries participated in the symposium. Comparing with the symposium which took place in 2017, the geographical distribution of the participants is significantly wider. Ukrainian, Serbian, Romanian, Malaysian researchers participated in the symposium for the first time. Natural science education requires constant researcher attention in the rapidly changing technological age, new educational methodologies and innovative educational approaches. The symposium reporter analysed topic spectrum is wide – from global warming of the climate, climate change to philosophical methodological and education evaluation questions. One of this international symposium ideas is – to show that Baltic region countries also actively participate in natural science and technology education research, especially in STEAM context. The second idea is that Baltic countries as well as other small regions of the world need corresponding close communication and cooperation.
Multi label restaurant classification using support vector machine

Many internet websites are hosted with a vast amount of information about restaurants which are not identified properly according to some predefined features to fit users' interests.Thus, restaurant classification was needed to solve this problem.Restaurant classification has become very important for individuals and food business applications to spread their services via the Internet.In this paper, a modest model is proposed to classify restaurants based on their predefined features which are used as factors affecting restaurant's ratings.The usage of multi label classification is utilized for labelling to maintain acceptable requirements for restaurant's services.Two proposed labels are suggested resulted from the output of two classifiers each operate on a specific set of features.Support vector machine is used for classification because of its effectiveness in restaurant's label separation.The final prediction label is yielded after applying the proposed hypothesis rules.The experimental results conducting Zomato dataset show that the proposed multi label model achieved approximately about 88% for prediction accuracy.Using the proposed model for classification had led to get a collection of accepted restaurants according to user favorites.
Optimizing BIKE for the Intel Haswell and ARM Cortex-M4

BIKE is a key encapsulation mechanism that entered the third round of the NIST post-quantum cryptography standardization process. This paper presents two constant-time implementations for BIKE, one tailored for the Intel Haswell and one tailored for the ARM Cortex-M4. Our Haswell implementation is much faster than the avx2 implementation written by the BIKE team: for bikel1, the level-1 parameter set, we achieve a 1.39x speedup for decapsulation (which is the slowest operation) and a 1.33x speedup for the sum of all operations. For bikel3, the level-3 parameter set, we achieve a 1.5x speedup for decapsulation and a 1.46x speedup for the sum of all operations. Our M4 implementation is more than two times faster than the non-constant-time implementation portable written by the BIKE team. The speedups are achieved by both algorithm-level and instruction-level optimizations.
Computer-Supported Knotworking

In this paper, we compare two cases of collaboration within healthcare in two European countries, Denmark and France respectively. In each of these two cases, we conducted a design case study, and we found that collaboration is ad hoc, temporary, and shifting with regards to collaborators, aims, and processes. We argue for the relevance of knotworking and its analytic potential for investigating the kind of collaborative work we observed. We also argue that our two cases present a higher complexity level than how knotworking has previously been described in the literature. We describe complex knotworking as having three characteristics: 1) collaboration happens between a dynamic number of actors (who are usually loosely connected), 2) collaboration happens in episodes, and 3) cooperative work arrangements are constantly negotiated. Using the concept of complex knotworking for a comparative analysis of our two design solutions, we outline generic design guidelines for developing computer support to manage complex knotworking situations.
Deep Multimodal Fusion Autoencoder for Saliency Prediction of RGB-D Images

In recent years, the prediction of salient regions in RGB-D images has become a focus of research. Compared to its RGB counterpart, the saliency prediction of RGB-D images is more challenging. In this study, we propose a novel deep multimodal fusion autoencoder for the saliency prediction of RGB-D images. The core trainable autoencoder of the RGB-D saliency prediction model employs two raw modalities (RGB and depth/disparity information) as inputs and their corresponding eye-fixation attributes as labels. The autoencoder comprises four main networks: color channel network, disparity channel network, feature concatenated network, and feature learning network. The autoencoder can mine the complex relationship and make the utmost of the complementary characteristics between both color and disparity cues. Finally, the saliency map is predicted via a feature combination subnetwork, which combines the deep features extracted from a prior learning and convolutional feature learning subnetworks. We compare the proposed autoencoder with other saliency prediction models on two publicly available benchmark datasets. The results demonstrate that the proposed autoencoder outperforms these models by a significant margin.
Analysis of Classroom Interaction Using IRF Pattern: A Case Study of EFL Conversation Class

&lt;p&gt;This study sheds lights on whether the use of Initiation, Response and Feedback (IRF) in teaching facilitates learner-initiated communication and gives learning opportunity for learners to engage in classroom interaction. This study applies the IRF framework proposed by Sinclair and Coulthard (1975), Initiation from (Mackey (2012) and teacher talk by CUllen (1988). The method applied for this study is qualitative method by classroom observation as the technique for data collection. The video records the classroom observation. The data are taken from classroom interaction of conversation class. The result of this study is the IRF pattern which mostly occurs in classroom interaction can be used to analyze classroom interaction. Additionally, the teaching using IRF pattern can facilitate learner-initiated communication and give learning opportunities for leaners to engage more in classroom interaction.&lt;/p&gt;&lt;p&gt;Key words: IRF Pattern, learner-initiated communication, classroom interaction&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Contrasting Ontology Modeling with Correlation Rules for Delivery Applications

With the increasing importance of knowledge management, variant management and the ever-growing quantity of data, ontologies emerged as a form of knowledge representation, especially in the field of technical communication for modelling metadata and to create correlations between them. In the area of delivery applications, the deliverable information objects receive a certain intelligence by semantic metadata. It is expected, that ontologies offer a higher level of intelligence which could lead to an improvement in classification, connection and delivery possibilities of content. On the contrary, creating those complex ontologies requires a time-consuming effort. Thus, the question arises, whether their use offers a decisive added benefit or if alternatives, such as untyped correlations, should be preferred. In that case, the concept of Semantic Correlation Rules can offer an opportunity to derive advantages from ontologies: By defining which classifications are connected to others, it is possible to present content tailored to user-specific information requirements. By developing use cases, we aim to evaluate the required level of intelligence of the metadata resulting from its modeling method to achieve this goal.
Nanomaterials in Protection of Buildings and Infrastructure Elements in Highly Aggressive Marine Environments

The 2030 Agenda and Sustainable Development Goals (SDG) are both an engineering challenge and an opportunity. Clean energy (SDG 7), sustainable cities and communities (SDG 11), and climate action (SDG 13) represent an effort to manage, plan, and develop our buildings and infrastructure. The purpose of this study is to contribute to this challenge by analysing nanomaterials in marine environment structures, both urban and maritime. To do this, we have analyzed different regulations of concrete properties in various countries, defining the characteristics of the cement, coating, water/cement rating, and chloride effect; the difference in durability based on conventional reinforcements and nanomaterials; and use on highly sensitive elements, buildings in marine environments, rubble mound structures, crown walls, and gravity-based foundations for wind power facilities. Division into overhead, underwater, or splash zones entails the use of epoxy resins or silica fume matrices in percentages far below ten percent. Using the most exposed and unfavorable structures, conclusions of application to buildings are established based on the recommendations in maritime engineering most exposed to the actions of the waves. The study concludes with recommendations regarding the durability, increased lifespan, and use of new materials in infrastructure elements in highly adverse marine environments.
Exploring Embeddings for MIMO Channel Decoding on Quantum Annealers

Quantum Annealing provides a heuristic method leveraging quantum mechanics for solving Quadratic Unconstrained Binary Optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for a wide range of use cases. In particular, a novel device, the D-Wave Advantage has been recently released. In this paper, we study the applicability of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in centralized RAN. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions can only use approximate methodologies, Kim et al. [1] leveraged Quantum Annealing for large MIMO problems with phase shift keying and quadrature amplitude modulation scenarios. Here, we extend upon their work and present embedding limits for both more complex modulation and higher receiver / transmitter numbers using the Pegasus P16 topology of the D-Wave Advantage system.
Upbringing of Student Teachers in Extracurricular Activities in the Context of Distance Learning

The introduction of distance technologies is necessary in the professional training of students, despite various assessments of its effectiveness. Hence the purpose of the study: to develop an upbringing system for students in a distance format and experimentally prove its impact on the main directions of upbringing of future teachers, in comparison with extracurricular activities in full-time mode. 156 undergraduate students took part in the experiment. Experimental research is the author's paradigm for the formation of youth upbringing in extracurricular activities, which includes six synergistically interrelated areas. The upbringing student system developed in the study was tested in two formats of implementation: in full-time format at the first stage of the study and in distance format in the second half of the study. The statistical data obtained during the study confirm the effectiveness of the implementation of extracurricular activities for the formation of the upbringing of future teachers only in full-time format. The introduction of distance technologies in the implementation of upbringing extracurricular activities does not have a positive effect on students, but in some areas has an opposite negative effect. The practical significance of the study is determined by the introduction of a self-analysis and monitoring system into the educational process through the use of Facebook and VKontakte social networks.
Location-sharing protocol for privacy protection in mobile online social networks

Abstract Location-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.
Pulse shaping for on-demand emission of single Raman photons from a quantum-dot biexciton

Semiconductor quantum dots embedded in optical cavities are promising on-demand sources of single photons. Here, we theoretically study single photon emission from an optically driven two-photon Raman transition between the biexciton and the ground state of a quantum dot. The advantage of this process is that it allows all-optical control of the properties of the emitted single photon with a laser pulse. However, with the presence of other decay channels and excitation-induced quantum interference, on-demand emission of the single Raman photon is generally difficult to achieve. Here we show that laser pulses with nontrivial shapes can be used to maintain excitation conditions for which, with increasing pulse intensities, the on-demand regime is reached. To provide a realistic picture of the achievable system performance, we include phonon-mediated processes in the theoretical calculations. While preserving both high photon purity and indistinguishability, we find that although based on a higher-order emission process, for realistic system parameters on-demand Raman photon emission is indeed achievable with suitably tailored laser pulses.
Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

Abstract Recent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.
WNV-Detector: automated and scalable detection of wireless network vulnerabilities

Abstract The security of wireless routers receives much attention given by the increasing security threats. In the era of Internet of Things, many devices pose security vulnerabilities, and there is a significant need to analyze the current security status of devices. In this paper, we develop WNV-Detector, a universal and scalable framework for detecting wireless network vulnerabilities. Based on semantic analysis and named entities recognition, we design rules for automatic device identification of wireless access points and routers. The rules are automatically generated based on the information extracted from the admin webpages, and can be updated with a semi-automated method. To detect the security status of devices, WNV-Detector aims to extract the critical identity information and retrieve known vulnerabilities. In the evaluation, we collect information through web crawlers and build a comprehensive vulnerability database. We also build a prototype system based on WNV-Detector and evaluate it with routers from various vendors on the market. Our results indicate that the effectiveness of our WNV-Detector, i.e., the success rate of vulnerability detection could achieve 95.5%.
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?

Anomaly detection is the process of identifying unexpected events or abnormalities in data, and it has been applied in many different areas such as system monitoring, fraud detection, healthcare, intrusion detection, etc. Providing real-time, lightweight, and proactive anomaly detection for time series with neither human intervention nor domain knowledge could be highly valuable since it reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous event occurs. To our knowledge, RePAD (Real-time Proactive Anomaly Detection algorithm) is a generic approach with all above-mentioned features. To achieve real-time and lightweight detection, RePAD utilizes Long Short-Term Memory (LSTM) to detect whether or not each upcoming data point is anomalous based on short-term historical data points. However, it is unclear that how different amounts of historical data points affect the performance of RePAD. Therefore, in this paper, we investigate the impact of different amounts of historical data on RePAD by introducing a set of performance metrics that cover novel detection accuracy measures, time efficiency, readiness, and resource consumption, etc. Empirical experiments based on real-world time series datasets are conducted to evaluate RePAD in different scenarios, and the experimental results are presented and discussed.