Reviewerly/exHarmony huggingface dataset:
Influence of cultural factors on freehand gesture design

In the design of gesture-based user interfaces, gesture elicitation methods are often used to obtain gesture preferences of end users. User choices of gestures can be affected by various factors, such as their cultural backgrounds. Considering cultural factors in designing gesture-based interfaces is important to systems that target for users from different cultures. However, so little empirical research has been conducted on the impact of cultures on gesture commands. This paper reports a study with a series of three experiments on the gesture preferences for tasks in three different application domains with participants from two cultures. We find that some gesture choices are strongly influenced by the cultural background of participants. We discuss the characteristics of those gestures that exhibit cultural dependence and those tasks that do not. We also provide some design guidelines for freehand gesture-based interfaces.
Predicting excited states from ground state wavefunction by supervised quantum machine learning

Abstract Excited states of molecules lie in the heart of photochemistry and chemical reactions. The recent development in quantum computational chemistry leads to inventions of a variety of algorithms that calculate the excited states of molecules on near-term quantum computers, but they require more computational burdens than the algorithms for calculating the ground states. In this study, we propose a scheme of supervised quantum machine learning which predicts the excited-state properties of molecules only from their ground state wavefunction resulting in reducing the computational cost for calculating the excited states. Our model is comprised of a quantum reservoir and a classical machine learning unit which processes the measurement results of single-qubit Pauli operators with the output state from the reservoir. The quantum reservoir effectively transforms the single-qubit operators into complicated multi-qubit ones which contain essential information of the system, so that the classical machine learning unit may decode them appropriately. The number of runs for quantum computers is saved by training only the classical machine learning unit, and the whole model requires modest resources of quantum hardware that may be implemented in current experiments. We illustrate the predictive ability of our model by numerical simulations for small molecules with and without noise inevitable in near-term quantum computers. The results show that our scheme reproduces well the first and second excitation energies as well as the transition dipole moment between the ground states and excited states only from the ground states as inputs. We expect our contribution will enhance the applications of quantum computers in the study of quantum chemistry and quantum materials.
Detection of Compromised Online Social Network Account with an Enhanced Knn

The primary threat to online social network (OSN) users is account compromisation. The challenge in detecting a compromised account is due to the trusted relationship established between the account owners, their friends, and the service providers. The available research which focuses on using machine learning has limitations with human experts involved in feature selection and a standardized dataset. The paper discusses users` various behaviors of users of OSN and the up-to-date approaches in detecting a compromised OSN account with emphasis on the limitations and challenges. Furthermore, we propose an enhanced machine learning approach Word Embedding and KNN (WE-KNN), which addresses the limitations faced by the previous techniques used. We detailed our proposed WE-KNN for feature extraction, selection of behavior of OSN users, and classification. Our proposed model is evaluated using the standard benchmark datasets, namely KDD Cup '99 and NSL-KDD and implemented it in WEKA. Besides, we used state-of-the-art evaluation metrics to assess the performance of our model. The results obtained depicts that the proposed approach in compromise account detection performs better.
A novel co-attention computation block for deep learning based image co-segmentation

The correlation between images is crucial for solving the image co-segmentation problem that is segmenting common and salient objects from a set of related images. This paper proposes a novel co-attention computation block to compute the visual correlation between images for improving the co-segmentation performance. Here ‘co-attention’ means that we obtain the co-attention features in encoded features of an image to guide the attention in another image. To this purpose, we firstly introduce top-k average pooling to compute the channel co-attention descriptor. Then we explore the correlation between features in different spatial positions to get the spatial co-attention descriptor. Finally, these two types of co-attention descriptors are multiplied to generate a fused one. We obtain such a fused co-attention descriptor for each image and use it to produce the co-attention augmented feature map for the following processing in the applications. We embed the proposed co-attention block into a U-shaped Siamese network for fulfilling the image co-segmentation. It is proven to be able to improve the performance effectively in the experiments. To our best knowledge, it leads to the currently best results on Internet dataset and iCoseg dataset.
The Value of PV Power Forecast and the Paradox of the “Single Pricing” Scheme: The Italian Case Study

One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms’ generation is important both for the producers/traders to minimize the imbalance costs and for the transmission system operators to ensure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale photovoltaic (PV) plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of the forecast accuracy and accuracy improvement in the context of the Italian energy market’s regulatory framework. Through this study, we further point out several important criticisms of the Italian “single pricing” system that brings paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome the undesired effects of the current dispatching regulations.
An index theorem for split-step quantum walks

Split-step quantum walks are models of supersymmetric quantum walk, and thus, their Witten indices can be defined. We prove that the Witten index of a split-step quantum walk coincides with the difference between the winding numbers of functions corresponding to the right limit of coins and the left limit of coins. As a corollary, we give an alternative derivation of the index formula for split-step quantum walks, which is recently obtained by Suzuki and Tanaka.
Quantum pricing with a smile: implementation of local volatility model on quantum computer

Abstract Quantum algorithms for the pricing of financial derivatives have been discussed in recent papers. However, the pricing model discussed in those papers is too simple for practical purposes. It motivates us to consider how to implement more complex models used in financial institutions. In this paper, we consider the local volatility (LV) model, in which the volatility of the underlying asset price depends on the price and time. As in previous studies, we use the quantum amplitude estimation (QAE) as the main source of quantum speedup and discuss the state preparation step of the QAE, or equivalently, the implementation of the asset price evolution. We compare two types of state preparation: One is the amplitude encoding (AE) type, where the probability distribution of the derivative’s payoff is encoded to the probabilistic amplitude. The other is the pseudo-random number (PRN) type, where sequences of PRNs are used to simulate the asset price evolution as in classical Monte Carlo simulation. We present detailed circuit diagrams for implementing these preparation methods in fault-tolerant quantum computation and roughly estimate required resources such as the number of qubits and T-count.
Upper Security Bounds for Coherent-One-Way Quantum Key Distribution

The performance of quantum key distribution (QKD) is severely limited by multiphoton pulses emitted by laser sources due to the photon-number splitting attack. Coherent-one-way (COW) QKD has been introduced as a promising solution to overcome this limitation, and thus extend the achievable distance of practical QKD. Indeed, thanks to its experimental simplicity, the COW protocol is already used in commercial applications. Here, we derive simple upper security bounds on its secret key rate, which demonstrate that it scales at most quadratically with the system's transmittance, thus solving a long-standing problem. That is, in contrast to what has been claimed, this approach is inappropriate for long-distance QKD transmission. Remarkably, our findings imply that all implementations of the COW protocol performed so far are insecure.
Capturing software architecture knowledge for pattern-driven design

Software architecture is a knowledge-intensive field. One mechanism for storing architecture knowledge is the recognition and description of architectural patterns. Selecting architectural patterns is a challenging task for software architects, as knowledge about these patterns is scattered among a wide range of literature. We report on a systematic literature review, intending to build a decision model for the architectural pattern selection problem. Moreover, twelve experienced practitioners at software-producing organizations evaluated the usability and usefulness of the extracted knowledge. An overview is provided of 29 patterns and their effects on 40 quality attributes. Furthermore, we report in which systems the 29 patterns are applied and in which combinations. The practitioners confirmed that architectural knowledge supports software architects with their decision-making process to select a set of patterns for a new problem. We investigate the potential trends among architects to select patterns. With the knowledge available, architects can more rapidly select and eliminate combinations of patterns to design solutions. Having this knowledge readily available supports software architects in making more efficient and effective design decisions that meet their quality concerns.
Design and Performance of Relay-Assisted Satellite Free-Space Optical Quantum Key Distribution Systems

This paper studies the design and performance analysis of relay-assisted satellite free-space optics (FSO) quantum key distribution (QKD) systems for secure vehicular networks. High-altitude platforms (HAPs) equipped with optical amplify-and-forward nodes are used as relay stations. Secrecy performances in terms of quantum bit error rate and ergodic secret-key rate are analytically investigated under the effects of transceiver misalignment, receiver's velocity variation, receiver noises, and atmospheric turbulence conditions. Based on the analyzed results, the design criteria for the legitimate user are determined so that the security of the considered system could be guaranteed.
A lightweight vehicle detection and tracking technique for advanced driving assistance systems

In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly a pipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the careful fusion of the employed algorithms where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. The particulars of the employed algorithms together with their implementation are described in detail. Additionally, these algorithms and their various integration combinations are tested and their performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark with 87% average precision achieved. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions.
A comparative user study of visualization techniques for cluster analysis of multidimensional data sets

This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, [Formula: see text], embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of [Formula: see text] as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with [Formula: see text] values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward’s hierarchical clustering) are likely to lead to estimates of [Formula: see text] that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.
Future Academic Teachers' Digital Skills: Polish Case-Study

Preparing quality academic teachers for modern digital native students is an essential task for higher educational institutions.Professional teachers' digital competencies combine professional, pedagogical, and technological knowledge and skills as well as beliefs regarding integrating technology in teaching practice.This article is an attempt to study the personal level of digital competence of PhD students as future academic teachers.The paper presents statements from an ongoing study (n=120) piloting large scale longitudinal qualitative teachers' surveys between 2017 and 2020.The first part of the research started in 2017 to attempt to recognize the personal level of digital competence of PhD students as future academic teachers.The study was conducted during 2017-2018 at the Wroclaw University of Environmental and Life Sciences.To study the levels of digital competence, a special questionnaire based on the European Competency Framework for the Digital Competence of Educators was used.The study results show different levels of digital competence depending on the students' field of study and specialty.The highest technological scores were demonstrated by PhD students of engineering specializations.However, the results also showed that on average PhD students do not feel well prepared for the practical use of digital techniques in teaching activities.The preliminary study results indicate the need to improve PhD training programs including various ways of developing students' skills for making better use of digital technology for teaching and learning.
A tight lower bound on the expected runtime of standard steady state genetic algorithms

Recent progress in the runtime analysis of evolutionary algorithms (EAs) has allowed the derivation of upper bounds on the expected runtime of standard steady-state GAs. These upper bounds have shown speed-ups of the GAs using crossover and mutation over the same algorithms that only use mutation operators (i.e., steady-state EAs) both for standard unimodal (i.e., OneMax) and multimodal (i.e., Jump) benchmark functions. These upper bounds suggest that populations are beneficial to the GA as well as higher mutation rates than the default 1/n rate. However, making rigorous claims was not possible because matching lower bounds were not available. Proving lower bounds on crossover-based EAs is a notoriously difficult task as it is hard to capture the progress that a diverse population can make. We use a potential function approach to prove a tight lower bound on the expected runtime of the (2 + 1) GA for OneMax for all mutation rates c/n with c < 1.422. This provides the last piece of the puzzle that completes the proof that larger population sizes improve the performance of the standard steady-state GA for OneMax for various mutation rates, and it proves that the optimal mutation rate for the (2 + 1) GA on OneMax is [EQUATION].
WeScream!

Prior psychology studies have shown that eating ice cream increases happiness, while human-computer interaction work has shown that interactive technology can enrich the eating experience. We explore the opportunity to combine these two through WeScream!, a playful social gustosonic system we designed-social gustosonic referring to the link between the acts of eating and listening as part of a social multisensory experience. WeScream! consists of two interdependent ice-cream cones that allow users to interact with musical sounds generated through the act of eating ice cream together. We report on an in-the-wild study that highlights how our system facilitated a "hard fun" experience through eating together, increased participants' awareness of relatedness, and drew shared attention to the ice cream's taste via increased face-to-face interaction. Drawing on these study insights, we also present three design tactics to guide designers in designing future social gustosonic experiences. Ultimately, we aim to contribute to a playful future of social eating experiences, supporting people in enjoying eating together.
Profit Maximization Strategy with Spot Allocation Quality Guaranteed Service in Cloud Environment

Cloud computing is more popular because of efficient way of providing services to the customers on demand and efficient usage of the resources. From the cloud provider's scenario profit is the ultimate goal and is determined by the system configuration of the cloud environment and from the customers point of view the payment to the provider is to be minimized. Many cloud providers adopt a long-term renting scheme from their customers, but it can't assure the guarantee on the service quality and it leads to a serious wastage of resources. The present research paper optimizes the sort term and long term pricing strategy to generate maximum profit by the cloud providers by maintaining the quality of services.
The Notion of Transparency Order, Revisited

Abstract We revisit the definition of transparency order (TO) and that of modified transparency order (MTO) as well, which were proposed to measure the resistance of substitution boxes (S-boxes) against differential power analysis (DPA). We spot a definitional flaw in original TO, which is proved to significantly affect the soundness of TO. Regretfully, MTO overlooks this flaw, yet it happens to incur no bad effects on the correctness of MTO, even though the start point of this formulation is highly questionable. It is also this neglect that made MTO consider a variant of multi-bit DPA attack, which was mistakenly thought to appropriately serve as an alternative powerful attack. This implies the soundness of MTO is also more or less arguable. Therefore, we fix this definitional flaw and provide a revised definition named reVisited TO (VTO). For demonstrating validity and soundness of VTO, we present simulated and practical DPA attacks on implementations of $4\times 4$ and $8\times 8$ S-boxes. In addition, we also illustrate the soundness of VTO in masked S-boxes. Furthermore, as a concrete application of VTO, we present the distribution of VTO values of optimal affine equivalence classes of $4\times 4$ S-boxes and give some recommended guidelines on how to select $4\times 4$ S-boxes with higher DPA resistance at the identical level of implementation cost.
A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes.Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention.Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features.Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes.In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach.We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency.To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects).Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets.The performances over object-based images are excellent and consistent on generic pictures.Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces.
VRtefacts

We explore how a combination of manipulations and transitions can extend Substitutional Reality to create a highly personal Virtual Reality experience. Our design aimed to meet two challenges faced by museums: the limitations of object handling and the desire for visitors to create their own interpretations. Using a Research-through-Design methodology, we built a performance-led Mixed Reality (MR) experience that lets museum visitors physically handle 3D prints or scans of museum objects to share personal stories about them. The stories are recorded and donated to the participating museum. We reflect on the complex design and the findings gained from a two-day in-the-wild deployment to explore engagement and disruption through manipulations of physicality, visuals, and scale; the transitions between spaces; and a trajectory of storytelling performance. We chart a wide scope for Performative Substitutional Reality and draw implications for VR, MR, and performance-led research in any context.