Reviewerly/exHarmony huggingface dataset:
Skeleton-based Action Recognition of People Handling Objects

In visual surveillance systems, it is necessary to recognize the behavior of people handling objects such as a phone, a cup, or a plastic bag. In this paper, to address this problem, we propose a new framework for recognizing object-related human actions by graph convolutional networks using human and object poses. In this framework, we construct skeletal graphs of reliable human poses by selectively sampling the informative frames in a video, which include human joints with high confidence scores obtained in pose estimation. The skeletal graphs generated from the sampled frames represent human poses related to the object position in both the spatial and temporal domains, and these graphs are used as inputs to the graph convolutional networks. Through experiments over an open benchmark and our own data sets, we verify the validity of our framework in that our method outperforms the state-of-the-art method for skeleton-based action recognition.
Automatic processing and solar cell detection in photovoltaic electroluminescence images

Electroluminescence (EL) imaging is a powerful and established technique for assessing the quality of photovoltaic (PV) modules, which consist of many electrically connected solar cells arranged in a grid.The analysis of imperfect real-world images requires reliable methods for preprocessing, detection and extraction of the cells.We propose several methods for those tasks, which, however, can be modified to related imaging problems where similar geometric objects need to be detected accurately.Allowing for images taken under difficult outdoor conditions, we present methods to correct for rotation and perspective distortions.The next important step is the extraction of the solar cells of a PV module, for instance to pass them to a procedure to detect and analyze defects on their surface.We propose a method based on specialized Hough transforms, which allows to extract the cells even when the module is surrounded by disturbing background and a fast method based on cumulated sums (CUSUM) change detection to extract the cell area of single-cell mini-module, where the correction of perspective distortion is implicitly done.The methods are highly automatized to allow for big data analyses.Their application to a large database of EL images substantiates that the methods work reliably on a large scale for real-world images.Simulations show that the approach achieves high accuracy, reliability and robustness.This even holds for low contrast images as evaluated by comparing the simulated accuracy for a low and a high contrast image.
Entangling protocols due to non-Markovian dynamics

It is widely spread in the literature that non-Markovianity (NM) may be regarded as a resource in quantum mechanics. However, it is still unclear how and when this alleged resource may be exploited. Here, we study the relationship between NM and quantum optimal control under the objective of generating entanglement within $M$ noninteracting subsystems, each one coupled to the same non-Markovian environment. Thus, we design a variety of entangling protocols that are only achievable due to the existence of the environment. We show that NM plays a crucial role in all the entangling protocols considered, revealing that the degree of NM completely determines the success of the entangling operation performed by the control. This is a demonstration of the virtues of NM and the way that it can be exploited in a general entangling setup.
Edge-labeling Graph Neural Network for Few-shot Learning

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on the node-labeling framework, which implicitly models the intra-cluster similarity and the inter-cluster dissimilarity. In contrast, the proposed EGNN learns to predict the edge-labels rather than the node-labels on the graph that enables the evolution of an explicit clustering by iteratively updating the edge-labels with direct exploitation of both intra-cluster similarity and the inter-cluster dissimilarity. It is also well suited for performing on various numbers of classes without retraining, and can be easily extended to perform a transductive inference. The parameters of the EGNN are learned by episodic training with an edge-labeling loss to obtain a well-generalizable model for unseen low-data problem. On both of the supervised and semi-supervised few-shot image classification tasks with two benchmark datasets, the proposed EGNN significantly improves the performances over the existing GNNs.
Face Recognition Based on Videos by Using Convex Hulls

A wide range of face appearance variations can be modeled by using set-based recognition approaches effectively, but computational complexity of current methods is highly dependent on the set and class sizes. This paper introduces new video-based classification methods designed for reducing the required disk space of data samples and speed up the testing process in large-scale face recognition systems. In the proposed method, image sets collected from videos are approximated with kernelized convex hulls and it was shown that it is sufficient to use only the samples that participate in shaping the image set boundaries in this setting. The kernelized support vector data description (SVDD) is used to extract those important samples that form the image set boundaries. Moreover, we show that these kernelized hypersphere models can also be used to approximate image sets for classification purposes. Then, we propose a binary hierarchical decision tree approach to improve the speed of the classification system even more. At last, we introduce a new video database that includes 285 people with 8 videos of each person, since the most popular video data sets used for set-based recognition methods include either a few people, or small number of videos per person. The experimental results on varying sized databases show that the proposed methods greatly improve the testing times of the classification system (we obtained speed-ups to a factor of 20) without a significant drop in accuracies.
Mobile Learning in Science: A Study in Secondary Education in Greece

Mobile technology can facilitate students' learning in anytime, anywhere.Different tools can be used to support science learning, while mobile devices with internet access can facilitate students' online investigations of simulations, video and virtual labs.However, there are few studies that reported mobile supported science activities and this topic is not covered within the Greek context.This paper presents a small scale study from Greece, with examples of mobile technology-supported learning activities in science lessons (in secondary schools).In parallel, it discusses studies on science learning with mobile technology and presents the context of mobile learning in Greece.Implications for educational policy makers, teachers and students are finally discussed.
Time–frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions

A joint time-frequency localized three-band biorthogonal wavelet filter bank to compress Electrocardiogram signals is proposed in this work. Further, the use of adaptive thresholding and modified run-length encoding resulted in maximum data volume reduction while guaranteeing reconstructing quality. Using signal-to-noise ratio, compression ratio (CR), maximum absolute error (EMA), quality score (Qs), root mean square error, compression time (CT) and percentage root mean square difference the validity of the proposed approach is studied. The experimental results deduced that the performance of the proposed approach is better when compared to the two-band wavelet filter bank. The proposed compression method enables loss-less data transmission of medical signals to remote locations for therapeutic usage.
R-Transformer: Recurrent Neural Network Enhanced Transformer

Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks. We have made the code publicly available at \url{https://github.com/DSE-MSU/R-transformer}.
Scheme for generation of three-photon entangled W state assisted by cross-Kerr nonlinearity and quantum dot

We represent an optical scheme using cross-Kerr nonlinearities (XKNLs) and quantum dot (QD) within a single-sided optical cavity (QD-cavity system) to generate three-photon entangled W state containing entanglement against loss of one photon of them. To generate W state (three-photon) with robust entanglement against loss of one photon, we utilize effects of optical nonlinearities in XKNLs (as quantum controlled operations) and QD-cavity system (as a parity operation) with linearly optical devices. In our scheme, the nonlinear (XKNL) gate consists of weak XKNLs, quantum bus beams, and photon-number-resolving measurement to realize controlled-unitary gate between two photons while another nonlinear (QD) gate employs interactions of photons and an electron of QD confined within a single-sided optical cavity for implementation of parity gate. Subsequently, for the efficiency and experimental feasibility of our scheme generating W state, we analyze the immunity of the controlled-unitary gate using XKNLs against decoherence effect and reliable performance of parity gate using QD-cavity system.
Identity-Based Encryption with Filtered Equality Test for Smart City Applications

With the growth of the urban population, the rapid development of smart cities has become the focus of urban regional development. Smart medical care is an indispensable part of smart city construction, which promotes the development of the medical industry. However, the security of data and timely service are the current problems faced by intelligent medical systems. Based on the public key encryption with filtered equality test and identity-based cryptography, an identity-based encryption with the filtered equality test (IBE-FET) is proposed for smart healthcare, in which a data receiver can use the private key and the message set to generate a warrant and send it to the cloud server. A cloud server can verify the equality between ciphertexts without decryption and check whether the encrypted message belongs to the same message set. Furthermore, the security analysis shows that the proposed scheme satisfies one-way security against the chosen identity and ciphertext attack in the random oracle model under the computational bilinear Diffie-Hellman assumption. The performance comparison shows that the scheme is feasible and practical in real life.
Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation.
Moving base driving simulators’ potential for carsickness research

We investigated whether motion sickness analogous to carsickness can be studied in a moving base simulator, despite the limited motion envelope. Importantly, to avoid simulator sickness, vision outside the simulator cabin was restricted. Participants (N = 16) were exposed blindfolded to 15-min lateral sinusoidal motion at 0.2 Hz and 0.35 Hz on separate days. These conditions were selected to realize optimal provocativeness of the stimulus given the simulator's maximum displacement and knowledge on frequency-acceleration interactions for motion sickness. Average motion sickness on an 11-point scale was 2.21 ± 1.97 for 0.2 Hz and 1.93 ± 1.94 for 0.35 Hz. The motion sickness increase over time was comparable to that found in studies using actual vehicles. We argue that motion base simulators can be used to incite motion sickness analogous to carsickness, provided considerable restrictions on vision. Future research on carsickness, potentially more prevalent in autonomous vehicles, could benefit from employing simulators.
On the Impossibility of Learning the Missing Mass

This paper shows that one cannot learn the probability of rare events without imposing further structural assumptions. The event of interest is that of obtaining an outcome outside the coverage of an i.i.d. sample from a discrete distribution. The probability of this event is referred to as the "missing mass". The impossibility result can then be stated as: the missing mass is not distribution-free learnable in relative error. The proof is semi-constructive and relies on a coupling argument using a dithered geometric distribution. Via a reduction, this impossibility also extends to both discrete and continuous tail estimation. These results formalize the folklore that in order to predict rare events without restrictive modeling, one necessarily needs distributions with "heavy tails".
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods.
Optimal Multi-Codec Adaptive Bitrate Streaming

We consider adaptive bitrate streaming system serving a population of mobile devices, where first subset of devices can only decode first codec (e.g. H.264), another subset of devices can only decode the second codec (e.g. HEVC), and where the third subset of devices can decode both codecs and can also seamlessly switch between them. We focus on a problem of design of encoding profiles, defining pluralities of streams encoded by both codecs, such that overall quality delivered by such system is optimal. We define this problem mathematically, show how it maps to known classes of optimization problems, and identify approach for solving it numerically. Examples of optimal ladders constructed for different models of networks as well as types of content are provided. It is shown that proposed approach leads to significant improvements compared to systems employing ABR ladders constructed separately for each codec.
Eye-gaze-triggered Visual Cues to Restore Attention in Educational VR

In educational virtual reality, it is important to deal with problems of student inattention to presented content. We are developing attention-restoring visual cues for display when gaze tracking detects that student focus shifts away from critical objects. These cues include novel aspects and variations of standard cues that performed well in prior work on visual guidance. For the longer term, we propose experiments to compare various cues and their parameters to assess effectiveness and tradeoffs, and to assess the impact of eye tracking. Eye tracking is used to both detect inattention and to control the appearance and location of cues.
A Review of Clustering Algorithms for Big Data

Big data is usually defined by five (05) characteristics called 5Vs +1C (Volume, Velocity, Variety, Veracity, Value and Complexity). It means to data that are too large, dynamic and complex with certain degree of accuracy. For that, data become difficult to analyze using traditional data analysis techniques because of their high complexity and computational cost. Clustering analysis technique is the most used method for cope with huge amount of data. The main goal of clustering is to classify data into clusters in manner that data grouped are more similar. In this paper, we provide an overview of various clustering techniques used for data analysis.
Domes, Rings, Ridges, and Polygons: Characteristics of Microbialites from Utah’s Great Salt Lake

By Michael D. Vanden Berg. Morphology of GSL microbialites and how local environmental conditions and periods of exposure and erosion contribute to growth location, grouping, shape, size, orientation, and internal structure.
Fault-tolerant quantum error correction on near-term quantum processors using flag and bridge qubits

Fault-tolerant (FT) computation by using quantum error correction (QEC) is essential for realizing large-scale quantum algorithms. Devices are expected to have enough qubits to demonstrate aspects of fault tolerance in the near future. However, these near-term quantum processors will only contain a small amount of noisy qubits and allow limited qubit connectivity. Fault-tolerant schemes that not only have low qubit overhead but also comply with geometrical interaction constraints are therefore necessary. In this work, we combine flag fault tolerance with quantum circuit mapping, to enable an efficient flag-bridge approach to implement FT QEC on near-term devices. We further show an example of performing the Steane code error correction on two current superconducting processors and numerically analyze their performance with circuit level noise. The simulation results show that the QEC circuits that measure more stabilizers in parallel have lower logical error rates. We also observe that the Steane code can outperform the distance-3 surface code using flag-bridge error correction. In addition, we foresee potential applications of the flag-bridge approach such as FT computation using lattice surgery and code deformation techniques.
Ancient Geez script recognition using deep learning

Handwritten text recognition is one of the most valuable recognition systems because of the unique characteristics of each person's handwriting. Thus, recognition systems need to be more adaptable to recognize same or different characters with different characteristics. On the other hand, one of the most challenging tasks in handwritten text recognition problems is recognizing ancient documents which include several noise within them. While digitizing these documents, these noise appear in different types which effects any recognition system. So, digitizing ancient documents, applying proper pre-processing techniques and performing effective classifier are the main steps of efficient recognition system. In this paper, a complete Ethiopian ancient Geez character recognition system using deep convolutional neural network is proposed in order to recognize twenty-six base characters of this alphabet. The proposed system obtained an accuracy of 99.39% with a model loss of 0.044 which demonstrates its efficiency.