Reviewerly/exHarmony huggingface dataset:
FPCR-Net: Feature pyramidal correlation and residual reconstruction for optical flow estimation

Optical flow estimation is a challenging problem in the field of video analytics yet. Features of different semantics levels in a convolutional neural network (CNN) provide information of different granularity. To exploit such flexible and comprehensive information, we propose a Feature Pyramidal Correlation and Residual Reconstruction Network (FPCR-Net) for optical flow estimation from frame pairs. It consists of two main modules: pyramid correlation mapping and residual reconstruction. The pyramid correlation mapping module takes advantage of the multi-scale correlations of global/local representation by aggregating features of different scales to form a multi-level cost volume. The residual reconstruction module aims at reconstructing the sub-band high-frequency residuals of finer optical flow at each stage. Based on the pyramid correlation mapping, we further propose a correlation-warping-normalization (CWN) module to efficiently exploit the correlation dependency. Furthermore, considering the characteristics of flow warping and alignment, we integrate unsupervised and supervised losses to explore the implicit relevance and explicit constraint. Experimental results show that the proposed network achieves the promising performance for two-frame-based optical flow estimation on the challenging Sintel and KITTI 2012/2015 datasets.
<i>SPEye:</i> A Calibration-Free Gaze-Driven Text Entry Technique Based on Smooth Pursuit

Gaze-based text entry is undoubtedly one of the most useful applications of eye-tracking technology for human-machine interaction, both in the assistive context (users with severe motor disabilities can exploit such writing modalities to communicate with the world) and as a way to allow touchless text input in everyday life. Different eye-driven text entry methods have been developed to date, and almost all of them require preliminary calibration procedures to work correctly. When a short text, such as a password or a PIN, needs to be entered without using hands or voice, calibration may be perceived as an unnecessary nuisance (and may not be properly maintained in public places due to &#x201C;ambient noise,&#x201D; caused, for example, by nearby people). Inadequate calibration may also be a problem in case of assistive uses. In this article we present <i>SPEye</i>, a calibration-free eye-controlled writing technique based on smooth pursuit. Although its writing speed is significantly lower than that of ordinary calibrated methods, the absence of an initial calibration makes it suitable for short text entry. The technique has been tested through several experiments, obtaining good performances in terms of key strokes per character and total error rate metrics, and receiving positive feedback from the participants in the tests.
Discriminating Quantum States with Quantum Machine Learning

An important use-case for machine learning (ML) is that of determining readout results in quantum computers. In quantum computing (QC), classical ML models are currently being used to discriminate in-phase and quadrature (IQ) signal data to discriminate between quantum states, which is a fundamental QC operation. In our research we propose a Quantum K-Means (QK-Means) clustering technique to discriminate quantum states on the IBM Bogota quantum device, and compare its performance to the K-Means technique (its classical counterpart). We used both algorithms to perform a correlation analysis and probe cross-talk between couples of qubits on the device. We observed that QK-Means obtained test and training scores at par with the classical K-Means, and testing scores that were marginally lower than K-Means when the clusters weren’t visually separable. The training times for QK-Means were observed to be at par with an implementation of the K-Means that was not optimized. In this case, we concluded a weak correlation with a Pearson correlation coefficient of 0.2 on the (1, 2) and (2, 3) qubit couples. After analyzing the training scores, we also conclude that the 1 qubit has the worst performance at readout evidenced by the signal data not being visually separable and the low scores obtained on both clustering algorithms compared to the other qubits. Its poor performance is further verified by the calibration data showing a high readout error of 8,4%. This technique can be used to find correlations present in readout of quantum circuits and to determine cross-talk.
Challenges, Security Mechanisms, and Research Areas in IoT and IioT

In recent years IoT technology has become widely accepted in the industrial sector creating the fourth industrial revolution, Industry 4.0, and Industrial Internet of Things (IIoT). This wide adoption of IoT has transformed the industries to smarter, efficient, and profitable ones. However with this adoption industry has inherited the vulnerabilities, challenges, and other issues faced by IoT. This paper conveys the challenges and issues created in this transformation and provides various security mechanisms to address the issues faced by the IoT and IIoT domain. The final section opens up various research areas in security that are still to be addressed in these domains and are also discussed in this paper.
Crime Scene Analysis Using Deep Learning

Gathering evidence is one of the key activities during a criminal investigation. Image processing can assist in going over the scene of crime, or even recreating it. It isn’t always possible to expect the crime scene to be untampered with. However, with visual documentation of crime scenes comprising of various objects detected, it could be possible to come back to the images. This could assist in looking for something that has been discovered in further developments of the case. Additionally, with CCTV footage capturing tons of potentially crucial information, it is difficult for law enforcers themselves to go through the footage. Crime scene images are hard to find, and no known dataset is available to train the models directly on crime scene images. However, other popular datasets for image captioning do possess some crime scene images. The proposed approach is to train the models on a general vast dataset, MSCOCO. If the crime scene images are directly passed to the models, they will grab the main idea and give the sentence implying a person is dead. The idea is, however, to capture the more minute details ignoring the obvious conclusion that a person is dead. For crime scene images, the images are segmented into 9 images and passed individually to the model. This will generate 9 sentences for the input crime scene image with insight into the crime scene as opposed to captioning the crime scene. The three deep learning models proposed to use for generating sentences are: Inceptionv3-LSTM network, VGG-16-LSTM network and ResNet-50-LSTM network, and 0.1771, 0.11 and 0.1784 are the respective BLEU scores obtained. The users preferred the Inceptionv3-LSTM model over ResNet-50-LSTM model with a difference of 14.8% votes.
Poisson image restoration using a novel directional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si8.svg"><mml:mrow><mml:mi>T</mml:mi><mml:msup><mml:mi>V</mml:mi><mml:mi>p</mml:mi></mml:msup></mml:mrow></mml:math> regularization

The degree of Poisson noise depends on the image intensity, which makes Poisson image restoration very challenging. Moreover, complex structures of images desire suitable regularizations to describe. In this paper, we propose a new image restoration model under Poisson noise based on an adaptive weighted directional TVp regularization. The rotation matrix can keep the diffusion of the corresponding Euler–Lagrange equation along with the tangential direction of the edge, and the adaptive weighted matrix can enhance the diffusion. Owing to its adaptivity, our proposed model can simultaneously handle several dominant directions. Besides, the ℓp-quasinorm regularization promotes the image sparsity. To solve it efficiently, we design an alternating direction method of multipliers (ADMM). The related ℓ2−ℓp subproblem is solved by using the half-quadratic algorithm with guaranteed convergence. Experimental results on natural and synthetic images show the effectiveness of the proposed method over the state-of-the-art variational methods.
Offline Arabic handwritten word recognition: A transfer learning approach

Offline Arabic handwritten word recognition is still a challenging task. Many deep learning approaches perform admirably on this task if the lexicon size is not too large and the number of training samples is sufficient for the training process. The transfer learning technique is commonly used to compensate for the lack of training samples, but there is a wide controversy about the effectiveness of applying it to cross-domain tasks. In this paper, we examine the performance of three deep convolution neural networks that have been randomly initialized for recognizing Arabic handwritten words. Then, we evaluate the performance of the ResNet18 model that has been pre-trained on the ImageNet dataset for the same task. Finally, we propose an approach based on sequentially transferring the mid-level word image representations through two consecutive phases using the ResNet18 model. We carried out four different sets of experiments using two popular offline Arabic handwritten word datasets: the AlexU-W and the IFN/ENIT (v2.0p1e) to figure out the most effective way of applying transfer learning. Our results demonstrate that using the ImageNet as a source dataset improves the recognition accuracy of the ten frequently misclassified words in the IFN/ENIT dataset by 14%, while our proposed approach gives a rise of 35.45%. In the whole dataset, we achieved recognition accuracy up to 96.11%, which is nearly a 2.5% enhancement compared with other state-of-the-art methods.
Optical Braille Recognition Using Object Detection Neural Network

Optical Braille recognition methods generally rely heavily on a Braille text’s geometric structure. They run into problems if this structure is distorted. Thus, they find it difficult to cope with images of book pages taken with a smartphone.We propose an optical Braille recognition method that uses an object detection convolutional neural network to detect whole Braille characters at once. The proposed algorithm is robust to deformations and perspective distortions of a Braille page displayed on an image. The algorithm is suitable for recognizing braille texts captured with a smartphone camera in domestic conditions. It can handle curved pages and images with perspective distortion. The proposed algorithm shows high performance and accuracy compared to existing methods.Additionally, we produced a new dataset containing 240 photos of Braille texts with annotation for each Braille letter. Both the proposed algorithm and the dataset are available at GitHub.
SchenQL: in-depth analysis of a query language for bibliographic metadata

Abstract Information access to bibliographic metadata needs to be uncomplicated, as users may not benefit from complex and potentially richer data that may be difficult to obtain. Sophisticated research questions including complex aggregations could be answered with complex SQL queries. However, this comes with the cost of high complexity, which requires for a high level of expertise even for trained programmers. A domain-specific query language could provide a straightforward solution to this problem. Although less generic, it can support users not familiar with query construction in the formulation of complex information needs. In this paper, we present and evaluate SchenQL, a simple and applicable query language that is accompanied by a prototypical GUI. SchenQL focuses on querying bibliographic metadata using the vocabulary of domain experts. The easy-to-learn domain-specific query language is suitable for domain experts as well as casual users while still providing the possibility to answer complex information demands. Query construction and information exploration are supported by a prototypical GUI. We present an evaluation of the complete system: different variants for executing SchenQL queries are benchmarked; interviews with domain-experts and a bipartite quantitative user study demonstrate SchenQL’s suitability and high level of users’ acceptance.
Publishing Graphs Under Node Differential Privacy

Differential privacy (DP) has become the de facto standard of privacy protection. For graphs, there are two widely used definitions of differential privacy, namely, edge differential privacy (edge-DP) and node differential privacy (node-DP), and node-DP is preferred when the minimal unit of interest is a node. To preserve node-DP, one can develop different methods to answer each specific graph query, or develop a graph publishing method to answer all graph queries. However, no existing works worked on such graph publishing methods. In this work, we propose two methods for publishing graphs under node-DP. One is the node-level perturbation algorithm which modifies the input graph by randomly inserting and removing nodes. The other one is the edge-level perturbation algorithm which randomly removing edges and inserting nodes. Both methods can achieve a flexible privacy guarantee by adjusting the running parameters. We conduct extensive experiments on both real-world and synthetic graphs to show the effectiveness and efficiency of proposed algorithms.
A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning

In social and service robotics, complex collaborative tasks are expected to be executed while interacting with humans in a natural and fluent manner. In this scenario, the robotic system is typically provided with structured tasks to be accomplished, but must also continuously adapt to human activities, commands, and interventions. We propose to tackle these issues by exploiting the concept of cognitive control, introduced in cognitive psychology and neuroscience to describe the executive mechanisms needed to support adaptive responses and complex goal-directed behaviors. Specifically, we rely on a supervisory attentional system to orchestrate the execution of hierarchically organized robotic behaviors. This paradigm seems particularly effective not only for flexible plan execution but also for human-robot interaction, because it directly provides attention mechanisms considered as pivotal for implicit, non-verbal human-human communication. Following this approach, we are currently developing a robotic cognitive control framework enabling collaborative task execution and incremental task learning. In this paper, we provide a uniform overview of the framework illustrating its main features and discussing the potential of the supervisory attentional system paradigm in different scenarios where humans and robots have to collaborate for learning and executing everyday activities.
OntoCSA

The modelling of agriculture with ontologies has been of interest to many authors in the past years. However, no research, currently, has focused on building a knowledge base ontology for the Climate Smart Agriculture (CSA) domain. This study attempts to fill this gap through the development of a Climate Smart Agriculture Ontology (OntoCSA). Information was gathered from secondary sources including websites, published research articles and reports as well as related ontologies, to formalize the OntoCSA ontology in Description Logics (DLs). The OntoCSA ontology was developed in Web Ontology Language (OWL) with Protégé. Furthermore, the OntoCSA ontology was successfully validated with the HermiT reasoner within Protégé. The resulting OntoCSA ontology is a machine-readable model of CSA that can be leveraged in web-based applications for the storage, open and automated access and sharing of CSA information/data, for research and dissemination of best practices
Unravelling the Effect of Image Distortions for Biased Prediction of Pre-trained Face Recognition Models

Identifying and mitigating bias in deep learning algorithms has gained significant popularity in the past few years due to its impact on the society. Researchers argue that models trained on balanced datasets with good representation provide equal and unbiased performance across subgroups. However, can seemingly unbiased pre-trained model become biased when input data undergoes certain distortions? For the first time, we attempt to answer this question in the context of face recognition. We provide a systematic analysis to evaluate the performance of four state-of-the-art deep face recognition models in the presence of image distortions across different gender and race subgroups. We have observed that image distortions have a relationship with the performance gap of the model across different subgroups.
Neurodynamic Network for Absolute Value Equations: A Fixed-Time Convergence Technique

In this brief, a novel projection neurodynamic network with fixed-time convergence is proposed for solving absolute value equations. In contrast to most existing projection neurodynamic networks, a conservative settling time of the proposed projection neurodynamic network is presented. It is shown that the solution of the proposed approach converges to the solution of the corresponding absolute value equation in fixed-time under some mild conditions. Finally, a numerical example is presented to validate the main results.
Secure Multi-Robot Adaptive Information Sampling

In a coordinated multi-robot information sampling scenario, robots often share their collected information with others for a better prediction. As with any other online data sharing technique, data integrity is a concern, but it has not yet been addressed in the multi-robot information sampling literature. In this paper, we study how to secure the information being shared among the robots in such a multi-robot network against integrity attacks and what is the cost of integrating such security techniques. To this end, we propose a Blockchain-based information sharing protocol that helps the robots reject fake data injection by a malicious entity. On the other hand, optimal information sampling is a compute-intensive technique and so are the popular Blockchain-based consensus protocols. Therefore, we also study the impact of adding such a security protocol on the execution time of the sampling algorithm, which in turn effects the energy spent by the robots. Results show that our proposed technique is effective against such data tampering attempts while the effect of the added computation varies largely on the consensus protocol used.
Word Embedding based Textual Semantic Similarity Measure in Bengali

Textual semantic similarity is a crucial constituent in many NLP tasks such as information retrieval, machine translation, information retrieval and textual forgery detection. It is a complicated task for rule-based techniques to address semantic similarity measures in low-resource languages due to the complex morphological structure and scarcity of linguistic resources. This paper investigates several word embedding techniques (Word2Vec, GloVe, FastText) to estimate the semantic similarity of Bengali sentences. Due to the unavailability of the standard dataset, this work developed a Bengali dataset containing 187031 text documents with 400824 unique words. Moreover, this work considers three semantic distance measures to compute the similarity between the word vectors using Cosine similarity with no weight, term frequency weighting and Part-of-Speech weighting. The performance of the proposed approach is evaluated on the developed dataset containing 50 pairs of Bengali sentences. The evaluation result shows that FastText with continuous bag-of-words with 100 vector size achieved the highest Pearson’s correlation (ρ) score of 77.28%.
The Path Planning Study of Multi-task Logistics UAVs Under Complex Low Airspace

The logistics unmanned aerial vehicles (UAVs) carry out goods to each mission point and return to the starting point, which is a typical TSP problem. However, in real life, logistics UAVs need to complete the TSP problem under the constraints of airspace obstacles. Aiming at this problem, a UAVs path planning algorithm based on the combination of ant colony algorithm and unnecessary point deletion strategy is proposed. First, use ant colony algorithm to complete the path planning without obstacles constraints. Secondly, considering obstacles constraints, obtain the corresponding turning points in the obstacle area, delete unnecessary points and update the turning points and distance. Finally, based on local updates, iterate to obtain global solution. The simulation results show that this method can realize the TSP path planning problem under the condition of multiple obstacles. The planned path is shorter and the convergence speed is faster. In this paper, the TSP problem under obstacle constraints is divided into traditional TSP problems and obstacle avoidance problems, and respectively solved by ant colony algorithm and unnecessary point deletion strategy. In addition, this paper deletes unnecessary points to make the algorithm obtain a shorter path under the premise of ensuring the safety of the path, which overcomes the situation that the ant colony algorithm is easy to fall into the local optimal solution.
Optimal Strategy for the Improvement of the Overall Performance of Dual-Axis Solar Tracking Systems

Solar Tracking Systems are useful to increase the generation efficiency of photovoltaic technology, mainly for concentration technology, where dual-axis is required on account of the high accurate alignment to the Sun. Even when there exists a strong relation between tracking error and energy efficiency, multiple technological and research developments have sought to solve these problems independently. The present research proposes a novel concurrent design methodology for optimizing the overall performance of two-axis trackers, allowing to keep a balance between the tracking error and the energy consumption from the design stage, from an optimization approach. The concurrent approach was implemented to design a Solar Tracker as a solar monitoring system, was compared with four commercial systems, obtaining a similar pointing accuracy with a mixed tracking error of 0.0942°. The system has the best energy balance, consuming only 0.9641% of the energy generated for the tracking action, below commercial models. Finally, a CO2 impact analysis was carried out, where the proposed tracker obtained the lowest value, with 25.7018 g. The results support the developed concurrent strategy for the optimization of the overall performance of dual-axis systems, allowing us to find a harmonic balance between the energy consumption and the required tracking accuracy.