Reviewerly/exHarmony huggingface dataset:
Semisupervised Ordinal Regression Based on Empirical Risk Minimization

Ordinal regression is aimed at predicting an ordinal class label. In this letter, we consider its semisupervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor. There are several metrics to evaluate the performance of ordinal regression, such as the mean absolute error, mean zero-one error, and mean squared error. However, the existing studies do not take the evaluation metric into account, restrict model choice, and have no theoretical guarantee. To overcome these problems, we propose a novel generic framework for semisupervised ordinal regression based on the empirical risk minimization principle that is applicable to optimizing all of the metrics mentioned above. In addition, our framework has flexible choices of models, surrogate losses, and optimization algorithms without the common geometric assumption on unlabeled data such as the cluster assumption or manifold assumption. We provide an estimation error bound to show that our risk estimator is consistent. Finally, we conduct experiments to show the usefulness of our framework.
Forensic examination of effects of Parkinsonism on various handwriting characteristics

Parkinsonism is a neurodegenerative syndrome that causes impairment of motor skills in affected persons. Thus, adverse effects may be produced in the handwriting of persons suffering from Parkinsonism. Medication used for the treatment of Parkinsonism is known to subside certain motor defects for specific time intervals, showing slight differences or improvement in certain handwriting characteristics during those intervals on the same day as compared to the ones executed before medication. Certain handwriting characteristics affected due to Parkinsonism may be mistaken as forged features due to poor line quality, which can cause suspicion upon the authenticity of important legal documents. The present research work has been carried out to determine the effects of Parkinsonism and medication used for its treatment on handwriting. Handwriting/signature samples executed before and after the onset of Parkinsonism (both pre- and post-medication) have been randomly collected from 70 participants. These handwritings have been evaluated separately and compared inter-se for various handwriting characteristics with qualitative and statistical approach. The results have demonstrated significant changes in most of the characteristics in both affected writings of majority of participants as compared to their corresponding earlier writings. Thus, forensic document experts should be aware of the detrimental effects of Parkinsonism on handwriting in pre- and post-medication conditions of this ailment.
Design and validation of robotic bionic eye with multiple flexible ropes parallel mechanism inspired by oculomotor law

Producing a stable and agile bionic eye for visual image acquisition in robotics is a challenging task. In this paper, we design a bionic eye with mirror-symmetric distribution and cross-connection of flexible ropes. This mechanism is based on oculomotor law and the physiological structure of the extraocular muscles (EOMs). Specifically, the basic structural parameters are determined by Listing’s law, and the unique connection of the flexible ropes can realize the functions of the recti and oblique muscles. Furthermore, to mimic the trochlea structure, a pulley mechanism is constructed to permit the free movement of the flexible ropes. Through simulation and physical experiments, it is demonstrated that the bionic eye mechanism can move with agility under the structural parameters. The experimental results indicate that the proposed bionic eye mechanism has a superior motion accuracy of 2.798 mm, which is 6.7% of the maximum motion distance, and the repeatable accuracy of the mechanism can up to 0.210 mm.
Internet of Things (IoT)

Technology today is an inevitable necessity for organization productivity, development of industries, economic growth, increment in a human development capacity, and livelihood improvement. Also, the advent of internet connection in technology has made it the cornerstone for every industry and a necessary fulcrum for effectiveness, efficiency and optimality. Among several industries, the construction industry is a formidable pillar in which construction projects are directly proportional to the economic development of countries. However, the construction industry has been faced with inadequacies in acquisition, management, utilization and forecasting of construction data, which has led to fluctuated productivity and inconsistent business value. Therefore, with the advent of several technologies, the Internet of Things (IoT) technology can recognise, configure, and transform data to optimize construction operations, leading to high productivity, increased business value, and cost-effectiveness.
Height Prediction and Refinement From Aerial Images With Semantic and Geometric Guidance

Deep learning provides a powerful new approach to many computer vision tasks. Height prediction from aerial images is one of those tasks which benefited greatly from the deployment of deep learning, thus replacing traditional multi-view geometry techniques. This manuscript proposes a two-stage approach to solve this task, where the first stage is a multi-task neural network whose main branch is used to predict the height map resulting from a single RGB aerial input image, while being augmented with semantic and geometric information from two additional branches. The second stage is a refinement step, where a denoising autoencoder is used to correct some errors in the first stage prediction results, producing a more accurate height map. Experiments on two publicly available datasets show that the proposed method is able to outperform state-of-the-art computer vision based and deep learning-based height prediction methods. Code is publicly available at : https://github.com/melhousni/DSMNet.
Shoot Down Drones with Your Answer, an Integration of a Questionnaire into a VR Experience

In this study, we propose the method of integrating a questionnaire into a VR experience. From the result of comparative experiment, our proposed method can alleviate the effect of transition from the VR experience to the questionnaire and maintain the presence in VR until measure.
Exploring Dynamic Task Loading in SGX-based Distributed Computing

Nowadays, data privacy is one of the most critical concerns in cloud computing, and many privacy-preserving distributed computing systems based on the trusted execution environment (e.g., Intel SGX) have been proposed to protect the user's privacy during cloud-outsourced computation. However, these SGX-based solutions are vulnerable to some traffic analyses, and loading all tasks into the enclave introduces much overhead for frequent EPC-paging. In this paper, we propose a T-SGX framework, which keeps the confidentiality of a distributed job and guarantees the system efficiency by allowing dynamically loading an enclave shared object for the task under processing. In T-SGX, all these objects are secretly shared and stored in a verifiably distributed share management system (SMS) outside the TCB. To mitigate the exposure of sensitive information, we present an efficient oblivious transfer (OT) protocol under the Decisional Diffie-Hellman (DDH) assumption for obliviously transmitting desired shares. Detailed security analysis demonstrates that the proposed T-SGX achieves the goal of secure distributed computing without privacy leakage to unauthorized parties. Finally, we benchmark the framework in six real-world applications, and the experimental results show that T-SGX significantly outperforms a state-of-the-art solution, with 11.9%-29.7% less overhead performing an SGX-based application.
An Interpretable Lane Change Detector Algorithm based on Deep Autoencoder Anomaly Detection

In this paper, we address the challenge of employing Machine Learning (ML) algorithms in safety critical driving functions. Despite ML algorithms demonstrating good performance in various driving tasks, e.g., detecting when other vehicles are going to change lanes, the challenge of validating these methods has been neglected. To this end, we introduce an interpretable Lane Change Detector (LCD) algorithm which takes advantage of the performance of modern ML-based anomaly detection methods. We independently train three Deep Autoencoders (DAEs) on different driving maneuvers: lane keeping, right lane changes, and left lane changes. The lane changes are subsequently detected by observing the reconstruction errors at the output of each DAE. Since the detection is purely based on the reconstruction errors of independently trained DAEs, we show that the classification outputs are completely interpretable. We compare the introduced algorithm with black-box Recurrent Neural Network (RNN)-based classifiers, and train all methods on realistic highway driving data. We discuss both the costs and the benefits of an interpretable classification, and demonstrate the inherent interpretability of the algorithm.
Intrinsic dimension estimation method based on correlation dimension and kNN method

In practical problems, high-dimensional data usually has a low-dimensional structure, or the data is located on a low-dimensional manifold. The dimension of this manifold is called the intrinsic dimension of the data. There are many intrinsic dimension estimation methods, among which methods based on the correlation dimension have received extensive attention. However, correlation dimension-based estimation methods often provide a dimension lower than the true intrinsic dimension of the dataset. To explore the reasons behind underestimation, the probabilities of underestimation, overestimation and proper estimation are analyzed using order statistics. The analysis results show that the probability of underestimation is much higher than that of the other two cases, and is verified by simulation experiments. Based on the above analysis, a new method for the estimation of the intrinsic dimension is proposed based on the correlation dimension and k -nearest neighbor method (kNN), which effectively reduces the underestimation. This method is implemented using two algorithms, namely a search algorithm and a matching algorithm. Comprehensive experimental studies on simulation datasets and real datasets show that the proposed algorithms are more effective than the comparison methods.
WebKE

The World Wide Web contains rich up-to-date information for knowledge graph construction. However, most current relation extraction techniques are designed for free text and thus do not handle well semi-structured web content. In this paper, we propose a novel multi-phase machine reading framework, called WebKE. It processes the web content on different granularity by first detecting areas of interest at DOM tree node level and then extracting relational triples for each area. We also propose HTMLBERT as an encoder the web content. It is a pre-trained markup language model that fully leverages the visual layout information and DOM-tree structure, without the need of hand engineered features. Experimental results show that the proposed approach outperforms state-of- the-art methods by a considerable gain. The source code is available at https://github.com/redreamality/webke.
Interpreting Convolutional Sequence Model by Learning Local Prototypes with Adaptation Regularization

In many high-stakes applications of machine learning models, outputting only predictions or providing statistical confidence is usually insufficient to gain trust from end users, who often prefer a transparent reasoning paradigm. Despite the recent encouraging developments on deep networks for sequential data modeling, due to the highly recursive functions, the underlying rationales of their predictions are difficult to explain. Thus, in this paper, we aim to develop a sequence modeling approach that explains its own predictions by breaking input sequences down into evidencing segments (i.e., sub-sequences) in its reasoning. To this end, we build our model upon convolutional neural networks, which, in their vanilla forms, associates local receptive fields with outputs in an obscure manner. To unveil it, we resort to case-based reasoning, and design prototype modules whose units (i.e., prototypes) resemble exemplar segments in the problem domain. Each prediction is obtained by combining the comparisons between the prototypes and the segments of an input. To enhance interpretability, we propose a training objective that delicately adapts the distribution of prototypes to the data distribution in latent spaces, and design an algorithm to map prototypes to human-understandable segments. Through extensive experiments in a variety of domains, we demonstrate that our model can achieve high interpretability generally, together with a competitive accuracy to the state-of-the-art approaches.
Multimodal Continuous Visual Attention Mechanisms

Visual attention mechanisms are a key component of neural network models for computer vision. By focusing on a discrete set of objects or image regions, these mechanisms identify the most relevant features and use them to build more powerful representations. Recently, continuous-domain alternatives to discrete attention models have been proposed, which exploit the continuity of images. These approaches model attention as simple unimodal densities (e.g. a Gaussian), making them less suitable to deal with images whose region of interest has a complex shape or is composed of multiple non-contiguous patches. In this paper, we introduce a new continuous attention mechanism that produces multimodal densities, in the form of mixtures of Gaussians. We use the EM algorithm to obtain a clustering of relevant regions in the image, and a description length penalty to select the number of components in the mixture. Our densities decompose as a linear combination of uni-modal attention mechanisms, enabling closed-form Jacobians for the backpropagation step. Experiments on visual question answering in the VQA-v2 dataset show competitive accuracies and a selection of regions that mimics human attention more closely in VQA-HAT, substantiating the impact of the structure induced by our visual prior. We present several examples that suggest how multimodal attention maps are naturally more interpretable than their unimodal counterparts, showing the ability of our model to automatically segregate objects from ground in complex scenes.
Solar Power Plant Suggestion for Charging Electric Vehicles and The Effects of The System on The Electric Network and CO2 Emission

In this study, the solar energy systems to be applied in parking lots that can contribute to the solution of the energy supply problem that will be encountered for these vehicles operating with electrical energy, considering that the number of electric vehicles, which are rapidly spreading all over the world and increasing their importance in Turkey, especially in recent years, will increase rapidly in the coming years, has been examined with various designs. As a result of this examination, the advantages and disadvantages of this application, which can be considered for energy supply to electric vehicles, have been revealed. Apart from this, since the utilization rate of fossil fuels in electricity production in Turkey is high, it has been shown how the greenhouse gas rates will decrease and the benefits that will emerge as a result of providing energy from renewable energy sources instead of fossil fuels for these vehicles operating with electrical energy. In addition, the greenhouse gas emission of electric vehicles was evaluated not only according to the production of the electric motor, but also according to the resources used by Turkey for electricity generation and production, and the greenhouse gas emission amounts of electric vehicles resulting from this energy consumption were examined.
Systematic review and exploration of new avenues for sorting algorithm

Sorting plays a crucial role in almost all algorithms that support data science applications. A quadratic sorting algorithm is one in which the time taken for the algorithm to sort a dataset grows as the square of the size of the dataset. This paper aims at developing a new quadratic sorting algorithm by addressing the limitations in existing sorting algorithms. We use the idea that an unsorted data sequence can be thought of as a set of disjoint sorted sequences of data items. This paper describes a novel approach to provide a solution towards an unsorted sequence of data items. We have compared our method with existing quadratic sorting algorithms to demonstrate its efficiency over other techniques.
Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks

The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.
DVMN: Dense Validity Mask Network for Depth Completion

LiDAR depth maps provide environmental guidance in a variety of applications. However, such depth maps are typically sparse and insufficient for complex tasks such as autonomous navigation. State of the art methods use image guided neural networks for dense depth completion. We develop a guided convolutional neural network focusing on gathering dense and valid information from sparse depth maps. To this end, we introduce a novel layer with spatially variant and content-depended dilation to include additional data from sparse input. Furthermore, we propose a sparsity invariant residual bottleneck block. We evaluate our Dense Validity Mask Network (DVMN) on the KITTI depth completion benchmark and achieve state of the art results. At the time of submission, our network is the leading method using sparsity invariant convolution.
Streamlined NTRU Prime on FPGA

Abstract We present a novel full hardware implementation of Streamlined NTRU Prime, with two variants: a high-speed, high-area implementation and a slower, low-area implementation. We introduce several new techniques that improve performance, including a batch inversion for key generation, a high-speed schoolbook polynomial multiplier, an NTT polynomial multiplier combined with a CRT map, a new DSP-free modular reduction method, a high-speed radix sorting module, and new encoders and decoders. With the high-speed design, we achieve the to-date fastest speeds for Streamlined NTRU Prime, with speeds of 5007, 10,989, and 64,026 cycles for encapsulation, decapsulation, and key generation, respectively, while running at 285 MHz on a Xilinx Zynq Ultrascale+. The entire design uses 40,060 LUT, 26,384 flip-flops, 36.5 Bram, and 31 DSP.
Off-chain management and state-tracking of smart programs on blockchain for secure and efficient decentralized computation

In the last decade various crypto-currencies opened new doors to a decentralized electronic monetary system. Emergence of new Blockchain platforms, such as Ethereum, introduced new features for the implementation of decentralized software or smart contracts. The applications of blockchain technology for secure and efficient information systems is rapidly increasing. However, due to the limitations of this platform including high computational and storage cost, it is not possible to run sophisticated and computationally expensive software on the blockchain. In this paper, a new framework for execution of smart programs on blockchain called the Smart Program Runner Framework (SPRF) is proposed. The proposed framework works by moving decentralized applications to the off-chain and only storing the hash of the state of the application on-chain. This framework can be implemented by creating a side-chain to an existing blockchain without any modifications. The proposed framework is implemented on Stellar blockchain and has various applications for secure and efficient information systems and can provide the required platform for execution of computationally demanding software on blockchain.
Cybersecurity Policy Compliance in Higher Education: A Theoretical Framework

HEIs Higher education institutions (HEIs) are open in their nature, decentralized, rich in population and private data, home to some ground-breaking research and innovations. They attract quite a substantial threat and cyberattacks due to their heavy reliance on the Internet. However, there is poor cybersecurity culture and low awareness that are motivated by generational differences that are characterized by “digital natives” and “digital residents” and gaps in technology savviness impacting cybersecurity compliance in specific higher education environments. The understanding of cybersecurity and organizational culture, compliance theory and protected motivated theory (PMT) respectively is key to achieving cybersecurity compliance.
Enhancing predictive maintenance architecture process by using ontology-enabled Case-Based Reasoning

A common milestone in systems architecture development is the logical architecture. It provides a detailed overview of the system components and their interfaces but keeps the architecture as generic as possible, meaning that no component is bound to a specific technology. Subsequently, the architect searches for physical/informational components to fulfill the logical architecture and can apply structured creativity to look for innovative solutions. This search can turn out to be a difficult and long-lasting task depending on the system complexity. Too many options may be available to fulfill the logical system components and not always the most suitable ones are identified. This problem is for instance encountered in the design of new predictive maintenance systems, especially when selecting the components to carry out the diagnostics and prognostics. The current study proposes to support the choice of suitable components combining case-based reasoning and ontologies. A domain ontology has been developed as a terminology framework to support the case base, case structure and similarity measures for a case-based reasoning Decision Support System (DSS). The DSS uses attributes of the new problem to solve and suggests the most similar cases from past experiences. The retrieved solutions can be adapted to develop a new predictive maintenance architecture. The decision support system has been tested with data coming from proved predictive maintenance solutions documented in scientific publications.
Classification of Solar Radio Spectrum Based on VGG16 Transfer Learning

Solar radio bursts are an important part of the study of solar activity, and automatic classification of solar radio spectrum can greatly improve the efficiency of solar activity research. Based on the preprocessing of the original solar radio spectrum images, this paper proposes a solar radio spectrum images classification method based on the VGG16 convolutional neural network and transfer learning. In this method, the pre-trained VGG model is applied to solar radio spectrum recognition. Trained on the generated target data set and adjusted the parameters. The experimental results show that compared with the traditional manual classification method and the existing deep learning classification method, the VGG16 transfer learning classification shows that the TPR of the solar radio burst is better than before. The situation has increased by 12.2%. For the overall classification result analysis, the experimental effect is greatly improved on the basis of the original classification.