Reviewerly/exHarmony huggingface dataset:
Intraoperative Use of Mixed Reality Technology in Median Neck and Branchial Cyst Excision

The paper considers the possibilities, prospects, and drawbacks of the mixed reality (MR) technology application using mixed reality smartglasses Microsoft HoloLens 2. The main challenge was to find and develop an approach that would allow surgeons to conduct operations using mixed reality on a large scale, reducing the preparation time required for the procedure and without having to create custom solutions for each patient. Research was conducted in three clinical cases: two median neck and one branchial cyst excisions. In each case, we applied a unique approach of hologram positioning in space based on mixed reality markers. As a result, we listed a series of positive and negative aspects related to MR surgery, along with proposed solutions for using MR in surgery on a daily basis.
BRISK: Dynamic Encryption Based Cipher for Long Term Security

Several emerging areas like the Internet of Things, sensor networks, healthcare and distributed networks feature resource-constrained devices that share secure and privacy-preserving data to accomplish some goal. The majority of standard cryptographic algorithms do not fit with these constrained devices due to heavy cryptographic components. In this paper, a new block cipher, BRISK, is proposed with a block size of 32-bit. The cipher design is straightforward due to simple round operations, and these operations can be efficiently run in hardware and suitable for software. Another major concept used with this cipher is dynamism during encryption for each session; that is, instead of using the same encryption algorithm, participants use different ciphers for each session. Professor Lars R. Knudsen initially proposed dynamic encryption in 2015, where the sender picks a cipher from a large pool of ciphers to encrypt the data and send it along with the encrypted message. The receiver does not know about the encryption technique used before receiving the cipher along with the message. However, in the proposed algorithm, instead of choosing a new cipher, the process uses the same cipher for each session, but varies the cipher specifications from a given small pool, e.g., the number of rounds, cipher components, etc. Therefore, the dynamism concept is used here in a different way.
IDEA: A utility-enhanced approach to incomplete data stream anonymization

The prevalence of missing values in the data streams collected in real environments makes them impossible to ignore in the privacy preservation of data streams. However, the development of most privacy preservation methods does not consider missing values. A few researches allow them to participate in data anonymization but introduce extra considerable information loss. To balance the utility and privacy preservation of incomplete data streams, we present a utility-enhanced approach for Incomplete Data strEam Anonymization (IDEA). In this approach, a slide-window-based processing framework is introduced to anonymize data streams continuously, in which each tuple can be output with clustering or anonymized clusters. We consider the dimensions of attribute and tuple as the similarity measurement, which enables the clustering between incomplete records and complete records and generates the cluster with minimal information loss. To avoid the missing value pollution, we propose a generalization method that is based on maybe match for generalizing incomplete data. The experiments conducted on real datasets show that the proposed approach can efficiently anonymize incomplete data streams while effectively preserving utility.
Scaled ReLU Matters for Training Vision Transformers

Vision transformers (ViTs) have been an alternative design paradigm to convolutional neural networks (CNNs). However, the training of ViTs is much harder than CNNs, as it is sensitive to the training parameters, such as learning rate, optimizer and warmup epoch. The reasons for training difficulty are empirically analysed in the paper Early Convolutions Help Transformers See Better, and the authors conjecture that the issue lies with the patchify-stem of ViT models. In this paper, we further investigate this problem and extend the above conclusion: only early convolutions do not help for stable training, but the scaled ReLU operation in the convolutional stem (conv-stem) matters. We verify, both theoretically and empirically, that scaled ReLU in conv-stem not only improves training stabilization, but also increases the diversity of patch tokens, thus boosting peak performance with a large margin via adding few parameters and flops. In addition, extensive experiments are conducted to demonstrate that previous ViTs are far from being well trained, further showing that ViTs have great potential to be a better substitute of CNNs.
Effective solution for underwater image enhancement

Degradation of underwater images severely limits people to exploring and understanding underwater world, which has become a fundamental but vital issue needing to be addressed in underwater optics. In this paper, we develop an effective solution for underwater image enhancement. We first employ an adaptive-adjusted artificial multi-exposure fusion (A-AMEF) and a parameter adaptive-adjusted local color correction (PAL-CC) to generate a contrast-enhanced version and a color-corrected version from the input respectively. Then we put the contrast enhanced version into the famous guided filter to generate a smooth base-layer and a detail-information containing detail-layer. After that, we utilize the color channel transfer operation to transfer color information from the color-corrected version to the base-layer. Finally, the color-corrected base-layer and the detail-layer are added together simply to reconstruct the final enhanced output. Enhanced results obtained from the proposed solution performs better in visual quality, than those dehazed by some current techniques through our comprehensive validation both in quantitative and qualitative evaluations. In addition, this solution can be also utilized for dehazing fogged images or improving accuracy of other optical applications such as image segmentation and local feature points matching.
SNR: <u>S</u> queezing <u>N</u> umerical <u>R</u> ange Defuses Bit Error Vulnerability Surface in Deep Neural Networks

As deep learning algorithms are widely adopted, an increasing number of them are positioned in embedded application domains with strict reliability constraints. The expenditure of significant resources to satisfy performance requirements in deep neural network accelerators has thinned out the margins for delivering safety in embedded deep learning applications, thus precluding the adoption of conventional fault tolerance methods. The potential of exploiting the inherent resilience characteristics of deep neural networks remains though unexplored, offering a promising low-cost path towards safety in embedded deep learning applications. This work demonstrates the possibility of such exploitation by juxtaposing the reduction of the vulnerability surface through the proper design of the quantization schemes with shaping the parameter distributions at each layer through the guidance offered by appropriate training methods, thus delivering deep neural networks of high resilience merely through algorithmic modifications. Unequaled error resilience characteristics can be thus injected into safety-critical deep learning applications to tolerate bit error rates of up to <?TeX $10\%$?> at absolutely zero hardware, energy, and performance costs while improving the error-free model accuracy even further.
Graph convolutional networks: analysis, improvements and results

Abstract A graph can represent a complex organization of data in which dependencies exist between multiple entities or activities. Such complex structures create challenges for machine learning algorithms, particularly when combined with the high dimensionality of data in current applications. Graph convolutional networks were introduced to adopt concepts from deep convolutional networks (i.e. the convolutional operations/layers) that have shown good results. In this context, we propose two major enhancements to two of the existing graph convolutional network frameworks: (1) topological information enrichment through clustering coefficients; and (2) structural redesign of the network through the addition of dense layers. Furthermore, we propose minor enhancements using convex combinations of activation functions and hyper-parameter optimization. We present extensive results on four state-of-art benchmark datasets. We show that our approach achieves competitive results for three of the datasets and state-of-the-art results for the fourth dataset while having lower computational costs compared to competing methods.
A wireless controlled intelligent healthcare system for diplegia patients

Rehabilitation engineering is playing a more vital role in the field of healthcare for humanity. It is providing many assistive devices to diplegia patients (The patients whose conditions are weak in terms of muscle mobility on both sides of the body and their paralyzing effects are high either in the arms or in the legs). Therefore, in order to rehabilitate such types of patients, an intelligent healthcare system is proposed in this research. The electric sticks and chairs are also a type of this system which was used previously to facilitate the diplegia patients. It is worth noting that a voice recognition system along with wireless control feature has been integrated intelligently in the proposed healthcare system in order to replace the common and conventional assistive tools for diplegia patients. These features will make the proposed system more user friendly, convenient and comfortable. The voice recognition system has been used for movements of system in any desired direction along with the ultrasonic sensor and light detecting technology. These sensors detect the obstacles and low light environment intelligently during the movement of the wheelchair and then take the necessary actions accordingly.
Trade Openness, Foreign Direct Investment and Sustainable Agriculture in Africa

This study applied a panel data of 37 African countries in examining the impact of trade openness and foreign direct investment on sustainable agriculture towards the attainment of the United Nation (UN) Sustainable Development Goals (SDGs), especially, SDG-2, with the aim of ending extreme hunger, achieve food security and improve nutrition and promote sustainable agriculture. Data for the study was sourced from the Country Policy and Institutional Assessment (CPIA) and World Development Indicators (WDI) of the World Bank, for the period 2005 – 2019. To control for endogeneity, the study engaged the system Generalised Method of Moments (GMM). The result shows that FDI and trade openness have significant negative impact on agricultural sustainability in Africa. This result implies that, increase in FDI may decrease agricultural sustainability by 0.00294%, while increase in trade openness may lower agricultural sustainability by 0.430066 %. Therefore, the study concludes that while trade openness is negative, policy to raise local production towards export promotion should be encouraged. In addition, FDI should be encouraged to augment local employment and investment towards increasing output and productivity in the Africa region.
Digitized adiabatic quantum factorization

Quantum integer factorization is a potential quantum computing solution that may revolutionize cryptography. Nevertheless, a scalable and efficient quantum algorithm for noisy intermediate-scale quantum computers looks far-fetched. We propose an alternative factorization method, within the digitized-adiabatic quantum computing paradigm, by digitizing an adiabatic quantum factorization algorithm enhanced by shortcuts to adiabaticity techniques. We find that this fast factorization algorithm is suitable for available gate-based quantum computers. We test our quantum algorithm in an IBM quantum computer with up to six qubits, surpassing the performance of the more commonly used factorization algorithms on the long way towards quantum advantage.
An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning

A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. That makes the teaching and learning task cumbersome for both parties. An intelligent process of students’ evaluation can make learning and teaching easier for both students and teachers. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. In our case, the task is about recognizing the exact pronunciation of Arabic alphabets according to the standards. A major challenge in the recognition of precise pronunciation of Arabic alphabets is the correct identification of a large number of short vowels, which cannot be dealt with using traditional statistical audio processing techniques and machine learning models. Therefore, we developed a model that classifies Arabic short vowels using Deep Neural Networks (DNN). The model is constructed from scratch by: (i) collecting a new audio dataset, (ii) developing a neural network architecture, and (iii) optimizing and fine-tuning the developed model through several iterations to achieve high classification accuracy. Given a set of unseen audio samples of uttered short vowels, our proposed model has reached the testing accuracy of 95.77%. We can say that our results can be used by the experts and researchers for building better intelligent learning support systems in Arabic speech processing.
Block Modeling-Guided Graph Convolutional Neural Networks

Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modelling into the framework of GCN so that it can realize “block-guided classified aggregation”, and automatically learn the corresponding aggregation rules for neighbors of different classes. By incorporating block modelling into the aggregation process, GCN is able to automatically aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree. We compared our algorithm with state-of-art methods which deal with the heterophily problem. Empirical results demonstrate the superiority of our new approach over existing methods in heterophilic datasets while maintaining a competitive performance in homophilic datasets.
SciCap: Generating Captions for Scientific Figures

Researchers use figures to communicate rich, complex information in scientific papers.The captions of these figures are critical to conveying effective messages.However, low-quality figure captions commonly occur in scientific articles and may decrease understanding.In this paper, we propose an end-to-end neural framework to automatically generate informative, high-quality captions for scientific figures.To this end, we introduce SCICAP, 1 a largescale figure-caption dataset based on computer science arXiv papers published between 2010 and 2020.After pre-processing -including figure-type classification, sub-figure identification, text normalization, and caption text selection -SCICAP contained more than two million figures extracted from over 290,000 papers.We then established baseline models that caption graph plots, the dominant (19.2%) figure type.The experimental results showed both opportunities and steep challenges of generating captions for scientific figures.
Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm

Recent progress in novel non-volatile memory-based synaptic device technologies and their feasibility for matrix-vector multiplication (MVM) has ignited active research on implementing analog neural network training accelerators with resistive crosspoint arrays. While significant performance boost as well as area- and power-efficiency is theoretically predicted, the realization of such analog accelerators is largely limited by non-ideal switching characteristics of crosspoint elements. One of the most performance-limiting non-idealities is the conductance update asymmetry which is known to distort the actual weight change values away from the calculation by error back-propagation and, therefore, significantly deteriorates the neural network training performance. To address this issue by an algorithmic remedy, Tiki-Taka algorithm was proposed and shown to be effective for neural network training with asymmetric devices. However, a systematic analysis to reveal the required asymmetry specification to guarantee the neural network performance has been unexplored. Here, we quantitatively analyze the impact of update asymmetry on the neural network training performance when trained with Tiki-Taka algorithm by exploring the space of asymmetry and hyper-parameters and measuring the classification accuracy. We discover that the update asymmetry level of the auxiliary array affects the way the optimizer takes the importance of previous gradients, whereas that of main array affects the frequency of accepting those gradients. We propose a novel calibration method to find the optimal operating point in terms of device and network parameters. By searching over the hyper-parameter space of Tiki-Taka algorithm using interpolation and Gaussian filtering, we find the optimal hyper-parameters efficiently and reveal the optimal range of asymmetry, namely the asymmetry specification . Finally, we show that the analysis and calibration method be applicable to spiking neural networks.
An adaptive high capacity reversible data hiding algorithm in interpolation domain

Reversible data hiding (RDH) algorithms using interpolation technology have the advantage of high capacity for single-layer embedding. However, existing algorithms usually embed secret data in the non-reference pixels based on simple additions without considering the properties of context-pixels, which results in that the visual quality of watermarked image is not optimal. To deal with this issue, a novel adaptive high capacity RDH algorithm is proposed. Firstly, a location-dependent weighted image interpolation method is proposed to improve the visual quality of the interpolated image. Then, the interpolated image is divided into overlapping blocks, which are sorted in an ascending order according to their standard deviations; secret data is preferentially embedded in the blocks with small deviation so that image quality is not distorted too much. Finally, to enhance embedding capacity as much as possible, the amount of secret data bits that can be embedded in non-reference pixels is calculated adaptively. We theoretically prove that the embedded secret data can be extracted correctly and there is no pixel overflow or underflow problem. Extensive experimental results showed that the proposed algorithm outperforms several state-of-the-art algorithms. In addition, our algorithm can resist steganalysis attacks, and demonstrated the effectiveness of the proposed algorithm.
Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot's mission in many cases depends on the properties of the robot's sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot's sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.
Academic Certificate Fraud Detection System Framework Using Blockchain Technology

The era of rapid disruption was accompanied by increased crime in data falsification and interception. The philosophy of Blockchain technology is related to its ontology; it can be characterized, applied, and realized by how it works in the world of education. Blockchain technology is used to verify the authenticity of academic degrees and certificates. Systems that use digital signature schemes and implementation timestamps in the development of blockchain technology use blockchain software. The impact of falsifying educational documents results in workers not being qualified for a particular position. The main interest of this service is to balance between graduates and employers by minimizing crime and falsifying educational documents. The method used in this research is a literature study from previous studies. As a result of maximizing the potential of blockchain technology, fees can also lead to checking the authenticity of certificates through trusted sources. The application and utilization of blockchain in the academy certificate fraud detection system can be a means of offering problems that exist from a global perspective and other stakeholders.