Reviewerly/exHarmony huggingface dataset:
Assessment of Composite Aluminum Adhesive Joints Using Digital Image Correlation

Traditional nondestructive evaluation (NDE) methods present significant challenges to detecting and characterizing kissing or weak bonds in adhesively bonded structures. These kissing or weak bonds also cannot transmit shear stresses or handle complex loading modes and, if not detected, can present a significant threat to the structural integrity of the components or systems. This paper demonstrates the digital image correlation (DIC) technique for evaluating adhesively bonded dissimilar materials joints subjected to kissing or weak bonds. The study employed four adhesively bonded carbon fiber reinforced plastics and aluminum (CFRP-Al) lap-shear test coupons with varied bond quality (i.e., with no contamination and three simulated kissing bond defects). The novelty of the approach presented in this paper was that this technique could detect and demonstrate changes in the normal strain (εyy) contour map of the contaminated specimens at relatively lower load levels. This load level corresponds to 15% of the failure load for the silicone and hydraulic oil contaminated sample and around 30% for the polyvinyl alcohol (PVA) contaminated sample. In addition, higher compressive strains along the overlap edges were observed in the strain map for the single lap joints due to the higher peeling stresses of the adherend and the stress concentration at the edges of an adhesively bonded joint.
The 44th European Conference on Visual Perception (ECVP) 2022, Nijmegen, The Netherlands

Restricted accessAbstractFirst published online December 21, 2022The 44th European Conference on Visual Perception (ECVP) 2022, Nijmegen, The NetherlandsVolume 51, Issue 1_supplhttps://doi.org/10.1177/03010066221141167
Improving face recognition using deep autoencoders and feature fusion

Uncontrolled environments are the main challenges of real face recognition systems, recent success of deep learning and features fusion has led to various performance improvements. This paper proposes a novel scheme called feature autoencoder (FAE), where an autoencoder model is not trained directly from the raw facial images, rather it uses a fusion of features constructed by Gabor filter, local binary pattern and local phase quantisation. For each feature, a linear discriminant analysis is applied to reduce its high dimensionality and a limited adaptive histogram equalisation process is employed for contrast enhancement. The proposed scheme has been evaluated using known datasets such as AR, ORL and YALE, and the experimental results carried out on these databases have been compared using three classifiers: k-nearest neighbour, multiclass support vector machine and softmax classifier, demonstrating the effectiveness of proposed approach and parameters. The experimental results obtained and compared with recent and similar approaches on six databases: ORL, YALE, AR, extended YALE B, CMU PIE, and LFWcrop, suggest that the proposed technique outperforms similar techniques. The recognition rates got from them are 100%, 100%, 99.66%, 99.40%, 97.31%, and 90.68% respectively.
Reflection Of the Integrative Relationship Between Six Sigma Technology Standards and Internal Control Elements According to The Coso Framework on Improving Performance Quality

In terms of the type of services, how customers evaluate services, and increasing customer satisfaction, organizations must apply a variety of management approaches and tactics.Six Sigma has been offered as a methodical and effective method for improving service quality and controlling costs.Six Sigma is a methodical and statistical strategy to identifying process performance flaws and reducing error in work processes that take a long time, cost a lot of money, and produce poor outcomes.The purpose of this paper is to investigate the reflection of the integrated relationship between Six Sigma technology standards and internal control elements according to the COSO framework in improving performance quality.This study is a review of the results of various studies that have implemented the Six Sigma approach in organizations, including the effects of this approach in terms of improving the time cycle and service delivery cycle, supply management, improving process management, improving employee scheduling, reducing errors.Saving and continuous training are among the necessities of its implementation.As a result, Six Sigma is a method for optimizing business processes through a systematic approach focused on customer needs and real-time analysis of active operations in each firm.In addition, any technique must be implemented in an organization that has established and accepted a quality culture.Also, quality improvement is a continuous process that is done by cooperation and interaction of working groups and the use of appropriate tools and methodology to solve any specific problem.
Introduction to TensorFlow

TensorFlow is an open source, high-performance machine learning library developed by Google and released for public use in 2015. It has interfaces for Python, C++, and Java programming languages. It has the option of running on multiple CPUs or GPUs. TensorFlow offers two modes of execution: eager mode that can be run immediately and graph mode that creates a dependency graph and executes nodes in that graph only where needed.
ShortcutFusion++: Optimizing an End-to-End CNN Accelerator for High PE Utilization

ShorcutFusion [1] is an end-to-end framework that effectively maps many well-known deep neural networks (DNNs), such as MobileNet-v2, EfficientNet-B0, ResNet-50, and YOLO-v3, to a generic CNN accelerator on FPGA. Nevertheless, its processing elements are not fully utilized when supporting various networks, leading to relatively low hardware utilization (e.g., 68.42% for YOLO-v3). This study aimed to enhance the performance of ShortcutFusion and introduce ShortcutFusion++ by proposing two simple but effective techniques for eliminating unnecessary stalls in conventional design. First, the prefetching scheme was re-designed to avoid bubble cycles when feeding data to the PE array. Second, the output buffer was reconstructed to pipeline the operations of PEs and the process of writing output feature maps to off-chip memory. The experimental results show that ShortcutFusion++ achieves a PE utilization of 80.95% for the well-known object detection network YOLO-v3, outperforming its baseline by 12.53%.
A Comprehensive Comparison Between Pre-trained and Custom Trained Object Detection Model for Indian Traffic Scenarios

Artificial intelligence is an emerging field that is undergoing humongous development. Self-driving technology is a major part of that development. Various companies like Tesla, Waymo, Ford, and Audi are part of research and development in this field. The modern self-driving vehicles that work on Level 5 autonomy utilize lidar technology along with computer vision frameworks to get a complete autonomous driving experience. These companies are based in the USA or well-developed countries. Their implementation in India, which is a developing country, is to be determined. To get a fair idea of how these vehicles and their technology will perform in the Indian scenario, a testing plan is devised in this work. In this paper, a YOLO object detection model was custom trained on images taken in the Indian traffic scenario and compared with a pre-trained model which has been trained on COCO dataset which contains traffic images from various foreign countries like the USA and Europe. The comparison has been done on the metrics like accuracy in detection of classes, precision, recall, and F1-score.
A Study on Sentiment Analysis of Twitter Data in Marathi Language for Measuring Depression

Sentiment analysis is nothing but an information retrieval system based on natural language processing (NLP). It is a machine learning algorithm. The rudimentary prototype of sentiment analysis comes under the classifier problem where the negative, positive, and neutral are the classes we would be expecting as an output, given text (tweets in our case) as an input. Feature selection methods (information gain and chi square) and feature extraction will be performed to get the output sentiment. After studying three classifiers, support vector machine (SVM), K-nearest neighbour (KNN), and Naive Bayes, the latter algorithm, i.e. Naive Bayes being most accurate among them would be considered for implementation. As mentioned, we would be focusing on major three output sentiment classes: positive, negative, and neutral. Further, we are getting into a detailed analysis of tweets reflecting a depressive state of mind due to sadness. Though our ultimate goal is to predict the mental health of a user by observing the consistency and frequency of tweets along with its sentiment analysis, for now, we are just dealing with one tweet per user at a time.
Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction

Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named $$C ^2RM $$ , which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our $$C ^2RM $$ can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.
Regulation of educational activities as a factor in ensuring the quality of higher education under restrictions (COVID-19)

The article presents the results of the study of standard-setting activities of higher education institutions (certain aspects of said activities under the conditions of the pandemic). The authors conclude that the conditions of the pandemic and the transition to remote work determined the need for corrections in the legislation governing the activities of educational institutions to maintain the level of quality of higher education. In particular, due to students’ mass transition to distance learning, there is a need to create quality standards for distance education and the attestation procedure for universities using blended and distance learning.
Securing Operating Systems Through Fine-Grained Kernel Access Limitation for IoT Systems

With the development of Internet of Things (IoT), it is gaining a lot of attention. It is important to secure the embedded systems with low overhead. The Linux Seccomp is widely used by developers to secure the kernels by blocking the access of unused syscalls, which introduces less overhead. However, there are no systematic Seccomp configuration approaches for IoT applications without the help of developers. In addition, the existing Seccomp configuration approaches are coarse-grained, which cannot analyze and limit the syscall arguments. In this article, a novel static dependent syscall analysis approach for embedded applications is proposed, which can obtain all of the possible dependent syscalls and the corresponding arguments of the target applications. So, a fine-grained kernel access limitation can be performed for the IoT applications. To this end, the mappings between dynamic library APIs and syscalls according with their arguments are built, by analyzing the control flow graphs and the data dependency relationships of the dynamic libraries. To the best of our knowledge, this is the first work to generate the fine-grained Seccomp profile for embedded applications.
Investigating User Embodiment of Inverse-Kinematic Avatars in Smartphone Augmented Reality

Smartphone Augmented Reality (AR) has already provided us with a plethora of social applications such as Pokemon Go or Harry Potter Wizards Unite. However, to enable smartphone AR for social applications similar to VRChat or AltspaceVR, proper user tracking is necessary to accurately animate the avatars. In Virtual Reality (VR), avatar tracking is rather easy due to the availability of hand-tracking, controllers, and HMD whereas smartphone AR has only the back-(and front) camera and IMUs available for this task. In this paper we propose ARIKA, a tracking solution for avatars in smartphone AR. ARIKA uses tracking information from ARCore to track the users hand position and to calculate a pose using Inverse Kinematics (IK). We compare the accuracy of our system against a commercial motion tracking system and compare both systems with respect to sense of agency, self-location, and body-ownership. For this, 20 participants observed their avatars in an augmented virtual mirror and executed a navigation and a pointing task. Our results show that participants felt a higher sense of agency and self location when using the full body tracked avatar as opposed to IK avatars. Interestingly and in favor of ARIKA, there were no significant differences in body-ownership between our solution and the full-body tracked avatars. Thus, ARIKA and it’s single-camera approach is valid solution for smartphone AR applications where body-ownership is essential.
Model Optimization in Imbalanced Regression

Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare) values. Recently, an evaluation metric was introduced: Squared Error Relevance Area (SERA). This metric posits a bigger emphasis on the errors committed at extreme values while also accounting for the performance in the overall target variable domain, thus preventing severe bias. However, its effectiveness as an optimization metric is unknown. In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used SERA as an objective function are practically better than the models produced by their respective standard boosting algorithms at the prediction of extreme values. This confirms that SERA can be embedded as a loss function into optimization-based learning algorithms for imbalanced regression scenarios.
Lifting Numeric Relational Domains to Algebraic Data Types

We present RAND, an input-output relational abstract domain that expresses relations between values of non-recursive algebraic data types (ADTs), and numeric relations between their scalar parts. RAND is parametrised on a user-provided numeric relational domain, that we lift to pairs of variables and projection paths. It is constructed as a disjunctive completion of a reduced product of domains for numeric relations, for equalities, and for cases of variant constructors. Using RAND, we define a modular, inter-procedural, input-output relational analysis for a while language with ADTs and function calls. The analysis computes function summaries, that describe relations between the inputs of programs and their outputs.
WITS: Wikipedia for Italian Text Summarization

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder.The best performing models also connect the encoder and decoder through an attention mechanism.We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU.On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.* Equal contribution.Listing order is random.Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea.Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work.Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail.Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor.Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations.Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.
Identification of the Radical Component from Images of Chinese Characters

The Chinese writing system is made of pictographic symbols known as hanzi or Han characters which are combinations of single or multiple smaller units in varying degrees of complexity. For every hanzi, there is a fundamental block, namely the radical, which defines its meaning, usage, and how words are organized in the dictionary. Given its importance, this work proposes and demonstrates an automatic radical identifier and localizer of hanzi from images, reaching an average precision of ~78.0% and an average recall of ~74.3% for the 30 radicals with most entries in the dictionary and on a random excerpt of a historical novel.
Plastic Photonic Synapse Based on VCSOA for Self-Learning in Photonic Spiking Neural Network

We propose and demonstrate a plastic photonic synapse to achieve all-optical synaptic plasticity based on the potentiated and depressed dynamics in commercially available vertical-cavity semiconductor optical amplifier (VCSOA). The effects of presynaptic and postsynaptic spike powers, bias current, initial wavelength detuning between presynaptic spike and VCSOA on the potentiated and depressed time windows are investigated. The numerical results show that all-optical synaptic plasticity can be achieved successfully based on VCSOA with delayed self-feedback loop. The proof-of-concept experimental results show that plastic photonic synapse can be achieved based on the VCSOA with subsequent iterations. Besides, the curve of VCSOA gain in the potentiated and depressed time windows is similar to that of spike timing dependent plasticity. Furthermore, all-optical unsupervised self-learning and recognition of first spike timing of an input pattern are realized numerically in the photonic neural network including two proposed plastic photonic synapses. The plastic photonic synapse based on a VCSOA is interesting and valuable for the self-learning in all-optical neural networks. Moreover, the proposed plastic photonic synapse offers great potential for dense, high-performance neuromorphic photonic systems.
Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database

Sentiment analysis (SA), often known as opinion mining, is the subjective examination of a written text. Moreover, SA is a critical technique in today's artificial intelligence (AI) field for extracting emotional information from huge amounts of data. The study is based on the Internet Movie Database (IMDB) dataset, which comprises movie reviews and the positive or negative labels that are connected with them. Our research experiment's objective is to identify the model with the best accuracy and the most generality. Text preprocessing is the first and most critical phase in a Natural Language Processing (NLP) system since it significantly impacts the overall accuracy of the classification algorithms. The experiment implements the Term Frequency-Inverse Document Frequency model (TFIDF) to feature selection and extractions. The following classifiers are used in this work: Linear Model and Naïve Bayes. Besides, we explore the possible options of loss functions such as square_hinge, huber, modified_huber, log, epsilon_insensitive, perceptron, and modified_huber. ComplementNB achieves the highest accuracy, 75.13%, for both classification reports based on our experiment result.
Research on the Management Mode of Library Information Resources Construction Project

With the continuous development of the times, the management mode of the library should keep pace with the times, and the advanced management mode should be applied to improve the management effect, so as to promote the rapid growth and development of the library and meet the current demand of people for knowledge.The knowledge management mode is an advanced management method at present, which can ensure that the knowledge integrated in the library can be comprehensive, meet people's knowledge needs and realize the modernization of the library.How to implement the knowledge management mode is the key research content in the current library management.The methods of knowledge management are studied in order to provide effective suggestions for library management.The value of information resources is different in different life cycle stages.According to the evolution and change characteristics of the life value of library digital information resources, the library can establish a library information resources management mode based on the information life cycle theory, establish a hierarchical digital resources storage standard and service system process, and strive to maximize the efficiency of library storage equipment and the utility of information resources, and improve the library's information service capability.
Embodiment of an Avatar with Unnatural Arm Movements

We developed an “unnatural avatar” of which the lower arms could be bent in reversed/unnatural directions. Participants controlled the avatar in a virtual environment to perform reaching tasks. We measured their senses of body ownership and agency. Compared to a normal avatar, the embodiment towards the unnatural avatar was significantly lower. However, senses of body ownership and agency towards both avatars were higher when the avatar's movements were synchronized with the movements of the participants compared to when there was a delay of one second. These results suggest that visuo- motor synchrony induces embodiment even towards avatars with reversed/unnatural limb movements.