Reviewerly/exHarmony huggingface dataset:
A Method of Learning Rate Range Control for Adam to Suppress Sudden Changes of Parameters in Early Learning Stage

Adam is one of the general optimization algorithms in neural networks. It can accelerate convergence speed while learning. It has, however, two problems. The first is that the final performance of a network trained by Adam, such as generalization ability, becomes worse than the one trained by SGD, in applications to large-scale networks. The second is that values of the learning rate tend to be large at the early learning stage; as a result, the values of network parameters, such as weights and bias, become too large by a first few iterations. In recent years, research has been conducted to solve these problems. AdaBound has been proposed for solving the first problem. This is a method switching dynamically from Adam to SGD. RAdam has also been proposed, for solving the second problem. This applies a method called WarmUp, which sets a small learning rate at the early learning stage and gradually increases it, to Adam. In this study, we propose to apply WarmUp to the upper limit of AdaBound's learning rate. The proposed algorithm prevents parameter updates at extremely large learning rates in the early learning stages. Therefore, more efficient learning can be expected than the conventional method. The proposed method has been applied to learning of some types of networks like CNN, ResNet, DenseNet and BERT. The results show that our method has improved performance compared to the traditional method, and an image classification task has shown a tendency to be more effective in large networks.
Extraction of Lifestyle Habits Described in Blogs of Diabetics

Tackling diabetes, an increasingly common lifestyle-related illness, requires information on patients’ lifestyle and habits. Blogs maintained by patients afflicted with the incurable illness may be useful for analyzing how lifestyle affects health-status. In this paper, we propose an intrinsic expression extractor that extracts keywords related to lifestyle and health-status from blogs of patients diagnosed with type-2 diabetes. To counter class imbalance and add to the corpus, the proposed method tags each keyword with information based on cue-words extracted from manually tagged data. The named-entity recognition (NER) for the extracted keywords uses a bidirectional gated recurrent unit neural network (BiGRU) and was evaluated for accuracy by cross-validation. We obtained F1-score of approximately 0.76. Although the accuracy of extraction can further be improved, the novel approach has applications in analyzing and improving the lifestyle of diabetes-afflicted patients.
Joint data learning panel summary

Artificial Intelligence/Deep Learning (AI/DL) techniques are based on learning a model using large available data sets. The data sets typically are from a single modality (e.g., imagery) and hence the model is based on a single modality. Likewise, multiple models are each built for a common scenario (e.g., video and natural language processing of text describing the situation). There are issues of robustness, efficiency, and explainability that need to be addressed. A second modality can improve efficiency (e.g., cueing), robustness (e.g., results cannot be fooled such as adversary systems), and explainability from different sources. The challenge is how to organize the data needed for joint data training and model building. For example, what is needed is (1) structure for indexing data as an object file, (2) recording of metadata for effective correlation, and (3) supporting methods of analysis for model interpretability for users. The Panel presents a variety of questions and responses discussed, explored, and analyzed for data fusion-based AI data fusion tools.
Breast Tumor Image Classification in Bright Challenge VIA Multiple Instance Learning and Deep Transformers

Artificial intelligence models have become increasingly promising in automated computed-aided cancer diagnos-tics. In this paper, a new deep learning method is proposed for solving breast tumor image classification in the BRIGHT Challenge. Given two types of data, regions of interest (ROIs) and whole slide images (WSIs), the proposed method first uti-lize ROI data to train a model that is able to select important image patches in WSI data and simultaneously capture a low-dimensional representation for image patches. With features extracted from important image patches in each WSI, another deep learning model following the Transformer framework is trained to perform the final WSI-level tumor classification, in which transfer learning is also employed to fully exploit ROI data. Evaluated on the test dataset, the proposed method achieves the 4th best results in the challenge. Ablation exper-iments are also carried out to analyze the proposed method in detail.
8-Channel Quantum Photonic Network Switch

We report a reconfigurable quantum photonic network switch with sub-μs rise times operating at visible wavelengths. Using hardware error correction, this system enables high-fidelity access to eight channels connected to spin memories.
Photonic Approach to Reinforcement Learning

Photonic approaches to reinforcement learning are reviewed. We present single-photon-based and chaotic-laser-based decision makers to solve multi-armed bandit problems as well as entangled-photon-based social decision-making by utilizing the unique attributes of classical and quantum light.
Efficient Hierarchical Signature Scheme with Batch Verification Function Suitable for ADS-B System

The federal aviation administration (FAA) has ruled that starting January 1, 2020, all aircraft flying over the United States must be equipped with an automatic dependent surveillance-broadcast (ADS-B) system to broadcast aircraft location information. The plaintext communication between the ADS-B system and the ground transmission tower is extremely vulnerable to interception and tampering by criminals. In addition, because of the increasing volume of airport air traffic, the airport tower is unable to quickly and efficiently verify large numbers of aircraft from all over the world. To address these issues, a three-level hierarchical signature scheme with batch verification function suitable for ADS-B system is proposed. Based on computational Diffie-Hellman (CDH) assumption, we prove that the proposed scheme is secure against existing unforgeability. Futhermore, we show the advantages of our scheme in computational cost through experiments.
A Time-Frequency Image Denoising Method via Neural Networks for Radar Waveform Recognition

Time-frequency images (TFIs) of radar emitter signals can reflect intra-pulse modulation information and be utilized to recognize waveforms, which is helpful for radar emitter identification. However, radar signals are usually interfered with by noise, therefore the robustness of TFIs needs to be improved. This letter proposes a TFI denoising method based on neural networks which can contribute to identifying radar waveforms at the low signal-to-noise ratio (SNR). Firstly, a neural network is trained to generate denoised spectrums from time-domain signals directly. Then the discrete Fourier transform operation in the short-time Fourier transform is replaced by the trained neural network to obtain denoised TFIs. Finally, several advanced convolutional neural networks (CNNs) are employed to classify these TFIs to realize waveform recognition and verify the effect of the proposed denoising method. In the experiment, 16 types of waveforms are simulated for recognition through several methods. The results show that the proposed method reduces the noise in the TFIs significantly, leading to the recognition accuracy being improved under different CNN structures.
Towards Interpretable and Lightweight Intrusion Detection for In-vehicle Network

Along with the improvement of informatization and intelligence in modern vehicles, the attacks aimed at in-vehicle network has been becoming an emerging threat. Current methods such as neural network-based methods have shown a state-of-the-art performance on intrusion detection based on in-vehicle network monitoring. However, the limitations in interpretability and resource consumption remain the non-negligible obstales for practical applications and deployments. In this paper, we propose and build an interpretable and lightweight framework based on shapelets model for in-vehicle network intrusion detection. We conduct massive experiments on a real-world dataset, namely Car-Hacking 2019 to evaluate our detection framework. Experiments demonstrate that our proposed method can reach or surpass state-of-the-art methods in four different adversarial scaneraios with extremely low time consumption, while providing interpretable results with visual explanations for further investigations.
Systematic Review for Selecting Methods of Document Clustering on Semantic Similarity of Online Laboratories Repository

In the era of digitalization, the number of electronic text documents has been rapidly increasing on the Internet. Organizing these documents into meaningful clusters is becoming a necessity by using several methods (i.e., TF-IDF, Word Embedding) and based on documents clustering. Document clustering is the process of dynamically arranging documents into clusters such that the documents contained within a cluster are very similar to those contained inside other clusters. Due to the fact that traditional clustering algorithms do not take semantic relationships between words into account and therefore do not accurately represent the meaning of documents. Semantic information has been widely used to improve the quality of document clusters by grouping documents according to their meaning rather than their keywords. In this paper, twenty-five papers have been systematically reviewed that are published in the last seven years (from 2016 to 2022) linked to semantic similarities which are based on document clustering. Algorithms, similarity measures, tools, and evaluation methods usage have been discussed as well. As result, the survey shows that researchers used different datasets for applying semantic similarity-based clustering regarding the text similarity. Hereby, this paper proposes methods of semantic similarity approach-based clustering that can be used for short text semantic similarity included in online laboratories repository.
Yet Another Canonical Nondeterministic Automaton

Several canonical forms of finite automata have been introduced over the decades. In particular, if one considers the minimal deterministic finite automaton (DFA), the canonical residual finite state automaton (RFSA), and the átomaton of a language, then the átomaton can be seen as the dual automaton of the minimal DFA, but no such dual has been presented for the canonical RFSA so far. We fill this gap by introducing a new canonical automaton that we call the maximized prime átomaton, and study its properties. We also describe how these four automata can be extracted from suitable observation tables used in the automata learning context.
Adaptive Bayesian Non-Parametric user profile classification in e-commerce websites

On e-commerce sites, users change their pref-erences over time concerning the products they want to purchase. Indeed, a product that is desired today may no longer be of interest tomorrow. This may be caused by a purchase of a similar product, a change of user&#x0027;s situation (marriage, birth, etc.) leading to the appearance of new preferences. Therefore, classifying users of an e-commerce site in a fixed class can induce inaccurate recommendations. Besides, a user may have different tastes and behaviors on the e-commerce site and therefore belong to several classes of user profiles. Finally, the number of classes of user profiles cannot be known a priori and may change over time since the membership of new users to the site may include the appearance of new classes. To overcome these issues, state-of-the-art approaches are mostly based on sequential models to keep a trade-off between the historical interests and the current session interests. However, these models fail to represent users&#x0027; relation and discover new trends in user profiles. In this paper, we handle these issues and propose a model-based Bayesian non-parametrics. Our proposed solution can select an adaptive number of users&#x0027; profiles and point out the discriminating hidden interests of users. Furthermore, the method could be applied to any e-commerce dataset and does not rely on feature engineering or specific parameters. Experiments are performed using real data from publicly available data sets, and the obtained results demonstrate the adaptability of the approach and its ability to infer the hidden features behind the appearance of user profiles.
Sensor fusion for media manipulation with ambiguous hypotheses, evidence alignment, and a novel belief combination rule

Fusing sensors that each test one hypothesis from a set of ambiguous, non-exclusive hypotheses about manipulation in media using only combination rules like those in Dempster-Shafer fusion ignores information about the overlap of the sensors’ beliefs, hypotheses, and evidences. We present a novel fusion approach for sensors that test ambiguous hypotheses using semantic evidence. Our approach measures the relevance of sensors to a given hypothesis and leverages the ambiguity of hypotheses and the similarity of evidences across sensors. These factors lead to a combination rule that captures the conceptual overlaps among hypotheses, evidences, and sensors.
A detective and corrective exercise assistant using computer vision and machine learning

Recently, Exercise Analysis has gained strong interest in the sport industry including athletes and coaches to understand and improve performance, as well as preventing injuries caused by incorrect exercise form. This work describes a system, USquat, that utilizes computer vision and machine learning for understanding and analyzing of a particular exercise, squatting, as proof of concept for the creation of a detective and corrective exercise assistant. Squatting was chosen as it is a complicated form of exercise and is often mis performed. For USquat, a Recurrent Neural Network is designed using Convolutional Neural Networks and Long Term Short Term networks. A sizable video library dataset containing numerous “bad” forms of squatting was created and curated to train the USquat system. When evaluated on test data, USquat achieved 90% accuracy on average. On a developed Android application that uses the resulting model, upon detection of “bad” squatting forms, it offers an instructive “good” video related specifically to the user’s bad form. Results including live application outcomes are demonstrated as well as challenging video results, problems, and areas of future work. Early work on the creation of a follow-on system to USquat that automatically generates a custom video of the user performing a correct action for the purpose of learning proper activity performance. Additionally, early work on a different version of USquat that explores an attention mechanism network is discussed.
EHR system tracks prisoner's data across countries to monitor COVID-19 in U.S. State and Federal Prisons

The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of our lives. While healthcare workers fight the virus in the front line, we do our part by creating an Electronic Health Records system that tracks state and federal prisoner&#x0027;s data through the countries in order to monitor COVID-19 cases in the U.S. The main objective of our system is to visualize the relationship between current data of deaths per day in both, state and federal prisons in U.S. State. In order to accomplish this process, we combine COVID-19 case and death rates into a single data collection which will show that state prisons have consistently reported greater rates of COVID-19 than federal prisons. The obtained results from the process satisfied our expectations and provide the efficiency of implementing Electronic Health Records Systems.
Enumeration of Both-Ends-Fixed &lt;i&gt;k&lt;/i&gt;-Ary Necklaces and Its Applications

We consider both-ends-fixed k-ary necklaces and enumerate all such necklaces of length n from the viewpoints of symbolic dynamics and β-expansions, where n and k(≥ 2) are natural numbers and β(> 1) is a real number. Recently, Sawada et al. proposed an efficient construction of k-ary de Bruijn sequence of length kn, which for each n ≥ 1, requires O(n) space but generates a single k-ary de Bruijn sequence of length kn in O(1)-amortized time per bit. Based on the enumeration of both-ends-fixed k-ary necklaces of length n, we evaluate auto-correlation values of the k-ary de Bruijn sequences of length kn constructed by Sawada et al. We also estimate the asymptotic behaviour of the obtained auto-correlation values as n tends to infinity.
EHDNet: Enhanced Human Detection Network for Search and Rescue

Human resources are a necessary cost for the search and rescue of people lost or stranded in the forest, which cannot be easily accessed in person. UAVs can be used to observe forests from up in the air without visiting directly. While UAVs are fly searching for people, they cannot be easily detected due to being too small and potentially being overlapped by obstacles such as grass, cars, and even the shadows of trees. We focus on detecting small objects even overlapped or hindered in the field and design an enhanced human detection Network (EHDNet) focusing on small objects. EHDNet enhances detection performance via an attentive feature pyramid network (AFPN) focused on small objects.
A predictive analytics of production planning model for yield performance in organic broiler industry using ensemble learning

Abstract The Organic broiler production requires farmers to use organic herbs without growth promoters or other chemical contents. The problem is uncertain production yield performance results. On the other hand, farmers want good performance results to increase income and minimize loss of production. Therefore, machine learning can support organic broiler production decisions. Meanwhile, a single prediction model has a lower level of accuracy, so a combination of various prediction models is needed. The paper aims to find an accurate performance of the yield production prediction model and has a high degree of accuracy using ensemble learning techniques. The attributes that affect performance as input include corn, rice bran, soybean meal, coconut cake, fish meal, and herbs. Then we evaluate the model using the confusion matrix. The result shows that ensemble learning has succeeded in predicting the yield performance of organic broilers with an accuracy of 98.96%. The evaluation model showed that accuracy was 87.98% in the data train and 94.23% in the data test.
Advances in Cloud Computing: Security Issues and Challenges in the Cloud

In the era of modern technology, the current trend is cloud computing, &#x201C;the practice of using the cloud.&#x201D; Nowadays, most of the people in the world have already utilized the cloud. The cloud is a virtual storage place where people can store, share and run their data thus making it a place to protect. Back when it first started out, the primary two companies that used cloud computing first were Amazon and Google back in 2002&#x2013;2009. At that time, the cloud&#x0027;s security level was not that sophisticated thus made it vulnerable to attacks. As time passes, people tried solving these issues and have leveled up the defense in the cloud. This paper will discuss cloud security levels from many different aspects to evaluate the secureness of the cloud in general. It will compare the different types of clouds and cloud services. It will also discuss different security risks and challenges across different cloud services.
Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness

In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probability scores of each class. In this type of architectures, the loss value of the classifier against any output class is directly proportional to the difference between the final probability score and the label value of the associated class. Standard White-box adversarial evasion attacks, whether targeted or untargeted, mainly try to exploit the gradient of the model loss function to craft adversarial samples and fool the model. In this study, we show both mathematically and experimentally that using some widely known activation functions in the output layer of the model with high temperature values has the effect of zeroing out the gradients for both targeted and untargeted attack cases, preventing attackers from exploiting the model's loss function to craft adversarial samples. We've experimentally verified the efficacy of our approach on MNIST (Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach substantially improves robustness against gradient-based targeted and untargeted attack threats. And, we showed that the increased non-linearity at the output layer has some ad-ditional benefits against some other attack methods like Deepfool attack.
An Algorithm for Anomalous Detection of Thermal Flow Value Based on Isolated Forest

In order to improve the accuracy and authenticity of the heat flow value collection data of the current building envelope, enhance the ability of on-site detection, and make each building maintain a more efficient energy saving state, it is particularly important to process the abnormal data after the heat flow value collection. Therefore, this paper proposes the feature weight-based iForest algorithm improved by the CRITIC weight method. First, we extract the data feature set and weight set, build W -iTree according to the weight, and finally integrate W-iForest to calculate the anomaly score of the data. This experiment compares the improved isolated forest algorithm with the traditional isolated forest algorithm. We show that the improved isolated forest algorithm detects anomaly data.
Chaotic spotted hyena optimizer for numerical problems

Abstract Spotted hyena optimizer (SHO) is a new metaheuristic algorithm that replicates spotted hyenas' hunting and social behaviour. This article proposes novel SHO algorithm that utilizes chaotic maps for fine‐tuning of control parameters. The chaotic maps help SHO to enhance the searching behaviour and preclude the solution to get trapped in local optima. The authors suggest 10 novel chaotic versions of SHO. The algorithms' performance is evaluated using 29 standardized test functions. The finding reveal that some of the presented algorithms outperform the standard SHO in terms of search capability and solution quality. In addition, five competitive approaches are compared with the suggested algorithms. It is observed from the results that chaos‐based spotted hyena optimizer (CSHO) achieved approximately 3% improvement over SHO in terms of fitness value. CSHO is also tested using five engineering design problems. CSHO achieved a 3%–5% improvement over the existing metaheuristic algorithms in terms of optimal design cost. Experimental results reveal that CSHO outperforms the existing metaheuristic algorithms.