Reviewerly/exHarmony huggingface dataset:
Exploring Human Activities Using eSense Earable Device

Detecting head- and mouth-related human activities of elderly people are very important for nurse care centers. They need to track different types of activities of elderly people like swallowing, eating, etc., to measure the health status of elderly people. In this regard, earable devices open up interesting possibilities for monitoring personal-scale behavioral activities. Here, we introduce activity recognition based on an earable device called ‘eSense’. It has multiple sensors that can be used for human activity recognition. ‘eSense’ has a 6-axis inertial measurement unit with a microphone and Bluetooth. In this paper, we propose an activity recognition framework using eSense device. We collect accelerometer and gyroscope sensor data from eSense device to detect head- and mouth-related activities along with other normal human activities. We evaluated the classification performance of the classifier using both accelerometer and gyroscope data. For this work, we develop a smartphone application for data collection from the eSense. Several statistical features are exploited to recognize head- and mouth-related activities (e.g., head nodding, head shaking, eating, and speaking), and regular activities (e.g., stay, walk, and speaking while walking). We explored different types of machine learning approaches like Convolutional Neural Network (CNN), Random Forest (RnF), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), etc., for classifying activities. We have achieved satisfactory results. Our results show that using both accelerometer and gyroscope sensors can improve performance. We achieve accuracy of 80.45% by LDA, 93.34% by SVM, 91.92% by RnF, 91.64% by KNN, and 93.76% by CNN while we exploit both accelerometer and gyroscope sensor data together. The results demonstrate the prospect of eSense device for detecting human activities in various healthcare monitoring system.
Superconducting Detector That Counts Microwave Photons Up to Two

We propose a detector of microwave photons which can distinguish the vacuum state, one-photon state, and the states with two or more photons. Its operation is based on the two-photon transition in a biased Josephson junction and detection occurs when it switches from a superconducting to a normal state. We model the detector theoretically. The detector performs with more than 90% success probability in several microseconds. It is sensitive for the 8.2GHz photons. The working frequency could be set at the design stage in the range from about 1GHz to 20GHz.
On Knowledge Distillation for Direct Speech Translation

Direct speech translation (ST) has shown to be a complex task requiring knowledge transfer from its sub-tasks: automatic speech recognition (ASR) and machine translation (MT). For MT, one of the most promising techniques to transfer knowledge is knowledge distillation. In this paper, we compare the different solutions to distill knowledge in a sequence-to-sequence task like ST. Moreover, we analyze eventual drawbacks of this approach and how to alleviate them maintaining the benefits in terms of translation quality.
Autoregressive Affective Language Forecasting: A Self-Supervised Task

Human natural language is mentioned at a specific point in time while human emotions change over time.While much work has established a strong link between language use and emotional states, few have attempted to model emotional language in time.Here, we introduce the task of affective language forecasting -predicting future change in language based on past changes of language, a task with real-world applications such as treating mental health or forecasting trends in consumer confidence.We establish some of the fundamental autoregressive characteristics of the task (necessary history size, static versus dynamic length, varying time-step resolutions) and then build on popular sequence models for words to instead model sequences of language-based emotion in time.Over a novel Twitter dataset of 1,900 users and weekly + daily scores for 6 emotions and 2 additional linguistic attributes, we find a novel dual-sequence GRU model with decayed hidden states achieves best results (r = .66).We make our anonymized dataset as well as task setup and evaluation code available for others to build on.
Improving the pilot selection process by using eye-tracking tools

This paper improves the understanding of the use of eye-tracking tools in the pilot selection process. Research of eye movement and attention distribution of candidate pilots may provide the capability for visual behavior prediction in more demanding flight training phases. The research included psychological testing, flight screening of subjects and their achievements in a flight simulator in combination with an eye-tracking device. Participants were divided into three categories: high performance, average performance, and low performance and separately regarded through psychological testing results and flight screening results. An eye-tracking device tracked visual behavior of subjects through the scope and speed of visual perception. The number of fixations and revisits recorded during the simulated visual flight conditions measured the difference in visual response between subjects. Comparison of results showed a positive correlation with psychological test results. Correlation with flight screening selection was not confirmed. We used the new network-based approach with three target importance measures to overcome the shortcomings of traditional eye movement metrics. The results of the adopted network approach presented in the form of graphs and analysis of normalized importance measures showed that it was possible to extract specific saccade strategy for each participant. Discovered differences between them positively detected week ones. In this way, Eye-tracking tools can potentially improve the pilot selection process and complement other tests and assessment methods.
Data trustworthiness signatures for nuclear reactor dynamics simulation

With the increased reliance on digitization in industrial control systems, the need for effective monitoring techniques has risen dramatically. Specifically, there is now a growing concern about the so-called false data injection (FDI) attacks. These attacks aim to alter the raw sensors’ data to cause malicious outcomes. Any serious FDI algorithm is based on an intimate knowledge of the system and its associated physics models, which renders conventional outlier/anomaly detection techniques almost obsolete in the face of such attacks. Thus, a critical need has emerged to develop a new class of defense methods that are capable of detecting FDI attacks under the assumption that the attacker has a strong familiarity with the system and its physics modeling. This class of defense methods are denoted by model-based defenses which are premised on the assumption that the attacker, while having a good understanding of the system, does not have full privileged access to all proprietary data and historical records of operation. However, (s)he is assumed to be capable of learning system behavior using self-learning techniques during an initial lie-in-wait period. To defend against this scenario, we propose a new model-based randomized window algorithm that searches time-series data for signatures that can serve as classifiers between normal and FDI scenarios. The classifiers are based on the correlations between the dominant degrees of freedom (DOFs) and the less-dominant DOFs (expected to be very sensitive to the system details that are unknown to the attacker). For demonstration, RELAP5 models are employed to calculate representative nuclear reactor behavior during a number of transient scenarios. Falsified data are injected into the RELAP5-simulated behavior, and the proposed signature-identification algorithm is employed to detect the injected data.
Imaginary Units of Publication Activities in Consumer Society

Consumer society is associated with abundance of goods and services. Usually the scientific activity is not among them. Meanwhile, with scientometric indicators introduction, science activity acquired characteristics of consumer society in which it runs. The publication activity in modern circumstances got in track of consumer focus: authors, journals and mediators became economical actors, and scientific text became the product of “sings absorption and absorbed by signs”. Was it possible to avoid it? - this question is without answer. It is clear that imposing quantitative scientometric indicators became the catalyst of this process.
Quantum Tomography of Pure States with Projective Measurements Distorted by Experimental Noise

The article undertakes the problem of pure state estimation from projective measurements based on photon counting. Two generic frames for qubit tomography are considered -- one composed of the elements of the SIC-POVM and the other defined by the vectors from the mutually unbiased bases (MUBs). Both frames are combined with the method of least squares in order to reconstruct a sample of input qubits with imperfect measurements. The accuracy of each frame is quantified by the average fidelity and purity. The efficiency of the frames is compared and discussed. The method can be generalized to higher-dimensional states and transferred to other fields where the problem of complex vectors reconstruction appears.
EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS VS CROSS-ENTROPY IN CLASSIFICATION TASKS

Modern neural architectures for classification tasks are trained using the cross-entropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be well-founded. We explore several major neural architectures and a range of standard benchmark datasets for NLP, automatic speech recognition (ASR) and computer vision tasks to show that these architectures, with the same hyper-parameter settings as reported in the literature, perform comparably or better when trained with the square loss, even after equalizing computational resources. Indeed, we observe that the square loss produces better results in the dominant majority of NLP and ASR experiments. Cross-entropy appears to have a slight edge on computer vision tasks. We argue that there is little compelling empirical or theoretical evidence indicating a clear-cut advantage to the cross-entropy loss. Indeed, in our experiments, performance on nearly all non-vision tasks can be improved, sometimes significantly, by switching to the square loss. Furthermore, training with square loss appears to be less sensitive to the randomness in initialization. We posit that training using the square loss for classification needs to be a part of best practices of modern deep learning on equal footing with cross-entropy.
SYSLITE: Syntax-Guided Synthesis of PLTL Formulas from Finite Traces

We present an efficient approach to learn past-time linear temporal logic formulas (PLTL) from a set of propositional variables and a sample of finite traces over those variables. The efficiency of our approach can be attributed to a careful encoding of the PLTL formula learning problem as a bit-vector function synthesis problem, and the use of an enhanced Syntax-Guided Synthesis (SyGuS) engine to solve the latter. We implemented our approach in a tool called Syslite and empirically evaluated its efficacy with two case studies. In these case studies, we observe that Syslite on average enjoys a speedup of 44x over current learning approaches for temporal formulas while learning the expected formulas in the vast majority of cases.
A relic sketch extraction framework based on detail-aware hierarchical deep network

As the first step of the restoration process of painted relics, sketch extraction plays an important role in cultural research. However, sketch extraction suffers from serious disease corrosion, which results in broken lines and noise. To overcome these problems, we propose a deep learning-based hierarchical sketch extraction framework for painted cultural relics. We design the sketch extraction process into two stages: coarse extraction and fine extraction. In the coarse extraction stage, we develop a novel detail-aware bi-directional cascade network that integrates flow-based difference-of-Gaussians (FDoG) edge detection and a bi-directional cascade network (BDCN) under a transfer learning framework. It not only uses the pre-trained strategy to extenuate the requirements of large datasets for deep network training but also guides the network to learn the detail characteristics by the prior knowledge from FDoG. For the fine extraction stage, we design a new multiscale U-Net (MSU-Net) to effectively remove disease noise and refine the sketch. Specifically, all the features extracted from multiple intermediate layers in the decoder of MSU-Net are fused for sketch predication. Experimental results showed that the proposed method outperforms the other seven state-of-the-art methods in terms of visual and quantitative metrics and can also deal with complex backgrounds.
From coarse to fine: A two stage conditional generative adversarial network for single image rain removal

Images captured in rainy days are often obscured by rain streaks which affect the accuracy of object detection, vehicle and pedestrian recognition. It is hard to restore the texture and color information of the de-rained image by some conventional rain removal algorithms. In order to address the problem, we propose a novel two stage conditional generative adversarial network (TS-CGAN), in which the generator network of the TS-CGAN contains two stage frameworks to better gradually remove rain streaks. In addition, compared with previous neural networks on the rain removal work, patch-GAN discriminator of the TS-CGAN can able to encourage generator to adversely produce satisfactory visual clean images that can solve the artifact problem. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method significantly outperforms recent state-of-the-art algorithms in terms of qualitative and quantitative measurement.
Towards Better Evaluation of Multi-target Regression Models

Multi-target models are machine learning models that simultaneously predict several target attributes. Due to a high number of real-world applications, the field of multi-target prediction is actively developing. With the growing number of multi-target techniques, there is a need for comparing them among each other. However, while established procedures exist for comparing conventional, single-target models, little research has been done on making such comparisons in the presence of multiple targets. In this paper, we highlight the challenges of evaluating multi-target models, focusing on multi-target regression algorithms. This paper reviews the common practice and discusses its shortcomings, indicating directions for future research.
A deeper look into natural sciences with physics-based and data-driven measures

With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools—latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mouse brain microscopy data. What sets these techniques apart is the relaxation of restrictive assumptions typical of many machine learning models and instead incorporating aspects that best fit the dynamical systems at hand.
An Intelligent Text Encryption System Using Musical Notes

Musical cryptograms have always been a matter of fascination for musicians and cryptologists across the world. In such cryptograms, each alphabet could be mapped to musical notes in numerous methods, which makes their detection difficult. The proposed system uses musical passwords to access the files and musical notes to encrypt and decrypt the text file data. When a user accesses a file, a musical keyboard appears and the user is prompted to play the musical password. Users define their own password as a sequence of musical notes of varying length. The musical password further serves as the key during encryption. The output would be a set of musical notes which represents the corresponding text in the file. The encrypted text file is then played as music. The proposed model is a symmetric model, where the same musical password used to encrypt should be used to decrypt data. The system ensures that a given plaintext with a given key generates different encrypted output each time due to usage of random mapping tables.
Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science.
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services

Deployment, operation and maintenance of large IT systems becomes increasingly complex and puts human experts under extreme stress when problems occur. Therefore, utilization of machine learning (ML) and artificial intelligence (AI) is applied on IT system operation and maintenance - summarized in the term AIOps. One specific direction aims at the recognition of re-occurring anomaly types to enable remediation automation. However, due to IT system specific properties, especially their frequent changes (e.g. software updates, reconfiguration or hardware modernization), recognition of reoccurring anomaly types is challenging. Current methods mainly assume a static dimensionality of provided data. We propose a method that is invariant to dimensionality changes of given data. Resource metric data such as CPU utilization, allocated memory and others are modelled as multivariate time series. The extraction of temporal and spatial features together with the subsequent anomaly classification is realized by utilizing TELESTO, our novel graph convolutional neural network (GCNN) architecture. The experimental evaluation is conducted in a real-word cloud testbed deployment that is hosting two applications. Classification results of injected anomalies on a cassandra database node show that TELESTO outperforms the alternative GCNNs and achieves an overall classification accuracy of 85.1%. Classification results for the other nodes show accuracy values between 85% and 60%.
Understanding Negative Sampling in Knowledge Graph Embedding

Knowledge graph embedding (KGE) is to project entities and relations of a knowledge graph (KG) into a low-dimensional vector space, which has made steady progress in recent years. Conventional KGE methods, especially translational distance-based models, are trained through discriminating positive samples from negative ones. Most KGs store only positive samples for space efficiency. Negative sampling thus plays a crucial role in encoding triples of a KG. The quality of generated negative samples has a direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks, such as recommendation, link prediction and node classification. We summarize current negative sampling approaches in KGE into three categories, static distribution-based, dynamic distribution-based and custom cluster-based respectively. Based on this categorization we discuss the most prevalent existing approaches and their characteristics. It is a hope that this review can provide some guidelines for new thoughts about negative sampling in KGE.
Research on Cooperate Search Path Planning of Multiple UAVs Using Dubins Curve

Firstly, the constraints on the evaluation of route planning results were proposed from the aspects of maximum target mission return, shortest track and minimum track difference. Secondly, the improved particle swarm optimization algorithm based on heuristic information was proposed. Finally, the simulation results were verified. The simulation results show that the multi UAV multi-target task allocation and path planning based on Dubins curve not only meet the requirements of fast track planning, but also meet certain flight constraints. The improved algorithm based on heuristic information particle swarm optimization has faster convergence speed and global optimization.
Automatic Blood-Cell Classification via Convolutional Neural Networks and Transfer Learning

The evaluation and diagnosis of cancer related diseases can be complex and lengthy. This is exacerbated due to manual analyses based on techniques that may take copious amount of time. In the last decade, different tools have been created to detect, analyze and classify different types of cancer in humans. However, there is still a lack of tools or models to automate the analysis of human cells to determine the presence of cancer. Such a model has the potential to improve early detection and prevention of said diseases, leading to more timely medical diagnoses. In this research, we present our current effort on the development of a Deep Learning Model capable of identifying blood cell anomalies. Our results show an accuracy that meets or exceeds the current state of the art, particularly achieving lower false negative rate in comparison to previous efforts reported.