Reviewerly/exHarmony huggingface dataset:
Anatomy of a Data Science Software Toolkit That Uses Machine Learning to Aid ‘Bench-to-Bedside’ Medical Research—With Essential Concepts of Data Mining and Analysis Explained

Data science and machine learning are buzzwords of the early 21st century. Now pervasive through human civilization, how do these concepts translate to use by researchers and clinicians in the life-science and medical field? Here, we describe a software toolkit, just large enough in scale, so that it can be maintained and extended by a small team, optimised for problems that arise in small/medium laboratories. In particular, this system may be managed from data ingestion statistics preparation predictions by a single person. At the system’s core is a graph type database, so that it is flexible in terms of irregular, constantly changing data types, as such data types are common during explorative research. At the system’s outermost shell, the concept of ’user stories’ is introduced to help the end-user researchers perform various tasks separated by their expertise: these range from simple data input, data curation, statistics, and finally to predictions via machine learning algorithms. We compiled a sizable list of already existing, modular Python platform libraries usable for data analysis that may be used as a reference in the field and may be incorporated into this software. We also provide an insight into basic concepts, such as labelled-unlabelled data, supervised vs. unsupervised learning, regression vs. classification, evaluation by different error metrics, and an advanced concept of cross-validation. Finally, we show some examples from our laboratory using our blood sample and blood clot data from thrombosis patients (sufferers from stroke, heart and peripheral thrombosis disease) and how such tools can help to set up realistic expectations and show caveats.
AdaRW training optimization algorithm for deep learning model of marine target detection based on SAR

Deep learning adjusts parameters to optimize the model through model training. Training algorithm is the key to model optimization and implementation. Therefore, the improvement of model training algorithm is of great significance to deep learning. Based on the original gradient algorithm, the paper proposes a new gradient descent algorithm AdaGrad Restricted by Windows (AdaRW) for Deep learning model training optimization. Aiming at the defects of AdaGrad algorithm, the new algorithm uses the subset of window to limit historical accumulation, so as to slow down the attenuation of learning rate, and improve the speed of model training. The paper constructs OceanTDA9, a Deep learning model of marine target detection for Synthetic Aperture Radar (SAR) data, and adopts the proposed AdaRW algorithm to train the model based on SAR data with a resolution of 10 m in the Bohai Sea. Experiment shows that the accuracy and loss of the algorithm are better than those of AdaGrad and Stochastic Gradient Descent (SGD) algorithms, and the standard deviation is better than that of Adam algorithm.
Comparing different nonlinearities in readout systems for optical neuromorphic computing networks

Nonlinear activation is a crucial building block of most machine-learning systems. However, unlike in the digital electrical domain, applying a saturating nonlinear function in a neural network in the analog optical domain is not as easy, especially in integrated systems. In this paper, we first investigate in detail the photodetector nonlinearity in two main readout schemes: electrical readout and optical readout. On a 3-bit-delayed XOR task, we show that optical readout trained with backpropagation gives the best performance. Furthermore, we propose an additional saturating nonlinearity coming from a deliberately non-ideal voltage amplifier after the detector. Compared to an all-optical nonlinearity, these two kinds of nonlinearities are extremely easy to obtain at no additional cost, since photodiodes and voltage amplifiers are present in any system. Moreover, not having to design ideal linear amplifiers could relax their design requirements. We show through simulation that for long-distance nonlinear fiber distortion compensation, using only the photodiode nonlinearity in an optical readout delivers BER improvements over three orders of magnitude. Combined with the amplifier saturation nonlinearity, we obtain another three orders of magnitude improvement of the BER.
A neural autonomous robotic manipulator with three degrees of freedom

This study focuses on the development of a robotic arm with three degrees of freedom designed for different applications, especially in industry, where the main contribution is the use of a low-cost robotic arm manipulator, which with the proper adjustments, mainly of speed, aims to add the concept of collaborative robotics in its most advanced versions. This study employs some classical principles of robotics, such as acquisition, image processing, and object manipulation. In this context, artificial neural networks were used for two essential functionalities: camera calibration and inverse kinematics resolution. From the results obtained with the use of the artificial neural networks (ANNs), it was possible to control the trajectory of the servo motors with the aid of software support. Also, by employing ANNs and using computer vision techniques, the robotic manipulator achieves the objectives of manipulating objects in space with a relatively acceptable error rate, considering the low-cost proposal applied. This manipulator, at first, has a didactic purpose, but the concepts and applications can be used in different areas of the industry.
A Secure Three Factor-Based Authentication Scheme for Telecare Medicine Information Systems With Privacy Preservation

Telecare Medicine Information System (TMIS) is now attracting field for remote healthcare, diagnosis and emergency health services etc. The major objective of this type of system is to provide medical facilities to patients who are critically ill and unable to attend hospitals or put in isolation for observations. A major challenge of such systems is to securely transmit patients' health related information to the medical server through an insecure channel. This collected sensitive data is further used by medical practitioners for diagnosis and treatment purposes. Therefore, security and privacy are essential for healthcare data. In this paper, a robust authentication protocol based on Chebyshev Chaotic map has been proposed for adequate security while transmitting data. The privacy preservation is maintained by a rule set which mainly controls the views. A detailed security analysis was performed for the proposed scheme.
Machine Learning-Based Grammar Error Detection Method in English Composition

The detection of grammatical errors in English composition is an important task in the field of NLP. The main purpose of this task is to check out grammatical errors in English sentences and correct them. Grammatical error detection and correction are important applications in the automatic proofreading of English texts and in the field of English learning aids. With the increasing influence of English on a global scale, a huge breakthrough has been made in the task of detecting English grammatical errors. Based on machine learning, this paper designs a new method for detecting grammatical errors in English composition. First, this paper implements a grammatical error detection model based on Seq2Seq. Second, this paper implements a grammatical error detection and correction scheme based on the Transformer model. The Transformer model performs better than most grammar models. Third, this paper realizes the application of the BERT model in grammar error detection and error correction tasks, and the generalization ability of the model has been significantly enhanced. This solves the problem that the forward and backward cannot be merged when the Transformer trains the language model. Fourth, this paper proposes a method of grammatical error detection and correction in English composition based on a hybrid model. According to specific application scenarios, the corresponding neural network model is used for grammatical error correction. Combine the Seq2Seq structure to encode the input sequence and automate feature engineering. Through the combination of traditional model and deep model, the advantages are complemented to realize grammatical error detection and automatic correction.
The dynamic of quantum entanglement of two dimensional harmonic oscillator in non-commutative space

Abstract In the present paper, we study the influence of non-commutativity on entanglement in a system of two oscillators-modes in interaction with its environment. The considered system is a two-dimensional harmonic oscillator in non-commuting spatial coordinates coupled to its environment. The dynamics of the covariance matrix, the separability criteria for two Gaussian states in non-commutative space coordinates, and the logarithmic negativity are used to evaluate the quantum entanglement in the system, which is compared to the commutative space coordinates case. The result is applied for two initially entangled states, namely the squeezed vacuum and squeezed thermal states. It can be observed that the phenomenon of entanglement sudden death appears more early in the system for the case of squeezed vacuum state than in the case of squeezed thermal state. Thereafter, it is also observed that non-commutativity effects lead to an increasing of entanglement of initially entangled quantum states, and reduce the separability in the open quantum system. It turns out that a separable state in the usual commutative quantum mechanics might be entangled in non-commutative extension.
The Sarcasm Detection in News Headlines Based on Machine Learning Technology

The work focuses on creating a program for determining sarcasm in news headlines using machine learning methods. This paper develops a program for determining sarcasm in the text in Python. The introduction describes the relevance, scientific novelty, and practical value of the developed program. The paper analyses research on the definition of sarcasm in the text and considers the main problems faced by previous scientists in predicting sarcastic statements. A dataset taken from the Kaggle site, where news headlines from two American websites are collected, is perfect for our task. Logistic regression and Bayesian classifier, simple tokenisation and vectors extraction based on TF-IDF, and neural networks creation such as LSTM and GRU were chosen. Glove and Word2Vec methods were selected for weight extraction. The data have been analysed. The program&#x0027;s design has been described, and its structure, the analysis of results of experimenters and work of the created models, have been carried out. The best accuracy was shown by the neural network model with scales created using the Glove method: 80.5&#x0025;. The following model of the Bayesian classifier using TF-IDF vectors: 78.9&#x0025;. Next is a model of a neural network with scales created using the Word2Vec method: 77.2&#x0025;. Following neural network without weights: 76.6&#x0025;. The lowest logical regression model in the table using TF-IDF vectors: 74&#x0025;.
Realistic talking face animation with speech-induced head motion

The recent advancements on talking face generation from speech have mostly focused on lip synchronization, realistic facial movements like eye blinks, eye brow motions but do not generate meaningful head motions according to the speech. This results in a lack of realism, especially in long speech. A very few recent methods try to animate the head motions, but they mostly rely on a short driving head motion video. In general, the prediction of head motion is largely dependent upon the prosodic information of the speech at a current time window. In this paper, we propose a method for generating speech-driven realistic talking face animation which has speech-coherent head motions with accurate lip sync, natural eye-blink, and high fidelity texture. In particular, we propose an attention-based GAN network to identify the highly correlated audio with the speaker's head motion and learn the relationship between the prosodic information of the speech and the corresponding head motions. Experimental results show that our animations are significantly better in terms of output video quality, realism of head movements, lip sync, and eye-blinks when compared to state-of-the-art methods, both qualitatively and quantitatively. Moreover, our user study shows that our speech-coherent head motions make the animation more appealing to the users.
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation

We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm. This problem is challenging when each robot considers its path without explicitly sharing observations with other robots and can lead to non-stationary issues in Deep Reinforcement Learning (DRL). The typical CTDE algorithm factorizes the joint action-value function into individual ones, to favor cooperation and achieve decentralized execution. Such factorization involves constraints (e.g., monotonicity) that limit the emergence of novel behaviors in an individual as each agent is trained starting from a joint action-value. In contrast, we propose a novel architecture for CTDE that uses a centralized state-value network to compute a joint state-value, which is used to inject global state information in the value-based updates of the agents. Consequently, each model computes its gradient update for the weights, considering the overall state of the environment. Our idea follows the insights of Dueling Networks as a separate estimation of the joint state-value has both the advantage of improving sample efficiency, while providing each robot information whether the global state is (or is not) valuable. Experiments in a robotic navigation task with 2 4, and 8 robots, confirm the superior performance of our approach over prior CTDE methods (e.g., VDN, QMIX).
Are You a Good Client? Client Classification in Federated Learning

Federated Learning (FL) is a distributed machine learning framework, where any raw data do not leave the participating clients' machines aiming for privacy preservation. Due to its distributed nature, federated learning is especially vulnerable to data poisoning attacks which degrade overall performance of the framework. Hence there is an arising need of early identification and removal of malicious clients. However, correctly identifying malicious clients is difficult. Clients with non-IID (Independently and Identically Distributed) data and those with malicious data, for example, are hardly distinguishable due to the dissimilar distribution of non-IID data and normal data. Prior works focus on improving the performance with either non-IID data or malicious data, but not both. On the other hand, this paper proposes a mechanism that identifies and classifies three types of clients: clients having IID, non-IID, and malicious data. Our findings can help future studies to remove malicious clients efficiently while training a model with diverse data.
Experience and Practice Teaching an Undergraduate Course on Diverse Heterogeneous Architectures

Heterogeneous computing is growing as an important hardware and software paradigm, both in high-performance computing and in application computing in general. Nevertheless, the topic is a relative newcomer to undergraduate curricula, and there is a dearth of guidance on suitable syllabi and lesson plans. The educational challenge of teaching this topic is exacerbated by the rapid pace of heterogeneous-hardware innovation and adoption, which can render parts of current textbooks obsolete.To help other educators facing these challenges, and to promote a conversation about a standardized approach toward teaching heterogeneous computing, this paper presents a case study for one semester-long class on the topic. It describes the goals, structure, challenges, and lessons learned from the introduction of a diverse heterogeneous hardware and software environment to computer science majors at Reed College, a small liberal-arts school. This paper also includes suggestions and ideas for future adoption, adaptation, and expansion of this class.
Group Flight Automation Using Tello EDU Unmanned Aerial Vehicle

UAVs are expected to play a key role in the future, providing support to various services to the population. Already today, their use helps to solve a huge range of scientific and applied problems related to ecology, meteorology, geology, zoology, agriculture, climate study, mineral exploration and more. Increasing the number of civilian, military and sports UAVs significantly complicates the problem of flight safety in a single airspace. The factors that have the strongest impact on the safety and effectiveness of UAVs include: destruction in contact with the ground; harm to life and health of people or their property on the ground; collisions in the air with other aircrafts. That is why the study of group flight parameters, especially in automatic and semi-automatic mode is an actual task. The problem of group flight automation based on the use of the popular Tello EDU UAV training model is solved in the work. The architecture, algorithm and model of information exchange in flight control are formed. UAV flight testing was performed and additional recommendations for further organization of experiments in the group were developed.
Towards a Standard Approach for the Design of a both Physical and Virtual Museum

Affluence in museums has gone through a substantial decrease worldwide over the years, recently also because of the COVID-19 pandemic social distancing policy. Many museums faced the issue proposing digital approaches, to make the collections more appealing and accessible; virtual, augmented and mixed reality (overall named XR) have shown to be a valuable support for this goal. In the current paper, we initially present a selection of success cases, then proceed proposing a set of guidelines for the exploitation of XR technologies in museums. Based on good practices coming from the user centered design of XR experiences, as well as museum project design and management, we propose an easy and standardized approach to the task.
A dual-layer context-based architecture for the detection of anomalous instructions sent to medical devices

Complex medical devices are controlled by instructions sent from a host personal computer (PC) to the device. Anomalous instructions can introduce many potentially harmful threats to patients (e.g., radiation overexposure), to physical device components (e.g., manipulation of device motors), or to functionality (e.g., manipulation of medical images). Threats can occur due to cyber-attacks, human error (e.g., using the wrong protocol, or misconfiguring the protocol's parameters by a technician), or host PC software bugs. Thus, anomalous instructions might represent an intentional threat to the patient or to the device, a human error, or simply a non-optimal operation of the device. To protect medical devices, we propose a new dual-layer architecture. The architecture analyzes the instructions sent from the host PC to the physical components of the device, to detect anomalous instructions using two detection layers: (1) an unsupervised context-free (CF) layer that detects anomalies based solely on the instruction's content and inter-correlations; and (2) a supervised context-sensitive (CS) layer that detects anomalies in both the clinical objective and patient contexts using a set of supervised classifiers pre-trained for each specific context. The proposed dual-layer architecture was evaluated in the computed tomography (CT) domain, using 4842 CT instructions that we recorded, including two types of CF anomalous instructions, four types of clinical objective context instructions and four types of patient context instructions. The CF layer was evaluated using 14 unsupervised anomaly detection algorithms. The CS layer was evaluated using six supervised classification algorithms applied to each context (i.e., clinical objective or patient). Adding the second CS supervised layer to the architecture improved the overall anomaly detection performance (by improving the detection of CS anomalous instructions [when they were not also CF anomalous]) from an F1 score baseline of 72.6%, to an improved F1 score of 79.1% to 99.5% (depending on the clinical objective or patient context used). Adding, the semantics-oriented CS layer enables the detection of CS anomalies using the semantics of the device's procedure, which is not possible when using just the purely syntactic CF layer. However, adding the CS layer also introduced a somewhat increased false positive rate (FPR), and thus reduced somewhat the specificity of the overall process. We conclude that by using both the CF and CS layers, a dual-layer architecture can better detect anomalous instructions to medical devices. The increased FPR might be reduced, in the future, through the use of stronger models, and by training them on more data. The improved accuracy, and the potential capability of adding explanations to both layers, might be useful for creating decision support systems for medical device technicians.
Essential Features in a Theory of Context for Enabling Artificial General Intelligence

Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI.
Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior

A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images.