Reviewerly/exHarmony huggingface dataset:
Designing and Integrating Electronics for Bespoke Rehabilitation Experiences in Virtual Reality

Nowadays the rehabilitation process involves the patient and the therapist, that must interact to recover the motion of limbs and the strength of related muscles to restore the initial functionalities.The therapy relies on the experience and sensitivity of the therapist that identifies the rehabilitation exercises which are necessary to recover the expected ability.To prevent inappropriate practices an interesting aid may come by mixing collaborative robots, namely Cobots, and additive manufacturing technologies.The proper integration of a Cobot assistant and custom-printed training objects enables a significant improvement in the effectiveness of the therapy action and the related user experience since the programmed trajectories can mimic the movements related to activities of daily living.To this aim, this work describes an integrated approach to support the design of Cobot assisted rehabilitative solutions.The object selected by the patient and therapist, the motion pattern, the clamping area, and loads on the limb represents the design requirements.The motion trajectories defining the specific training tasks are the starting point to the optimal placement within the Cobot workspace.Specifically, manipulability maps can provide an objective evaluation of the locations where the exercises are performed at the best of workspace and configuration of the Cobot.A simple upper limb rehabilitation exercise based on a demonstrative handle has been selected to prove the effectiveness of the proposed approach.The results confirm that the manipulability index can be adopted to drive the preliminary design of the Cobotic solution toward a feasible configuration.
Black-Box Attack using Adversarial Examples: A New Method of Improving Transferability

Adversarial examples (AEs) are malicious test-data samples (typically images) generated by applying carefully calculated perturbations to clean samples. The added perturbations are usually human-imperceptible but the AEs can fool a machine learning (ML) model to make misclassifications. Although multiple methods were proposed to generate AEs, the ability to generalize is very limited; that is, they easily overfit to their source, single, white-box ML models and the generated AEs rarely work for other models. In this paper, we propose a black-box attack approach that crafts transferable AEs that can attack a wide range of ML models without knowing those model details. Our novel method consists of an elastic momentum (EM) that expedites gradient descent to avoid early overfitting, and a random erasure (RE) technique that increases the diversity of perturbations and reduces gradient fluctuations. Our method can be applied to any gradient-based attacks to make those attacks become more transferable. We evaluate our proposed method by attacking seven state-of-the-art (SOTA) deep learning models and comparing against five SOTA attacks; we also attack nine advanced defense mechanisms that are integrated into the above models. Our results demonstrate significant improvement on the attack success rate (ASR) and transferability when using our method alone, and that it can also be easily applied to other baseline methods (which are gradient-based) to substantially improve their performance.
Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Training

Edema is a common symptom of kidney disease, and quantitative measurement of edema is desired.This paper presents a method to estimate the degree of edema from facial images taken before and after dialysis of renal failure patients.As tasks to estimate the degree of edema, we perform pre-and postdialysis classification and body weight prediction.We develop a multi-patient pre-training framework for acquiring knowledge of edema and transfer the pre-trained model to a model for each patient.For effective pre-training, we propose a novel contrastive representation learning, called weight-aware supervised momentum contrast (WeightSupMoCo).WeightSupMoCo aims to make feature representations of facial images closer in similarity of patient weight when the pre-and post-dialysis labels are the same.Experimental results show that our pre-training approach improves the accuracy of pre-and post-dialysis classification by 15.1% and reduces the mean absolute error of weight prediction by 0.243 kg compared with training from scratch.The proposed method accurately estimate the degree of edema from facial images; our edema estimation system could thus be beneficial to dialysis patients.
Multiscale attention-based detection of tiny targets in aerial beach images

Tiny target detection in marine scenes is of practical importance in marine vision applications such as personnel search and rescue, navigation safety, and marine management. In the past few years, methods based on deep convolutional neural networks (CNN) have performed well for targets of common sizes. However, the accurate detection of tiny targets in marine scene images is affected by three difficulties: perspective multiscale, tiny target pixel ratios, and complex backgrounds. We proposed the feature pyramid network model based on multiscale attention to address the problem of tiny target detection in aerial beach images with large field-of-view, which forms the basis for the tiny target recognition and counting. To improve the ability of the tiny targets’ feature extraction, the proposed model focuses on different scales of the images to the target regions based on the multiscale attention enhancement module. To improve the effectiveness of tiny targets’ feature fusion, the pyramid structure is guided by the feature fusion module in order to give further semantic information to the low-level feature maps and prevent the tiny targets from being overwhelmed by the information at the high-level. Experimental results show that the proposed model generally outperforms existing models, improves accuracy by 8.56 percent compared to the baseline model, and achieves significant performance gains on the TinyPerson dataset. The code is publicly available via Github.
Weighted Graph Embedding Feature with Bi-Directional Long Short-Term Memory Classifier for Multi-Document Text Summarization

In this digital era, there is a tremendous increase in the volume of data, which adds difficulties to the person who utilizes particular applications, such as websites, email, and news. Text summarization targets to reduce the complexity of obtaining statistics from the websites as it compresses the textual document to a short summary without affecting the relevant information. The crucial step in multi-document summarization is obtaining a relationship between the cross-sentence. However, the conventional methods fail to determine the inter-sentence relationship, especially in long documents. This research develops a graph-based neural network to attain an inter-sentence relationship. The significant step in the proposed multi-document text summarization model is forming the weighted graph embedding features. Furthermore, the weighted graph embedding features are utilized to evaluate the relationship between the document’s words and sentences. Finally, the bidirectional long short-term memory (BiLSTM) classifier is utilized to summarize the multi-document text summarization. The experimental analysis uses the three standard datasets, the Daily Mail dataset, Document Understanding Conference (DUC) 2002, and Document Understanding Conference (DUC) 2004 dataset. The experimental outcome demonstrates that the proposed weighted graph embedding feature + BiLSTM model exceeds all the conventional methods with Precision, Recall, and F1 score of 0.5352, 0.6296, and 0.5429, respectively.
Adiabatic Amplification of the Harmonic Oscillator Energy When the Frequency Passes through Zero

We study the evolution of the energy of a harmonic oscillator when its frequency slowly varies with time and passes through a zero value. We consider both the classical and quantum descriptions of the system. We show that after a single frequency passage through a zero value, the famous adiabatic invariant ratio of energy to frequency (which does not hold for a zero frequency) is reestablished again, but with the proportionality coefficient dependent on the initial state. The dependence on the initial state disappears after averaging over the phases of initial states with the same energy (in particular, for the initial vacuum, the Fock and thermal quantum states). In this case, the mean proportionality coefficient is always greater than unity. The concrete value of the mean proportionality coefficient depends on the power index of the frequency dependence on a time near the zero point. In particular, the mean energy triplicates if the frequency tends to zero linearly. If the frequency attains zero more than once, the adiabatic proportionality coefficient strongly depends on the lengths of time intervals between zero points, so that the mean energy behavior becomes quasi-stochastic after many passages through a zero value. The original Born-Fock theorem does not work after the frequency passes through zero. However, its generalization is found: the initial Fock state becomes a wide superposition of many Fock states, whose weights do not depend on time in the new adiabatic regime. When the mean energy triplicates, the initial Nth Fock state becomes a superposition of, roughly speaking, 6N states, distributed nonuniformly. The initial vacuum and low-order Fock states become squeezed, as well as the initial thermal states with low values of the mean energy.
CNS: Hybrid Explainable Artificial Intelligence-Based Sentiment Analysis on COVID-19 Lockdown Using Twitter Data

COVID-19 preventive measures have been a hindrance to millions of people over the globe not only affecting their daily routine but also affecting the mental stability. Among several preventive measures for COVID-19 spread, the lockdown is an important measure which helps considerably reduce the number of cases. The updated news about the COVID-19 is drastically spread in social media. Particularly, Twitter is widely used to share posts and opinions about the COVID-19 pandemic. Sentiment analysis (SA) on tweets can be used to determine different emotions such as anger, disgust, sadness, joy, and trust. But transparence is needed to understand how a given sentiment is evaluated with the black-box machine learning models. With this motivation, this paper presents a new explainable artificial intelligence (XAI)-based hybrid approach to analyze the sentiments of the tweets during different COVID-19 lockdowns. The proposed model attempted to understand the public’s emotions during the first, second, and third lockdowns in India by analyzing tweets on social media, and demonstrates the novelty of the work. A new hybrid model is derived by integrating surrogate model and local interpretable model-agnostic explanation (LIME) model to categorize and predict different human emotions. At the same time, the Top j Similarity evaluation metric is employed to determine the similarity between the original and surrogate models. Furthermore, top words using the feature importance are identified. Finally, the overall emotions during the first, second, and third lockdowns are also estimated. For validating the enhanced outcomes of the proposed method, a series of experimental analysis was performed on the IEEE port and Twitter API dataset. The simulation results highlighted the supremacy of the proposed model with higher average precision, recall, F-score, and accuracy of 95.69%, 96.80%, 95.04%, and 96.76%, respectively. The outcome of the study reported that the public initially had a negative feeling and then started experiencing positive emotions during the third lockdown.
Quantum Teleportation in the Commuting Operator Framework

We introduce a notion of teleportation scheme between subalgebras of semi-finite von Neumann algebras in the commuting operator model of locality. Using techniques from subfactor theory, we present unbiased teleportation schemes for relative commutants $$N'\cap M$$ of a large class of finite-index inclusions $$N\subseteq M$$ of tracial von Neumann algebras, where the unbiased condition means that no information about the teleported observables is contained in the classical communication sent between the parties. For a large class of subalgebras N of matrix algebras $$M_n({\mathbb {C}})$$ , including those relevant to hybrid classical/quantum codes, we show that any tight teleportation scheme for N necessarily arises from an orthonormal unitary Pimsner–Popa basis of $$M_n({\mathbb {C}})$$ over $$N'$$ , generalising work of Werner (J Phys A 34(35):7081–7094, 2001). Combining our techniques with those of Brannan–Ganesan–Harris (J Math Phys 63(11): 112204, 2022) we compute quantum chromatic numbers for a variety of quantum graphs arising from finite-dimensional inclusions $$N\subseteq M$$ .
Edge Weight Updating Neural Network for Named Entity Normalization

Discriminating the matched named entity pairs or identifying the entities' canonical forms are critical in text mining tasks. More precise named entity normalization in text mining will benefit other subsequent text analytic applications. We built the named entity normalization model with a novel edge weight updating neural network. We, next, verify our model's performance on NCBI disease, BC5CDR disease, and BC5CDR chemical databases, which are widely used named entity normalization datasets in the bioinformatics field. We also tested our model with our own financial named entity normalization dataset to validate the efficacy for more general applications. Using the constructed dataset, we differentiate named entity pairs. Our model achieved the highest named entity normalization performances in terms of various evaluation metrics. Our proposed model when tested on four different datasets achieved state-of-the-art results.
Machine Learning-Based Social Distance Detection: An Approach Using OpenCV and YOLO Framework

In the fight against coronavirus, social distance has proven to be a very effective tool. To minimize the risk of the virus spreading through physical contact or proximity, the public is being advised to limit their contact with one another. It has previously been demonstrated that deep learning can solve a variety of issues. In our proposed system, we utilize Python, image analysis, and other learning techniques to monitor social distance in public areas and offices to corroborate the social distancing protocol. By analysing live video feeds from cameras, this tool will track if people remain within a safe distance from each other. With this tool, it is possible to predict people at malls, company offices, and stores to see if they are at an appropriate distance from one another.
Leveraging heuristic client selection for enhanced secure federated submodel learning

As the number of clients for federated learning (FL) has expanded to the billion level, a new research branch named secure federated submodel learning (SFSL) has emerged. In SFSL, mobile clients only download a tiny ratio of the global model from the coordinator’s global. However, SFSL provides little guarantees on the convergence and accuracy performance as the covered items may be highly biased. In this work, we formulate the problem of client selection through optimizing unbiased coverage of item index set for enhancing SFSL performance. We analyze the NP-hardness of this problem and propose a novel heuristic multi-group client selection framework by jointly optimizing index diversity and similarity. Specifically, heuristic exploration on some random client groups are performed progressively for an empirical approximate solution. Meanwhile, private set operations are used to preserve the privacy of participated clients. We implement the proposal by simulating large-scale SFSL application in a lab environment and conduct evaluations on two real-world data-sets. The results demonstrate the performance (w.r.t., accuracy and convergence speed) superiority of our selection algorithm than SFSL. The proposal is also shown to yield significant computation advantage with similar communication performance as SFSL.
Analysis of Steaming Ecoprint Techniques on Various Fabrics

Experimental analysis research is an effort to test textile materials that are suitable for making textile motifs with the stem eco print technique in order to find good quality textile materials. The development of fashion has had a major impact on one's creativity in processing and developing its products. The ecoprint technique is an option in making product variations. Textile materials suitable for stem techniques and raw materials to produce unique fabric motifs. This type of research is descriptive qualitative to observe the color results of the same type of leaves on different types of fabrics. The eco print results from three fabric materials, namely, ero, mori, and cotton, there are significant differences, the influence of the raw material for leaves as the media looks the best for ecoprint media, there are 2, namely cotton. Mori Cotton and Japanese Cotton for clothing materials. While the calico cloth is for household linen
You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding

Stochastic rounding is a critical technique used in low-precision deep neural networks (DNNs) training to ensure good model accuracy. However, it requires a large number of random numbers generated on the fly. This is not a trivial task on the hardware platforms such as FPGA and ASIC. The widely used solution is to introduce random number generators with extra hardware costs. In this paper, we innovatively propose to employ the stochastic property of DNN training process itself and directly extract random numbers from DNNs in a self-sufficient manner. We propose different methods to obtain random numbers from different sources in neural networks and a generator-free framework is proposed for low-precision DNN training on a variety of deep learning tasks. Moreover, we evaluate the quality of the extracted random numbers and find that high-quality random numbers widely exist in DNNs, while their quality can even pass the NIST test suite.
Video Dialog as Conversation About Objects Living in Space-Time

It would be a technological feat to be able to create a system that can hold a meaningful conversation with humans about what they watch. A setup toward that goal is presented as a video dialog task, where the system is asked to generate natural utterances in response to a question in an ongoing dialog. The task poses great visual, linguistic, and reasoning challenges that cannot be easily overcome without an appropriate representation scheme over video and dialog that supports high-level reasoning. To tackle these challenges we present a new object-centric framework for video dialog that supports neural reasoning dubbed $$\texttt{COST}$$ –which stands for Conversation about Objects in Space-Time. Here dynamic space-time visual content in videos is first parsed into object trajectories. Given this video abstraction, $$\texttt{COST}$$ maintains and tracks object-associated dialog states, which are updated upon receiving new questions. Object interactions are dynamically and conditionally inferred for each question, and these serve as the basis for relational reasoning among them. $$\texttt{COST}$$ also maintains a history of previous answers, and this allows retrieval of relevant object-centric information to enrich the answer forming process. Language production then proceeds in a step-wise manner, taking the context of the current utterance, the existing dialog, and the current question. We evaluate $$\texttt{COST}$$ on the AVSD test splits (DSTC7 and DSTC8), demonstrating its competitiveness against state-of-the-arts.
Image Inpainting Using Multi-Scale Feature Joint Attention Model

Nowadays, image inpainting methods based on deep learning would lead to information loss when acquiring deep features, which is not conducive to the restoration of texture details and ignores the inpainting of semantic features. Besides, great majority of them generate inpainting results with unreasonable structures. In response to the above problems, an image inpainting network based on a multi-scale feature joint attention model is proposed. First of all, an image inpainting network using multi-scale feature joint attention model is advanced. When acquiring image depth features, multi-scale feature fusion is used to reduce the loss of information in the convolution process. Afterwards, a joint attention mechanism not only strengthens the model’s ability to repair image semantics, but also ensures the model could generate inpainting results with distinct texture. Last but not least, the style loss and perceptual loss are introduced into the network for the purpose of ensuring the consistency of the detail and style of the inpainting results. The qualitative experimental results on the CelebA-HQ and Places2 datasets and commonly used evaluation indicators such as PSNR and SSIM verify the method is superior to the existing image inpainting methods. Compared with the comparison methods, the proposed method improves PSNR and SSIM by 0.4%−6% and 0.4%−3% respectively.
Local Point Matching Network for Stabilized Crowd Counting and Localization

Accurately estimating the number and location of individuals in a crowd is a difficult but essential task. Most existing methods usually apply density maps or pseudo boxes as regression targets. Those methods are incapable of providing precise localization of people in dense crowds. Recently, some methods for directly predicting a set of head coordinates have shown great superiority. They match all proposal points and target points globally to carry out one-to-one label assignments. However, we find that the global matching algorithm will cause potential instability in the training process, especially in the early stage of training. To alleviate this, we design a local matching point-based framework which only uses anchor points near head centers as positive candidate samples, conducts the one-to-many assignment using a proposed iterative Hungarian algorithm at the first several epochs and performs one-to-one matching before the end of training. Besides, we load the pre-training object detection model to help our network pay more attention to informative regions in the early training stage. Extensive experiments show that our network achieves state-of-the-art counting and localization performance with higher training efficiency on various standard benchmarks, e.g., ShanghaiTech, UCF-CC-50, UCF_QNRF, NUPW.
An RE’22 Workshop on Environment-Driven Requirements Engineering (EnviRE’22)

We organize a one-day workshop on Environment-Driven Requirements Engineering (EnviRE’22) in conjunction with the 30th IEEE International Requirements Engineering Conference. With the rising influence of AI, IoT, and cyber-physical systems, we realize that the environment, in which the software operates, becomes more open and evolves rapidly with stakeholders’ changing needs. EnviRE’22 features one keynote and five accepted papers. Overall, the workshop is aimed at bringing the interested researchers and practitioners together, exchanging ideas and visions, and exploring a set of open problems to pursue in the years to come.