Reviewerly/exHarmony huggingface dataset:
Federated learning: Applications, challenges and future directions

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions.
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts

Existing and planned legislation stipulates various obligations to provide information about machine learning algorithms and their functioning, often interpreted as obligations to “explain”. Many researchers suggest using post-hoc explanation algorithms for this purpose. In this paper, we combine legal, philosophical and technical arguments to show that post-hoc explanation algorithms are unsuitable to achieve the law’s objectives. Indeed, most situations where explanations are requested are adversarial, meaning that the explanation provider and receiver have opposing interests and incentives, so that the provider might manipulate the explanation for her own ends. We show that this fundamental conflict cannot be resolved because of the high degree of ambiguity of post-hoc explanations in realistic application scenarios. As a consequence, post-hoc explanation algorithms are unsuitable to achieve the transparency objectives inherent to the legal norms. Instead, there is a need to more explicitly discuss the objectives underlying “explainability” obligations as these can often be better achieved through other mechanisms. There is an urgent need for a more open and honest discussion regarding the potential and limitations of post-hoc explanations in adversarial contexts, in particular in light of the current negotiations of the European Union’s draft Artificial Intelligence Act.
Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.
GMLight: Lighting Estimation via Geometric Distribution Approximation

Inferring the scene illumination from a single image is an essential yet challenging task in computer vision and computer graphics. Existing works estimate lighting by regressing representative illumination parameters or generating illumination maps directly. However, these methods often suffer from poor accuracy and generalization. This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation. We parameterize illumination scenes in terms of the geometric light distribution, light intensity, ambient term, and auxiliary depth, which can be estimated by a regression network. Inspired by the earth mover's distance, we design a novel geometric mover's loss to guide the accurate regression of light distribution parameters. With the estimated light parameters, the generative projector synthesizes panoramic illumination maps with realistic appearance and high-frequency details. Extensive experiments show that GMLight achieves accurate illumination estimation and superior fidelity in relighting for 3D object insertion. The codes are available at https://github.com/fnzhan/Illumination-Estimation.
Implementation and Evaluation of Intelligence Incorporated Tutoring System

This research article elaborates the explication of domain knowledge into a tutorable form and illustrates the prediction of tutoring strategy prior to beginning of the tutoring session. The proposed intelligent tutoring system is christened as SeisTutor. It impersonates human intelligence by adjudging best tutoring strategy for the learner. An empirical analysis has been performed on group of applicants having fundamental knowledge of the domain. Experimental analysis reveals that the learners who have undergone tutoring through ‘SeisTutor’ embedded with intelligence present an overall 44.5 % learning gain as against that of 24.8 % shown by learners who underwent tutoring through the ‘SeisTutor’, while it does not impersonate intelligence by choosing content and style for the learner.
PackerRobo: Model-based robot vision self supervised learning in CART

Robots are most widely used to replace human contribution with machine generated response. When humans interact with robots, its mandatory for both to forecast actions based on current conditions. Huge efforts have been channelized towards attaining this perfect coordination. To decipher complex environments, the inference of robotic mobility and alteration of random unstructured scenarios is a complicated task in the field of visual processing and imaging. To address this issue, a new Vision-Based Interaction Model based on deep neural networks has been suggested. The proposed model solves the error amplification issue by the application of past inputs through features as reposed by a Deep Belief Network (DBN). In addition, a novel Vision-Based Robotics Learning model is also proposed for scene understanding and recognition using deep neural network understanding. Moreover, a vision theory-based smart learning algorithm is also suggested to decide positive possible outcomes.Therefore, the model is capable of using object motions to extract relevant information used for Turning, Griping and object mobility.To validate the suggested model, a number of experiments have been performed on benchmark datasets and it showed a higher performance as evaluated against some of the niche methods.
Understanding and Improving Critical Metrology. Quenching Superradiant Light-Matter Systems Beyond the Critical Point

We carefully examine critical metrology and present an improved critical quantum metrology protocol which relies on quenching a system exhibiting a superradiant quantum phase transition beyond its critical point. We show that this approach can lead to an exponential increase of the quantum Fisher information in time with respect to existing critical quantum metrology protocols relying on quenching close to the critical point and observing power law behaviour. We demonstrate that the Cram\'er-Rao bound can be saturated in our protocol through the standard homodyne detection scheme. We explicitly show its advantage using the archetypal setting of the Dicke model and explore a quantum gas coupled to a single-mode cavity field as a potential platform. In this case an additional exponential enhancement of the quantum Fisher information can in practice be observed with the number of atoms $N$ in the cavity, even in the absence of $N$-body coupling terms.
Blockchain technology acceptance by investment professionals: a decomposed TPB model

Purpose The rapid emergence and acceptance of blockchain applications by investment professionals has made this study significant. The study aims to examine the degree of trust and acceptance of blockchain technology in the Indian financial services industry. Design/methodology/approach The decomposed theory of planned behaviour (DTPB) model is investigated using responses from 200 investment professionals to an online survey on blockchain technology adoption, and partial least squares structural equation modelling using SmartPLS 3.0 is used to analyse the results. Findings In general, the results support a DTPB and offer the best fit to the data. This study may have significant drawbacks in predicting blockchain technology’s acceptance by investment professionals. Furthermore, the findings suggest that the research instruments used in this study, which engage users in business settings, are equally successful in the context of investment professionals. Research limitations/implications In predicting blockchain acceptance by investing experts, this study may have significant drawbacks. Practical implications As a result of TAM studies, it has been determined that perceived usefulness is more essential than perceived ease of use. Therefore, investment professionals may have discovered significant subjective normative impacts on behavioural intention. Management must devise techniques for implementing blockchain technology in digital financial services that are compatible with users’ workplaces. Originality/value Users may benefit from this research by concentrating on blockchain acceptance to improve digital banking services.
Telecom-Wavelength Quantum Repeater Node Based on a Trapped-Ion Processor

A quantum repeater node is presented based on trapped ions that act as single-photon emitters, quantum memories, and an elementary quantum processor. The node's ability to establish entanglement across two 25-km-long optical fibers independently, then to swap that entanglement efficiently to extend it over both fibers, is demonstrated. The resultant entanglement is established between telecom-wavelength photons at either end of the 50 km channel. Finally, the system improvements to allow for repeater-node chains to establish stored entanglement over 800 km at hertz rates are calculated, revealing a near-term path to distributed networks of entangled sensors, atomic clocks, and quantum processors.
The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms

In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their confidential nature. This has hampered the development of reproducible and generalisable machine learning applications in health care. Here we introduce the Health Gym - a growing collection of highly realistic synthetic medical datasets that can be freely accessed to prototype, evaluate, and compare machine learning algorithms, with a specific focus on reinforcement learning. The three synthetic datasets described in this paper present patient cohorts with acute hypotension and sepsis in the intensive care unit, and people with human immunodeficiency virus (HIV) receiving antiretroviral therapy. The datasets were created using a novel generative adversarial network (GAN). The distributions of variables, and correlations between variables and trends in variables over time in the synthetic datasets mirror those in the real datasets. Furthermore, the risk of sensitive information disclosure associated with the public distribution of the synthetic datasets is estimated to be very low.
Lattice-Based SNARKs: Publicly Verifiable, Preprocessing, and Recursively Composable

A succinct non-interactive argument of knowledge (SNARK) allows a prover to produce a short proof that certifies the veracity of a certain NP-statement. In the last decade, a large body of work has studied candidate constructions that are secure against quantum attackers. Unfortunately, no known candidate matches the efficiency and desirable features of (pre-quantum) constructions based on bilinear pairings. In this work, we make progress on this question. We propose the first lattice-based SNARK that simultaneously satisfies many desirable properties: It (i) is tentatively post-quantum secure, (ii) is publicly-verifiable, (iii) has a logarithmic-time verifier and (iv) has a purely algebraic structure making it amenable to efficient recursive composition. Our construction stems from a general technical toolkit that we develop to translate pairing-based schemes to lattice-based ones. At the heart of our SNARK is a new lattice-based vector commitment (VC) scheme supporting openings to constant-degree multivariate polynomial maps, which is a candidate solution for the open problem of constructing VC schemes with openings to beyond linear functions. However, the security of our constructions is based on a new family of lattice-based computational assumptions which naturally generalises the standard Short Integer Solution (SIS) assumption.
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection

In this paper, we delve into two key techniques in Semi-Supervised Object Detection (SSOD), namely pseudo labeling and consistency training. We observe that these two techniques currently neglect some important properties of object detection, hindering efficient learning on unlabeled data. Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance. To address the problems incurred by noisy pseudo boxes, we design Noisy Pseudo box Learning (NPL) that includes Prediction-guided Label Assignment (PLA) and Positive-proposal Consistency Voting (PCV). PLA relies on model predictions to assign labels and makes it robust to even coarse pseudo boxes; while PCV leverages the regression consistency of positive proposals to reflect the localization quality of pseudo boxes. Furthermore, in consistency training, we propose Multi-view Scale-invariant Learning (MSL) that includes mechanisms of both label- and feature-level consistency, where feature consistency is achieved by aligning shifted feature pyramids between two images with identical content but varied scales. On COCO benchmark, our method, termed PSEudo labeling and COnsistency training (PseCo), outperforms the SOTA (Soft Teacher) by 2.0, 1.8, 2.0 points under 1%, 5%, and 10% labelling ratios, respectively. It also significantly improves the learning efficiency for SSOD, e.g., PseCo halves the training time of the SOTA approach but achieves even better performance. Code is available at https://github.com/ligang-cs/PseCo .
FedALA: Adaptive Local Aggregation for Personalized Federated Learning

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy. Code is available at https://github.com/TsingZ0/FedALA.
Use of the IEEE Codes of Conduct to Resolve Legal Disputes

In the absence of other published standards of care, it is reasonable for contractual parties to rely on an applicable, widely available code of conduct to guide expectations. The authors illustrate the current application of CoCs with a fictional enterprise resource planning (ERP) system implementation failure that is a compilation of real-life cases. Subject to binding panel arbitration, the plaintiff and defendant in the case presented conflicting interpretations of the same facts: From the plaintiff's perspective, the defendant failed to migrate the ERP system as promised; the defendant countered that defective and poor-quality data delayed the migration. Using the ACM/IEEE-CS CoC as a reference, expert testimony convinced the arbitration panel that the defendant's position was untenable, and the panel accordingly awarded the plaintiff a multimillion-dollar judgment.
Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising

The sparse autoencoder stacked inside deep neural network has been a powerful tool for image inpainting. We propose a new method for video inpainting as well as fingerprint denoising based on the Iterative Application of Autoencoders (IAA). Instead of using either a single sparse autoencoder or denoising autoencoder, multiple autoencoders are concatenated in an iterative manner until a desired output is acquired from the last stage. This method allows us to reduce loss via iteration and reuse a well-defined network. Results from two public challenges on video inpainting and fingerprint denoising suggest that performance is excellent and it can be a useful approach for image inpainting in general. Our codes are available online.
Human Motion Sequence Recognition Based on Feature Selection and Support Vector Machine

Aiming at the problem of human motion sequence recognition, algorithm based on feature selection and support vector machine is proposed. Firstly, the feature extraction of human motion sequences is obtained by key frame and human joint angle calculation. Then, based on the Pearson correlation coefficient and CFS evaluation function, the algorithm of relevance feature selection is used to search the optimal feature subset from the original feature set. By reducing the dimension of the feature set, the difficulty of classification recognition is reduced. In the classification process, the support vector machine is used as the classifier to complete the recognition task of the human motion sequence. Through the recognition experiment and the contrast experiment, the effectiveness of the recognition algorithm based on feature selection and support vector machine is proved.
Smart Health: Natural Language Processing based Question and Answering Retrieval System in Healthcare

The healthcare system may be a data-driven trade that consists of large and growing volumes of narrative data obtained from discharge summaries/reports, physicians case notes, pathologists similarly as radiologists reports. This data is typically held on in unstructured and non-standardized formats in electronic healthcare systems that create it tough for the systems to grasp {the data the knowledge the data} contents of the narrative information. Thus, access to valuable and pregnant attention data for deciding may be a challenge. all the same, natural language processing (NLP) techniques are accustomed structure narrative data inattention. Thus, NLP techniques have the potential to capture unstructured healthcare data, analyze its grammatical structure. Question and Answering System is one of the major researches is in Natural Language. Main challenges of Question and Answer system gives an exact answer of question which gives by Patient (User). Question and Answering system can be classified into three categories are an open domain, closed domain, and restricted domain. Using advanced Natural Language Processing tool, we will be developed a framework for question answering system. In this paper, we work on the restricted domain question answering system. Proposed system works on keyword and question matching and returns a precise answer to the question.
The Correlation between Students’ Motivation in Learning English and Their Achievement at Grade XI of SMK Negeri 1 Pleret in the Academic Year 2019/2020

This research aims at finding: (1) the motivation of the second grade students at SMKN 1 Pleret (2) the English achievement of second grade students at SMKN 1 Pleret (3) the correlation between students’ motivation in learning English and their achievement of the second grade students of SMK Negeri 1 Pleret Bantul. Thus, this research belongs to correlation research. The population of this research was the second grade students of SMK Negeri 1 Pleret with the total population were 232 students. The number of samples were 142 students, to know the motivation of the student the reseacher taken data by the random sampling technique. The data of students’ motivation in learning english was collected by distributing questionnaire and to know the data of students’achievement was collected with documentation from the English teacher this English learning score was obtained from the students’ midterm test. Then the questionaire was adopted from Children (MALESC) by Carreira about motivation questionnaire for learners. Data analysis was carried out using descriptive statistic and inferential statistic using IBM SPSS. The descriptive analysis used maximum, minimum, means, and standard deviation of student’s motivation and students achievement. The inferential analysis was done by normality, linearity, and hypothesis tests. The results of this study indicated that there was no significant correlation between student’s motivation and their english achievement at SMK Negeri 1 Pleret Bantul in Academic Year 2019/2020. It was proved by the result of the sig value. The sig value compared to the correlation coefficient was .308 > 0.05 and the value of r coefficient was - 0.86 (negative correlation) that indicated there was a weak correlation.Thus, thehypothesis (H1) which states that there was significant correlation between students motivation in learning English and their achievement was rejected and the null hypothesis (H0) that there was no significant correlation between students motivation in learning English and their achievement was accepted.