Reviewerly/exHarmony huggingface dataset:
Automation of Minutes of Meeting (MoM) using Natural Language Processing (NLP)

Minutes of Meeting (i.e MoM) is a part of almost every professional meeting which paves way to track or handle workflow, and helps gather gist of the meeting content for the attendees as well as non attendees of the meet who may need the information to act on. Taking MoM has been made manual till now which has one person dedicatedly listening to each and every conversation and taking notes on what needs to be cached. Automating this process helps any organization or management to have their MoM companion staged anytime, anywhere they need without human dependence.
Gaussian Process Self-triggered Policy Search in Weakly Observable Environments

The environments of such large industrial machines as waste cranes in waste incineration plants are often weakly observable, where little information about the environ-mental state is contained in the observations due to technical difficulty or maintenance cost (e.g., no sensors for observing the state of the garbage to be handled). Based on the findings that skilled operators in such environments choose predetermined control strategies (e.g., grasping and scattering) and their durations based on sensor values, we propose a novel non-parametric policy search algorithm: Gaussian process self-triggered policy search (GPSTPS). GPSTPS has two types of control policies: action and duration. A gating mechanism either maintains the action selected by the action policy for the duration specified by the duration policy or updates the action and duration by passing new observations to the policy; therefore, it is categorized as self-triggered. GPSTPS simultaneously learns both policies by trial and error based on sparse GP priors and variational learning to maximize the return. To verify the performance of our proposed method, we conducted experiments on garbage-grasping-scattering task for a waste crane with weak observations using a simulation and a robotic waste crane system. As experimental results, the proposed method acquired suitable policies to determine the action and duration based on the garbage's characteristics.
Quantum network coding based on six-qubit entangled state

In order to improve the security and transmission efficiency of quantum network coding (QNC), we propose a new QNC scheme based on butterfly network. The scheme uses two groups of GHZ states to distribute quantum entangled states through intermediate nodes, so as to establish the connection between multiple nodes and achieve safe and reliable quantum traffic. This paper can not only improve the security of QNC, but also reduce the transmission of particles and improve the anti-interference ability of information. Compared with the traditional network coding method based on a priori entanglement, our scheme is a better attempt.
Application of Gesture Recognition Based on Spatiotemporal Graph Convolution Network in Virtual Reality Interaction

Aiming at the low recognition rate of traditional gesture, a gesture recognition algorithm based on spatiotemporal graph convolution network is proposed in this paper. Firstly, the dynamic gesture data were preprocessed, including removing invalid gesture frames, completing gesture frame data and normalization of joint length. Then, the key frame of the gesture is extracted according to the given coordinate information of the hand joint. A connected graph is constructed according to the natural connection of time series information and gesture skeleton. A spatio-temporal convolutional network with multi-attention mechanism is used to learn spatio-temporal features to predict gestures. Finally, experiments are carried out on 14 types of gesture dataset in DHG-14 dynamic gesture dataset. Experimental results show that this method can recognize gestures accurately.
Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation

This paper provides a solution for multi-target tracking with unknown detection probability. For the standard Poisson Multi-Bernoulli Mixture (PMBM) filter, the detection probability is generally considered a priori. However, affected by sensors, the features used for detection, and other environmental factors, the detection probability is time-varying and unknown in most multi-target tracking scenarios. Therefore, the standard PMBM filter is not feasible in practical scenarios. In order to overcome these practical restrictions, we improve the PMBM filter with unknown detection probability using the feature used for detection. Specifically, the feature is modeled as an inverse gamma distribution and the target kinematic state is modeled as a Gaussian distribution; the feature is integrated into the target kinematic state to iteratively estimate the target detection probability with the motion state. Our experimental results show that the proposed method outperforms the standard PMBM filter and the robust PMBM filter based on Beta distribution in the scenarios with unknown and time-varying detection probability. Further, we apply the proposed filter to a simulated infrared image to confirm the effectiveness and robustness of the filter.
What is the Cryptocurrency? Is it a Threat to Our National Security, Domestically and Globally?

Since the rise of Bitcoin in 2009 and recent interest in digital currency is on an ascending trend, the question is how far will it penetrate in financial system other industries? How stable is this currency? With recent comments from CEO of Tesla Elon Mask, we saw a crash of 14% in Bitcoin stock overnight trades of any other existing crypto currency stock in open market came to a halt. Thus, one should be concern about stability of digital currency, providing no centralized banking standing behind any of these digital crypto currencies like we see in traditional banking business where currencies are being backed up and insured by Federal governments like FDIC in United States. In this short review, we have brought a high level of information from the web at a very holistic level, yet in a layman language for those readers that want to have a quick understanding of Crypto Currencies or Digital Currencies that seem a hot topic of today’s market.
VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution

Most of the existing video face super-resolution (VFSR) methods are trained and evaluated on VoxCeleb1, which is designed specifically for speaker identification and the frames in this dataset are of low quality. As a consequence, the VFSR models trained on this dataset can not output visual-pleasing results. In this paper, we develop an automatic and scalable pipeline to collect a high-quality video face dataset (VFHQ), which contains over 16, 000 high-fidelity clips of diverse interview scenarios. To verify the necessity of VFHQ, we further conduct experiments and demonstrate that VFSR models trained on our VFHQ dataset can generate results with sharper edges and finer textures than those trained on VoxCeleb1. In addition, we show that the temporal information plays a pivotal role in eliminating video consistency issues as well as further improving visual performance. Based on VFHQ, by analyzing the benchmarking study of several state-of-the-art algorithms under bicubic and blind settings.
Federated Learning with Dynamic Model Exchange

Large amounts of data are needed to train accurate robust machine learning models, but the acquisition of these data is complicated due to strict regulations. While many business sectors often have unused data silos, researchers face the problem of not being able to obtain a large amount of real-world data. This is especially true in the healthcare sector, since transferring these data is often associated with bureaucratic overhead because of, for example, increased security requirements and privacy laws. Federated Learning should circumvent this problem and allow training to take place directly on the data owner’s side without sending them to a central location such as a server. Currently, there exist several frameworks for this purpose such as TensorFlow Federated, Flower, or PySyft/PyGrid. These frameworks define models for both the server and client since the coordination of the training is performed by a server. Here, we present a practical method that contains a dynamic exchange of the model, so that the model is not statically stored in source code. During this process, the model architecture and training configuration are defined by the researchers and sent to the server, which passes the settings to the clients. In addition, the model is transformed by the data owner to incorporate Differential Privacy. To trace a comparison between central learning and the impact of Differential Privacy, performance and security evaluation experiments were conducted. It was found that Federated Learning can achieve results on par with centralised learning and that the use of Differential Privacy can improve the robustness of the model against Membership Inference Attacks in an honest-but-curious setting.
InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol

Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein docking or structure prediction. One such method is Rosetta FlexPepDock which consistently refines coarse peptide-protein models into sub-Ångström precision using Monte-Carlo simulations and statistical potentials. Deep learning has recently seen increased use in protein structure prediction, with graph neural networks used for protein model quality assessment.Here, we introduce a graph neural network, InterPepScore, as an additional scoring term to complement and improve the Rosetta FlexPepDock refinement protocol. InterPepScore is trained on simulation trajectories from FlexPepDock refinement starting from thousands of peptide-protein complexes generated by a wide variety of docking schemes. The addition of InterPepScore into the refinement protocol consistently improves the quality of models created, and on an independent benchmark on 109 peptide-protein complexes its inclusion results in an increase in the number of complexes for which the top-scoring model had a DockQ-score of 0.49 (Medium quality) or better from 14.8% to 26.1%.InterPepScore is available online at http://wallnerlab.org/InterPepScore.Supplementary data are available at Bioinformatics online.
Big Data Analysis for E-Trading Flower Shop Management System

Flower shops in Europe have traditionally been a billion-euro business. From a financial, market, customer and competitive standpoint, the study aims to investigate the business strategy in further detail. It provides evidence-based guidance on how to run a floral store. When it comes to running a flower store, they talk about the difficulties they've run into when trying to find the best floral suppliers. When it comes to impressing consumers, flower arrangements and services need to be on point at all times. There is no doubt that they will come back. That is to say, those with more disposable money frequently spend a greater amount to have a more unique shopping experience. The fragile economic climate may have a negative impact on sales, and the company must plan for this possibility. If a firm is successful, its business model will be copied by competitors as well. Even more crucially, the flower business must continually reinvent itself.
Generation of high-quality image using generative adversarial network

Deep learning enables a computer to construct complex concepts from simpler ones by learning data representation with abstraction. However, deep learning requires a large number of samples for training to prevent over-fitting. Generative adversarial network (GAN) is a kind of unsupervised learning that can be used to synthesize images. Generating higher quality images using GAN is conducive to improving the training efficiency of deep learning. How to generate high quality and high-diversity samples using GAN to expand the sample size for deep learning training is quite appealing. In this paper, the concept of generative adversarial network including network structure and four popular loss functions is introduced. Furthermore, three typical GAN-based models to improve the image quality or diversity have been presented. Finally, the paper discusses the shortcomings of GANs to steer future research in the right direction. Generation of high quality images using GAN has great potential to help deep learning models to reduce the over-fitting problem in the near future.
Thermal Fisher information and entropy squeezing for superconducting qubits

We investigate the effects of temperature on quantum Fisher information (QFI), fidelity, and entropy squeezing in two interacting superconducting qubits (SCQs). We show how the variations in these quantum quantifiers may be controlled by carefully selecting the model's physical parameters (PPM). We obtain a trapping phenomena for Fisher information, allowing us to improve the QFI and retain the amount of information while also preserving it from information loss due to temperature impacts. We evaluate the fidelity of the SCQ to explain the dynamic aspects of the QFI in relation to the PPM. Furthermore, we examine the influence of the PPM on the variation in the squeezing entropy for SCQs. The results indicate how, in terms of quantum measurement theory, the system under consideration might be valuable in enabling the realization of experiments under optimal conditions.
Fisher, Bayes, and Predictive Inference

We review historically the position of Sir R.A. Fisher towards Bayesian inference and, particularly, the classical Bayes–Laplace paradigm. We focus on his Fiducial Argument.
An REE-independent Approach to Identify Callers of TEEs in TrustZone-enabled Cortex-M Devices

Internet of Things (IoT) devices are becoming increasingly ubiquitous in our lives, from personal health monitoring to house and factory management. Further, IoT devices are becoming increasingly complex, and ensuring their security is of paramount importance. As a result, they started to include Trusted Execution Environments (TEEs) to protect security-critical IoT operations. This paper focuses on improving the security of the next-generation IoT devices by introducing Secure Informer, an identification and authentication mechanism for ARM TrustZone for Cortex-M. Under Secure Informer, the TEE can directly determine the Rich Execution Environment (REE) context without introducing additional communication or dependency on the REE software stack. We implement our solution for ARMV8-M architecture, showing its efficacy with no need to change the source code of the REE. Empirical results show that Secure Informer can help mitigate confused deputy attacks targeting TEE-running services while only incurring an average 3.2% overhead on the device performance and requiring an additional 464 lines (52 bytes in the compiled binary file) to the TEE.
Communication-Efficient Federated Learning For Massive MIMO Systems

Federated learning (FL) is an emerging distributed learning algorithm where the process of data acquisition and computation are decoupled to preserve users&#x2019; data privacy. In the training process, model weights have to be updated at both base station (BS) and local users sides. These weights, when exchanged between users and BS, are subjected to imperfections in uplink (UL) and downlink (DL) transmissions due to limited reliability of wireless channels. In this paper, for a FL algorithm in a single-cell massive MIMO cellular communication system, we investigate the impacts of both DL and UL transmissions and improve the communication-efficiency by adjusting global communication rounds, transmit power and average codeword length after quantization. Simulation results on standard MNIST dataset with both i.i.d and non-i.i.d training data distributions are also presented. Our simulation results have shown accelerated learning for various local steps and transmit power. The network energy consumption has been reduced while achieving similar testing accuracy at higher iterations.
Stealthy Inference Attack on DNN via Cache-based Side-Channel Attacks

The advancement of deep neural networks (DNNs) motivates the deployment in various domains, including image classification, disease diagnoses, voice recognition, etc. Since some tasks that DNN undertakes are very sensitive, the label information is confidential and contains a commercial value or critical privacy. This paper demonstrates that DNNs also bring a new security threat, leading to the leakage of label information of input instances for the DNN models. In particular, we leverage the cache-based side-channel attack (SCA), i.e., Flush-Reload on the DNN (victim) models, to observe the execution of computation graphs, and create a database of them for building a classifier that the attacker can use to decide the label information of (unknown) input instances for victim models. Then we deploy the cache-based SCA on the same host machine with victim models and deduce the labels with the attacker's classification model to compromise the privacy and confidentiality of victim models. We explore different settings and classification techniques to achieve a high attack success rate of stealing label information from the victim models. Additionally, we consider two attacking scenarios: binary attacking identifies specific sensitive labels and others while multi-class attacking targets recognize all classes victim DNNs provide. Last, we implement the attack on both static DNN models with identical architectures for all inputs and dynamic DNN models with an adaptation of architectures for different inputs to demonstrate the vast existence of the proposed attack, including DenseNet 121, DenseNet 169, VGG 16, VGG 19, MobileNet v1, and MobileNet v2. Our experiment exhibits that MobileNet v1 is the most vulnerable one with 99% and 75.6% attacking success rates for binary and multi-class attacking scenarios, respectively.
CR-Spectre: Defense-Aware ROP Injected Code-Reuse Based Dynamic Spectre

Side-channel attacks have been a constant threat to computing systems. In recent times, vulnerabilities in the architecture were discovered and exploited to mount and execute a state-of-the-art attack such as Spectre. The Spectre attack exploits a vulnerability in the Intel-based processors to leak confidential data through the covert channel. There exist some defenses to mitigate the Spectre attack. Among multiple defenses, hardware-assisted attack/intrusion detection (HID) systems have received overwhelming response due to its low overhead and efficient attack detection. The HID systems deploy machine learning (ML) classifiers to perform anomaly detection to determine whether the system is under attack. For this purpose, a performance monitoring tool profiles the applications to record hardware performance counters (HPC), utilized for anomaly detection. Previous HID systems assume that the Spectre is executed as a standalone application. In contrast, we propose an attack that dynamically generates variations in the injected code to evade detection. The attack is injected into a benign application. In this manner, the attack conceals itself as a benign application and gen-erates perturbations to avoid detection. For the attack injection, we exploit a return-oriented programming (ROP)-based code-injection technique that reuses the code, called gadgets, present in the exploited victim's (host) memory to execute the attack, which, in our case, is the CR-Spectre attack to steal sensitive data from a target victim (target) application. Our work focuses on proposing a dynamic attack that can evade HID detection by injecting perturbations, and its dynamically generated variations thereof, under the cloak of a benign application. We evaluate the proposed attack on the MiBench suite as the host. From our experiments, the HID performance degrades from 90% to 16%, indicating our Spectre-CR attack avoids detection successfully.
Research on self‐learning system with “Internet + Education” innovative talents education mode under big data background

Abstract In the “Internet + Education” mode, a big data analysis of innovative talents education model is conducted to improve the quantitative evaluation ability of innovative talent education and training, and the “Internet + Education” innovative talent education model under the background of big data is proposed based on segmented information fusion and regression statistical analysis. More and more attention is attracted by the continuous advancement of innovative talent training models. The resource data mining and information processing research of innovative talent education models are conducted in related literature and have achieved certain research results. A big data analysis model for the cultivation of innovative talents is constructed and a structured big data information reorganization method is adopted to conduct information fusion processing of the “Internet + Education” innovative talents education model. Features describing the associated information of the talent cultivation model are extracted and the segmented information fusion method is adopted for feature clustering processing. The autoregression analysis of innovative talents cultivation evaluation ability is conducted based on feature clustering results and a test statistic model is constructed for effective analysis of the “Internet + Education” innovative talents education model under big data background. As a result, comprehensive and fuzzy decision‐making on the talent education model is realized according to judgment statistics. Simulation results show that the accuracy of a quantitative assessment of the “Internet + Education” innovative talents education model is higher with this method and the convergence of regression analysis is better. This shows that this method can effectively instruct the innovation of the talent education model and novel technologies can be more utilized by the efficiency and performance of the method.
How the Notion of “Knowledge Rich Context” Can Be Characterized Today

This paper studies how the notion of Knowledge Rich Contexts (KRCs), initially defined as passages of text allowing access to elements that define terms, has evolved. It discusses related work in Natural Language Processing (NLP), which has led to disciplinary convergence. It mainly presents the way in which KRCs can manifest themselves within a corpus, in the form of explicit or implicit markers and lexico-syntactic or morphological markers. Extra-linguistic elements are examined through the study of the domain and the textual genre of the corpus studied, and by the adaptation of KRCs to a particular demand (evolution in time, interdisciplinarity…). All types of functioning are illustrated with real data from different corpus-based studies. The study of the notion of KRC allows us to address fundamental questions in the linguistic study of specialized texts, such as the link between form and meaning and between linguistic and extra-linguistic contexts.
Wasserstein Uncertainty Estimation for Adversarial Domain Matching

Domain adaptation aims at reducing the domain shift between a labeled source domain and an unlabeled target domain, so that the source model can be generalized to target domains without fine tuning. In this paper, we propose to evaluate the cross-domain transferability between source and target samples by domain prediction uncertainty, which is quantified via Wasserstein gradient flows. Further, we exploit it for reweighting the training samples to alleviate the issue of domain shift. The proposed mechanism provides a meaningful curriculum for cross-domain transfer and adaptively rules out samples that contain too much domain specific information during domain adaptation. Experiments on several benchmark datasets demonstrate that our reweighting mechanism can achieve improved results in both balanced and partial domain adaptation.