Reviewerly/exHarmony huggingface dataset:
Entangled states shaping with CV states of definite parity

We present a new method to entangle continuous variable (CV) states of certain parity and photonic states for the purpose of generating optical hybrid cluster (HC) states. To do it we introduce two families of the CV states of definite parity which stems from single mode squeezed vacuum (SMSV) state. Potential to apply the CV states of certain parity is high. We report on the generation of the even/odd Schrödinger cat state like (SCS-like) states whose fidelities with even/odd SCS of amplitude of [Formula: see text] are more of [Formula: see text], when 30,31 photons are detected in auxiliary mode of input SMSV state initially mixed with single photon. We show that the quantum efficiency of a photon number resolving (PNR) detector is crucial to maintaining the success rate of even/odd SCSs generator at an acceptable level. The scheme with delocalized photon implements deterministic imperfect entanglement operation between macro and micro states. We show that the beam splitter implements the two-qubits operation [Formula: see text] (CZ) for input CV states of definite parity and photonic states, provided that certain result is detected in measurement mode. An extension of the entangling operation for two entangled delocalized photons (TEDP) allows one to realize three-qubit HC state. Seven-qubit HC state is the result of conjunction of two three-qubit HC states through TEDP state.
Incremental Learning Vector Auto Regression for Forecasting with Edge Devices

It is common to forecast time-series data in a cloud server environment by building a forecasting model after collecting all the time-series data at the server-side. However, this may not be efficient in time-critical forecasting, control, and decision-making due to high latency, bandwidth, and network connectivity issues. Hence, edge devices can be employed to make quick forecasting on a real-time basis. However, due to limited computing resources and processing power, edge devices cannot handle a huge volume of multivariate time-series data. Therefore, it is desirable to develop an algorithm that trains and updates a forecasting model incrementally. This can be done by using a small chunk of multivariate time-series data without sacrificing the forecasting accuracy, while training and inference can be executed in the edge device itself. In this context, we propose a new forecasting method called Incremental Learning Vector Auto Regression (ILVAR). It works by minimizing the variance difference between actual and forecasted values as a new chunk of time-series data arrives sequentially and thereby it updates the forecasting model incrementally. To show the effectiveness of the proposed method, experiments were performed on 11 publicly available datasets from diverse domains using Raspberry Pi-2 as an edge device and evaluated using five metrics such as MAPE, RMSE, $\mathrm{R}^{2}$ score, Computational time, and Memory consumption for 1-step and 24-step ahead forecasting tasks. The performance was compared with the state-of-the-art methods such as Vector Auto Regression (VAR), Incremental Learning Extreme Learning Machine (ILELM), and Incremental Learning Long Short-Term Memory (ILLSTM). These experimental results suggest that our proposed method performs better than existing methods and is able to achieve the desired performance for forecasting with edge devices.
Single Image And Video Dehazing: A Dark Channel Prior (DCP)-based Approach

One of the major challenges associated with real-time image and video processing is severe deterioration in the signal quality due to the presence of detrimental environmental factors such as fog, haze and smoke. With the recent advancements in computer vision and image processing, several image and video dehazing techniques have been proposed in existing literature. This paper presents a state-of-the-art comparison of popular dehazing schemes along with an extension of the dark channel prior (DCP)-based approach for haze removal in videos. Lastly, all schemes are implemented to present an exhaustive performance comparison in terms of accuracy and other quantitative indices.
Performance Evaluation of a DQN-Based Autonomous Aerial Vehicle Mobility Control Method in an Indoor Single-Path Environment with a Staircase

The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms, which uses deep neural network structure to estimate the Q-value in Q-learning. In the previous work, we designed and implemented a DQN-based Autonomous Aerial Vehicle (AAV) testbed and proposed a Tabu List Strategy based DQN (TLS-DQN). In this paper, we propose a DQN-based AAV mobility control method. The performance evaluation results show that the proposed method can decide and reach the destination in an indoor single-path environment with a staircase.
Training Artificial Neural Networks with Gradient and Coarse-Level Correction Schemes

A two-level learning scheme for multi-layer neural networks is presented. This scheme combines a standard gradient scheme with a two-level correction procedure in such a way that the reduction of the value of the given loss function is improved. This construction is based on the knowledge of the Hessian of the loss function, which is used to estimate an optimal learning rate for the gradient scheme and in the construction the two-level correction procedure. The proposed two-level approach is supported by theoretical consideration in the framework of multilevel optimization schemes and by numerical investigation. Results of numerical experiments with nonlinear regression on small datasets successfully validate the proposed two-level learning scheme.
FastKeeper: A Fast Algorithm for Identifying Top-k Real-time Large Flows

Precise identification of large flows is a critical task in network traffic measurement. Previous works focus on identification of elephant flows (i.e. large flows from the beginning of the measurement). However, we generally observe that the flow rates change periodically and abruptly. In addition, the large flows may become small flows over time. Thus, elephant flows are not equal to the real-time large flows, and previous works cannot be used for the identification of the real-time large flows that is more meaningful for modern network applications. Nevertheless, identification of real-time large flows is challenging in that it requires accurate measurement of real-time flow rates and timely replacement of flows that have become small in the measurement data structure. In this paper, we propose FastKeeper to identify real-time large flows with a primary goal of simultaneously achieving low over-head, high performance and high accuracy. FastKeeper employs a sliding-window-based algorithm for accurate measurement of real-time flow rates and a bitmap-voting algorithm for timely replacement of flows that have become small in the measurement data structure. We evaluate it on DPDK using the traces from an operator network, and the evaluation demonstrates that it achieves high accuracy (98&#x0025;) and processing throughput (25.33Mpps).
A Wavelength Tunable Optical Neuron Based on a Fiber Laser

With the increased applications of 5G communications, Internet of Things, autonomous driving etc., the world has been producing a large amount of data. Processing massive data intelligently with higher speed and lower energy consumption is hence extensively demanded. Photonic neuromorphic computing, which mimics the information processing manner of biological neural networks like human brains with optical components and systems, has attracted growing attention due to its great potential of meeting the aforementioned requirements. To this end, investigations and realizations of nonlinear optical systems that demonstrate similar spiking dynamics to biological neurons are of fundamental interests and importance. In this paper, we experimentally built a wavelength tunable fiber laser and numerically investigate its pulsing and nonlinear characteristics. The fiber laser exhibits excitability and behavesanalogously to a leaky integrate-and-fire (LIF) neuron. Such an optical neuron responds rapidly with maximum response frequency of about 17 kHz, at least 170 times faster than typical biological neurons. By adding a tunable optical bandpass filter (TOBPF) into the fiber laser cavity and adjusting its bandpass window, the operating central wavelength of the fiber laser can vary from 1530 nm to 1570 nm. With the spectral tunability, more possibilities and advantages in terms of computing performances by utilizing the wavelength degree of freedom could be envisioned, just like the case in optical communications where communication capacity can be easily enlarged with wavelength division multiplexing. We also numerically simulated the pulsing and nonlinear dynamics of the fiber laser with rate equations. In both experiment and simulation, the fiber laser shows the threshold behavior, temporal integration of nearby pulses, nonlinear transformation of input pulses, and features of refractory period, confirming its neuromorphic properties. With such a wavelength tunable neuromorphic neuron, we propose several applications. One is for typical logical digital computing such as AND and OR operation but with additional wavelength specificity; the second is for recognition of combined temporal and spectral patterns; the third is for spiking neural networks with wavelength multiplexing.
<i>k</i>-Uniform States in Heterogeneous Systems

We study <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -uniform states in heterogeneous systems whose local dimensions are not all the same. Based on the connections between mixed orthogonal arrays with certain minimum Hamming distance, irredundant mixed orthogonal arrays and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -uniform states, we present two constructions of 2-uniform states in heterogeneous systems. We also construct two families of 3-uniform states in heterogeneous systems, which solves a question posed in [D. Goyeneche <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , Phys. Rev. A <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">94</b> , 012346 (2016)]. We show two methods of generating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k-1)$ </tex-math></inline-formula> -uniform states from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -uniform states. Some results on the nonexistence of absolutely maximally entangled states are provided. For the applications, we present an orthogonal basis consisting of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -uniform states with the minimum support, and we show that some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -uniform bases are locally irreducible. Moreover, we connect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -uniform states with quantum information masking.
Image Steganography System based on Hybrid Edge Detector

In the field of image steganography, edge detection based implantation methods play vital rules in providing stronger security of hided data. In this arena, researcher applies a suitable edge detection method to detect edge pixels in an image. Those detected pixels then conceive secret message bits. A very recent trend is to employ multiple edge detection methods to increase edge pixels in an image and thus to enhance the embedding capacity. The uses of multiple edge detectors additionally boost up the data security. Like as the demand for embedding capacity, many applications need to have the modified image, i.e., stego image, with good quality. Indeed, when the message payload is low, it will not be a better idea to finds more local pixels for embedding that small payload. Rather, the image quality will look better, visually and statistically, if we could choose a part but sufficient pixels to implant bits. In this article, we propose an algorithm that uses multiple edge detection algorithms to find edge pixels separately and then selects pixels which are common to all edges. This way, the proposed method decreases the number of embeddable pixels and thus, increases the image quality. The experimental results provide promising output.
Weight Annotation in Information Extraction

The framework of document spanners abstracts the task of information extraction from text as a function that maps every document (a string) into a relation over the document's spans (intervals identified by their start and end indices). For instance, the regular spanners are the closure under the Relational Algebra (RA) of the regular expressions with capture variables, and the expressive power of the regular spanners is precisely captured by the class of VSet-automata -- a restricted class of transducers that mark the endpoints of selected spans. In this work, we embark on the investigation of document spanners that can annotate extractions with auxiliary information such as confidence, support, and confidentiality measures. To this end, we adopt the abstraction of provenance semirings by Green et al., where tuples of a relation are annotated with the elements of a commutative semiring, and where the annotation propagates through the positive RA operators via the semiring operators. Hence, the proposed spanner extension, referred to as an annotator, maps every string into an annotated relation over the spans. As a specific instantiation, we explore weighted VSet-automata that, similarly to weighted automata and transducers, attach semiring elements to transitions. We investigate key aspects of expressiveness, such as the closure under the positive RA, and key aspects of computational complexity, such as the enumeration of annotated answers and their ranked enumeration in the case of ordered semirings. For a number of these problems, fundamental properties of the underlying semiring, such as positivity, are crucial for establishing tractability.
A Lightweight Deep Learning Network for Sensor-based Human Activity Recognition using IMU sensors of a Low-Power Wearable Device

Human Activity Recognition (HAR) is an intriguing approach to healthcare monitoring that necessitates the ongoing utilization of wearable sensors to capture everyday activities. The most advanced studies using wearable devices have used a cloud computing paradigm that categorized data at remote systems. This method has drawbacks in terms of energy consumption, latency, and privacy. As a result, we adhere to a low-power computing structure in which wearable device solutions achieve an acceptable level of performance while being energy and memory-efficient. This study proposes a lightweight deep learning model termed a gate recurrent unit (GRU) network for energy-efficient HAR appropriate for low-power wearable devices. The introduced GRU network is evaluated against a benchmark dataset named the w-HAR dataset, including essential deep learning networks. The proposed GRU surpasses baseline deep learning networks in terms of overall accuracy, with a score of 95.160&#x0025;.
Immortal time bias

I am planning a study to evaluate the relationship between infection and mechanical ventilation.Because we are interested in a time-to-event outcome, I am wondering whether there is a potential risk of introducing immortal time bias and how to avoid it.In studies with a time-to-event (survival) outcome, immortal time refers to a period of follow-up during which, by design, death or the event of interest cannot occur. 1,2In general, immortal time could happen in an exposure/treatment group if the subjects need to be followed long enough to experience an exposure or receive a treatment and be event-free prior to the exposure/treatment.
Who is the digital forensic expert and what is their expertise?

Abstract Given the importance of digital evidence to many inquiries, the services of practitioners in the digital forensic field are often sought. In some cases, a “digital forensic expert witness” may be required to conduct specific investigatory work and provide opinion evidence. Here we explore the concept of a digital forensic expert witness while also considering whether this term may now be potentially misleading to those outside of this domain. A discussion of the challenges that exist when trying to establish and assess the validity of expertise in this field is offered. Suggestions for ways to both evidence and correctly identify relevant expertise are made, taking into account the viewpoints of both the potential expert and client. This article is categorized under: Digital and Multimedia Science &gt; Multimedia Forensics
Designing an AI-assisted toolbox for fitness activity recognition based on deep CNN

Online applications for virtual meetings have be-come handy for many fitness instructors these days. However, currently used applications have limitations in tracking clients' performance simultaneously, particularly for many participants. In this paper, we proposed a fitness AI-assisted toolbox to detect the fitness activities of athletes from steamed frames. The innovative Magnitude flow method is introduced to compute the dense optical flow of each frame and provides details of human body poses during exercising. The two-stream convolutional neural network is proposed to only detect fitness actions from RGB frames and improved magnitude flows. The two-stream CNN is designed with a MobileNetVl backbone and a Multi-layer perceptron (MLP) network as the classification head. Moreover, we introduced an Observer network as a score fusion approach to improve the final detection accuracy and reduce the uncertainties between the two networks. Our proposed method is tested over selected fitness activities of the UCF dataset. The plenary results were acquired by testing selected activities and considering their ability in detecting major fitness actions. The efficient accuracies and confusion matrices describe that our proposed model is an effective solution to assist fitness instructors in tracking, analyzing, and conveniently supervising many participants.
MP-CRJ: Multi-parallel cycle reservoirs with jumps for industrial multivariate time series predictions

Nowadays, echo state networks (ESNs) with a variety of model structures are available for industrial process time series predictions. However, most existing ESNs employ a uniform timescale for data knowledge learning, which obviously ignores the influences of multivariate status at different timescales to the prediction target, usually leading to unsatisfactory model approximation performances. In response to this problem, this paper proposes a multi-parallel cycle reservoir with jumps (MP-CRJ) which is embedded with the feature knowledge of different timescales contained in the multivariate data. The MP-CRJ uses a more concise and superior circular jump reserve pool and a more memorable leaky integral neuron-filled parallel structure able to reduce the spatial complexity resulted from parallel ESNs and relatively improve the dynamic diversity of reserve pools. In addition, grey relational analysis algorithms are used to select relevant variables contributing to the prediction in filtering unnecessary data information. Applying to practical plant data for methanol productions, it shows that the MP-CRJ can help increase the prediction accuracy while maintain prediction speeds, as well as enjoy better adaptions to dynamics of complex industrial processes.
Why IT Security Needs Therapy

Abstract Over the past decade, researchers investigating IT security from a socio-technical perspective have identified the importance of trust and collaboration between different stakeholders in an organisation as the basis for successful defence. Yet, when employees do not follow security rules, many security practitioners attribute this to them being “weak” or “careless”; many employees in turn hide current practices or planned development because they see security as “killjoys” who “come and kill our baby”. Negative language and blaming others for problems are indicators of dysfunctional relationships. We collected a small set of statements from security experts’ about employees to gauge how widespread this blaming is. To understand how employees view IT security staff, we performed a prolific survey with 100 employees (n = 92) from the US &amp; UK, asking them about their perceptions of, and emotions towards, IT security staff. Our findings indicate that security relationships are indeed often dysfunctional. Psychology offers frameworks for identifying relationship and communication flows that are dysfunctional, and a range of interventions for transforming them into functional ones. We present common examples of dysfunctionality, show how organisations can apply those interventions to rebuild trust and collaboration, and establish a positive approach to security in organisations that seizes human potential instead of blaming the human element. We propose Transactional Analysis (TA) and the OLaF questionnaire as measurement tools to assess how organisations deal with error, blame and guilt. We continue to consider possible interventions inspired by therapy such as conditions from individual and group therapy which can be implemented, for example, in security dialogues or the use of humour and clowns.
Improvement of AnyNet-based end-to-end phased binocular stereo matching network

To improve the accuracy of the parallax maps obtained by the binocular stereo matching network for better 3D reconstruction, this thesis is based on the AnyNet network, with the corresponding optimization to get MyNet. The Hourglass structure allows for the acquisition of multiscale image information. The original 3D Conv is replaced by a 3D residual block in the parallax network to avoid the gradient disappearance or explosion and model degradation problems of traditional convolutional neural network models, thus further improving the cost volume obtained. The experiments were tested on the KITTI2012 and KITTI2015 datasets and showed some improvement compared to the original algorithm. The experimental results show that the accuracy of the parallax map has improved significantly with almost the same time spent.