Reviewerly/exHarmony huggingface dataset:
The Role of DC Component and Trend Term in FFT Transform Spectrum Analysis

Abstract Most of the actual signals are analyzed and processed with zero spectral line values. This paper describes the causes of this phenomenon and the solutions. Through the analysis of a group of specific experimental data, the N-point FFT transform is carried out. By observing the original data and the frequency-shifted spectrum, the original can be intuitively understood. The signal has strong DC component and trend term (trend term is also divided into linear trend term and polynomial trend term). Because of this, the frequency spectrum obtained by FFT is mostly zero. In this paper, the DC component of the signal is calculated and the trend term is eliminated by using the digital signal processing function of MATLAB, and the whole spectrum is obtained. Finally, the specific area is selected according to the actual requirements. The spectrum analysis was carried out to obtain the qualified spectrum.
Mapping Schools in Areas Prone to Natural Hazards in India: Understanding Context to Plan for Long-Term Benefit

Mapping often plays a very important role to understand and identify vulnerable areas, risk zones and to predict the risks in any particular area. With the advent of mapping technologies, all the countries across the world have prepared numerous maps that show the hazardous zones, vulnerability, risk, etc. Still, there is a dearth of mapping in India at micro levels for some particular areas which are vulnerable and where vulnerability directly impacts the children in various ways. These are mainly school children in the areas prone to natural or man-made hazards. This paper poses an argument that mapping of schools in India is important to understand the context of schools within a community in a holistic manner. Mapping with basic database generation for schools is specifically relevant and useful in the challenging locations facing natural hazards because it not only helps to locate the schools, but also helps to design any appropriate change intervention as per the context which can be beneficial for the school children for generations. Context mapping is useful for understanding the community, and therefore, to accelerate the process of involving the community in participatory mode of approach to arrive at a context-specific solution. Such a practice can enhance the process of decentralisation following a path of devolution. While mapping the context, apart from mere technical and locational mapping, emphasis must be given to mapping all possible aspects that influence the local schools, schooling trends, processes and practices. The paper has discussed the process of mapping schools in a few disaster-prone areas of India. During the field study, the data were collected from the schools, local community and administrative functionaries. Here, an attempt has been made to explain how this mapping process and its final outcomes can help in regional planning for the schools for long term in various regions of India. The arguments are supported by evidences from other relevant literatures from similar country context and also evidences from the experimentations made so far in the field. At the same time, the paper also has discussed about the potential of mapping the context in areas prone to natural hazards and/or disasters which might be useful for the regional planners as well as potential researchers willing to contribute meaningfully to similar processes and interventions.
Designing effective knowledge presentation techniques for large digital collections

ABSTRACT We are developing a prototype knowledge presentation system for enhancing the access to, and use of, large digital document collections. We have started a preliminary user interface design for our prototype system which will employ the state‐of‐the‐art knowledge visualization techniques. The techniques, which will be based on knowledge extraction and interactive visualization, will also be tested on open digital text document collections.
Multi-value Classification of Ambiguous Personal Data

Addressing privacy regulation such as GDPR requires organizations to find and classify sensitive and personal data in their datastores. First, data discovery tools are applied to identify the data. Then, data classification tools are applied on the data that was discovered. Organizations must classify the data into concrete categories to manage data appropriately. In this paper we focus on multi-value classification, where the classifier provides a category to set of values all from the same category. Traditional classifiers usually apply single-value classification methods to a multi-value data set. However, in many cases this resulting an incorrect classification when, for example, domain categories overlap. In this paper, we address this scenario and provide two methods to overcome this problem.
A Universal Descent Model for Saliency Detection

We propose an unsupervised method for saliency detection with multiple functions by a universal descent model. The basic descent model is concise, which is a minimization equation conducted by a relationship matrix and background prior. The relationship matrix is calculated by the contrast of features which contains much information between superpixels. So saliency maps with high performance can be obtained even if only one feature is included. Besides, various terms can be appended to the basic descent model to achieve multiple functions. For example, considering an neighbor matrix into the basic model can further smooth saliency maps. The refinement term can also refine existing results by introducing prior saliency maps, demonstrating the universal applications of our model. Extensive experiments on different datasets indicate that the basic model outperforms state-of-the-art methods. The universal descent models with smoothness and refinement terms are able to promote the performance of state-of-the-art methods by a large margin.
Design of Medical Device Product Using Multiphysics Simulations

The primary function of kidneys is to remove waste and other impurities from the blood of a human being on a daily basis. When the kidneys stop functioning properly, the toxins from the blood have to be removed through an external device, dialyser. The dialyser mimics the kidney and draws the blood out of the patient, purifies the blood and infuses them back into the patient’s body. When the blood is infused back to the human body, it has to be at 37.6 ℃ failing which would lead to hypothermia, a fatal condition. To avoid this fatal condition, the body is heated to human body blood temperature 37.6 ℃. The blood flow rateFlow rate and heat supply in the bloodline can vary over a wide range but still the blood has to be heated to 37.6 ℃ which is human body blood temperature. To raise the human body temperature to 37.6 ℃, a sub-component in the dialyser machine called blood warmerBlood warmer takes care of heating. Since it is a medical device which interacts with the patient, the precision of 0.5 ℃ is expected over a range of flow ratesFlow rate and heat supply based on patient’s medical condition. The scope of this work is the design of a precise blood warmerBlood warmer which is effective than existing. The multiphysicsMultiphysics simulations helped the engineers to visualize, observe and understand the physics involved during the design stage.
XR for XR – Context Craft: An extended reality platform that transforms local environments through play

The aim of Context Craft is to develop a platform that shrinks the gap between imagination and reality.Players become crafters of their own context -hacking their reality for the benefit of themselves and their communities.Imagine if you could take what you made in Lego or Minecraft and make it in reality with household tools and some globally accessible waste (such as soda bottles, water bottles, sticks for core framework, plastic bags, fabric, cans etc.) and "mine" reality in order to transform it via polygon structures that can be used to make anything from furniture, to bike trailers to shelters and community buildings.XR (Cross or Extended Reality) is a recent, controversial, umbrella term that attempts to simplify the understanding and adoption of the full range of immersive technologies that generate new forms of reality, recognising that in the future we will see people interacting with the virtual world and real world in seamless and continuous ways, not bound by delineations of experience between VR, AR, and MR.Recently, another organisation Extinction Rebellion also adopted the acronym XR.This serendipity led to the questioning how can these XR technologies relate to our Extinction Rebellion (XR)?Extended reality (XR).Cross reality.Extinction Rebellion.
Change Detection for Streaming Data Using Wavelet-Based Least Squares Density–Difference

HereMijatovic, Nenad, weHaber, Rana presentMoyou, Mark a novelSmith, Anthony O. algorithmPeter, Adrian M. for detecting changes in a continuous time series stream based on the $${\ell }_{2}$$ distance between two distributions. The distributions are non-parametrically modeled using wavelet expansions, inspiring the name of our method: Wavelet-based Least Squares Density–Difference (WLSDD). Using the least squares method, we show that the $${\ell }_{2}$$ distance between two wavelet expanded densities results in a closed-form expression of their coefficients. This circumvents the need to evaluate the densities and, instead, allows us to work directly with the differences between the corresponding scaling and wavelet coefficients. The method demonstrated superior change detection performance on both synthetic and real datasets, stationary or nonstationary, in comparison to other competing techniques.
Review based on Image Understanding Approaches

Image Understanding provides a description of a scene without the help of a user. To make this ability feasible, machine learning (ML) performs an imperative role. Learning algorithm permits the system to understand an image reliant on its training algorithm. The notable areas of mining algorithms are contents-based image retrieval, change detection, video sequence analysis, video retrieval, image knowledge extraction, object recognition and model learning. The combination of deep learning and mining algorithms are a great boon to image understanding. This paper handles various methods involved in image understanding. As image understanding is a popular area of present research, it is significant to know how far the researchers contributed to this field until today. This paper analyzes various techniques and models for image understanding.
A novel tracker based on the kernelized correlation filter

Correlation filter based tracking methods are the core component of most trackers which achieve the excellent performance in term of the accuracy and robustness in visual tracking. However, there are still lots of challenging situations, such as occlusion or illumination, which confines and limits the performance of trackers. To cope with the above problems, in this paper, we suggest an effective tracking method via part-based strategy. Compared with the conventional tracking algorithms based on correlation filter, our tracker employs the novel strategy to validate and estimate the target’s final position, avoiding merely utilizing the maximum response in the response map as the target position which is often prone to drift away from the target. In addition, to effectively deal with occlusion, we divide the sample into multiple parts. When the sample is partly occluded, the visible part can still provide effective clues for tracking, ensuring the robustness of tracker. A large number of conveys are conducted on the public databases, and experimental results show that the proposed algorithm has obvious performance improvement in the case of dealing with target occlusion, and the real-time performance is also pretty good.
Improved Cubature Kalman Filtering-Based Estimation of the Jacobian Matrix for Environment Mutation

To improve the accuracy of online estimation of Jacobian matrix (JCM) in image-based visual servo, an improved cubature Kalman filtering (CKF) algorithm is proposed to estimate the Jacobian matrix online. The key idea of the proposed method is to introduce the fading factor into the measurement update process, which can improve the capability when coping with the environment mutation. In addition, to avoid the emergence of the non-positive covariance matrix, the Cholesky decomposition of the estimation is replaced by the singular value decomposition (SVD). To verify the effect of the proposed method, two sets of experiments are carried out. Three-dimensional motion tracking is employed to prove tracking effect and the eye in hand manipulator positioning based on the Puma560 simulation is adopted to testify stability of this method. Simulation results show that the proposed method is feasible and effective.
Natural Language Inference Based on the LIC Architecture with DCAE Feature

Natural Language Inference (NLI), which is also known as Recognizing Textual Entailment (RTE), aims to identify the logical relationship between a premise and a hypothesis. In this paper, a DCAE (Directly-Conditional-Attention-Encoding) feature based on Bi-LSTM and a new architecture named LIC (LSTM-Interaction-CNN) is proposed to deal with the NLI task. In the proposed algorithm, Bi-LSTM layers are used to modeling sentences to obtain a DCAE feature, then the DCAE feature is reconstructed into images through an interaction layer to enrich the relevant information and make it possible to be dealt with convolutional layers, finally the CNN layers are applied to extract high-level relevant features and relation patterns and the discriminant result obtained through a MLP (Multi-Layer Perceptron). Advantages of LSTM layers in sequence information processing and CNN layers in feature extraction are fully combined in this proposed algorithm. Experiments show this model achieving state-of-the-art results on the SNLI and Multi-NLI datasets.
Control based motion primitives for quadrotor trajectory generation

The local motion planning is critical for the safe operation of an autonomous vehicle such as the quadrotor in which motion primitives are usually adopted to find valid and efficient reaction. We present in this work a method that is capable of generating a class of motion primitives for the quadrotor. Compared to the previous approaches, these primitives are capable of minimizing multiple types of cost functions while satisfying the motion constraints of the quadrotor. Moreover, neural networks are adopted to approximate the generated motions to further boost the online evaluation efficiency. Finally, the local motion planning is achieved through solving a non-convex optimization problem with particle swarm techniques in a receding horizon fashion. The optimization process is a natural extension of the previous tree searching based methods. The proposed approach is computationally efficient and has been tested in a real environment on an actual quadrotor platform.
Towards Mixed-Reality in Software Engineering of Safety-Critical Systems

Software engineering safety-critical systems exact a high cost because of the required system reliability. Safety-critical systems are characterized by the resulting harm or loss of life should such unanticipated system failure occur. This demand for high reliability has fostered the application of various approaches towards realizing this system reliability. Some of these approaches include model-driven development methodologies, application domain profile modeling, formal specification techniques, and various rigorous testing techniques. Software engineering researchers seek new and innovative techniques towards increasing safety-critical systems development. One emerging technique that may provide new prospects for developing highly reliable safety-critical software systems is that of mixed-reality, which is a combination of virtual and augmented realities. In this report a framework for the use of mixed-reality in safety-critical software system development is presented. This proposed mixed-reality software engineering methodology incorporates model-driven techniques along with formal specification techniques and builds on prior work in defining a model-driven software development methodology.
Boosting Network Weight Separability via Feed-Backward Reconstruction

This paper proposes a new evaluation metric and a boosting method for weight separability in neural network design. In contrast to general visual recognition methods designed to encourage both intra-class compactness and inter-class separability of latent features, we focus on estimating linear independence of column vectors in weight matrix and improving the separability of weight vectors. To this end, we propose an evaluation metric for weight separability based on semi-orthogonality of a matrix, Frobenius distance, and the feed-backward reconstruction loss, which explicitly encourages weight separability between the column vectors in the weight matrix. The experimental results on image classification and face recognition demonstrate that the weight separability boosting via minimization of feed-backward reconstruction loss can improve the visual recognition performance, hence universally boosting the performance on various visual recognition tasks.
Optical d-Level Frequency-Time-Based Cluster States

Cluster states, a specific class of multi-partite entangled states, are of particular importance for quantum science, as such systems are equivalent to the realization of one-way (or measurement-based) quantum computers [1]. In this scheme, algorithms are implemented through high-fidelity measurements on the parties of the state [2]. While two-level (i.e. qubit) cluster states have been realized so far, increasing the number of particles to boost the computational resource comes at the price of significantly reduced coherence time and detection rates, as well as increased sensitivity to noise, restricting the realization of discrete cluster states to a record of eight qubits. In contrast, the demonstration of d-level (i.e. qudit) cluster states has the potential to i) increase quantum resources without modifying the number of particles; ii) enable the implementation of highly efficient computational protocols; iii) reduce the noise sensitivity of the states. Up till now, the realization of discrete d-level cluster states has not been shown in any quantum platform. We here demonstrate the realization of d-level cluster states, perform d-level one-way quantum processing operations on the states, and show that higher-dimensional forms of cluster states are more noise tolerant than lower dimensional realizations.
Smart Recommendation System Based on Understanding User Behavior with Deep Learning

Consumer behavior is one of the most important issues that has been discussed in recent decades. Organizations always want to understand how consumer make decisions so that they can use it to design their products and services. Having a correct understanding of the consumers and the consumption process has many advantages. These advantages include helping managers make decisions, providing a cognitive basis through consumer analysis, helping legislators and regulators legislate on the purchase and sale of goods and services, and ultimately helping consumers make better decisions. Here is a solution for recommending goods based on the users’ past behavior over deep learning. The architecture expressed for deep learning is trained by users’ past behavioral data. Amazon data was studied and the results indicated that the proposed method has a much higher accuracy than similar methods.
Control of Neuromorphic Dynamics in Two State Quantum Dot Lasers

Summary form only given. Quantum dot (QD) lasers display the unique ability to lase from multiple energy states [1]. Depending on pump current a QD laser can lase from the ground state (GS), from the first excited state (ES), or simultaneously from both. Although a lot of work has been done investigating the neuromorphic dynamics for the GS state, very little has been done involving the ES. In [2] antiphase ES and GS bursting intensity oscillations were reported while in [3], a novel Type I dual state excitable dynamic was reported. Here we show that we can controllably produce GS dark pulses and an associated ES bright pulse using sufficiently large perturbations to deterministically trigger them. We also identify and show techniques that can be used to control the number of spikes in the bursts and the refractory time in the dual state excitability.
Stato, problemi, applicazioni critiche della filologia dannunziana

The paper traces some general reflections on the problems, methods and perspectives of authorial philology applied to d’Annunzio’s work and in relation to some recent scientific contributions. Following Contini’s philological sensitivity, the d’Annunzio variant studies have dealt with a peculiar processing method that manifests itself almost instantly, in currenti calamo interventions. In critical editions, they are given an adequate representation through multilayered apparatuses and through the distinction between text prehistory and protohistory. The critical applications of these studies have since been the most attractive prospects for an investigation into the elaboration of d’Annunzio’s text.
Automatic Construction of Chinese Typo-Pairs Based on Web Corpus

With the development of big data, the amount of text data is growing bigger and bigger in which errors are also more and more. The traditional human-correction cannot meet the actual demand. It is a trend for automatic text proofing by using computer data processing. Chinese text errors can be divided into two categories: non-word error and real-word error. One or more character in a Chinese word replaced by other character will result in the word does not belong to the Chinese dictionary, which we call "non-word error". The word segmentation is firstly performed on the corpus in Chinese NLP, and non-word error will be divided into several disperse strings, which bring Chinese text proofreading several problems, because there are single-character words and multi-characters words in Chinese dictionary. In this paper, an approach is proposed to construct Chinese typo-pairs from Web corpus, which can be used in Chinese text automatic proofreading efficiently. Firstly, the method adds similar words into a candidate set using fuzzy matching algorithm, and then validates the similar words in the candidate set using statistical models, and finally constructs the typo-pairs.
JPEG fragment Carving based on Pixel Similarity of MED_ED

JPEG fragment carving technology is a hot and difficult research field in data recovery and computer forensics. Most of the adjacent Pixel Similarity algorithms for JPEG images have high accuracy when the pixels are continuous and smooth, Low accuracy will occur when image contains sharp areas such as there are strips, vertical strips and angles. SoD and ED only focus on the similarity between adjacent edge pixels, rather than the integrity and smoothness of the whole image. MED uses the surrounding pixel values to predict the target pixel value, which is better than SoD and ED in the prediction of pixel integrity. In order to solve this problem, we provide a new Euclidean distance adjacent pixel detection algorithm MED-ED based on median edge detector, according to the local stability and the characteristics of MED. Secondly, the design algorithm to judge the original cluster size, according to the Features of the file system, in order to solve the problem of inefficiency under the situation of the current storage device capacity soaring and high image quality. The experimental results show that the proposed method can effectively improve the accuracy and efficiency.