Reviewerly/exHarmony huggingface dataset:
Robustness of deformed catlike states under dissipative decoherence

The generation of coherent superposition of distinct physical systems and the construction of robust entangled states under decoherence are the most experimental challenges of quantum technologies. In this work, we investigate the behaviors of catlike states of a deformed harmonic oscillator under dissipative decoherence. Varying the deformation parameters, we obtain catlike states having more resistance against decoherence than catlike states of the ordinary harmonic oscillator. Furthermore, we study nonclassical properties and entanglement of different catlike states subjects to decoherence caused by a dissipative interaction with a large environment. Depending on different deformation parameters, we reveal that the nonclassical properties of catlike states under dissipative interaction can be more retarded and preserved in the time.
Risk Analysis and Early Warning of Food Safety Testing Based on Big Data

With the development of the market economy, people’s income is increasing, and food health is becoming more and more important. Especially in recent years, the frequent occurrence of food safety accidents has exacerbated people’s attention to food safety. The inspection for food safety is mainly done by manpower, which is not only inefficient, but also has disadvantages such as long inspection cycle and one-sided inspection data. With the development of Internet information technology, cloud computing, the Internet of Things and other emerging technologies have entered the market. The above technologies are combined by big data, which can effectively improve the effectiveness of food safety monitoring, and not only can provide detailed data for regulatory authorities, but also provide comprehensive data services to manufacturers, consumers, and third-party supervisors. With the in-depth development and maturity of related technologies, this model will definitely become a major development trend in the future. This study explores the risk characteristics of food safety from the perspective of big data through the study of big data related theories, and creates a food safety risk analysis and early warning framework based on operational data sources and big data technology as the core, so as to provides theoretical basis for applied research of relevant field in the future.
Neural Network-Based Random-Valued Impulsive Noise Suppression Scheme

Denoising of digital images is one of the important pre-processing stages in image processing. In this paper, a new efficient back-propagation neural network-based impulse noise suppression technique is presented. The proposed technique works in two stages, i.e., impulse noise detection and noise filtering. In noise-detection stage, a feed-forward neural network is trained by back-propagation training algorithm to classify the noisy and non-noisy pixels in the test image. The input patterns for training are selected carefully to make the detector more robust for impulse detection. An adaptive median filtering is employed in the second stage for restoring the value of the noisy pixels identified in the first stage keeping the non-noisy ones unaltered. The restoration ability of the proposed method is evaluated using different standard images at different noise levels. It has been observed after several experiments on standard images that the performances of the proposed techniques are superior in terms of both visual quality and quantitative measurement.
Optimization of Kalman Filter for Target Tracking Applications

The computational complexity, area overhead and power consumption are the major drawbacks of Kalman filter that limits its use for various real-time applications including target tracking. This paper aims to optimize the Kalman algorithm to meet the growing demands of today’s tracking system. A reduced recursive scalar Kalman filter is proposed by simplifying the calculations of the posterior error co-variance estimate. This reduces the hardware cost and computational time. For hardware implementation, the architecture is divided into two parts. The first part calculates Kalman gain and posterior error co-variance estimate, and the second part calculates the output estimate. Both pipelining and parallel processing techniques are employed to increase the speed of computation. The area and power consumption are reduced by time sharing the architecture as the algorithm is recursive. The proposed technique is implemented on FPGA, and the performance of the Kalman filter is verified. The implementation requires nine adders, three subtractors, six multipliers and two dividers. A maximum operating frequency of 145 MHz is obtained with a worst case computational delay of seven clock cycles (6.881 ns). A parabolic path is used to verify the tracking performance of the architecture, and the simulation results show an accuracy loss of 1.98 $$\%$$ between the actual and the estimated path. The architecture consumes a power of 0.128 W at 100 MHz.
Private Information Delivery

We introduce the problem of private information delivery (PID), comprised of K messages, a user, and N servers (each holds M ≤ K messages) that wish to deliver one out of K messages to the user privately, i.e., without revealing the delivered message index to the user. The information theoretic capacity of PID, C, is defined as the maximum number of bits of the desired message that can be privately delivered per bit of total communication to the user. For the PID problem with K messages, N servers, M messages stored per server, and N ≥ ΓMK⌉, we provide an achievable scheme of rate 1/ΓMK⌉ and an information theoretic converse of rate M/K, i.e., the PID capacity satisfies 1/ΓKM⌉ ≤ C ≤ M/K. This settles the capacity of PID whenMKis an integer. When K/M is not an integer, we show that the converse rate of M/K is achievable if N ≥ K/gcd(K,M ) - ( M/gcd(K,M) - 1)(⌊K/M⌋- 1), and the achievable rate of 1/ΓMK⌉ is optimal if N =ΓK/M⌉. Otherwise if ΓK/M⌉ <; N<; K/gcd(K,M) -( M/gcd(K,M) -1)(⌊K/M⌋-1), we give an improved achievable scheme and prove its optimality for several small settings.
Rule Discovery for (Semi-)automatic Repairs of ETL Processes

A data source integration layer, commonly called extract-transform-load (ETL), is one of the core components of information systems. It is applicable to standard data warehouse (DW) architectures as well as to data lake (DL) architectures. The ETL layer runs processes that ingest, transform, integrate, and upload data into a DW or DL. The ETL layer is not static, since the data sources being integrated by this layer change their structures. As a consequence, an already deployed ETL process stops working and needs to be re-designed (repaired). Companies typically have deployed from thousands to hundreds of thousands of ETL processes. For this reason, a technique and software support for repairing semi-automatically a failed ETL processes is of vital practical importance. This problem has been only partially solved by technology or research, but the solutions still require an immense work of an ETL administrator. Our solution is based on a case-based-reasoning combined with repair rules. In this paper, we contribute a method for automatic discovery of repair rules from a stored history of repair cases.
A 435MHz, 2.5Mbps/W Side-Channel-Attack Resistant Crypto-Processor for Secure RSA-4K Public-Key Encryption in 14nm CMOS

A 0.072mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SCA-Resistant RSA-4K crypto-processor fabricated in 14nm CMOS achieves peak encryption throughput of 0.08Mbps at 750mV, 25°C. Exponent magnitude/timing randomization with dynamic RF addressing provide 711× lower means-separation in current/EM trace magnitudes, reducing attacker's accuracy to an ineffective random guess classification while limiting area/performance overhead to 3%.
Pedagogy of life beyond extinction

Purpose The purpose of this study is a reaction to COVID-19 and is intended to transcend the regular thoughts to deeper issues towards humanity and nature. The study explores the notion of extinction, and what we value, and alludes to humanity increasingly having fractured self-awareness in the context of the uncharted health threat of the global pandemic of 2020. The study focusses on a holistic perspective towards pedagogy that explores related issues of knowledge, spirituality, self-awareness and embodied actions. Design/methodology/approach This study adopts a reflective, and exploratory, style that is an enabler for future research into pedagogy that is focussed on humanity and nature. Findings The conceptual paper explores an holistic perspective towards pedagogy that considers issues of knowledge, spirituality, self-awareness and embodied actions. Originality/value This study’s intention is to extend our notion of pedagogy that looks beyond educational institutions in seeking a wider understanding of humanity and nature. The concept of “pedagogy of life beyond extinction” is used as a way to identify a selfless way in life that is underpinned by self-awareness. This provides a lens to intrinsic and extrinsic values that come from an interpretation of truth in our existence beyond self-interest.
Classification and Recommendation With Data Streams

Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.
Sensor-Based Benchmark Datasets: Comparison and Analysis

Human Activity Recognition (HAR) using installed sensors has made renowned progress in the field of pattern recognition and human-computer interaction. To make efficient machine learning models, researchers need publicly available benchmark datasets. In this chapter, we have bestowed a comprehensive survey on sensor-based benchmark datasets. We have not considered RGB or RGB-Depth video-based action or activity-related datasets in this book. We have performed a complete analysis of benchmark datasets, that incorporates information about sensors, attributes, activity classes, etc. These datasets sum up a good number of sensor-based daily activities, medical activities, fitness activities, device usage, fall detection, transportation activity, and hand gesture data.
Flow Diagrams, Assertions, and Formal Methods

This paper examines the early history of the flow diagram notation developed by Herman Goldstine and John von Neumann in the mid-1940s. It emphasizes the motivation for the notation’s mathematical aspects and the provision made for formally checking the consistency of diagrams. Goldstine and von Neumann’s introduction of assertion boxes is considered in detail. The practical use of flow diagrams is considered briefly, and the paper then reads Turing’s 1949 essay on “Checking a large routine” in the light of his likely knowledge of the Goldstine/von Neumann notation. In particular, his different use of the term “assertion” is considered, and related to the earlier work.
Using Machine Learning for Automated Assessment of Misclassification of Goods for Fraud Detection

The paper is devoted to providing automated solutions to an actual problem of misclassification of goods in cross-border trade. In this paper, we introduce a hybrid approach to Harmonized System (HS) code assessment that combines the knowledge derived from textual descriptions of products, assigned to them HS codes and taxonomy of HS codes nomenclature. We use machine learning for providing HS code’s predictions and recommendations on the basis of a model learned from the textual descriptions of the products. In order to perform an assessment of misclassification of goods we present a novel combined similarity measure based on cosine similarity of texts and semantic similarity of HS codes based on HS code taxonomy (ontology). The method is evaluated on the real open source data set of Bill of Lading Summary 2017 [1] using Gensim Python library [4].
The Combination of YAKE and Language Processing for Unsupervised Term Extraction Ontology Learning

Abstract Information that is spread on the internet is available in the form of unstructured texts that can only be understood by humans, but difficult for machines to understand. Ontology learning is a method that can transform information in unstructured forms, into information that can be understood by machines, namely ontology. In ontology learning, the extraction term is one of the stages that must be passed. This stage produces important terms related to a topic before finally being grouped in certain classes. In this study, the term extraction method used is YAKE. The contribution of this research is to investigate the effects of language processing such as stemming and stopword removal when combined with the YAKE method at the term extraction stage. The language processing technique is then applied to the corpus of the test, after that it is as the input to the YAKE term extraction. Testing is conducted with several scenarios, namely: plain YAKE, stemming+YAKE, stopword removal+YAKE, or a combination three of them. These extraction scenario are evaluated by expert for measure the term correctness. The research shows that the combination of stopword removal+YAKE provide the best accuracy of 48%.
Controllable quantum interference from two-photon scattershot sources

We describe a multi-mode passive linear optical network which emulates the two-photon number statistics of a beam splitter, irrespective on where the two photons enter the network. This is done by firstly defining general properties that the generator of the network matrix must fulfil. We then show that the network's effective transmission coefficient can be freely set to replicate either the absence of coincidence counts (i.e. the Hong-Ou-Mandel dip), or a 100% coincidence rate, as well as all possible two-photon beam splitter statistics between these two extremal points. Finally, we demonstrate that this network, in comparison to simpler systems, provides a better sampling rate and other resource advantages.
Calibration and system error evaluation of a high-accuracy 3D pavement profilometer based on interference fringe

In order to realize the accurate calibration of pavement profilometers and trace the pavement profilometry error sources according to the metrological standard, a high-accuracy interference fringe-based 3D pavement profilometer is developed. Based on the known dimensions of the standard component, the measurement accuracy of the proposed system is assessed. Moreover, the measurement errors of the system at different measurement heights are obtained. Finally, the error sources are analyzed. It is demonstrated that the system has better resolution and accuracy than most 3D pavement profilometers currently used in pavement evaluation. The experimental evaluations indicated that the highest resolution is 5.3 × 10−5 m and the best accuracy can reach ± 2.8 × 10−4 m. The system errors come from the measured height error and background light intensity. The proposed system paves the way for achieving the accurate calibration for the pavement profilometers used in pavement condition assessments.
Discriminative Subspace Learning for Cross-view Classification with Simultaneous Local and Global Alignment

With the wide applications of cross-view data, cross-view Classification tasks draw much attention in recent years. Nevertheless, an intrinsic imperfection existed in cross-view data is that the data of the different views from the same semantic space are further than that within the same view but from different semantic spaces. To solve this special phenomenon, we design a novel discriminative subspace learning model via low-rank representation. The model maps cross-view data into a low-dimensional subspace. The main contributions of the proposed model include three points. 1) A self-representation model based on dual low-rank models is adopted, which can capture the class and view structures, respectively. 2) Two local graphs are designed to enforce the view-specific discriminative constraint for instances in a pair-wise way. 3) The global constraint on the mean vector of different classes is developed for further cross-view alignment. Experimental results on classification tasks with several public datasets prove that our proposed method outperforms other feature learning methods.
A Multi-Criteria Decision Approach for Movie Recommendation Using Machine Learning Techniques

The challenges in decision making can be viewed as selecting an appropriate method from multi-criteria decision making (MCDM). The MCDM methods do not offer similar answers to decision makers. In such cases, selection of the best answer becomes an issue. This is possible when there may be a better choice which was not considered or the unavailability of the right information at that time. MCDM contains a limited number of alternatives for every problem. In the initial process for the solution, all these solutions are explicitly identified. In this work, we have used machine learning techniques to fulfill the objective of MCDM also known as multi-criteria decision making (MCDM). The objective is to rank the movies by using various alternatives based on the weights of several criterions. In this proposed work, different kinds of movies like science fiction, cartoon, and adventures are used to generate the ranking. Other criterions that are used such as various factors like price, movie reviews and duration are used for movie ranking. The movie ranking is generated based on the weightage.
A Simple Method for Speaker Recognition and Speaker Verification

Speaker recognition is developing and is very much useful in the modern life. This paper clearly explained the speaker recognition as a technique of automatically identifying the persons liable upon the data incorporated in signals of speech, classification of the speaker recognition method, speaker identification and verification method. It also explained the recognition text-dependent and text-independent methods, different phases involved in speaker recognition systems such as speaker verification process and speaker identification process. This paper explained the speech production technique which involves voiced, unvoiced and perceived speech.
Modelling Earthquakes: Characterizing Magnitudes and Inter-Arrival Times

Statistical modelling of earthquakes is a challenging and delicate topic: research activity is vivid in this respect, and tailored to an improved understanding of the seismic phenomena and of their dynamics over time and space in all its shades. By surfing on some of the available literature, a critical investigation of the probability distributions best fitting earthquake sizes and inter-arrival times is performed, by using data on the Pacific Ring of Fire as illustrative example. As a by-product of our analysis, new ideas about adequate modelling of earthquake sizes and inter-event times together with the location of the earthquakes are advanced, which in turn could pave the way to further developments in a directional perspective.
Two-Level Text Summarization Using Topic Modeling

Since the dawn of the Internet, the size of textual data has been steadily growing, every single day, due to frequent usage of digital libraries, social media, and online search engines, concomitant with storage of a mountainous amount of raw text. Gleaning useful content toward generation of a credible summary is a challenging task. This work reports on the implementation of a two-level summarization document, using latent Dirichlet allocation (LDA). The proposed method is constituted of two steps: the first step involves usage of maximal marginal relevance (MMR) and (b) text rank (TR) summarization procedures. These techniques generate summaries of a document, for a given corpus with multiple topics. In the second step, LDA modeling algorithm is applied to the summaries generated by MMR and TR, in the previous step. This process generates more shortened summaries, with differentiated topics. We used customers opinion reviews on products, hotels. [Section 4.1] as input corpus. The performance of this two-level document summarization (DS) using LDA is compared with MMR and TR. The comparison results show the two-level document summarization using LDA generates better summaries Sect. 5.
Benchmarking Human Performance in Semi-Automated Image Segmentation

Abstract Semi-automated segmentation algorithms hold promise for improving extraction and identification of objects in images such as tumors in medical images of human tissue, counting plants or flowers for crop yield prediction or other tasks where object numbers and appearance vary from image to image. By blending markup from human annotators to algorithmic classifiers, the accuracy and reproducability of image segmentation can be raised to very high levels. At least, that is the promise of this approach, but the reality is less than clear. In this paper, we review the state-of-the-art in semi-automated image segmentation performance assessment and demonstrate it to be lacking the level of experimental rigour needed to ensure that claims about algorithm accuracy and reproducability can be considered valid. We follow this review with two experiments that vary the type of markup that annotators make on images, either points or strokes, in tightly controlled experimental conditions in order to investigate the effect that this one particular source of variation has on the accuracy of these types of systems. In both experiments, we found that accuracy substantially increases when participants use a stroke-based interaction. In light of these results, the validity of claims about algorithm performance are brought into sharp focus, and we reflect on the need for a far more control on variables for benchmarking the impact of annotators and their context on these types of systems.
Implementation of Block Chain Technology in Public Distribution System

PDS is the scheme which was started by government for the distribution of necessary good to the people who are unable to purchase the goods or who doesn’t have proper income to take the goods. There are various frauds and malicious activities carried out while distributing the goods to individual like making the non-availability of goods and also making of the fraud ration cards, to reduce the frauds like this government has applied many of the schemes to make the system digital. We are trying to build a block chain process which help in governing the PDS which consist of the network having Farmers, Central Government, State Government, Fair price shop and customers. The block chain stores the logs of all the food transfer. Food transfers includes supply of food stock from Farmer to Central Government, Central Government to State Government, food distribution to Fair Price Shop by State government and food supply to customer by fair rice shop. Implementing the block chain technology in this system will help us to make the system decentralized and also will provide the transparency among the every individual who is working in the system or connected to the system. As block chain is the most secured technology it will be the best way to make this area free from all the malicious activities. The implementation will help the customers to get rid of the activities who are not in favour of customer also they will have the rights to see what was transferred to the fair price shop by government also to the customers who have taken the good and the stock left with the shop owner. The whole process will become decentralized and there will be the transparency among every person connected in the system.