Reviewerly/exHarmony huggingface dataset:
Large Scale Category-Structured Image Retrieval for Object Identification Through Supervised Learning of CNN and SURF-Based Matching

In the modern era of Internet, mobile and digital information technology, image retrieval for object identification, just as wine label retrieval from a wine bottle image, has become an important and urgent problem in artificial intelligence.In comparison with the general image retrieval, it is rather challenging because there are a huge number of object identification or brand images which are very similar and difficult to discriminate, and the number of different brand images in the given dataset changes greatly, that is, the samples are strongly unbalanced for these brands.In this paper, we propose a CNN-SURF Consecutive Filtering and Matching (CSCFM) framework for this kind of image retrieval, specifically focalizing on wine label retrieval.In particular, Convolutional Neural Network (CNN) is utilized to filter out the impossible main-brands (manufacturers) for narrowing down the range of retrieval and the Speeded Up Robust Features (SURF) matching is improved by adopting the RANdom SAmple Consensus (RANSAC) mechanism and the modified Term Frequency-Inverse Document Frequency (TF-IDF) distance for the accurate retrieval of the sub-brand (item attribute under the manufacture).The experiments are conducted on a dataset containing approximately 548k images of wine labels with 17, 328 main-brands and 260, 579 subbrands.It is demonstrated by the experimental results that our proposed method can solve the wine label retrieval problem effectively and efficiently.Moreover, our proposed method is further evaluated on two pubic benchmarks of the object identification image retrieval tasks, Oxford Buildings Benchmark (Oxford5k) and the University of Kentucky of Indoor Things Benchmark (UKB), and achieves 88.3% mean average precision and 3.92 N-S score in Oxford5k and UKB, respectively.
Performance analysis based on probabilistic modelling of Quaid-e-Azam Solar Park (QASP) Pakistan

The solar photovoltaic (PV) technology has gained global importance to overcome the global warming and meet future energy needs. The performance of a solar PV plant depends on many factors such as solar irradiance, weather conditions, various types of energy losses and system degradation over time. Although the deterministic models nicely predict the PV performance at a single instant in time, however, they fail to account for the uncertainty and randomness in the input parameters. Probabilistic models, in contrast, are more useful to predict the system performance over a time span under real conditions. In this study, a probabilistic model has been developed for the performance analysis of a recently commissioned 100 MW power plant at Bahawalpur, Pakistan. The model is based on Monte-Carlo simulation method and uses the probable range of input data from the site of the power plant. The performance of the power plant is presented in terms of monthly and seasonal electricity generation. The associated energy losses are discussed in detailed. Furthermore, a comprehensive cost analysis of the power plant has been provided. According to results from the model, the power produced in the first year of operation of the plant is 136,700 MWh and the projected cumulative energy produced during a plant lifetime of 25 years is 3,108,450 MWh. The levelized cost of energy (LCOE) estimated by the model is 0.0795 $/kWh, which is quite reasonable in comparison to the average 0.1 $/kWh cost of electricity to a domestic customer in Pakistan.
Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling (DFJSS) is a very valuable practical application problem that can be applied in many fields such as cloud computing and manufacturing. In DFJSS, machine assignment and operation sequencing decisions need to be made simultaneously in dynamic environments with unpredicted events such as new job arrivals. Scheduling heuristic is an ideal candidate for solving the DFJSS problem due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, GP has a huge search space, and the traditional search algorithms do not utilise effectively the information obtained from the evolutionary process. This paper proposes a new method to make better use of the information during the evolutionary process of GP to further enhance the ability of GP. To be specific, this paper proposes two adaptive search strategies based on the frequency of features in promising individuals to guide GP to evolve effective rules. This paper examines the proposed algorithm on six different DFJSS scenarios. The results show that the proposed GP with adaptive search can converge faster and achieve significantly better performance than the GP without adaptive search in most scenarios while no worse in all other scenarios without increasing the computational cost.
Continuous‐Variable Entanglement and Wigner‐Function Negativity via Adding or Subtracting Photons

Abstract The performance of adding or subtracting photons on two‐mode squeezed thermal states via examining the Einstein–Podolsky–Rosen (EPR) correlation, the Hillery–Zubairy (HZ) correlation, the fidelity of teleportation, and the negativity of Wigner function is theoretically investigated. The normalization factors and the teleportation fidelity are related to Jacobi polynomials, and the (evolved) Wigner functions are simply associated with two‐variable Hermite polynomials. Compared with the original squeezed thermal states, the EPR correlation and the teleportation fidelity can be enhanced by photon subtraction and basically weakened by photon addition symmetric operations, but they cannot be enhanced for both photon addition and subtraction asymmetric cases. Also, HZ correlation can provide a better option relative to the EPR correlation in detecting the entanglement, and the fidelity for teleporting a squeezed state with a large squeezing can also be enhanced via photon addition symmetric operations, in contrast to teleporting a coherent state. Additionally, the nonclassicality is discussed in terms of the negativity of the (evolved) Wigner functions, which shows that photon addition and subtraction and the squeezing cannot restrain the deteriorate of nonclassicality, and the evolved Wigner functions become Gaussian (corresponding to vacuum) with long decay times as a result of amplitude decay.
Effect of Character and Word Features in Bidirectional LSTM-CRF for NER

Named entity recognition (NER) is a challenging task in natural language processing (NLP). Nowadays, the NLP research community pays attention to the NER system, adopting a well-known deep neural network (DNN) techniques to extract the named entity in a text. So, we studied the effect of the existing Glove and Fasttext word embedding to improve NER performance. We also examined the impact of a combination of additional word and character input features to CNN, Bidirectional LSTM with and without the CRF models. In the proposed work, we did not preprocess the data and did not use any lexicon for further enhancement. Our experiment mainly focuses on the effectiveness of the word and character features for NER. The F1 measure was used for comparing the effectiveness of our additional input features with Chui's word and character feature. To our knowledge, the best result is obtained using Glove 840B as word embedding along with word pattern, and character pattern input for CNN and two-layers of Bidirectional LSTM with CRF. The experiment achieves 91.10% on the CoNLL-2003 and outperforms Chiu state of the art (SOTA) results.
Hierarchical Identity-Based Encryption with Tight Multi-challenge Security

We construct the first hierarchical identity-based encryption (HIBE) scheme with tight adaptive security in the multi-challenge setting, where adversaries are allowed to ask for ciphertexts for multiple adaptively chosen identities. Technically, we develop a novel technique that can tightly introduce randomness into user secret keys for hierarchical identities in the multi-challenge setting, which cannot be easily achieved by the existing techniques for tightly multi-challenge secure IBE. In contrast to the previous constructions, the security of our scheme is independent of the number of user secret key queries and that of challenge ciphertext queries. We prove the tight security of our scheme based on the Matrix Decisional Diffie-Hellman Assumption, which is an abstraction of standard and simple decisional Diffie-Hellman assumptions, such as the k-Linear and SXDH assumptions. Finally, we also extend our ideas to achieve tight chosen-ciphertext security and anonymity, respectively. These security notions for HIBE have not been tightly achieved in the multi-challenge setting before.
Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy

In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class measurement devices for irradiance and other weather conditions. For this reason, the use of reanalysis-based or satellite-based data is currently of significant interest in the PV community and combining the data with decomposition and transposition irradiance models, the actual Plane-of-Array operating conditions can be determined. In this paper, we are proposing an efficient and accurate approach for PV output energy modelling by combining a new data filtering procedure and fast machine learning algorithm Light Gradient Boosting Machine (LightGBM). The applicability of the procedure is presented on three levels of irradiance data accuracy (low, medium, and high) depending on the source or modelling used. A new filtering algorithm is proposed to exclude erroneous data due to system failures or unreal weather conditions (i.e., shading, partial snow coverage, reflections, soiling deposition, etc.). The cleaned data is then used to train three empirical models and three machine learning approaches, where we emphasize the advantages of the LightGBM. The experiments are carried out on a 17 kW roof-top PV system installed in Ljubljana, Slovenia, in a temperate climate zone.
Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

With the development of computer network technology and the expansion of network system, sensitive data is facing the threat of hacker attack.Intrusion detection is an active network security defense measure, which is an attempt to invade, an ongoing intrusion or an intrusion that has occurred to identify the process.At present, the detection rate of intrusion detection method is low, the false alarm rate and false alarm rate is high, and the real-time performance is poor.It needs a large number of or complete data to achieve better detection performance.In this paper, the concept, characteristics, classification, research contents and difficulties of traditional intrusion detection for mass multimedia data transmission network are described.Then, the basic principle of neural network and particle swarm optimization (PSO) algorithm and the basic idea of particle swarm optimization algorithm with quantum (QPSO) behaviour are introduced.It is emphasized that QPSO has better convergence performance than PSO algorithm in global optimization problems.In this paper, the concept, characteristics and structure of neural network are described, and the algorithm and classification of wavelet neural network are introduced.Then taking wavelet neural network (WNN) as the object, using the QPSO algorithm as the training algorithm, the concrete operation process is given.The research work in this paper shows that the performance of neural network trained by QPSO algorithm and improved QPSO algorithm is better than that of other intelligent algorithms such as PSO algorithm and genetic algorithm, and the convergence speed is faster than that of PSO algorithm or GA algorithm.QPSO is a high performance neural network training algorithm, which can play a good role in neural network anomaly detection.
Regularized least squares locality preserving projections with applications to image recognition

Locality preserving projection (LPP), as a well-known technique for dimensionality reduction, is designed to preserve the local structure of the original samples which usually lie on a low-dimensional manifold in the real world. However, it suffers from the undersampled or small-sample-size problem, when the dimension of the features is larger than the number of samples which causes the corresponding generalized eigenvalue problem to be ill-posed. To address this problem, we show that LPP is equivalent to a multivariate linear regression under a mild condition, and establish the connection between LPP and a least squares problem with multiple columns on the right-hand side. Based on the developed connection, we propose two regularized least squares methods for solving LPP. Experimental results on real-world databases illustrate the performance of our methods.
Masking in Fine-Grained Leakage Models: Construction, Implementation and Verification

We propose a new approach for building efficient, provably secure, and practically hardened implementations of masked algorithms. Our approach is based on a Domain Specific Language in which users can write efficient assembly implementations and fine-grained leakage models. The latter are then used as a basis for formal verification, allowing for the first time formal guarantees for a broad range of device-specific leakage effects not addressed by prior work. The practical benefits of our approach are demonstrated through a case study of the PRESENT S-Box: we develop a highly optimized and provably secure masked implementation, and show through practical evaluation based on TVLA that our implementation is practically resilient. Our approach significantly narrows the gap between formal verification of masking and practical security.
Keefektifan Penggunaan Media Pembelajaran Berbasis Game Edukasi Quizizz Terhadap Hasil Belajar Teknologi Perkantoran Siswa Kelas X SMK Ketintang Surabaya

This research aims to determine the effectiveness of the effectiveness of learning media based educational game quizizz on student outcomes office technology grade X SMK Ketintang Surabaya. This type of research is using quasi experimental design. The circuit was conducted on all class X OTKP 2 students as a control class and X OTKP 3 as an experimental class. Analytical techniques use homogeneity test, normality test, hypothesis test and gain score test. The results of the study expressed the Posttest value obtained by t-test of 2.154 with a signification of 0.036 and a value of 2.002 with a level of significance 0.005 which means signification (0.036) &lt; 0.05 and t-test (2.154) &gt; ttable (2.002).The result of Gain Score the Pretest and Posttest value is 3,212 with a signication 0,002 and the value ttable is 2,002 with signification 0,05. So it can be concluded that the use of learning media based on Quizizz educational Game effectively improve student learning outcomes on the subject of office technology class X OTKP in SMK Ketintang Surabaya.
Generalisation error in learning with random features and the hidden manifold model

We study generalised linear regression and classification for a synthetically generated dataset encompassing different problems of interest, such as learning with random features, neural networks in the lazy training regime, and the hidden manifold model. We consider the high-dimensional regime and using the replica method from statistical physics, we provide a closed-form expression for the asymptotic generalisation performance in these problems, valid in both the under- and over-parametrised regimes and for a broad choice of generalised linear model loss functions. In particular, we show how to obtain analytically the so-called double descent behaviour for logistic regression with a peak at the interpolation threshold, we illustrate the superiority of orthogonal against random Gaussian projections in learning with random features, and discuss the role played by correlations in the data generated by the hidden manifold model. Beyond the interest in these particular problems, the theoretical formalism introduced in this manuscript provides a path to further extensions to more complex tasks.
Active Learning for Coreference Resolution using Discrete Annotation

We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent.This simple modification, when combined with a novel mention clustering algorithm for selecting which examples to label, is much more efficient in terms of the performance obtained per annotation budget.In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour.Future work can use our annotation protocol to effectively develop coreference models for new domains.Our code is publicly available. 1* *Work done while at the University of Washington. 1 https://github.com/belindal/discrete-active-learning-coref A volcano in Mexico, known to locals as Po-po , just started spewing molten rock.Are the two mentions coreferent?No What is the first appearance of the entity that the yellowhighlighted text refers to?A volcano in Mexico
What are the Goals of Distributional Semantics?

Distributional semantic models have become a mainstay in NLP, providing useful features for downstream tasks.However, assessing long-term progress requires explicit long-term goals.In this paper, I take a broad linguistic perspective, looking at how well current models can deal with various semantic challenges.Given stark differences between models proposed in different subfields, a broad perspective is needed to see how we could integrate them.I conclude that, while linguistic insights can guide the design of model architectures, future progress will require balancing the often conflicting demands of linguistic expressiveness and computational tractability.
Quantum Communication between Multiplexed Atomic Quantum Memories

The use of multiplexed atomic quantum memories (MAQM) can significantly enhance the efficiency to establish entanglement in a quantum network. In the previous experiments, individual elements of a quantum network, such as the generation, storage, and transmission of quantum entanglement have been demonstrated separately. Here we report an experiment to show the compatibility and integration of these basic operations. Specifically, we generate photon-atom entanglement from any chosen pair of memory cells in a 6×5 MAQM, convert the spin-wave to time-bin photonic excitation after a controllable storage time, and then store and retrieve the photon in a second MAQM for another controllable storage time. The preservation of quantum information in this process is verified by measuring the state fidelity. We also demonstrate that higher dimension quantum states can be transferred between the two distant MAQMs.
The Usability Analysis Online Learning Site for Supporting Computer programming Course Using System Usability Scale (SUS) in a University

Computer programming subject is the most-feared subject by students because it is hard to learn. At present, various learning media for programming are available, therefore, the learning process which was often considered as boring and hard to learn becomes interesting and easy to do. One of those media is through internet. This condition is parallel with various number of learning-based online sites that currently grow, including CodeSaya; one of the learning websites for computer programming languages. Usability is an important aspect to be noticed for the users to feel facilitated in using it, acquiring the required knowledge, and be interested to always rely on the site within the learning process. By collecting the user perception about user interfaces, questionnaire might help identifying the weakness in website usability to perform improvements. This research was done to analyze the success of online learning site, whether it meets the usability criteria by implementing System Usability Scale (SUS) questionnaire as the research instrument. SUS is an effective and reliable instrument to measure the usability of various products and services. The research questionnaire consisted of 10 questions which disseminated toward 162 respondents divided into two groups, namely familiar users that amounted to 81 respondents and unfamiliar users that amounted to 81 respondents as well. The usability of CodeSaya site acquired 72.1 of score for familiar users and 70 of score for unfamiliar users. The score acquired from the rating given by familiar respondents only a bit higher compared to the unfamiliar respondents. The rating in alphabet resulted in B or Good. It can be concluded that this computer programming learning site has met the usability criteria, therefore, it can be implemented as a learning medium that is feasible to be used by the users.
Application of Blockchain Technology for iLearning Student Assessment

Blockchain is the core technology used to create cryptocurrencies, such as Bitcoin. As one part of the fourth industrial Revolution since the invention of steam engines, electricity, and information technology, blockchain technology has begun to be applied in areas such as finance, judiciary, and trade. Blockchain technology uses decentralized, distributed and transparency techniques for data security. This research aims to determine the implementation of blockchain technology in the field of education, especially in the Data Security section. The study uses two methods namely the mind mapping method and literature review. The results of this study showed that with the presence of blockchain technology, the data is more secure from attacks from both inside and outside because it passes through two levels of security, namely encryption and decentralized data.
A Survey of Planning and Learning in Games

In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.