Reviewerly/exHarmony huggingface dataset:
Comparative study among solar radiation data estimated by satellite images and measured by meteorological stations in the Paraíba state

Solar energy availability can be determined by different methodologies, the most used being those estimated by satellites or by using irradiation data obtained from a chain of surface meteorological stations. The aim of the present study is to compare the Glocal Horizontal Irradiaton (GHI) data obtained by Automated Meteorological Stations, provided by National Institute of Meteorology (INMET) with estimated data from satellite images, offered by the National Renewable Energy Laboratory (NREL) in the National Solar Radiation Database (NSRDB), for cities of Paraíba State, in the northeastern region of Brasil. To execute the comparisons, statistical methods were used such as Pearson's correlation coefficient, the bias, the root of mean squared error and hypothesis test. The results presented a good correlation between the databases. It was verified that the estimated data by satellite images are slightly higher than those observed in soil. While applying test t, it was observed that only one of the localities presented evidences of difference between the averages. The present study assisted to a better understanding of the differences between GHI data, estimated by satellite images and observed in surface meteorological stations, for the state of Paraíba.
Texture Analysis-based Features Extraction &amp; Classification of Lung Cancer Using Machine Learning

Lung cancer is a form of carcinoma that develops as a result of aberrant cell growth or mutation in the lungs. Most of the time, this occurs due to daily exposure to hazardous chemicals. However, this is not the only cause of lung cancer; additional factors include smoking, indirect smoke exposure, family medical history, and so on. Cancer cells, unlike normal cells, proliferate inexorably and cluster together to create masses or tumors. The symptoms of this disease do not appear until cancer cells have moved to other parts of the body and are interfering with the healthy functioning of other organs. As a solution to this problem, Machine Learning (ML) algorithms are used to diagnose lung cancer. The image datasets for this study were obtained from Kaggle. The images are preprocessed using various approaches before being used to train the image model. Texture-based Feature Extraction (FE) algorithms such as Generalized Low-Rank Models (GLRM) and Gray-level co-occurrence matrix (GLCM) are then used to extract the essential characteristics from the image dataset. To develop a model, the collected features are given into ML classifiers like the Support Vector Machine (SVM) and the k-nearest neighbor's algorithm (k-NN).&lt;br&gt;
Comparative study among solar radiation data estimated by satellite images and measured by meteorological stations in the paraíba state

Solar energy availability can be determined by different methodologies, the most used being those estimated by satellites or by using irradiation data obtained from a chain of surface meteorological stations. The aim of the present study is to compare the Glocal Horizontal Irradiaton (GHI) data obtained by Automated Meteorological Stations, provided by National Institute of Meteorology (INMET) with estimated data from satellite images, offered by the National Renewable Energy Laboratory (NREL) in the National Solar Radiation Database (NSRDB), for cities of Paraíba State, in the northeastern region of Brasil. To execute the comparisons, statistical methods were used such as Pearson's correlation coefficient, the bias, the root of mean squared error and hypothesis test. The results presented a good correlation between the databases. It was verified that the estimated data by satellite images are slightly higher than those observed in soil. While applying test t, it was observed that only one of the localities presented evidences of difference between the averages. The present study assisted to a better understanding of the differences between GHI data, estimated by satellite images and observed in surface meteorological stations, for the state of Paraíba.
Dynamics of quantum-memory assisted entropic uncertainty and entanglement in two-dimensional graphene

We explore the quantum-memory assisted entropic uncertainty (QMA-EU), Jensen-Shannon coherence and entanglement dynamics in a Graphene sheet of disordered electrons in the presence of the intrinsic decoherence. The Graphene sheet containing two sublattices in a two-dimensional honeycomb lattice, which results due to the impurity-potentials interaction of two Dirac points. Entropic uncertainty and logarithmic negativity are used to investigate the generation and preservation of QMA-EU and entanglement under the major factors of Graphene material, including the band structure parameter, the wave numbers as well as the decoherence effect. For the initial uncorrelated two-lattice-point-qubit state, it is found that the lattice-point interactions have high capacity to generated partially/maximally two-lattice-point-qubit entangled and hence the partial/perfect Bob's ability to guessing the outcome of Alice's measurement. The increase of the graphite band structure parameter, the wave numbers enhance the generated lattice-point entanglement and Jensen-Shannon coherence, and hence the quantum memory game has a high prediction accuracy. For the intrinsic decoherence, the ability to generate entanglement, Jensen-Shannon coherence. and guessing the Alice's measurement outcomes weakens. The sudden death-birth phenomenon in the logarithmic-negativity dynamics appears. The increase of the graphite band structure parameter weakens the robustness dynamics against the decoherence effect, while the increase of the wave number operators enhances this robustness. For initial maximally correlated state, the robustness dynamics of the QMA-EU, Jensen-Shannon coherence, and logarithmic-negativity entanglement is very sensitive to the increase of the band structure, wave numbers, and intrinsic decoherence.
Assessing syntactic and lexicogrammatical use in second language Mandarin writing samples

General indices of syntactic complexity (e.g. mean length of T-unit, clauses per T-Unit) have long been used to measure the writing proficiency of adult language learners (Bulté & Housen, 2012; Norris & Ortega, 2006; Ortega, 2003; Wolfe-Quintero et al., 1998). In contrast, a number of recent studies have focused on measuring adult second language writing proficiency using methods rooted in usage-based theories of language learning. The present study extends previous research (e.g., Kyle & Crossley, 2017) by comparing usage-based and general indices of syntactic and lexicogrammatical use in learners of Mandarin. It also extends previous work by investigating whether meaningful, but non-linear trends exist. To do this, it first compares multiple linear regression models to polynomial models for general and usage-based indices respectively. Then it compares the strongest model for each type of index to determine which is better. Consistent with Kyle and Crossley (2017), it finds that in Mandarin usage-based indices are better predictors of proficiency than general indices and that the frequency of the verb decreases over time while the strength of association between verb and VAC increases. Furthermore, non-linear trajectories were found to exist in general indices while usage-based indices were linear.
Exploring the Accuracy of Machine Learning in Detecting Fake News

Identifying fake news is crucial in the fight against misinformation. To achieve this goal, our project employs SVM and NB algorithms. We also utilize sentiment information from labeled and unlabeled data to improve the sentiment classifiers’ understanding of fake news in each trend. With the proliferation of the internet, there is a growing volume of dubious and intentional lym is leading content. The quality of fake news can be so high that it can be challenging to differentiate it from authentic news. Thus, the use of deep learning and machine learning methods for identifying fake news automatically has become significantly crucial. In our project, we pre-process the text using techniques such as stemming, lemmatization and stop word removal from creating text representations for our models. Our system’s essential features are based on two observations: first, we aim to classify words, and second, our customers receive a filtered subset of fake news. To categorize fake news based on the social transmission of false news, we experiment with a simple set of language-independent criteria.
Locally Consistent Decomposition of Strings with Applications to Edit Distance Sketching

In this paper we provide a new locally consistent decomposition of strings. Each string x is decomposed into blocks that can be described by grammars of size O(k) (using some amount of randomness). If we take two strings x and y of edit distance at most k then their block decomposition uses the same number of grammars and the i-th grammar of x is the same as the i-th grammar of y except for at most k indexes i. The edit distance of x and y equals to the sum of edit distances of pairs of blocks where x and y differ. Our decomposition can be used to design a sketch of size O(k2) for edit distance, and also a rolling sketch for edit distance of size O(k2). The rolling sketch allows to update the sketched string by appending a symbol or removing a symbol from the beginning of the string.
Memory-Sample Lower Bounds for Learning with Classical-Quantum Hybrid Memory

In a work by Raz (J. ACM and FOCS 16), it was proved that any algorithm for parity learning on n bits requires either Ω(n2) bits of classical memory or an exponential number (in ‍n) of random samples. A line of recent works continued that research direction and showed that for a large collection of classical learning tasks, either super-linear classical memory size or super-polynomially many samples are needed. All these works consider learning algorithms as classical branching programs, which perform classical computation within bounded memory. However, these results do not capture all physical computational models, remarkably, quantum computers and the use of quantum memory. It leaves the possibility that a small piece of quantum memory could significantly reduce the need for classical memory or samples and thus completely change the nature of the classical learning task. Despite the recent research on the necessity of quantum memory for intrinsic quantum learning problems like shadow tomography and purity testing, the role of quantum memory in classical learning tasks remains obscure. In this work, we study classical learning tasks in the presence of quantum memory. We prove that any quantum algorithm with both, classical memory and quantum memory, for parity learning on n bits, requires either Ω(n2) bits of classical memory or Ω(n) bits of quantum memory or an exponential number of samples. In other words, the memory-sample lower bound for parity learning remains qualitatively the same, even if the learning algorithm can use, in addition to the classical memory, a quantum memory of size c n (for some constant c>0). Our result is more general and applies to many other classical learning tasks. Following previous works, we represent by the matrix M: A × X → {−1,1} the following learning task. An unknown x is sampled uniformly at random from a concept class X, and a learning algorithm tries to uncover x by seeing streaming of random samples (ai, bi = M(ai, x)) where for every i, ai∈ A is chosen uniformly at random. Assume that k,ℓ,r are integers such that any submatrix of M of at least 2−k·|A| rows and at least 2−ℓ·|X| columns, has a bias of at most 2−r. We prove that any algorithm with classical and quantum hybrid memory for the learning problem corresponding to M needs either (1) Ω(k · ℓ) bits of classical memory, or (2) Ω(r) qubits of quantum memory, or (3) 2Ω(r) random samples, to achieve a success probability at least 2−O(r). Our results refute the possibility that a small amount of quantum memory significantly reduces the size of classical memory needed for efficient learning on these problems. Our results also imply improved security of several existing cryptographical protocols in the bounded-storage model (protocols that are based on parity learning on n bits), proving that security holds even in the presence of a quantum adversary with at most c n2 bits of classical memory and c n bits of quantum memory (for some constant c>0).
The Power of Unentangled Quantum Proofs with Non-negative Amplitudes

Quantum entanglement is a fundamental property of quantum mechanics and it serves as a basic resource in quantum computation and information. Despite its importance, the power and limitations of quantum entanglement are far from being fully understood. Here, we study entanglement via the lens of computational complexity. This is done by studying quantum generalizations of the class NP with multiple unentangled quantum proofs, the so-called QMA(2) and its variants. The complexity of QMA(2) is known to be closely connected to a variety of problems such as deciding if a state is entangled and several classical optimization problems. However, determining the complexity of QMA(2) is a longstanding open problem, and only the trivial complexity bounds ⊆ (2) ⊆ are known.
Constant-Round Arguments from One-Way Functions

We study the following question: what cryptographic assumptions are needed for obtaining constant-round computationally-sound argument systems? We focus on argument systems with almost-linear verification time for subclasses of P, such as depth-bounded computations. Kilian’s celebrated work [STOC 1992] provides such 4-message arguments for P (actually, for NP) using collision-resistant hash functions. We show that one-way functions suffice for obtaining constant-round arguments of almost-linear verification time for languages in P that have log-space uniform circuits of linear depth and polynomial size. More generally, the complexity of the verifier scales with the circuit depth. Furthermore, our argument systems (like Kilian’s) are doubly-efficient; that is, the honest prover strategy can be implemented in polynomial-time. Unconditionally sound interactive proofs for this class of computations do not rely on any cryptographic assumptions, but they require a linear number of rounds [Goldwasser, Kalai and Rothblum, STOC 2008]. Constant-round interactive proof systems of linear verification complexity are not known even for NC (indeed, even for AC1).
Hard Languages in NP ∩ coNP and NIZK Proofs from Unstructured Hardness

The existence of “unstructured” hard languages in NP ∩ coNP is an intriguing open question. Bennett and Gill (SICOMP, 1981) asked whether P is separated from NP ∩ coNP relative to a random oracle, a question that remained open ever since. While a hard language in NP ∩ coNP can be constructed in a black-box way from a one-way permutation, for which only few (structured) candidates exist, Bitansky et al. (SICOMP, 2021) ruled out such a construction based on an injective one-way function, an unstructured primitive that is easy to instantiate heuristically. In fact, the latter holds even with a black-box use of indistinguishability obfuscation.
Automated multi-objective system identification using grammar-based genetic programming

In order to use existing identification tools effectively, a user must make critical choices a priori that ultimately determine the quality of estimated models. Furthermore, while estimated models are typically optimized for a single identification criterion, engineering applications typically impose multiple performance specifications that may contradict each other. In this contribution, we develop a system identification methodology that automatically selects parametric model structures from a wide range of dynamic system models based on measured data. The problem of inferring model structures and estimating model parameters within these structures is encapsulated in a bi-level optimization problem. The optimization problem is formulated for multiple user-specified identification objectives. Finally, the range of dynamical systems considered for the optimization problem is specified using Tree Adjoining Grammar. A solution approach based on genetic programming is developed, and its asymptotic properties and computational complexity is analysed. The empirical performance of the proposed identification techniques is studied using a simulation example.
Fingerprint construction of optical transmitters based on the characteristic of electro-optic chaos for secure authentication

In this paper, an optical transmitter authentication method using hardware fingerprints based on the characteristic of electro-optic chaos is proposed. By means of phase space reconstruction of chaotic time series generated by an electro-optic feedback loop, the largest Lyapunov exponent spectrum (LLES) is defined and used as the hardware fingerprint for secure authentication. The time division multiplexing (TDM) module and the optical temporal encryption (OTE) module are introduced to combine chaotic signal and the message to ensure the security of the fingerprint. Support vector machine (SVM) models are trained to recognize legal and illegal optical transmitters at the receiver. Simulation results show that LLES of chaos has the fingerprint characteristic and is highly sensitive to the time delay of the electro-optic feedback loop. The trained SVM models can distinguish electro-optic chaos generated by different feedback loops with a time delay difference of only 0.03ns and have a good anti-noise ability. Experimental results show that the recognition accuracy of the authentication module based on LLES can reach 98.20% for both legal and illegal transmitters. Our strategy can improve the defense ability of optical networks against active injection attacks and has high flexibility.
Recent Advances in Immersive Multimedia

This special issue focuses on the recent advances in immersive multimedia, and presents eight articles that tackle a wide spectrum of challenges related to the adoption of immersive multimedia in novel applications spreading across various domains. Immersive multimedia has become a reality due to the active development of extended reality , which is an umbrella term for emulating a physical world in a virtual world, leveraging various human senses. One driving force of such development is the appearance of consumer-grade head-mounted displays and the immediate market of computer games. Immersive multimedia, however, has other novel applications in many domains, such as occupational training, health care, manufacturing, education, tourism, and real estate, to name a few. Adopting immersive multimedia for novel applications in these domains is quite challenging from both technology and sociology perspectives, and, thus, requires interdisciplinary discussions. From the technology perspective, immersive multimedia dictates high bandwidth, short latency, and reliable transmission of media content, which can be better fulfilled with edge/cloud architectures and advanced networks. From the sociology perspective, the success of immersive multimedia depends on the human—computer interface, which is highly application dependent. This special issue offers a timely collection of research updates to benefit researchers and practitioners working in fields ranging across multimedia, networking, cloud computing, and human—computer interfaces, with the aim of motivating social researchers and business agencies to deploy novel immersive multimedia in these fields.
Deep CNN-Based Facial Recognition for a Person Identification System Using the Inception Model

Reliable person identification is a primary factor and administered in the initial stages for safety measures. One of the contactless person identifications for tracing techniques is using an image-based face identification system. In this project, an algorithm of machine vision-based person identification using the Inception V3 is proposed, and the performance of the model is analyzed to gain the desired results for contactless person identification. A collection of facial image datasets had been created and each of the seven subjects was contributing 600 facial images; thus, 4200 images in total were stored in the database. The dataset is physically available at https://bit.ly/3jVhApz . A well-known Haar Cascade Classifier was used to capture and extract the facial region from the raw images. Data augmentation is carried out to increase the variation of the facial features within the training dataset by manipulating images with transformations such as horizontal flipping, random cropping, zooming, and color space augmentations. Thus, minimizing the distance between the training set and validation set, and the dataset was split using the Holdout method. This project has produced a good performance of face recognition system using the Inception V3 model with validation and testing accuracy rates of 99.7% and 99.4%, respectively. From the result, it shows that the model is able to give good performance in identifying a set of persons by using facial features in an image.
Technology for the CLIL Preschool

Busy teachers may find it difficult to implement technology in their classroom due to busy schedules or limited amount of time outside the class. Online technology progresses at a rapid pace and it is necessary to find open educational resources as well as pay-resources that can facilitate the integration of technology in the CLIL classroom at an early age. These resources should be easy to apply in the classroom and demand the minimal time investment from Preschool practitioners. This paper looks at the need of Preschool teachers to implement technology in their classroom by addressing the conditions and providing a list of resources that can be used. It also considers the constraints that many teachers find and suggests ways to reduce their fears and their anxiety due to the lack of knowledge or time. The paper has a significant section on resources that are easy to implement, that can serve in a variety of courses and whose access is either free or has a low cost. This chapter concludes that easily accessible technology has a remarkable value in teaching Preschool through CLIL.