Reviewerly/exHarmony huggingface dataset:
A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection

A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%; precision 85.30, 83.87%; recall 88.41%, 83.89; F-measure 86.81, 83.87%; classification error 14.75, 17.70; and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers.
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants’ satisfaction with their computational notebook.
Texture and Trace Element Composition of Rutile in Orogenic Gold Deposits

Abstract Rutile from a wide range of orogenic gold deposits and districts, including representative world-class deposits, was investigated for its texture and trace element composition using scanning electron microscopy, electron probe microanalysis, and laser ablation-inductively coupled plasma-mass spectrometry. Deposits are hosted in various country rocks including felsic to ultramafic igneous rocks and sedimentary rocks, which were metamorphosed from lower greenschist to middle amphibolite facies and with ages of mineralization that range from Archean to Phanerozoic. Rutile presents a wide range of size, texture, and chemical zoning. Rutile is the dominant TiO2 polymorph in orogenic gold mineralization. Elemental plots and partial least square-discriminant analysis suggest that the composition of the country rocks exerts a strong control on concentrations of V, Nb, Ta, and Cr in rutile, whereas the metamorphic facies of the country rocks controls concentrations of V, Zr, Sc, U, rare earth elements, Y, Ca, Th, and Ba in rutile. The trace element composition of rutile in orogenic gold deposits can be distinguished from rutile in other deposit types and geologic settings. Elemental ratios Nb/V, Nb/Sb, and Sn/V differentiate the rutile trace element composition of orogenic gold deposits compared with those from other geologic settings and environments. A binary plot of Nb/V vs. W enables distinction of rutile in metamorphic-hydrothermal and hydrothermal deposits from rutile in magmatic-hydrothermal deposits and magmatic environments. The binary plot Nb/Sb vs. Sn/V distinguishes rutile in orogenic gold deposits from other geologic settings and environments. Results are used to establish geochemical criteria to constrain the source of rutile for indicator mineral surveys and potentially guide mineral exploration.
Optimal Scaling Quantum Linear-Systems Solver via Discrete Adiabatic Theorem

Recently, several approaches to solving linear systems on a quantum computer have been formulated in terms of the quantum adiabatic theorem for a continuously varying Hamiltonian. Such approaches have enabled near-linear scaling in the condition number κ of the linear system, without requiring a complicated variable-time amplitude amplification procedure. However, the most efficient of those procedures is still asymptotically suboptimal by a factor of log﻿(κ). Here, we prove a rigorous form of the adiabatic theorem that bounds the error in terms of the spectral gap for intrinsically discrete-time evolutions. In combination with the qubitized quantum walk, our discrete adiabatic theorem gives a speed-up for all adiabatic algorithms. Here, we use this combination to develop a quantum algorithm for solving linear systems that is asymptotically optimal, in the sense that the complexity is strictly linear in κ, matching a known lower bound on the complexity. Our O[κlog﻿(1/ϵ)] complexity is also optimal in terms of the combined scaling in κ and the precision ϵ. Compared to existing suboptimal methods, our algorithm is simpler and easier to implement. Moreover, we determine the constant factors in the algorithm, which would be suitable for determining the complexity in terms of gate counts for specific applications.2 MoreReceived 3 December 2021Accepted 18 July 2022DOI:https://doi.org/10.1103/PRXQuantum.3.040303Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAdiabatic quantum optimizationQuantum algorithmsQuantum simulationQuantum Information
Quantum Distributed Unit Commitment: An Application in Microgrids

The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment problem complexity is expected to increase in the future because of the trend toward the increase of penetration of intermittent renewables. Even though quantum computing has proven effective for solving a host of problems, its applications for power systems’ problems have been rather limited. In this paper, a quantum unit commitment is innovatively formulated and the quantum version of the decomposition and coordination alternate direction method of multipliers (ADMM) is established. The above is achieved by devising quantum algorithms and by exploiting the superposition and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible solutions. The main contributions of this paper include: 1) the innovative development of a quantum model for Unit Commitment; 2) development of decomposition and coordination-supported framework which paves the way for the utilization of limited quantum resources to potentially solve the large-scale discrete optimization problems; 3) devising the novel quantum distributed unit commitment (QDUC) to solve the problem in a larger scale than currently available quantum computers are capable of solving. The QDUC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing.
How to Train BERT with an Academic Budget

While large language models à la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few wellfunded industry labs can afford.How can one train such models with a more modest budget?We present a recipe for pretraining a masked language model in 24 hours using a single lowend deep learning server.We demonstrate that through a combination of software optimizations, design choices, and hyperparameter tuning, it is possible to produce models that are competitive with BERT BASE on GLUE tasks at a fraction of the original pretraining cost. 1
Programmable photonic neural networks combining WDM with coherent linear optics

Abstract Neuromorphic photonics has relied so far either solely on coherent or Wavelength-Division-Multiplexing (WDM) designs for enabling dot-product or vector-by-matrix multiplication, which has led to an impressive variety of architectures. Here, we go a step further and employ WDM for enriching the layout with parallelization capabilities across fan-in and/or weighting stages instead of serving the computational purpose and present, for the first time, a neuron architecture that combines coherent optics with WDM towards a multifunctional programmable neural network platform. Our reconfigurable platform accommodates four different operational modes over the same photonic hardware, supporting multi-layer, convolutional, fully-connected and power-saving layers. We validate mathematically the successful performance along all four operational modes, taking into account crosstalk, channel spacing and spectral dependence of the critical optical elements, concluding to a reliable operation with MAC relative error $$&lt; 2\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&lt;</mml:mo> <mml:mn>2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> .
An image encryption scheme based on chaotic logarithmic map and key generation using deep CNN

A secure and reliable image encryption scheme is presented in this study. The encryption scheme hereby introduces a novel chaotic log-map, deep convolution neural network (CNN) model for key generation, and bit reversion operation for the manipulation process. Thanks to the sensitive key generation, initial values and control parameters are produced for the hyperchaotic log-map, and thus a diverse chaotic sequence is achieved for encrypting operations. The scheme then encrypts the images by scrambling and manipulating the pixels of images through four operations: permutation, DNA encoding, diffusion, and bit reversion. The encryption scheme is precisely examined for the well-known images in terms of various analyses such as keyspace, key sensitivity, information entropy, histogram, correlation, differential attack, noisy attack, and cropping attack. To corroborate the scheme, the visual and numerical results are even compared with available outcomes of the state of the art. Therefore, the proposed log-map based image encryption scheme is successfully verified and validated by the superior absolute and comparative results.
Weakly Supervised Video Moment Localization with Contrastive Negative Sample Mining

Video moment localization aims at localizing the video segments which are most related to the given free-form natural language query. The weakly supervised setting, where only video level description is available during training, is getting more and more attention due to its lower annotation cost. Prior weakly supervised methods mainly use sliding windows to generate temporal proposals, which are independent of video content and low quality, and train the model to distinguish matched video-query pairs and unmatched ones collected from different videos, while neglecting what the model needs is to distinguish the unaligned segments within the video. In this work, we propose a novel weakly supervised solution by introducing Contrastive Negative sample Mining (CNM). Specifically, we use a learnable Gaussian mask to generate positive samples, highlighting the video frames most related to the query, and consider other frames of the video and the whole video as easy and hard negative samples respectively. We then train our network with the Intra-Video Contrastive loss to make our positive and negative samples more discriminative. Our method has two advantages: (1) Our proposal generation process with a learnable Gaussian mask is more efficient and makes our positive sample higher quality. (2) The more difficult intra-video negative samples enable our model to distinguish highly confusing scenes. Experiments on two datasets show the effectiveness of our method. Code can be found at https://github.com/minghangz/cnm.
A large scale group decision making system based on sentiment analysis cluster

Nowadays, group decision making is an everyday occurrence in different scenarios, such as marketing or social networks. These social networks have facilitated communication between experts because they do not need to meet in person. However, communication between experts through the internet generates three problems: the management of large amounts of information, the fact that experts often provide their information using natural language, and the lack of analysis of experts’ intentions. In this paper, we propose a novel large scale group decision making method to manage the information generated by a large number of experts, using the natural language processing approach, specifically sentiment analysis. This approach makes it possible to detect the degree of positivity and aggressiveness of each expert and thus proceed to a classification. Once the behaviours are detected, the experts are grouped according to them and, for each group, a weight and a unique preference relation is obtained. In addition, we propose an optimised consensus analysis process, in which it is not necessary to compare all experts with each other, but only groups of experts.
On Learning Contrastive Representations for Learning with Noisy Labels

Deep neural networks are able to memorize noisy labels easily with a softmax cross entropy (CE) loss. Previous studies attempted to address this issue focus on incorporating a noise-robust loss function to the CE loss. However, the memorization issue is alleviated but still remains due to the non-robust CE loss. To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where label noise does not dominate the representation learning. By theoretically investigating the representations induced by the proposed regularization function, we reveal that the learned representations keep information related to true labels and discard information related to corrupted labels. Moreover, our theoretical results also indicate that the learned representations are robust to the label noise. The effectiveness of this method is demonstrated with experiments on benchmark datasets.
Data Privacy and Security

Part I: Data Encryption: Monoalphabetic Substitution Ciphers.- Transposition Ciphers.- Polyalphabetic Substitution Ciphers.- Random Numbers.- The Enigma.- Stream Ciphers.- Block ciphers.- Public-Key Cryptography.- Quantum Cryptography.- Part II: Data Hiding: Data Hiding in Text.- Data Hiding in Images.- Other Methods Appendixes: Convolution.- Hashing.- Cyclic Redundancy Codes.- Galois Fields.- Glossary.- Index.
The On-Line Encyclopedia of Integer Sequences

The On-Line Encyclopedia of Integer Sequences (or OEIS) is a database of some 130000 number sequences. It is freely available on the Web (http://www.research.att.com/~njas/sequences/) and is widely used.There are several ways in which it benefits research: 1 It serves as a dictionary, to tell the user what is known about a particular sequence. There are hundreds of papers which thank the OEIS for assistance in this way. 1 The associated Sequence Fans mailing list is a worldwide network which has evolved into a powerful machine for tackling new problems. 1 As a direct source of new theorems, when a sequence arises in two different contexts. 1 As a source of new research, when one sees a sequence in the OEIS that cries out to be analyzed. The 40-year history of the OEIS recapitulates the story of modern computing, from punched cards to the internet.The talk will be illustrated with numerous examples, emphasizing new sequences that have arrived in the past few months. Many open problems will be mentioned.Because of the profusion of books and journals, volunteers play an important role in maintaining the database. If you come across an interesting number sequence in a book, journal or web site, please send it and the reference to the OEIS. (You do not need to be the author of the sequence to do this.) There is a web site for sending in Comments or New sequences.Several new features have been added to the OEIS in the past year. Thanks to the work of Russ Cox, searches are now performed at high speed, and thanks to the work of Debby Swayne, there is a button which displays plots of each sequence. Finally, a listen button enables one to hear the sequence played on a musical instrument (try Recamaan's sequence A005132!).
Type-2 Fuzzy Logic-Based Linguistic Pursuing Strategy Design and Its Deployment to a Real-World Pursuit Evasion Game

This paper presents a systematic and interpretable design approach to generate type-2 (T2) fuzzy logic-based linguistic pursuing strategies (PSs) and their deployment to a real-world pursuit-evasion game (PEG). First, we have developed a novel T2 fuzzy logic-based strategy planner (T2-FSP). Then, through detailed theoretical investigations on the input-output mapping of the T2-FSP, it has been shown that it is possible to design a linguistic PS which defines both pursuer's approaching behavior (aggressive, smooth) and side (left or right) to the evader by simply tuning the footprint of uncertainty (FOU) sizes of the T2 fuzzy sets. Hence, an interpretable relationship has been revealed between the FOU sizes and the PSs through comparative theoretical explorations and derivations. Additionally, as there is a need to employ different PSs in a dynamic PEG environment, a type-1 fuzzy decision making (T1-FDM) mechanism has been designed to tune the FOU sizes of the T2-FSP and, thus, adjust the PS to be employed in real time. A real-world game environment is constructed in order to validate the developed T2 fuzzy logic-based PSs and T1-FDM mechanism in real time. Comparative experimental results have been presented to show that the T2 fuzzy logic-based PSs have satisfactory performance against a human user.
Alignment-Supervised Bidimensional Attention-Based Recursive Autoencoders for Bilingual Phrase Representation

Exploiting semantic interactions between the source and target linguistic items at different levels of granularity is crucial for generating compact vector representations for bilingual phrases. To achieve this, we propose alignment-supervised bidimensional attention-based recursive autoencoders (ABattRAE) in this paper. ABattRAE first individually employs two recursive autoencoders to recover hierarchical tree structures of bilingual phrase, and treats the subphrase covered by each node on the tree as a linguistic item. Unlike previous methods, ABattRAE introduces a bidimensional attention network to measure the semantic matching degree between linguistic items of different languages, which enables our model to integrate information from all nodes by dynamically assigning varying weights to their corresponding embeddings. To ensure the accuracy of the generated attention weights in the attention network, ABattRAE incorporates word alignments as supervision signals to guide the learning procedure. Using the general stochastic gradient descent algorithm, we train our model in an end-to-end fashion, where the semantic similarity of translation equivalents is maximized while the semantic similarity of nontranslation pairs is minimized. Finally, we incorporate a semantic feature based on the learned bilingual phrase representations into a machine translation system for better translation selection. Experimental results on NIST Chinese-English and WMT English-German test sets show that our model achieves substantial improvements of up to 2.86 and 1.09 BLEU points over the baseline, respectively. Extensive in-depth analyses demonstrate the superiority of our model in learning bilingual phrase embeddings.
Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads

The recent upsurge in the available amount of health data and the advances in next-generation sequencing are setting the ground for the long-awaited precision medicine. To process this deluge of data, bioinformatics workloads are becoming more complex and more computationally demanding. For this reasons they have been extended to support different computing architectures, such as GPUs and FPGAs, to leverage the form of parallelism typical of each of such architectures. The paper describes how a genomic workload such as k-mer frequency counting that takes advantage of a GPU can be offloaded to one or even more FPGAs. Moreover, it performs a comprehensive analysis of the FPGA acceleration comparing its performance to a non-accelerated configuration and when using a GPU. Lastly, the paper focuses on how, when using accelerators with a throughput-oriented workload, one should also take into consideration both kernel execution time and how well each accelerator board overlaps kernels and PCIe transferred. Results show that acceleration with two FPGAs can improve both time- and energy-to-solution for the entire accelerated part by a factor of 1.32x. Per contra, acceleration with one GPU delivers an improvement of 1.77x in time-to-solution but of a lower 1.49x in energy-to-solution due to persistently higher power consumption. The paper also evaluates how future FPGA boards with components (i.e., off-chip memory and PCIe) on par with those of the GPU board could provide an energy-efficient alternative to GPUs.
OSA: Orientation Search Algorithm

Random based inventive algorithms are being widely used for optimization. An important category of these algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed algorithm for optimizing the orientation game is a series of optimization algorithms that are formed with the idea of an old game and search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the game referee's orders. The best position is achieved by the laws are there in this game .In this paper, the real version of the algorithm is presented. The results of optimization of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed algorithm over the genetic algorithm and the particle swarm optimization algorithm.