Reviewerly/exHarmony huggingface dataset:
Qualitative analysis of clay minerals and swelling potential using gamma-ray spectrometry logs: A case study of the Bahariya Formation in the Western Desert, Egypt

Gamma-ray spectrometry logs are verified as a qualitative analysis tool for clay minerals where clay swelling depends on the clay mineral type. The data are obtained from three random wells (Kh-24, Bassel-1X, and Hayat-1X wells) in Western Desert oil fields. X-ray diffraction, scanning electron microscopy, and gamma-ray spectrometry are performed and the swelling potential index is obtained for surface samples from the Bahariya Formation. The swelling values and the total counts of gamma radiation, equivalent Th, equivalent U, and K are plotted with correlation coefficients (r) of 0.71, 0.81, 0.62, and 0.07, respectively. The swelling percentage ranges from 33% to 110%. This swelling behavior is reflected in the great difference in the composition of the clay minerals. Active clay minerals such as montmorillonite are widely observed with mixed-layer clay, illite, chlorite, and kaolinite. The correlation coefficients clarify a strong relation occurs in the cases of total gamma radiation and equivalent Th versus the swelling index. Consequently, gamma-ray spectrometry logs provide a new approach for identifying not only clay minerals but also their swelling trend. Moreover, they help to give an expected drilling risk, such as stuck while drilling.
Encrypted Image Retrieval Method using SIFT and ORB in Cloud

With the increasing development of image retrieval techniques, we proposes an encryption based retrieval of images by the use of local image information SIFT and ORB in the cloud environment. In this encrypted image retrieval work, to extract features of an image by using SIFT, and ORB. To generate the protected index on the features Random Projection with min-hash algorithm is proposed. For preserving the privacy, encrypting the images by using position scrambling method. This proposed method outperformed other methods with the precision of 93%.
An AI based design of student performance prediction and evaluation system in college physical education

College physical education is too one-sided, which makes the teaching process evaluation meaningless. Based on this, based on neural network technology, this article combines artificial intelligence teaching system to build an artificial intelligence sports teaching evaluation model based on neural network. The artificial intelligence model starts from the process evaluation and the final evaluation. Moreover, it uses a recurrent neural network for data training and analysis, and introduces a new decoder to perform data processing, and introduces a simplified gated neural network internal structure diagram to build the internal structure of the model.In addition, this study designs a control experiment to evaluate the performance of the model constructed in this study. The research results show that the artificial intelligence model constructed in this paper has a good effect in the performance prediction and evaluation of college sports students.
Ensemble Knowledge Distillation for Learning Improved and Efficient Networks.

Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements. In this paper, we present a framework for learning compact CNN models with improved classification performance and model generalization. For this, we propose a CNN architecture of a compact student model with parallel branches which are trained using ground truth labels and information from high capacity teacher networks in an ensemble learning fashion. Our framework provides two main benefits: i) Distilling knowledge from different teachers into the student network promotes heterogeneity in feature learning at different branches of the student network and enables the network to learn diverse solutions to the target problem. ii) Coupling the branches of the student network through ensembling encourages collaboration and improves the quality of the final predictions by reducing variance in the network outputs. Experiments on the well established CIFAR-10 and CIFAR-100 datasets show that our Ensemble Knowledge Distillation (EKD) improves classification accuracy and model generalization especially in situations with limited training data. Experiments also show that our EKD based compact networks outperform in terms of mean accuracy on the test datasets compared to state-of-the-art knowledge distillation based methods.
A Real-Time Garbage Truck Supervision and Data Statistics Method Based on Object Detection

Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.
Foveated Encoding for Large High-Resolution Displays

Collaborative exploration of scientific data sets across large high-resolution displays requires both high visual detail as well as low-latency transfer of image data (oftentimes inducing the need to trade one for the other).In this work, we present a system that dynamically adapts the encoding quality in such systems in a way that reduces the required bandwidth without impacting the details perceived by one or more observers.Humans perceive sharp, colourful details, in the small foveal region around the centre of the field of view, while information in the periphery is perceived blurred and colourless.We account for this by tracking the gaze of observers, and respectively adapting the quality parameter of each macroblock used by the H.264 encoder, considering the so-called visual acuity fall-off.This allows to substantially reduce the required bandwidth with barely noticeable changes in visual quality, which is crucial for collaborative analysis across display walls at different locations.We demonstrate the reduced overall required bandwidth and the high quality inside the foveated regions using particle rendering and parallel coordinates.
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning

Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper, we investigate the nonconvex-strongly-convex bilevel optimization problem. For deterministic bilevel optimization, we provide a comprehensive finite-time convergence analysis for two popular algorithms respectively based on approximate implicit differentiation (AID) and iterative differentiation (ITD). For the AID-based method, we orderwisely improve the previous finite-time convergence analysis due to a more practical parameter selection as well as a warm start strategy, and for the ITD-based method we establish the first theoretical convergence rate. Our analysis also provides a quantitative comparison between ITD and AID based approaches. For stochastic bilevel optimization, we propose a novel algorithm named stocBiO, which features a sample-efficient hypergradient estimator using efficient Jacobian- and Hessian-vector product computations. We provide the finite-time convergence guarantee for stocBiO, and show that stocBiO outperforms the best known computational complexities orderwisely with respect to the condition number κ and the target accuracy ϵ. We further validate our theoretical results and demonstrate the efficiency of bilevel optimization algorithms by the experiments on meta-learning and hyperparameter optimization.
Land Registration: Use-case of e-Governance using Blockchain Technology

e-Governance is a medium to offer various services to citizens through a web portal, that exists in many countries nowadays.The existing e-Governance technology is a vast, centrally managed database and a set of applications that connect to it via web interfaces.Despite the modernisation of services, it remains with the lack of transparency.Thus, the existing infrastructure of e-Governance paves the way for corrupt practises by the bureaucrats.e-Governance needs a powerful underlying technology which doesn't provide any way to allow tampering of the record and which in turn eliminates corruption.In this paper, we took land registration as a use-case for building e-Governance by keeping Blockchain as an underlying technology, to put off the corrupt practices and to bring transparency.Once transactions in land registration added to the Blockchain, it is immutable as it is cryptographically secured.Besides, the blockchain technology is secured as the ledger is distributed over the network.If a hacker wants to modify the ledger, he needs to hack every node in the blockchain network.Hyperledger Fabric, a permissioned Blockchain adopted for implementation and Hyperledger Caliper for performance analysis with these evaluation metrics such as throughput, latency and execution time.
An Audio-Enriched BERT-Based Framework for Spoken Multiple-Choice Question Answering

In a spoken multiple-choice question answering (SMCQA) task, given a passage, a question, and multiple choices all in the form of speech, the machine needs to pick the correct choice to answer the question. While the audio could contain useful cues for SMCQA, usually only the auto-transcribed text is utilized in system development. Thanks to the large-scaled pre-trained language representation models, such as the bidirectional encoder representations from transformers (BERT), systems with only auto-transcribed text can still achieve a certain level of performance. However, previous studies have evidenced that acoustic-level statistics can offset text inaccuracies caused by the automatic speech recognition systems or representation inadequacy lurking in word embedding generators, thereby making the SMCQA system robust. Along the line of research, this study concentrates on designing a BERT-based SMCQA framework, which not only inherits the advantages of contextualized language representations learned by BERT, but integrates the complementary acoustic-level information distilled from audio with the text-level information. Consequently, an audio-enriched BERT-based SMCQA framework is proposed. A series of experiments demonstrates remarkable improvements in accuracy over selected baselines and SOTA systems on a published Chinese SMCQA dataset.
Security science as a scientific discipline - technological aspects

The paper argues the facts that the growth and expansion of its domain, in combination with its own procedures and methodologies, justifies the existence of Security Science. The paper starts with the global opinion overview of security as a scientific discipline. With the focus on technological segments, a possible scientific approach to security analysis (SA) is presented in detail. Finally, examples are provided regarding the use of mathematics modelling and tools for data analysis and interpretation in security domain.
Speech Separation Based on Multi-Stage Elaborated Dual-Path Deep BiLSTM with Auxiliary Identity Loss

Deep neural network with dual-path bi-directional long short-term memory (BiLSTM) block has been proved to be very effective in sequence modeling, especially in speech separation. This work investigates how to extend dual-path BiLSTM to result in a new state-of-the-art approach, called TasTas, for multi-talker monaural speech separation (a.k.a cocktail party problem). TasTas introduces two simple but effective improvements, one is an iterative multi-stage refinement scheme, and the other is to correct the speech with imperfect separation through a loss of speaker identity consistency between the separated speech and original speech, to boost the performance of dual-path BiLSTM based networks. TasTas takes the mixed utterance of two speakers and maps it to two separated utterances, where each utterance contains only one speaker's voice. Our experiments on the notable benchmark WSJ0-2mix data corpus result in 20.55dB SDR improvement, 20.35dB SI-SDR improvement, 3.69 of PESQ, and 94.86\% of ESTOI, which shows that our proposed networks can lead to big performance improvement on the speaker separation task. We have open sourced our re-implementation of the DPRNN-TasNet here (this https URL), and our TasTas is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be reproduced with ease.
A Safe-Region Imputation Method for Handling Medical Data with Missing Values

Medical data usually have missing values; hence, imputation methods have become an important issue. In previous studies, many imputation methods based on variable data had a multivariate normal distribution, such as expectation-maximization and regression-based imputation. These assumptions may lead to deviations in the results, which sometimes create a bottleneck. In addition, directly deleting instances with missing values may have several problems, such as losing important data, producing invalid research samples, and leading to research deviations. Therefore, this study proposed a safe-region imputation method for handling medical data with missing values; we also built a medical prediction model and compared the removed missing values with imputation methods in terms of the generated rules, accuracy, and AUC. First, this study used the kNN imputation, multiple imputation, and the proposed imputation to impute the missing data and then applied four attribute selection methods to select the important attributes. Then, we used the decision tree (C4.5), random forest, REP tree, and LMT classifier to generate the rules, accuracy, and AUC for comparison. Because there were four datasets with imbalanced classes (asymmetric classes), the AUC was an important criterion. In the experiment, we collected four open medical datasets from UCI and one international stroke trial dataset. The results show that the proposed safe-region imputation is better than the listing imputation methods and after imputing offers better results than directly deleting instances with missing values in the number of rules, accuracy, and AUC. These results will provide a reference for medical stakeholders.
Eliminating cross-camera bias for vehicle re-identification

Vehicle re-identification (reID) often requires to recognize a target vehicle in large datasets captured from multi-cameras. It plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic in recent years. However, the appearance of vehicle images is easily affected by the environment that various illuminations, different backgrounds and viewpoints, which leads to the large bias between different cameras. To address this problem, this paper proposes a cross-camera adaptation framework (CCA), which smooths the bias by exploiting the common space between cameras for all samples. CCA first transfers images from multi-cameras into one camera to reduce the impact of the illumination and resolution, which generates the samples with the similar distribution. Then, to eliminate the influence of background and focus on the valuable parts, we propose an attention alignment network (AANet) to learn powerful features for vehicle reID. Specially, in AANet, the spatial transfer network with attention module is introduced to locate a series of the most discriminative regions with high-attention weights and suppress the background. Moreover, comprehensive experimental results have demonstrated that our proposed CCA can achieve excellent performances on benchmark datasets VehicleID and VeRi-776.
Project Esky: Enabling High Fidelity Augmented Reality on an Open Source Platform

This demonstration showcases a complete Open-Source Augmented Reality (AR) modular platform capable of high fidelity natural hand-interactions with virtual content, high field of view, and spatial mapping for environment interactions. We do this via several live desktop demonstrations. Finally, included in this demonstration is a completed open source schematic, allowing anyone interested in utilizing our proposed platform to engage with high fidelity AR. It is our hope that the work described in this demo will be a stepping stone towards bringing high-fidelity AR content to researchers and commodity users alike.
Research on Basketball Players’ Training Strategy Based on Artificial Intelligence Technology

Abstract The current artificial intelligence technology, as a new frontier science and technology, can track and analyze a single sports goal in the training methods of basketball players, and use convenient training strategies to achieve the best training effect. This article first elaborates the current development trend of artificial intelligence technology, and also analyzes the development of artificial intelligence at home and abroad, expounds the current status of artificial intelligence in sports, and finally focuses on the training strategy of artificial intelligence in basketball players. In-depth analysis and research have been done in the application of other aspects, the purpose is to provide theoretical support and guidance for the further development of modern basketball. Based on artificial intelligence, the basketball player training system should fully understand and evaluate the athlete’s physical condition and athletic ability, timely measure the basketball player’s sports skills, give training strategies for the basketball player’s sports situation, and optimize the training plan for the athletes. In the software and hardware architecture, the Baum-Welch algorithm should be contacted to collect athletes’ sports data, and a data management platform should be established to fully explore the athletes’ exercise laws and strengthen the athletes’ daily training. The experimental research results show that the use of artificial intelligence technology can greatly improve the training efficiency of basketball players and fully stimulate the potential of basketball players. The use of artificial intelligence technology can not only provide basketball players with convenient and effective strategies, but also develop different training plans according to the physical fitness of each basketball player.
Are Some Words Worth More than Others?

Current evaluation metrics for language modeling and generation rely heavily on the accuracy of predicted (or generated) words as compared to a reference ground truth. While important, token-level accuracy only captures one aspect of a language model’s behavior, and ignores linguistic properties of words that may allow some mis-predicted tokens to be useful in practice. Furthermore, statistics directly tied to prediction accuracy (including perplexity) may be confounded by the Zipfian nature of written language, as the majority of the prediction attempts will occur with frequently-occurring types. A model’s performance may vary greatly between high- and low-frequency words, which in practice could lead to failure modes such as repetitive and dull generated text being produced by a downstream consumer of a language model. To address this, we propose two new intrinsic evaluation measures within the framework of a simple word prediction task that are designed to give a more holistic picture of a language model’s performance. We evaluate several commonly-used large English language models using our proposed metrics, and demonstrate that our approach reveals functional differences in performance between the models that are obscured by more traditional metrics.
Quantum Modular Multiplication

Quantum modular multiplication circuit is one of the basic quantum computation circuits which are basic functions in quantum algorithms. However, since quantum-quantum modular multipliers require a high cost reversible modular inversion routine for modular multiplication, researchers have been unable to propose a feasible quantum-quantum modular multiplier. In this paper, we proposed efficient quantum-classical modular multipliers and the first quantum-quantum modular multipliers that do not require a reduction stage by transforming the partial product used in multiplication utilizing bit-shift operation. Then, we calculated quantum resource complexity and analyzed it compared to other quantum modular multipliers and utilized ETRI (Electronics and Telecommunications Research Institute) Qcrypton to analyze quantum resource complexity in the practical quantum computing situation. The proposed quantum modular multipliers show an improvement of 50% in terms of gates and circuit depth compared to the most recently proposed high-performance quantum modular multipliers.
Backpropagating Linearly Improves Transferability of Adversarial Examples

The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community. In this paper, we study the transferability of such examples, which lays the foundation of many black-box attacks on DNNs. We revisit a not so new but definitely noteworthy hypothesis of Goodfellow et al.'s and disclose that the transferability can be enhanced by improving the linearity of DNNs in an appropriate manner. We introduce linear backpropagation (LinBP), a method that performs backpropagation in a more linear fashion using off-the-shelf attacks that exploit gradients. More specifically, it calculates forward as normal but backpropagates loss as if some nonlinear activations are not encountered in the forward pass. Experimental results demonstrate that this simple yet effective method obviously outperforms current state-of-the-arts in crafting transferable adversarial examples on CIFAR-10 and ImageNet, leading to more effective attacks on a variety of DNNs.
Domain-Specific Knowledge Graph Construction for Semantic Analysis

Knowledge graphs are widely used for systematic representation of real-world data. Large-scale, general purpose knowledge graphs, having millions of facts, have been constructed through automated techniques from publicly available datasets such as Wikipedia. However, these knowledge graphs are typically incomplete and often fail to correctly capture the semantics of the data. This holds true particularly for domain-specific data, where the generic techniques for automated knowledge graph creation often fail due to several challenges, such as lack of training data, semantic ambiguities and absence of representative ontologies. The focus of this thesis is on automated knowledge graph construction for the cultural heritage domain. The goal is to tackle the research challenges encountered during the creation of an ontology and a knowledge graph from digitized collections of cultural heritage data. This paper identifies the specific research problems for these tasks and presents a methodology and approach for a solution, along with preliminary results.