Reviewerly/exHarmony huggingface dataset:
Pterodactyl: Mechanical Designs for Integrated Control Design of a Mechanically Deployable Entry Vehicle

As space exploration missions continue to get more complex, greater demands for mass and volume are being placed on planetary entry vehicles. At the same time, the desire for precision entry targeting to enable surface missions at very specific locations is becoming common. Deployable aeroshells are one type of technology that is being developed to increase the down-mass capability of planetary entry vehicles while complying with current launch vehicle volume constraints. Propulsive control can be used for precision entry targeting but can require a large percentage of the vehicle’s total mass or volume for propellant. The Pterodactyl project is studying alternative entry control systems on a Deployable Entry Vehicle to potentially introduce a mass and volume efficient entry system that can achieve precision targeting. This paper presents the mechanical design work undertaken to support the Pterodactyl trade study of three entry vehicle control system options: 1) Flaps, 2) Mass Movement, and 3) Reaction Control System.
Universal Image Segmentation Technique for Cancer Detection in Medical Images

Image segmentation is key area of research and focus due to the complexity of the problem statements. Based on the application and source of images the parameters of processing the image as well as the techniques employed to get efficient segmentation varies greatly. The works in this field thus far have been on a particular modality and utilized a sinlg e or combination of techniques for improving segmentation for those modalities.This paper focuses on a universal method of identification of cancerous regions in medical images captured from different image sources such as Computerized Tomography (CT) Scan, Magnetic Resonance Imaging (MRI), X-Ray and Ultrasound. The parameters utilized in this model have been optimized so as to avoid any and all human intercention in the identification of cancer in medical images. The proposed method employs a double mask technique in order to autoidentify region of interest without any manual intervention.
Text analysis for Bengali Text Summarization using Deep Learning

Text summarization is an approach by which the size of one or more document is shortened and the shorten passage presents the core information of the document. In this modern era of information technology, we are over flooded with online data which raised the necessity of summary of the original text. Many methods have already implemented for English text and the effort for Bengali text are gaining alongside. In this paper, we propose an extractive text summarization technique based on a deep learning model of Recurrent Neural Network (RNN) for single document summary. Our method is to classify the sentences as significant or not for the summary. We have used Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) based RNN. Between them, we found LSTM more promising and we achieved average F1 scores- 0.63, 0.59, 0.56 for Rouge-1, Rouge-2 and Rouge-3 in some respects.
Cryptanalysis cipher text using new modeling: Text encryption using elliptic curve cryptography

Cryptanalysis is the investigation of cipher content, ciphers and cryptosystems with the point of seeing how they work and finding and improving procedures for crushing or debilitating. Elliptic Curve Cryptography is one of the newest fields in cryptography field. This paper has proposed a new technique where classical techniques are replaced with new technique by changing characters with affine points. This technique avoids costly mapping.
CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks

Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution. However, when a high level of distortion has been applied to a CAPTCHA to make it resistant to automated attacks, the CAPTCHA becomes difficult for a human to recognize. In this work, we propose a method for generating a CAPTCHA image that will resist recognition by machines while maintaining its recognizability to humans. The method utilizes the style transfer method, and creates a new image, called a style-plugged-CAPTCHA image, by incorporating the styles of other images while keeping the content of the original CAPTCHA. In our experiment, we used the TensorFlow machine learning library and six CAPTCHA datasets in use on actual websites. The experimental results show that the proposed scheme reduces the rate of recognition by the DeCAPTCHA system to 3.5% and 3.2% using one style image and two style images, respectively, while maintaining recognizability by humans.
Entity Profiling in Knowledge Graphs

Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiles generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs.
Optimizing Ontology Alignment in Vector Space

Ontology matching technique aims at determining the identical entities, which can effectively solve the ontology heterogeneity problem and implement the collaborations among ontology-based intelligent systems. Typically, an ontology consists of a set of concepts which are described by various properties, and they deﬁne a space such that each distinct concept and property represents one dimension in that space. Therefore, it is an effective way to model an ontology in a vector space, and use the vector space based similarity measure to calculate two entities’ similarity. In this work, the entities’ structure information is utilized to model an ontology in a vector space, and then, their linguistic information is used to reduce the number of dimensions, which can improve the eﬃciency of the similarity calculation and entity matching process. After that, a discrete optimization model is constructed for the ontology matching problem, and a compact Evolutionary Algorithm (cEA) based ontology matching technique is proposed to eﬃciently address it. The experiment uses the benchmark track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the comparing results with state-of-the-art ontology matching systems show that our approach can eﬃciently determine high-quality ontology alignments.
Two-wavelength phase-shifting method with four patterns for three-dimensional shape measurement

Fringe projection technique has been widely used for three-dimensional (3D) shape measurement. However, it remains challenging to achieve high-speed measurement. A two-wavelength phase-shifting profilometry method with only four patterns is presented. Specifically, all these four patterns contain two wavelength components. The short wavelength component was used to compute the wrapped phase map, while the long one was used to unwrap the wrapped phase map. The performance of the proposed method was validated by both simulation study and experimental results.
Image-based marker tracking and registration for intraoperative 3D image-guided interventions using augmented reality

Augmented reality has the potential to improve operating room workflow by allowing physicians to "see" inside a patient through the projection of imaging directly onto the surgical field. For this to be useful the acquired imaging must be quickly and accurately registered with patient and the registration must be maintained. Here we describe a method for projecting a CT scan with Microsoft Hololens and then aligning that projection to a set of fiduciary markers. Radio-opaque stickers with unique QR-codes are placed on an object prior to acquiring a CT scan. The location of the markers in the CT scan are extracted and the CT scan is converted into a 3D surface object. The 3D object is then projected using the Hololens onto a table on which the same markers are placed. We designed an algorithm that aligns the markers on the 3D object with the markers on the table. To extract the markers and convert the CT into a 3D object took less than 5 seconds. To align three markers, it took $0.9 \pm 0.2$ seconds to achieve an accuracy of $5 \pm 2$ mm. These findings show that it is feasible to use a combined radio-opaque optical marker, placed on a patient prior to a CT scan, to subsequently align the acquired CT scan with the patient.
Updating Algerian solar atlas using MEERA-2 data source

According to its geographical location, Algeria holds one of the highest solar potential in the Mediterranean basin, as indicated in many studies. Unfortunately, an important lack of data of the solar global radiation is observed. With a huge area of 2 381 741 km2, it is necessary to have numerous stations performing radiation measurement in order to quantify in an objective way the solar potential. Previously, several studies have been conducted dealing with solar potential evaluation. In this study, an update of the Algerian solar atlas is presented. Thus, a solar mapping on the national territory was carried out based this time on data measured over a period of almost 37 years. In fact MEERA-2 ”The Modern-Era Retrospective Analysis for Research and Applications, Version 2, NASA”, were used in this study. Therefore, solar maps for solar radiation on different tilting planes were plotted thanks to the use of SURFER® software, through Kriging interpolation method. Furthermore, measured data for five (05) locations across the country were used in order to make comparison to those given by MERRA-2.
Modeling the forecasted power of a photovoltaic generator using numerical weather prediction and radiative transfer models coupled with a behavioral electrical model

The intermittency of the solar radiation is the big challenger for the management of electrical SMART GRID network. So the forecasting of the solar radiation remains the crucial objective to anticipate the injection of the electrical power produced by a photovoltaic generator. In this study, we use the Numerical Weather Prediction model WRF coupled to a radiative transfer model libRadtran to forecast the global, direct and diffuse solar radiations. The NMM core of WRF model was used to predict the vertical profiles of the atmospheric meteorological parameters such as liquid and ice water concentration, cloud cover, relative humidity, temperature, etc. These data are used as input in libRadtran to calculate the hourly solar radiations components (Direct, Diffuse and Global) in real meteorological situations. The radiation values are obtained on regular geographical grid points. A behavioral electrical power model is used to compute the electrical power produced by a photovoltaic generator located at the Center of Development of Renewable Energy in the city of Algiers in Algeria. The comparative study shows well that the results are very encouraging.
Towards Automatic Detection and Explanation of Hate Speech and Offensive Language

The use of hate speech and offensive language online has become widely recognized as a critical social problem plaguing today's Internet users. Previous research in the detection of hate speech and offensive language has primarily focused on using machine learning approaches to naively detect hate speech and offensive language, without explaining the reasons for their detection. In this work, we introduce a novel hate speech and offensive language defense system called HateDefender, which consists of a detection model based on deep Long Short-term Memory (LSTM) neural networks and an explanation model based on the gating signals of LSTMs. HateDefender effectively detects hate speech and offensive language (average accuracy of 90.82% and 89.10% on hate speech and offensive language, respectively) and explains their factors by pinpointing the exact words that are responsible for causing them. Our system uses these explanations for the effective intervention of such incidents online.
Positionality-aware machine learning

Positionality is a person's unique and always partial view of the world which is shaped by social and political contexts. Machine Learning (ML) systems have positionality, too, as a consequence of the choices we make when we develop ML systems. Being positionality-aware is key for ML practitioners to acknowledge and embrace the necessary choices embedded in ML by its creators.
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis

Duplicate Question Identification (DQI) improves the processing efficiency and accuracy of large-scale community question answering and automatic QA system.The purpose of DQI task is to identify whether the paired questions are semantically equivalent.However, how to distinguish the synonyms or homonyms in paired questions is still challenging.Most previous works focus on the word-level or phrase-level semantic differences.We firstly propose to explore the asking emphasis of a question as a key factor in DQI.Asking emphasis bridges semantic equivalence between two questions.In this paper, we propose an attention model with multifusion asking emphasis (MFAE) for DQI.At first, BERT is used to obtain the dynamic pre-trained word embeddings.Then we get inter-and intra-asking emphasis by summing inter-attention and self-attention, respectively; the idea is that, the more a word interacts with others, the more important the word is.Finally, we use eight-way combinations to generate multi-fusion asking emphasis and multi-fusion word representation.Experimental results demonstrate that our model achieves state-of-the-art performance on both Quora Question Pairs and CQADupStack data.In addition, our model can also improve the results for natural language inference task on SNLI and MultiNLI datasets.
Benchmarking Manifold Learning Methods on a Large Collection of Datasets

Manifold learning, a non-linear approach of dimensionality reduction, assumes that the dimensionality of multiple datasets is artificially high and a reduced number of dimensions is sufficient to maintain the information about the data. In this paper, a large scale comparison of manifold learning techniques is performed for the task of classification. We show the current standing of genetic programming (GP) for the task of classification by comparing the classification results of two GP-based manifold leaning methods: GP-Mal and ManiGP - an experimental manifold learning technique proposed in this paper. We show that GP-based methods can more effectively learn a manifold across a set of 155 different problems and deliver more separable embeddings than many established methods.
Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations

Constructing accurate and automatic solvers of math word problems has proven to be quite challenging. Prior attempts using machine learning have been trained on corpora specific to math word problems to produce arithmetic expressions in infix notation before answer computation. We find that custom-built neural networks have struggled to generalize well. This paper outlines the use of Transformer networks trained to translate math word problems to equivalent arithmetic expressions in infix, prefix, and postfix notations. In addition to training directly on domain-specific corpora, we use an approach that pre-trains on a general text corpus to provide foundational language abilities to explore if it improves performance. We compare results produced by a large number of neural configurations and find that most configurations outperform previously reported approaches on three of four datasets with significant increases in accuracy of over 20 percentage points. The best neural approaches boost accuracy by almost 10% on average when compared to the previous state of the art.
Knowing what you know: valid confidence sets in multiclass and multilabel prediction.

We develop conformal prediction methods for constructing valid predictive confidence sets in multiclass and multilabel problems without assumptions on the data generating distribution. A challenge here is that typical conformal prediction methods---which give marginal validity (coverage) guarantees---provide uneven coverage, in that they address easy examples at the expense of essentially ignoring difficult examples. By leveraging ideas from quantile regression, we build methods that always guarantee correct coverage but additionally provide (asymptotically optimal) conditional coverage for both multiclass and multilabel prediction problems. To address the potential challenge of exponentially large confidence sets in multilabel prediction, we build tree-structured classifiers that efficiently account for interactions between labels. Our methods can be bolted on top of any classification model---neural network, random forest, boosted tree---to guarantee its validity. We also provide an empirical evaluation, simultaneously providing new validation methods, that suggests the more robust coverage of our confidence sets.
SCANViz: Interpreting the Symbol-Concept Association Captured by Deep Neural Networks through Visual Analytics

Two fundamental problems in machine learning are recognition and generation. Apart from the tremendous amount of research efforts devoted to these two problems individually, finding the association between them has attracted increasingly more attention recently. Symbol-Concept Association Network (SCAN) is one of the most popular models for this problem proposed by Google DeepMind lately, which integrates an unsupervised concept abstraction process and a supervised symbol-concept association process. Despite the outstanding performance of this deep neural network, interpreting and evaluating it remain challenging. Guided by the practical needs from deep learning experts, this paper proposes a visual analytics attempt, i.e., SCANViz, to address this challenge in the visual domain. Specifically, SCANViz evaluates the performance of SCAN through its power of recognition and generation, facilitates the exploration of the latent space derived from both the unsupervised extraction and supervised association processes, empowers interactive training of SCAN to interpret the model’s understanding on a particular visual concept. Through concrete case studies with multiple deep learning experts, we validate the effectiveness of SCANViz.
Uncertainty Treemaps

Rectangular treemaps visualize hierarchical numerical data by recursively partitioning an input rectangle into smaller rectangles whose areas match the data. Numerical data often has uncertainty associated with it. To visualize uncertainty in a rectangular treemap, we identify two conflicting key requirements: (i) to assess the data value of a node in the hierarchy, the area of its rectangle should directly match its data value, and (ii) to facilitate comparison between data and uncertainty, uncertainty should be encoded using the same visual variable as the data, that is, area. We present Uncertainty Treemaps, which meet both requirements simultaneously by introducing the concept of hierarchical uncertainty masks. First, we define a new cost function that measures the quality of Uncertainty Treemaps. Then, we show how to adapt existing treemapping algorithms to support uncertainty masks. Finally, we demonstrate the usefulness and quality of our technique through an expert review and a computational experiment on real-world datasets.
AutoHAS: Differentiable Hyper-parameter and Architecture Search.

Neural Architecture Search (NAS) has achieved significant progress in pushing state-of-the-art performance. While previous NAS methods search for different network architectures with the same hyper-parameters, we argue that such search would lead to sub-optimal results. We empirically observe that different architectures tend to favor their own hyper-parameters. In this work, we extend NAS to a broader and more practical space by combining hyper-parameter and architecture search. As architecture choices are often categorical whereas hyper-parameter choices are often continuous, a critical challenge here is how to handle these two types of values in a joint search space. To tackle this challenge, we propose AutoHAS, a differentiable hyper-parameter and architecture search approach, with the idea of discretizing the continuous space into a linear combination of multiple categorical basis. A key element of AutoHAS is the use of weight sharing across all architectures and hyper-parameters which enables efficient search over the large joint search space. Experimental results on MobileNet/ResNet/EfficientNet/BERT show that AutoHAS significantly improves accuracy up to 2% on ImageNet and F1 score up to 0.4 on SQuAD 1.1, with search cost comparable to training a single model. Compared to other AutoML methods, such as random search or Bayesian methods, AutoHAS can achieve better accuracy with 10x less compute cost.
Random circuit block-encoded matrix and a proposal of quantum LINPACK benchmark

The LINPACK benchmark reports the performance of a computer for solving a system of linear equations with dense random matrices. Although this task was not designed with a real application directly in mind, the LINPACK benchmark has been used to define the list of TOP500 supercomputers since the debut of the list in 1993. We propose that a similar benchmark, called the quantum LINPACK benchmark, could be used to measure the whole machine performance of quantum computers. The success of the quantum LINPACK benchmark should be viewed as the minimal requirement for a quantum computer to perform a useful task of solving linear algebra problems, such as linear systems of equations. We propose an input model called the RAndom Circuit Block-Encoded Matrix (RACBEM), which is a proper generalization of a dense random matrix in the quantum setting. The RACBEM model is efficient to be implemented on a quantum computer, and can be designed to optimally adapt to any given quantum architecture, with relying on a black-box quantum compiler. Besides solving linear systems, the RACBEM model can be used to perform a variety of linear algebra tasks relevant to many physical applications, such as computing spectral measures, time series generated by a Hamiltonian simulation, and thermal averages of the energy. We implement these linear algebra operations on IBM Q quantum devices as well as quantum virtual machines, and demonstrate their performance in solving scientific computing problems.
Mobile Application System Supported BUC Students Services and Learning

&lt;p&gt;&lt;em&gt;Smartphones and applications are one of the most used things in learning and management systems. They make everyone connected with society, and those who feel sorry for it, especially college students are always looking for things that benefit them at a university. In this research, there is a need to apply a new application for student education service with an easy way of managing data and generate progression on reports feedback in his or her university life. We conducted a survey with the students of Al Buraimi University College and we reached a positive result that helps in the work. Smartphones have become pervasive and sales of all categories have many functions, in search (application of mobile-based devices in education design) to talk about applications in smart devices that serve education and take their information for management purpose and ease of use and follow them in any way. &lt;/em&gt;&lt;/p&gt;