Reviewerly/exHarmony huggingface dataset:
On Evaluating Runtime Performance of Interactive Visualizations

As our field matures, evaluation of visualization techniques has extended from reporting runtime performance to studying user behavior. Consequently, many methodologies and best practices for user studies have evolved. While maintaining interactivity continues to be crucial for the exploration of large data sets, no similar methodological foundation for evaluating runtime performance has been developed. Our analysis of 50 recent visualization papers on new or improved techniques for rendering volumes or particles indicates that only a very limited set of parameters like different data sets, camera paths, viewport sizes, and GPUs are investigated, which make comparison with other techniques or generalization to other parameter ranges at least questionable. To derive a deeper understanding of qualitative runtime behavior and quantitative parameter dependencies, we developed a framework for the most exhaustive performance evaluation of volume and particle visualization techniques that we are aware of, including millions of measurements on ten different GPUs. This paper reports on our insights from statistical analysis of this data, discussing independent and linear parameter behavior and non-obvious effects. We give recommendations for best practices when evaluating runtime performance of scientific visualization applications, which can serve as a starting point for more elaborate models of performance quantification.
EcForest: Extractive document summarization through enhanced sentence embedding and cascade forest

Summary We present EcForest, an extractive summarization model through Enhanced Sentence Embedding and Cascade Forest. Sentence representation is of great significance for many summarization methods. Bag‐of‐words mostly fails to grasp the semantics, and typical embedding models cannot capture more complex semantic features, such as polysemy and the meaning of a phrase, which is usually ignored by simply averaging the word embeddings included in a sentence. To this end, we propose Enhanced Sentence Embedding (ESE) model to solve such drawbacks via mapping several valid features to dense vectors. Essentially, the enhanced sentence embedding is a novel model for improving the distributed representation of sentence. Our sentence embedding model is universally applicable and it can be adapted to other NLP tasks. Moreover, deep forest is used as a sentence extraction algorithm for its robustness to the hyper‐parameters and its efficient training algorithm compared to deep neural network. The evaluation of variant models proposed in this work proves the validation of the enhanced sentence embedding. The comparison results between EcForest and several baselines on two different datasets demonstrate that the proposed summarization model performs better than or with high competitiveness to the state‐of‐the‐art.
Authentication of area fingerprint scanners

Each area fingerprint scanner (sensor) has a unique scanner pattern that can be used to distinguish one scanner from another. The pattern results from imperfections in the conversion of the signal acquired from the object, applied to the scanner, into a digital image. The scanner pattern is a sufficiently unique, persistent, and unalterable intrinsic characteristic of the fingerprint scanners even to those of the same technology, manufacturer, and model. We propose a model of the image acquisition in widely-used capacitive area fingerprint scanners and a method to extract the pattern from a single image and compare it with a similarly extracted pattern from another image. The method is very simple and computationally efficient yet extremely accurate, achieving an equal error rate in the order of 1e-7 for 22 scanners of exactly the same model, and is also robust in a wide range of conditions. The scanner pattern can be used to enhance the security of a biometric system by detecting an attack in which an image containing the fingerprint pattern of the legitimate user and acquired by the authentic fingerprint scanner has been replaced by another image that may still contain the fingerprint pattern of the legitimate user but has been acquired by another, unauthentic fingerprint scanner, i.e., for scanner authentication.
A Secure Shuffling Mechanism for White-Box Attack-Resistant Unmanned Vehicles

Unmanned Vehicles (UVs) have been being utilized for various applications, such as surveillance, search/rescue, and monitoring. The information they transmit is critical for decision-making. However, UVs are vulnerable to white-box attacks due to improvements in reverse engineering techniques and the openness of their software. Therefore, attackers with sufficient knowledge of a target UV can steal secret information stored in the UV by exploiting its vulnerabilities. Recently, several white-box cryptography techniques have been introduced to protect secret keys from being extracted by converting them into large look-up tables. However, none of them provide approaches to securely update the look-up tables. Thus, once a remote attacker succeeds in extracting the static look-up table from a UV, he/she can use it to decrypt past/future communications or to send false information to the control station by impersonating the UV. In this paper, we propose a look-up table shuffling mechanism that supports white-box cryptography with dynamics. The mechanism makes it hard for attackers to determine the positions of the table entries, and thus to decrypt/encrypt ciphertexts/plaintexts. To show the practicality of the block cipher with our mechanism, we implemented it on a board equipped with a GPU and show its GPU-accelerated performance.
Informed Weighted Non-Negative Matrix Factorization Using αβ-Divergence Applied to Source Apportionment

In this paper, we propose informed weighted non-negative matrix factorization (NMF) methods using an α β -divergence cost function. The available information comes from the exact knowledge/boundedness of some components of the factorization—which are used to structure the NMF parameterization—together with the row sum-to-one property of one matrix factor. In this contribution, we extend our previous work which partly involved some of these aspects to α β -divergence cost functions. We derive new update rules which are extendthe previous ones and take into account the available information. Experiments conducted for several operating conditions on realistic simulated mixtures of particulate matter sources show the relevance of these approaches. Results from a real dataset campaign are also presented and validated with expert knowledge.
Towards Hierarchical Classification of Data Streams

In data stream mining, state-of-the-art machine learning algorithms for the classification task associate each event with a class belonging to a finite, devoid of structural dependencies and usually small, set of classes. However, there are more complex dynamic problems where the classes we want to predict make up a hierarchal structure. In this paper, we propose an incremental method for hierarchical classification of data streams. We experimentally show that our stream hierarchical classifier present advantages to the traditional online setting in three real-world problems related to entomology, ichthyology, and audio processing.
Lightweight Blockchain Logging for Data-Intensive Applications

With the recent success of cryptocurrency, Blockchain’s design opens the door of building trustworthy distributed systems. A common paradigm is to repurpose the Blockchain as an append-only log that logs the application events in time order for subsequent auditing and query verification. While this paradigm reaps the security benefit, it faces technical challenges especially when being used for data-intensive applications. Instead of treating Blockchain as a time-ordered log, we propose to lay the log-structured merge tree (LSM tree) over the Blockchain for efficient and lightweight logging. Comparing other data structures, the LSM tree is advantageous in supporting efficient writes while enabling random-access reads. In our system design, only a small digest of an LSM tree is persisted in the Blockchain and minimal store operations are carried out by smart contracts. With the implementation in Ethereum/Solidity, we evaluate the proposed logging scheme and demonstrate its performance efficiency and effectiveness in cost saving.
Two Stream Deep CNN-RNN Attentive Pooling Architecture for Video-Based Person Re-identification

Person re-identification (re-ID), is the task of associating the relationship among the images of a person captured from different cameras with non-overlapping field of view. Fundamental and yet an open issue in re-ID is extraction of powerful features in low resolution surveillance videos. In order to solve this, a novel Two Stream Convolutional Recurrent model with Attentive pooling mechanism is presented for person re-ID in videos. Each stream of the model is a Siamese network which is aimed at extracting and matching most differentiated feature maps. Attentive pooling is used to select most informative video frames. The output of two streams is fused to formulate one combined feature map, which helps to deal with major challenges of re-ID e.g. pose and illumination variation, clutter background and occlusion. The proposed technique is evaluated on three challenging datasets: MARS, PRID-2011 and iLIDS-VID. Experimental evaluation shows that the proposed technique performs better than existing state-of-the-art supervised video based person re-ID models. The implementation is available at https://github.com/re-identification/Person_RE-ID.git .
The Development of Integrated Bali Tourism Information Portal using Web Scrapping and Clustering Methods

This study aims to develop integrated tourism information portal prototypes in Bali by using web scrapping and Clustering methods.This study will use the waterfall development model. This study focused on the stages of needs analysis, system design for Tourism information portals in Bali and prototype development at the implementation stage. The initial stage of the web scrapping process is to do web searching in the form of url requests based on the keywords entered. After the data from the web scrapping process is collected, the next step is text processing which consists of several processes, namely parsing or tokenization, stemming in the form of root word search process, and stop word removal or removal of non-essential words. Before the clustering process begins, each word will be given weighting with the TF-IDF method which will form the vector space model as a representation of a text document. Similarity between vector space models will be calculated using the cosine similarity method. After the weighting process, the last step is to cluster the words which then continue to group the websites based on the results of clustering.The results of this study are a website portal that can display tourism information in Bali.
Numerical procedures and their practical application in PV modules analyses. Part I: air mass

The subject of the article is aspects of PV modules and cells measurement, with the use of natural sunlight.A light source is an important element during calibration and measurements of solar cells and modules.All designers of artificial light sources try to recreate natural light using so called measurement tables.The correctly performed measurement, i.e. meeting all the appropriate atmospheric conditions, guarantees obtaining the result with the use of a reference spectrum.The article has two main aims.The first aim of the article is to answer the question asked earlier -if the sunlight spectrum registered in appropriate conditions is so good that it serves as the reference spectrum -then, in practice, during measurements carried out with its use, certain problems occur regarding the correct measurement results or their interpretation.The second aim regards presenting detailed numeric procedures in order to enable readers to associate air mass with geographical coordinates and Local Solar Time of their study/laboratory location.Moreover, having the data from their local meteorological station, they will be able to estimate the occurrence of the measurement spectral error of the tested cell/module not only from the group referred to in the article but also for others, for which they have a dedicated characteristics of spectral response.
Human Edge Segmentation From 2D Images By Histogram of Oriented Gradients and Edge Matching Algorithm

Current research performance is limited to segment precise human edge. In this paper, we proposed an edge matching algorithm for human edge segmentation from 2D images by means of the histogram of oriented gradients technique and SVM classification. The algorithm having four steps, namely image sequence acquisition, human detection, edge segmentation and human edge segmentation, were carried out in this work. Data was collected from 710 full body human image. Based on the finding of this work, error results of the human edge in the parts of head, neck, body, and leg were 9.86, 13.60, 6.63, and 637 pixels, respectively. The advantages of the proposed method is this method can segment human from image by using only one image and a small group of databases.
Color Sorting Robotic Arm

This paper represents the design and implementation of color sorting robotic arm which can detect the exact position of an object and can pick up the object to place it in designated place. This robotic arm is like human arm which can rotate according to its predefined angles. On the other hand, detecting an object on color basis is done by ultrasonic sensor & color sorting sensor. The heart of this project is microcontroller board ATMEGA328P which controls servo motors used in base, elbow, wrist and grip. The prototype of this project was made and used for picking red, green and blue color objects.
Challenges in Usage of Mobile Phone Regarding Agricultural and Marketing Information among Farmers in Sindh, Pakistan

Objective: This study was conducted to discover the farmers’ behavioral intention to use mobile phones for agricultural information and how mobile phones impact farmers’ abilities to obtain the latest weather and market information. Method: In this way, this study surveyed 1500 farmers in ten districts of Sindh, Pakistan, 150 from each district. The list was obtained from Sindh Government Agriculture Department and Sindh Irrigation & Drainage Authority (SIDA). Findings: The results mentioned that 97.3% respondents own their personal mobile phone and 64.5% of the respondents call to the market directly for seeking crop information. Moreover, in Sindh, Pakistan, farmers make use of mobile phones to keep current with the market; contact buyers and very limited 7.9% of the respondents get weather information. In some places information and communication technology disseminates information rapidly around the world to the benefit of many communities. Furthermore, the farmers were found unaware of the proper mobile phone usage. Improvements: Therefore, there is still a need to enhance farmers’ capacity for new technology and provide new opportunities and facilities to farmers for enhance their product. Keywords: Agriculture Development, Farmers, Marketing, Mobile Phone
Explainable Clustering Using Hyper-Rectangles for Building Energy Simulation Data

Clustering has become a very popular machine learning technique for identifying groups of data points with common features in a set of data points. In several applications, there is a need to explain the clusters so that the user can understand the underlying commonalities. One such application is in the area of building energy simulation. There is a need to cluster solutions obtained by parametric energy simulation runs and explain the characteristics of each cluster for human consumption. This paper demonstrates how the axis-aligned hyper-rectangles based clustering, on building energy simulation data, can help identify clusters and describe the governing rules for each cluster. We are calling these rules design strategies. Instead of the distance-based clustering methods that are unable to extract simple rules from the underlying commonalities in each cluster, this method is able to overcome this limitation. This method is applied to identify design strategies from a parametric run of a simple five-zone rectangular building model. Based on a user-given threshold, low energy solutions are selected for clustering. Each axis-aligned hyper-rectangle cluster is a unique design strategy that can be easily communicated to the user.
Designing a Waterless Toilet Prototype for Reusable Energy Using a User-Centered Approach and Interviews

User-oriented community engagement can reveal insights into ways of improving a community and solving complex public issues, such as natural resource scarcity. This study describes the early process of co-designing a novel, waterless toilet to respond to the water scarcity problem in the Republic of Korea. It presents how we designed a toilet focusing on three factors—a sanitization function, an ergonomic posture, and clean aesthetics—by conducting focus group interviews as part of a user engagement approach to understand what community users want from a toilet and ways of improving their toilet experiences. The results not only supported the development of an experiential service design project to raise community awareness of water scarcity but also supported scientists and engineers in experimenting with and developing new technologies by collaborating closely with designers.
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis

Recent work has shown significant progress in the direction of synthetic data generation using Generative Adversarial Networks (GANs). GANs have been applied in many fields of computer vision including text-to-image conversion, domain transfer, super-resolution, and image-to-video applications. In computer vision, traditional GANs are based on deep convolutional neural networks. However, deep convolutional neural networks can require extensive computational resources because they are based on multiple operations performed by convolutional layers, which can consist of millions of trainable parameters. Training a GAN model can be difficult and it takes a significant amount of time to reach an equilibrium point In this paper, we investigate the use of depthwise separable convolutions to reduce training time while maintaining data generation performance. Our results show that a DepthwiseGAN architecture can generate realistic images in shorter training periods when compared to a StarGan architecture, but that model capacity still plays a significant role in generative modelling. In addition, we show that depthwise separable convolutions perform best when only applied to the generator. For quality evaluation of generated images, we use the Fréchet Inception Distance (FID), which compares the similarity between the generated image distribution and that of the training dataset.
The Hizeh‐Jan Kaolin Deposit of NW Iran: the Tetrad Effect in REE Distribution Patterns

Abstract The Hizeh‐Jan kaolin deposit (northwest of Varzeghan, East‐Azarbaidjan Province, NW Iran) is a product of the alteration of Eocene andesitic rocks. Based on mineralogical examinations, kaolinite, quartz, smectite, pyrophyllite, muscovite‐illite, alunite, calcite, diaspore, goethite and hematite are the most abundant mineral phases in this deposit. The geochemical indicators, such as Y/Ho and Zr/Hf, indicate the non‐CHARAC (non‐Charge‐radius control) behavior of these pairs, which are likely to be due to the occurrence of the tetrad effect phenomenon in this deposit. Simultaneous concave and convex shapes in the chondrite‐normalized REE distribution patterns are a remarkable feature of the kaolin samples. Bivariate diagrams of the size of the third tetrad effect (T 3 ) versus geochemical parameters such as Y/Ho, Nb/Ta and Zr/Hf ratios display two distinct populations for the kaolin samples. The first population is characterized by high T 3 values (&gt;0.13), which are near or on the fault zone. The second population is characterized by low T 3 values (&lt;0.13), and are farther from the fault zone. The obtained results from the geochemical data have furnished compelling evidence that fluid‐rock interaction, overprint of hypogene processes by supergene ones, and structural control, are key controlling factors for the occurrence of tetrad effects in REE distribution patterns in the Hizeh‐Jan kaolin deposit.
A formalisation of nominal α-equivalence with A, C, and AC function symbols

This paper describes a formalisation in Coq of nominal syntax extended with associative (A), commutative (C) and associative-commutative (AC) operators. This formalisation is based on a natural notion of nominal α-equivalence, avoiding the use of an auxiliary weak α-relation used in previous formalisations of nominal AC equivalence. A general α-relation between terms with A, C and AC function symbols is specified and formally proved to be an equivalence relation. As corollaries, one obtains the soundness of α-equivalence modulo A, C and AC operators. General α-equivalence problems with A operators are log-linearly bounded in time while if there are also C operators they can be solved in O(n2log⁡n); nominal α-equivalence problems that also include AC operators can be solved with the same running time complexity as in standard first-order AC approaches. This development is a first step towards verification of nominal matching, unification and narrowing algorithms modulo equational theories in general.
Fast Verification of Signatures With Shared ECQV Implicit Certificates

In many bandwidth-critical environments, including vehicle-to-vehicle and vehicle-to-infrastructure communications, implicit certificates such as elliptic curve Qu-Vanstone (ECQV) are frequently employed as a means to reduce the bandwidth requirement of digital signatures. In these environments, however, certain recipients such as roadside units need to verify signatures for multiple messages within considerably short time periods. In this paper, we propose two efficient methods of reducing the computational costs of signature verification when multiple digital signatures are provided together with the ECQV implicit certificates. The first method achieves this by sharing the intermediate results that are computed when public keys are extracted from ECQV certificates. The second guarantees a further speed improvement by merging the public key extraction processes into a batch signature verification process. We also present an algorithm that adaptively chooses the best verification method for the given signatures in real time. Our experimental results show that the proposed methods allow for a signature verification that is five times faster than existing methods.