Reviewerly/exHarmony huggingface dataset:
The origins of Objective-C at PPI/Stepstone and its evolution at NeXT

The roots of Objective-C began at ITT in the early 1980s in a research group led by Tom Love investigating improving programmer productivity by an order of magnitude, a concern motivated by the perceived "software crisis" articulated in the late 1960s. In 1981, Brad Cox, a member of this group, began to investigate Smalltalk and object-oriented programming for this purpose, but needed a language compatible with the Unix and C environments used by ITT. That year, Cox quickly wrote up the Object-Oriented Pre-Compiler (OOPC) that would translate a Smalltalk-like syntax into C. Love felt there was a market for object-oriented solutions that could coexist with legacy languages and platforms, and after a brief stint at Schlumberger-Doll, co-founded with Cox Productivity Products International (PPI), later renamed as Stepstone, to pursue this. At PPI, Cox developed OOPC into Objective-C. Cox saw Objective-C as a crucial link in his larger vision of creating a market for "pre-fabricated" software components ("software-ICs"), which could be bought off the shelf and which, Cox believed, would unleash a "software industrial revolution." Steve Naroff joined Stepstone in 1986 as Steve Jobs' NeXT Computer became an important customer for Objective-C, as it was being used in its NeXTSTEP operating system. Naroff became the primary Stepstone developer addressing NeXT's issues with Objective-C, solving a key fragility problem preventing NeXT from deploying forwards-compatible object libraries. Impressed with NeXT, Naroff left Stepstone for NeXT in 1988, and once there, added Objective-C support to Richard Stallman's GNU GCC compiler, which NeXT was using as its C compiler, removing the need to use Stepstone's ObjC to C translator. Over the next several years, Naroff and others would add significant new features to Objective-C, such as "categories," "protocols," and the ability to mix in C++ code. When Stepstone folded in 1994, all rights to Objective-C were acquired by NeXT. This eventually transferred to Apple when NeXT was acquired by Apple in 1997. Objective-C became the basis for Apple's Mac OS X and then iOS platforms, and Naroff and others at Apple added additional features to the language in the late 2000s as the iPhone App Store greatly expanded Objective-C's user base.
A method for constructing sliding windows leak from noisy cache timing information

Abstract This paper presents a method for constructing an operation sequence of sliding window exponentiation from the noisy cache information of RSA, which can be used for a cache attack using sliding windows leak (SWL). An SWL attack can retrieve the secret keys of RSA with non-negligible probability if the SWL is correctly captured. However, in practice, it is not always possible for an attacker to acquire a complete and correct operation sequence from cache information observation. In this paper, we first show that the capture errors in an operation sequence can be evaluated based on the Levenshtein distance between correct and estimated sequences. The dynamic time warping algorithm is employed for quantitative evaluation. Then, we present a method of accurately estimating a complete and correct operation sequence from noisy sequences obtained through multiple observations. Furthermore, we show the effectiveness of the proposed method through a set of experiments performed using RSA software in Libgcrypt. As a result, we can identify the correct operation sequence from approximately 100 observations of cache traces.
Application of the stochastic transition matrix as a prediction pattern for the occurrence of disasters in Aceh province in 2017-2021

Abstract Disaster is a series of events that threaten people’s lives. The causes can be from natural factors and human factors that can cause casualties, environmental damage, property losses and psychological impacts. Historically, there have been many disasters in Indonesia, especially in the Aceh province, which has stimulated the government to produce policies of reducing the incidence of disasters, especially disasters caused by human error. The number of floods, fires and landslides occurring annually tends to be similar. Unlike the catastrophic tidal waves, tornadoes, earthquakes, and droughts, such disasters occur randomly and some never occur. Therefore, a method is needed to predict disaster event opportunities, one of which is the Markov chain method. To find out the predicted value of disaster opportunities in the Aceh province for 2017-2021. Based on the results of the study, it is known that the predicted probability of disasters in the province of Aceh in 2017-2021 does not increase and does not decrease in extreme ways from year to year, the accuracy of the predictive value of opportunities for disasters caused by humans (landslides, fires, and floods) in the Province of Aceh in 2017-2021 are classified as reasonable with error value of 26.93%.
DEVELOPMENT AND IMPLEMENTATION OF PROGRAM OUTCOME ASSESSMENT TOOL FOR POLYTECHNIC COURSES

In the present scenario every polytechnic is mandatory to apply for NBA (National Board of Acreditation), so every course teacher has to find out concerned level of PO (Program Outcome) attainment for the concerned theory as well as practical courses.This paper proposes a simplified tool for determining PO attainment level using performance in direct methods viz.test examination, semester examination and practical examination and in indirect method rubrics undertaken for various activities as per CO (Course Outcome), PO mapping matrix which has been designed by concerned faculty.In order to find out the PO attainment, outcomes of a particular course are mapped with PO (PO1 to PO7).This mapping is a co-relation between CO's and PO's of each course over the scale of 1 to 3. The percentage attainment levels are defined over the scale of 1 to 3 to compute the values of Direct Assessment of each course by using CO-PO Matrix.In order to justify the proposed tool a typical case-study has been presented.The Attainment levels obtained with Average Scores of Indirect Assessment with Rubrics was higher for the course-Feedback Control system as compared to that of Indirect Assessment without Rubrics for each PO's from PO1 to PO7.
Graph Regularization for Multi-lingual Topic Models

Unsupervised multi-lingual language modeling has gained attraction in the last few years and poly-lingual topic models provide a mechanism to learn aligned document representations. However, training such models require translation-aligned data across languages, which is not always available. Also, in case of short texts like tweets, search queries, etc, the training of topic models continues to be a challenge. In this work, we present a novel strategy of creating a pseudo-parallel dataset followed by training topic models for sponsored search retrieval, that also mitigates the short text challenge. Our data augmentation strategy leverages easily available bipartite click-though graph that allows us to draw similar documents in different languages. The proposed methodology is evaluated on sponsored search system whose performance is measured on correctly matching the user intent, presented via the query, with ads provided by the advertiser. Our experiments substantiate the goodness of the method on EuroParl dataset and live search-engine traffic.
An AHP Approach for Transform Selection in DHCT-Based Image Compression

In this paper,Morita, Masaki we discuss an Analytic Hierarchy Process (AHP) approach to high-resolution image compression.Kimura, Yuto Generally, in image compression, it is necessary to remove redundant components in order to achieve significant data compression. Therefore, it is most important to minimize image distortion for a given bit rate. The computational cost is also important in the case of high-resolution image compression.Yamatani, Katsu The DHCT is a frequency transform using an orthogonal combination with the discrete cosine transform and Haar transform. Minimizing image distortion for a given bit rate and reducing the computational cost, we have already provided a DHCT-based compression method using p-norm for the transform selection. In this paper, we use the AHP method to determine the transform selection parameter p. The evaluation criteria used to formulate AHP utilizes the level of negative impact caused by the three types of noise-mosquito noise, jagged noise, and block noise. The computational cost is also required for the overall priority setting. Furthermore, numerical experiments using standard images demonstrate subjectively and objectively the superiority of the method over previous methods.
MAKING THE MOST OF MAKERS: AN EXPLORATION OF THE NEED FOR A PROTOTYPING TOOL FOR COMPANIES TO ENGAGE WITH THE MAKER MOVEMENT

Abstract Makers have proven to be skilled at prototyping and therefore present a unique opportunity for companies, who seek to improve their capabilities, to learn from them. In this study, a mixed methods approach was used to understand possible benefits to both companies and makers from collaborating in prototyping, and to identify a set of design considerations to guide the future development of a tool to facilitate such collaboration. Despite challenges to collaboration, a tool designed to help companies engage with makers in prototyping could be beneficial to both and should be developed.
Deep learning based 3-dimensional liver motion estimation using 2-dimensional ultrasound images

Purpose: Radiofrequency ablation therapy support system aims to display the true tumor position by tracking the tumor during the treatment. This sys-tem utilizes 2-dimensional ultrasound images. As a major cause of the tracking error, the tumor moves in the direction which is perpendicular to the scan plane of the ultra-sound image. Methods: To cope with this, in the proposed method, the six-axis movement amount is estimated from two 2-dimensional ultrasound images by a convolution neural network. In addition, we tried to improve the processing speed by using multitask learning. The liver moves differently from other tissues in the ultra-sound image. In particular, in order to improve the accuracy, a heatmap as a result of segmentation estimation of the liver region was utilized for estimating the amount of movement. By this method, we could estimate the amount of movement in the direction which is perpendicular to the ultrasound image plane so as to track the organ motion more precisely and robustly. We conducted an experiment to confirm the estimating performance of the amount of probe movement relative to the liver phantom, and evaluated estimation accuracy and measured processing speed. Results: The number of the used image sets was 9,506 for the learning and 1,337 for the evaluation. Conclusion: The experimental results show the effectiveness of our novel method. This article does not contain any studies with human participants or animals performed by any of the authors.
Enhanced Global and Local Curvature Properties for Corner Detection

Abstract Corner detection is basically a methods used to extract certain kind of features in images which could produce some information including the location or position of the corner points. Thus, in this paper an enhancement shape corner detection method is proposed to detect true corners of shape images. The overall performance of the proposed enhanced shape corner detector and six other existing shape detectors and descriptors including Harris, SUSAN, Harris-Laplace, CSS, SIFT and global and local curvature properties is presented. The experimental results of corner detection methods are tested using the benchmark binary image MPEG-7 Core Experiment Shape-1 Part B dataset. To measure the performance of corner detection evaluation, an appropriate number of true corners were determined.
Neighborhood Aggregation Embedding Model for Link Prediction in Knowledge Graphs

Link prediction has become a hot topic of knowledge graphs (KGs) in recent years. It aims at predicting missing links between entities to complement KGs. The most successful methods for this problem are embedding-based. Most previous works only consider the triples to learn the embeddings of entities and relations, so the information they can utilize is limited. However, KGs are graph-structured data, we can use the neighborhood information to improve the quality of embeddings, thus improving the performance of link prediction task. In this paper, we propose NAE (neighborhood aggregation embedding model), a novel approach for link prediction. NAE consists of an aggregator and a predictor. The aggregator aggregates the embeddings of multi-order neighbors with different weights to generate a new embedding for each entity. Further analysis shows that the performance of some existing methods such as TransE and DistMult can be improved by integrating our aggregators. The predictor predicts the probability distributions of target entities. It uses convolutional neural network (CNN) to capture more interactions between the new entity embeddings and the relation embeddings. We also propose a highly parameter efficient model NAE-S by simplifying the predictor, which can obtain competitive performance with fewer parameters. Compared with DistMult, NAE-S achieves the same performance with 16x fewer parameters. Experimental results show that our method outperforms several state-of-the-art methods on benchmark datasets.
An unsupervised lower-baseline localization method based on writing style features for historical documents

There is a lot of cultural heritage information in historical documents that have not been explored or exploited yet. Lower-Baseline Localization (LBL) is the first step in information retrieval from images of manuscripts where groups of handwritten text lines representing a message are identified. An LBL method is described depending on how the features of the writing style of an author are treated: the character shape and size, gap between characters and between lines, the shape of ascendant and descendant strokes, character body, space between characters, words and columns, and touching and overlapping lines. For example, most of the supervised LBL methods only analyze the gap between characters as part of the preprocessing phase of the document and the rest of features of the writing style of the author are left for the learning phase of the classifier. For such reason, supervised LBL methods tend to learn particular styles and collections. This paper presents an unsupervised LBL method that explicit analyses all the features of the writing style of the author and processes the document by windows. In this sense, the proposed method is more independent from the writing style of the author, and it is more reliable with new collections in real scenarios. According to the experimentation, the proposed method surpasses the state-of-the-art methods with the standard READ-BAD historical collection with 2,036 manuscripts and 132,124 manually annotated baselines from 9 libraries in 500 years.
Event-driven Fuzzy Inference System Implementation in Node-RED

Fuzzy Inference System can be applied in the Internet of Things solutions. Wireless devices in Internet of Things (IoT) solutions are communicating with messages. The message based communication in these systems can be represented as event-driven control network. In event-driven control networks the input values of the system not available at the same time. The aim of this paper is to show a possible solution to describe Takagi-Sugeno Fuzzy Inference System (FIS) as an event-driven network. This paper describes the implementation and validation of event-driven control network based Takagi-Sugeno FIS in Node-RED programming tool.
Identification of topics in News Articles Using Algorithm of Porter Stemmer Enhancement and Likelihood Classifier

Abstract Every piece of information contained in a story sometimes has a variety of themes and seems not specific so there is difficulty in digesting information simultaneously. This requires grouping based on the topic relevance of the news. This grouping can make it easier for readers to get the information in accordance with the topic you want to read. Each news group must have different information characteristics so that we need a special algorithm that is able to handle topic discovery and classification using training data on many Indonesian news articles. This research will apply an algorithm of Porter Stemmer Enhancement in the stemming process and Likelihood method for news classification based on categories and identification of topics. Based on the test results using 900 training data and 90 test data, obtained a fairly high accuracy, namely 95.56% for category classification and 97.78% for topic identification.
Improving Spanish classroom assessment via logistic regression: lessons from the Rasch model

ABSTRACT Each year in the United States, hundreds of thousands of undergraduate students enroll in introductory Spanish courses. For many students, passing these classes is a prerequisite for graduation. Consequently, it is imperative that the exams used in these courses are of the highest possible quality. For decades, the Rasch model (a special case of logistic regression) has been used to evaluate high-stakes language assessment, but has rarely been applied to exams used in classrooms. The current study employs the Rasch model to evaluate a midterm exam administered in the fourth week of an introductory Spanish class (n = 399 across 17 sections). This model identifies which questions best evaluate students at different proficiency levels and shows that the test reliably assigns scores near the pass-fail cutoff. The results of this study suggest that for this sample, items testing article-noun agreement were the most appropriate for the average student.
Post-Processing Network Based on Dense Inception Attention for Video Compression

Traditional video coding standards, such as HEVC and VVC, have achieved significant compression performance. To further improve the coding efficiency, a post-processing network is proposed to enhance the compressed frames in this paper. Specifically, the proposed network, namely DIA_Net, contains multiple inception blocks, attention mechanism and dense residual structure. The DIA_Net can efficiently extract information of multiple scale and fully exploit the extracted feature to improve image quality. In addition, the DIA_Net is integrated into the latest test model of VVC (VTM-8.0) to post-process the reconstructed frames of the decoder for better compression performance. The proposed scheme has achieved the best performance in the sense of PSNR at the similar bitrate in the validation sets of challenge on learned image compression (CLIC), which demonstrates the superiority of our approach.
Analog Image Modeling for 3D Single Image Super Resolution and Pansharpening

Image super-resolution is an image reconstruction technique which attempts to reconstruct a high resolution image from one or more under-sampled low-resolution images of the same scene. High resolution images aid in analysis and inference in a multitude of digital imaging applications. However, due to limited accessibility to high-resolution imaging systems, a need arises for alternative measures to obtain the desired results. We propose a three-dimensional single image model to improve image resolution by estimating the analog image intensity function. In recent literature, it has been shown that image patches can be represented by a linear combination of appropriately chosen basis functions. We assume that the underlying analog image consists of smooth and edge components that can be approximated using a reproducible kernel Hilbert space function and the Heaviside function respectively. We also extend the proposed method to pansharpening, a technology to fuse a high resolution panchromatic image with a low resolution multi-spectral image for a high resolution multi-spectral image. Various numerical results of the proposed formulation indicate competitive performance when compared to some state-of-the-art algorithms.
Research on Neural Networks Integration for Object Classification in Video Analysis Systems

Object recognition with the help of outdoor video surveillance cameras is an important task in the context of ensuring the security at enterprises, public places and even private premises. There have long existed systems that allow detecting moving objects in the image sequence from a video surveillance system. Such a system is partially considered in this research. It detects moving objects using a background model, which has certain problems. Due to this some objects are missed or detected falsely. We propose to combine the moving objects detection results with the classification, using a deep neural network. This will allow determining whether a detected object belongs to a certain class, sorting out false detections, discarding the unnecessary ones (sometimes individual classes are unwanted), to divide detected people into the employees in the uniform and all others, etc. The authors perform a network training in the Keras developer-friendly environment that provides for quick building, changing and training of network architectures. The performance of the Keras integration into a video analysis system, using direct Python script execution techniques, is between 6 and 52 ms, while the precision is between 59.1% and 97.2% for different architectures. The integration, made by freezing a selected network architecture with weights, is selected after testing. After that, frozen architecture can be imported into video analysis using the TensorFlow interface for C++. The performance of such type of integration is between 3 and 49 ms. The precision is between 63.4% and 97.8% for different architectures.
Improve Image Codec’s Performance By Variating Post Enhancing Neural Network:Submission of zxw for CLIC2020

Adding post enhancing filter after traditional image decoder to improve reconstruction quality is nowadays a very common method [1],[2],[3],[5]. Researchers use a large network filter or repeatedly stack single/multiple types of relative simple filters together. They all achieved better results. On the other hand the training materials and training time increases exponentially for a larger network scale, and the performance improvement becomes less and little. We learn this experience from the CLIC2019 low-rate track, where we proposed the VimicroABCnet and VimicroSpeed[4], with 2 post filters of different scale. The later one(5 time larger than the small one) achieved the final test's PSNR by improvement of only 0.02db@0.15bpp. In this paper, we propose a method to variate an existing post network filter(base filter). The base filter is altered into different ones, alternation only happens to weights. The key of the method is to divide the training data into different groups. Based on the pre-trained base filter, different altered filters are individually fine-trained with different group of training data. There are different ways to divide the training data, and we use a relative simple one. Sort by compression rate(with traditional codec) and bin the training images in to 4/8 group of training data subsets. With the new filters plus the base one, we now have 5/9 filters candidates in encoding phase and choose the best. The CLIC2019 test data show that PSNR increases 0.04db@0.15bpp and 0.06db@0.15bpp than the one filter VimicroSpeed method. This method requires the same training data and perfectly suitable for multi-GPU training scheme, and retraining the altered filters is much easier and consuming less time than training a relative large network filter. Also the result is better(5 filters scheme@0.04db vs VimicroABCnet@0.02db).