Reviewerly/exHarmony huggingface dataset:
Efficient natural language classification algorithm for detecting duplicate unsupervised features

This paper focuses on capturing the meaning of Natural Language Understanding (NLU) text features to detect the duplicate unsupervised features. The NLU features are compared with lexical approaches to prove the suitable classification technique. The transfer-learning approach is utilized to train the extraction of features on the Semantic Textual Similarity (STS) task. All features are evaluated with two types of datasets that belong to Bosch bug and Wikipedia article reports. This study aims to structure the recent research efforts by comparing NLU concepts for featuring semantics of text and applying it to IR. The main contribution of this paper is a comparative study of semantic similarity measurements. The experimental results demonstrate the Term Frequency–Inverse Document Frequency (TF-IDF) feature results on both datasets with reasonable vocabulary size. It indicates that the Bidirectional Long Short Term Memory (BiLSTM) can learn the structure of a sentence to improve the classification.
sboxgates: A program for finding low gate count implementations of S-boxes

S-boxes are often the only nonlinear components in modern block ciphers.They are commonly selected to comply with very specific criteria in order to make a cipher secure against, for example, linear and differential attacks.An M × N S-box can be thought of as a lookup table that relates an M -bit input value to an N -bit output value, or as a set of N boolean functions of M variables (Schneier, 1996).Although cipher specifications generally describe S-boxes using their lookup tables, they can also be described as boolean functions or logic gate circuits.sboxgates, which is presented here, finds equivalent logic gate circuits for S-boxes, given their lookup table specification.Generated circuits are output in a human-readable XML format.The software can convert the output files into C or CUDA (a parallel computing platform for Nvidia GPUs) source code.The generated circuits can also be converted to the DOT graph description language for visualization with Graphviz (Ellson et al., 2002).
Sparse within Sparse Gaussian Processes using Neighbor Information

Approximations to Gaussian processes based on inducing variables, combined with variational inference techniques, enable state-of-the-art sparse approaches to infer GPs at scale through mini batch-based learning. In this work, we address one limitation of sparse GPs, which is due to the challenge in dealing with a large number of inducing variables without imposing a special structure on the inducing inputs. In particular, we introduce a novel hierarchical prior, which imposes sparsity on the set of inducing variables. We treat our model variationally, and we experimentally show considerable computational gains compared to standard sparse GPs when sparsity on the inducing variables is realized considering the nearest inducing inputs of a random mini-batch of the data. We perform an extensive experimental validation that demonstrates the effectiveness of our approach compared to the state-of-the-art. Our approach enables the possibility to use sparse GPs using a large number of inducing points without incurring a prohibitive computational cost.
Perencanaan Partisipatif Tanggap Darurat Bencana Tsunami Di Pesisir Selatan Watulimo, Trenggalek

The research aims to mapping the tsunami-prone areas in Watulimo District using numerical simulation methods and spatial analysis as well as compiling tsunami response planning by determining shelters and evacuation routes in a participatory manner according to the characteristics of residents local wisdom of Watulimo District using weighting analysis and network analysis with ArcGIS. The most prone area to tsunami is Prigi Village because it has the lowest topography compared to the other two villages. Determination of shelter is carried out by considering the building function, number of floors, building area, building capacity, distance from the coastline, location from the road, and building construction. Public facilities with the number of floors &gt;1 such as flat, trade facilities; religious facilities; educational facilities; government facilities; as well as health facilities in Watulimo District can be used as shelters during tsunami. Based on the analysis, the shelters obtained can accommodate the population in tsunami-prone areas. The evacuation route can be determined by considering the distance, road width, road conditions, road class, and travel time to obtain the fastest route from settlement to the nearest shelter. The evacuation routes that can be used in Watulimo District are the existing roads. However, there is a need for road repairs in damaged/ potholed road conditions, road widening, adding new roads to facilitate the evacuation process of disaster victims to the shelter, and carrying out routine road maintenance on roads that will be used as evacuation routes, so that these roads are always in good condition.
Digital Archaeological Archiving in Sweden: the Swedish National Data Service perspective

In the past, data from archaeological investigations, as well as research projects led by universities have not been archived or made publicly accessible. Synthetic publications such as papers, reports and articles have been available, but not the underlying data files containing the original data to be reused or combined with new/other datasets for further research. Archaeological investigations are regulated within Sweden, but it has only recently been possible for that data to be preserved and disseminated in a more streamlined way. The mandatory requirement to archive research data at universities is often not enforced, resulting in a loss of data that is very problematic. This is now slowly changing owing to requirements from both governmental bodies and funding agencies, and therefore the future of archaeological data in Sweden looks a bit brighter.
Objective / subjective information strategies in electronic German media

The article is devoted to the research of linguistic implementation of objective / subjective information strategies in the complex of electronic texts (article + comments) from modern German media. The material of the research was the primary texts of messages on topical issues from the newspaper ZEIT ONLINE and the magazine Focus Online as well as secondary texts of messages in the form of comments from the general reader. According to the analysis it is obvious that the strategic program in the studied discursive space is a complex multicomponent system due to both the intentional attitudes of the sender of the message and the special conditions of interaction in the Internet space. The topicality of the research is determined by consideration of verbal organization and implementation of the information strategies chosen for analysis and sorted on the objectivity subjectivity scale. The author emphasizes the use of means of different levels of the German language in the process of overlapping and intentional change of strategies. The emphasis is made on the fact that the strategic program of the message sender in the electronic environment can be guessed by the recipient, but there is no further continuation in the comments. The scientific novelty of the research is determined by the fact that in electronic German media a commentator, choosing suitable strategies of his own can turn the polemics based on what he read in the article into a new direction. In the course of the language analysis, it is noticed that for an electronic article, strategies of objective information are the most typically ones (detail strategy, information and interpretation strategy, logical persuasion strategy and evasion of the truth strategy), and for an electronic comment, it is more common the choice of subjective information strategy (control over the topic strategy, feelings appeal strategies and evaluation strategies).
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions

Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents. We focus here on the problem of learning control flow that deviates from a strict step-by-step execution of instructions -- that is, control flow that may skip forward over parts of the instructions or return backward to previously completed or skipped steps. Demand for such flexible control arises in two fundamental ways: explicitly when control is specified in the instructions themselves (such as conditional branching and looping) and implicitly when stochastic environment dynamics require re-completion of instructions whose effects have been perturbed, or opportunistic skipping of instructions whose effects are already present. We formulate an attention-based architecture that meets these challenges by learning, from task reward only, to flexibly attend to and condition behavior on an internal encoding of the instructions. We test the architecture's ability to learn both explicit and implicit control in two illustrative domains -- one inspired by Minecraft and the other by StarCraft -- and show that the architecture exhibits zero-shot generalization to novel instructions of length greater than those in a training set, at a performance level unmatched by two baseline recurrent architectures and one ablation architecture.
System characterization report on the WorldView-3 Imager

a radiometric performance in the range of -0.121 to 1.420 (offset and slope); and a spatial performance in the range of 1.2 to 1.7 pixels at full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.093 to 0.185.
Machine Learning Integrity and Privacy in Adversarial Environments

Machine learning is increasingly being used for automated decisions in applications such as health care, finance, autonomous vehicles, and personalized recommendations. These critical applications require strong guarantees on both the integrity of the machine learning models and the privacy of the user data used to train these models. The area of adversarial machine learning studies the effect of adversarial attacks against machine learning models and aims to design robust defense algorithms. The main challenges in this space are the development of realistic adversarial models that consider the specifics of real-world applications, and the design of machine learning algorithms resilient against a wide range of threats.
Desain Game Edukasi Berbasis Android pada Materi Transformasi

The purpose of this research is to produce a product in the form of media game android based education on transformation material. This research uses research and development (methods R&amp;D ). with model procedure Four D (4-D). This research was conducted at one of the state junior high schools in Bengkalis Regency. The data collection technique was in the form of a questionnaire using a Likert scale. The data analysis technique was analyzed by transforming the average scale into qualitative sentences. Research.This results indesign in game adventure educational trans go an android-based accordance with the analysis of the needs and problems encountered at school. The results of the feasibility study show the validity of 86% with very valid criteria. In conclusion, products game android based education on transformation material are feasible and can be implemented in mathematics learning.
Fully Transformer Networks for Semantic Image Segmentation

Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent progress has demonstrated that combining such Transformers with CNN-based semantic image segmentation models is very promising. However, it is not well studied yet on how well a pure Transformer based approach can achieve for image segmentation. In this work, we explore a novel framework for semantic image segmentation, which is encoder-decoder based Fully Transformer Networks (FTN). Specifically, we first propose a Pyramid Group Transformer (PGT) as the encoder for progressively learning hierarchical features, meanwhile reducing the computation complexity of the standard Visual Transformer (ViT). Then, we propose a Feature Pyramid Transformer (FPT) to fuse semantic-level and spatial-level information from multiple levels of the PGT encoder for semantic image segmentation. Surprisingly, this simple baseline can achieve better results on multiple challenging semantic segmentation and face parsing benchmarks, including PASCAL Context, ADE20K, COCOStuff, and CelebAMask-HQ. The source code will be released on https://github.com/BR-IDL/PaddleViT.
Meta-Graph Adaptation for Visual Object Tracking

Existing deep trackers typically use offline-learned backbone networks for feature extraction across various online tracking tasks. However, for unseen objects, offline-learned representations are still limited due to the lack of adaptation. In this paper, we propose a Meta-Graph Adaptation Network (MGA-Net) to adapt backbones of deep trackers to specific online tracking tasks in a meta-learning fashion. Our MGA-Net is composed of a gradient embedding module (GEM) and a filter adaptation module (FAM). GEM takes gradients as an adaptation signal, and applies graph-message propagation to learn smoothed low-dimensional gradient embeddings. FAM utilizes both the learned gradient embeddings and the target exemplar to adapt the filter weights for the specific tracking task. MGA-Net can be end-to-end trained in an offline meta-learning way, and runs completely feed-forward for testing, thus enabling highly-efficient online tracking. We show that MGA-Net is generic and demonstrate its effectiveness in both template matching and correlation filter tracking frameworks.
Disrupting Model Training with Adversarial Shortcuts

When data is publicly released for human consumption, it is unclear how to prevent its unauthorized usage for machine learning purposes. Successful model training may be preventable with carefully designed dataset modifications, and we present a proof-of-concept approach for the image classification setting. We propose methods based on the notion of adversarial shortcuts, which encourage models to rely on non-robust signals rather than semantic features, and our experiments demonstrate that these measures successfully prevent deep learning models from achieving high accuracy on real, unmodified data examples.
Effectiveness of Syntactic Dependency Information for Higher-Order Syntactic Attention Network

Recently, as a replacement of syntactic tree-based approaches, such as tree-trimming, Long Short-Term Memory (LSTM)-based methods have been commonly used to compress sentences because LSTM can generate fluent compressed sentences. However, the performance of these methods degrades significantly while compressing long sentences because they do not explicitly handle long-distance dependencies between the words. To solve this problem, we proposed a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states. Furthermore, to avoid the influence of incorrect parse results, we trained HiSAN by maximizing the probability of a correct output together with the attention distribution. Experiments on the Google sentence compression dataset show that our method improved the performance from baselines in terms of F1 as well as ROUGE-1, -2, and -L scores. In subjective evaluations, HiSAN outperformed baseline methods in both readability and informativeness. Besides, in this study, we additionally investigated the performance of HiSAN after training it without any syntactic dependency tree information. The results of our investigation show that HiSAN can compress sentences without relying on any syntactic dependency information while maintaining accurate compression rates, and also shows the effectiveness of syntactic dependency information in compressing long sentences with higher F1 scores.
Data Cleaning Tools for Token Classification Tasks

Human-in-the-loop systems for cleaning NLP training data rely on automated sieves to isolate potentially-incorrect labels for manual review.We have developed a novel technique for flagging potentially-incorrect labels with high sensitivity in named entity recognition corpora.We incorporated our sieve into an end-to-end system for cleaning NLP corpora, implemented as a modular collection of Jupyter notebooks built on extensions to the Pandas DataFrame library.We used this system to identify incorrect labels in the CoNLL-2003 corpus for English-language named entity recognition (NER), one of the most influential corpora for NER model research.Unlike previous work that only looked at a subset of the corpus's validation fold, our automated sieve enabled us to examine the entire corpus in depth.Across the entire CoNLL-2003 corpus, we identified over 1300 incorrect labels (out of 35089 in the corpus).We have published our corrections, along with the code we used in our experiments.We are developing a repeatable version of the process we used on the CoNLL-2003 corpus as an open-source library.
Diagnosis of physical conditions for the implementation of a reverse logistics management model in a supply chain

Abstract The objective of this research was to structure a Reverse Logistics Management model applied to the furniture sector in the city of San José de Cúcuta, Colombia, based on the identification and analysis of the current business practices adopted by the companies that are part of this sector. For this purpose, engineering methods and techniques were used to propose alternatives that contribute to achieve a competitive growth, a better use of resources and environmental impact through strategies for the reuse of products or materials that, based on their physical properties, represent the best feasibility conditions. The type of research used was descriptive, with a qualitative approach. The methods used for data collection were mainly surveys, interviews and direct inspection of the companies studied, which made it possible to evaluate the physical variables of the object of study. It was possible to demonstrate that furniture manufacturing companies can adopt better reverse logistics practices in the manufacturing processes through referencing and articulation with strategic allies in the distribution channels. It was possible to establish the importance of implementing this model as a strategy to diagnose physical conditions and improve production systems to achieve a more efficient use of physical manufacturing resources.
Traffic Sign Detection by Image Preprocessing and Deep Learning

Detection of traffic sign is quite important in autonomous driving and driver assistance systems. In this study, Faster R-CNN as a very powerful object detection system is integrated with a feature extractor Inception Resnet V2 system, and it is evaluated and tested for detection and recognition of traffic sign. In addition, to enhance the effectiveness of this presented architecture, a new procedure of preprocessing the image to augment the detection performance of a traffic sign detector is also proposed. This new image preprocessing is introduced to reduce noise by blurring statistically some regions in an image, in which existence possibility of a traffic sign is lower. Hence, a more efficient training of object detector approach is presented. In the experiments, the publicly available Faster R-CNN Inception Resnet V2 model pre-trained on Microsoft COCO dataset is imported from Tensorflow model zoo on Github and is trained again on German Traffic Sign Detection Benchmark dataset. Also, to recognize detected traffic signs, German Traffic Sign Recognition Benchmark is used to train a CNN classifier. The experimental study illustrates the effectiveness of the presented approach while benchmarking with existing methodologies.
A MLP-Hedge-Algebras Admittance Controller for Physical Human–Robot Interaction

Recently, the identification of inertia and damping matrices (IIDM) and safety issues, as well as natural cooperation, are interestingly considered to enhance the quality of the physical human–robot interaction (pHRI). To cover all of these issues, advanced admittance controllers, such as those based on fuzzy logic or hedge algebras, have been formulated and successfully applied in several industrial problems. However, the inference mechanism of those kinds of controllers causes the discreteness of the super surface describing the input–output relationship in the Cartesian coordinates. As a consequence, the quality of the safe-natural cooperation between humans and robots is negatively affected. This paper presents an alternative admittance controller for pHRI by using a combination of hedge algebras and multilayer perceptron neural network (MLP), whose purpose is to create a more accurate inference mechanism for the admittance controller. To our best knowledge, this is the first time that such a neural network is considered for the inference mechanism of hedge algebras and also the first time that such an admittance controller is used for pHRI. The proposed admittance controller is verified on a teaching task using a 6-DOF manipulator. Experimental results have shown that the proposed method provides better cooperation compared with previous methods.
Photometric stereo-based high-speed inline battery electrode inspection

Abstract Battery technology is a key component in current electric vehicle applications and an important building block for upcoming smart grid technologies. The performance of batteries depends largely on quality control during their production process. Defects introduced in the production of electrodes can lead to degraded performance and, more importantly, to short circuits in final cells, which is highly safety-critical. In this paper, we propose an inspection system architecture that can detect defects, such as missing coating, agglomerates, and pinholes on coated electrodes. Our system is able to acquire valuable production quality control metrics, like surface roughness. By employing photometric stereo techniques (PS), a shape from shading algorithm, our system surmounts difficulties that arise while optically inspecting the black to dark gray battery coating materials. We present in detail the acquisition concept of the proposed system architecture, and analyze its acquisition-, as well as, its surface reconstruction performance in experiments. We carry these out utilizing two different implementations that can operate at a production speed of up to 2000 mm/s at a resolution of 50 µm per pixel. In this work we aim to provide a system architecture that can provide a reliable contribution to ensuring optimal performance of produced battery cells.