Reviewerly/exHarmony huggingface dataset:
A Bird’s Eye View on Aspect Oriented Requirements Engineering

Aspect-oriented software development builds on the concept of separation of concerns. It is concerned with addressing and handling the scattering and tangling issues related to the object-oriented paradigm. The traditional requirements engineering approach cannot address crosscutting concerns properly, which results in the occurrence of the tyranny of the dominant decomposition. Aspect-oriented requirements engineering (AORE) supports identifying and handling crosscutting concerns at the early stages of software development. The approaches and techniques of AORE are an extension of the traditional requirements engineering approaches like viewpoints, use cases, and goal-oriented, while some approaches are purely designed with aspect orientation in mind. This paper surveys the most relevant and popular AORE approaches. Most of the approaches support analysis and design. The focus is on the approaches in the requirements analysis phase. A critical evaluation is set to compare each proposal against the defined activity in requirements analysis. The evaluation of the approaches through the defined criteria showed that the validation and verification of AORE artifacts are not given much attention and focus compared to the identification and treatment of concerns and aspects. The key elements of validation and verification supported by AORE approaches are outlined and discussed.
Adaptive Spatio-Temporal Tube for Fast Motion Segments Extraction of Videos

Existing motion segments extraction methods suffer from the problem of high computation complexity. To address this issue, we propose a method called adaptive spatio-temporal tube for fast motion segments extraction of videos. Firstly, initial spatio-temporal flow of sub-videos divided from input video is computed by adopting a novel Area-adjusted Spatio-Temporal Tunnel (A-STT) to screen preliminarily moiton segments. Secondly, the Sampling-line Adjustment Mechanism (SAM) is presented to avoid processing the entire amount of video spatial data and reduce computational complexity. The SAM is created by analyzing object consistency to produce a Sampling-line Adjustment Factor (SAF) which is used to dynamically obtain the sampling-line of various sub-videos. Finally, the adaptive spatio-temporal tubes are generated by integrating the initial spatio-temporal flow and SAF, which ensures the robustness of the proposed method. The proposed method is experimented on the public datasets VISOR, CAVIR and self-collected dataset. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both computing speed and accuracy.
Analysis of Machine Learning Techniques for Carbonate Outcrop Image Classification in Landsat 8 Satellite Data

Carbonate outcrops are widely studied due to their character-istic analogous to oil and gas reservoirs. Analyzes made with data from these outcrops are used to assist in the description of reservoirs located at great depths. These rocky expo-sures can be easily detected in satellite images using machine learning. Considering the diversity of techniques used in the identification/classification of features on the satellite image data, this work aimed to evaluate the best machine learning technique for identifying carbonate outcrops in Landsat 8 data, considering the carbonate outcrops from the Jandaíra formation, in northeast Brazil. We employed techniques like Grid Search, cross-validation, and validation statistics like Friedman and Nemenyi. Considering the metrics F1 score and Mathews Correlation Coefficient, the MLP technique presented the best results, achieving a F1 score of 0.823, against the SVM with a F1 score of 0.810 and the Random Forest technique with a F1 score of 0.812.
KRNet: Towards Efficient Knowledge Replay

The knowledge replay technique has been widely used in many tasks such as continual learning and continuous domain adaptation. The key lies in how to effectively encode the knowledge extracted from previous data and replay them during current training procedure. A simple yet effective model to achieve knowledge replay is autoencoder. However, the number of stored latent codes in autoencoder increases linearly with the scale of data and the trained encoder is redundant for the replaying stage. In this paper, we propose a novel and efficient knowledge recording network (KRNet) which directly maps an arbitrary sample identity number to the corresponding datum. Compared with autoencoder, our KRNet requires significantly (400×) less storage cost for the latent codes and can be trained without the encoder sub-network. Extensive experiments validate the efficiency of KRNet, and as a showcase, it is successfully applied in the task of continual learning.
Proposal of a Stair Climbing Robot with a Transformable Body

This study aims to develop a cargo handling robot that can be put to daily use, considering factors such as high throughput, high mobility, smoothness, simplicity and low cost of construction. An inverted pendulum-wheeled mobile robot with a transformable body is proposed, which adapts to different road surfaces by switching between two and four wheels traveling modes. The four-wheeled mode state can traverse obstacles, where as the two-wheeled mode transforms into an inverted pendulum robot with higher maneuverability. This enables turning in tight spaces and moving smoothly and quickly in flat terrains.
Exploiting Semantic and Syntactic Diversity for Diverse Task-oriented Dialogue

Task-oriented dialogues have one-to-many property from semantic and syntactic perspectives, with many suitable dialogue acts and syntactic forms for a given post. However, current state-of-the-art task-oriented dialogue systems attempt to improve the quality of dialogues in terms of the most popular metrics (i.e., BLEU and entity F1), measuring the similarity between the generated responses and the human annotations, while the diversity of task-oriented dialogues remains less explored. This paper aims to improve the diversity of task-oriented dialogues from both semantic and syntactic perspectives by proposing a structural causal model to learn the causality composition of the dialogue acts and syntactic forms. Specifically, the disentangled understanding module decouples the dialogue into semantic and syntactic spaces and learns one-to-many property with multiple reference training. Then the casual collaboration generation module is proposed to apply Structural Causal Mechanism (SCM) to learn the causality composition relationship of the semantic and syntactic representations to generate the diverse response. Extensive experiments on the MultiWOZ datasets demonstrate that the proposed method achieves significantly better diversity than solid competitors.
An IoT Privacy-Oriented selective disclosure credential system

Abstract Personal credentials, such as passports and drivers’ licenses, can be implemented electronically using multi-show protocols. In this paper, we introduce an IoT Privacy-Oriented selective disclosure credential system, i.e. based on bilinear pairings and multilinear maps. The proposed system consists of three protocols, which allow users to be in control of their personal credentials. The Credentials Authority (CA) verifies and attests to the users credentials. Once the CA signs these credentials, the users cannot modify any of them. Moreover, the users can mask these credentials in every showing process to protect their identity from being revealed through a collusion between the CA and the verifiers. The proposed system maintains unlinkability between the issuing and showing protocols. Furthermore, it achieves unlinkability in the showing protocol such that the verifier cannot distinguish a user in two different sessions of the showing protocol. The proposed system is novel and practical in terms of introducing a new multi-show credential system that supports selective disclosure (Some credentials can be disclosed and others kept secret during the showing protocol.) The proposed system is the first that utilizes multilinear maps in the identification protocol. Making use of bilinear pairings and multilinear maps are suitable for IoT devices that have limited capabilities in terms of power consumption, key storage, and computing power. The security analysis of the proposed system is discussed using Burrows–Abadi–Needham (BAN) logic.
A Pedagogical Note on the Computation of Relative Entropy of Two n-Mode Gaussian States

A formula for the relative entropy $$S(\rho \vert \vert \sigma )=\textrm{Tr}\,\rho \,(\log \,\rho -\log \,\sigma )$$ of two gaussian states $$\rho $$ , $$\sigma $$ in the boson Fock space $$\varGamma (\mathbb {C}^n)$$ is presented. It is shown that the relative entropy has a classical and a quantum part: The classical part consists of a weighted linear combination of relative Shannon entropies of n pairs of Bernouli trials arising from the thermal state composition of the gaussian states $$\rho $$ and $$\sigma $$ . The quantum part has a sum of n terms, that are functions of the annihilation means and the covariance matrices of 1-mode marginals of the gaussian state $$\rho '$$ , which is equivalent to $$\rho $$ under a disentangling unitary gaussian symmetry operation of the state $$\sigma $$ . A generalized formula for the Petz-Rényi relative entropy $$S_\alpha (\rho \vert \vert \sigma )=-\frac{1}{\alpha -1}\log \textrm{Tr}\, \rho ^{\alpha }\sigma ^{1-\alpha },\ 0<\alpha <1$$ for gaussian states $$\rho $$ , $$\sigma $$ is also presented. Furthermore it is shown that $$S_{\alpha }(\rho \vert \vert \sigma )$$ converges to the limit $$S(\rho \vert \vert \sigma )$$ as $$\alpha $$ increases to 1.
Cross-Model Operator Batching for Neural Network Architecture Search

Recently, automated machine learning (AutoML) and neural architecture search (NAS), regarded as promising techniques to design deep learning (DL) models automatically, have received increasing attention from both industry and academia. NAS will generate a large number of candidate models, which typically consist of numerous common substructures, providing a vast opportunity for cross-model optimization (e.g., operator batching) to improve training efficiency. However, most of the existing AutoML frameworks do not make use of operator batching and we also lack an efficient batching strategy. In this work, we propose a heuristic scheme named DPBat to guide the operator batching among multiple models in NAS. For most models, the operator batching of DPBat can be finished in just a few seconds, which is negligible compared to the subsequent training. We adopt Microsoft’s open source AutoML framework NNI to implement DPBat to real NAS scenarios. Extensive experiments show that DPBat is highly effective in improving training efficiency and reducing the overhead of operator batching, with a throughput 3.7 $$\times $$ higher than the standard practice of running each job without batching.
<i>Collection management in the cloud: a guide for using cloud computing technologies in libraries</i>

"Collection management in the cloud: a guide for using cloud computing technologies in libraries." Journal of Electronic Resources Librarianship, 34(4), pp. 352–353
Local Alignment with Global Semantic Consistence Network for Image–Text Matching

Image-text matching is a major task in cross-modal information processing, which refers to measuring the similarity between an image and a sentence. The existing methods are mainly divided into global embedding and local alignment. Recently, local alignment methods that uses the fine-grained features to explore the correspondence between image regions and text words have achieved impressive results. However, the local alignment methods only focus on the matching between significant objects and ignores the importance of global semantics. To solve this problem, we propose a novel Local Alignment with Global Semantic Consistence Network (LAGSC).which performs cross-modal matching at global and local levels. Our method provides supervisory information for image-text pairs through label vectors of image regions, so as to maintain global semantic consistency. Experiment results on two benchmark datasets Flickr30K and MS-COCO prove the effectiveness of our method.
A New Visualization-guided Attention-ResNeXt Network for Trash classification

The recycling of trash can help guarantee the sustainable and healthy development of our society. Techniques for effective trash classification have received widespread attention. In this study, after testing six state-of-the-art classification models, we chose ResNeXt-101, which had the best classification accuracy, as our baseline model. To further understand the model's learning process, Grad-CAM visualization technology is applied, and a phenomenon of the dispersion of attention weights is found. To address this issue, the convolutional block attention module (CBAM) is introduced to ResNeXt model, and a new architecture, Attention-ResNeXt, is proposed. In addition, to overcome the limitation of insufficient samples, seven data enhancement methods are compared, including grayscale, rotation, and adjustment of contrast, etc., and the best performing dataset enhancement strategy is proposed to further improve the accuracy of the network. The final results indicate that our proposed method, Attention-ResNeXt, yields a 98.23% accuracy for the TrashNet dataset and outperforms the other five state-of-the art methods for trash classification.
A Novel Real-Time 3D Object Detection Network in Autonomous Driving Using Reformed RS-ResNet network

Extending the RS-ResNets architecture for 3D bounding box detection from 2D images to 3D point clouds that have varying degrees of visibility, a regression module with a Euler Region Proposal Network that performs complex angle regression is added to predict the direction of bounding boxes around detected objects. Geometrical overlap of predicted labels and ground truth is identified for every set of Lidar point cloud and the corresponding image data and compared with the state-of-the-art architectures. With the ability to scale to different architectures of GPU/TPU, the RS-ResNet architecture achieves faster frame rates of reaching threshold value of 70 ms/frame and APH/L2 = 66, on a NVIDIA Tesla V100 GPU and capability to scale to new and more powerful GPU/TPU architectures. The predictions are made to two levels of label hierarchy and three levels of Lidar range images with the associated camera images that are considered as single set. The achieved frame rates and detected AP are compared to the latest state-of-the-art architectures such as Complex-YOLOV4 and other leaderboard in the Waymo 2021 3D object detection challenge.
Design of Enterprise Financial and Economic Data Accurate Classification Management System Based on Random Forest

The conventional system has the problems of low recall rate, accuracy rate and high false positive rate of financial data classification. Therefore, an accurate classification management system of enterprise financial and economic data based on random forest is proposed. In the hardware, the front end, middle layer, server end and enterprise financial and economic data display end are used to form the overall architecture of the system, optimize the data memory of the server end, and transform the serial communication circuit of the development board; In the software design, abnormal financial data are filtered, a decision tree is established for each sample data, the utility function value is learned through the membership of the decision tree, and the optimal classification category is selected for the data through the random forest classifier. The experimental results show that the designed system improves the recall and accuracy of data classification, reduces the false positive rate, and the financial data classification results are more accurate and reliable.
HyperGix - A User-Friendly, Open-Source Hyperspectral Imagery Application

This paper describes the design and development of HyperGix, a desktop application for downloading, viewing, and processing hyperspectral data. HyperGix's graphical interface was implemented using Python and is designed to be user-friendly and intuitive to use without much instruction necessary. The software presently consists of three main modules-the Image Viewer, the Spectra Manager, and the USGS Search modules. In the Image Viewer, the user can browse their hyperspectral image library, examine the spectra of any pixel in the image, assign pixels to material classes such as ‘Water’ or ‘Vegetation’, and perform Principal Component Analysis and automatic spectra classification using a spectral angle mapping. The Spectra Manager module allows the user to create custom material classes of their own and examine the spectra of pixels assigned to each class. Finally, the USGS Search module provides an easy search engine to query the United States Geological Survey's vast repository of Hyperion satellite sensor images and download them for analysis in the application. HyperGix is open-source and still in development. Compared to other current free / open-source solutions, the platform provides a full workflow, from data search to processing, is easy to use and expand, making it a valuable tool for citizen science projects and overall lowering the barriers of access to both data and methods in hyperspectral imagery.
DUMA: Dual Mask for Multivariate Time Series Anomaly Detection

As a major category of unsupervised anomaly detection methods for multivariate time series, autoregression-based methods train a predictor to model the normal pattern from only normal time series, and then detect anomalies by prediction error. However, we find that the discrepancy of input data between the training and inference stages is a crucial challenge, which may lead to volatile results when detectors take abnormal time series as input. Furthermore, the correlations among multiple sensors are intricate, where irrelevant sensors may bring noise dependencies. This article proposes an autoregression-based time series anomaly detection method named DUal Masked self-Attention (DUMA). First, we propose a block-mask mechanism to enhance the robustness of the predictor for abnormal input data. Then a max-mask self-attention is proposed to reduce the noise dependencies between irrelevant sensors. Experiments on three cyber-physical systems (CPSs) datasets show that DUMA outperforms the state-of-the-art baseline methods.
Social-Politic in a Pandemics Indonesian Citizens’ Twitter Conversation Analysis using Latent Dirichlet Allocation

During the COVID-19 pandemic, there has been an increase in communication through social media. However, this is not accompanied by the level of citizen discipline where the high spread of false issues causes problems in the social and political fields. This study aims to look at the views of the community during the COVID-19 pandemic regarding Social and Political. The research method uses Latent Dirichlet Allocation Analysis to extract topic cluster of political and social during COVID-19 pandemic in Indonesia. The results of this study indicate a public view that considers the COVID-19 pandemic to be a tool, interest, or political game, and social concerns have increased considerably during the COVID-19 pandemic. During COVID-19, the increase in public communication through social media has become a driving force for other social activities that can ease the burden on the community. Besides that, the community seems wise enough to choose which news is fake and what is true.
Multimodal Neural Machine Translation for Mongolian to Chinese

Multimodal Machine Translation (MMT) aims to enhance translation quality by incorporating information from other modalities (usually images). However, dominant MMT models do not consider that visual features not only provide supplementary information also introduce much noise. In this paper, we propose the visual features filter to solve this issue. Specifically, we adopt a soft-lookup function to select the visual features relevant to the text and then use these visual features as pseudo-words concatenating with a text representation. In addition, our model conducts two-pass decoding. The secondarypass decoding amounts to polishing which can identify errors in draft translations. The reason is that polishing expands the view in the process of decoding each target token, providing more contextual information. Besides, since most words in draft translations can be copied to final translations, we further equip our model with the copying mechanism to reserve those words that do not need to be corrected. MMT has achieved success in some mainstream languages at present. In order to promote the development of MMT in low-resource languages such as Mongolian, we deploy our model to the Mongolian→Chinese translation task. We expand Multi30k dataset to synthetic Mongolian and Chinese descriptions. Experiments on synthetic Mongolian and Chinese datasets demonstrate that our model can bring significant improvements.
Noise-aware Canny Algorithm for Edge Detection

As people pay more and more attention to computer vision, many methods for edge detection have been created. However, these methods also have certain loopholes. The current popular "Canny operator" also lacks the selection value and improper use of sub-methods, resulting in the object edge detection weak edges cannot be detected clearly, some types of noise cannot be avoided. In this paper, the method of "Canny operator" is improved to achieve more accurate detection results. By preprocessing a certain image and comparing the results with the results of the original "Canny operator", we can draw the corresponding conclusions. This article through the improvement of the traditional "Canny operator", The influence of subtle noise on the picture can be properly avoided, and weak edges can also be clearly presented. And subsequent computer vision work has a relatively large impact.
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images

In this work, we investigate a methodology to perform anomaly detection and localization in images. The method leverages both sparse representation learning and the adoption of a pre-trained neural network for classification purposes. The objective is to assess the effectiveness of the K-SVD sparse dictionary learning algorithm and understand the role of neural network activation maps as data descriptors. We extract meaningful representation features and build a sparse dictionary of the most expressive ones. The dictionary is built only over features coming from images without anomalies. Thus, images containing anomalies will either have a non-sparse representation as linear combinations of the dictionary elements or a high reconstruction error. We show that the proposed pipeline achieves state-of-the-art performance in terms of AUC-ROC score over benchmarks such as MVTec Anomaly Detection, Rd-MVTec Anomaly Detection, Magnetic Tiles Defect, BeanTech Anomaly Detection, Kolektor Surface Defect datasets.
Face Recognition in Mobile-Based Test Systems Using FaceNet

Almost all activities are currently implemented with applications, including the implementation of exams, especially the covid-19 pandemic has caused online exams to have begun to be widely used by various kinds of educational institutions, online exams are carried out generally using computer devices that require a lot of devices and space for infrastructure. This study aims to create an online exam system that is simple and easy to do by utilizing mobile devices. This system utilizes the face recognition feature for security and minimizes the occurrence of fraud. The method used is FaceNet taking an input image quality of 160x160 pixels with an accuracy percentage of 85.7%.