Reviewerly/exHarmony huggingface dataset:
SG-TCN: Semantic Guidance Temporal Convolutional Network for Action Segmentation

While recent approaches based on multi-stage temporal convolutional network (TCN) can achieve good accuracy in action segmentation, they cannot get an excellent F1-score, which makes them difficult to be applied in practice. The main issue we investigated is that the TCN lacks the max-pool and hence it is difficult to capture sufficient semantic information which leads to over-segmentation. To reduce the occurrence of over-segmentation, we propose the Semantic Guidance module (SG) to capture high-level semantic features and guide the TCN. In addition, we consider the role of each stage in a multi-stage architecture and deploy a lighter parameter-sharing TCN (PS-TCN) as the backbone, which achieves higher accuracy and reduces about 16% parameters than the most popular backbone. Simultaneously, our proposed Video Speed Prediction module (VSP) explores temporal information and improves temporal modeling ability. Combining PS-TCN with VSP and using SG for guidance yield an accurate and robust segmentation model. Extensive experiments demonstrate that our model is much better than the benchmark MS-TCN++ (e.g. from 45.9% to 56.4% F1@50 on Breakfast) and achieves state-of-the-art performance on two challenging datasets. The code is available at https://github.com/zhangylll/SG-TCN
Completeness of composite refactorings for smell removal

Code smells are problems in the internal structural quality. Refactoring is a technique commonly used to remove code smells. A single refactoring rarely suffices to assist developers in achieving a full removal of a code smell. Thus, developers frequently apply composite refactorings (or, simply, composites) with the goal of fully removing a smell. A composite is formed by two or more interrelated single refactorings. Studies report that developers often fail in fully removing code smells through composite refactoring. In this context, a composite refactoring is considered incomplete whenever it does not fully remove a smell. Either incomplete or complete composite is formed by several refactorings; thus, both may inadvertently degrade other parts of the software. However, the literature on (in)complete composites and their effects on structural quality is still scarce. This lack of knowledge hampers the design of empirically-based recommendations to properly assist developers in performing effective complete composites, i.e., those not causing any harm in related parts of the program. This doctoral research investigates the effect of composite (in)completeness on structural quality and proposes a catalog with composite recommendations for the full removal of popular code smell types. We investigated 618 composites in 20 software projects. We found that 58% of incomplete composites did not change the internal structural quality, and 64% of complete composites are formed by refactoring types that were not actually previously recommended in the literature or elsewhere. The expected contributions are a list of findings, guidelines, and a catalog to support developers on how to successfully perform complete composites.
Construction of $4\times 4$ MDS Matrices for Lightweight Block Ciphers

Diffusion is one of the fundamental properties while designing block ciphers. Diffusion layer is derived with a linear diffusion matrix which converts an i/p vector to o/p vectors by the diffusion operations. To resist the differential and linear attacks, it is indispensable to strengthen the diffusion power of the matrix for a block cipher. Maximizing the branch number is certainly helpful to obtain the stronger security requirements along with better computational performance. The matrix having maximum branch number is an ideal and most suitable diffusion layer. We call this matrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{M}$</tex> aximal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{D}$</tex> istance Separable (MDS) matrix. MDS matrices are important constituent for block ciphers and are widely used in various ciphers such as AES [1], LED [2], SKINNY [3] etc. It is essential to minimize the computational complexity and implementation costs while constructing the diffusion layer. This paper presents an algorithm to construct <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\times 4$</tex> dimension matrices. Utilization of some specific structure of matrices and minimal XOR counts construct these matrices as lightweight in nature. The practical adaptability and optimality of the proposed approach is also discussed through various experimental evaluations.
(Crypto) Exchanges as Regulators?

The cryptocurrency market has proven notoriously challenging to regulate. Even as “crypto-winter” extinguished two trillion dollars in value, leaving ordinary savers wiped out and crypto-ventures collapsing, regulation has shown itself to be woefully under-protective to either mitigate risks ex ante or help cover the damage ex post. This Article makes the case for mandating self-regulation by crypto-exchanges as a first step toward comprehensive reform. It offers three rationales in support. Firstly, a mandate for self-regulation brings crypto-exchanges in line with traditional financial exchanges that have long supervised their market. This delegation is rationally rooted. By promising safety, exchanges can more fully perform their core economic function of enabling fluid trading, drawing users, helping them transact on predictable terms and building network effects as numbers grow in response to increasing efficiencies and integrity. This designation of exchange-as-regulator helps align the private self-interest of exchanges with the public good. Secondly, crypto-exchanges occupy a unique position from which to perform oversight. As a centralizing firm connected to an array of crypto-users, technologies and ventures, exchanges possess systemic perspective and economic power. Especially within an ecosystem comprised of more decentralized infrastructure, the highly centralized and connected set-up of most crypto-exchanges lowers their cost of delivering oversight and increases its coercive power. Finally, by bringing crypto-exchanges within the regulatory perimeter, a mandate for self-regulation creates a cost for failure. It forces venues to re-structure their governance and operational practices and places them – and, by extension the market – on a surer, more organized footing. In concluding, this Article lays out a roadmap for regulation to introduce a mandate for self-regulation in crypto-markets, setting out strategies to overcome administrative hold-up. While recognizing that self-regulation has shortcomings, it views crypto-exchange oversight as a first step toward comprehensive reform – one that is urgently needed to address the growing significance of the crypto-economy and its deeper entanglement within the financial system.
An ontology and neural networks based platform for improving impact policy assessment

Research and development along with technology transfer play an essential role in the long-lasting development of any country. However, governmental institutions may struggle nowadays to adopt convenient innovation policies due to the exponential growth of data as well as the variety of information formats. A relevant example is Romania, a European country, which faces a challenge in processing the huge amount of unstructured, unlinked and heterogeneous data related to the above-mentioned fields. Using data from official sources, we aim to build a system that can enable experts in national and international policies to identify, analyze and visualize dependencies between the gross domestic expenditure on the research and development sector and the results in research, innovation and technology transfer fields. In order to achieve this, we propose an ontology-based platform that can allow policy-makers to ask queries, explore datasets, view statistics and study predictions using neural networks related to the evolution of certain research and technology transfer indicators.
PR-MVI: Efficient Missing Value Imputation over Data Streams by Distance Likelihood

Predicting missing attribute values in data streams is useful in boosting the accuracies of analytical results in many applications. Many algorithms (i.e., Distance Likelihood Maximization (DLM)) have been proposed for permanently-stored data. They can be used to handle data streams, but the performance is bad when the data streams have a different distribution from the training data. Some works (i.e., Autoregressive Integrated Moving Average (ARIMA)) can deal with data streams, but they cannot handle categorical data. This paper proposes Past and Recent neighboring approaches for Missed attribute Value Imputation by distance likelihood, called “PR-MVI”, over data streams. PR-MVI learns from both past training data and the set of the most recent complete records. It can handle both numerical and categorical data streams that have similar and/or different distribution from that of the past training data. Extensive experiments have shown that PR-MVI can better predict the missed attribute values over data streams than other existing approaches.
Edge Guided Learning for Image Super-resolution with Realistic Textures

Super-resolution (SR) methods are used to reconstruct details in images to obtain an improved resolution. Recently, SR methods based on generative adversarial networks (GANs) have become seminal due to their effectiveness in generating textures. However, a common problem is the presence of unpleasant artifacts. In this paper, an edge-guided SR neural network (Edge-SRN) is proposed by introducing a plug-in edge detection module and incorporating a new edge loss, which increases the reconstruction accuracy and reduces artifacts. We also use the Edge-SRN as a teacher network to a knowledge distillation framework for training a lightweight student SR model. The student model learned from Edge-SRN outperforms its counterparts learned from GAN-based teachers or from the ground-truth HR images in both reconstruction accuracy and perceptual quality, which indicates the ability of reconstructing realistic textures can be transferred well from Edge-SRN to a small model. Extensive experiments on diverse criteria show the promising performance of our method compared with several state-of-the-art SR methods in the qualitative and quantitative evaluations. Our code is available at https://github.com/lizhangray/Edge-SRN
Multi-Label Learning with Missing Labels via Common and Label-Specific Features

In multi-label learning with missing labels, previous works usually only consider all features, or only consider label-specific features. It is obviously inappropriate to select a subset of features only considering which have a great discriminability for all labels or for a label. Besides, they are built on an assumption that if two labels are correlated, their regression coefficient vectors should be similar. However, it is hard to hold this assumption in real-world applications. Therefore, we propose a novel multi-label classification approach for missing labels via considering common and label-specific features with correlation information. More specifically, we firstly utilize <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{1}$</tex> - norm regularizer over label correlation matrix, and a new sup-plementary label matrix is augmented from the incomplete label matrix by learning label correlations. Then, for a better label recovery, we introduce <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,1}$</tex> -norm and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{1}$</tex> -norm regularizers to learn common and label-specific features simultaneously. Thirdly, to make more rational use of label correlation to solve missing labels, we use a regularizer to constrain label correlations on outputs of labels instead of regression coefficient matrix. Finally, an objective function is designed in terms of the above processing. Extensive experiments conducted on eight data sets demonstrate the effectiveness of the proposed approach.
Software Engineering Education Meets DevOps: an Experience Report

DevOps is one of the mainstream topics in the software industry nowadays. Because of this, in recent years, some universities have started to include DevOps related content in their programs. In some cases, specific DevOps courses were created while in other cases DevOps topics were included in existing ones. This article describes an experience of including DevOps topics in a Software Engineering advanced course. Details of the practices and tools covered are provided along with explanations of the teaching approach and the cloud services used to deliver the course.
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis

Visual sentiment analysis has received increasing attention in recent years. However, the quality of the dataset is a concern because the sentiment labels are crowd-sourcing, subjective, and prone to mistakes. This poses a severe threat to the data-driven models especially the deep neural networks. The deep models would generalize poorly on the testing cases when trained to over-fit the training samples with noisy sentiment labels. Inspired by the recent progress on learning with noisy labels, we propose a robust learning method to perform robust visual sentiment analysis. Our method relies on an external memory to aggregate and filter noisy labels during training and thus can prevent the model from overfitting the noisy cases. The memory is composed of the prototypes with corresponding labels, both of which can be updated online. We establish a benchmark for visual sentiment analysis with label noise using publicly available datasets. The experimental results comprehensively show the effectiveness of our method.
Object interaction in virtual school environment

This article is devoted to the matter of interaction with 3D objects in web browsers mainly on desktop devices. It explains the content of virtual school environment as well as principle of the calculations needed to perform movement of a 3D object in a 3D scene from 2D input data. It includes scene visualization, object physics and descriptive algorithms of the provided solution.
Weakly Supervised Pedestrian Segmentation for Person Re-Identification

Person re-identification (RelD) is an important problem in intelligent surveillance and public security. Among all the solutions to this problem, existing mask-based methods first use a well-pretrained segmentation model to generate a foreground mask, in order to exclude the background from ReID. Then they perform the RelD task directly on the segmented pedestrian image. However, such a process requires extra datasets with pixel-level semantic labels. In this paper, we propose a Weakly Supervised Pedestrian Segmentation (WSPS) framework to produce the foreground mask directly from the RelD datasets. In contrast, our WSPS only requires image-level subject ID labels. To better utilize the pedestrian mask, we also propose the Image Synthesis Augmentation (ISA) technique to further augment the dataset. Experiments show that the features learned from our proposed framework are robust and discriminative. Compared with the baseline, the mAP of our framework is about 4.4%, 11.7%, and 4.0% higher on three widely used datasets including Market-1501, CUHK03, and MSMT17. The code will be available soon.
Misogyny Speech Detection Using Long Short-Term Memory and BERT Embeddings

Freedom of speech on social media is often abused to express inappropriate speech against individuals or groups. One form of social media abuse is misogyny utterance. Misogyny is hatred directed at women. The number of misogyny utterances on social media keeps increasing. Detection of misogyny needs to be done to prevent misogyny utterances. This work examines misogyny on Twitter and proposes a method to detect it. The dataset used comes from tweets on social media Twitter related to women. The experiment in this work was done by comparing the effect of BERT Embedding for Logistic Regression, Convolutional Neural Network, and Long Short-Term Memory. The performance of the methods tested was evaluated based on the resulting accuracy. This work showed that BERT with LSTM surpassed other methods with 86.15% accuracy. The model could outperform other methods because it understands more about the context of the data.
A Relation Extraction Model Based on BERT Model in the Financial Regulation Field

Relation extraction is a natural language processing (NLP) task to extract semantic relations between entities from unstructured texts, and is an important research content of constructing knowledge graph. Based on the documents issued by the China Banking and Insurance Regulatory Commission, we construct a knowledge graph in the field of financial regulation through relation extraction, which helps bank staff to realize intelligent query of regulatory documents and locate business-related regulatory requirements quickly and accurately. This paper proposes the Financial Regulation BERT (FR-BERT) model for relation extraction in the financial regulation field, aiming at the dataset's characteristics of rich semantic information of target entities, clear keywords and long texts. FR-BERT uses the BERT model to obtain sentence vector information containing each word vector, then obtains the target entity vectors and keyword vector by locating the sentence vector information, and obtains the text vector by sending the whole sentence vector information into Bi-directional Long Short-Term Memory (BiLSTM). Finally, the model uses the above vectors for classification information. FR-BERT integrates target entity vectors, keyword vector, and text vector output by BiLSTM to achieve relation extraction between entities. Compared with other relation extraction models selected in this paper, the experimental results show that FR-BERT performs better such as Macro-F1 on the relation extraction task in the financial regulation field, which verifies the effectiveness of the method.
Secure Similar Sequence Query over Multi-source Genomic Data on Cloud

Cloud computing has been shown promising in enabling various analyses over large-scale genomic data integrated across multiple data sources. However, outsourcing data to remote cloud servers raises data-privacy concerns, therefore demands secure computing measures over the data analyzing process on the untrusted cloud servers. Due to the scale of genomic dataset and the length of each genomic sequence, it is challenging to evaluate data-analysis functions on outsourced genomic data securely and efficiently. In this work, we study the secure similar-sequence-query (SSQ) problem over outsourced genomic data. To address the challenges of security and efficiency, we propose a set of two-party computing protocols in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixed form</i> , which combine secure secret sharing, garbled circuit, and partial homomorphic encryptions together and use them to jointly fulfill the secure SSQ function. Moreover, our scheme supports the fusion of genomic data from multiple data owners to generate a deduplicated-joint genomic dataset, therefore reduces the redundancy in the dataset. The performance improvements of our scheme are validated through extensive experiments on a commercial cloud platform over a real-world genomic dataset.
Threat Hunting for Digital Forensic Using GRR Rapid Response with NIST Framework

There are presently a huge number of internet users. Conventional security systems such as Firewall and IDS/IPS might not be enough to secure the system so far as those cannot detect the cyber-attack timely. Moreover, the attacker has more complicated tools and methods resulting in continuously growth in cyber criminality. This paper presents the use of a digital forensic process on a computer system following standards of NIST framework SP800-86 to analyze, track, and inspect incidents caused by the cyber-attack systematically and effectively. It demonstrated a use of a forensic tool called GRR rapid response framework which is a remote live forensic, convenient, and fast tool for digital forensic investigation. It works based on client-server which collects and analyzes digital evidence via web interface. The paper validated efficiency to detect cyber-attack of the framework in different network environments. Three types of attacks were imitated in this paper including DDos, Memory, and Target application in 3 different network environments which covered server zone, user zone and wireless zone. The result revealed that the GRR rapid response framework showed the highest percentage of incident response to DDos at 100% when the attack took place on all three network zones. Whereas Memory attack showed second-most significant incident response followed by Target Application attack.
Pooling Time-Series of Cross-Section Data

For the general linear model y = Xβ + u with E(uu′) = Ω, a necessary and sufficient condition for OLS to be equivalent to GLS is given by $$\mathrm {X}^{\prime }\Omega ^{-1}\bar {\mathrm {P}}_{\mathrm {X}}$$ where $$\bar {\mathrm {P}}_{\mathrm {X}}=\mathrm {I}-\mathrm {P_{X}}$$ and PX = X(X′X)−1X′, see Eq. (9.7) of Chap. 9 . For Eq. (12.12), this condition can be written as $$\displaystyle (\mathrm {X}^{\prime }\mathrm {Q})(\mathrm {Q}/\upsigma _\upnu ^2)\bar {\mathrm {P}}_{\mathrm {QX}}=0 $$ using the fact that Q is idempotent, the left hand side can be written as $$\displaystyle (\mathrm {X}^{\prime }\mathrm {Q})\bar {\mathrm {P}}_{\mathrm {QX}}/\upsigma _\upnu ^2 $$ which is clearly 0, since $$\bar {\mathrm {P}}_{\mathrm {QX}}$$ is the orthogonal projection of QX.
RESLearn: Comprehensive Mobile Application for Remote Sensing Learning

Due to unprecedented growth in freely and easily available remote sensing data, it is being used in plenty of applications and creating opportunities to contribute in this field. At the same time at the educational perspectives, very few countries are paying attention to remote sensing education at wider scale. Many of the developing countries still do not have exposure to remote sensing education. Therefore, it is required to have a learning platform to reach out to the learners using simple and easily available technologies. We are proposing mobile based remote sensing learning platform which will focus understanding, training, and research in remote sensing domain and have maximum outreach to all interested learners.