Reviewerly/exHarmony huggingface dataset:
Students’ Self-Efficacy and Motivation in Emergency Remote Learning

Quick transformation of class activities to an online format in the middle of the term due to the pandemic of COVID-19 caused a lot of attention of educators worldwide. They stated that there is a difference between online learning (e-learning) and ‘emergency remote learning’, the main defining feature being their design. It was assumed that rapid transition to the new format of studying affected students and their attitude to studying, some of them having reported that their self-efficacy and motivation was affected by emergency remote learning. The main purpose of this study was to collect specific information about students’ self-efficacy and motives that make them to continue studying and attend remote classes. This includes comparing the levels of self-efficacy of the same group before and after their transition to emergency remote learning. The sample of 75 students responded to the questionnaire devised to assess the level of self-efficacy and distinguish the motives to attend distance classes. The findings demonstrate that students’ levels of self-efficacy in 2019 and 2020 are moderate. However, no statistically significant differences were found between the levels of self-efficacy of this group (z = 1.177). As a result, this group of students possesses the same level of self-efficacy before and during the emergency remote learning. The most reported motive to attend classes is pleasure of studying and desire to solve difficult tasks.
Calibration for Non-Exemplar Based Class-Incremental Learning

Catastrophic forgetting is the central challenge in incremental learning. Notable studies address the problem by using regularization or experience replay strategies. However, the performance is far from ideal without storing previous data, especially in the scenario of class-incremental learning (CIL). In CIL setting, an important factor causing catastrophic forgetting is the severe bias between the new and previously learned classes, in both classifier and feature extractor. In this paper, we propose calibrateCIL which contains two simple modifications to calibrate the bias in non-exemplar based CIL. Specifically, local softmax is proposed to calibrate the classifier, and cutout training is used to calibrate the feature extractor by learning richer, more generalizable and transferable features. Our method can give balance class scores without any post-processing technique. We show that our method outperforms state-of-the-art non-exemplar based methods on the challenging problem of CIL, and the ablation study demonstrates the effectiveness of the two modifications.
Deep learning-based model for breast cancer histopathology image classification

Breast cancer is the most commonly found cancer in India and the world. As per Global Cancer Observatory, in 2020 this cancer alone was the reason for the death of more than 6.8 million women throughout the world. There is no single cause of breast cancer but it could be a combination of one's environment, one's genes and the way one lives her life. These breast tumors can be benign (non- cancerous) or malignant (potentially-cancerous). Therefore, it becomes essential to identify them properly for appropriate treatment. Although histopathology images are used to judge breast cancer, there is always a chance of human error. This paper aims to simplify the breast cancer classification process for everyone. We propose a deep learning based Convolutional Neural Network model to detect and classify the breast cancer using histopathology images. This method uses pretrained neural network DenseNet-201 for features extraction from the images, which then used to predict for classification. Our model reached an accuracy of 97.05%.
RefDetect: A Multi-Language Refactoring Detection Tool Based on String Alignment

Refactoring is performed to improve software quality while leaving the behaviour of the software unchanged.Identifying refactorings applied to a software system is an important activity that leads to a better understanding of the evolution of the software system, and several techniques have been proposed and implemented to address this issue.The vast majority of existing refactoring detection techniques are language-specific, including the accepted state of the art, RMiner, which is exclusively Java-based.Although impressive performance has been achieved to date, there is scope for improvement in refactoring detection and such improvement would enhance both refactoring research and practice.In this paper, we propose a novel, language-neutral technique to identify refactorings in commit histories.Our approach is motivated by a desire to explore the use of string alignment algorithms in refactoring detection, and to determine if such approaches are competitive with the state of the art.The proposed approach has been implemented in a tool called RefDetect, evaluated, and compared with the current state-of-the-art refactoring detection tool: RMiner.In experiments we applied RefDetect to 514 commits of 185 Java applications containing 5,058 true refactoring instances, achieving an f-score slightly better than that achieved by RMiner (87.3% vs. 86%).RefDetect clearly outperformed RMiner in method and class based refactorings, achieving f-scores respectively of 87.7% vs. 81.7%for method-level refactorings and 92.1% vs. 86.9%for class-level refactorings.To demonstrate the language-independence of RefDetect, we conducted a further study with four C++ applications, achieving high values for both precision (96.1%) and recall (94.1%).The achieved results indicate that RefDetect performs better than the current state of the art in refactoring detection and is demonstrably capable of handling different programming languages.
An experimental framework for improving the performance of BFT consensus for future permissioned blockchains

Permissioned Blockchains are increasingly considered in enterprise use-cases, many of which do not require geo-distribution, or even disallow it due to legislation. Examples include countrywide networks, such as Alastria, or those deployed using cloud-based platforms such as IBM Blockchain Platform. We expect these blockchains to eventually run in environments with high bandwidth and low latency modern networks, as well as with advanced programmable hardware accelerators.
Ontology-Based Knowledge Description Model for Climate Change

Ontology modeling and visualization is quite a tedious task, but it is of utmost importance for highly specialized domains. Modeling and visualization of domains seem ordinary, but the technical aspects and variations of such domains are missed out. Such specialized domains that seem shallow needs Ontology modeling considering several technical factors. Sometimes there can be an entirely new perspective to a domain that needs to be considered when modeling ontologies. This paper presents an Ontology model for climate change from a geological perspective considering socio geological aspects. It is developed upon extensive research on the domain. The knowledge is represented as classes associated with each other by a link. The Ontology is visualized using Web VOWL and converted to owl format using Web Protege. The proposed climate Ontology has been evaluated both qualitatively and quantitatively. A reuse ratio of 0.95 is obtained.
See through Walls: Detecting Malware in SGX Enclaves with SGX-Bouncer

Intel Software Guard Extensions (SGX) offers strong confidentiality and integrity protection to software programs running in untrusted operating systems. Unfortunately, SGX may be abused by attackers to shield suspicious payloads and conceal misbehaviors in SGX enclaves, which cannot be easily detected by existing defense solutions. There is no comprehensive study conducted to characterize malicious enclaves. In this paper, we present the first systematic study that scrutinizes all possible interaction interfaces between enclaves and the outside (i.e., cache-memory hierarchy, host virtual memory, and enclave-mode transitions), and identifies seven attack vectors. Moreover, we propose SGX-Bouncer, a detection framework that can detect these attacks by leveraging multifarious side-channel observations and SGX-specific features. We conduct empirical evaluations with existing malicious SGX applications, which suggests SGX-Bouncer can effectively detect various abnormal behaviors from malicious enclaves.
Combine Early and Late Fusion Together: A Hybrid Fusion Framework for Image-Text Matching

Image-text matching is a challenging task in cross-modal learning due to the discrepancy of data representation be-tween different modalities of images and texts. The main-stream methods adopt the late fusion to generate image-text similarity on encoded cross-modal features, and put effort to capture intra-modality associations with considerably high training cost. In this work, we propose to Combine Early and Late Fusion Together (CELFT), which is a universal hybrid fusion framework that can effectively overcome the above shortcomings of the late fusion scheme. In the pro-posed CELFT framework, the hybrid structure with early fusion and late fusion could facilitate the interaction between image and text modalities at early stage. Moreover, these two kinds of fusion strategies complement each other in capturing the inter-modal and intra-modal information, which ensure to learn more accurate image-text similarity. In the experiments, we choose four latest approaches based on the late fusion scheme as the base models, and integrate them with our CELFT framework. The results on two widely used image-text datasets MSCOCO and Flickr30K show that the matching performance of all base models is significantly improved with remarkably reduced training time.
Privately Publishing Internet of Things Data: Bring Personalized Sampling Into Differentially Private Mechanisms

Massive Internet of Things (IoT) data sets are possessed by big institutions serving daily life because IoT devices are widely used in our daily life such as wearable devices and smart home devices. Publishing these data sets among various institutions causes an increasing number of users to concern their personal privacy. Differential privacy is the state-of-the-art concept of privacy preservation, but it suffers from the low accuracy. In this article, we improve differentially private mechanisms including the Laplace mechanism as well as the sample and aggregation mechanism by bringing the personalized sampling technology into these mechanisms so that IoT data sets can be privately published through differentially private mechanisms. In particular, improved mechanisms assign a personalized sampling probability to each data record in a way that their accuracy can be improved. We analyse improved mechanisms in terms of their privacy and accuracy. Then, we empirically demonstrate that the performance of improved mechanisms is better than original mechanisms through extensive experiments on synthetic data sets and real-world data sets.
Multi-View Image Clustering via Representations Fusion Method With Semi-Nonnegative Matrix Factorization

Multi-view clustering aims to obtain the perfect clusters with a set of feature sets.Many methods learn a common agreement among views to achieve this.However, they may fail to use the specific unique feature of each view, which is important to describe the intrinsic characteristics of data.Unlike many works, we treat each view as a subset feature of data and fuse representations from those unique views to learn an integrated graph for clustering.We propose a novel representations fusion method for MVC.In this method, a regularized semi-nonnegative matrix factorization is proposed to learn the low-dimensional representation of each view, in which, a regularization term is designed to keep the neighboring structure in new low-dimensional space, and it is further updated and utilized to guide the learning process.Then the learned representations is fused to get an integrated graph for describing the image data.Finally, extensive experiments on several real image datasets show that our method achieves better performance than the stateof-the-art methods.
Utilizing Corpus Statistics for Assamese Word Sense Disambiguation

Classification or categorization of a word based on its meaning in respect to a context is one of the major problems in Natural Language Processing (NLP). Such a problem is termed as Word Sense Disambiguation (WSD), and the mentioned problem is seen to be prevalent in all languages across the globe. However, in Indian languages, WSD poses greater challenges due to limitation of digital resources and lack of UNICODE. In this paper, we have made an attempt to highlight the efforts put by researchers to overcome WSD. It is also to be mentioned that for the said purpose, two WSD algorithms for Assamese language WSD are contrasted while asserting the corpus statistics in the approach. Of the two aforementioned WSD algorithms, the first is applied using the Lesk algorithm simpler, while the second is exercised to determine the probability of words and phrases on grounds of condition that co-occur with every meaning of an ambiguous word in disambiguation. Both the algorithms delivered affirmative results for a trained set of corpus. However, compared to the second, the Lesk algorithm yielded better results in terms of overall efficiency of the system developed in comparison to words and phrases co-occurrence.
Neural monocular 3D human motion capture with physical awareness

We present a new trainable system for physically plausible markerless 3D human motion capture, which achieves state-of-the-art results in a broad range of challenging scenarios. Unlike most neural methods for human motion capture, our approach, which we dub "physionical", is aware of physical and environmental constraints. It combines in a fully-differentiable way several key innovations, i.e., 1) a proportional-derivative controller, with gains predicted by a neural network, that reduces delays even in the presence of fast motions, 2) an explicit rigid body dynamics model and 3) a novel optimisation layer that prevents physically implausible foot-floor penetration as a hard constraint. The inputs to our system are 2D joint keypoints, which are canonicalised in a novel way so as to reduce the dependency on intrinsic camera parameters---both at train and test time. This enables more accurate global translation estimation without generalisability loss. Our model can be finetuned only with 2D annotations when the 3D annotations are not available. It produces smooth and physically-principled 3D motions in an interactive frame rate in a wide variety of challenging scenes, including newly recorded ones. Its advantages are especially noticeable on in-the-wild sequences that significantly differ from common 3D pose estimation benchmarks such as Human 3.6M and MPI-INF-3DHP. Qualitative results are provided in the supplementary video.
Modern Symmetric Ciphers—DES and AES

In a symmetric cryptosystem, the key used for encrypting a message is the same as the key used for decrypting a message. Although this does place a burden of proper key management and security on the users of such a cryptosystem, there have been two major cryptosystems, the Digital Encryption Standard (DES) and the Advanced Encryption Standard (AES), promulgated by the National Institute of Standards and Technology. DES was controversial due to the manner in which it was promulgated, and it has largely been superseded by the AES, about which almost no controversy exists. AES is currently in wide use in part because it is the NIST standard and in part because its design makes it fast and usable on a variety of platforms with different computational capabilities. This chapter covers the technical aspects of AES. Code for AES and test results appear in Appendix B so that code testing can be done and the encryption process observed.
A Computer Assisted Decision Support System for Education Planning

The advances in technology are eliminating the demand for certain occupations and creating new opportunities. Thus, the universities, teachers, and students have to collaboratively work together to restructure their departments, course offerings, and course contents. Failure to realize the aforementioned initiatives may lead to a loss of quality and competitiveness. This study proposes a decision support system capable of maintaining the quality and competitiveness of the departments and the course offerings. The proposed system consists of three stages. The first stage is the data collection stage. At this stage, data are collected from the internet using web scraping methods. In the second stage, the collected data are turned into meaningful and processable information by natural language processing methods. In the third stage, the alternatives are ranked using multi-criteria decision-making methods. The proposed decision support system provides useful information to several educational stakeholders. First, universities are informed on which departments to create or close as well as the relevant course offerings. Second, information are provided to the teachers to create new courses or shape the course contents. Finally, students are better informed on how to go about choosing the universities, departments, courses, or career paths to pursue. The applicability and reliability of the proposed decision support system were experimentally proven through the use of computer engineering-related job postings and course contents of the universities in Turkey.
Robust Object Detection Fusion Against Deception

Deep neural network (DNN) based object detection has become an integral part of numerous cyber-physical systems, perceiving physical environments and responding proactively to real-time events. Recent studies reveal that well-trained multi-task learners like DNN-based object detectors perform poorly in the presence of deception. This paper presents FUSE, a deception-resilient detection fusion approach with three novel contributions. First, we develop diversity-enhanced fusion teaming mechanisms, including diversity-enhanced joint training algorithms, for producing high diversity fusion detectors. Second, we introduce a three-tier detection fusion framework and a graph partitioning algorithm to construct fusion-verified detection outputs through three mutually reinforcing components: objectness fusion, bounding box fusion, and classification fusion. Third but not least, we provide a formal analysis of robustness enhancement by FUSE-protected systems. Extensive experiments are conducted on eleven detectors from three families of detection algorithms on two benchmark datasets. We show that FUSE guarantees strong robustness in mitigating the state-of-the-art deception attacks, including adversarial patches - a form of physical attacks using confined visual distortion.
When the Recursive Diversity Anonymity Meets the Ring Signature

In privacy-preserving blockchain systems, to protect a sender's identity of a transaction in privacy-preserving blockchain systems, ring signature (RS) schemes have been widely implemented, which allow users to obscure consumed tokens via including "mixin'' (i.e., chaff tokens). However, recent works point out that existing RS schemes are vulnerable to the "chain-reaction'' analysis, where adversaries eliminate mixins of RSs by utilizing the fact that each token can only be consumed in a RS. By "chain-reaction'' analysis, adversaries can find some definite token-RS pair sets (DTRSs) to confirm the sender's identity of a RS. Besides, the existing RS schemes do not consider the diversity of mixins when generating a RS. Moreover, since the transaction fee is proportional to the number of mixins, a use is motivated to use a RS with the minimum number of mixins. In this paper, we formally define the diversity-aware mixins selection (DA-MS) problem, which aims to generate a RS with the minimum number of mixins satisfying the constraints of its diversity and the anonymity of other RSs. We prove the DA-MS problem is $\#P$ and propose a breadth-first search algorithm to get the optimal solution. Furthermore, to efficiently solve the DA-MS problem, we propose two practical configurations and two approximation algorithms with theoretic guarantees. Through comprehensive experiments on real data sets as well as synthetic data sets, we illustrate the effectiveness and the efficiency of our solutions.
PolyU-CBS at the FinSim-2 Task: Combining Distributional, String-Based and Transformers-Based Features for Hypernymy Detection in the Financial Domain

In this contribution, we describe the systems presented by the PolyU CBS Team at the second Shared Task on Learning Semantic Similarities for the Financial Domain (FinSim-2), where participating teams had to identify the right hypernyms for a list of target terms from the financial domain.
Beyond archetypes: Advancing the knowledge of narrative fiction in future scenarios

• Examples of narrative fiction and literary theory are shown to advance futuring research that uses of narrative archetypes. • Highlights the limitations of using science fiction as the dominant resource for understanding narrative fiction. • Highlights the limitations of the way socio-technological change is often conceptualised in science fiction. • Highlights the limitations of using archetypes as units of narrative meaning. • A fictional scenario is used to show how narrative fiction can aid in envisioning alternative futures. This article argues that examples of literary fiction and research from literary theory can complement futuring research that makes use of narrative archetypes. Specifically, such examples can offer distinctive insight into the longer timescale, multi-layered complexities and ambiguity that characterises socio-technological change. This article briefly outlines the limitations of futuring research that derives narrative archetypes from science fiction and of archetypes more generally as units of narrative meaning. It then closely analyses Alan Hollinghurst’s novel The Stranger’s Child (2011) and draws on research from literary theory to highlight the subtleties of construction evident in this work that go beyond narrative structures based on archetypes and offer a number of complicating perspectives on socio-technological change. Hollinghurst’s narrative is then used to inform a series of impressionistic scenarios involving smart home technology in order to indicate more specifically how a deeper understanding of literary craft might aid in envisioning alterative futures. To conclude, the article suggests finding the right balance between schematization, on the one hand, and accounting for complexity and ambiguity, on the other hand, is a key challenge when developing resources from literary narratives.
Robust Facial Image Super-Resolution by Kernel Locality-Constrained Coupled-Layer Regression

Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the low-resolution ( LR ) and the corresponding high-resolution ( HR ) training patch pairs. To simultaneously utilize the contextual information of the target position and the manifold structure of the primitive HR space, in this work, we design a robust context-patch facial image super-resolution scheme via a kernel locality-constrained coupled-layer regression (KLC2LR) scheme to obtain the desired HR version from the acquired LR image. Here, KLC2LR proposes to acquire contextual surrounding patches to represent the target patch and adds an HR layer constraint to compensate the detail information. Additionally, KLC2LR desires to acquire more high-frequency information by searching for nearest neighbors in the HR sample space. We also utilize kernel function to map features in original low-dimensional space into a high-dimensional one to obtain potential nonlinear characteristics. Our compared experiments in the noisy and noiseless cases have verified that our suggested methodology performs better than many existing predominant facial image super-resolution methods.