Reviewerly/exHarmony huggingface dataset:
MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA) aims to adapt models from a labeled source domain to a different but related target domain, from which unlabeled data and a small set of labeled data are provided. In this paper we propose a new approach for SSDA, which is to explicitly decompose SSDA into two sub-problems: a semi-supervised learning (SSL) problem in the target domain and an unsupervised domain adaptation (UDA) problem across domains. We show that these two sub-problems yield very different classifiers, which we leverage with our algorithm MixUp Co-training (MiCo). MiCo applies Mixup to bridge the gap between labeled and unlabeled data of each individual model and employs co-training to exchange the expertise between the two classifiers. MiCo needs no adversarial and minmax training, making it easily implementable and stable. MiCo achieves state-of-the-art results on SSDA datasets, outperforming the prior art by a notable 4% margin on DomainNet.
Big Data-Driven Feature Extraction and Clustering Based on Statistical Methods

Big data-driven feature extraction is a challenging process because it contains a variety and voluminous of data.But, in the current scenario of the Internet and the multimedia datadriven necessitates handling of complex data.Nowadays, it becomes a significant challenge to the Internet-based service provider to store voluminous data.To overcome this difficulty, this article provides a novel technique for big data-driven feature extraction, based on statistical methods.At first, the proposed method preprocesses the given input key-frame, that is, normalizes and removes noise.The noise-removed key-frames are separated into background scenes and forefront objects; features are extracted from the background scenes and forefront objects.The extracted features formulated as a feature vector.To validate the extracted features that whether it correctly represents the specific frame or similar frames, the feature vector is associated with the feature vectors in the feature vector catalogue.The proposed feature extraction method matches and retrieves the frames from the video database.It yields average correct retrieval rate of 95.29 per cent.The results obtained from experiments show that the proposed feature extraction method gives the average retrieval precision of 95.29 per cent.The enactment of the proposed feature extraction method is analogous to the existing methods.
Neural Sarcasm Detection using Conversation Context

Social media platforms and discussion forums such as Reddit, Twitter, etc. are filled with figurative languages.Sarcasm is one such category of figurative language whose presence in a conversation makes language understanding a challenging task.In this paper, we present a deep neural architecture for sarcasm detection.We investigate various pre-trained language representation models (PLRMs) like BERT, RoBERTa, etc. and fine-tune it on the Twitter dataset 1 .We experiment with a variety of PLRMs either on the twitter utterance in isolation or utilizing the contextual information along with the utterance.Our findings indicate that by taking into consideration the previous three most recent utterances, the model is more accurately able to classify a conversation as being sarcastic or not.Our best performing ensemble model achieves an overall F1 score of 0.790, which ranks us second 2 on the leaderboard of the Sarcasm Shared Task 2020.
Whole-Slide Imaging Allows Pathologists to Work Remotely in Regions with Severe Logistical Constraints Due to Covid-19 Pandemic

In this study, we report on our experience using digital pathology to overcome the severe limitations imposed on health care by the Covid-19 outbreak in Northern Italy. Social distancing had a major impact on public transportation, causing it to run with reduced timetables. This resulted in a major challenge for hospital commuters. To limit the presence in our hospital of no more than two pathologists at a time out of four, a web-based digital pathology system (DPS) was employed to work remotely.We used a DPS in which a scanner, a laboratory information system, a storage device, and a web server were interfaced so that tissue slides could be viewed over the Internet by whole-slide imaging (WSI). After a brief internal verification test, the activity on the DPS was recorded, taking track of a set of performance and efficiency indicators. At the end of the study, 405 cases were signed out remotely.Of 693 cases, 58.4% were signed out remotely by WSI, while 8.4% needed to be kept on hold to return to the original microscope slide. In three cases, at least one slide had to be rescanned. In eight cases, one slide was recut. Panel discussion by WSI was necessary in 34 cases, a condition in which all pathologists were asked for their opinion. A consultation with a more experienced colleague was necessary in 17 cases.We show that WSI easily allows pathologists to overcome the problems caused by the severe social distancing measures imposed by the Covid-19 pandemic. Our experience shows that soon there will not be alternatives to digital pathology, given that there is no assurance that other similar outbreaks will not occur.
Rapidly Verifiable XMSS Signatures

This work presents new speed records for XMSS (RFC 8391) signature verification on embedded devices. For this we make use of a probabilistic method recently proposed by Perin, Zambonin, Martins, Custódio, and Martina (PZMCM) at ISCC 2018, that changes the XMSS signing algorithm to search for rapidly verifiable signatures. We improve the method, ensuring that the added signing cost for the search is independent of the message length. We provide a statistical analysis of the resulting verification speed and support it by experiments. We present a record setting RFC compatible implementation of XMSS verification on the ARM Cortex-M4. At a signing time of about one minute on a general purpose CPU, we create signatures that are verified about 1.44 times faster than traditionally generated signatures. Adding further well-known implementation optimizations to the verification algorithm we reduce verification time by over a factor two from 13.85 million to 6.56 million cycles. In contrast to previous works, we provide a detailed security analysis of the resulting signature scheme under classical and quantum attacks that justifies our selection of parameters. On the way, we fill a gap in the security analysis of XMSS as described in RFC 8391 proving that the modified message hashing in the RFC does indeed mitigate multi-target attacks. This was not shown before and might be of independent interest.
FAST AND ACCURATE MULTI-FRAME SUPER-RESOLUTION OF SATELLITE IMAGES

Abstract. Recent constellations of small satellites, such as Planet’s SkySats, offer new acquisition modes where very short videos or bursts of images are acquired instead of a single still image. Compared to sequences of multi-date images, these sequences of consecutive video frames yield a large redundancy of information within the range of seconds. This redundancy enables to increase the spatial resolution using multi-frame super-resolution algorithms. In this paper, we propose a novel super-resolution method based on a high-order spline interpolation model that combines multiple low-resolution frames to produce a high-resolution image. Moreover this method can be implemented efficiently on GPU to process entire images from real satellite acquisitions. Synthetic and real experiments show that the proposed method is able to recover fine details, and measurements of the resulting resolution indicate a gain of 10 cm / pixel with respect to Planet’s SkySat standard imagery products.
Optical Potts machine through networks of three-photon down-conversion oscillators

Abstract In recent years, there has been a growing interest in optical simulation of lattice spin models for applications in unconventional computing. Here, we propose optical implementation of a three-state Potts spin model by using networks of coupled parametric oscillators with phase tristability. We first show that the cubic nonlinear process of spontaneous three-photon down-conversion is accompanied by a tristability in the phase of the subharmonic signal between three states with 2 π /3 phase contrast. The phase of such a parametric oscillator behaves like a three-state spin system. Next, we show that a network of dissipatively coupled three-photon down-conversion oscillators emulates the three-state planar Potts model. We discuss potential applications of the proposed system for all-optical optimization of combinatorial problems such as graph 3-COL and MAX 3-CUT.
A Novel Class Noise Detection Method for High-Dimensional Data in Industrial Informatics

The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of high-dimensional mislabeling. The traditional method usually adopts a two-step solution, first finding the relevant subspace and then using it for mislabeling detection. This two-step method struggles to provide the optimal mislabeling detection performance, since it separates the procedures of feature selection and label error detection. To solve this problem, in this article, we integrate the two steps and propose a sequential ensemble noise filter (SENF). In the SENF, relevant features are selected and used to generate a noise score for each instance. Continuously, these noise scores guide feature selection in the regression learning. Thus, the SENF falls in the scope of sequential ensemble learning. We evaluate our approach on several benchmark datasets with high dimensionality and much label noise. It is shown that the SENF is significantly better than other existing label noise detection methods.
Fast Network Alignment via Graph Meta-Learning

Network alignment (NA) - i.e., linking entities from different networks (also known as identity linkage) - is a fundamental problem in many application domains. Recent advances in deep graph learning have inspired various auspicious approaches for tackling the NA problem. However, most of the existing works suffer from efficiency and generalization, due to complexities and redundant computations.We approach the NA from a different perspective, tackling it via meta-learning in a semi-supervised manner, and propose an effective and efficient approach called Meta-NA - a novel, conceptually simple, flexible, and general framework. Specifically, we reformulate NA as a one-shot classification problem and address it with a graph meta-learning framework. Meta-NA exploits the meta-metric learning from known anchor nodes to obtain latent priors for linking unknown anchor nodes. It contains multiple sub-networks corresponding to multiple graphs, learning a unified metric space, where one can easily link entities across different graphs. In addition to the performance lift, Meta-NA greatly improves the anchor linking generalization, significantly reduces the computational overheads, and is easily extendable to multi-network alignment scenarios. Extensive experiments conducted on three real-world datasets demonstrate the superiority of Meta-NA over several state-of-the-art baselines in terms of both alignment accuracy and learning efficiency.
Discovering and Categorising Language Biases in Reddit

We present a data-driven approach using word embeddings to discover and categorise language biases on the discussion platform Reddit. As spaces for isolated user communities, platforms such as Reddit are increasingly connected to issues of racism, sexism and other forms of discrimination, signalling the need to monitor the language of these groups. One of the most promising AI approaches to trace linguistic biases in large textual datasets involves word embeddings, which transform text into high-dimensional dense vectors and capture semantic relations between words. Yet, previous studies require predefined sets of potential biases to study, e.g., whether gender is more or less associated with particular types of jobs. This makes these approaches unfit to deal with smaller and community-centric datasets such as those on Reddit, which contain smaller vocabularies and slang, as well as biases that may be particular to that community. This paper proposes a data-driven approach to automatically discover language biases encoded in the vocabulary of online discourse communities on Reddit. In our approach, protected attributes are connected to evaluative words found in the data, which are then categorised through a semantic analysis system. We verify the effectiveness of our method by comparing the biases we discover in the Google News dataset with those found in previous literature. We then successfully discover gender bias, religion bias, and ethnic bias in different Reddit communities. We conclude by discussing potential application scenarios and limitations of this data-driven bias discovery method.
National-level innovation in Africa

Why are some African nations moving ahead in innovation? We develop seven hypotheses for what determines national-level innovation in Africa using seven well established lines of argumentation grouped into two distinct policy areas: international and domestic. The international policy areas are trade arrangements (benefits from economic integration), export specialization (resource curse problem) and inward migration (social capital spillovers). The domestic policy areas are education (investment in human capital), control of corruption (institutional quality), financial inclusion ('reaching the unbanked') and internet usage (closing the digital divide). Testing these hypotheses on a unique dataset collated from diverse secondary sources and representing up to 39 African countries, we find that trade arrangements and financial inclusion are the most consistent predictors while the remaining arguments find inconsistent or no support. Exploratory analysis reveals two pathways to innovation in Africa: one internationally oriented, based on trade arrangements for relatively poorer countries, and the other domestically oriented, based on financial inclusion for relatively higher-income countries. The relevance of our findings for policy makers and research is discussed.
Privacy-preserving model training architecture for intelligent edge computing

With the rapid development of artificial intelligence and increasing data generated by end devices, the traditional cloud-centric data processing is gradually replaced by intelligent edge computing to achieve faster and nearer service via breaking the limit of network bandwidth and communication delay. However, training machine learning (ML) models on end devices is severely resource-constrained; besides, the privacy protection and continuous improvement of ML models are challenging. To address these problems, we propose an ML model training architecture to achieve intelligent edge computing in a novel cloud-edge-device cooperative manner, which is consisted of two phases: (1) the cooperative federated pre-training phase between the cloud and edge server is inspired by federated learning, coming with an incentive mechanism for fair reward allocation according to the contribution of edge servers for pre-training the model; (2) the privacy-preserving model segmentation training phase between the edge server and device leverages homomorphic encryption to realize model improvement and protection on end devices while transferring a large amount of computation to edge servers. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and feasibility of our proposed framework.
Immersive visual scripting based on VR software design patterns for experiential training

Virtual reality (VR) has re-emerged as a low-cost, highly accessible consumer product, and training on simulators is rapidly becoming standard in many industrial sectors. However, the available systems are either focusing on gaming context, featuring limited capabilities or they support only content creation of virtual environments without any rapid prototyping and modification. In this project, we propose a code-free, visual scripting platform to replicate gamified training scenarios through rapid prototyping and VR software design patterns. We implemented and compared two authoring tools: a) visual scripting and b) VR editor for the rapid reconstruction of VR training scenarios. Our visual scripting module is capable of generating training applications utilizing a node-based scripting system, whereas the VR editor gives user/developer the ability to customize and populate new VR training scenarios directly from the virtual environment. We also introduce action prototypes, a new software design pattern suitable to replicate behavioral tasks for VR experiences. In addition, we present the training scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum. Finally, a user-based evaluation of the proposed solution indicated that users—regardless of their programming expertise—can effectively use the tools to create and modify training scenarios in VR.
Enhancing Data Security Using Digital Watermarking

Knowledge is a power that can grant anyone leverage over others if the intention is malicious enough. With the advent of the internet, data is being produced daily and at unfathomable levels. As it stands the amount of data produced daily has already exceeded the amount of humans that ever walked the surface of the Earth. With that much data it is imperative to keep it secure at all costs because no matter which data is stolen or used maliciously, someone will get affected by it. With the help of cloud computing, more and more companies are now adopting cloud computing services allowing them to keep up with the large influx of data and at the same time keep them secure from being leaked. However, no matter what not everything can be secure especially when technology is concerned because technology is evolving every day and so are the methods hackers use to gain unauthorized access to data. The only thing possible to do is to reduce the number of data breaches by either updating the current security policies already in place or by implementing new security techniques and polices. In this paper, wediscuss the benefits of cloud computing and cloud storage, from its flexibility in terms of costs to security features and explain about data security and the threats to data security. Paper also explains what watermarking is and how digital watermarking may be used to better improve data security.
Learning to Detect Anomalous Wireless Links in IoT Networks

After decades of research, Internet of Things (IoT) is finally permeating real-life and helps improve the efficiency of infrastructures and processes as well as our health. As massive number of IoT devices are deployed, they naturally incurs great operational costs to ensure intended operations. To effectively handle such intended operations in massive IoT networks, automatic detection of malfunctioning, namely anomaly detection, becomes a critical but challenging task. In this paper, motivated by a real-world experimental IoT deployment, we introduce four types of wireless network anomalies that are identified at the link layer. We study the performance of threshold- and machine learning (ML)-based classifiers to automatically detect these anomalies. We examine the relative performance of three supervised and three unsupervised ML techniques on both non-encoded and encoded (autoencoder) feature representations. Our results demonstrate that; i) selected supervised approaches are able to detect anomalies with F1 scores of above 0.98, while unsupervised ones are also capable of detecting the said anomalies with F1 scores of, on average, 0.90, and ii) OC-SVM outperforms all the other unsupervised ML approaches reaching at F1 scores of 0.99 for SuddenD, 0.95 for SuddenR, 0.93 for InstaD and 0.95 for SlowD.
A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject–predicate–object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks.
Adversarial Attention-Based Variational Graph Autoencoder

Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space. However, previous methods ignore the structure and properties of the reconstructed graph, or they do not consider the potential data distribution in the graph, which typically leads to unsatisfactory graph embedding performance. In this paper, we propose the adversarial attention variational graph autoencoder (AAVGA), which is a novel framework that incorporates attention networks into the encoder part and uses an adversarial mechanism in embedded training. The encoder involves node neighbors in the representation of nodes by stacking attention layers, which can further improve the graph embedding performance of the encoder. At the same time, due to the adversarial mechanism, the distribution of the potential features that are generated by the encoder are closer to the actual distribution of the original graph data; thus, the decoder generates a graph that is closer to the original graph. Experimental results prove that AAVGA performs competitively with state-of-the-art popular graph encoders on three citation datasets.
A Web Based Automated Tool for Course Teacher Evaluation System (TTE)

For any educational institution course or teacher evaluation is an integral part for the betterment and effective education system.Student's feedback could be taken as one of parts of teacher evaluation.This paper has tried to evaluate the effectiveness of the course teacher evaluation system from the students' feedback of their corresponding courses.At Stamford University Bangladesh currently the teacher's evaluation task is running by manually which is very time consuming, slow and a lengthy process.It also needs number of human resources for completing this task.This paper has presented a web based automated tool for Course Teacher Evaluation System (TTE).In this technique student's opinions are taken from some predefined questions in a web based platform for evaluating a teacher of any particular course.And the result from the data analysis is automatically generated along with a graphical representation.From the generated report it becomes very easy for the teacher to understand and focus on the area where they need to emphasize for their personal and professional growth.As the results are generated automatically from the survey it saves time as well as man power.