Reviewerly/exHarmony huggingface dataset:
A Novel Machine Learning Methodology for Detecting Phishing Attacks in Real Time

Phishing is a cybercriminal activity where the criminal masquerades as a trusted entity and attacks the righteous users to gain personal information illegally. Many phishing detection techniques have been proposed in the past which use blacklist/whitelist, heuristic, search engine, visual similarity and machine learning. The statistics say that the average lifespan of any phishing website is 8–10 h which makes it strenuous for most of the above-mentioned techniques to identify and detect it accurately. Blacklist/whitelist and Search Engine based techniques work in real time but may fail to handle zero day phishing attacks. To tackle this problem, it is essential to have an approach that studies the dynamic behavior of the websites and predicts the new phishing website accurately. Machine Learning has been used in the past to handle dynamic behavior of phishing websites. In this paper, we propose a method in which a browser extension makes an API call to the pre-trained machine learning model to fetch the results, thus making machine learning work in real-time. Six machine learning classifiers have been rigorously trained and tested on a dataset of 5430 legitimate URLs and 5147 phished URLs. We have used a novel feature in which HTTPS URLs can be accurately identified as phished or legitimate based on Certificate validation. This method also detects the phishing websites hidden behind the short URLs along with the normal URLs, thus making it more robust. This methodology has a quick response time of 1.74 s along with an accuracy of 99.93% which is better than the previous works.
Lightweight Action Recognition with Sequence-Specific Global Context

With the emergence of a large number of video resources, video action recognition is attracting much attention. Recently, realizing the outstanding performance of three-dimensional (3D) convolutional neural networks (CNNs), many works have began to apply them for action recognition and obtained satisfactory results. However, high computational over-heads greatly reduce the efficiency of 3D CNNs. To make up for the shortcoming, in this paper, we first propose two innovations - the Xwise Separable Convolution and the SS block, both of which are lightweight. Then we build an efficient 3D CNN called the XwiseNet based on our innovations. Our work aims to make 3D CNNs lightweight without reducing the recognition accuracy. The key idea of the Xwise Separable Convolution is extremely decoupling the 3D convolution in channel, spatial, and temporal dimensions. The SS block can capture temporal long-range dependencies via aggregating sequence-specific global context to each sequence feature. Experiments have verified that our XwiseNet achieves competitive performance with the least computational overhead.
IoT Devices and the Need to Inform Utility Users of Collecting, Controlling and Processing of Personal Data

The introduction of smart devices into households presents new challenges in terms of privacy and protection of personal data, given the wide set of data they collect. Replacing various meters that measure electrical energy, water or natural gas consumption, and with smart meters bring many benefits, such as the ability to read data automatically, an approach to managing energy distribution based on real-time energy consumption, although on the other hand it presents a new security risk. For example, by introducing a smart meter for electricity or water which can be monitored to even obtain information on how many household members are present in the household, and when the household is empty, and what are their life habits. Under the current consumer protection framework in the EU, service providers are under no obligation to notify customers that new consumer connected devices capture a significantly larger set of data about them and pose a potential privacy risk. In this paper, we consider the basic dangers to privacy and personal information brought by using IoT devices, and outline proposals to change the legal framework to inform consumers about the capabilities of these utilities to control devices bidirectional communication, coordination and the very need to monitor the data collected.
Grammatical Error Detection with Self Attention by Pairwise Training

Automatic grammatical error detection system is useful for language learners to identify whether the texts written by themselves have errors. Researches have paid more attention on different models to deal with this task, various approaches have been proposed and better results have been achieved compare with rules base methods. It is known that artificially generated incorrect texts can further improve the performance of grammatical error correction and pairwise training is necessary for many recommendation algorithms. We incorporating these two techniques together to solve the error detection task with pre-trained words embeddings from BERT in this paper. It is the first work that adopt pairwise training with pairs of samples to detect grammatical errors since all previous work were training models with batches of samples piontwisely. Pairwise training is useful for models to capture the differences within the pair of samples, which are intuitive useful for model to distinguish errors. Extensive experiments have been carried out to prove the effectiveness of pairwise training mechanism. The experimental results shown that the proposed method can achieve the state of the art performance on four different standard benchmarks. With the help of data augmentation and filtering, the value of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0:5</sub> can be further improved. The overall improvements among the four test set are around 2.5% which demonstrate the generality of pairwise training for datasets from differen domains.
Learning with Partial Multi-Outlooks

How to deal with data that abounds in heterogeneous applications, known as multi-outlook learning, is of imperative importance to the creation of general-purpose and flexible intelligent systems. Prior works envision that each data instance appears in all outlooks, however, in practice, it is often the case that every outlook suffers from information incompleteness due to the data availability issue (e.g., privacy concerns). In such a case, existing learning models tend to be fooled by the ambiguous semantics conveyed by the missing outlooks. To fill the gap, we in this paper propose a new learning paradigm, named Generative Outlook Reproducing via Repository (GORR), which draws insight from the human analogy of capturing the commonalities among outlooks to reconstruct the missing outlooks. Specifically, GORR leverages the feature relatedness across outlooks to construct an outlook repository. The instances, once being projected onto the outlook repository, would have complete feature representations, where the missing outlooks are generated from the observed ones. Learner trained on the outlook repository then enjoys a complete feature information and thus is capable to perform accurate predictions. Extensive experiments are carried out on both synthetic and real data sets, demonstrating the effectiveness of GORR.
Retrieving and Highlighting Action with Spatiotemporal Reference

In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlighting, which visualizes where and when actions occur in an untrimmed video setting. Action highlighting is a fine-grained task, compared to conventional action recognition tasks which focus on classification or window-based localization. Leveraging weak supervision from annotated captions, our framework acquires spatiotemporal relevance maps and generates local embeddings which relate to the nouns and verbs in captions. Through experiments, we show that our model generates various maps conditioned on different actions, in which conventional visual reasoning methods only go as far as to show a single deterministic saliency map. Also, our model improves retrieval recall over our baseline without alignment by 2-3% on the MSR-VTT dataset.
The timer compression of information

This paper is devoted to a transform-based quantitative data compression technique involving rapid enumeration in a unary-binary time-based numeral system (NS). The symbols comprising the alphabets of human-computer interaction languages (HCIL), which are used in an informational message (IM), are collected in primary code tables, such as the ASCII table. The statistical-oriented data compression method using unconventional timer encryption and encoding information are proposed by us. It was constructed probability - discrete model of the set of character sequences and characterized some probabilistic algorithms associated with the recovery of text by its public key and its cipher. We find the possibility of parallel implementation of this method by building a block of timer tags. The necessary complex assessments are made. The method can be used to compress SMS messages. Probabilistic-statistical analysis and evaluation of their effectiveness are obtained.
Learning Style Correlation for Elaborate Few-Shot Classification

Few-shot classification is defined as a task where the network aims to classify unseen classes given only a few samples. Recent approaches, especially metric-based methods, have great progress in few-shot classification. However, the existing metric-based methods have a limitation in deploying discriminative features for elaborate comparison. They usually extract features from the embedding network without direct consideration of the relationship between support and query sets. To address the relationship, we propose a novel architecture, Style Correlated Module (SCM) to learn style correlation between support and query sets for few-shot classification. The proposed module leads support and query feature maps to focus on significant style correlated features and encourage the metric network to conduct an elaborate comparison. Furthermore, the proposed module can be generally applied to the existing metric-based approaches by adding the SCM behind the embedding network. We evaluate our proposed method with comprehensive experiments on two publicly available datasets and demonstrate its effectiveness with comparable results.
Taxonomic Soil Regions of Nevada

Nevada is divided here into 17 soil regions based on great-group associations, which include 25 dominant great groups. The text is accompanied by a new General Soil Map of Nevada, which shows great-group associations at a scale of 1:2 million.
Controlling parameter estimation precision by designing the spin environment

The artificial intervention of quantum environment is a basic task of quantum information engineering. In this paper, we investigate the dynamical behaviors of a single system coupled to a spin environment by a z–x-type interaction. We focus on studying how to enhance the precision of parameter by the pre-designed factors of the environment. The numerical simulation results show that the initial state of the environment obviously affects the dynamics of coherence of the central spin system. We find that the environment should be prepared in a block entangled state with Bell state. This option is beneficial to increasing the quantum Fisher information. Further, if the environment is dominated by higher-frequencies particles, the decoherence in noisy environment can be effectively suppressed and the precision of parameter estimation can be enhanced.
Extricating from GroundTruth: An Unpaired Learning Based Evaluation Metric for Image Captioning

Recently, instead of pursuing high performance on classical evaluation metrics, the research focus of image captioning has shifted to generating sentences which are more vivid and stylized than human-written ones. However, there are still no applicable metrics which can judge how close the generated captions are to the human-written ones. In this paper, we propose a novel learning-based evaluation metric, namely Unpaired Image Captioning Evaluation (UICE), which can be trained to distinguish between human-written and generated captions. Unlike existing metrics, our UICE consists of two parts: the semantic alignment module measuring the semantic distance between extracted image features and caption meanings, and the syntactic discriminating module syntactically judging how human-like the candidate caption is. The semantic alignment module is implemented by mapping the image features and the word embedding into a unified tensor space. And the syntactic discriminating module is designed to be learning-based, and thereby can be trained to be stylized by users' own, fed with additional personalized corpus during the training process. Extensive experiments indicate that our metric can correctly judge the grammatical correctness of generated captions and the semantic consistency between captions and corresponding images.
Legal Aspects of Genetic Testing Regarding Insurance and Employment in Cyprus

This text aims to explore the uncharted waters of using genetic testing in the context of insurance and employment in Cyprus. Cyprus’ existing legislative vacuum is filled by recourse to international, European, and national texts, of both a hard and soft law nature. The text begins by presenting different definitions of the terms “genetic testing” and “genetic data.” Then, it examines the need for special status recognition for genetic data and raises the key question of whether genetic testing poses a threat to legal principles and human rights. Next, it explores which legally binding instruments of European and Cypriot origin apply to genetic testing performance in general (e.g. the Council of Europe’s Oviedo Convention), as well as to the processing of the resulting genetic data (e.g. the EU General Data Protection Regulation). Finally, the knotty problem of unregulated genetic testing for insurance and employment purposes is addressed through Opinions of national authorities (e.g. the Cyprus National Bioethics Committee) or advisory bodies (e.g. the European Group of Ethics), as well as Recommendations from international organisations (e.g. the Council of Europe).
Cascaded Context Dependency: An Extremely Lightweight Module For Deep Convolutional Neural Networks

In this paper, we present a cascaded context dependency module, which is a highly lightweight module that can improve the performance of deep convolutional neural networks for various visual tasks. Inspired by the feature pyramid work in object detection and the context dependency work in image recognition, we consider to cascade the contexts of multiscale feature maps to aggregate the locality and globality in a local region. We further extract the dependency between original input and cascaded contexts for feature recalibration. Without employing learnable layers, our method introduces almost no additional parameters and computations. Furthermore, Our module can be seamlessly plugged into many existing CNN architectures to improve the performance. Experiments on ImageNet and MS COCO benchmarks indicate that our method can achieve results on par with or better than related work. Qualitatively, we achieve an absolute 1.42% (77.3137% vs. 75.8974%) top-1 classification accuracy improvement based on ResNet50 on ImageNet 2012 validation set with negligible computational overhead. Besides, our method yields significant gains on the MS COCO benchmark for the object detection task. All codes and models are made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We submit all the codes, pre-trained models, and training log files to https://github.com/13952522076/ParameterFree.
Novel local information kernelized fuzzy <scp>C</scp>‐means algorithm for image segmentation

Abstract In MRI, the image with poor quality, especially the image with noise interference or low contrast, may provide insufficient data for the visual interpretation of the affected part. Image segmentation provides an effective method to facilitate early detection and further diagnosis. By introducing a Particle Swarm Optimization (PSO) initialization step and a novel dissimilarity measure metric, we present a local information kernelized fuzzy C‐means (LIKFCM) algorithm for image segmentation. The dissimilarity measure metric, considering an adaptive tradeoff weighted factor, incorporates the Mahalanobis distance and outliers‐rejection‐based spatial term which eliminates unreliable neighboring information. By using this dissimilarity measure metric, the new algorithm could take reliable contextual information into account and achieve better segmentation results on images with complexed boundaries. Furthermore, the adaptive tradeoff factor depends on a fast noise estimation algorithm. This factor avoids subjective adjustment and makes the LIKFCM algorithm more universal. To evaluate the performance of the proposed algorithm both quantitatively and qualitatively, experiments are conducted both on synthetic images and real‐world images with different kinds of noise. Segmentation Accuracy (SA) and Comparison scores are used to evaluate the performance of both proposed algorithm and other methods. Experimental results illustrate that the proposed algorithm has better performance on denoising and reserving useful edges. The LIKFCM algorithm not only shows more robustness to noise but also preserves the texture details of the images.
The FEMA Plan for Education Educating During National Emergencies

In response to the spread of COVID-19 in the spring of 2020, the CARES Act was signed into law, providing students with computers without taking into consideration students’ access to internet services or a guaranteed safe place to keep the expensive equipment. According to the National Center for Homeless Education, based on federal data for school year 2017-2018, over 1.5 million registered students in the United States are homeless (National Center for Homeless Education, p. iii). COVID-19 surely increased this population as evidenced by the increased joblessness rate. In addition to issues with internet access for privileged users during the pandemic, and existing struggles of schools to provide even basic educational needs prior to COVID-19, we must consider an alternative form of education other than just providing students with computers. Not only are we in a pandemic, but severe weather compounded with lack of availability of specialized service workers could create and prolong power outages (Oberhaus, 2020, para. 4; North American Electric Reliability Corporation, 2020, p. 3). In the event that natural and unnatural disasters occur in the United States, education must not stop. We must not wait months to years for infrastructures to be built or rebuilt, as in the case of tribal nations and Puerto Rico after hurricane Maria (“FCC”, pp. 11-12; Maitland & Peha, 2018). We must be as versatile as third world countries, where business runs as usual when confronted with common power outages. We must educate now. I called this the FEMA Plan for Education, and this paper outlines how a newly created department should operate nationally to provide support to all students in our nation.
A Re-Ranking Framework for Knowledge Graph Completion

Knowledge graph completion, one of the most important research questions in knowledge graphs, aims at predicting missing links in a given graph. Current mainstream approaches adopt high-quality embeddings of entities and relations of the graph to improve their performances. However, it is not easy to devise a universal embedding learner that can fit various scenarios. In this paper, we propose a general-purpose framework which can be employed to improve the performance of knowledge graph completion. Specifically, given an arbitrary knowledge graph completion model, we first run the original model to get a ranked entity list. Then, we combine the query and the top ranked entities with attention mechanism, re-rank all these entities by feeding the combined vector into a neural network. The proposed re-ranking phase can be conveniently added to a variety of models to improve their performance without substantial modification. We conduct experiments on four datasets: WN18, FB15k, WN18RR, and FB15k-237. We choose TransE, TransH, TransD, DistMult, and ANALOGY as base models. Experiments on these datasets and models validate the effectiveness of the proposed re-ranking framework. We further explore the influence of the number of top ranked entities used in the re-ranking phase. We also test other attention mechanism to determine the most effective one, and found that vanilla attention mechanism can balance accuracy and complexity.
A Boosting-based Quantile Autoregressive Tree Model for the COVID-19 Time Series

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Play to Improve: Gamifying Usability Evaluations in Virtual Reality

Objective: The research study focuses on evaluating how usability engineering related activities would look like in virtual reality from an exploratory point of view. Research questions for the study are: a) What quantitative impact does an environment have on the usability evaluation of software interface components? b) How is this impact different in virtual environment as compared to physical environment? c) What could be the best interaction design representation in virtual reality which could have a similar mental model for the users as having a mouse and keyboard in physical environment? d) What role does another user/a virtual mannequin and other elements/objects play on influencing the usability evaluation results in virtual reality?Background: As per ISO 62366 and Food and drugs administration (FDA) guidelines, simulating usage environment when evaluating software components is crucial. However, with conventional lab environment usability testing sessions have no environment simulated in it.The research focuses on how the transition path (moving from physical to virtual environment) would look like if a researcher wants to thoroughly evaluate a design concept in virtual reality.Method: Participants (N = 8) participated in the experiment to evaluate 3 interaction design concepts in virtual reality (1) gaze timer: seeing the virtual monitor for 3 s to go to the next page in the workflow, (2) gaze click: seeing the virtual monitor and using controller to aim and go to the next page in the workflow, and (3) gaze gesture: seeing the virtual monitor and using controller to pick-drag-drop a page in the workflow stack to another location. The three interaction design concepts varied in physical workload, cognitive workload, familiarity, learning curve and readability. The experiment design was a within subject design.Results: Participants preferred gaze click interaction design concept over gaze timer and gaze gesture concept.Conclusion: Having a virtual environment added to a conventional lab/physical environment, transition could be possible. Replacement of controls like mouse and keyboard could be done by adding gaze click interaction.Application: Results of the study could serve as providing design guidelines for simulation of software interfaces’ usability evaluation in virtual reality.