Reviewerly/exHarmony huggingface dataset:
Phishing Websites Detection using Machine Learning

Tremendous resources are spent by organizations guarding against and recovering from cybersecurity attacks by online hackers who gain access to sensitive and valuable user data.Many cyber infiltrations are accomplished through phishing attacks where users are tricked into interacting with web pages that appear to be legitimate.In order to successfully fool a human user, these pages are designed to look like legitimate ones.Since humans are so susceptible to being tricked, automated methods of differentiating between phishing websites and their authentic counterparts are needed as an extra line of defense.The aim of this research is to develop these methods of defense utilizing various approaches to categorize websites.Specifically, we have developed a system that uses machine learning techniques to classify websites based on their URL.We used four classifiers: the decision tree, Naïve Bayesian classifier, support vector machine (SVM), and neural network.The classifiers were tested with a data set containing 1,353 real world URLs where each could be categorized as a legitimate site, suspicious site, or phishing site.The results of the experiments show that the classifiers were successful in distinguishing real websites from fake ones over 90% of the time.
ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E

We describe an efficient implementation of given clause selection in saturation-based automated theorem provers, extending the previous ENIGMA approach. Unlike in the first ENIGMA implementation where a fast linear classifier is trained and used together with manually engineered features, we have started to experiment with more sophisticated state-of-the-art machine learning methods such as gradient boosted trees and recursive neural networks. In particular, the latter approach poses challenges in terms of efficiency of clause evaluation, however, we show that deep integration of the neural evaluation with the ATP data-structures can largely amortize this cost and lead to competitive real-time results. Both methods are evaluated on a large dataset of theorem proving problems and compared with the previous approaches. The resulting methods improve on the manually designed clause guidance, providing the first practically convincing application of gradient-boosted and neural clause guidance in saturation-style automated theorem provers.
Conformal Prediction Under Covariate Shift

We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distribution-free prediction intervals for problems in which the test and training covariate distributions differ, but the likelihood ratio between the two distributions is known---or, in practice, can be estimated accurately from a set of unlabeled data (test covariate points). Our weighted extension of conformal prediction also applies more broadly, to settings in which the data satisfies a certain weighted notion of exchangeability. We discuss other potential applications of our new conformal methodology, including latent variable and missing data problems.
A Benchmark for Anomaly Segmentation.

Detecting out-of-distribution examples is important for safety-critical machine learning applications such as self-driving vehicles. However, existing research mainly focuses on small-scale images where the whole image is considered anomalous. We propose to segment only the anomalous regions within an image, and hence we introduce the Combined Anomalous Object Segmentation benchmark for the more realistic task of large-scale anomaly segmentation. Our benchmark combines two novel datasets for anomaly segmentation that incorporate both realism and anomaly diversity. Using both real images and those from a simulated driving environment, we ensure the background context and a wide variety of anomalous objects are naturally integrated, unlike before. Additionally, we improve out-of-distribution detectors on large-scale multi-class datasets and introduce detectors for the previously unexplored setting of multi-label out-of-distribution detection. These novel baselines along with our anomaly segmentation benchmark open the door to further research in large-scale out-of-distribution detection and segmentation.
Efficient drone hijacking detection using two-step GA-XGBoost

With the fast growth of civilian drones, their security problems meet significant challenges. A commercial drone may be hijacked by Global Positioning System (GPS)-spoofing attacks for illegal activities, such as terrorist attacks. Ideally, comparing positions respectively estimated by GPS and Inertial Navigation System (INS) can detect such attacks, while the results may always get fault because of the accumulated errors over time in INS. Therefore, in this paper, we propose a two-step GA-XGBoost method to detect GPS-spoofing attacks that just uses GPS and Inertial Measurement Unit (IMU) data. However, tunning the proper values of XGBoost parameters directly on the drone to achieve high prediction results consumes lots of resources which would influence the real-time performance of the drone. The proposed method separates the training phase into offboard step and onboard step. In offboard step, model is first trained by flight logs, and the training parameter values are automatically tuned by Genetic Algorithm (GA). Once the offboard model is trained, it could be uploaded to drones. To adapt our method to drones with different types of sensors and improve the correctness of prediction results, in onboard step, the model is further trained when a drone starts a mission. After onboard training finishes, the proposed method switches to the prediction mode. Besides, our method does not require any extra onboard hardware. The experiments with a real quadrotor drone also show the detection correctness is 96.3% and 100% in hijacked and non-hijacked cases at each sampling time respectively. Moreover, our method can achieve 100% detection correctness just within 1 s just after the attacks start.
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks

Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order to deploy it later for identification of real anomalies in streaming data. It is thus important to design anomalies detectors that can correctly detect anomalies without access to labeled training data. Our idea is to adapt the Online evolving Spiking Neural Network (OeSNN) classifier to the anomaly detection task. As a result, we offer an Online evolving Spiking Neural Network for Unsupervised Anomaly Detection algorithm (OeSNN-UAD), which, unlike OeSNN, works in an unsupervised way and does not separate output neurons into disjoint decision classes. OeSNN-UAD uses our proposed new two-step anomaly detection method. Also, we derive new theoretical properties of neuronal model and input layer encoding of OeSNN, which enable more effective and efficient detection of anomalies in our OeSNN-UAD approach. The proposed OeSNN-UAD detector was experimentally compared with state-of-the-art unsupervised and semi-supervised detectors of anomalies in stream data from the Numenta Anomaly Benchmark and Yahoo Anomaly Datasets repositories. Our approach outperforms the other solutions provided in the literature in the case of data streams from the Numenta Anomaly Benchmark repository. Also, in the case of real data files of the Yahoo Anomaly Benchmark repository, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.
Federated Learning for Vision-and-Language Grounding Problems

Recently, vision-and-language grounding problems, e.g., image captioning and visual question answering (VQA), has attracted extensive interests from both academic and industrial worlds. However, given the similarity of these tasks, the efforts to obtain better results by combining the merits of their algorithms are not well studied. Inspired by the recent success of federated learning, we propose a federated learning framework to obtain various types of image representations from different tasks, which are then fused together to form fine-grained image representations. The representations merge useful features from different vision-and-language grounding problems, and are thus much more powerful than the original representations alone in individual tasks. To learn such image representations, we propose the Aligning, Integrating and Mapping Network (aimNet). The aimNet is validated on three federated learning settings, which include horizontal federated learning, vertical federated learning, and federated transfer learning. Experiments of aimNet-based federated learning framework on two representative tasks, i.e., image captioning and VQA, demonstrate the effective and universal improvements of all metrics over the baselines. In image captioning, we are able to get 14% and 13% relative gain on the task-specific metrics CIDEr and SPICE, respectively. In VQA, we could also boost the performance of strong baselines by up to 3%.
A Sentiment Polarity Categorization Technique for Online Product Reviews

Sentiment analysis is also known as opinion mining which shows the people's opinions and emotions about certain products or services.The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral.Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations.In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos.A comprehensive data set of five hundred thousand online reviews is used in our research.There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive).We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization.Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent.
An endorsement-based trust bootstrapping approach for newcomer cloud services

This paper addresses the challenge of providing trustworthy recommendations on newly deployed cloud services/resources for which little or no evidence about their trustworthiness is available. We also provide a two-level dishonesty discouragement mechanism to fight against unfair recommendations at both the collection and aggregation levels. Our solution consists of a (1) mechanism to allow users to self-assess the accuracy of their recommendations and autonomously decide on whether to participate in the recommendation process or not, (2) machine learning technique that generates reliable endorsements on newcomer items through extracting hidden similarities among the specifications of new and existing ones, (3) dishonesty-aware aggregation technique for endorsements coming from multiple advisors, (4) credibility update mechanism that captures the dynamism in the endorsers’ credibility, and (5) incentive mechanism to motivate advisors to participate in the endorsement process. Experiments conducted on the CloudHarmony and Epinions datasets show that our solution improves the accuracy of classifying newly deployed cloud services and yields better performance in protecting the recommendation process against Sybil attacks, in comparison with four existing recommendation approaches.
The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

The discovery of software faults at early stages plays an important role in improving software quality; reduce the costs, time, and effort that should be spent on software development.Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault.Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields.In this study, two deep learning algorithms are studied, Multi-layer perceptron's (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms.The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached.Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm.To validate our assumptions, the experiments are conducted on four common NASA datasets.The result shows how the addressed factors might increase or decrease the fault detection rate measurement.The improvement rate was as follows up to 43.5% for PC1, 8% for KC1, 18% for KC2 and 76.5% for CM1.
More than Words in Medical Question-and-Answer Sites: A Content-Context Congruence Perspective

Application Abstract
Facial Landmark-Based Emotion Recognition via Directed Graph Neural Network

Facial emotion recognition (FER) has been an active research topic in the past several years. One of difficulties in FER is the effective capture of geometrical and temporary information from landmarks. In this paper, we propose a graph convolution neural network that utilizes landmark features for FER, which we called a directed graph neural network (DGNN). Nodes in the graph structure were defined by landmarks, and edges in the directed graph were built by the Delaunay method. By using graph neural networks, we could capture emotional information through faces’ inherent properties, like geometrical and temporary information. Also, in order to prevent the vanishing gradient problem, we further utilized a stable form of a temporal block in the graph framework. Our experimental results proved the effectiveness of the proposed method for datasets such as CK+ (96.02%), MMI (69.4%), and AFEW (32.64%). Also, a fusion network using image information as well as landmarks, is presented and investigated for the CK+ (98.47% performance) and AFEW (50.65% performance) datasets.
Continual Relation Learning via Episodic Memory Activation and Reconsolidation

Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations.Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent training is an effective solution for such a challenging problem.However, these memorybased methods usually suffer from overfitting the few memorized examples of old relations, which may gradually cause inevitable confusion among existing relations.Inspired by the mechanism in human long-term memory formation, we introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning.Every time neural models are activated to learn both new and memorized data, EMAR utilizes relation prototypes for memory reconsolidation exercise to keep a stable understanding of old relations.The experimental results show that EMAR could get rid of catastrophically forgetting old relations and outperform the state-of-the-art continual learning models.The code and datasets are released on https://github.com/thunlp/ContinualRE.
Penelitian Kepustakaan (Library Research) dalam Penelitian Pendidikan IPA

Thesis is a scientific paper compiled by undergraduate students to complete their education. Many methods are used by students to obtain data or information in the preparation of this thesis. This form of activity is often used by students to obtain data by conducting field research. However, this type of research cannot always be carried out, especially in a co-19 pandemic emergency. Literature research is the right way to produce scientific work. But not all students are ready to do this library research. One reason is that there are no guidelines and examples they can guide to conducting this research. So the purpose of this writing is to provide guidelines for students and lecturers to carry out library research in the field of education.
BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection

Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity.Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released.In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea.The comments are annotated regarding social bias and hate speech since both aspects are correlated.The inter-annotator agreement Krippendorff's alpha score is 0.492 and 0.496, respectively.We provide benchmarks using CharCNN, BiL-STM, and BERT, where BERT achieves the highest score on all tasks.The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue.Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined.We make our dataset publicly available and open competitions with the corpus and benchmarks.
Heimdall

We present Heimdall, a mobile GPU coordination platform for emerging Augmented Reality (AR) applications. Future AR apps impose an explored challenging workload: i) concurrent execution of multiple Deep Neural Networks (DNNs) for physical world and user behavior analysis, and ii) seamless rendering in presence of the DNN execution for immersive user experience. Existing mobile deep learning frameworks, however, fail to support such workload: multi-DNN GPU contention slows down inference latency (e.g., from 59.93 to 1181 ms), and rendering-DNN GPU contention degrades frame rate (e.g., from 30 to ≈12 fps). Multi-tasking for desktop GPUs (e.g., parallelization, preemption) cannot be applied to mobile GPUs as well due to limited architectural support and memory bandwidth. To tackle the challenge, we design a Pseudo-Preemption mechanism which i) breaks down the bulky DNN into smaller units, and ii) prioritizes and flexibly schedules concurrent GPU tasks. We prototyped Heimdall over various mobile GPUs (i.e., recent Adreno series) and multiple AR app scenarios that involve combinations of 8 state-of-the-art DNNs. Our extensive evaluation shows that Heimdall enhances the frame rate from ≈12 to ≈30 fps while reducing the worst-case DNN inference latency by up to ≈15 times compared to the baseline multi-threading approach.
COVID-19 testing and infection surveillance: Is a combined digital contact-tracing and mass-testing solution feasible in the United States?

In December 2019, the novel COVID-19 virus spread from a cluster of pneumonia cases in Wuhan, China, to every corner of the globe, creating a worldwide pandemic pushing hospital systems past capacity and bringing economies worldwide to a halt. The COVID-19 pandemic is unique in comparison to prior coronavirus epidemics in its superior ability to be spread by asymptomatic and presymptomatic patients, allowing the virus to silently evade traditional symptoms-based screening approaches. Countries have implemented cutting-edge digital solutions to enhance traditional contact-tracing methodologies in combination with novel testing strategies to combat the virus, with variable levels of success. Despite having one of the most advanced and expensive health care systems in the world, the United States (U.S.) response is arguably one of the world's largest failures, as it leads the globe in case number as well as deaths. Until a successful vaccine can be broadly distributed, it is imperative that the U.S. curb the viral spread by rapidly developing a framework implementing both enhanced tracing and testing strategies balancing the needs of public health while respecting individual liberties. This review will explore the role of technology-augmented contact-based surveillance in tracking the outbreak in select countries in comparison to the current U.S. approach. It will evaluate barriers in the U.S. to implementing similar technologies, focusing on privacy concerns and a lack of unified testing and tracing strategy. Finally, it will explore strategies for rapidly scaling testing in a cost-effective manner.
Edge-Aware Graph Representation Learning and Reasoning for Face Parsing

Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their efficiency in face parsing, which however overlook the correlation among different face regions. The correlation is a critical clue about the facial appearance, pose, expression, etc., and should be taken into account for face parsing. To this end, we propose to model and reason the region-wise relations by learning graph representations, and leverage the edge information between regions for optimized abstraction. Specifically, we encode a facial image onto a global graph representation where a collection of pixels (“regions”) with similar features are projected to each vertex. Our model learns and reasons over relations between the regions by propagating information across vertices on the graph. Furthermore, we incorporate the edge information to aggregate the pixel-wise features onto vertices, which emphasizes on the features around edges for fine segmentation along edges. The finally learned graph representation is projected back to pixel grids for parsing. Experiments demonstrate that our model outperforms state-of-the-art methods on the widely used Helen dataset, and also exhibits the superior performance on the large-scale CelebAMask-HQ and LaPa dataset. The code is available at https://github.com/tegusi/EAGRNet.