Reviewerly/exHarmony huggingface dataset:
Measuring the changing cost of cybercrime

In 2012 we presented the rst systematic study of the costs of cybercrime. In this paper,we report what has changed in the seven years since. The period has seen major platformevolution, with the mobile phone replacing the PC and laptop as the consumer terminalof choice, with Android replacing Windows, and with many services moving to the cloud.The use of social networks has become extremely widespread. The executive summary isthat about half of all property crime, by volume and by value, is now online. We hypothesisedin 2012 that this might be so; it is now established by multiple victimisation studies.Many cybercrime patterns appear to be fairly stable, but there are some interesting changes.Payment fraud, for example, has more than doubled in value but has fallen slightly as aproportion of payment value; the payment system has simply become bigger, and slightlymore ecient. Several new cybercrimes are signicant enough to mention, including businessemail compromise and crimes involving cryptocurrencies. The move to the cloud means thatsystem misconguration may now be responsible for as many breaches as phishing. Somecompanies have suered large losses as a side-eect of denial-of-service worms released bystate actors, such as NotPetya; we have to take a view on whether they count as cybercrime.The infrastructure supporting cybercrime, such as botnets, continues to evolve, and speciccrimes such as premium-rate phone scams have evolved some interesting variants. The overallpicture is the same as in 2012: traditional oences that are now technically `computercrimes' such as tax and welfare fraud cost the typical citizen in the low hundreds of Euros/dollars a year; payment frauds and similar oences, where the modus operandi has beencompletely changed by computers, cost in the tens; while the new computer crimes cost inthe tens of cents. Defending against the platforms used to support the latter two types ofcrime cost citizens in the tens of dollars. Our conclusions remain broadly the same as in 2012:it would be economically rational to spend less in anticipation of cybercrime (on antivirus,rewalls, etc.) and more on response. We are particularly bad at prosecuting criminals whooperate infrastructure that other wrongdoers exploit. Given the growing realisation amongpolicymakers that crime hasn't been falling over the past decade, merely moving online, wemight reasonably hope for better funded and coordinated law-enforcement action.
Controllable Text Simplification with Lexical Constraint Loss

We propose a method to control the level of a sentence in a text simplification task.Text simplification is a monolingual translation task translating a complex sentence into a simpler and easier to understand the alternative.In this study, we use the grade level of the US education system as the level of the sentence.Our text simplification method succeeds in translating an input into a specific grade level by considering levels of both sentences and words.Sentence level is considered by adding the target grade level as input.By contrast, the word level is considered by adding weights to the training loss based on words that frequently appear in sentences of the desired grade level.Although existing models that consider only the sentence level may control the syntactic complexity, they tend to generate words beyond the target level.Our approach can control both the lexical and syntactic complexity and achieve an aggressive rewriting.Experiment results indicate that the proposed method improves the metrics of both BLEU and SARI.
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation

Pivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach is still challenged by the large pivot detection problem that should be solved, and by the inherent instability of LSTMs. In this paper we propose a Task Refinement Learning (TRL) approach, in order to solve these problems. Our algorithms iteratively train the PBLM model, gradually increasing the information exposed about each pivot. TRL-PBLM achieves stateof- the-art accuracy in six domain adaptation setups for sentiment classification. Moreover, it is much more stable than plain PBLM across model configurations, making the model much better fitted for practical use.
Novelty search

Novelty Search is an exploration algorithm driven by the novelty of a behavior. The same individual evaluated at different generations has different fitness values. The corresponding fitness landscape is thus constantly changing and if, at the scale of a single generation, the metaphor of a fitness landscape with peaks and valleys still holds, this is not the case anymore at the scale of the whole evolutionary process. How does this kind of algorithms behave? Is it possible to define a model that would help understand how it works? This understanding is critical to analyse existing Novelty Search variants and design new and potentially more efficient ones. We assert that Novelty Search asymptotically behaves like a uniform random search process in the behavior space. This is an interesting feature, as it is not possible to directly sample in this space: the algorithm has a direct access to the genotype space only, whose relationship to the behavior space is complex. We describe the model and check its consistency on a classical Novelty Search experiment. We also show that it sheds a new light on results of the literature and suggests future research work.
Hierarchical User and Item Representation with Three-Tier Attention for Recommendation

Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Deep Neural Networks for Predicting Task Time Series in Cloud Computing Systems

A large number of cloud services provided by cloud data centers have become the most important part of Internet services. In spite of numerous benefits, cloud providers face some challenging issues in accurate large-scale task time series prediction. Such prediction benefits providers since appropriate resource provisioning can be performed to ensure the full satisfaction of their service-level agreements with users without wasting computing and networking resources. In this work, we first perform a logarithmic operation before task sequence smoothing to reduce the standard deviation. Then, the method of a Savitzky-Golay (S-G) filter is chosen to eliminate the extreme points and noise interference in the original sequence. Next, this work proposes an integrated prediction method that combines the S-G filter with Long Short-Term Memory network models to predict task time series at the next time slot. We further adopt a gradient clipping method to eliminate the gradient exploding problem. Furthermore, in the process of model training, we choose optimizer Adam to achieve the best results. Experimental results demonstrate that it achieves better prediction results than some commonly-used prediction methods.
A Selective Image Encryption Scheme Based on 2D DWT, Henon Map and 4D Qi Hyper-Chaos

In recent years hyperchaos has found applications in several fields such as medicine and engineering.It has been applied in image-based applications which require reliable, fast, and robust security systems to store or/and transfer images.Given the increased use of images, there is a fundamental requirement among security in ensuring protection, confidentiality, privacy, integrity, and authenticity of such images.This, in essence, has become a major concern.Many preventive and protective algorithms, including chaos-based schemes, have been developed over the past years in trying to prevent directed attacks.However, many existing chaos-based encryption schemes have been successfully decrypted.One of the main reasons for these chaos-based image encryption schemes being successfully attacked is that the degree of randomness of the chaotic system used is not high enough to guarantee robust encryption.The key space lengths of many of the existing cryptosystems are not long enough to guarantee the desired security properties of chaos-based ciphers.Thus, the security level is lowered.In this paper, a selective image encryption algorithm based on 2D Discrete Wavelet Transform, Henon's Map and 4D Qi Hyper Chaos is proposed.This paper is developed such that 2D DWT is used to decompose the image into details and approximations, Henon chaotic map is used to shuffle the decomposed image pixels' positions.The shuffled image is further encrypted with the bit streams generated by Qi hyperchaos using XOR operation.The experimental results demonstrate that the proposed algorithm provides a large key space, high security, and resistance to different types of attacks.When compared to some existing algorithms, the performance of the proposed method displays a better performance with respect to security and encryption making it suited for real-time applications.
Protecting Privacy of Users in Brain-Computer Interface Applications

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations.
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop “rubric sampling” approach to tackle the “zero shot” feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student’s solution and can articulate a student’s misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world’s largest programming education platform.
Hate Speech Detection on Indonesian Long Text Documents Using Machine Learning Approach

Due to the growth of hate speech on social media in recent years, it is important to understand this issue. An automatic hate speech detection system is needed to help to counter this problem. There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecting hate speech on Indonesian long documents using machine learning approach. We build a new Indonesian hate speech dataset from Facebook. The experiment showed that the best performance obtained by Support Vector Machine (SVM) as its classifier algorithm using TF-IDF, char quad-gram, word unigram, and lexicon features that yield f1-score of 85%.
Recall What You See Continually Using GridLSTM in Image Captioning

The goal of image captioning is to automatically describe an image with a sentence, and the task has attracted research attention from both the computer vision and natural-language processing research communities. The existing encoder-decoder model and its variants, which are the most popular models for image captioning, use the image features in three ways: first, they inject the encoded image features into the decoder only once at the initial step, which does not enable the rich image content to be explored sufficiently while gradually generating a text caption; second, they concatenate the encoded image features with text as extra inputs at every step, which introduces unnecessary noise; and, third, they using an attention mechanism, which increases the computational complexity due to the introduction of extra neural nets to identify the attention regions. Different from the existing methods, in this paper, we propose a novel network, Recall Network, for generating captions that are consistent with the images. The recall network selectively involves the visual features by using a GridLSTM and, thus, is able to recall image contents while generating each word. By importing the visual information as the latent memory along the depth dimension LSTM, the decoder is able to admit the visual features dynamically through the inherent LSTM structure without adding any extra neural nets or parameters. The Recall Network efficiently prevents the decoder from deviating from the original image content. To verify the efficiency of our model, we conducted exhaustive experiments on full and dense image captioning. The experimental results clearly demonstrate that our recall network outperforms the conventional encoder-decoder model by a large margin and that it performs comparably to the state-of-the-art methods.
NIR to RGB Domain Translation Using Asymmetric Cycle Generative Adversarial Networks

Near infrared (NIR) images have clear textures but do not contain color. In this paper, we propose NIR to RGB domain translation using asymmetric cycle generative adversarial networks (ACGANs). The RGB image (3 channels) has richer information than the NIR image (1 channel), which makes NIR-RGB domain translation asymmetric in information. We adopt asymmetric cycle GANs that have different network capacities according to the translation direction. We combine UNet and ResNet in generator and use the feature pyramid networks (FPNs) in discriminator. With the help of a 128 × 128 large receptive field, we capture rich spatial context information with a multiscale architecture. Experimental results show that the proposed method achieves natural looking NIR colorization results with high generalization ability, i.e. feasible in category unaware cases, and outperforms state-of-the-art ones in realistic colorization and resistance to unregistration.
Halt the use of facial-recognition technology until it is regulated

Until appropriate safeguards are in place, we need a moratorium on biometric technology that identifies individuals, says Kate Crawford. Until appropriate safeguards are in place, we need a moratorium on biometric technology that identifies individuals, says Kate Crawford.
Invisible Backdoor Attacks Against Deep Neural Networks

Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific input (called triggers), trick the model into producing unexpected behavior. In this paper, we create covert and scattered triggers for backdoor attacks, invisible backdoors, where triggers can fool both DNN models and human inspection. We apply our invisible backdoors through two state-of-the-art methods of embedding triggers for backdoor attacks. The first approach on Badnets embeds the trigger into DNNs through steganography. The second approach of a trojan attack uses two types of additional regularization terms to generate the triggers with irregular shape and size. We use the Attack Success Rate and Functionality to measure the performance of our attacks. We introduce two novel definitions of invisibility for human perception; one is conceptualized by the Perceptual Adversarial Similarity Score (PASS) and the other is Learned Perceptual Image Patch Similarity (LPIPS). We show that the proposed invisible backdoors can be fairly effective across various DNN models as well as four datasets MNIST, CIFAR-10, CIFAR-100, and GTSRB, by measuring their attack success rates for the adversary, functionality for the normal users, and invisibility scores for the administrators. We finally argue that the proposed invisible backdoor attacks can effectively thwart the state-of-the-art trojan backdoor detection approaches, such as Neural Cleanse and TABOR.
Survey On Image Registration

Picture enrollment has crucial applications in therapeutic picture examination.The term picture enlistment fundamentally means the procedure of arrangement of pictures in which the last data is picked up from various information sources.It is a key preprocessing step where at least two pictures are adjusted into a typical arrange framework.Out of different sorts of enlistment techniques, a famous class is the one, which utilizes the entire picture substance to determine a reasonable change for overlaying the information pictures.Picture enlistment itself is made out of various stages like change, interjection, processing closeness metric and streamlining of the change parameters (interpretation, pivot, shearing and so forth).A central point that decides the achievement and adequacy of any enrollment technique is the methodology we utilize for accomplishing the ideal arrangement of change vectors.The search for change parameters for picture enlistment has been managed for the most part as a multidimensional improvement issue, which processes the geometric just as force changes at which the information pictures are having maximal closeness with each other.Escalate, the quick enhancement in the enclosure of military programmed target confession remote cartography, PC vision, picture combination, therapeutic imaging, and astrophotography has built up the requirement for the advancement of good picture enrollment procedure.A few writing approaches are fundamentally looked into, by exploring changes and hybridizations with Evolutionary Strategies.Since biomedical picture enlistment speaks to a difficult clinical undertaking, the exploratory discoveries energize further research contemplates sooner rather than later
Easy-to-Deploy API Extraction by Multi-Level Feature Embedding and Transfer Learning

Application Programming Interfaces (APIs) have been widely discussed on social-technical platforms (e.g., Stack Overflow). Extracting API mentions from such informal software texts is the prerequisite for API-centric search and summarization of programming knowledge. Machine learning based API extraction has demonstrated superior performance than rule-based methods in informal software texts that lack consistent writing forms and annotations. However, machine learning based methods have a significant overhead in preparing training data and effective features. In this paper, we propose a multi-layer neural network based architecture for API extraction. Our architecture automatically learns character-, word- and sentence-level features from the input texts, thus removing the need for manual feature engineering and the dependence on advanced features (e.g., API gazetteers) beyond the input texts. We also propose to adopt transfer learning to adapt a source-library-trained model to a target-library, thus reducing the overhead of manual training-data labeling when the software text of multiple programming languages and libraries need to be processed. We conduct extensive experiments with six libraries of four programming languages which support diverse functionalities and have different API-naming and API-mention characteristics. Our experiments investigate the performance of our neural architecture for API extraction in informal software texts, the importance of different features, the effectiveness of transfer learning. Our results confirm not only the superior performance of our neural architecture than existing machine learning based methods for API extraction in informal software texts, but also the easy-to-deploy characteristic of our neural architecture.
Golden speaker builder – An interactive tool for pronunciation training

The type of voice model used in Computer Assisted Pronunciation Instruction is a crucial factor in the quality of practice and the amount of uptake by language learners. As an example, prior research indicates that second-language learners are more likely to succeed when they imitate a speaker with a voice similar to their own, a so-called “golden speaker”. This manuscript presents Golden Speaker Builder (GSB), a tool that allows learners to generate a personalized “golden-speaker” voice: one that mirrors their own voice but with a native accent. We describe the overall system design, including the web application with its user interface, and the underlying speech analysis/synthesis algorithms. Next, we present results from a series of listening tests, which show that GSB is capable of synthesizing such golden-speaker voices. Finally, we present results from a user study in a language-instruction setting, which show that practising with GSB leads to improved fluency and comprehensibility. We suggest reasons for why learners improved as they did and recommendations for the next iteration of the training.
The “A” of FAIR – As Open as Possible, as Closed as Necessary

In order to provide responsible access to health data by reconciling benefits of data sharing with privacy rights and ethical and regulatory requirements, Findable, Accessible, Interoperable and Reusable (FAIR) metadata should be developed. According to the H2020 Program Guidelines on FAIR Data, data should be “as open as possible and as closed as necessary”, “open” in order to foster the reusability and to accelerate research, but at the same time they should be “closed” to safeguard the privacy of the subjects. Additional provisions on the protection of natural persons with regard to the processing of personal data have been endorsed by the European General Data Protection Regulation (GDPR), Reg (EU) 2016/679, that came into force in May 2018. This work aims to solve accessibility problems related to the protection of personal data in the digital era and to achieve a responsible access to and responsible use of health data. We strongly suggest associating each data set with FAIR metadata describing both the type of data collected and the accessibility conditions by considering data protection obligations and ethical and regulatory requirements. Finally, an existing FAIR infrastructure component has been used as an example to explain how FAIR metadata could facilitate data sharing while ensuring protection of individuals.