Reviewerly/exHarmony huggingface dataset:
Evaluation of Uncertainty Quantification in Deep Learning

Artificial intelligence (AI) is nowadays included into an increasing number of critical systems. Inclusion of AI in such systems may, however, pose a risk, since it is, still, infeasible to build AI systems that know how to function well in situations that differ greatly from what the AI has seen before. Therefore, it is crucial that future AI systems have the ability to not only function well in known domains, but also understand and show when they are uncertain when facing something unknown. In this paper, we evaluate four different methods that have been proposed to correctly quantifying uncertainty when the AI model is faced with new samples. We investigate the behaviour of these models when they are applied to samples far from what these models have seen before, and if they correctly attribute those samples with high uncertainty. We also examine if incorrectly classified samples are attributed with an higher uncertainty than correctly classified samples. The major finding from this simple experiment is, surprisingly, that the evaluated methods capture the uncertainty differently and the correlation between the quantified uncertainty of the models is low. This inconsistency is something that needs to be further understood and solved before AI can be used in critical applications in a trustworthy and safe manner.
Coronavirus Social Engineering Attacks: Issues and Recommendations

During the current coronavirus pandemic, cybercriminals are exploiting people's anxieties to steal confidential information, distribute malicious software, perform ransomware attacks and use other social engineering attacks.The number of social engineering attacks is increasing day by day due to people's failure to recognize the attacks.Therefore, there is an urgent need for solutions to help people understand social engineering attacks and techniques.This paper helps individuals and industry by reviewing the most common coronavirus social engineering attacks and provides recommendations for responding to such an attack.The paper also discusses the psychology behind social engineering and introduces security awareness as a solution to reduce the risk of social engineering attacks.
IA-SUWO: An Improving Adaptive semi-unsupervised weighted oversampling for imbalanced classification problems

As the essence of machine learning, classification is widely used in real life, however, imbalanced data has brought great challenges to classification problems. This is because standard classifiers tend to favor the majority instances and ignore the minority instances. The new oversampling algorithms (e.g. A-SUWO) based on the improving majority weighted minority oversampling (IMWMO) method assign weights through the Euclidean distances from majority instances to hard-to-learn minority instances, and then guide the synthesis of minority samples according to the weights to address the offset of the classification hyperplanes. A-SUWO has achieved better results than traditional oversampling algorithms (e.g. SMOTE and MWMOTE, etc.), when its parameters are well adjusted. However, A-SUWO may give minority training samples inappropriate weights in some irregularly distributed scenarios and make learning tasks even more harder. Additionally, A-SUWO’s knn synthesizing method may not obtain wider and more effective instances. Therefore, we propose an improving adaptive semi-unsupervised weighted oversampling (IA-SUWO) technique to address the imbalanced classification problems more effectively. The improvement of IA-SUWO mainly focuses on the following two aspects: (1) comprehensively considering the least squares support numerical spectrum values and the IMWMO method to assign weights to minority instances, and (2) synthesizing new instances using the k* information nearest neighbors (k*INN) method. IA-SUWO aims to maximize the probability that all important minority samples will be drawn and generates more efficient (more scattered) boundary instances. Results demonstrate that IA-SUWO achieves significantly better results in most datasets compared with other 10 oversampling algorithms and 2 ensemble algorithms.
Learning with Multiple Complementary Labels

A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the problem setting only allows a single CL for each example, which notably limits its potential since our labelers may easily identify multiple CLs (MCLs) to one example. In this paper, we propose a novel problem setting to allow MCLs for each example and two ways for learning with MCLs. In the first way, we design two wrappers that decompose MCLs into many single CLs, so that we could use any method for learning with CLs. However, the supervision information that MCLs hold is conceptually diluted after decomposition. Thus, in the second way, we derive an unbiased risk estimator; minimizing it processes each set of MCLs as a whole and possesses an estimation error bound. We further improve the second way into minimizing properly chosen upper bounds. Experiments show that the former way works well for learning with MCLs but the latter is even better.
Dialogue Coherence Assessment Without Explicit Dialogue Act Labels

Recent dialogue coherence models use the coherence features designed for monologue texts, e.g.nominal entities, to represent utterances and then explicitly augment them with dialogue-relevant features, e.g., dialogue act labels.It indicates two drawbacks, (a) semantics of utterances is limited to entity mentions, and (b) the performance of coherence models strongly relies on the quality of the input dialogue act labels.We address these issues by introducing a novel approach to dialogue coherence assessment.We use dialogue act prediction as an auxiliary task in a multi-task learning scenario to obtain informative utterance representations for coherence assessment.Our approach alleviates the need for explicit dialogue act labels during evaluation.The results of our experiments show that our model substantially (more than 20 accuracy points) outperforms its strong competitors on the Dai-lyDialogue corpus, and performs on par with them on the SwitchBoard corpus for ranking dialogues concerning their coherence.We release our source code 1 .
Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation

We tackle the task of building supervised event trigger identification models which can generalize better across domains.Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance.ADA uses adversarial training to construct representations that are predictive for trigger identification, but not predictive of the example's domain.It requires no labeled data from the target domain, making it completely unsupervised.Experiments with two domains (English literature and news) show that ADA leads to an average F1 score improvement of 3.9 on outof-domain data.Our best performing model (BERT-A) reaches 44-49 F1 across both domains, using no labeled target data.Preliminary experiments reveal that finetuning on 1% labeled data, followed by self-training leads to substantial improvement, reaching 51.5 and 67.2 F1 on literature and news respectively. 1
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering

Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations.Unfortunately, most existing MCQA datasets are small in size, which increases the difficulty of model learning and generalization.To address this challenge, we propose a multi-source meta transfer (MMT) for low-resource MCQA.In this framework, we first extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains.To bridge the distribution gap between training sources and the target, we further introduce the meta transfer that can be integrated into the multi-source meta training.More importantly, the proposed MMT is independent of backbone language models.Extensive experiments demonstrate the superiority of MMT over state-of-the-arts, and continuous improvements can be achieved on different backbone networks on both supervised and unsupervised domain adaptation settings.
On the boomerang uniformity of some permutation polynomials

Abstract The boomerang attack, introduced by Wagner in 1999, is a cryptanalysis technique against block ciphers based on differential cryptanalysis. In particular it takes into consideration two differentials, one for the upper part of the cipher and one for the lower part, and it exploits the dependency of these two differentials. At Eurocrypt’18, Cid et al. introduced a new tool, called the Boomerang Connectivity Table (BCT), that permits to simplify this analysis. Next, Boura and Canteaut introduced an important parameter for cryptographic S-boxes called boomerang uniformity, that is the maximum value in the BCT. Very recently, the boomerang uniformity of some classes of permutations (in particular quadratic functions) have been studied by Li, Qu, Sun and Li, and by Mesnager, Tang and Xiong. In this paper we further study the boomerang uniformity of some non-quadratic differentially 4-uniform functions. In particular, we consider the case of the Bracken-Leander cubic function and three classes of 4-uniform functions constructed by Li, Wang and Yu, obtained from modifying the inverse functions.
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction

Open Information Extraction systems extract ("subject text", "relation text", "object text") triples from raw text.Some triples are textual versions of facts, i.e., non-canonicalized mentions of entities and relations.In this paper, we investigate whether it is possible to infer new facts directly from the open knowledge graph without any canonicalization or any supervision from curated knowledge.For this purpose, we propose the open link prediction task, i.e., predicting test facts by completing ("subject text", "relation text", ?) questions.An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained.For example, facts can appear in different paraphrased textual variants, which can lead to test leakage.To this end, we propose an evaluation protocol and a methodology for creating the open link prediction benchmark OLPBENCH.We performed experiments with a prototypical knowledge graph embedding model for open link prediction.While the task is very challenging, our results suggests that it is possible to predict genuinely new facts, which can not be trivially explained.
DROCC: Deep Robust One-Class Classification

Classical approaches for one-class problems such as one-class SVM and isolation forest require careful feature engineering when applied to structured domains like images. State-of-the-art methods aim to leverage deep learning to learn appropriate features via two main approaches. The first approach based on predicting transformations (Golan &amp; El-Yaniv, 2018; Hendrycks et al., 2019a) while successful in some domains, crucially depends on an appropriate domain-specific set of transformations that are hard to obtain in general. The second approach of minimizing a classical one-class loss on the learned final layer representations, e.g., DeepSVDD (Ruff et al., 2018) suffers from the fundamental drawback of representation collapse. In this work, we propose Deep Robust One-Class Classification (DROCC) that is both applicable to most standard domains without requiring any side-information and robust to representation collapse. DROCC is based on the assumption that the points from the class of interest lie on a well-sampled, locally linear low dimensional manifold. Empirical evaluation demonstrates that DROCC is highly effective in two different one-class problem settings and on a range of real-world datasets across different domains: tabular data, images (CIFAR and ImageNet), audio, and time-series, offering up to 20% increase in accuracy over the state-of-the-art in anomaly detection. Code is available at https://github.com/microsoft/EdgeML.
Unsupervised Domain Adaptation in Semantic Segmentation: a Review

The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attracting a wide interest, since semantic segmentation models require a huge amount of labeled data and the lack of data fitting specific requirements is the main limitation in the deployment of these techniques. This problem has been recently explored and has rapidly grown with a large number of ad-hoc approaches. This motivates us to build a comprehensive overview of the proposed methodologies and to provide a clear categorization. In this paper, we start by introducing the problem, its formulation and the various scenarios that can be considered. Then, we introduce the different levels at which adaptation strategies may be applied: namely, at the input (image) level, at the internal features representation and at the output level. Furthermore, we present a detailed overview of the literature in the field, dividing previous methods based on the following (non mutually exclusive) categories: adversarial learning, generative-based, analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. Novel research directions are also briefly introduced to give a hint of interesting open problems in the field. Finally, a comparison of the performance of the various methods in the widely used autonomous driving scenario is presented.
Accelerating photonic computing by bandwidth enhancement of a time-delay reservoir

Abstract Semiconductor lasers (SLs) that are subject to delayed optical feedback and external optical injection have been demonstrated to perform information processing using the photonic reservoir computing paradigm. Optical injection or optical feedback can under some conditions induce bandwidth-enhanced operation, expanding their modulation response up to several tens of GHz. However, these conditions may not always result in the best performance for computational tasks, since the dynamical and nonlinear properties of the reservoir might change as well. Here we show that by using strong optical injection we can obtain an increased frequency response and a significant acceleration in the information processing capability of this nonlinear system, without loss of performance. Specifically, we demonstrate numerically that the sampling time of the photonic reservoir can be as small as 12 ps while preserving the same computational performance when compared to a much slower sampling rate. We also show that strong optical injection expands the reservoir’s operating conditions for which we obtain improved task performance. The latter is validated experimentally for larger sampling times of 100 ps. The above attributes are demonstrated in a coherent optical communication decoding task.
Development of an electronic health records datamart to support clinical and population health research

Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research; however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research Datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators.The CRDM was designed to (1) contain most of the patient-level data elements needed for research studies; (2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members); (3) be queried via a code-based system to promote reproducibility and consistency across studies; and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information.We provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource.The CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.
Organization of the Educational Process: Opportunities for Individualization of Training

The possibilities of real individualization of education in Russian universities are constrained by the peculiarities of the type of organization of the educational process. Almost all Russian universities are characterized by stream-group organization of training, in which study groups and streams are formed once for the entire period of study. Students of each study group during the entire period of study have a common curriculum and a common schedule of training sessions. Opportunities for individualization of curricula and programs are very limited and confined to the choice of a foreign language and one of several disciplines of the socio-economic cycle. In modern higher education, the stream-group organization of education is preserved only in Russian universities, universities of the post-soviet and partially post-socialist space. Most universities in the rest of the world uphold the principles of individually-oriented organization of training, according to which each student has an opportunity to choose from the curriculum modules to study in the next semester, choose the teachers and the time of classes. In accordance with the choice of students, groups and streams are formed, which retain only during the current semester. Thus, we emphasize, not individual persons but all the students of modern universities have individual curricula and schedules of training sessions. The article discusses the opportunities and prospects of the individualization of education at Russian universities.
Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.
Collaborative Learning for Faster StyleGAN Embedding

The latent code of the recent popular model StyleGAN has learned disentangled representations thanks to the multi-layer style-based generator. Embedding a given image back to the latent space of StyleGAN enables wide interesting semantic image editing applications. Although previous works are able to yield impressive inversion results based on an optimization framework, which however suffers from the efficiency issue. In this work, we propose a novel collaborative learning framework that consists of an efficient embedding network and an optimization-based iterator. On one hand, with the progress of training, the embedding network gives a reasonable latent code initialization for the iterator. On the other hand, the updated latent code from the iterator in turn supervises the embedding network. In the end, high-quality latent code can be obtained efficiently with a single forward pass through our embedding network. Extensive experiments demonstrate the effectiveness and efficiency of our work.
Cloud applications monitoring: An industrial study

Modern software systems employ large IT infrastructures hosted in on-premise clouds or using “rented” cloud resources from specific vendors. The unifying force across any cloud strategy is incremental product and application improvement against conservation of those resources. This is where monitoring of cloud applications becomes a key asset To shed light over the status of monitoring practices in industry, we study: (a) monitoring practices and tools adoption in industry; (b) size and complexity of industrial monitoring problems; (c) the role of software architecture and software process with respect to monitoring strategies. We conduct mixed-methods empirical research featuring interviews and a web survey featuring 140+ practitioners from over 70 different organizations. Even if the market makes available a significant set of monitoring tools, our results show a rather unappealing picture of industrial monitoring: (a) industrial decision-makers do not perceive monitoring as a key asset even though the downtime of their applications correlates heavily with the level of automation and responsiveness enabled by monitoring; (b) monitoring is done with crude technology, mostly MySQL querying or similar (e.g., Nagios); finally, (c) incidents are discovered by clients rather than application owners. We conclude that the road toward the industrial adoption of cutting-edge monitoring technology is still one of the less travelled, presumably in connection to the considerable investment required. Furthermore, the lack of industrial cloud monitoring standards does not help in addressing the proliferation of multiple tool combinations, with varying effectiveness. Further research should be invested in looking into and addressing these major concerns.
Reflections on welfare and political economy aspects of a central bank digital currency

Abstract The point of departure of this short paper is that, in order to preserve the effectiveness of monetary policy in a world increasingly flooded by private digital currencies, central banks (CBs) will eventually have to issue their own digital currencies. The paper presents two proposals for the implementation of such a currency: A moderate proposal in which only the banking sector continues to have access to deposits at the CB and a radical one in which the entire private sector is allowed to hold digital currency deposits at the CB. The paper contrasts the implications of those two polar paths to a CBDC for the funding of banks, the allocation of credit to the economy, for welfare and for political feasibility. One section of the paper shows that the radical implementation may pave the way toward a narrow banking system and dramatically reduce the need for deposit insurance in the long run. The paper evaluates the relative merits of issuing a currency on a blockchain using a permissionless distributed ledger technology in comparison to a centralized (permissioned) blockchain ledger operated by the CB and concludes that the latter dominates the former in more than one dimension.