Reviewerly/exHarmony huggingface dataset:
Partitions in the S-Box of Streebog and Kuznyechik

Streebog and Kuznyechik are the latest symmetric cryptographic primitives standardized by the Russian GOST. They share the same S-Box, π, whose design process was not described by its authors. In previous works, Biryukov, Perrin and Udovenko recovered two completely different decompositions of this S-Box.We revisit their results and identify a third decomposition of π. It is an instance of a fairly small family of permutations operating on 2m bits which we call TKlog and which is closely related to finite field logarithms. Its simplicity and the small number of components it uses lead us to claim that it has to be the structure intentionally used by the designers of Streebog and Kuznyechik.The 2m-bit permutations of this type have a very strong algebraic structure: they map multiplicative cosets of the subfield GF(2m)* to additive cosets of GF(2m)*. Furthermore, the function relating each multiplicative coset to the corresponding additive coset is always essentially the same. To the best of our knowledge, we are the first to expose this very strong algebraic structure.We also investigate other properties of the TKlog and show in particular that it can always be decomposed in a fashion similar to the first decomposition of Biryukov et al., thus explaining the relation between the two previous decompositions. It also means that it is always possible to implement a TKlog efficiently in hardware and that it always exhibits a visual pattern in its LAT similar to the one present in π. While we could not find attacks based on these new results, we discuss the impact of our work on the security of Streebog and Kuznyechik. To this end, we provide a new simpler representation of the linear layer of Streebog as a matrix multiplication in the exact same field as the one used to define π. We deduce that this matrix interacts in a non-trivial way with the partitions preserved by π.
Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives

Objective The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports. Methods The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals. Each of them was labeled as presence (1) or absence (0) of HAI. We started by normalizing the records using a list of preprocessing techniques. We calculated an overall performance metric, the F1 Score, to compare a deep learning method (convolutional neural network [CNN]) with the most popular conventional ML models (Bernoulli and multi-naïve Bayes, k-nearest neighbors, logistic regression, random forests, extra-trees, gradient boosting, support vector machines). We applied the hyperparameter Bayesian optimization for each model based on its HAI identification performances. We included the set of text representation as an additional hyperparameter for each model, using four different text representations (bag of words, term frequency–inverse document frequency, word2vec, and Glove). Results CNN outperforms all other conventional ML algorithms for HAI classification. The best F1 Score of 97.7% ± 3.6% and best area under the curve score of 99.8% ± 0.41% were achieved when CNN was directly applied to the processed clinical notes without a pretrained word2vec embedding. Through receiver operating characteristic curve analysis, we could achieve a good balance between false notifications (with a specificity equal to 0.937) and system detection capability (with a sensitivity equal to 0.962) using the Youden's index reference. Conclusions The main drawback of CNNs is their opacity. To address this issue, we investigated CNN inner layers' activation values to visualize the most meaningful phrases in a document. This method could be used to build a phrase-based medical assistant algorithm to help the infection control practitioner to select relevant medical records. Our study demonstrated that deep learning approach outperforms other classification learning algorithms for automatically identifying HAIs in medical reports.
Neural Grammatical Error Correction with Finite State Transducers

Grammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet superseded more traditional symbolic models. Hybrid systems combining phrase-based statistical machine translation (SMT) and neural sequence models are currently among the most effective approaches to GEC. However, both SMT and neural sequence-to-sequence models require large amounts of annotated data. Language model based GEC (LM-GEC) is a promising alternative which does not rely on annotated training data. We show how to improve LM-GEC by applying modelling techniques based on finite state transducers. We report further gains by rescoring with neural language models. We show that our methods developed for LM-GEC can also be used with SMT systems if annotated training data is available. Our best system outperforms the best published result on the CoNLL-2014 test set, and achieves far better relative improvements over the SMT baselines than previous hybrid systems.
Hyperconnectivity and (Im)mobility: Uber and Surveillance Capitalism by the Global South

Apart from governments’ increased opportunities to monitor citizens, businesses, civil servants, and services, companies are mobilizing personal data to build profitable, algorithmically based business models with profound ramifications. With companies that have rapidly become giants in this sector, such as Uber, the phenomenon is spreading to various services at the same overwhelming speed as many companies bet on what is known as Uberization. In this paper, we aim to use one example of such a phenomenon from the Global South to show how a potential hyperconnected society is, in fact, creating the possibility for expanded patterns of immobilization for certain groups. We aim to show how highly indirect corporate surveillance involved in businesses such as Uber can run in parallel with a specific direct form of worker surveillance that, without any legal or social safeguards, increases the vulnerability of the weakest link in this chain.
Towards a Comprehensive Blockchain Architecture Continuum.

The increasingly need for companies to keep a high level of synchronization globally and the advent of new technologies are pushing more and more to move decision-making and operational power from the centre of organizations to their edges.The blockchain could be the key technology to make this change possible.However, there is no bridge yet capable of shortening the still long distance between this new technological phenomenon and today's business realities.Our work aims precisely at this goal; we propose a framework of blockchain models to help practitioners understanding and potentially implement new solutions based on this technological paradigm.In particular, we have developed an ontology that helps to identify and clarify in detail what are the concepts and structures revolving around this technology, and built a continuum of blockchain architectural solutions, ranging from a classic centralized IT architecture to one completely distributed within a public ecosystem.
Learning rapid and precise skills.

A theory is presented about how instruction and experience combine to produce human fluency in a complex skill. The theory depends critically on 4 aspects of the ACT-R architecture. The first is the timing of various modules, particularly motor timing, which results in behavior that closely matches human behavior. The second is the ability to interpret declarative representations of instruction so that they lead to action. The third aspect concerns how practice converts this declarative knowledge into a procedural form so that appropriate actions can be quickly executed. The fourth component, newly added to the architecture, is a Controller module that learns the setting of control variables for actions. The overall theory is implemented in a computational model that is capable of simulating human learning. Its predictions are confirmed in a first experiment involving 2 games derived from the experimental video game Space Fortress. The second experiment tests predictions from the Controller module about lack of transfer between video games. Across the 2 experiments a single model, with the same parameter settings, is shown to simulate human learning of 3 video games. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
BLOCKCHAIN AUDITING – ACCELERATING THE NEED FOR AUTOMATED AUDITS!

This article explores issues related to auditing Blockchain ledgers (BC) or Distributed Ledgers (DL). These are very new technologies and audits thereof are in the infancy stage. While auditing BC/DL networks in the future is still being studied, we believe this paper will provide food for thought and discussion. That said, we also believe that due to the structure of these new technologies audit approaches are sure to include the use of continuous controls/analytics and continuous monitoring. We discuss BC attributes that have resulted in assumptions that the BC is self-auditing. While in some respects they are, there are significant new risks on blockchains which require new controls and audit programs. New control features in a BC/DL include the concept that all the users have a complete copy of the ledger with all the data and all the transactions and system features that make transactions immutable – plainly speaking, they cannot be changed. We explain the Consensus Mechanism for a BC and how it and public/private keys replace heretofore traditional controls. Assertions such as occurrence, completeness, cutoff, accuracy, etc., are all addressed by BC systems providing significant improvements in control precision. There are, however, new assertions and controls that need to be addressed with BCs and new risks especially with the introduction of smart contracts. We have assumed the reader has a basic level of understanding about a BC. For further background we recommend you consult our bibliography. As noted, we do not believe this paper has all the answers or addresses all the risks associated with BC/DL. We trust new approaches will be leveraging a whole new set of controls and audit practices unique to this exciting rapidly emerging technology.
Effective Contexts for UAV Vehicle Detection

Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task for the small size of objects, complex background, and the imbalance of various vehicle samples. This paper proposes a high-performance UAV vehicle detector. We use the single-shot refinement neural network (RefineDet) as a base network, which employs the top-down architecture to offer contextual information, achieving accurate detection. However, for the small size of vehicles, the top-down architecture introduces too much context, which brings surrounding interference. We present a multi-scale adjacent connection module (ACM) to provide effective contextual information and reduce interference for vehicle detection. In addition, we adopt an alternate double loss training strategy (ADT) to solve the problem of imbalance between hard and easy examples during training, and we design suitable default boxes according to the distribution of the UAV dataset to improve the recall rate. Our method achieves 92.0% and 90.4% accuracy on the collected UAV dataset and the publicly available Stanford drone dataset, respectively. And, the proposed detector can run at 58 FPS on a single GPU.
Scalable and Secure Internet of Things Connectivity

The Internet of things (IoT) technology, which is currently considered the new growth engine of the fourth industrial revolution, affects our daily life and has been applied to various industrial fields. Studies on overcoming the limitations of scalability and stability in a centralized IoT operating environment by employing distributed blockchain technology have been actively conducted. However, the nature of IoT that ensures connectivity with multiple objects at any time and any place increases security threats. Further, it extends the influence of the cyber world into the physical domain, resulting in serious damage to human life and property. Therefore, we aim to study a method to increase the security of IoT devices and effectively extend them simultaneously. To this end, we analyze the authentication methods and limitations of traditional IoT devices and examine cases for improving IoT environments by using blockchain technology. Accordingly, we propose a framework that allows IoT devices to be securely connected and extended to other devices by automatically evaluating security using blockchain technology and the whitelist. The method proposed in this paper restricts the extension of devices vulnerable to security risks by imposing penalties and allows only devices with high security to be securely and quickly authenticated and extended without user intervention. In this study, we applied the proposed method to IoT network simulation environments and observed that the number of devices vulnerable to security was reduced by 48.5% compared with traditional IoT environments.
Validation of radiologists’ findings by computer-aided detection (CAD) software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software

Background Computer-aided detection software for automated breast ultrasound has been shown to have potential in improving the accuracy of radiologists. Alternative ways of implementing computer-aided detection, such as independent validation or preselecting suspicious cases, might also improve radiologists’ accuracy. Purpose To investigate the effect of using computer-aided detection software to improve the performance of radiologists by validating findings reported by radiologists during screening with automated breast ultrasound. Material and Methods Unilateral automated breast ultrasound exams were performed in 120 women with dense breasts that included 60 randomly selected normal exams, 30 exams with benign lesions, and 30 malignant cases (20 mammography-negative). Eight radiologists were instructed to detect breast cancer and rate lesions using BI-RADS and level-of-suspiciousness scores. Computer-aided detection software was used to check the validity of radiologists' findings. Findings found negative by computer-aided detection were not included in the readers’ performance analysis; however, the nature of these findings were further analyzed. The area under the curve and the partial area under the curve for an interval in the range of 80%–100% specificity before and after validation of computer-aided detection were compared. Sensitivity was computed for all readers at a simulation of 90% specificity. Results Partial AUC improved significantly from 0.126 (95% confidence interval [CI] = 0.098–0.153) to 0.142 (95% CI = 0.115–0.169) ( P = 0.037) after computer-aided detection rejected mostly benign lesions and normal tissue scored BI-RADS 3 or 4. The full areas under the curve (0.823 vs. 0.833, respectively) were not significantly different ( P = 0.743). Four cancers detected by readers were completely missed by computer-aided detection and four other cancers were detected by both readers and computer-aided detection but falsely rejected due to technical limitations of our implementation of computer-aided detection validation. In this study, validation of computer-aided detection discarded 42.6% of findings that were scored BI-RADS ≥3 by the radiologists, of which 85.5% were non-malignant findings. Conclusion Validation of radiologists’ findings using computer-aided detection software for automated breast ultrasound has the potential to improve the performance of radiologists. Validation of computer-aided detection might be an efficient tool for double-reading strategies by limiting the amount of discordant cases needed to be double-read.
Low light image enhancement based on non‐uniform illumination prior model

Images captured under low-light conditions are often of low visibility. To improve visualisation, a novel low light image enhancement method is presented based on the non-uniform illumination prior model. First, the k-means method is used to process the value channel in the hue-saturation-value (HSV) colour space after space conversion of the input image. Then, the initial illumination of segmented scenes is estimated by an improved maximum red–green–blue method. Next, an illumination preservation method is presented to maintain the naturalness of the enhanced image. Furthermore, the non-uniform illumination prior model is proposed to enhance the textural details in the enhanced image. Fast Fourier transformation is used to accelerate the optimisation. Since an adaptive weight is assigned, the proposed method can preserve the edges and textures at the bright and edge areas. Experimental analysis shows that the results using the proposed method have less noise, better illumination, improved contrast, and satisfactory naturalness. In addition, the proposed method can provide better quality images in terms of subjective and objective assessments.
Health timeline: an insight-based study of a timeline visualization of clinical data

The increasing complexity and volume of clinical data poses a challenge in the decision-making process. Data visualizations can assist in this process by speeding up the time required to analyze and understand clinical data. Even though empirical experiments show that visualizations facilitate clinical data understanding, a consistent method to assess their effectiveness is still missing. The insight-based methodology determines the quality of insights a user acquires from the visualization. Insights receive a value from one to five points based on a domain-specific criteria. Five professional psychiatrists took part in the study using real de-identified clinical data spanning 4 years of medical history. A total of 50 assessments were transcribed and analyzed. Comparing a total of 558 insights using Health Timeline and 576 without, the mean value using the Timeline (1.7) was higher than without (1.26; p<0.01), similarly the cumulative value with the Timeline (11.87) was higher than without (10.96: p<0.01). The average time required to formulate the first insight with the Timeline was higher (13.16 s) than without (7 s; p<0.01). Seven insights achieved the highest possible value using Health Timeline while none were obtained without it. The Health Timeline effectively improved understanding of clinical data and helped participants recognize complex patterns from the data. By applying the insight-based methodology, the effectiveness of the Health Timeline was quantified, documented and demonstrated. As an outcome of this exercise, we propose the use of such methodologies to measure the effectiveness of visualizations that assist the clinical decision-making process.
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation

Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries over a single table.We focus on the Spider dataset, a complex and crossdomain text-to-SQL task, which includes complex queries over multiple tables.In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address the Spider task.Each of the clause-specific decoders consists of a set of sub-modules, which is defined by the syntax of each clause.Additionally, our model works recursively to support nested queries.When evaluated on the Spider dataset, our approach achieves 4.6% and 9.8% accuracy gain in the test and dev sets, respectively.In addition, we show that our model is significantly more effective at predicting complex and nested queries than previous work.
SpeechYOLO: Detection and Localization of Speech Objects

In this paper, we propose to apply object detection methods from the vision domain on the speech recognition domain, by treating audio fragments as objects. More specifically, we present SpeechYOLO, which is inspired by the YOLO algorithm for object detection in images. The goal of SpeechYOLO is to localize boundaries of utterances within the input signal, and to correctly classify them. Our system is composed of a convolutional neural network, with a simple least-mean-squares loss function. We evaluated the system on several keyword spotting tasks, that include corpora of read speech and spontaneous speech. Our system compares favorably with other algorithms trained for both localization and classification.
Exponential Separations in Local Differential Privacy

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Exponential Separations in Local Differential PrivacyMatthew Joseph, Jieming Mao, and Aaron RothMatthew Joseph, Jieming Mao, and Aaron Rothpp.515 - 527Chapter DOI:https://doi.org/10.1137/1.9781611975994.31PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connection to prove sample complexity lower bounds for locally differentially private protocols as straightforward corollaries of results from communication complexity. In particular, we 1) use a communication lower bound for the hidden layers problem to prove an exponential sample complexity separation between sequentially and fully interactive locally private protocols, and 2) use a communication lower bound for the pointer chasing problem to prove an exponential sample complexity separation between k-round and (k + 1)-round sequentially interactive locally private protocols, for every k. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Generating and Analyzing Chatbot Responses using Natural Language Processing

Customer support has become one of the most important communication tools used by companies to provide before and after-sale services to customers.This includes communicating through websites, phones, and social media platforms such as Twitter.The connection becomes much faster and easier with the support of today's technologies.In the field of customer service, companies use virtual agents (Chatbot) to provide customer assistance through desktop interfaces.In this research, the main focus will be on the automatic generation of conversation "Chat" between a computer and a human by developing an interactive artificial intelligent agent through the use of natural language processing and deep learning techniques such as Long Short-Term Memory, Gated Recurrent Units and Convolution Neural Network to predict a suitable and automatic response to customers' queries.Based on the nature of this project, we need to apply sequence-to-sequence learning, which means mapping a sequence of words representing the query to another sequence of words representing the response.Moreover, computational techniques for learning, understanding, and producing human language content are needed.In order to achieve this goal, this paper discusses efforts towards data preparation.Then, explain the model design, generate responses, and apply evaluation metrics such as Bilingual Evaluation Understudy and cosine similarity.The experimental results on the three models are very promising, especially with Long Short-Term Memory and Gated Recurrent Units.They are useful in responses to emotional queries and can provide general, meaningful responses suitable for customer query.LSTM has been chosen to be the final model because it gets the best results in all evaluation metrics.