Reviewerly/exHarmony huggingface dataset:
Multi-scale Temporal Memory for Clinical Event Time-Series Prediction

The objective of this work is to develop and study dynamic patient-state models and patient-state representations that are predictive of a wide range of future events in the electronic health records (EHRs). One challenge to overcome when building predictive EHRs representations is the complexity of multivariate clinical event time-series and their short and long-term dependencies. We address this challenge by proposing a new neural memory module called Multi-scale Temporal Memory (MTM) linking events in a distant past with the current prediction time. Through a novel mechanism implemented in MTM, information about previous events on different time-scales is compiled and read on-the-fly for prediction through memory contents. We demonstrate the efficacy of MTM by combining it with different patient state summarization methods that cover different temporal aspects of patient states. We show that the combined approach is 4.6% more accurate than the best result among the baseline approaches and it is 16% more accurate than prediction solely through hidden states of LSTM.
Deep Hashing with Hash-Consistent Large Margin Proxy Embeddings

Image hash codes are produced by binarizing the embeddings of convolutional neural networks (CNN) trained for either classification or retrieval. While proxy embeddings achieve good performance on both tasks, they are non-trivial to binarize, due to a rotational ambiguity that encourages non-binary embeddings. The use of a fixed set of proxies (weights of the CNN classification layer) is proposed to eliminate this ambiguity, and a procedure to design proxy sets that are nearly optimal for both classification and hashing is introduced. The resulting hash-consistent large margin (HCLM) proxies are shown to encourage saturation of hashing units, thus guaranteeing a small binarization error, while producing highly discriminative hash-codes. A semantic extension (sHCLM), aimed to improve hashing performance in a transfer scenario, is also proposed. Extensive experiments show that sHCLM embeddings achieve significant improvements over state-of-the-art hashing procedures on several small and large datasets, both within and beyond the set of training classes.
Building Implicit Vector Representations of Individual Coding Style

We propose a new approach to building vector representations of individual developers by capturing their individual contribution style, or coding style. Such representations can find use in the next generation of software development team collaboration tools, for example by enabling the tools to track knowledge transfer in teams. The key idea of our approach is to avoid using explicitly defined metrics of coding style and instead build the representations through training a model for authorship recognition and extracting the representations of individual developers from the trained model. By empirically evaluating the output of our approach, we find that implicitly built individual representations reflect some properties of team structure: developers who report learning from each other are represented closer to each other.
Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

The technical progress in the last decades makes photo and video recording devices omnipresent. This change has a significant impact, among others, on police work. It is no longer unusual that a myriad of digital data accumulates after a criminal act, which must be reviewed by criminal investigators to collect evidence or solve the crime. This paper presents the VICTORIA Interactive 4D Scene Reconstruction and Analysis Framework (“ISRA-4D” 1.0), an approach for the visual consolidation of heterogeneous video and image data in a 3D reconstruction of the corresponding environment. First, by reconstructing the environment in which the materials were created, a shared spatial context of all available materials is established. Second, all footage is spatially and temporally registered within this 3D reconstruction. Third, a visualization of the hereby created 4D reconstruction (3D scene + time) is provided, which can be analyzed interactively. Additional information on video and image content is also extracted and displayed and can be analyzed with supporting visualizations. The presented approach facilitates the process of filtering, annotating, analyzing, and getting an overview of large amounts of multimedia material. The framework is evaluated using four case studies which demonstrate its broad applicability. Furthermore, the framework allows the user to immerse themselves in the analysis by entering the scenario in virtual reality. This feature is qualitatively evaluated by means of interviews of criminal investigators and outlines potential benefits such as improved spatial understanding and the initiation of new fields of application.
Freeware Solution for Preventing Data Leakage by Insider for Windows Framework

Every organisation has some crucial data that holds the reason for its competitive advantage over others. This data includes intellectual property, trade secrets, salary details etc. Non-ethical disclosure of such data can have fatal impacts. Recent incidents of data leaks cannot be overlooked, therefore every organisation should preferably use Data Loss Prevention(DLP) system to avoid the risk of data leakage. The aim of this work is to develop a freeware DLP that will help small and medium scale organizations to protect their covert data. There are numerous channels of data exfiltration such as Bluetooth, E-mail, Universal Serial Bus(USB) etc. The USB channel being portable and fast to use, it is favoured for data transfer. This DLP system is developed to work on windows framework. It targets to block transfer of confidential files through a USB port, according to the policies set by an administrator. This solution uses emerging technologies and integrates kernel space modules and machine learning approach to deliver a novel solution. It intercepts file transfer actions through a USB port and checks the contents of the file. In case, contents of the file are found to be confidential, the copy action will be blocked. This solution is implemented in a way that makes it effective and simplistic to use. It will definitely help the organizations to protect their data. There is a plethora of research going on in this area to secure sensitive information from being leaked. Incorporating Machine learning to accurately detect leaks is a new challenge in this field.
Securing the Private Key in Your Blockchain Wallet: A Continuous Authentication Approach Based on Behavioral Biometric

Abstract As blockchain is increasingly valued, the application of blockchain and its security has become a research hotspot. Not only that, more and more ordinary users are paying attention to the blockchain and beginning to trade commodities in cryptocurrencies. The blockchain wallet is a program for users to manage and stores their private keys. Furthermore, the private key is the user’s unique credential to the cryptocurrency in the blockchain. But for now, blockchain wallets are only using passwords to authenticate users, which poses a potential risk to private key security. To mitigate this risk, this paper proposes a continuous authentication approach based on user mouse behavioral biometrics. The approach was experimentally shown to be able to authenticate once per second with an average FAR and FRR of 6.92% and 6.18%, respectively.
Asymmetric and Square Convolutional Neural Network for SAR Ship Detection from Scratch

Despite there have been little research on synthetic aperture radar (SAR) ship detection from scratch, the transfer learning-based studies are still the mainstream. Due to some limitations of the fixed structures of the backbone, it is hard to improve and optimize these parts of networks and there is between domain mismatch which restricts the performance of the convolutional neural network (CNN) to some extent. Addressing the issue, we design an asymmetric and square convolution block (A-S CB) which is easy to be embedded into any CNNs and helps to significantly reduce the number of parameters and computations without serious damages to detection accuracy. We integrated the proposed A-S CB to the classic SSD, namely A-S SSD, which can be trained from scratch. Experiments on RDISD_SAR proves our approach reaches competitive performance in both accuracy and detection speed compared with the typical DSOD, and in contrast with SSD, it holds some superiorities in aspects of accuracy, speed, number of parameters, and computations.
An experimental analysis on the similarity of argumentation semantics

In this paper we ask whether approximation for abstract argumentation is useful in practice, and in particular whether reasoning with grounded semantics – which has polynomial runtime – is already an approximation approach sufficient for several practical purposes. While it is clear from theoretical results that reasoning with grounded semantics is different from, for example, skeptical reasoning with preferred semantics, we investigate how significant this difference is in actual argumentation frameworks. As it turns out, in many graphs models, reasoning with grounded semantics actually approximates reasoning with other semantics almost perfectly. An algorithm for grounded reasoning is thus a conceptually simple approximation algorithm that not only does not need a learning phase – like recent approaches – but also approximates well – in practice – several decision problems associated to other semantics.
Neural candidate-aware language models for speech recognition

This paper presents novel neural network based language models that can correct automatic speech recognition (ASR) errors by using speech recognizer outputs as a context. Our proposed models, called neural candidate-aware language models (NCALMs), estimate the generative probability of a target sentence while considering ASR outputs including hypotheses and their posterior probabilities. Recently, neural network language models have achieved great success in ASR field because of their ability to learn long-range contexts and model the word representation in continuous space. However, they estimate a sentence probability without considering other candidates and their posterior probabilities, even though the competing hypotheses are available and include important information to increase the speech recognition accuracy. To overcome this limitation, our idea is to utilize ASR outputs in both the training phase and the inference phase. Our proposed models are conditional generative models consisting of a Transformer encoder and a Transformer decoder. The encoder embeds the candidates as context vectors and the decoder estimates a sentence probability given the context vectors. We evaluate the proposed models in Japanese lecture transcription and English conversational speech recognition tasks. Experimental results show that a NCALM has better ASR performance than a system including a deep neural network-hidden Markov model hybrid system. We further improve ASR performance by using a NCALM and a Transformer language model simultaneously.
Efficient Graph Search

Welcome to Drill Bits , a new column about programming. This inaugural episode shows how graph search algorithms can avoid unnecessary work. A simple modification to classic breadth-first search improves the lower bound on its running time: Whereas classic BFS always requires time proportional to the number of vertices plus the number of edges, the improved "Efficient BFS" sometimes runs in time proportional to the number of vertices alone. Both asymptotic analysis and experiments show that Efficient BFS can be much faster than classic BFS. All software used in the experiments is available for download, and suggestions for further explorations are provided.
A role-based architecture for self-adaptive cyber-physical systems

Today's computing world features a growing number of cyber-physical systems that require the cooperation of many physical devices. Examples include autonomous cars and co-working robots, which are expected to appropriately adapt to any possible context they find themselves in (e.g. the presence of a nearby human).
Blockchain to improve Academic Governance

Today's society is in constant transition to keep up with the ever evolving technological advances, which constantly challenge the traditional paradigms. One such being the fundamentals of governance. The current system of campus governance is running out of fuel; riddled with mishandled records, lack of transparency and unjustifiable time delays. In today's age of digitization and instantaneous transactions, the academic system has failed to keep up and is stuck in the clutches of legacy systems and bureaucracy. Also, the conventional labour intensive process makes it too easy for the students to tamper their original academic documents for employment. It is the need of the hour that today's academic institutes serve the fast moving needs of the education system with state-of-the-art technology. We propose a system for modern academic governance by harnessing the power of Blockchain technology. We demonstrate how Blockchain can ensure transparency and build back trust into the system while simultaneously ensuring speed of execution by discussing the pervasive use cases of student transcript verification, tender management and certificate verification and presenting an implementation of the same on the VJTI Blockchain.
Uncertainty-guided testing and robustness enhancement for deep learning systems

Deep learning (DL) systems, though being widely used, still suffer from quality and reliability issues. Researchers have put many efforts to investigate these issues. One promising direction is to leverage uncertainty, an intrinsic characteristic of DL systems when making decisions, to better understand their erroneous behavior. DL system testing is an effective method to reveal potential defects before the deployment into safety- and security-critical applications. Various techniques and criteria have been designed to generate defect-triggers, i.e. adversarial examples (AEs). However, whether these test inputs could achieve a full spectrum examination of DL systems remains unknown and there still lacks understanding of the relation between AEs and DL uncertainty. In this work, we first conduct an empirical study to uncover the characteristics of AEs from the perspective of uncertainty. Then, we propose a novel approach to generate inputs that are missed by existing techniques. Further, we investigate the usefulness and effectiveness of the data for DL robustness enhancement.
Temperature Controller Using the Takagi-Sugeno-Kang Fuzzy Inference System for an Industrial Heat Treatment Furnace

The industrial welding industry has a high energy consumption due to the heating processes carried out. The heat treatment furnaces used for reheating equipment made of steel require a good regulator to control the temperature at each stage of the process, thereby optimizing resources. Considering dynamic and variable temperature behavior inside the oven, this paper proposes the design of a temperature controller based on a Takagi-Sugeno-Kang (TSK) fuzzy inference system of zero order. Considering the reaction curve of the temperature process, the plant model has been identified with the Miller method and a subsequent optimization based on the descending gradient algorithm. Using the conventional plant model, a TSK fuzzy model optimized by the recursive least square’s algorithm is obtained. The TSK fuzzy controller is initialized from the conventional controller and is optimized by descending gradient and a cost function. Applying this controller to a real heat treatment system achieves an approximate minimization of 15 min with respect to the time spent with a conventional controller. Improving the process and integrated systems of quality management of the service provided.
Deep Morphological Filter Networks For Gaussian Denoising

This paper presents a deep network based on morphological filters for Gaussian denoising. The opening and closing, which are basic morphological filters, can eliminate small peaks and valleys of intensities of images. Top-hat transforms are defined as subtractions between an original image and its opening or closing and extract peaks or valleys of the image. Each layer of the proposed network consists of top-hat transforms to attenuate peaks and valleys of noise components. Noise components are iteratively reduced in the proposed deep network structure. In this paper, the extensions of opening and closing are introduced by linear combinations of the morphological filters for the deep network. Multiplications are required for only the linear combination of the morphological filters in the proposed network. Since almost parameters of the network are structuring elements of the morphological filters, the feature maps and parameters can be represented in short bit-length integers. Denosing examples show the proposed network obtains denosing results comparable to BM3D [1] without linear convolutions with the number of parameters that is about 1/10 of full scale deep convolutional neural networks [2].
How should we respond to the life-altering crises that education is facing?

Prior literature suggests that to address the problems facing education, researchers and practitioners of online and flexible learning should avoid placing too much emphasis on the potential of technology and consult the history and literature of the field. In this reflective article, I argue that in addition to these activities, we should expand our efforts to broaden the reach and impact of our field and engage in speculative work that asks: What should the future of digital, online, and flexible education look like?
Usage visualisation for the AWS services

The goal of this work is to elaborate a lightweight solution to manage multiple Amazon Web Services (AWS) accounts in one place. Currently, no such system is available. In this paper, we present a system that solves this task and allows to visualise the usage of AWS services across a number of customer’s accounts. This system can be also used to enable optimisation cost and performance of the AWS services across the customer’s accounts.
Super-Resolution by Image Enhancement Using Texture Transfer

Recent deep learning approaches in single image super-resolution (SISR) can generate high-definition textures for super-resolved (SR) images. However, they tend to hallucinate fake textures and even produce artifacts. An alternative to SISR, reference-based SR (RefSR) approaches use high-resolution (HR) reference (Ref) images to provide HR details that are missing in the low-resolution (LR) input image. We propose a novel framework that leverages existing SISR approaches and enhances them with RefSR. Specifically, we refine the output of SISR methods using neural texture transfer, where HR features are queried from the Ref images. The query is conducted by computing the similarity of textural and semantic features between the input image and the Ref images. The most similar HR features, patch-wise, to the LR image is used to augment the SR image through an augmentation network. In the case of dissimilar Ref images from the LR input image, we prevent performance degradation by including the similarity scores in the input features of the network. Furthermore, we use random texture patches during the training to condition our augmentation network to not always trust the queried texture features. Different from past RefSR approaches, our method can use arbitrary Ref images and its lower-bound performance is based on the SR image. We showcase that our method drastically improves the performance of the base SISR approach.
Mixed-Models Method Based on Machine Learning in Detecting WebShell Attack

WebShell is a command execution environment in the form of web files and also a remote administration tool in web containers. However, it is also a web page backdoor for attackers. Malicious WebShell endangers safety of the Web services. Traditional detection methods, which suitable for general WebShell attack scripts, are based on rule matching. Effectively detecting mutant WebShell has become a great difficulty in computer security worldwide. Mutant scripts of PHP WebShell are the most numerous, complicated and difficult to detect in all kinds of mutant WebShell. This paper proposed a mixed model based on Machine Learning that is used to detect WebShell in different classifications. Using many feature engineering and sample balancing algorithms, the model mixes Machine Learning algorithms of Random Forest (RF) and Convolutional Neural Networks (CNN). It proposed a practicable intelligent solution for mutant WebShell attack detection with the optimized precision rate over 97%.
Triple Attention For Robust Video Crowd Counting

Traditional static-image based crowd counting methods work well on public datasets. However, due to the complexity and variability of real-world scenarios, their performance tends to drop dramatically in practice. Aiming to solve the robust problem of crowd counting, we propose to use a co-attention mechanism to extract correlation features lying behind adjacent video frames which can enhance the distinguish-ability between background and foreground. Also, we combine co-attention with spatial attention and multi-scale self-attention. Three different and complementary attention-based modules jointly reinforce the robustness of the counting model. Experiments on two widely used video crowd datasets demonstrate the effectiveness of the proposed approach.
Hybrid Planning Using Learning and Model Checking for Autonomous Systems

Self-adaptive software systems rely on planning to make adaptation decisions autonomously. Planning is required to produce high-quality adaptation plans in a timely manner; however, quality and timeliness of planning are conflicting in nature. This conflict can be reconciled with hybrid planning, which can combine reactive planning (to quickly provide an emergency response) with deliberative planning that take time but determine a higher-quality plan. While often effective, reactive planning sometimes risks making the situation worse. Hence, a challenge in hybrid planning is to decide whether to invoke reactive planning until the deliberative planning is ready with a high-quality plan. To make this decision, this paper proposes a novel learning-based approach. We demonstrate that this learning-based approach outperforms existing techniques that are based on specifying fixed conditions to invoke reactive planning in two domains: enterprise cloud systems and unmanned aerial vehicles.
Playing in virtual nature

Age-related physical, mental, and social limitations can severely reduce the quality of life of elderly people. Research has shown that virtual reality (VR) can provide stimulating experiences, elicit positive emotions, and provoke relaxation. In this paper, we present an approach that integrates stimulating cognitive tasks, playful elements, and virtual natural environments in one VR app tailored for elderly people. We developed a virtual walk in the forest where the players can solve cognitive tasks in two mini-games. To support a feeling of social presence and to reduce loneliness, we integrated a virtual dog as a companion who accompanies the players. We conducted an evaluation with 14 participants aged 66 to 84 years. The results show a change of positive and negative affect scores after using the VR experience. That supports our assumption, even though the differences were not significant. The participants enjoyed the VR app and were especially impressed by the experience of presence. Despite their inexperience in using VR technology, the participants behaved naturally and were interested in using it again. Our results support the assumption that elderly people can benefit from VR games in their everyday lives.