Reviewerly/exHarmony huggingface dataset:
Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners

Robot navigation is a fundamental problem in robotics and various approaches have been developed to cope with this problem. Despite the great success of previous approaches, learning-based methods are receiving growing interest in the research community. They have shown great efficiency in solving navigation tasks and offer considerable promise to build intelligent navigation systems. This paper presents a goal-directed robot navigation system that integrates global planning based on goal-directed end-to-end learning and local planning based on reinforcement learning (RL). The proposed system aims to navigate the robot to desired goal positions while also being adaptive to changes in the environment. The global planner is trained to imitate an expert’s navigation between different positions by goal-directed end-to-end learning, where both the goal representations and local observations are incorporated to generate actions. However, it is trained in a supervised fashion and is weak in dealing with changes in the environment. To solve this problem, a local planner based on deep reinforcement learning (DRL) is designed. The local planner is first implemented in a simulator and then transferred to the real world. It works complementarily to deal with situations that have not been met during training the global planner and is able to generalize over different situations. The experimental results on a robot platform demonstrate the effectiveness of the proposed navigation system.
An assertive reasoning method for emergency response management based on knowledge elements C4.5 decision tree

The correct selection of knowledge elements is the key to emergency management. Using emergency knowledge elements, this study constructs an assertive reasoning selection methodology by improving acquisition on the balance coefficient in the C4.5 algorithm. Through hierarchical representation, a two level model selection method, based on a top model construction process as well as an underlying model selection process, is proposed. Specifically, the top model construction process is based on assertive reasoning and an underlying model selection process is based on the C4.5 decision tree. This method reduces the requirements for emergency domain knowledge, and improves the accuracy and timeliness. Lastly, the methodology is applied to the 2015 Tianjin explosions as a case study.
Secure Computation for Machine Learning With SPDZ

Secure Multi-Party Computation (MPC) is an area of cryptography that enables computation on sensitive data from multiple sources while maintaining privacy guarantees. However, theoretical MPC protocols often do not scale efficiently to real-world data. This project investigates the efficiency of the SPDZ framework, which provides an implementation of an MPC protocol with malicious security, in the context of popular machine learning (ML) algorithms. In particular, we chose applications such as linear regression and logistic regression, which have been implemented and evaluated using semi-honest MPC techniques. We demonstrate that the SPDZ framework outperforms these previous implementations while providing stronger security.
An IBE-based design for assured deletion in cloud storage

Assured deletion of outsourced data in cloud storage is one of the most important issues in cloud storage. The use of encryption is a promising approach to address this issue. In this paper, we propose a design to achieve assured deletion in cloud storage built upon identity based cryptography (IBE). Compared with other designs known in the literature, foremost among them being FADE, the proposed design comes with a lightweight key management infrastructure. Furthermore, it offers other key management benefits inherent in identity-based cryptography such as key delegation and revocation. To demonstrate the practicality of the proposed design, we implement a fully featured prototype and evaluate its performance. Results analysis shows that the overhead time cost of the proposed design does not exceed 3 percent of the plaintext design. This rate decreases significantly for files with larger sizes, which makes it a relevant choice for use in real applications.
Investigating Generalisation in Continuous Deep Reinforcement Learning

Deep Reinforcement Learning has shown great success in a variety of control tasks. However, it is unclear how close we are to the vision of putting Deep RL into practice to solve real world problems. In particular, common practice in the field is to train policies on largely deterministic simulators and to evaluate algorithms through training performance alone, without a train/test distinction to ensure models generalise and are not overfitted. Moreover, it is not standard practice to check for generalisation under domain shift, although robustness to such system change between training and testing would be necessary for real-world Deep RL control, for example, in robotics. In this paper we study these issues by first characterising the sources of uncertainty that provide generalisation challenges in Deep RL. We then provide a new benchmark and thorough empirical evaluation of generalisation challenges for state of the art Deep RL methods. In particular, we show that, if generalisation is the goal, then common practice of evaluating algorithms based on their training performance leads to the wrong conclusions about algorithm choice. Finally, we evaluate several techniques for improving generalisation and draw conclusions about the most robust techniques to date.
Common Knowledge Based and One-Shot Learning Enabled Multi-Task Traffic Classification

Deep neural networks have been used for traffic classifications and promising results have been obtained. However, most of the previous work confined to one specific task of the classification, where restricts the classifier potential performance and application areas. The traffic flow can be labeled from a different perspective which might help to improve the accuracy of classifier by exploring more meaningful latent features. In addition, deep neural network (DNN)-based model is hard to adapt the changes in new classification demand, because of training such a new model costing not only many computing resources but also lots of labeled data. For this purpose, we proposed a multi-output DNN model simultaneously learning multi-task traffic classifications. In this model, the common knowledge of traffic is exploited by the synergy among the tasks and improves the performance of each task separately. Also, it is showed that this structure shares the potential of meeting new demands in the future and meanwhile being able to achieve the classification with advanced speed and fair accuracy. One-shot learning, which refers to the learning process with scarce data, is also explored and our approach shows notable performance.
An Open Source Domain-Specific Avionics System Architecture Model for the Design Phase and Self-Organizing Avionics

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;State-of-the-art avionics systems are standardized, e.g. the computing system of the flying vehicle is composed of pre-defined and pre-qualified modules of a standardized avionics platform. Integrated Modular Avionics (IMA) is the most popular representative, but not the only one. Two challenges of standardized avionics platform are system design and configuration. Since the high numbers of functions, modules, and constraints for modern air vehicles, bringing up the optimal system architecture is a difficult job if carried out manually. The subsequent process of creating millions of configuration parameters is time consuming and error prone. Both issues are similar and are, in general, processable by algorithms. Algorithms proved to provide significant support for current system design issues and might be mandatory in future, when avionics become self-organizing and the design and configuration are derived by the platform itself. Automated design already proved its advantages and self-organizing platforms started to be in development. Having the right and rigid data format for this purpose is mandatory. A suitable data format must hold all requirements necessary to proof the validity of the avionics architecture and take reliable organization decisions. It must be independent of technologies, in terms of hardware, software, and configuration. It must have a structure simple enough to be used in qualified embedded systems. Existing modeling approaches, e.g. AADL, have different purposes as detailed system design, dynamic simulations, virtual qualification, and lack especially rigidity and simplicity. The Open Avionics Architecture Model (OAAM) is a domain-specific model representing avionics system’s architectures designed to be used offline or online in the automated organization of avionics systems. It was implemented using the EMOF modeling standard and the Eclipse Modeling Framework (EMF). It is available as open source. This article explains the structure and the purpose of OAAM.&lt;/div&gt;&lt;/div&gt;
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations

Most artificial networks today rely on dense representations, whereas biological networks rely on sparse representations. In this paper we show how sparse representations can be more robust to noise and interference, as long as the underlying dimensionality is sufficiently high. A key intuition that we develop is that the ratio of the operable volume around a sparse vector divided by the volume of the representational space decreases exponentially with dimensionality. We then analyze computationally efficient sparse networks containing both sparse weights and activations. Simulations on MNIST and the Google Speech Command Dataset show that such networks demonstrate significantly improved robustness and stability compared to dense networks, while maintaining competitive accuracy. We discuss the potential benefits of sparsity on accuracy, noise robustness, hyperparameter tuning, learning speed, computational efficiency, and power requirements.
Fourth Order MCA and Chaos-Based Image Encryption Scheme

This paper presents a fast and efficient cryptosystem for enciphering digital images. It employs two of the most prominent dynamical systems-chaotic maps and cellular automata. The key streams in the proposed encryption scheme are derived from the SHA-256 hash function. Hash functions produce the digest of the input plaintext, known as a hash value, which can be considered as a unique signature of the input. This makes the keys more plaintext dependent, which is a desirable property of a robust cryptosystem. These key streams are used as the secret keys (i.e., initial conditions and control parameters) of an improved one-dimensional (1-D) chaotic map, i.e., the Logistic-Sine map. As far as we know, this paper is a first that combines the well-known diffusion-confusion architecture and the fourth order 1-D memory cellular automata (MCA) for image encryption. First, a pixel-wise XOR operation is applied to the original image, followed by a pixel-wise random permutation. The resulting image is decomposed into four blocks according to the quadtree decomposition strategy. Then, a fourth order reversible MCA is applied, the blocks obtained from the quadtree decomposition are considered as the initial MCA configurations, and the transition rules are determined using the chaotic map. The performance analyses show that the proposed encryption scheme presents a high immunity against all kind of attacks while maintaining a low complexity, which outcome a notably better performance/complexity trade-off compared to some recently proposed image schemes.
A Literature Study of Embeddings on Source Code

Natural language processing has improved tremendously after the success of word embedding techniques such as word2vec. Recently, the same idea has been applied on source code with encouraging results. In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code. The articles in this survey have been collected by asking authors of related work and with an extensive search on Google Scholar. Each article is categorized into five categories: 1. embedding of tokens 2. embedding of functions or methods 3. embedding of sequences or sets of method calls 4. embedding of binary code 5. other embeddings. We also provide links to experimental data and show some remarkable visualization of code embeddings. In summary, word embedding has been successfully applied on different granularities of source code. With access to countless open-source repositories, we see a great potential of applying other data-driven natural language processing techniques on source code in the future.
Unveiling your keystrokes: A Cache-based Side-channel Attack on Graphics Libraries

Operating systems use shared memory to improve performance.However, as shown in recent studies, attackers can exploit CPU cache side-channels associated with shared memory to extract sensitive information.The attacks that were previously attempted typically only detect the presence of a certain operation and require significant manual analysis to identify and evaluate their effectiveness.Moreover, very few of them target graphics libraries which are commonly used, but difficult to attack.In this paper, we consider the execution time of shared libraries as the side-channel, and showcase a completely automated technique to discover and select exploitable side-channels on shared graphics libraries.In essence, we first collect the cache lines accessed by a victim process during different key presses offline, and then use machine learning to infer the best cache lines (e.g., easily measurable, robust to noise, high information leakage) for a flush and reload attack.We are able to discover effective strategies to classify what keys have been pressed.Using this approach, we not only preclude the need for manual analyses of code and tracesthe automated system discovered many previously unknown sidechannels of the type we are interested in, but also achieve high precision in terms of inferring the sensitive information entered on desktop and Android platforms.We show that our approach infers the passwords with lowercase letters and numbers 10,000 -1,000,000 times faster than random guessing.For a large fraction of PINs consisting of 4 to 6 digits, we are able to infer them within 20 and 80 guesses respectively.Finally, we suggest ways to mitigate these attacks.
A New Ratio Image Based CNN Algorithm for SAR Despeckling

In SAR domain many application like classification, detection and segmentation are impaired by speckle. Hence, despeckling of SAR images is the key for scene understanding. Usually despeckling filters face the trade-off of speckle suppression and information preservation. In the last years deep learning solutions for speckle reduction have been proposed. One the biggest issue for these methods is how to train a network given the lack of a reference. In this work we proposed a convolutional neural network based solution trained on simulated data. We propose the use of a cost function taking into account both spatial and statistical properties. The aim is two fold: overcome the trade-off between speckle suppression and details suppression; find a suitable cost function for despeckling in unsupervised learning. The algorithm is validated on both real and simulated data, showing interesting performances.
Maintaining quality diagnosis with digital pathology: a practical guide to ISO 15189 accreditation

An ever-increasing number of clinical pathology departments are deploying, or planning to deploy digital pathology systems for all, or part of their diagnostic output. Digital pathology is an evolving technology, and it is important that departments uphold or improve on current standards. Leeds Teaching Hospitals NHS Trust has been scanning 100% of histology slides since September 2018, and has developed validation and validation protocols to train 38 histopathology consultants in primary digital diagnosis. In this practical paper, we will share our approach to ISO inspection of our digital pathology service, which resulted in successful ISO accreditation for primary digital diagnosis. We will offer practical advice on what types of procedure and documentation are necessary, both from the point of view of the laboratory and your reporting pathologists. We will explore topics including risk assessment, standard operating procedures, validation and training, calibration and quality assurance, and provide a checklist of the key digital pathology components you need to consider in your inspection preparations. The continuous quest for quality and safety improvements in our practice should underpin everything we do in pathology, including our digital pathology operations. We hope this publication will make it easier for subsequent departments to successfully achieve ISO 15189 accreditation and feel confident in their digital pathology services.
Working principles of binary differential evolution

We conduct a first fundamental analysis of the working principles of binary differential evolution (BDE), an optimization heuristic for binary decision variables that was derived by Gong and Tuson (2007) from the very successful classic differential evolution (DE) for continuous optimization. We show that unlike most other optimization paradigms, it is stable in the sense that neutral bit values are sampled with probability close to 1/2 for a long time. This is generally a desirable property, however, it makes it harder to find the optima for decision variables with small influence on the objective function. This can result in an optimization time exponential in the dimension when optimizing simple symmetric functions like OneMax. On the positive side, BDE quickly detects and optimizes the most important decision variables. For example, dominant bits converge to the optimal value in time logarithmic in the population size. This enables BDE to optimize the most important bits very fast. Overall, our results indicate that BDE is an interesting optimization paradigm having characteristics significantly different from classic evolutionary algorithms or estimation-of-distribution algorithms (EDAs). On the technical side, we observe that the strong stochastic dependencies in the random experiment describing a run of BDE prevent us from proving all desired results with the mathematical rigor that was successfully used in the analysis of other evolutionary algorithms. Inspired by mean-field approaches in statistical physics we propose a more independent variant of BDE, show experimentally its similarity to BDE, and prove some statements rigorously only for the independent variant. Such a semi-rigorous approach might be interesting for other problems in evolutionary computation where purely mathematical methods failed so far.
Everything Is in the Name – A URL Based Approach for Phishing Detection

Phishing attack, in which a user is tricked into revealing sensitive information on a spoofed website, is one of the most common threat to cybersecurity. Most modern web browsers counter phishing attacks using a blacklist of confirmed phishing URLs. However, one major disadvantage of the blacklist method is that it is ineffective against newly generated phishes. Machine learning based techniques that rely on features extracted from URL (e.g., URL length and bag-of-words) or web page (e.g., TF-IDF and form fields) are considered to be more effective in identifying new phishing attacks. The main benefit of using URL based features over page based features is that the machine learning model can classify new URLs on-the-fly even before the page is loaded by the web browser, thus avoiding other potential dangers such as drive-by download attacks and cryptojacking attacks. In this work, we focus on improving the performance of URL based detection techniques. We show that, although a classifier trained on traditional bag-of-words features (tokenized using special characters) works well in many cases, it fails to recognize a very prevalent class of phishing URLs that combines a popular brand with one or more words (e.g., www.paypalloginsecure.com and paypalhelpservice.simdif.com) among others. To overcome these flaws, we explore various alternative feature extraction techniques based on word segmentation and $$n-$$ grams. We also construct and use a phishy-list of popular words that are highly indicative of phishing attacks. We verify the efficacy of each of these feature sets by training a logistic regression classifier on a large dataset consisting of 100,000 URLs. Our experimental results reveal that features based on word segmentation, phishy-list and numerical features (e.g., URL length) perform better than all other features, as measured by misclassification and false negative rates.
Multi-Dimensional Regression Host Utilization algorithm (MDRHU) for Host Overload Detection in Cloud Computing

The use of cloud computing data centers is growing rapidly to meet the tremendous increase in demand for high-performance computing (HPC), storage and networking resources for business and scientific applications. Virtual machine (VM) consolidation involves the live migration of VMs to run on fewer physical servers, and thus allowing more servers to be switched off or run on low-power mode, as to improve the energy consumption efficiency, operating cost and CO2 emission. A crucial step in VM consolidation is host overload detection, which attempts to predict whether or not a physical server will be oversubscribed with VMs. In contrast to the majority of previous work which use CPU utilization as the sole indicator for host overload, a recent study has proposed a multiple regression host overload detection algorithm, which takes multiple factors into consideration: CPU, memory and network BW utilization. This paper provides further improvement along two directions. First, we provide Multi-Dimensional Regression Host Utilization (MDRHU) algorithms that combine CPU, memory and network BW utilization via Euclidean Distance (MDRHU-ED) and absolute summation (MDRHU-AS), respectively. This leads to improved results in terms of energy consumption and service level agreement violation. Second, the study explicitly takes real-world HPC workloads into consideration. Our extensive simulation study further illustrates the superiority of our proposed algorithms over existing methods. In particular, as compared to the most recently proposed multiple regression algorithm that is based on Geometric Relation (GR), our proposed algorithms provide an improvement of at least 12% in energy consumption, and an improvement of at least 80% in a metric that combines energy consumption, service-level-violation, and number of VM migrations.