Reviewerly/exHarmony huggingface dataset:
Model Agnostic Defence Against Backdoor Attacks in Machine Learning

Machine learning (ML) has automated a multitude of our day-to-day decision-making domains, such as education, employment, and driving automation. The continued success of ML largely depends on our ability to trust the model we are using. Recently, a new class of attacks called backdoor attacks have been developed. These attacks undermine the user’s trust in ML models. In this article, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> , a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models. For a given image classification model, our approach analyzes the inputs it receives and determines if the model is backdoored. In addition to this feature, we also mitigate these attacks by determining the correct predictions of the poisoned images. We have implemented <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> and evaluated it against three state-of-the-art poisoned models. In our evaluation, we show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> can detect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 88% of the poisoned inputs on average and it is as fast as 4.4 ms per input image. We also compare our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> approach with the state-of-the-art defence methodologies proposed for backdoor attacks. Our evaluation reveals that despite being a blackbox approach, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> is more effective in thwarting backdoor attacks than the existing techniques. Finally, we also reconstruct the exact poisoned input for the user to effectively test their systems.
Learning a virtual codec based on deep convolutional neural network to compress image

In this paper, we propose a standard-compliant image compression framework based on image representation network (IRN) and post-processing neural network (PNN), which are trained by learning a virtual codec network (VCN). Firstly, we use a mixed-resolution image coding considering different types of distortions caused by image compression with different quality factors. Secondly, the VCN is introduced to learn a differentiable soft-projection from the represented image to the post-processed image to resolve the non-differentiable problem of hard quantization. Thirdly, the PNN is used to greatly enhance the quality of decoded images, since standard codecs always result in visually unpleasant blocking artifacts and ringing artifacts. Finally, our framework is trained in an end-to-end manner, whose convolutional kernels of the IRN, PNN and VCN are initialized by pre-training an auto-encoder network. Experimental results verify that our method has higher coding efficiency than the newest image representation-based compression method and many post-processing approaches.
Query-Based Video Synopsis for Intelligent Traffic Monitoring Applications

Synopsis of a long-duration video has many applications in intelligent transportation systems. It can help to monitor traffic with lesser manpower. However, generating meaningful synopsis of a long-duration video recording can be challenging. Often summarized outputs include redundant contents or activities that may not be helpful to the observer. Moving object trajectories are possible sources of information that can be used to generate the synopsis of long-duration videos. The synopsis generation faces challenges due to object tracking, grouping of the trajectories with respect to activity type, object category, and contextual information, and generating smooth synopsis according to a query. In this paper, we propose a method to generate meaningful and smooth synopsis of long-duration videos according to the users' query. We have tracked moving objects and adopted deep learning to classify the objects into known categories (e.g., car, bike, and pedestrians). We then identify regions in the surveillance scene with the help of unsupervised clustering. Each tube (spatiotemporal object trajectory) is represented by the source and the destination. In the final stage, we take a query from the user and generate the synopsis video by smoothly blending the appropriate tubes over the background frame through energy minimization. The proposed method has been evaluated on two publicly available datasets and our own surveillance datasets. We have compared the method with popular state-of-the-art techniques. The experiments reveal that the proposed method is superior to the existing techniques and it produces visually seamless video synopsis.
Secure and transparent audit logs with BlockAudit

Audit logs serve as a critical component in enterprise business systems and are used for auditing, storing, and tracking changes made to the data. However, audit logs are vulnerable to a series of attacks enabling adversaries to tamper data and corresponding audit logs without getting detected. Among them, two well-known attacks are “the physical access attack,” which exploits root privileges, and “the remote vulnerability attack,” which compromises known vulnerabilities in database systems. In this paper, we present BlockAudit: a scalable and tamper-proof system that leverages the design properties of audit logs and security guarantees of blockchain to enable secure and trustworthy audit logs. Towards that, we construct the design schema of BlockAudit and outline its functional and operational procedures. We implement our design on a custom-built Practical Byzantine Fault Tolerance (PBFT) blockchain system and evaluate the performance in terms of latency, network size, payload size, and transaction rate. Our results show that conventional audit logs can seamlessly transition into BlockAudit to achieve higher security and defend against the known attacks on audit logs.
MPPDS: Multilevel Privacy-Preserving Data Sharing in a Collaborative eHealth System

With the development of smart healthcare services, there is a growing demand for healthcare environments in which a patient can be collaboratively treated by multiple healthcare providers. To enable such collaborative treatments, it is essential to have a collaborative eHealth system that is capable of storing and managing large-scale health data generated by multiple healthcare providers as well as patients themselves in one place. However, this system may raise serious privacy concerns because of unauthorized access to sensitive health data of a data owner by other users. Usually, such privacy concerns can be resolved using attribute-based encryption (ABE), which provides fine-grained data access control mechanisms. Through ABE, in which access control is expressed by a set of attributes, the sensitive data of a data owner can be selectively shared with a set of data users by encrypting it with a set of attributes. However, with the growing popularity of smart healthcare services, which are enabled by analyzing large-scale health data collected from diverse populations, a binary access control policy akin to this is not sufficient for supporting the ever-growing request for sharing health data. Hence, in this paper, we develop a novel collaborative eHealth system that supports Multilevel Privacy-Preserving Data Sharing, MPPDS. In MPPDS, a data owner is able to share his or her health data with various data users within a collaborative eHealth system, under different levels of privacy protections. Specifically, our approach leverages local differential privacy to preserve the privacy of data owners in the process of data sharing and relies on ABE to support multilevel privacy guarantee mechanisms depending on the degree of trust between data owners and data users. We also present a method to effectively estimate aggregate statistics based on the collection of health data shared by different data owners under MPPDS. Experimental results with real and synthetic data sets demonstrate that the proposed MPPDS scheme enables more trusted data users to compute more accurate aggregate statistics than less trusted data users in a collaborative eHealth system.
A personal data store approach for recommender systems: enhancing privacy without sacrificing accuracy

Recommender systems have become extremely common in recent years, and are applied in a variety of domains. Existing recommender systems exhibit two major limitations: (1) Privacy - each service provider holds a database that contains information about all of its users; and (2) Partial view - when recommending to users, each such service can rely only on data that were collected by the service itself. The Open Personal Data Store (openPDS) architecture was recently suggested for storing personal data in a privacy preserving way. Inspired by openPDS, we suggest a novel architecture for recommender systems that overcomes the two limitations mentioned above. The suggested architecture allows the recommender system to utilize rich data collected about the user (possibly through other services) to produce more accurate recommendations, while allowing its users to manage and gain control over their own data. We evaluate the suggested architecture on two different use cases: movies and web browsing, and compare its performance with that of a popular non-privacy-aware collaborative-filtering algorithm. We find that in comparison to the alternative approach, our approach is able to enhance privacy significantly without sacrificing the accuracy level of the recommendations (and in some cases providing even higher level of accuracy).
Personalized Estimation of Engagement From Videos Using Active Learning With Deep Reinforcement Learning

Perceiving users' engagement accurately is important for technologies that need to respond to learners in a natural and intelligent way. In this paper, we address the problem of automated estimation of engagement from videos of child-robot interactions recorded in unconstrained environments (kindergartens). This is challenging due to diverse and person-specific styles of engagement expressions through facial and body gestures, as well as because of illumination changes, partial occlusion, and a changing background in the classroom as each child is active. To tackle these difficult challenges, we propose a novel deep reinforcement learning architecture for active learning and estimation of engagement from video data. The key to our approach is the learning of a personalized policy that enables the model to decide whether to estimate the child's engagement level (low, medium, high) or, when uncertain, to query a human for a video label. Queried videos are labeled by a human expert in an offline manner, and used to personalize the policy and engagement classifier to a target child over time. We show on a database of 43 children involved in robot-assisted learning activities (8 sessions over 3 months), that this combined human-AI approach can easily adapt its interpretations of engagement to the target child using only a handful of labeled videos, while being robust to the many complex influences on the data. The results show large improvements over a non-personalized approach and over traditional active learning methods.
Modeling and Planning Manipulation in Dynamic Environments

In this paper we propose a new model for sequential manipulation tasks that also considers robot dynamics and time-variant environments. From this model we automatically derive constraint-based controllers and use them as steering functions in a kinodynamic manipulation planner. The resulting plan is not a trajectory but a sequence of controllers that react online to disturbances. We validated our approach in simulation and on a real robot. In the experiments our approach plans and executes dual-robot manipulation tasks with online collision avoidance and reactions to estimates of object poses.
Leakage Certification Revisited: Bounding Model Errors in Side-Channel Security Evaluations

Leakage certification aims at guaranteeing that the statistical models used in side-channel security evaluations are close to the true statistical distribution of the leakages, hence can be used to approximate a worst-case security level. Previous works in this direction were only qualitative: for a given amount of measurements available to an evaluation laboratory, they rated a model as “good enough” if the model assumption errors (i.e., the errors due to an incorrect choice of model family) were small with respect to the model estimation errors. We revisit this problem by providing the first quantitative tools for leakage certification. For this purpose, we provide bounds for the (unknown) Mutual Information metric that corresponds to the true statistical distribution of the leakages based on two easy-to-compute information theoretic quantities: the Perceived Information, which is the amount of information that can be extracted from a leaking device thanks to an estimated statistical model, possibly biased due to estimation and assumption errors, and the Hypothetical Information, which is the amount of information that would be extracted from an hypothetical device exactly following the model distribution. This positive outcome derives from the observation that while the estimation of the Mutual Information is in general a hard problem (i.e., estimators are biased and their convergence is distribution-dependent), it is significantly simplified in the case of statistical inference attacks where a target random variable (e.g., a key in a cryptographic setting) has a constant (e.g., uniform) probability. Our results therefore provide a general and principled path to bound the worst-case security level of an implementation. They also significantly speed up the evaluation of any profiled side-channel attack, since they imply that the estimation of the Perceived Information, which embeds an expensive cross-validation step, can be bounded by the computation of a cheaper Hypothetical Information, for any estimated statistical model.
The Market Generator

We propose to use a special type of generative neural networks - a Restricted Boltzmann Machine (RBM) - to build a powerful generator of synthetic market data that can replicate the probability distribution of the original market data. An RBM constructed with stochastic binary activation units in both the hidden and the visible layers (Bernoulli RBM) can learn complex dependence structures while avoiding overfitting. In this paper we consider an efficient data transformation and sampling approach that allows us to operate Bernoulli RBM on real-valued data sets and control the degree of autocorrelation and non-stationarity in the generated time series.
A new internal index based on density core for clustering validation

Clustering validation which is applied to the evaluation of clustering results has been recognized as one of the vital issues for clustering application. Density core, a set of connected maximum local density peaks, can approximately represent the structure of a cluster without being affected by noises. Therefore, a density-core-based clustering validation index (DCVI) with minimum spanning tree (MST) is proposed to solve the problem that noises and arbitrary shapes have great influence on the performance of widely used internal validation measures. As for data sets containing arbitrarily shaped clusters with noises, DCVI can effectively obtain the optimal number of clusters. Moreover, experimental results of both synthetic and real data sets indicate that DCVI outperforms most existing measures.
A large-scale empirical study of compiler errors in continuous integration

Continuous Integration (CI) is a widely-used software development practice to reduce risks. CI builds often break, and a large amount of efforts are put into troubleshooting broken builds. Despite that compiler errors have been recognized as one of the most frequent types of build failures, little is known about the common types, fix efforts and fix patterns of compiler errors that occur in CI builds of open-source projects. To fill such a gap, we present a large-scale empirical study on 6,854,271 CI builds from 3,799 open-source Java projects hosted on GitHub. Using the build data, we measured the frequency of broken builds caused by compiler errors, investigated the ten most common compiler error types, and reported their fix time. We manually analyzed 325 broken builds to summarize fix patterns of the ten most common compiler error types. Our findings help to characterize and understand compiler errors during CI and provide practical implications to developers, tool builders and researchers.
Image classification toward breast cancer using deeply-learned quality features

Image classification plays an important role in computer vision and its applications, such as scene categorization, image retrieval. Convolutional neural network based methods have shown competitive performance in image classification, which aims to exploit deep feature of training images. In this paper, based on CNN methods and image quality assessment (IQA) algorithms, we propose a novel method for medical application, that is breast cancer classification. First, we leverage CNN architecture to calculate the number of pixels in the lesions, where maximum pooling layers are used. Then, large density of pixel regions will be assigned with large quality scores, which reflect more texture and grayscale features. Finally, we construct a multi-SVM based image kernel using obtained quality scores to achieve breast cancer classification. Experimental results show our proposed method outperforms single recognition based image classification methods such as pixel grayscale or gradient.
Text Presentation for Augmented Reality Applications in Dual-Task Situations

We investigate how reading text in augmented reality (AR) glasses and the simultaneous execution of three real-world tasks interfere with each other. The three tasks are a visual stimulus-response task (VSRT), a simple walking task and a walking obstacle course. Also, we investigate the effects of different AR text positions on primary task and reading performance as well as subjective preference. We propose a novel out of sight body-locked text placement for AR text presentation to be used in dual-task situations and compare it to head-locked text placement, each in two heights. AR reading affected performance in all tasks and reading speed was affected in all dual-task conditions. Participants subjectively preferred the body-locked text presentation, while objective measures do not reflect that preference. Differences between the tasks and several interaction effects between task and AR text placement demonstrate the necessity to carefully consider the context of use when designing AR reading UIs. The presented study with 12 participants provides insights into the effects of AR glasses usage in dual-task situations and several design recommendations are derived from the results.
BIKER: a tool for Bi-information source based API method recommendation

Application Programming Interfaces (APIs) in software libraries play an important role in modern software development. Although most libraries provide API documentation as a reference, developers may find it difficult to directly search for appropriate APIs in documentation using the natural language description of the programming tasks. We call such phenomenon as knowledge gap, which refers to the fact that API documentation mainly describes API functionality and structure but lacks other types of information like concepts and purposes. In this paper, we propose a Java API recommendation tool named BIKER (Bi-Information source based KnowledgE Recommendation) to bridge the knowledge gap. We implement BIKER as a search engine website. Given a query in natural language, instead of directly searching API documentation, BIKER first searches for similar API-related questions on Stack Overflow to extract candidate APIs. Then, BIKER ranks them by considering the query's similarity with both Stack Overflow posts and API documentation. Finally, to help developers better understand why each API is recommended and how to use them in practice, BIKER summarizes and presents supplementary information (e.g., API description, code examples in Stack Overflow posts) for each recommended API. Our quantitative evaluation and user study demonstrate that BIKER can help developers find appropriate APIs more efficiently and precisely.
A Coupled Variable Input LCG Method and its VLSI Architecture for Pseudorandom Bit Generation

The dual-coupled-linear congruential generator (LCG) (dual-CLCG) is a secure pseudorandom bit gene-rator (PRBG) method among various linear feedback shift register (LFSR), LCG, and chaotic-based PRBG methods for generating a pseudorandom bit sequence. The hardware implementation of this method has a bottleneck due to the involvement of inequality equations. Initially, a direct architectural mapping of the dual-CLCG method is performed. Since two inequality equations are involved for coupling, it generates pseudorandom bit at unequal interval of time that leads to large variation in output latency. In addition, it consumes a large area and fails to achieve the maximal period. Hence, to overcome the aforesaid drawbacks, a new efficient PRBG method, i.e., “coupled-variable input LCG (CVLCG),” and its architecture are proposed. The novelty of the proposed method is the coupling of two newly formed variable input LCGs that generates pseudorandom bit at every uniform clock rate, attains maximum length sequence, and reduces one comparator area as compared to the dual-CLCG architecture. The proposed architecture is implemented using Verilog-HDL and prototyped on the commercially available field-programmable gate array (FPGA) device. Furthermore, the sequences are captured through the logic analyzer and evaluated for randomness using the National Institute of Standard and Technology (NIST) standard test tool. The experimental result reports that the proposed PRBG method passes all the randomness tests with a high degree of consistency.
A UK Case Study on Cybersecurity Education and Accreditation

This paper presents a national case study-based analysis of the numerous dimensions to cybersecurity education and how they are prioritised, implemented and accredited; from understanding the interaction of hardware and software, moving from theory to practice (and vice versa), to human factors, policy and politics (as well as various other important facets).A multitude of model curricula and recommendations have been presented and discussed in international fora in recent years, with varying levels of impact on education, policy and practice.This paper address three key questions: i) what is taught and what should be taught for cybersecurity to general computer science students; ii) should cybersecurity be taught stand-alone or in an integrated manner to general computer science students; and iii) can accreditation by national professional, statutory and regulatory bodies enhance the provision of cybersecurity within a body's jurisdiction?Evaluating how cybersecurity is taught in all aspects of computer science is clearly a task of considerable size, one that is beyond the scope of this paper.Instead a case study-based research approach -primarily focusing on the UK -has been adopted to evaluate the evidence of the teaching of cybersecurity within general computer science to university-level students.Thus, in the context of widespread international computer science/engineering curriculum reform, what does this need to embed cybersecurity knowledge and skills mean more generally for institutions and educators, and how can we teach this subject more effectively?Through this UK case study, and by contrasting with related initiatives in the US, we demonstrate the positive effect that national accreditation requirements can have, and offer some recommendations both for future research and curriculum developments.