Reviewerly/exHarmony huggingface dataset:
Enhancing Security in the Industrial IoT Sector using Quantum Computing

The development of edge computing and machine learning technologies have led to the growth of Industrial IoT systems. Autonomous decision making and smart manufacturing are flourishing in the current age of Industry 4.0. By providing more compute power to edge devices and connecting them to the internet, the so-called Cyber Physical Systems are prone to security threats like never before. Security in the current industry is based on cryptographic techniques that use pseudorandom number keys. Keys generated by a pseudo-random number generator pose a security threat as they can be predicted by a malicious third party. In this work, we propose a secure Industrial IoT Architecture that makes use of true random numbers generated by a quantum random number generator (QRNG). CITRIOT's FireConnect IoT node is used to show the proof of concept in a quantum-safe network where the random keys are generated by a cloud based quantum device. We provide an implementation of QRNG on both real quantum computer and quantum simulator. Then, we compare the results with pseudorandom numbers generated by a classical computer.
Multi-modal Code Summarization Fusing Local API Dependency Graph and AST

Automatically obtaining descriptions of the functions of code snippets in natural language, i.e., code summarization, is an important issue in software engineering. In recent years, abstract syntax tree (AST)-based code summarization models for modelling syntactic structures have continued to emerge. In addition to syntactics, the semantics of the code are gradually gaining attention. In this paper, we propose a code summarization approach that incorporates local-ADG and AST, called GTsum. In particular, we introduce a novel local-ADG-based approach that can effectively filter out irrelevant semantics in the modelling of the semantic structure of code. GTsum learns semantics and syntactics in the local-ADG and AST through graph convolutional networks (GCNs) and then fuses them using Transformer. We evaluate our model on two Java language datasets with several metrics. The results demonstrate that our model achieves state-of-the-art performance compared to the existing models.
Towards a Secure Integrated Heterogeneous Platform via Cooperative CPU/GPU Encryption

Nowadays, emerging integrated heterogeneous platforms play major roles to host autonomous systems. However, the security issue that comes with such heterogeneous architectures has not been thoroughly explored and imposes great threats and vulnerabilities to these systems. We set out to explore the security issues for the heterogeneous architectures and the corresponding mitigation mechanisms. We investigate the side-channel timing attack in a modern integrated CPU/GPU platform and propose a CPU/GPU co-encryption mechanism CoENC to mitigate the timing attack to provide a secure platform for autonomous systems. Evaluations demonstrate CoENC can effectively enhance the security 29~44 times compared to the baseline with an extra 14%~31% latency overhead.
Accelerating FPGA-Implementations for Mobile Medical Devices with high-level AI libraries: an Object Detection Model for Colorectal Polyp Images

Colorectal polyps are an important cause of colon cancer and can be effectively detected by colonoscopy or Wireless Capsule Endoscopy (WCE). Up to now, many studies have applied Deep Learning (DL) to polyp detection and have obtained promising results, but all the algorithms are run on GPUs. They cannot be transferred to the fast-growing segment of mobile medical devices which require low power uptake. To bridge this gap, we designed a polyp detection algorithm for a Field Programmable Gate Array (FPGA). We present the detailed procedure of Deep Neural Network (DNN) design and the FPGA implementation and show that the FPGA-based polyp detection outperforms the GPU-based one in terms of energy efficiency and lower latency. We thus demonstrate the feasibility of an application scenario for energy-efficient mobile medical devices without highly specialized FPGA skills.
2-Phase Adiabatic Logic for Low-Energy and CPA-Resistant Implantable Medical Devices

Designing a low-energy and secure lightweight cryptographic coprocessor is the primary design constraint for modern wireless Implantable Medical Devices (IMDs). The lightweight cryptographic ciphers are the preferred cryptographic solution for low-energy encryption. This article proposes 2-SPGAL, the 2-phase sinusoidal clocking implementation of Symmetric Pass Gate Adiabatic Logic (SPGAL) for IMDs. The proposed 2-SPGAL is energy-efficient and secure against the Correlation Power Analysis (CPA) attack. The proposed 2-SPGAL was evaluated with the integration of synchronous resonant Power Clock Generators (PCGs): (i) 2N2P-PCG, and (ii) 2N-PCG. The case study implementation of one round of PRESENT-80 encryption using 2-SPGAL, with 2N2P-PCG integrated into the design, shows an average of 47.50% of energy saving compared to its CMOS counterpart, over the frequency range of 50 kHz to 250 kHz. The same 2-SPGAL based case study, with 2N-PCG integrated into the design, shows 51.18% of an average energy saving compared to its CMOS counterpart, over 50 kHz to 250 kHz. Further, the 2-SPGAL based PRESENT-80 one round shows an average energy saving of 16.62% and 28.90% respectively for 2N2P-PCG and 2N-PCG integrated into the design compared to existing 2-phase adiabatic logic called 2-EE-SPFAL. We also subjected PRESENT-80 design of 2-SPGAL and CMOS against CPA attack. The 2-SPGAL, with 2N2P-PCG and 2N-PCG, integrated into one round of PRESENT-80 design protects the encryption key. However, the encryption key was successfully revealed in one round of PRESENT-80 design using CMOS logic. Therefore, the proposed 2-SPGAL logic can be useful to design energy-efficient and CPA resilient Implantable Medical Devices (IMDs).
On the Use of Evolutionary Coupling for Software Architecture Recovery

Software architecture documentation can be partially obtained automatically by means of software architecture recovery tools. These tools mainly cluster software modules to provide a high level structural organization of these modules. They use dependency graphs as input. These graphs reflect various types of coupling among software modules. In this paper, we present an empirical evaluation of using evolutionary coupling as a complementary source of information for software architecture recovery. We use 3 open source projects as subject systems. We derive inter-module dependencies for these systems based on various levels of evolutionary coupling among their modules. We investigate the accuracy of software architecture recovery when input dependency graphs are extended with these additional dependencies. Results show that involving evolutionary coupling in the process can increase the accuracy of architecture recovery by up to 40%.
Generating Active Explicable Plans in Human-Robot Teaming

Intelligent robots are redefining a multitude of critical domains but are still far from being fully capable of assisting human peers in day-to-day tasks. An important requirement of collaboration is for each teammate to maintain and respect an understanding of the others’ expectations of itself. Lack of which may lead to serious issues such as loose coordination between teammates, reduced situation awareness, and ultimately teaming failures. Hence, it is important for robots to behave explicably by meeting the human’s expectations. One of the challenges here is that the expectations of the human are often hidden and can change dynamically as the human interacts with the robot. However, existing approaches to generating explicable plans often assume that the human’s expectations are known and static. In this paper, we propose the idea of active explicable planning to relax this assumption. We apply a Bayesian approach to model and predict dynamic human belief and expectations to make explicable planning more anticipatory. We hypothesize that active explicable plans can be more efficient and explicable at the same time, when compared to explicable plans generated by the existing methods. In our experimental evaluation, we verify that our approach generates more efficient explicable plans while successfully capturing the dynamic belief change of the human teammate.
MAKE: A matrix action key exchange

Abstract We offer a public key exchange protocol based on a semidirect product of two cyclic (semi)groups of matrices over <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi mathvariant="double-struck">Z</m:mi> </m:mrow> <m:mrow> <m:mi>p</m:mi> </m:mrow> </m:msub> </m:math> {{\mathbb{Z}}}_{p} . One of the (semi)groups is additive, and the other one is multiplicative. This allows us to take advantage of both operations on matrices to diffuse information. We note that in our protocol, no power of any matrix or of any element of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi mathvariant="double-struck">Z</m:mi> </m:mrow> <m:mrow> <m:mi>p</m:mi> </m:mrow> </m:msub> </m:math> {{\mathbb{Z}}}_{p} is ever exposed, so standard classical attacks on Diffie–Hellman-like protocols are not applicable.
Active Learning for the Subgraph Matching Problem

The subgraph matching problem arises in a number of modern machine learning applications including segmented images and meshes of 3D objects for pattern recognition, bio-chemical reactions and security applications. This graph-based problem can have a very large and complex solution space especially when the world graph has many more nodes and edges than the template. In a real use-case scenario, analysts may need to query additional information about template nodes or world nodes to reduce the problem size and the solution space. Currently, this query process is done by hand, based on the personal experience of analysts. By analogy to the well-known active learning problem in machine learning classification problems, we present a machine-based active learning problem for the subgraph match problem in which the machine suggests optimal template target nodes that would be most likely to reduce the solution space when it is otherwise overly large and complex. The humans in the loop can then include additional information about those target nodes. We present some case studies for both synthetic and real world datasets for multichannel subgraph matching.
A Conceptual Framework for Blockchain-based Intelligent Healthcare Data Management

Blockchain is a distributed ledger technology, frequently used in the creation of new digital currencies. The rapid development of blockchain technology has resulted in numerous new successes in industry and academia. This architecture is now widely used in e-finance as a widely accepted electronic money. It possesses critical characteristics such as immutability, decentralisation, and transparency, which may aid in the resolution of major healthcare challenges. Highlighted issues are like, incomplete treatment records, restricted access to patients’ own health records and so on. A blockchain architecture stores multiple copies of a single piece of data in various locations and on various devices. Hence, even if one copy of data is damaged or tampered, other copies remain immutable. The link between the blocks prevents any of the information from being changed or a new block from being placed between two existing blocks. A healthcare record management system based on blockchain technology is proposed in this paper. The proposed model is a multilayer architecture with different components representing various entities related to the healthcare system. Patients, doctors, clinics or hospitals, medical records, and so on are examples of entities. Effective sharing of medical data, ensuring Authentication and Authorization, is one of the most important aspects of this research.
Logic of informal provability with truth values

Abstract Classical logic of formal provability includes Löb’s theorem, but not reflection. In contrast, intuitions about the inferential behavior of informal provability (in informal mathematics) seem to invalidate Löb’s theorem and validate reflection (after all, the intuition is, whatever mathematicians prove holds!). We employ a non-deterministic many-valued semantics and develop a modal logic T-BAT of an informal provability operator, which indeed does validate reflection and invalidates Löb’s theorem. We study its properties and its relation to known provability-related paradoxical arguments. We also argue that T-BAT is a fairly sensible candidate for a formal logic of informal provability.
Evaluation of Machine Learning Algorithms for Multiclass Classification of Voice Calls from Power Systems Operations

The Brazilian electric system is operated by a nonprofit institution called the National Electric System Operator (ONS) which coordinates and controls the operation of energy generation and transmission. For this, the main form of operation occurs through verbal communication between the operators of the system. All telephone calls made between operators are recorded and stored on a server. In this sense, from the transcription and labeling of the audios, it is possible to use Natural Language Processing (NLP) and Machine Learning (ML) techniques to classify verbal communication in the operation. The Python programming language will be used together with a scikit-learn library to perform a comparison of multiclass classifiers, Complement Naive Bayes, Linear Support Vector Classification, Stochastic Gradient Descent Classifier, K-Nearest Neighbors Classifier, Multi-Layer Perceptron Classifier and Random Forest Classifier by tunning the hyperparameters and applying evaluation metrics for classification models. As result the model with best performance is the Multi-Layer Perceptron, reaching up to 85&#x0025; of accuracy and F1 weighted. This work contributes to generation of products that assist the operation of this sector, it can produce performance indicators and even automatization of tasks and actions.
Integrated Single Shot Multi-Box Detector and Efficient Pre-Trained Deep Convolutional Neural Network for Partially Occluded Face Recognition System

Partial occlusion is a key issue in face recognition as it decreases the recognition accuracy. As a result, current face recognition systems are limited to operate under constrained environments. To resolve the partial occlusion problem, we propose a system that adopts the convolutional neural network but with a pre-trained model for robust face recognition and facial feature extraction. The model improves the accuracy of partially occluded face recognition. Moreover, the face detection network utilizes the feature pyramids to reduce the number of network parameters and achieve scale invariance. The image context module is also incorporated to increase the receptive field, as it effectively improves the detection accuracy and reduces memory usage. Experiment results show that the proposed method performs better than existing state-of-the-art methods for the detection and recognition of occluded faces.
Cherry: A Distributed Task-Aware Shuffle Service for Serverless Analytics

While there has been a lot of effort in recent years in optimising Big Data systems like Apache Spark and Hadoop, the all-to-all transfer of data between a MapReduce computation step, i.e., the shuffle data mechanism between cluster nodes remains always a serious bottleneck. In this work, we present Cherry, an open-source distributed task-aware Caching sHuffle sErvice for seRveRless analYtics. Our thorough experiments on a cloud testbed using realistic and synthetic workloads showcase that Cherry can achieve an almost 23% to 39% reduction in completion of the reduce stage with small shuffle block sizes, a 10% reduction in execution time on real workloads, while it can efficiently handle Spark execution failures with a constant task time re-computation overhead compared to existing approaches.
Towards leveraging conversational agents for instructors and learners to find and access learning resources

Learning material and resources are a necessary input for educational courses and the process of academic knowledge-gain. In this paper, we outline a proposal for a cloud-based tool to find and access learning resources via a conversational agent. As the literature shows, chatbots are a suitable tool for content discovery and provide a low-entry utility, as well as showing previous implementation projects in the past. As the evolution of the web progressed, more media-types, especially videos for learning emerged. The primary goal of our proposed architecture is combining one search query from the user to trigger multiple crawlers and discoverability ‘agents’ to shorten search time and support the learning experience. This will be especially useful for users with less technical knowledge and in time-constraint searching scenarios. As a work-in-progress, the proposed implementation will be part of a undergraduate capstone project following a design-thinking engineering process.
The Applications of Blockchains in Addressing the Integration and Security of IoT Systems: A Survey

The Internet of Things (IoT) has already changed our daily lives by integrating smart devices together towards delivering high quality services to its clients. These devices when integrated together form a network through which massive amount of data can be produced, transferred, and shared. A critical concern is the security and integrity of such a complex platform to ensure the sustainability and reliability of these IoT-based systems. Blockchain is an emerging technology that has demonstrated its unique features and capabilities for different problems and application domains including IoT-based systems. This survey paper reviews the adaptation of Blockchain in the context of IoT to represent how this technology is capable of addressing the integration and security problems of devices connected to IoT systems. The innovation of this survey is that we present a survey based upon the integration approaches and security issues of IoT data and discuss the role of Blockchain in connection with these issues.
Quantum Key Distribution Polygon

The main aim of this article is to describe the basics of quantum key distribution (QKD) and its possible applications in the near future. A connection to a conventional aggregated network is modeled using the VPIphotonics Design Suite. Simulations are performed based on ID Quantique (IDQ) devices called Clavis <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and their coherent one-way (COW) protocol.
Domain adaptation based on rough adjoint inconsistency and optimal transport for identifying autistic patients

Computer aided diagnosis technology has been widely used to diagnose autism spectrum disorder (ASD) from neural images. The performance of the model usually depends largely on a sufficient number of training samples that reflect the real sample distribution. Due to the lack of labelled neural images data, multisite data are often pooled together to expand the sample size. However, the heterogeneity among sites will inevitably lead to a decline in the generalization of models. To solve this problem, we propose a multisource unsupervised domain adaptation method using rough adjoint inconsistency and optimal transport.First, we define the concept of rough adjoint inconsistency and propose a double quantization method based on rough adjoint inconsistency and Dempster-Shafer (D-S) evidence theory to estimate the weight coefficient of each source domain to accurately describe the importance of each source domain to the target domain. Second, using optimal transport theory, we weaken the data distribution differences between domains and solve the problem of class imbalance by adjusting the sampling weights among classes.The ASD recognition accuracy of the proposed method is improved on all eight tasks, which are 70.67%, 64.86%, 62.50%, 70.80%, 73.08%, 71.19%, 75.41% and 75.76%, respectively. Our proposed model achieves superior performance compared to traditional machine learning methods and other recently proposed deep learning model.Our method demonstrates that the fusion of rough adjoint inconsistency and optimal transport can be a powerful tool for identifying ASD and quantifying the correlations between domains.
Dissecting traffic fingerprinting CNNs with filter activations

HTTPS encrypted traffic flows leak information on underlying contents through various statistical properties such as packet lengths and timing, enabling traffic fingerprinting attacks. Recent traffic fingerprinting attacks leveraged Convolutional Neural Networks (CNNs) to record very high accuracies undermining state-of-the-art defenses. In this paper, we analyze such CNNs to understand their inner workings which helps in building efficient traffic classifiers and effective defenses. First, we experiment on three datasets and show that website fingerprinting CNNs focus majorly on transitions between uploads and downloads in trace fronts while video fingerprinting CNNs focus more on finer shapes of periodic bursts. Next, we show that traffic fingerprinting CNNs exhibit transfer-learning capabilities allowing identification of new websites with fewer data. We also demonstrate how traffic fingerprinting CNNs outperform Recurrent Neural Networks (RNNs) due to their resilience to random shifts in data, which is common in network traces. We further generalize these observations on other publicly available network traffic datasets. Leveraging our observations, we propose two new defenses against traffic fingerprinting. Our first defense FRONT-U, defends website visits by obfuscating transitions between uploads and downloads in trace fronts and provides similar privacy as the state-of-the-art defense FRONT, with half the data overhead. Our second defense STOMA, defends streaming traffic by obfuscating the finer sub-bursts within major bursts of a trace using only the next few seconds as opposed to using the entire trace as in the state-of-the-art.
Multi-Objective Optimization and Decision-Making in Context Steering

This work concentrates on decision-making for autonomous movement of agents to simultaneously optimize several objectives which occur in their local environment. Such behavior can be achieved with steering algorithms, which have originally been designed for moving numerous agents simultaneously where occasional uncertainties are not noticeable by players. Nevertheless, concentrating on single individuals can reveal major flaws in their movement patterns such as oscillatory movement. For avoiding such problems, game makers are forced to develop higher-level abstractions for handling game-relevant special cases. Thus, eliminating the initial benefit of steering behaviors to be highly modular, lightweight, and controllable. This work enhances the context steering approach by Fray, which introduced discretized contextual information in the aggregation of a steering behavior's components. We combine this method with multi-criteria decision-making for controlling the agent's velocity direction and magnitude. The resulting approach is tested based on selected scenarios which show that the resulting approach is well suited to improve the agent's smooth and natural movement. Based on our observations we propose suitable parameterizations of the designed method and discuss advantages and disadvantages of made enhancements.