Reviewerly/exHarmony huggingface dataset:
Backdoor Defense with Machine Unlearning

Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper, we propose BAERASER, a novel method that can erase the backdoor injected into the victim model through machine unlearning. Specifically, BAERASER mainly implements backdoor defense in two key steps. First, trigger pattern recovery is conducted to extract the trigger patterns infected by the victim model. Here, the trigger pattern recovery problem is equivalent to the one of extracting an unknown noise distribution from the victim model, which can be easily resolved by the entropy maximization based generative model. Subsequently, BAERASER leverages these recovered trigger patterns to reverse the backdoor injection procedure and induce the victim model to erase the polluted memories through a newly designed gradient ascent based machine unlearning method. Compared with the previous machine unlearning solutions, the proposed approach gets rid of the reliance on the full access to training data for retraining and shows higher effectiveness on backdoor erasing than existing fine-tuning or pruning methods. Moreover, experiments show that BAERASER can averagely lower the attack success rates of three kinds of state-of-the-art backdoor attacks by 99% on four benchmark datasets.
Cybersecurity Attacks on Software Logic and Error Handling Within AIS Implementations: A Systematic Testing of Resilience

To increase situational awareness of maritime vessels and other entities and to enable their exchange of various information, the International Maritime Organization mandated the use of the Automatic Identification System (AIS) in 2004.The AIS is a self-reporting system that uses the VHF radio link.However, any radio-based self-reporting system is prone to forgery, especially in situations where authentication of the message is not designed into the architecture.As AIS was designed in the 1990s when cyberattacks were in their infancy, it does not implement authentication or encryption; thus, it can be seen as fundamentally vulnerable against cyberattacks.This paper demonstrates and evaluates the impact of multiple cyberattacks on AIS via remote radio frequency (RF) links using transmission-enabled software-defined radio (SDR).Overall, we implemented and tested a total of 11 different tests/attacks on 19 AIS setups, using a controlled environment.The tested configurations were derived from heterogeneous platforms such as Windows, Android, generic receivers, and commercial transponders.Our aim is to enhance the early discovery of new vulnerabilities in AIS to effectively address AIS attacks in the nearest future.The results showed that approximately 89% of the setups were affected by Denial-of-Service (DoS) attacks at the AIS protocol level.Besides implementing some existing attack ideas (e.g., spoofing, DoS, and flooding), we showed some novel attack concepts in the AIS context such as a coordinated attack, overwhelming alerts, and logical vulnerabilities, all of which have the potential to cause software/system crashes in the worst-case scenarios.Moreover, an implementation/specification flaw related to the AIS preamble was identified during the experiments, which may affect the interoperability of different AIS devices.The error-handling system in AIS was also investigated.Unlike the aviation sector's Automatic Dependent Surveillance-Broadcast (ADS-B), the maritime sector's AIS does not effectively support any error correction method, which may contribute to RF pollution and less effective use of the overall system.The consistency of our results for a comprehensive range of hardware-software configurations indicated the reliability of our approach, test system, and evaluation results.
OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes

Ambient assisted living (AAL) makes it possible to build assistance for older adults according to the person’s context. Understanding the person’s context sometimes involves transforming one’s home into a smart home. Typically, this is carried out using nonintrusively distributed sensors and calm technologies. Older adults often have difficulty performing activities of daily living, such as taking medication, drinking coffee, watching television, using certain electronic devices, and dressing. This difficulty is even greater when these older adults suffer from cognitive impairments. Defining an assistance solution requires a multidisciplinary and iterative collaborative approach. It is necessary, therefore, to reason about the imperatives and solutions of this multidisciplinary collaboration (e.g., clinical), as well as the adaptation of technical constraints (e.g., technologies). A common approach to reasoning is to represent knowledge using logic-based formalisms, such as ontologies. However, there is not yet an established ontology that defines concepts such as multidisciplinary collaboration in successive stages of the assistance process. This article presents OntoDomus, an ontology that describes, at several levels, the semantic interactions between ambient assisted living, context awareness, smart home, and Internet of Things, based on multidisciplinarity. It revolves around two main notions: multidisciplinarity, based on specific sub-ontologies and the ambient feedback loop. OntoDomus combines SPARQL queries and OWL 2 models to improve the reusability of domain terminology, allowing stakeholders to represent their knowledge in different collaborative and adaptive situations. The ontological model is validated, first by its reuse in more specific works—specific to an aspect of ambient assistance. Second, it is validated by the structuring of ambient knowledge and inferences of the formalization in a case study that includes instances for a particular activity of daily living. It places the ambient feedback loop at the center of the ontology by focusing on highly expressive domain ontology formalisms with a low level of expressiveness between them.
ARPCNN: Auxiliary Review-Based Personalized Attentional CNN for Trustworthy Recommendation

Convolutional neural network (CNN)-based recommender systems are playing an increasingly significant role in the vigorous development of Industrial Internet of Things, and have made great contributions to analyzing and mining a large amount of data to provide various services for terminal users. However, as the lack of explainability in deep learning, users often have low trust in the system due to their incomprehension of recommendation results. In addition, recommender systems have been facing a serious sparsity problem, and relying only on sparse rating data to learn user preferences and similarities may face malicious recommendation attacks. The abovementioned problems have been hindering the further improvement of recommendation performance. Therefore, in order to effectively alleviate the sparsity problem and meanwhile enhance the trustworthiness, an auxiliary review-based personalized attentional CNN (ARPCNN) is proposed in this article. By applying the proposed personalized word-level attention mechanism and personalized review-level attention mechanism in parallel CNNs, critical words and informative reviews are given high attention weights. Moreover, a user auxiliary network is proposed, which regards the reviews written by kindred spirits who have a trust relationship with the user as auxiliary reviews, and effectively extracts the user’s auxiliary review features, thereby achieving more accurate user modeling to improve the recommendation performance. Extensive experiments are conducted on four real-world datasets, and the results show that the performance of the proposed model is better than that of baselines, which verifies the effectiveness of ARPCNN.
Low power residue number system using lookup table decomposition and finite state machine based post computation

In this paper, memory optimization and architectural level modifications are introduced for realizing the low power &lt;span lang="EN-US"&gt;residue number system (RNS) with improved flexibility for electroencephalograph (EEG) signal classification. The proposed RNS framework is intended to maximize the reconfigurability of RNS for high-performance finite impulse response (FIR) filter design. By replacing the existing power-hungry RAM-based reverse conversion model with a highly decomposed lookup table (LUT) model which can produce the results without using any post accumulation process. The reverse conversion block is modified with an appropriate functional unit to accommodate FIR convolution results. The proposed approach is established to develop and execute pre-calculated inverters for various module sets. Therefore, the proposed LUT-decomposition with RNS multiplication-based post-accumulation technology provides a high-performance FIR filter architecture that allows different frequency response configuration elements. Experimental results shows the superior performance of decomposing LUT-based direct reverse conversion over other existing reverse conversion techniques adopted for energy-efficient RNS FIR implementations. When compared with the conventional RNS FIR design with the proposed FSM based decomposed RNS FIR, the logic elements (LEs) were reduced by 4.57%, the frequency component is increased by 31.79%, number of LUTs is reduced by 42.85%, and the power dissipation was reduced by 13.83%.&lt;/span&gt;
A new color image encryption algorithm using multiple chaotic maps with the intersecting planes method

This paper proposes a new and efficient color image encryption algorithm based on multiple chaotic maps (Logistic map, Sine map, and Chebyshev map) and the intersecting planes method inside a cube. All maps were used to fill the three adjacent faces of the cube. These faces represent the three channels of the color image (red, green, and blue). The first phase of our method begins with extracting all the pixels from the original image and looking for the corresponding values of each pixel on the three faces of the cube. Then, we used a circular rotation operation based on the position of each pixel (row, column). This rotation prevents two identical pixels to have the same encrypted value. Afterward, we used the intersecting planes method with the corresponding face to encrypt the pixels. For the confusion part, we chose the 2D transformation Arnold Cat Map to shuffle all the pixels and change their positions according to the parameters calculated from all the pixels of the original image. We evaluated the performance of our algorithm against various differential and statistical attacks and based on multiple factors, such as histogram, entropy, correlation coefficient, UACI, NPCR, and PSNR. We compared the proposed method with several efficient methods in the field. The various results got by our method show its performance and reliability in terms of safety, precision, confidentiality, and robustness.
Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images

Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, such that the complex deformation field can be recovered incrementally. (iii) We also propose both the adaptive smoothing layer and the tissue-aware anti-folding constraint into the registration network to ensure the physiological plausibility of estimated deformations without degrading the registration accuracy. Experimental results, in comparison to the state-of-the-art registration methods, indicate that our proposed method achieves the highest registration accuracy while still preserving the smoothness of the deformation field. The implementation of our proposed registration network is available online https://github.com/Barnonewdm/ACTA-Reg-Net.
Deep Self-representative Concept Factorization Network for Representation Learning

In this paper, we technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features. To improve the representation and clustering abilities, DSCF-Net explicitly considers discovering hidden deep semantic features, enhancing the robustness properties of the deep factorization to noise and preserving the local manifold structures of deep features. Specifically, DSCF-Net integrates the robust deep concept factorization, deep self-expressive representation and adaptive locality preserving feature learning into a unified framework. To discover hidden deep representations, DSCF-Net designs a hierarchical factorization architecture using multiple layers of linear transformations, where the hierarchical representation is performed by formulating the problem as optimizing the basis concepts in each layer to improve the representation indirectly. DSCF-Net also improves robustness by subspace recovery for sparse error correction firstly and then performs deep factorization in the recovered visual subspace. To obtain localitypreserving representations, we also present an adaptive deep self-representative weighting strategy by using the coefficient matrix as adaptive weights to keep the locality of representations. Extensive results show that DSCF-Net delivers state-of-the-art performance on several public databases.
Human Activity Analysis using Machine Learning Classification Techniques

In recent times, smart phones are playing a vital role to recognize the human activities and became a well-known field of research. Detail overview of various research papers on human activity recognition are discussed in this paper. Artificial Intelligence(AI) models are developed to recognize the activity of the human from the provided UCI online storehouse. The data chosen is multivariate and we have applied various machine classification techniques Random Forest, kNN, Neural Network, Logistic Regression, Stochastic Gradient Descent and Naïve Bayes to analyse the human activity. Besides building AI models, the dimension of the dataset is reduced through feature selection process.Precision and Recall values were calculated and a Confusion Matrix for each model was made. Experiment results proved that the Neural Network and logistic regression provides better accuracy for human activity recognition compared to other classifiers such as k-nearest neighbor (KNN), SGD , Random Forest and Naïve Bayes though they take higher computational time and memory resources.
A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series

Early prediction of sepsis onset can notify clinicians to provide timely interventions to patients to improve their clinical outcomes.The key question motivating this work is: given a retrospective patient cohort consisting of multivariate clinical time series (e.g., vital signs and lab measurement) and patients' demographics, how to build a model to predict the onset of sepsis six hours earlier?To tackle this challenge, we first used a recurrent imputation for time series (RITS) approach to impute missing values in multivariate clinical time series.Second, we applied temporal convolutional networks (TCN) to the RITS-imputed data.Compared to other sequence prediction models, TCN can effectively control the size of sequence history.Third, when defining the loss function, we assigned custom timedependent weights to different types of errors.We achieved 9th place (team name = prna, utility score = 0.328) at the 2019 PhysioNet Computing in Cardiology Challenge, which evaluated our proposed model on a real-world sepsis patient cohort.At a follow-up 'hackathon' event, held by the challenge organizers, an improved version of our algorithm achieved 2nd place (utility score = 0.342).
A Flood Prediction System Developed Using Various Machine Learning Algorithms

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Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology

Abstract Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions. We compare the effectiveness of model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are compared. Our experiments focus on two domain shift scenarios: a shift to a different medical center and to an underrepresented subtype of cancer. Our results show that uncertainty estimates increase reliability by reducing a model’s sensitivity to classification threshold selection as well as by detecting between 70 and 90% of the mispredictions done by the model. Overall, the deep ensembles method achieved the best performance closely followed by TTA.
Specific features of universal competences of higher education in Russia in the context of competence-based education: conceptual analysis

Problem and goal. Amid the currently advancing (technological, social, techno-social) trends, prominent attention is drawn to skills and competences, which are universal for all spheres of activity – soft skills, key competences. They are known as universal competences in Russian higher education. The aim of the article is to pinpoint universal competences (UCs) and to analyze their specific features in the context of competence-based education. Materials and methods. The authors use methods of etymological, conceptual and comparative analysis combined with dialectical and diachronic approaches and discursive reflection. The study was based on competence-based education-related documents and projects of EU, OECD, UNESCO, the World Bank as well as on monographs and articles of top experts and research groups. Results. During the study, the term of universal competences was differentiated from the associated terms (soft skills, key competencies, generic competencies, and 21st century skills); the role of universal competences was identified as a balancer in higher education for equilibrating its two missions: Preparing students for successful career and helping them become well-rounded personalities. It has been found that the terminological confusion and the lack of consensus on many aspects of competence-based education have an ambivalent character. The idea of universal competences was identified as a Russian version of a tool (and the outcome) of the integration of two opposite intentions of the national educational policy: Blending in the mainstream of the competence-based approach and retaining its uniqueness. A contribution was made to expanding the conception of universalism as the foundation for competence-based education, including the one in Russia. Conclusion. We acknowledge that the concept of universal competences can make competence-based education more balanced in Russian environment or, on the contrary, it can aggravate its inherent imbalance.
DigiQ: A Scalable Digital Controller for Quantum Computers Using SFQ Logic

The control of cryogenic qubits in today&#x2019;s super-conducting quantum computer prototypes presents significant scalability challenges due to the massive costs of generating/routing the analog control signals that need to be sent from a classical controller at room temperature to the quantum chip inside the dilution refrigerator. Thus, researchers in industry and academia have focused on designing in-fridge classical controllers in order to mitigate these challenges. Due to the maturity of CMOS logic, many industrial efforts (Microsoft, Intel) have focused on Cryo-CMOS as a near-term solution to design in-fridge classical controllers. Meanwhile, Supercon-ducting Single Flux Quantum (SFQ) is an alternative, less mature classical logic family proposed for large-scale in-fridge controllers. SFQ logic has the potential to maximize scalability thanks to its ultra-high speed and very low power consumption. However, architecture design for SFQ logic poses challenges due to its unconventional pulse-driven nature and lack of dense memory and logic. Thus, research at the architecture level is essential to guide architects to design SFQ-based classical controllers for large-scale quantum machines.In this paper, we present DigiQ, the first system-level design of a Noisy Intermediate Scale Quantum (NISQ)-friendly SFQ-based classical controller. We perform a design space exploration of SFQ-based controllers and co-design the quantum gate decompositions and SFQ-based implementation of those decompositions to find an optimal SFQ-friendly design point that trades area and power for latency and control while ensuring good quantum algorithmic performance. Our co-design results in a single instruction, multiple data (SIMD) controller architecture, which has high scalability, but imposes new challenges on the calibration of control pulses. We present software-level solutions to address these challenges, which if unaddressed would degrade quantum circuit fidelity given the imperfections of qubit hardware.To validate and characterize DigiQ, we first implement it using hardware description languages and synthesize it using state-of-the-art/validated SFQ synthesis tools. Our synthesis results show that DigiQ can operate within the tight power and area budget of dilution refrigerators at &#x003E;42,000-qubit scales. Second, we confirm the effectiveness of DigiQ in running quantum algorithms by modeling the execution time and fidelity of a variety of NISQ applications. We hope that the promising results of this paper motivate experimentalists to further explore SFQ-based quantum controllers to realize large-scale quantum machines with maximized scalability.
Ensemble Knowledge Tracing: Modeling interactions in learning process

Knowledge Tracing (KT) aims to continuously estimate students’ evolving knowledge state during their learning process, which has attracted much research attention due to its potential for delivering personalized and optimal experiences to students in intelligent learning systems. The learning process is essentially the pairwise interactions of Students, Concepts, and Questions (S–C, S–Q, C–Q for short). Modeling all these interactions will improve the performance of KT. However, existing KT methods hardly exploit all the interactions in a single model. Specifically, Bayesian Knowledge Tracing (BKT) and most of its variants neglect C–Q; Deep Knowledge Tracing (DKT) and other deep neural network approaches mostly neglect S–Q and C–Q. We propose the Ensemble Knowledge Tracing (EnKT), which models all three types of interactions. The base model of EnKT is a hybrid of BKT and DKT. We also present an ensemble algorithm Recurrent Boosting (RB), which extends AdaBoost to deal with KT sequential data. Inspired by BKT, EnKT represents S–C and S–Q using learning and performance parameters, respectively. Besides, EnKT defines C–Q as the correlation complexity among the concepts involved in a question. Experiments show EnKT significantly outperforms state-of-the-art methods (by up to 6% in AUC in some cases) on four real-world benchmark datasets and illustrate better interpretability by several typical case studies.