Reviewerly/exHarmony huggingface dataset:
Two-Stage Alignments Framework for Unsupervised Domain Adaptation on Time Series Data

Unsupervised Domain Adaptation (UDA) aims to free models from labeled information of target domain by minimizing the discrepancy of distributions between different domains. Most existing methods are designed to learn domain-invariant features either by domain discrimination or by matching lower-order moments. However, these methods are not robust due to the limited representation of statistical characteristics for non-Gaussian distributions and thus fail in domain matching. In addition, they often focus on matching distributions while not considering class decision boundaries between domains. To address these issues, we propose a novel Two-Stage Alignments Framework (TSAF) for UAD, which not only performs arbitrary-order moment matching to approximately characterize complex non-Gaussian distributions, but also utilizes domain-specific decision boundaries to align the probabilistic outputs of classifiers. Moreover, the reconstruction-based task is introduced to enhance the representation of the inherent characteristics for specific distribution. Extensive experiments on three real-world time series datasets demonstrate that: 1) our model evidently outperforms many state-of-the-art domain adaptation methods in cross-domain classification tasks; 2) TSAF can learn domain-invariant features efficiently.
Federated Learning for Network Intrusion Detection in Ambient Assisted Living Environments

Given the Internet of Things rapid expansion and widespread adoption, it is of great concern to establish secure interaction between devices without worsening the quality of their performance.Using machine learning techniques has been shown to improve detecting anomalous behavior in these types of networks, but their implementation leads to poor performance and compromised privacy.To better address these shortcomings, federated learning is being introduced.It enables devices to collaboratively train and evaluate a shared model while keeping personal data on-site (e.g., smart homes, intensive care units, hospitals, etc.), thus minimizing the possibility of an attack and fostering real-time distribution of models and learning.The paper investigates the performance of federated learning in comparison to deep learning, with respect to network intrusion detection in ambient assisted living environments.The results demonstrate comparable performances of federated learning with deep learning, while achieving improved data privacy and security.THE INCREASE in sensors, cloud and big data analytics, as well as the need to automate and ease processes, is contributing to the fast-paced development of Internet of things (IoT) networks.IoT facilitates many diverse functions that are provided for the household, industry, infrastructure, transportation, by a massive number of unique devices from diverse manufacturers.It has also received attention from the medical community as a promising way for early diagnosis, prevention, treatment, and administration of drugs while the patients remain in the comfort of their own homes
Federated Learning Resource Optimization and Client Selection for Total Energy Minimization Under Outage, Latency, and Bandwidth Constraints With Partial or No CSI

We consider the problem of minimizing the total energy consumption due to the computation and communication tasks of federated learning (FL) under bandwidth and latency constraints.To avoid channel state information (CSI) feedback to the transmitter, we adopt outage probability as an additional constraint in the energy minimization problem.First, we define a feasibility metric based on the system design parameters to exclude slow clients (stragglers).Then, we propose a novel client selection algorithm, after excluding stragglers, based on dividing the remaining clients into clusters, where clients within the same cluster collaborate in a communication round to train their local models.For each communication round, one client cluster is selected in a round-robin fashion.Furthermore, we formulate and solve a resource allocation problem to optimize the transmit power, clock frequency, allocated bandwidth, and communication latency of clients within each cluster to minimize the total energy subject to total bandwidth, latency, and outage constraints.Moreover, we extend our FL design framework to the case of no CSI at both the client and server ends using differential transmission to eliminate CSI estimation pilot overhead and complexity at comparable total energy consumption and learning accuracy to coherent transmission.We test our proposed algorithms over MNIST and Fashion-MNIST datasets in iid and non-iid settings.Our proposed client selection algorithm reduces the number of participating clients per communication round by 41% compared to the baseline while maintaining the same learning accuracy.Moreover, our results demonstrate that increasing the number of receive antennas at the server from one to four can reduce the number of communication rounds required to reach a predetermined testing accuracy level by up to 53% for the Fashion-MNIST dataset.
Computational capabilities of a multicellular reservoir computing system

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions-computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.
The effect of variable labels on deep learning models trained to predict breast density

Purpose. High breast density is associated with reduced efficacy of mammographic screening and increased risk of developing breast cancer. Accurate and reliable automated density estimates can be used for direct risk prediction and passing density related information to further predictive models. Expert reader assessments of density show a strong relationship to cancer risk but also inter-reader variation. The effect of label variability on model performance is important when considering how to utilise automated methods for both research and clinical purposes.Methods. We utilise subsets of images with density labels from the same 13 readers and 12 reader pairs, and train a deep transfer learning model which is used to assess how label variability affects the mapping from representation to prediction. We then create two end-to-end models: one that is trained on averaged labels across the reader pairs and the second that is trained using individual reader scores, with a novel alteration to the objective function. The combination of these two end-to-end models allows us to investigate the effect of label variability on the model representation formed.Results. We show that the trained mappings from representations to labels are altered considerably by the variability of reader scores. Training on labels with distribution variation removed causes the Spearman rank correlation coefficients to rise from 0.751 ± 0.002 to either 0.815 ± 0.026 when averaging across readers or 0.844 ± 0.002 when averaging across images. However, when we train different models to investigate the representation effect we see little difference, with Spearman rank correlation coefficients of 0.846 ± 0.006 and 0.850 ± 0.006 showing no statistically significant difference in the quality of the model representation with regard to density prediction.Conclusions. We show that the mapping between representation and mammographic density prediction is significantly affected by label variability. However, the effect of the label variability on the model representation is limited.
Vision-Based Hybrid Detection For Pick And Place Application In Robotic Manipulators

Pick and place using cobots is a common application in industry. In a cyber-physical system, a smarter cobot with vision sensing can decrease the uncertainty in decision-making for acquiring the position of objects in a scene. In this paper, a UR5 cobot is used as a cobot to automatically detect objects on a tabletop utilizing a monocular wrist camera. An improved statistical method and error reduction approach is designed by formulation of a mathematical relation for estimating the position of the object (w.r.t to the manipulator coordinate system) using the object detected in the camera coordinate system. Firstly, correspondence between simulated world coordinates and image coordinates is used to make a relation. This is followed by an error reduction approach by capturing multiple images and gradually moving towards the target centre. The object location accuracy of 99.785% was achieved using the statistical method. The error is reduced up to 2mm which is compensated since the gripper is still able to pick up the object. Our proposed method can be used to accurately approach an object's location and can be used effectively in pick-and-place applications using robotic manipulators. Fruit sorting has been used as a sample application however the proposed method is generic and can be applied to any object.
Modular Component-Based Quantum Circuit Synthesis

In this article, we present a novel method for synthesizing quantum circuits from user-supplied components. Given input-output state vectors and component quantum gates, our synthesizer aims to construct a quantum circuit that implements the provided functionality in terms of the supplied component gates. To achieve this, we basically use an enumerative search with pruning. To accelerate the procedure, however, we perform the search and pruning at the module level; instead of simply enumerating candidate circuits by appending component gates in sequence, we stack modules, which are groups of gate operations. With this modular approach, we can effectively reduce the search space by directing the search in a way that bridges the gap between the current circuit and the input-output specification. Evaluation on 17 benchmark problems shows that our technique is highly effective at synthesizing quantum circuits. Our method successfully synthesized 16 out of 17 benchmark circuits in 96.6 seconds on average. On the other hand, the conventional, gate-level synthesis algorithm succeeded in 10 problems with an average time of 639.1 seconds. Our algorithm increased the speed of the baseline by 20.3x for the 10 problems commonly solved by both approaches.
ReGR: Relation-aware graph reasoning framework for video question answering

As one of the challenging cross-modal tasks, video question answering (VideoQA) aims to fully understand video content and answer relevant questions. The mainstream approach in current work involves extracting appearance and motion features to characterize videos separately, ignoring the interactions between them and with the question. Furthermore, some crucial semantic interaction details between visual objects are overlooked. In this paper, we propose a novel Relation-aware Graph Reasoning (ReGR) framework for video question answering, which first combines appearance–motion and location–semantic multiple interaction relations between visual objects. For the interaction between appearance and motion, we design the Appearance–Motion Block, which is question-guided to capture the interdependence between appearance and motion. For the interaction between location and semantics, we design the Location–Semantic Block, which utilizes the constructed Multi-Relation Graph Attention Network to capture the geometric position and semantic interaction between objects. Finally, the question-driven Multi-Visual Fusion captures more accurate multimodal representations. Extensive experiments on three benchmark datasets, TGIF-QA, MSVD-QA, and MSRVTT-QA, demonstrate the superiority of our proposed ReGR compared to the state-of-the-art methods.
Adversarial Robustness of Phishing Email Detection Models

Developing robust detection models against phishing emails has long been the main concern of the cyber defense community. Currently, public phishing/legitimate datasets lack adversarial email examples which keeps the detection models vulnerable. To address this problem, we developed an augmented phishing/legitimate email dataset, utilizing different adversarial text attack techniques. Next, the models were retrained with the adversarial dataset. Results showed that accuracy and F1 score of the models improved under subsequent attacks. In another experiment, synthetic phishing emails were generated using a fine-tuned GPT-2 model. The detection model was retrained with a newly formed synthetic dataset. Subsequently, we observed that the accuracy and robustness of the model did not improve significantly under black box attack methods. In the last experiment, we proposed a defensive technique to classify adversarial examples to their true labels using a K-Nearest Neighbor approach with 94% accuracy in our prediction.
Verus: Verifying Rust Programs using Linear Ghost Types

The Rust programming language provides a powerful type system that checks linearity and borrowing, allowing code to safely manipulate memory without garbage collection and making Rust ideal for developing low-level, high-assurance systems. For such systems, formal verification can be useful to prove functional correctness properties beyond type safety. This paper presents Verus, an SMT-based tool for formally verifying Rust programs. With Verus, programmers express proofs and specifications using the Rust language, allowing proofs to take advantage of Rust's linear types and borrow checking. We show how this allows proofs to manipulate linearly typed permissions that let Rust code safely manipulate memory, pointers, and concurrent resources. Verus organizes proofs and specifications using a novel mode system that distinguishes specifications, which are not checked for linearity and borrowing, from executable code and proofs, which are checked for linearity and borrowing. We formalize Verus' linearity, borrowing, and modes in a small lambda calculus, for which we prove type safety and termination of specifications and proofs. We demonstrate Verus on a series of examples, including pointer-manipulating code (an xor-based doubly linked list), code with interior mutability, and concurrent code.
How can we encourage students to manage their own learning?

The report investigates several methods of managing a student's educational activity. The educational discipline "Practicum of problem solving in informatics" has been chosen for students in their third year of study, future informatics teachers, in order to realize pedagogical conditions of computer-oriented management of students' educational activity. The major aim behind building the courseware was to move away from direct management and toward co-management, subsidiary management, and self-management. The information and communication educational environment is built on the Moodle learning management system platform. Moodle's Workshop components were critical in managing students' instructional engagement. Our educational observation and assessment results demonstrated the efficacy of the proposed technique. On the basis of the experimental results, it was also demonstrated that pupils lacked competency in time management and reflection.
A novel triangular stereo model for 3D reconstruction of uniaxial MEMS-based structured light system

Benefiting from the small size and low driven voltage of the Micro Electro Mechanical Systems (MEMS) mirror, MEMS-based structured light (SL) 3D reconstruction has recently attracted much attention. In this paper, we propose a novel triangular stereo model for uniaxial MEMS-based 3D reconstruction. Existing SL methods based on MEMS mirrors often require the construction of a look-up table(LUT), but the proposed method only requires 34 parameters for accurate reconstruction. By analyzing the anatomy of the MEMS mirror, we put forward a transition function that allows the MEMS mirror to be regarded as an inverse camera model. Then, the triangular stereo principle can be directly employed to calculate 3D information. Furthermore, we present a calibration method to obtain the parameters of our model, since MEMS mirrors can only perform unidirectional fringe projection. Extensive experiments are performed to verify the feasibility and accuracy of our proposed mathematical model and calibration method.
Contextualized medication event extraction with levitated markers

Automatic extraction of patient medication histories from free-text clinical notes can increase the amount of relevant information to clinicians for developing treatment plans. In addition to detecting medication events, clinical text mining systems must also be able to predict event context, such as negation, uncertainty, and time of occurrence, in order to construct accurate patient timelines. Towards this goal, we introduce Levitated Context Markers (LCMs), a novel transformer-based model for contextualized event extraction. LCMs are an adaptation of levitated markers -originally developed for relation extraction- that allow pretrained transformer models to utilize global input representations while also focusing on event-related subspans using a sparse attention mechanism. In addition to outperforming a strong baseline model on the Contextualized Medication Event Dataset, we show that LCMs' sparse attention can provide interpretable predictions by detecting relevant context cues in an unsupervised manner.
Breast mass characterization using sparse approximations of patch-sampled deep features

Diagnosis of breast cancer is often achieved through expert radiologist examination of medical images such as mammograms. Computer-aided diagnosis (CADx) methods can be useful tools in the medical field with applications such as aiding radiologists in making diagnosis decisions. However, such CADx systems require a sufficient amount of data to train on, in conjunction with efficient machine learning techniques. Our Spatially Localized Ensembles Sparse Analysis using Deep Features (DF-SLESA) machine learning model uses local information of features extracted from deep neural networks to learn and classify breast imaging patterns based on sparse approximations. We have also developed a new technique of patch sampling for learning sparse approximations and making classification decisions that we denote as PatchSample decomposition. The PatchSample method differs from our previous approach, our BlockBoost method, in that larger dictionaries are constructed that hold not just spatial-specific information, but a larger collective of visual information from all locations in the region of interest (ROI). Of note is that we trained and tested our method on a merged dataset of mammograms obtained from two sources. Experimental results have reached up to 67.80% classification accuracy (ACC) and 73.21% area under the ROC curve (AUC) using PatchSample decomposition on a merged dataset consisting of the MLO view regions of interest of the MIAS and CBIS-DDSM datasets.
A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting system based on vision detection. In this scheme, the YOLOX (You Only Look Once) object detection model and the DeepSORT (Deep Simple Online and Realtime Tracking) multiple object tracking algorithm are improved and combined to make them more suitable for plastic sorting. For plastic detection, multiple data augmentations are combined to improve the detection effect, while BN (Batch Normalization) layer fusion and mixed precision inference are adopted to accelerate the model. For plastic tracking, the improved YOLOX is used as a detector, and the tracking effect is further improved by optimizing the deep cosine metric learning and the metric in the matching stage. Based on this, virtual detection lines are set up to filter and extract information to determine the sorted objects. The experimental results show that the scheme proposed in this paper makes full use of vision information to achieve dynamic and real-time detection of plastics. The system is effective and versatile for sorting complex objects.
An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database

Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will result in extremely high real-time data processing requirements. The effective and reliable geometric positioning of remote sensing images is the foundation of remote sensing applications. In this paper, we propose an optical remote sensing image matching method based on a simple stable feature database. This method entails building the stable feature database, extracting local invariant features that are comparatively stable from remote sensing images using an iterative matching strategy, and storing useful information about the features. Without reference images, the feature database-based matching approach potentially saves storage space for reference data while increasing image processing speed. To evaluate the performance of the feature database matching method, we train the feature database with various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Furthermore, we carried out matching comparison experiments with various satellite images to confirm the viability and stability of the feature database-based matching method. In comparison with direct matching using the classical feature algorithm, the feature database-based matching method in this paper can essentially improve the correct rate of feature point matching by more than 30% and reduce the matching time by more than 40%. This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by the reference data, and has great potential for fast matching of optical remote sensing images.