Reviewerly/exHarmony huggingface dataset:
Adaptive Neural Network-based OFDM Receivers

We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel conditions. This online adaptation via retraining is mainly motivated by two reasons: First, receiver design typically focuses on the universal optimal performance for a wide range of possible channel realizations. However, in actual applications and within short time intervals, only a subset of these channel parameters is likely to occur, as macro parameters, e.g., the maximum channel delay, can assumed to be static. Second, in-the-field alterations like temporal interferences or other conditions out of the originally intended specifications can occur on a practical (real-world) transmission. While conventional (filter-based) systems would require reconfiguration or additional signal processing to cope with these unforeseen conditions, NN-based receivers can learn to mitigate previously unseen effects even after their deployment. For this, we showcase on-the-fly adaption to current channel conditions and temporal alterations solely based on recovered labels from an outer forward error correction (FEC) code without any additional piloting overhead. To underline the flexibility of the proposed adaptive training, we showcase substantial gains for scenarios with static channel macro parameters, for out-of-specification usage and for interference compensation.
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning

Multi-task learning (MTL) is a novel framework to learn several tasks simultaneously with a single shared network where each task has its distinct personalized header network for fine-tuning. MTL can be implemented in federated learning settings as well, in which tasks are distributed across clients. In federated settings, the statistical heterogeneity due to different task complexities and data heterogeneity due to non-iid nature of local datasets can both degrade the learning performance of the system. In addition, tasks can negatively affect each other’s learning performance due to negative transference effects. To cope with these challenges, we propose FedGradNorm which uses a dynamic-weighting method to normalize gradient norms in order to balance learning speeds among different tasks. FedGradNorm improves the overall learning performance in a personalized federated learning setting. We provide convergence analysis for FedGradNorm by showing that it has an exponential convergence rate. We also conduct experiments on multi-task facial landmark (MTFL) and wireless communication system dataset (RadComDynamic). The experimental results show that our framework can achieve faster training performance compared to equal-weighting strategy. In addition to improving training speed, FedGradNorm also compensates for the imbalanced datasets among clients.
Decentralized Federated Learning for Industrial IoT With Deep Echo State Networks

Federated learning (FL) has recently been adopted to train shared models across industrial Internet of Things (IoT) devices without revealing their private raw data. Conventional FL usually relies on a central server for coordination. However, in reality, the central server is not fully trusted, which means that it may be collecting data, raising concerns about data leakage and misuse. Here, we propose a decentralized FL algorithm based on deep neural networks to address the problem of untrusted central servers. The original problem is decomposed into several subproblems with consensus constraints, which can be solved by local computation and communication. The proposed algorithm combines the decentralized average consensus and alternating direction method of multipliers. Several decentralized algorithms are employed for comparison, and the issue of heterogeneous data is discussed. Experimental evaluation shows the effectiveness of our proposed algorithm.
Scene inversion reveals distinct patterns of attention to semantically interpreted and uninterpreted features

Semantic guidance theories propose that attention in real-world scenes is strongly associated with semantically informative scene regions. That is, we look where there are recognizable and informative objects that help us make sense of our visual environment. In contrast, image guidance theories propose that local differences in semantically uninterpreted image features such as luminance, color, and edge orientation primarily determine where we look in scenes. While it is clear that both semantic guidance and image guidance play a role in where we look in scenes, the degree of their relative contributions and how they interact with each other remains poorly understood. In the current study, we presented real-world scenes in upright and inverted orientations and used general linear mixed effects models to understand how semantic guidance, image guidance, and observer center bias were associated with fixation location and fixation duration. We observed distinct patterns of change under inversion. Semantic guidance was severely disrupted by scene inversion, while image guidance was mildly impaired and observer center bias was enhanced. In addition, we found that fixation durations for semantically rich regions decreased when viewing inverted scenes relative to upright scene viewing, while fixation durations for image salience and center bias were unaffected by inversion. Together these results provide important new constraints on theories and computational models of attention in real-world scenes.
Applying graph convolution neural network in digital breast tomosynthesis for cancer classification

Digital breast tomosynthesis, or 3D mammography, has advanced the field of breast imaging diagnosis. It has been rapidly replacing the traditional full-field digital mammography because of its diagnostic superiority. However, automatic detection of breast cancer using digital breast tomosynthesis images has remained challenging, mainly due to their high resolution, high volume, and complexity. In this study, we developed a novel model for more precise detection of cancerous 3D mammogram images. The proposed model first, represents 3D mammograms as graphs, then employs a self-attention graph convolutional neural network model to effectively and efficiently learn the features of 3D mammograms, and finally, using the extracted features, identifies the cancerous 3D mammograms. We trained and evaluated the performance of the proposed model using public and private datasets. We compared the performance of the proposed model with those of multiple state-of-the-art CNN-based models as baseline models. The results show that the proposed model outperforms all the baseline models in terms of accuracy, precision, sensitivity, F1, and AUC.
Security of Optical Beam Splitter in Quantum Key Distribution

The optical beam splitter is an essential device used for decoding in quantum key distribution. The impact of optical beam splitters on the security of quantum key distribution was studied, and it was found that the realistic device characteristics closely influence the error rate introduced by the wavelength-dependent attack on optical beam splitters. A countermeasure, combining device selection and error rate over-threshold alarms, is proposed to protect against such attacks. Beam splitters made of mirror coatings are recommended, and the variation of splitting ratio should be restricted to lower than 1 dB at 1260–1700 nm. For the partial attack scenario where the eavesdropper attacks only a portion of the quantum signal, a modified secure key rate formula is proposed to eliminate the revealed information of the attacked portion. Numerical results show that the QKD system adopting this countermeasure exhibits good performance with a secure key rate of over 10 kbps at 100 km and a maximum transmission distance of over 150 km, with only a small difference from the no-attack scenario. Additionally, a countermeasure to monitor the light intensity of different wavelengths is proposed to protect against the wavelength-dependent attack on optical beam splitters.
Algorithmic cooling for resolving state preparation and measurement errors in quantum computing

State preparation and measurement errors are commonly regarded as indistinguishable. The problem of distinguishing state preparation (SPAM) errors from measurement errors is important to the field of characterizing quantum processors. In this work, we propose a method to separately characterize SPAM errors using a novel type of algorithmic cooling protocol called measurement-based algorithmic cooling (MBAC). MBAC assumes the ability to perform (potentially imperfect) projective measurements on individual qubits, which is available on many modern quantum processors. We demonstrate that MBAC can significantly reduce state preparation error under realistic assumptions, with a small overhead that can be upper bounded by measurable quantities. Thus, MBAC can be a valuable tool not only for benchmarking near-term quantum processors, but also for improving the performance of quantum processors in an algorithmic manner.
Solving arithmetic word problems by synergizing syntax-semantics extractor for explicit relations and neural network miner for implicit relations

Abstract This paper presents a relation-centric algorithm for solving arithmetic word problems (AWPs) by synergizing a syntax-semantics extractor for extracting explicit relations, and a neural network miner for mining implicit relations. This is the first algorithm that has a specific component to acquire implicit knowledge items for solving AWPs. This paper proposes a three-phase scheme to decompose the challenging task of designing an algorithm for solving AWPs into three smaller tasks. The first phase proposes a state-action paradigm; the second phase instantiates the paradigm into a relation-centric approach; and the third phase implements a relation-centric algorithm for solving AWPs. There are two main steps in the proposed algorithm: problem understanding and symbolic solver. By adopting the relation-centric approach, problem understanding becomes a task of relation acquisition. For conducting the task of relation acquisition, a relaxed syntax-semantics method first extracts a group of explicit relation candidates. In parallel, a neural network miner acquires implicit relation candidates. The miner computes the vectors encoded by BERT to determine which implicit relations should be added. Thus, problem understanding can acquire both explicit relations and implicit relations, which addresses the challenge of building a problem understanding method that can acquire all the knowledge items to find the solution. In the subsequent step of symbolic solver, a fusion procedure forms a distilled set of relations from all the candidates by discarding unnecessary relations. Experimentation on nine benchmark datasets validates the superiority of the proposed algorithm that outperforms the state-of-the-art algorithms.
Management of Implicit Requirements Data in Large SRS Documents

Implicit Requirements (IMR) identification is part of the Requirements Engineering (RE) phase in Software Engineering during which data is gathered to create SRS (Software Requirements Specifications) documents. As opposed to explicit requirements clearly stated, IMRs constitute subtle data and need to be inferred. Research has shown that IMRs are crucial to the success of software development. Many software systems can encounter failures due to lack of IMR data management. SRS documents are large, often hundreds of pages, due to which manually identifying IMRs by human software engineers is not feasible. Moreover, such data is evergrowing due to the expansion of software systems. It is thus important to address the crucial issue of IMR data management. This article presents a survey on IMRs in SRS documents with the definition and overview of IMR data, detailed taxonomy of IMRs with explanation and examples, practices in managing IMR data, and tools for IMR identification. In addition to reviewing classical and state-of-the-art approaches, we highlight trends and challenges and point out open issues for future research. This survey article is interesting based on data quality, hidden information retrieval, veracity and salience, and knowledge discovery from large textual documents with complex heterogeneous data.
Alternating Minimization-Based Sparse Least-Squares Classifier for Accuracy and Interpretability Improvement of Credit Risk Assessment

When dealing with complex and redundant data classification problems, many classifiers cannot provide high predictive accuracy and interpretability. We also find that the least-squares support vector classifiers (LSSVCs) hardly identify important instances and features from data, so they cannot give an interpretable prediction. Although the LSSVC has the properties of low bias and high robustness, its high variance often gives a poor predictive performance. In this paper, we propose an alternating minimization-based sparse least-squares classifier (AMSLC) approach in the framework of LSSVCs to address the aforementioned problems. Based on the reconstructed row- and column-wise kernel matrices, the sparsity-induced [Formula: see text]-norm approximation function is introduced to the LSSVC model. By alternately solving two unconstrained quadratic optimization problems or two systems of linear equations, AMSLC can predict the class labels of given instances and extract the least number of important instances and features to obtain the interpretable classification. Compared with SVC, LSSVC, [Formula: see text]-norm SVC (L1SVC), [Formula: see text]-norm SVC (L0SVC), the least absolute shrinkage and selection operator classifier (LASSOC), and multiple kernel learning SVC (MKLSVC) on four real credit datasets, the experimental results show that the proposed AMSLC method generally obtains the best predictive accuracy and the interpretable classification with the minimum number of important instances and features.
Privacy protection against attack scenario of federated learning using internet of things

Laws and regulations for privacy protection have been promulgated one after another, and the phenomenon of data islands has become a significant bottleneck hindering the development of big data and artificial intelligence technologies. From the perspective of the historical development, concepts, and architecture classification of federated learning, the technical advantages of federated learning are explained using Internet of Things. Simultaneously, numerous attack methods and classifications of federated learning systems are examined, as well as the distinctions between different federated learning encryption algorithms. Finally, it reviews research in the subject of federal learning privacy protection and security mechanisms, as well as identifies difficulties and opportunities.
FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis

Objective.Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from 'hematoxylin and eosin' (HE) stained 'whole slide images' (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs.Approach.We introduce FEEDNet, a novel encoder-decoder network that uses LSTM units and 'feature enhancement blocks' (FE-blocks). Our proposed FE-block avoids the loss of location information incurred by pooling layers by concatenating the downsampled version of the original image to preserve pixel intensities. FEEDNet uses an LSTM unit to capture multi-channel representations compactly. Secondly, for datasets that provide class information, we train a multiclass segmentation model, which generates masks corresponding to each class at the output. Using this information, we generate more accurate binary masks than that generated by conventional binary segmentation models.Main results.We have thoroughly evaluated FEEDNet on CoNSeP, Kumar, and CPM-17 datasets. FEEDNet achieves the best value of PQ (panoptic quality) on CoNSeP and CPM-17 datasets and the second best value of PQ on the Kumar dataset. The 32-bit floating-point version of FEEDNet has a model size of 64.90 MB. With INT8 quantization, the model size reduces to only 16.51 MB, with a negligible loss in predictive performance on Kumar and CPM-17 datasets and a minor loss on the CoNSeP dataset.Significance.Our proposed idea of generalized class-aware binary segmentation is shown to be accurate on a variety of datasets. FEEDNet has a smaller model size than the previous nuclei segmentation networks, which makes it suitable for execution on memory-constrained edge devices. The state-of-the-art predictive performance of FEEDNet makes it the most preferred network. The source code can be obtained fromhttps://github.com/CandleLabAI/FEEDNet.
Testing of statistical significance of semantic changes detected by diachronic word embedding

In recent years, methods based on word embedding models have been widely used for solving problems of semantic change estimation. The models are trained on text corpora of various years. Semantic change is detected by analyzing changes in distance between words using vector space alignment or by analyzing changes in a set of words that are most similar in meaning to a target word. Testing for statistical significance of the detected effects has not been detailly discussed in previous studies. This paper focuses on the problem of testing statistical significance of semantic change. Besides, we consider the problem of finding a confidence interval of estimates of semantic distance between words. We allow for the influence of two random factors. The first one is associated with the use of random initial conditions and stochastic optimization when training the model, the second one results from a random selection of texts for a training corpus. The proposed approach is based on the use of resampling of a training set of texts. The proposed method is tested on the COHA corpus.
Evolutionary Computational Intelligence-Based Multi-Objective Sensor Management for Multi-Target Tracking

In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data. However, when the number of sensors to be selected is unknown in advance, sensor selection is essentially non-deterministic polynomial-hard (NP-hard), and finding the optimal solution is computationally unacceptable. To alleviate these issues, we propose a novel sensor selection approach based on evolutionary computational intelligence for tracking multiple targets in the MSSs. The sensor selection problem is formulated in a partially observed Markov decision process framework by modeling multi-target states as labeled multi-Bernoulli random finite sets. Two conflicting task-driven objectives are considered: minimization of the uncertainty in posterior cardinality estimates and minimization of the number of selected sensors. By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset of sensors at each time step. Numerical studies are used to evaluate the performance of the proposed approach, where the MSS tracks multiple moving targets with nonlinear/linear dynamic models and nonlinear measurements. The results show that our method not only significantly reduces the number of selected sensors but also provides superior tracking accuracy compared to generic sensor selection methods.
Abs-CAM: a gradient optimization interpretable approach for explanation of convolutional neural networks

The black-box nature of deep neural networks severely hinders its performance improvement and application in specific scenes. In recent years, class activation mapping-based method has been widely used to interpret the internal decisions of models in computer vision tasks. However, when this method uses backpropagation to obtain gradients, it will cause noise in the saliency map and even locate features that are irrelevant to decisions. In this paper, we propose an absolute value class activation mapping-based (Abs-CAM) method, which optimizes the gradients derived from the backpropagation and turns all of them into positive gradients to enhance the visual features of output neurons’ activation and improve the localization ability of the saliency map. The framework of Abs-CAM is divided into two phases: generating initial saliency map and generating final saliency map. The first phase improves the localization ability of the saliency map by optimizing the gradient, and the second phase linearly combines the initial saliency map with the original image to enhance the semantic information of the saliency map. We conduct qualitative and quantitative evaluation of the proposed method, including Deletion, Insertion, and Pointing Game. The experimental results show that the Abs-CAM can obviously eliminate the noise in the saliency map, and can better locate the features related to decisions, and is superior to the previous methods in recognition and localization tasks.
Domain adaptive state representation alignment for reinforcement learning

In recent years, deep reinforcement learning (RL) has shown excellent performance in robot control, video games, and multi-agent systems. However, most of existing RL models do not generalize. Even a small visual change will greatly deteriorate the performance of RL agents, which limits the generalization and flexibility of RL in real-world applications. To address this problem, we propose a two-stage model in which reinforcement learning agents learn adaptation to changes in the visual environment before learning optimal behavioral policies. In the first stage, we employ domain adaptation to align the distribution of domain-invariant state representations from different domains in the latent feature space. Specifically, we introduce feature-level and pixel-level multi-granularity adversarial loss to constrain the learning of domain-invariant state representations. In the second stage, the RL agent is trained based on the learned domain-invariant state representations. Since the adjusted observation is domain-invariant, the learned policy has strong cross-domain generalization performance. We name the proposed method as Adversarial-based Domain Invariant State Representation (Ad-DISR). At last, we evaluate Ad-DISR on various variants of Car-Racing games and CARLA, an autonomous driving simulator. The results show that our method can achieve better performance on both reward scores and living time in both source and target domains.