Reviewerly/exHarmony huggingface dataset:
Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning

Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyperparameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely, genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyperparameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR-10 dataset with only 10 training rounds.
Foundations of structural causal models with cycles and latent variables

Structural causal models (SCMs), also known as (nonparametric) structural equation models (SEMs), are widely used for causal modeling purposes. In particular, acyclic SCMs, also known as recursive SEMs, form a well-studied subclass of SCMs that generalize causal Bayesian networks to allow for latent confounders. In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles. We show that in the presence of cycles, many of the convenient properties of acyclic SCMs do not hold in general: they do not always have a solution; they do not always induce unique observational, interventional and counterfactual distributions; a marginalization does not always exist, and if it exists the marginal model does not always respect the latent projection; they do not always satisfy a Markov property; and their graphs are not always consistent with their causal semantics. We prove that for SCMs in general each of these properties does hold under certain solvability conditions. Our work generalizes results for SCMs with cycles that were only known for certain special cases so far. We introduce the class of simple SCMs that extends the class of acyclic SCMs to the cyclic setting, while preserving many of the convenient properties of acyclic SCMs. With this paper we aim to provide the foundations for a general theory of statistical causal modeling with SCMs.
The EBS-A* algorithm: An improved A* algorithm for path planning

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the traditional A* algorithm has some limitations, such as slow planning speed, close to obstacles. In this paper, we propose an improved A*-based algorithm, called the EBS-A* algorithm, that introduces expansion distance, bidirectional search, and smoothing into path planning. The expansion distance means keeping an extra space from obstacles to improve path reliability by avoiding collisions. Bidirectional search is a strategy searching path from the start node and the goal node simultaneously. Smoothing improves path robustness by reducing the number of right-angle turns. In addition, simulation tests for the EBS-A* algorithm are performed, and the effectiveness of the proposed algorithm is verified by transferring it to a robot operating system (ROS). The experimental results show that compared with the traditional A* algorithm, the proposed algorithm improves the path planning efficiency by 278% and reduces the number of critical nodes by 91.89% and the number of right-angle turns by 100%.
Towards Predicting the Best Answers in Community-based Question-Answering Services

Community-based question-answering (CQA) services contribute to solving many difficult questions we have. For each question in such services, one best answer can be designated, among all answers, often by the asker. However, many questions on typical CQA sites are left without a best answer even if when good candidates are available. In this paper, we attempt to address the problem of predicting if an answer may be selected as the best answer, based on learning from labeled data. The key tasks include designing features measuring important aspects of an answer and identifying the most importance features. Experiments with a Stack Overflow dataset show that the contextual information among the answers should be the most important factor to consider.
Learning Fully Dense Neural Networks for Image Semantic Segmentation

Semantic segmentation is pixel-wise classification which retains critical spatial information. The “feature map reuse” has been commonly adopted in CNN based approaches to take advantage of feature maps in the early layers for the later spatial reconstruction. Along this direction, we go a step further by proposing a fully dense neural network with an encoderdecoder structure that we abbreviate as FDNet. For each stage in the decoder module, feature maps of all the previous blocks are adaptively aggregated to feedforward as input. On the one hand, it reconstructs the spatial boundaries accurately. On the other hand, it learns more efficiently with the more efficient gradient backpropagation. In addition, we propose the boundary-aware loss function to focus more attention on the pixels near the boundary, which boosts the “hard examples” labeling. We have demonstrated the best performance of the FDNet on the two benchmark datasets: PASCAL VOC 2012, NYUDv2 over previous works when not considering training on other datasets.
Shot boundary detection based on orthogonal polynomial

Shot boundary detection (SBD) is a substantial step in video content analysis, indexing, retrieval, and summarization. SBD is the process of automatically partitioning video into its basic units, known as shots, through detecting transitions between shots. The design of SBD algorithms developed from simple feature comparison to rigorous probabilistic and using of complex models. Nevertheless, accelerate the detection of transitions with higher accuracy need to be improved. Extensive research has employed orthogonal polynomial (OP) and their moments in computer vision and signal processing owing to their powerful performance in analyzing signals. A new SBD algorithm based on OP has been proposed in this paper. The Features are derived from orthogonal transform domain (moments) to detect the hard transitions in video sequences. Moments are used because of their ability to represent signal (video frame) without information redundancy. These features are the moments of smoothed and gradients of video frames. The moments are computed using a developed OP which is squared Krawtchouk-Tchebichef polynomial. These moments (smoothed and gradients) are fused to form a feature vector. Finally, the support vector machine is utilized to detect hard transitions. In addition, a comparison between the proposed algorithm and other state-of-the-art algorithms is performed to reinforce the capability of the proposed work. The proposed algorithm is examined using three well-known datasets which are TRECVID2005, TRECVID2006, and TRECVID2007. The outcomes of the comparative analysis show the superior performance of the proposed algorithm against other existing algorithms.
Artpedia: A New Visual-Semantic Dataset with Visual and Contextual Sentences in the Artistic Domain

As vision and language techniques are widely applied to realistic images, there is a growing interest in designing visual-semantic models suitable for more complex and challenging scenarios. In this paper, we address the problem of cross-modal retrieval of images and sentences coming from the artistic domain. To this aim, we collect and manually annotate the Artpedia dataset that contains paintings and textual sentences describing both the visual content of the paintings and other contextual information. Thus, the problem is not only to match images and sentences, but also to identify which sentences actually describe the visual content of a given image. To this end, we devise a visual-semantic model that jointly addresses these two challenges by exploiting the latent alignment between visual and textual chunks. Experimental evaluations, obtained by comparing our model to different baselines, demonstrate the effectiveness of our solution and highlight the challenges of the proposed dataset. The Artpedia dataset is publicly available at: http://aimagelab.ing.unimore.it/artpedia .
Double circulant self-dual and LCD codes over Galois rings

This paper investigates the existence, enumeration, and asymptotic performance of self-dual and LCD double circulant codes over Galois rings of characteristic $p^2$ and order $p^4$ with $p$ an odd prime. When $p \equiv 3 \ ({\rm mod} \ 4),$ we give a method to construct a duality preserving bijective Gray map from such a Galois ring to $\mathbb{Z}_{p^2}^2.$ Closed formed enumeration formulas for double circulant codes that are self-dual (resp. LCD) are derived as a function of the length of these codes. Using random coding, we obtain families of asymptotically good self-dual and LCD codes over $\mathbb{Z}_{p^2}$ with respect to the metric induced by the standard ${\mathbb{F}}_p$ -valued Gray maps.
Long-Lived Entanglement Generation of Nuclear Spins Using Coherent Light

Nuclear spins of noble-gas atoms are exceptionally isolated from the environment and can maintain their quantum properties for hours at room temperature. Here we develop a mechanism for entangling two such distant macroscopic ensembles by using coherent light input. The interaction between the light and the noble-gas spins in each ensemble is mediated by spin-exchange collisions with alkali-metal spins, which are only virtually excited. The relevant conditions for experimental realizations with ^{3}He or ^{129}Xe are outlined.
Ethereum, Smart Contracts, DApps

On February 28, 2012, an 18-year-old high school student wrote “If BitcoinBitcoin is to achieve mainstream success, it cannot stop at the limited crowds of InternetInternet geeks, libertarians, and privacy advocates that it is hitting now, and it must find some way to attract the mainstream public” (Buterin in Bitcoin adoption opportunity: teenagers, 2012).
Improving the dynamics of quantum sensors with reinforcement learning

Abstract Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare. Here, we use the cross-entropy method of reinforcement learning (RL) to optimize the strength and position of control pulses. Compared to the quantum-chaotic sensors with periodic control pulses in the presence of superradiant damping, we find that decoherence can be fought even better and measurement precision can be enhanced further by optimizing the control. In some examples, we find enhancements in sensitivity by more than an order of magnitude. By visualizing the evolution of the quantum state, the mechanism exploited by the RL method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.
Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm

We present a detailed circuit implementation of Szegedy's quantization of the Metropolis-Hastings walk. This quantum walk is usually defined with respect to an oracle. We find that a direct implementation of this oracle requires costly arithmetic operations and thus reformulate the quantum walk in a way that circumvents the implementation of that specific oracle and which closely follows the classical Metropolis-Hastings walk. We also present heuristic quantum algorithms that use the quantum walk in the context of discrete optimization problems and numerically study their performances. Our numerical results indicate polynomial quantum speedups in heuristic settings.
Learning nonlinear input–output maps with dissipative quantum systems

In this paper, we develop a theory of learning nonlinear input-output maps with fading memory by dissipative quantum systems, as a quantum counterpart of the theory of approximating such maps using classical dynamical systems. The theory identifies the properties required for a class of dissipative quantum systems to be {\em universal}, in that any input-output map with fading memory can be approximated arbitrarily closely by an element of this class. We then introduce an example class of dissipative quantum systems that is provably universal. Numerical experiments illustrate that with a small number of qubits, this class can achieve comparable performance to classical learning schemes with a large number of tunable parameters. Further numerical analysis suggests that the exponentially increasing Hilbert space presents a potential resource for dissipative quantum systems to surpass classical learning schemes for input-output maps.
Blockchain-Based Data Storage Mechanism for Industrial Internet of Things

With the development of the Industrial Internet of Things and the continuous expansion of application scenarios, many development bottlenecks have followed. Its data security issue has become an obstacle to its widespread application. It has attracted substantial attention from both academia and industry. Blockchain technology has the characteristics of decentralization, openness and transparency and non-tampering. It has natural advantages in solving the security of the Industrial Internet of Things. Accordingly, this paper first analyzes the security risks associated with data storage in the Industrial Internet of Things and proposes the use of blockchain technology to ensure the secure storage of data in the Industrial Internet of Things. In traditional blockchains, the data layer uses Merkle hash trees to store data; however, the Merkle hash tree not able to provide non-member proof, which makes it unable to resist attacks from malicious nodes in the network. To solve this problem, this paper replaces the Merkle hash tree with a password accumulator to provide member proof and non-member proof. Moreover, the existing accumulators have trapdoors and cannot be updated in batches, and unable to meet the blockchain’s expansion requirements. This paper presents an improved RSA accumulator and gives the definition of the accumulator. Finally, this paper uses RSA to construct a batch update accumulator scheme without trapdoor, and shows that the scheme is feasible through correctness and security
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications

Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell's images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results.
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack.Victim models can maintain competitive performance on clean samples while behaving abnormally on samples with a specific trigger word inserted.Previous backdoor attacking methods usually assume that attackers have a certain degree of data knowledge, either the dataset which users would use or proxy datasets for a similar task, for implementing the data poisoning procedure.However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples.Experimental results on sentiment analysis and sentence-pair classification tasks show that our method is more efficient and stealthier.We hope this work can raise the awareness of such a critical security risk hidden in the embedding layers of NLP models.Our code is available at https://github.com/lancopku/Embedding-Poisoning.
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning

Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks.However, conventional pre-training objectives do not explicitly model relational facts in text, which are crucial for textual understanding.To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text.Specifically, we define two novel pre-training tasks to better understand entities and relations: (1) the entity discrimination task to distinguish which tail entity can be inferred by the given head entity and relation; (2) the relation discrimination task to distinguish whether two relations are close or not semantically, which involves complex relational reasoning.Experimental results demonstrate that ERICA can improve typical PLMs (BERT and RoBERTa) on several language understanding tasks, including relation extraction, entity typing and question answering, especially under low-resource settings. 1
Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images

Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019–9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.
An intelligent cyber security phishing detection system using deep learning techniques

Recently, phishing attacks have become one of the most prominent social engineering attacks faced by public internet users, governments, and businesses. In response to this threat, this paper proposes to give a complete vision to what Machine learning is, what phishers are using to trick gullible users with different types of phishing attacks techniques and based on our survey that phishing emails is the most effective on the targeted sectors and users which we are going to compare as well. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails that are growing at an alarming rate in recent years, thus will discuss the techniques of mitigation of phishing by Machine learning algorithms and technical solutions that have been proposed to mitigate the problem of phishing and valuable awareness knowledge users should be aware to detect and prevent from being duped by phishing scams. In this work, we proposed a detection model using machine learning techniques by splitting the dataset to train the detection model and validating the results using the test data , to capture inherent characteristics of the email text, and other features to be classified as phishing or non-phishing using three different data sets, After making a comparison between them, we obtained that the most number of features used the most accurate and efficient results achieved. the best ML algorithm accuracy were 0.88, 1.00, and 0.97 consecutively for boosted decision tree on the applied data sets.
Methods of using mobile Internet devices in the formation of the general professional component of bachelor in electromechanics competency in modeling of technical objects

The article describes the components of methods of using mobile Internet devices in the formation of the general professional component of bachelor in electromechanics competency in modeling of technical objects: using various methods of representing models; solving professional problems using ICT; competence in electric machines and critical thinking. On the content of learning academic disciplines “Higher mathematics”, “Automatic control theory”, “Modeling of electromechanical systems”, “Electrical machines” features of use are disclosed for Scilab, SageCell, Google Sheets, Xcos on Cloud in the formation of the general professional component of bachelor in electromechanics competency in modeling of technical objects. It is concluded that it is advisable to use the following software for mobile Internet devices: a cloud-based spreadsheets as modeling tools (including neural networks), a visual modeling systems as a means of structural modeling of technical objects; a mobile computer mathematical system used at all stages of modeling; a mobile communication tools for organizing joint modeling activities.