Reviewerly/exHarmony huggingface dataset:
Refinement Types: A Tutorial

Refinement types enrich a language's type system with logical predicates that circumscribe the set of values described by the type, thereby providing software developers a tunable knob with which to inform the type system about what invariants and correctness properties should be checked on their code. In this article, we distill the ideas developed in the substantial literature on refinement types into a unified tutorial that explains the key ingredients of modern refinement type systems. In particular, we show how to implement a refinement type checker via a progression of languages that incrementally add features to the language or type system.
Hybrid Deep Learning Approach for Aspect Detection on Reviews

With the rapid growth of social networks and e-commerce, aspect-based sentiment analysis has gradually become a vital tool to analyze and evaluate the customers' feedback through social networking platforms or online sales websites. We could capture the customer insights as well as political opinions and predict the future social trend by analyzing the customer feedback in terms of aspects. In this research, we propose a hybrid deep learning method to solve the aspect detection problem. The aspect detection is the task to identify of the entity E and attribute A pairs expressed in the text. This model combines the advantages of each of the convolutional neural network (CNN) and long short-term memory (LSTM) methods, in which CNN works well in extracting spatial features, while LSTM works effectively in data classification. Experimental results on the Vietnamese VLSP 2018 dataset show that the proposed method achieves better results than the previous research methods if it only relies on a single method.
Adapting Multilingual Neural Machine Translation to Unseen Languages

Multilingual Neural Machine Translation (MNMT) for low-resource languages (LRL) can be enhanced by the presence of related high-resource languages (HRL), but the relatedness of HRL usually relies on predefined linguistic assumptions about language similarity. Recently, adapting MNMT to a LRL has shown to greatly improve performance. In this work, we explore the problem of adapting an MNMT model to an unseen LRL using data selection and model adaptation. In order to improve NMT for LRL, we employ perplexity to select HRL data that are most similar to the LRL on the basis of language distance. We extensively explore data selection in popular multilingual NMT settings, namely in (zero-shot) translation, and in adaptation from a multilingual pre-trained model, for both directions (LRL-en). We further show that dynamic adaptation of the model's vocabulary results in a more favourable segmentation for the LRL in comparison with direct adaptation. Experiments show reductions in training time and significant performance gains over LRL baselines, even with zero LRL data (+13.0 BLEU), up to +17.0 BLEU for pre-trained multilingual model dynamic adaptation with related data selection. Our method outperforms current approaches, such as massively multilingual models and data augmentation, on four LRL.
PT-CapsNet: A Novel Prediction-Tuning Capsule Network Suitable for Deeper Architectures

Capsule Networks (CapsNets) create internal representations by parsing inputs into various instances at different resolution levels via a two-phase process – part-whole transformation and hierarchical component routing. Since both of these internal phases are computationally expensive, CapsNet have not found wider use. Existing variations of CapsNets mainly focus on performance comparison with the original CapsNet, and have not outperformed CNN-based models on complex tasks. To address the limitations of the existing CapsNet structures, we propose a novel Prediction-Tuning Capsule Network (PT-CapsNet), and also introduce fully connected PT-Capsules (FC-PT-Caps) and locally connected PT-Capsules (LC-PT-Caps). Different from existing CapsNet structures, our proposed model (i) allows the use of capsules for more difficult vision tasks and provides wider applicability; and (ii) provides better than or comparable performance to CNN-based baselines on these complex tasks. In our experiments, we show robustness to affine transformations, as well as the lightweight and scalability of PT-CapsNet via constructing larger and deeper networks and performing comparisons on classification, semantic segmentation and object detection tasks. The results show consistent performance improvement and significant parameter reduction compared to various baseline models. Code is available at https://github.com/Christinepan881/PT-CapsNet.git.
SAN: Attention‐based social aggregation neural networks for recommendation system

The recommender system is of great significance to alleviate information overload. The rise of online social networks leads to a promising direction—social recommendation. By injecting the interaction influence among social users, recommendation performance has been further improved. Successful as they are, we argue that most social recommendation methods are still not sufficient to make full use of social network information. Existing solutions typically either considered only the local neighbors or treat neighbors’ information equally, even or both. However, few studies have attempted to solve these social recommendation problems jointly from both the perspective of social depth and social strength. Recently, graph convolutional neural networks have shown great potential in learning graph data by modeling the information propagation and aggregation process. Thus, we propose an attention-based social aggregation neural networks (abbreviated as SAN) model to build a recommendation system. Different from previous work, our proposed SAN model simulates the recursive social aggregation process to spread the global social influence, and simultaneously introduces social attention mechanism to incorporate the heterogeneous influences for better model user embedding. Instead of a shallow linear interaction function, we adopt multi-layer perception to model the complex user–item interaction. Extensive experiments on two real-world datasets show the effectiveness of our proposed model SAN, and further analysis verifies the generalization and flexibility of the model.
Reconstructing the wave function through the momentum weak value

The wave function is the cornerstone of quantum mechanics, although it is still open to different interpretations. It has been shown that the amplitude and the phase of a wave function are manifested in the imaginary and real parts of the momentum weak value, respectively. Based on this, we develop a weak measurement scheme to reconstruct the wave function through measuring the momentum weak value. A proof-of-principle experiment is made to confirm the feasibility of our measurement scheme, in which the polarization states and the momentum of light are coupled. We believe the experimental protocol has the potential to be applied in diverse fields such as wave-front sensing, edge detection, and optical communication, and measurement of the momentum weak value may offer valuable insight to better understand quantum mechanics.
EVA-Planner: Environmental Adaptive Quadrotor Planning

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Deep Adversarial Network Alignment

Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure. However, most existing network alignment methods have added assumptions of additional constraints to guide the alignment, such as having a set of seed node-node correspondences across the networks or the existence of side-information. Instead, we seek to develop a general unsupervised network alignment algorithm that makes no additional assumptions. Recently, network embedding has proven effective in many network analysis tasks, but embeddings of different networks are not aligned. Thus, we present our Deep Adversarial Network Alignment (DANA) framework that first uses deep adversarial learning to discover complex mappings for aligning the embedding distributions of the two networks. Then, using our learned mapping functions, DANA performs an efficient nearest neighbor node alignment. Furthermore, we present an unsupervised heuristic to perform model selection for DANA. We perform experiments on real world datasets to show the effectiveness of our framework for first aligning the graph embedding distributions and then discovering node alignments that outperform existing methods.
A Lightweight Isolation Mechanism for Secure Branch Predictors

Recently exposed vulnerabilities reveal that branch predictors shared by different processes leave the attackers with the opportunities for malicious training and perception. Instead of flush-based or physical isolation of hardware resources, we want to achieve isolation of the content in these hardware tables with some lightweight processing using randomization as follows. (1) Content encoding. We propose to use hardware-based thread-private random numbers to encode the contents of the branch predictor tables. It achieves a similar effect of logical isolation but adds little in terms of space or time overheads. (2) Index encoding. We propose a randomized index mechanism of the branch predictor. This disrupts the correspondence between the branch instruction address and the branch predictor entry, thus increases the noise for malicious perception attacks. Our analyses using an FPGA-based RISC-V processor prototype and additional auxiliary simulations suggest that the proposed mechanisms incur a very small performance cost while providing strong protection.
Confidential Computing—a brave new world

The semiconductor industry is witnessing a nascent security paradigm shift in the rise of Confidential Computing. Driven by the need to protect computations delegated to co-tenanted machines operated by Cloud Computing services, mainstream instruction set architectures are gradually introducing novel features that can be used to establish protected isolates offering strong integrity and confidentiality guarantees to code and data contained within. Coupled with a Remote Attestation protocol, a third-party may request the launch of an isolate on an otherwise untrusted machine and know—with a high degree of assurance—that a payload of code and data was indeed loaded into a legitimate isolate with a particular configuration.We argue that this ability to reliably establish a safe “beach-head” on an untrusted third-party’s machine has far-reaching consequences with applications beyond protecting workloads delegated to Cloud Computing services. In a future world where facilities for Confidential Computing are widely deployed and used, we imagine a utopia where inadvertent data leakage is a curiosity of a bygone age, with encrypted data moving from isolate to isolate and never resting in plaintext. Moreover, data is only released in explicitly delimited ways for processing, with systems and individuals exhibiting fine-grained control over data.We report on recent activities within Arm in attempting to realize this vision, and hope that this paper acts as a “call to arms” to others to join with us in fully exploring the potential of these emerging technologies.
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks

Graph Neural Networks (GNNs) apply deep learning to inputs represented as graphs. They use fully-connected layers to extract features from the nodes/edges of a graph and aggregate these features using message passing between nodes, thereby combining two distinct computational patterns: dense, regular computations and sparse, irregular computations. To address the computational challenges posed by GNNs, we propose GNNERATOR, an accelerator with heterogeneous compute engines optimized for these two patterns. Further, we propose feature-blocking, a novel GNN dataflow that beneficially trades off irregular memory accesses during aggregation for regular memory accesses during feature extraction. We show that GNNERATOR achieves speedups of 5.7-37x over an NVIDIA RTX 2080-Ti, and 2.3x-3.8x over HyGCN, a state-of-the-art GNN accelerator.
Blockchain as a tool to facilitate property rights protection in the Global South: lessons from India’s Andhra Pradesh state

The lack of a comprehensive property rights system is an issue of pressing concern in most economies in the Global South. Property rights-related issues have important social, economic and environmental consequences. This paper argues that one of the most impactful uses of blockchain in the Global South could be in the creation, implementation and enforcement of property rights. The article provides an in‐depth analysis of a blockchain-based land registry project in the Andhra Pradesh state of India. It also delves into facilitators for and barriers to a large-scale adoption and deployment of blockchain for this purpose. The paper shows that with blockchain, the benefits of digitisation of land records can be amplified and some of the major drawbacks of digitisation can be avoided.
Alleviating estimation problems in small sample structural equation modeling—A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches.

Small sample structural equation modeling (SEM) may exhibit serious estimation problems, such as failure to converge, inadmissible solutions, and unstable parameter estimates. A vast literature has compared the performance of different solutions for small sample SEM in contrast to unconstrained maximum likelihood (ML) estimation. Less is known, however, on the gains and pitfalls of different solutions in contrast to each other. Focusing on three current solutions-constrained ML, Bayesian methods using Markov chain Monte Carlo techniques, and fixed reliability single indicator (SI) approaches-we bridge this gap. When doing so, we evaluate the potential and boundaries of different parameterizations, constraints, and weakly informative prior distributions for improving the quality of the estimation procedure and stabilizing parameter estimates. The performance of all approaches is compared in a simulation study. Under conditions with low reliabilities, Bayesian methods without additional prior information by far outperform constrained ML in terms of accuracy of parameter estimates as well as the worst-performing fixed reliability SI approach and do not perform worse than the best-performing fixed reliability SI approach. Under conditions with high reliabilities, constrained ML shows good performance. Both constrained ML and Bayesian methods exhibit conservative to acceptable Type I error rates. Fixed reliability SI approaches are prone to undercoverage and severe inflation of Type I error rates. Stabilizing effects on Bayesian parameter estimates can be achieved even with mildly incorrect prior information. In an empirical example, we illustrate the practical importance of carefully choosing the method of analysis for small sample SEM. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Sharing is Caring! Joint Multitask Learning Helps Aspect-Category Extraction and Sentiment Detection in Scientific Peer Reviews

The peer-review process is the benchmark of research validation. Peer-reviewed texts are the artifacts via which the editors/chairs decide the inclusion/exclusion of a paper in a journal or conference proceedings. Hence it is important for the editors/chairs to carefully analyze the peer-review text from various aspects of the paper (e.g., novelty, substance, soundness, etc.), identify the underlying sentiment of the reviewers, and thereby validate the informativeness of the reviews before making a decision. With the rise in research paper submissions, the current peer-review system is experiencing an unprecedented information overload. Sometimes it becomes stressful for the chairs/editors to make a reasonable decision within the stringent timelines. Here in this work, we attempt an interesting problem to automatically extract the aspect and sentiment from the peer-review texts. We design an end-to-end deep multitask learning model to perform aspect extraction and sentiment classification simultaneously. We show that both these tasks help each other in the predictions. We achieve encouraging performance on a recently released dataset of peer-review texts. We make our codes available for further research <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://www.iitp.ac.in/~ai-nlp-ml/resources.html#aspect-category-sentiment.
A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network (ANN) research, ANN model structure and function are improving towards diversification and intelligence. However, the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough. Hence, a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper. Firstly, four classical neural network models are illustrated: Back Propagation (BP) network, Deep Belief Network (DBN), LeNet5 network, and olfactory bionic model (KIII model), and the neuron transmission mode and equation, network structure, and weight updating principle of the models are analyzed qualitatively. The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models, and the LeNet5 network simulates the nervous system in depth. Secondly, evaluation indexes of ANN are constructed from the perspective of bionics in this paper: small-world, synchronous, and chaotic characteristics. Finally, the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics. The experimental results show that the DBN network, LeNet5 network, and BP network have synchronous characteristics. And the DBN network and LeNet5 network have certain chaotic characteristics, but there is still a certain distance between the three classical neural networks and actual biological neural networks. The KIII model has certain small-world characteristics in structure, and its network also exhibits synchronization characteristics and chaotic characteristics. Compared with the DBN network, LeNet5 network, and the BP network, the KIII model is closer to the real biological neural network.
Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor

To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid method by combining principal component analysis (PCA) and boosted C5.0 decision tree algorithm with penalty factor is proposed to address this issue. PCA is used to reduce the dimension of feature subset. The boosted C5.0 decision tree algorithm is utilized as an ensemble classifier for classification. Penalty factor is used to optimize the classification result. To demonstrate the efficiency of the proposed method, it is implemented on biased-representative breast cancer datasets from the University of California Irvine(UCI) machine learning repository. Given the experimental results and further analysis, our proposal is a promising method for breast cancer and can be used as an alternative method in class imbalance learning. Indeed, we observe that the feature extraction process has helped us improve diagnostic accuracy. We also demonstrate that the extracted features considering breast cancer issues are essential to high diagnostic accuracy.
Human‐centered recommender systems: Origins, advances, challenges, and opportunities

Abstract From the earliest days of the field, Recommender Systems research and practice has struggled to balance and integrate approaches that focus on recommendation as a machine learning or missing‐value problem with ones that focus on machine learning as a discovery tool and perhaps persuasion platform. In this article, we review 25 years of recommender systems research from a human‐centered perspective, looking at the interface and algorithm studies that advanced our understanding of how system designs can be tailored to users objectives and needs. At the same time, we show how external factors, including commercialization and technology developments, have shaped research on human‐centered recommender systems. We show how several unifying frameworks have helped developers and researchers alike incorporate thinking about user experience and human decision‐making into their designs. We then review the challenges, and the opportunities, in today's recommenders, looking at how deep learning and optimization techniques can integrate with both interface designs and human performance statistics to improve recommender effectiveness and usefulness
ISE-YOLO: Improved Squeeze-and-Excitation Attention Module based YOLO for Blood Cells Detection

Accurate detection of human peripheral blood cells is of great significance to assist doctors to diagnose blood-related diseases. Traditional clinical detection of peripheral blood cells is usually identified by manual microscopy. However, such artificial analysis and processing methods are easily affected by subjective factors, which will cause certain errors. In recent years, the convolutional neural network has been applied to various medical image processing tasks and has shown satisfactory performance. However, traditional CNNs are limited by the lack of feature expression ability. This work introduces the idea of visual attention mechanism into the deep learning detection model and design an improved Squeeze-and-Excitation based YOLO-v3 detection model (ISE-YOLO). This model adds our improved SE module into the different structural blocks of the YOLO to strengthen the network information discrimination of input features and improve the detection performance. Experimental results demonstrate that the proposed ISE-YOLO improves the performance over 96.5% on WBCs, 92.7% on RBCs, and 89.6% on platelets, and outperforms other advanced classification methods.