Reviewerly/exHarmony huggingface dataset:
Learning Higher‐Order Transitional Probabilities in Nonhuman Primates

The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the cooccurrence of events A and B appearing in a sequence is measured by the transitional probability (TP) between these events, and it corresponds to the probability of the second stimulus given the first (i.e., p(B|A)). In the present study, nonhuman primates (Guinea baboons, Papio papio) were exposed to a serial version of the XOR (i.e., exclusive-OR), in which they had to process sequences of three stimuli: A, B, and C. In this manipulation, first-order TPs (i.e., AB and BC) were uninformative due to their transitional probabilities being equal to .5 (i.e., p(B|A) = p(C|B) = .5), while second-order TPs were fully predictive of the upcoming stimulus (i.e., p(C|AB) = 1). In Experiment 1, we found that baboons were able to learn second-order TPs, while no learning occurred on first-order TPs. In Experiment 2, this pattern of results was replicated, and a final test ruled out an alternative interpretation in terms of proximity to the reward. These results indicate that a nonhuman primate species can learn a nonlinearly separable problem such as the XOR. They also provide fine-grained empirical data to test models of statistical learning on the interaction between the learning of different orders of TPs. Recent bioinspired models of associative learning are also introduced as promising alternatives to the modeling of statistical learning mechanisms.
One-step model agnostic meta-learning using two-phase switching optimization strategy

Abstract Conventional training mechanisms often encounter limited classification performance due to the need of large training samples. To counter such an issue, the field of meta-learning has shown great potential in fine tuning and generalizing to new tasks using mini dataset. As a variant derived from the concept of Model Agnostic Meta-Learning (MAML), an one-step MAML incorporated with the two-phase switching optimization strategy is proposed in this paper to improve performance using less iterations. One-step MAML uses two loops to conduct the training, known as the inner and the outer loop. During the inner loop, gradient update is performed only once per task. At the outer loop, gradient is updated based on losses accumulated by the evaluation set during each inner loop. Several experiments using the BERT-Tiny model are conducted to analyze and compare the performance of the one-step MAML with five benchmark datasets. The performance of evaluation shows that the best loss and accuracy can be achieved using one-step MAML that is coupled with the two-phase switching optimizer. It is also observed that this combination reaches its peak accuracy with the fewest number of steps.
Continuous variable quantum teleportation through turbulent channels

Abstract The turbulence channel, as a potential medium for communications, plays a major role in establishing global quantum networks. However, the weather-tunable channel parameters have an influence on performance of quantum communication system. In this paper, we consider effects of weather conditions on attenuation of the transmission of turbulent channel and thus on performance of continuous variable quantum teleportation (CVQT). The serious weather conditions that involve snow, rain, fog and haze have been employed for security analysis of the practical system. Moreover, a linear optical amplifier is used for performance improvement in terms of the fidelity and transmission distance as well. The simulation results show that the noiseless linear optical amplifier can increase the fidelity and maximal transmission distance of the CVQT system that involves serious weather conditions. This has certain reference guides significance for information transmission in the face of various extreme weathers.
Distributed D-core decomposition over large directed graphs

Given a directed graph G and integers k and l , a D-core is the maximal subgraph H ⊆ G such that for every vertex of H , its in-degree and out-degree are no smaller than k and l , respectively. For a directed graph G , the problem of D-core decomposition aims to compute the non-empty D-cores for all possible values of k and l. In the literature, several peeling-based algorithms have been proposed to handle D-core decomposition. However, the peeling-based algorithms that work in a sequential fashion and require global graph information during processing are mainly designed for centralized settings, which cannot handle large-scale graphs efficiently in distributed settings. Motivated by this, we study the distributed D-core decomposition problem in this paper. We start by defining a concept called anchored coreness , based on which we propose a new H-index-based algorithm for distributed D-core decomposition. Furthermore, we devise a novel concept, namely skyline coreness , and show that the D-core decomposition problem is equivalent to the computation of skyline corenesses for all vertices. We design an efficient D-index to compute the skyline corenesses distributedly. We implement the proposed algorithms under both vertex-centric and block-centric distributed graph processing frameworks. Moreover, we theoretically analyze the algorithm and message complexities. Extensive experiments on large real-world graphs with billions of edges demonstrate the efficiency of the proposed algorithms in terms of both the running time and communication overhead.
Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?

Provide guidance on sample size considerations for developing predictive models by empirically establishing the adequate sample size, which balances the competing objectives of improving model performance and reducing model complexity as well as computational requirements.We empirically assess the effect of sample size on prediction performance and model complexity by generating learning curves for 81 prediction problems (23 outcomes predicted in a depression cohort, 58 outcomes predicted in a hypertension cohort) in three large observational health databases, requiring training of 17,248 prediction models. The adequate sample size was defined as the sample size for which the performance of a model equalled the maximum model performance minus a small threshold value.The adequate sample size achieves a median reduction of the number of observations of 9.5%, 37.3%, 58.5%, and 78.5% for the thresholds of 0.001, 0.005, 0.01, and 0.02, respectively. The median reduction of the number of predictors in the models was 8.6%, 32.2%, 48.2%, and 68.3% for the thresholds of 0.001, 0.005, 0.01, and 0.02, respectively.Based on our results a conservative, yet significant, reduction in sample size and model complexity can be estimated for future prediction work. Though, if a researcher is willing to generate a learning curve a much larger reduction of the model complexity may be possible as suggested by a large outcome-dependent variability.Our results suggest that in most cases only a fraction of the available data was sufficient to produce a model close to the performance of one developed on the full data set, but with a substantially reduced model complexity.
Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks

Video streaming has become extremely widespread, especially with the growing number of users and the spread of mobile devices, along with the increase in the availability and diversity of multimedia applications and communication technologies. Real-time video communication requires awareness of the quality of experience (QoE) to provide customers with a satisfactory service, for example, in smart cities that use video surveillance systems. The quality of service (QoS) is dependent on network performance, which directly affects the QoE. However, reliance on traditional network infrastructure and routing protocols cannot assure QoS. The emergence of software defined networks (SDN) may eliminate current network limitations. Due to SDN’s global view and programmability characteristics, such capabilities could help in providing an automated QoS control and management. This paper introduces video streaming adaptive QoS-based routing and resource reservation (VQoSRR), which gives SDN networks the ability to meet video demands and enhance user experience over best effort networks, such as the ones required for video surveillance in smart cities. In order to implement QoS-based routing (QBR), we developed algorithms for calculating routing, installing routing paths in the forwarding devices, and shifting traffic to an alternative path when QoE is violated. As well, we used queuing mechanisms to allocate resources based on the QoE requirements of video streaming. Our results indicate that resource reservation mechanisms combined with QoS-based routing enable effective control over routes and resources. Our framework guarantees the video quality as well. This technique of using video streaming would improve the tools and applications used for smart cities such as surveillance systems for hospitals and civil defense organizations.
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation

The principal task in supervised neural machine translation (NMT) is to learn to generate target sentences conditioned on the source inputs from a set of parallel sentence pairs, and thus produce a model capable of generalizing to unseen instances.However, it is commonly observed that the generalization performance of the model is highly influenced by the amount of parallel data used in training.Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples.In this paper, we present a novel data augmentation paradigm termed Continuous Semantic Augmentation (CSANMT), which augments each training instance with an adjacency semantic region that could cover adequate variants of literal expression under the same meaning.We conduct extensive experiments on both rich-resource and low-resource settings involving various language pairs, including WMT14 English→{German,French}, NIST Chinese→English and multiple low-resource IWSLT translation tasks.The provided empirical evidences show that CSANMT sets a new level of performance among existing augmentation techniques, improving on the state-of-theart by a large margin. 1
Detection of Diseases Using Machine Learning Image Recognition Technology in Artificial Intelligence

With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processing, and other fields. Purpose. This article aims to apply the image processing technology based on machine learning in the detection of childhood diseases and propose the application of image processing technology to the detection of childhood diseases. This article introduces machine learning, image recognition technology, and related algorithms in detail and experiments on image recognition technology based on machine learning. The experimental results show that image recognition technology based on machine learning can well identify white blood cells that are difficult to distinguish with the naked eye, with a recognition rate of up to 90%. Applying image recognition technology based on machine learning in disease diagnosis has greatly improved the level of medical diagnosis.
An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning

Abstract A global path planning method based on improved particle swarm optimization (PSO) algorithm was proposed to find a high quality flight trajectory in three-dimensional complex environment under multiple threats for UAV. The improved path planning algorithm combines the standard PSO with A* method to compensate for the slow convergence rate of PSO. The objective function with multiple constraints of A* method is used to evaluate the quality of the waypoint, and the objective function of PSO algorithm is designed to evaluate the quality of the candidate path. To verify the effectiveness of the improved algorithm, the improved PSO algorithm is used to compare with basic PSO algorithms. The experiment in complex environment shows it has stronger search ability, convergence ability due to the improvement of population diversity and convergence speed.
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED

DocRED is a widely used dataset for documentlevel relation extraction.In the large-scale annotation, a recommend-revise scheme is adopted to reduce the workload.Within this scheme, annotators are provided with candidate relation instances from distant supervision, and they then manually supplement and remove relational facts based on the recommendations.However, when comparing Do-cRED with a subset relabeled from scratch, we find that this scheme results in a considerable amount of false negative samples and an obvious bias towards popular entities and relations.Furthermore, we observe that the models trained on DocRED have low recall on our relabeled dataset and inherit the same bias in the training data.Through the analysis of annotators' behaviors, we figure out the underlying reason for the problems above: the scheme actually discourages annotators from supplementing adequate instances in the revision phase.We appeal to future research to take into consideration the issues with the recommend-revise scheme when designing new models and annotation schemes.The relabeled dataset is released at https://github.com/AndrewZhe/Revisit-DocRED, to serve as a more reliable test set of document RE models.
Optimizing quantum control pulses with complex constraints and few variables through autodifferentiation

Applying optimal control algorithms on realistic quantum systems confronts two key challenges: to efficiently adopt physical constraints in the optimization and to minimize the variables for the convenience of experimental tuneups. To resolve these issues, we propose an algorithm by incorporating multiple constraints into the gradient optimization over piecewise pulse constant values, which are transformed to contained numbers of the finite Fourier basis for bandwidth control. Such complex constraints and variable transformation involved in the optimization introduce extreme difficulty in calculating gradients. We resolve this issue efficiently utilizing autodifferentiation on the machine learning platform TensorFlow. We test our algorithm by finding smooth control pulses to implement single-qubit and two-qubit gates for superconducting transmon qubits with always-on interaction, which remains a challenge of quantum control in various qubit systems. Our algorithm provides a promising optimal quantum control approach that is friendly to complex and optional physical constraints.
Automated transpilation of imperative to functional code using neural-guided program synthesis

While many mainstream languages such as Java, Python, and C# increasingly incorporate functional APIs to simplify programming and improve parallelization/performance, there are no effective techniques that can be used to automatically translate existing imperative code to functional variants using these APIs. Motivated by this problem, this paper presents a transpilation approach based on inductive program synthesis for modernizing existing code. Our method is based on the observation that the overwhelming majority of source/target programs in this setting satisfy an assumption that we call trace-compatibility: not only do the programs share syntactically identical low-level expressions, but these expressions also take the same values in corresponding execution traces. Our method leverages this observation to design a new neural-guided synthesis algorithm that (1) uses a novel neural architecture called cognate grammar network (CGN) and (2) leverages a form of concolic execution to prune partial programs based on intermediate values that arise during a computation. We have implemented our approach in a tool called NGST2 and use it to translate imperative Java and Python code to functional variants that use the Stream and functools APIs respectively. Our experiments show that NGST2 significantly outperforms several baselines and that our proposed neural architecture and pruning techniques are vital for achieving good results.
Augmented Convolutional Neural Network Models with Relative Multi-Head Attention for Target Recognition in Infrared Images

Deep convolutional neural network (CNN) models are typically trained on high-resolution images. When we apply them directly to low-resolution infrared images, for example, the performances will not always be satisfactory. This is due to CNN layers that operate in a local neighborhood, which is already poor in information for infrared images. To overcome these weaknesses and increase information of global nature, a hybrid architecture based on CNN with self-attention mechanism is proposed. This later provides information about the global context by capturing the long-range interactions between the different parts of an image. In this paper, we have incorporated a convolutional–attentional form in the top layers of two pre-trained networks VGGNet and ResNet. The convolutional–attentional form is a concatenation of two paths; the original convolutional feature maps of the pre-trained network, and the output of a relative multi-head attentional block. Extensive experiments are conducted in the FLIR starter thermal dataset, where we achieve a [Formula: see text] overall accuracy in the four-class FLIR starter thermal dataset. Moreover, the proposed architectures exceed the state of the art in target recognition on two-class FLIR starter thermal dataset with a [Formula: see text] improvement in overall classification accuracy. In addition, a study on the effect of different hyper-parameters and error analysis is carried out to give some research forward directions.
Ethics of Conversational User Interfaces

Building on the prior workshops on conversational user interfaces (CUIs) [2, 40], we tackle the topic of ethics of CUIs at CHI 2022. Though commercial CUI developments continue to rapidly advance, our scholarly dialogue on ethics of CUIs is underwhelming. The CUI community has implicitly been concerned with ethics, yet making it central to the growing body of work thus far has not been adequately done. Since ethics is a far-reaching topic, perspectives from philosophy, design, and engineering domains are integral to our CUI research community. For instance, philosophical traditions, e.g., deontology or virtue ethics, can guide ethical concepts that are relevant for CUIs, e.g., autonomy or trust. The practice of design through approaches like value sensitive design can inform how CUIs should be developed. Ethics comes into play with technical contributions, e.g., privacy-preserving data sharing between conversational systems. By considering such multidisciplinary angles, we come to a special topic of interest that ties together philosophy, design, and engineering: conversational disclosure, e.g., sharing personal information, transparency, e.g., as how to transparently convey relevant information in a conversational manner, and vulnerability of diverse user groups that should be taken into consideration.
Elipmoc: advanced decompilation of Ethereum smart contracts

Smart contracts on the Ethereum blockchain greatly benefit from cutting-edge analysis techniques and pose significant challenges. A primary challenge is the extremely low-level representation of deployed contracts. We present Elipmoc, a decompiler for the next generation of smart contract analyses. Elipmoc is an evolution of Gigahorse, the top research decompiler, dramatically improving over it and over other state-of-the-art tools, by employing several high-precision techniques and making them scalable. Among these techniques are a new kind of context sensitivity (termed “transactional sensitivity”) that provides a more effective static abstraction of distinct dynamic executions; a path-sensitive (yet scalable, through path merging) algorithm for inference of function arguments and returns; and a fully context sensitive private function reconstruction process. As a result, smart contract security analyses and reverse-engineering tools built on top of Elipmoc achieve high scalability, precision and completeness. Elipmoc improves over all notable past decompilers, including its predecessor, Gigahorse, and the state-of-the-art industrial tool, Panoramix, integrated into the primary Ethereum blockchain explorer, Etherscan. Elipmoc produces decompiled contracts with fully resolved operands at a rate of 99.5% (compared to 62.8% for Gigahorse), and achieves much higher completeness in code decompilation than Panoramix—e.g., up to 67% more coverage of external call statements—while being over 5x faster. Elipmoc has been the enabler for recent (independent) discoveries of several exploitable vulnerabilities on popular protocols, over funds in the many millions of dollars.
Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities From Visual SLAM

Feature-based visual simultaneous localization and mapping (SLAM) methods only estimate the depth of extracted features, generating a sparse depth map. To solve this sparsity problem, depth completion tasks that estimate a dense depth from a sparse depth have gained significant importance in robotic applications like exploration. Existing methodologies that use sparse depth from visual SLAM mainly employ point features. However, point features have limitations in preserving structural regularities owing to texture-less environments and sparsity problems. To deal with these issues, we perform depth completion with visual SLAM using line features, which can better contain structural regularities than point features. The proposed methodology creates a convex hull region by performing constrained Delaunay triangulation with depth interpolation using line features. However, the generated depth includes low-frequency information and is discontinuous at the convex hull boundary. Therefore, we propose a mesh depth refinement (MDR) module to address this problem. The MDR module effectively transfers the high-frequency details of an input image to the interpolated depth and plays a vital role in bridging the conventional and deep learning-based approaches. The Struct-MDC outperforms other state-of-the-art algorithms on public and our custom datasets, and even outperforms supervised methodologies for some metrics. In addition, the effectiveness of the proposed MDR module is verified by a rigorous ablation study.
Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery

Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks due to the innate task-specific training paradigm. To alleviate it, unsupervised RL, a framework that pre-trains the agent in a task-agnostic manner without access to the task-specific reward, leverages active exploration for distilling diverse experience into essential skills or reusable knowledge. For exploiting such benefits also in robotic manipulation, we propose an unsupervised method for transferable manipulation skill discovery that ties structured exploration toward interacting behavior and transferable skill learning. It not only enables the agent to learn interaction behavior, the key aspect of the robotic manipulation learning, without access to the environment reward, but also to generalize to arbitrary downstream manipulation tasks with the learned task-agnostic skills. Through comparative experiments, we show that our approach achieves the most diverse interacting behavior and significantly improves sample efficiency in downstream tasks including the extension to multi-object, multitask problems.