Reviewerly/exHarmony huggingface dataset:
Motivational engine and long-term memory coupling within a cognitive architecture for lifelong open-ended learning

This paper considers a cognitive architecture that revolves around a network memory based Long-Term Memory and how it can lead to a working lifelong learning system that can deal with open-ended learning. It focuses on the mutual interaction between the Motivational Engine and the Long-Term Memory and, in particular, on autonomously producing high-level utility representations in order to allow for development. Thus, the main point is to study how this architecture allows to start from primitive policies and models operating over continuous and large state/action spaces and progressively move towards higher level structures defined over smaller and discrete state/action spaces. This progression is demonstrated in a series of experiments carried out on a real robotic setup that involves different contexts, both in terms of domains (worlds) and tasks (goals).
Hate Speech Detection In Indonesian Language Instagram

Hate speech is a form of communication which contains hatred by doing things, such as inciting, insulting, disparaging, or demeaning a person or group. Hate speech issues in Indonesia often have linkages to politics. In 2018 and 2019, for example, the hate speech relates to the local leader and presidential elections. The hate speech actors commonly use social networks, such as Instagram, to spread their hatred words. About 60% of hate speech is found in the comments of the posts and it will be a real threat if not quickly detected. Our study aims to detect hate speech in Instagram comments. We propose the use of a word2vec method with skip-gram models and a modified TextCNN to learn and detect hate speech texts. Furthermore, random oversampling, random under sampling, and class weight was used to solve imbalanced dataset problems. The results show that the best accuracy, in term of F-score, is 93.70%, gained from a combination of word2vec skip-gram with window size 15, a modified TextCNN, and random oversampling methods.
Online adaptation for consistent mesh reconstruction in the wild

This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild. Without requiring annotations of 3D mesh, 2D keypoints, or camera pose for each video frame, we pose video-based reconstruction as a self-supervised online adaptation problem applied to any incoming test video. We first learn a category-specific 3D reconstruction model from a collection of single-view images of the same category that jointly predicts the shape, texture, and camera pose of an image. Then, at inference time, we adapt the model to a test video over time using self-supervised regularization terms that exploit temporal consistency of an object instance to enforce that all reconstructed meshes share a common texture map, a base shape, as well as parts. We demonstrate that our algorithm recovers temporally consistent and reliable 3D structures from videos of non-rigid objects including those of animals captured in the wild -- an extremely challenging task rarely addressed before.
Comparing Process Calculi Using Encodings

Encodings or the proof of their absence are the main way to compare process calculi.To analyse the quality of encodings and to rule out trivial or meaningless encodings, they are augmented with encodability criteria.There exists a bunch of different criteria and different variants of criteria in order to reason in different settings.This leads to incomparable results.Moreover, it is not always clear whether the criteria used to obtain a result in a particular setting do indeed fit to this setting.This paper provides a short survey on often used encodability criteria, general frameworks that try to provide a unified notion of the quality of an encoding, and methods to analyse and compare encodability criteria.
WITHDRAWN: Village precision poverty alleviation and smart agriculture based on FPGA and machine learning

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/about/our-business/policies/article-withdrawal ). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Robustness of Noether’s Principle: Maximal Disconnects between Conservation Laws and Symmetries in Quantum Theory

To what extent does Noether's principle apply to quantum channels? Here, we quantify the degree to which imposing a symmetry constraint on quantum channels implies a conservation law, and show that this relates to physically impossible transformations in quantum theory, such as time-reversal and spin-inversion. In this analysis, the convex structure and extremal points of the set of quantum channels symmetric under the action of a Lie group $G$ becomes essential. It allows us to derive bounds on the deviation from conservation laws under any symmetric quantum channel in terms of the deviation from closed dynamics as measured by the unitarity of the channel. In particular, we investigate in detail the $U(1)$ and $SU(2)$ symmetries related to energy and angular momentum conservation laws. In the latter case, we provide fundamental limits on how much a spin-$j_A$ system can be used to polarise a larger spin-$j_B$ system, and on how much one can invert spin polarisation using a rotationally-symmetric operation. Finally, we also establish novel links between unitarity, complementary channels and purity that are of independent interest.
Generalized Adaptive Differential Evolution algorithm for Solving CEC 2020 Benchmark Problems

The effort devoted in introducing new numerical optimization benchmarks has attracted the attention to develop new optimization algorithms to solve them. Very recently, a new suite on bound constrained optimization problems is proposed as a new addition to CEC benchmark series. Differential Evolution (DE) is a simple Evolutionary Algorithm (EA) which shows superior performance to solve many CEC benchmark during the past years. This paper presents a new extension to DE algorithm through extending the line of research for AGDE algorithm. The new algorithm, which we name GADE, enhanced the DE algorithm by introducing a generalized adaptive framework for enhancing the performance of DE. Numerical experiments on a set of 10 test problems from the CEC2020 benchmarks for 5, 10, 15 and 20 dimensions, including a comparison with state-of-the-art algorithm are executed. Comparative analysis indicates that GADE is superior to other state-of-the-art algorithms in terms of stability, robustness, and quality of solution.
The Average Mixing Kernel Signature

We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks. The average mixing kernel holds information on the average transition probabilities of a quantum walk between each pair of vertices of the mesh until a time T. We define the AMKS by decomposing the spectral contributions of the kernel into several bands, allowing us to limit the influence of noise-dominated high-frequency components and obtain a more descriptive signature. We also show through a perturbation theory analysis of the kernel that choosing a finite stopping time T leads to noise and deformation robustness for the AMKS. We perform an extensive experimental evaluation on two widely used shape matching datasets under varying level of noise, showing that the AMKS outperforms two state-of-the-art descriptors, namely the Heat Kernel Signature (HKS) and the similarly quantum-walk based Wave Kernel Signature (WKS) .
Granger causality detection in high-dimensional systems using feedforward neural networks

This paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure: first, we maximize the transfer of information between input and output variables in the network in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.
Influence of the receptive field size on accuracy and performance of a convolutional neural network

Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. In this paper we study the size of the receptive field of deep convolutional neural networks, in particular, we check the idea of a "redundant" receptive field. We run a set of experiments on two common CNN models - VGG16 and ResNet18 - in order to explore the influence of receptive field size on CNN's training time, accuracy, and performance. We run experiments using the MakiFlow framework on the CALTECH256 dataset. The experiments' results show that the optimization of neural networks (NNs) by reducing the size of the receptive field allows to reduce the NN's training time by 5-7% while maintaining the accuracy of the network.
Hyperspectral face recognition based on sparse spectral attention deep neural networks

Inspired by the robust capability and outstanding performance of convolutional neural networks (CNN) in image classification tasks, CNN-based hyperspectral face recognition methods are worthy of further exploration. However, hyperspectral imaging poses new challenges including high data dimensionality and interference between bands on spectral dimension. High data dimensionality can result in high computational costs. Moreover, not all bands are equally informative and discriminative. The usage of a useless spectral band may even introduce noises and weaken the performance. For the sake of solving those problems, we proposed a novel CNN framework, which adopted a channel-wise attention mechanism and Lasso algorithm to select the optimal spectral bands. The framework is termed as the sparse spectral channel-wise attention-based network (SSCANet) where the SSCA-block focuses on the inter-band channel relationship. Different from other methods which usually select the useful bands manually or in a greedy fashion, SSCA-block can adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing less useful ones. Especially, a Lasso constraint strategy can zero out the bands during the training of the network, which can boost the training process by making the weights of bands sparser. Finally, we evaluate the performance of the proposed method in comparison of other state-of-the-art hyperspectral face recognition algorithms on three public datasets HK-PolyU, CMU, and UWA. The experimental results demonstrate that SSCANet based method outperforms the state-of-the-art methods for face recognition on the benchmark.
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training

We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we explore its use as an alternative to data augmentation and validate its performance against a number of distortion types and transformations including adversarial examples. In our image classification experiments using ImageNet data stability training performs on a par or even outperforms data augmentation for specific transformations, while consistently offering improved robustness against a broader range of distortion strengths and types unseen during training, a considerably smaller hyperparameter dependence and less potentially negative side effects compared to data augmentation.
Exempla gratis (E.G.): code examples for free

Modern software engineering often involves using many existing APIs, both open source and – in industrial coding environments– proprietary. Programmers reference documentation and code search tools to remind themselves of proper common usage patterns of APIs. However, high-quality API usage examples are computationally expensive to curate and maintain, and API usage examples retrieved from company-wide code search can be tedious to review. We present a tool, EG, that mines codebases and shows the common, idiomatic us-age examples for API methods. EG was integrated into Facebook's internal code search tool for the Hack language and evaluated on open-source GitHub projects written in Python. EG was also compared against code search results and hand-written examples from a popular programming website called ProgramCreek. Compared with these two baselines, examples generated by EG are more succinct and representative with less extraneous statements. In addition, a survey with Facebook developers shows that EG examples are preferred in 97% of cases.
Distinct Two-Stream Convolutional Networks for Human Action Recognition in Videos Using Segment-Based Temporal Modeling

The Two-stream convolution neural network (CNN) has proven a great success in action recognition in videos. The main idea is to train the two CNNs in order to learn spatial and temporal features separately, and two scores are combined to obtain final scores. In the literature, we observed that most of the methods use similar CNNs for two streams. In this paper, we design a two-stream CNN architecture with different CNNs for the two streams to learn spatial and temporal features. Temporal Segment Networks (TSN) is applied in order to retrieve long-range temporal features, and to differentiate the similar type of sub-action in videos. Data augmentation techniques are employed to prevent over-fitting. Advanced cross-modal pre-training is discussed and introduced to the proposed architecture in order to enhance the accuracy of action recognition. The proposed two-stream model is evaluated on two challenging action recognition datasets: HMDB-51 and UCF-101. The findings of the proposed architecture shows the significant performance increase and it outperforms the existing methods.
Understanding the impact of GitHub suggested changes on recommendations between developers

Recommendations between colleagues are effective for encouraging developers to adopt better practices. Research shows these peer interactions are useful for improving developer behaviors, or the adoption of activities to help software engineers complete programming tasks. However, in-person recommendations between developers in the workplace are declining. One form of online recommendations between developers are pull requests, which allow users to propose code changes and provide feedback on contributions. GitHub, a popular code hosting platform, recently introduced the suggested changes feature, which allows users to recommend improvements for pull requests. To better understand this feature and its impact on recommendations between developers, we report an empirical study of this system, measuring usage, effectiveness, and perception. Our results show that suggested changes support code review activities and significantly impact the timing and communication between developers on pull requests. This work provides insight into the suggested changes feature and implications for improving future systems for automated developer recommendations, such as providing situated, concise, and actionable feedback.
Exploring in the City with Your Personal Guide:Design and User Study of T-Leap, a Telepresence System

This paper describes a field study conducted with our system, T-Leap, a telepresence system connecting one person (the Viewer), situated indoors, with multiple destinations (the Nodes), that roam outdoors. Here, each Node is a person wearing a module that includes a 360-degree camera and a microphone-speaker. Through our study, we demonstrate that T-Leap enables the Viewer to perform various interactions with the Nodes including being helped by them, collaborating with them, and guiding them. These interactions were demonstrated through three studies completing different tasks: 1) Nodes purchasing souvenirs for the Viewer, 2) Nodes finding objects in the park, and 3) Viewer guiding Nodes to purchase things. The studies were primarily conducted with Taiwanese locals and Japanese visitors in Taipei. Throughout the studies, we found that T-Leap worked especially well for mediating communication between a Viewer with local knowledge acting as a guide and several Nodes who were being guided. To conclude the paper, we broadly discuss our findings, the lessons we learned from our field study, and present recommendations for the future development of mobile and wearable telepresence systems.
UAV Anomaly Detection Using Active Learning and Improved S3VM Model

Unmanned aerial vehicles can complete various specific tasks and play an increasingly important role in various fields. However, UAVs often have anomalies during flight, which is likely to cause huge losses. Therefore, the use of data-driven anomaly detection methods has attracted attention. Because UAV sensors have less labeled data and more unlabeled data, semi-supervised support vector machine (S3VM) classification method is introduced. Considering that unlabeled instances may not have the information content and the distribution assumptions used may not reflect the real time series distribution well, the pure S3VM classification method may not necessarily achieve the ideal classification effect. For existing time series data, active sampling is used to mine the most valuable data of the classifier model through margin sampling (MS). In addition, due to the overlapping of different types of data, the classification boundary of the S3VM classifier cannot be located in a low-density region. By adjusting the distance of most samples to the classification boundary, the classification boundary is in a low-density region, which meets the theoretical design requirements of the classifier. Experiments show that combining MS active learning and improved S3VM classification method will achieve the ideal classification effect. Using the UAV time series data, according to the classification prediction results of the classifier model during the operation stage of the training data training, an estimate of the prediction uncertainty is given. Anomaly detection is realized by comparing the predicted value with the uncertainty interval for classification. This paper uses three sets of UAV channel telemetry data for experimental verification. Using MS active learning and the improved S3VM algorithm compared with SVM and S3VM algorithms, it verifies the effectiveness of the algorithm in different UAV data sets for anomaly detection.
On Structured Design Space Exploration for Mapping of Quantum Algorithms

Quantum algorithms can be expressed as quantum circuits when the circuit model of computation is adopted.Such a circuit description is usually hardware-agnostic, that is, it does not consider the limitations that the quantum hardware might have.In order to make quantum algorithms executable on quantum devices they need to comply to their constraints, which mainly affect the parallelism of quantum operations and the possible interactions between the qubits.The process of adapting a quantum circuit to meet the quantum chip restrictions is known as mapping.The resulting circuit usually has a higher number of gates and depth, decreasing the algorithm's reliability.Different mapping solutions have been already proposed.Most of them are meant for a specific quantum processor and differ in methodology, approach and features.In addition, they are usually only compared in terms of added gates, circuit depth and compilation time.No thorough comparative analysis of the different mapping solutions performance and features has been performed so far.In this paper, we propose to apply structured design space exploration (DSE) methodologies to the mapping procedures.This will allow not only to have a more in depth and structured analysis of their performance but also to identify what features are key and worth to implement.By using DSE we will be able to: i) determine in what regimes some mapping solutions outperform others; ii) derive optimal mapping strategies for specific quantum algorithms and quantum processors; and iii) perform an scalability analysis.In addition, DSE techniques cannot only be applied to the mapping layer that is key for bridging quantum applications to quantum devices, but also to the full-stack quantum computing system allowing for its crosslayer co-design.
PNEL: Pointer Network Based End-To-End Entity Linking over Knowledge Graphs

Question Answering systems are generally modelled as a pipeline consisting of a sequence of steps. In such a pipeline, Entity Linking (EL) is often the first step. Several EL models first perform span detection and then entity disambiguation. In such models errors from the span detection phase cascade to later steps and result in a drop of overall accuracy. Moreover, lack of gold entity spans in training data is a limiting factor for span detector training. Hence the movement towards end-to-end EL models began where no separate span detection step is involved. In this work we present a novel approach to end-to-end EL by applying the popular Pointer Network model, which achieves competitive performance. We demonstrate this in our evaluation over three datasets on the Wikidata Knowledge Graph.
The OpBench Ethereum opcode benchmark framework: Design, implementation, validation and experiments

Ethereum is a public, permissionless blockchain, with Ether as cryptocurrency, and with Turing-complete smart contracts to implement arbitrarily complex distributed applications. Correct operation of Ethereum relies on appropriately rewarding participating nodes (called miners) for the resources used to run the blockchain. In Ethereum the Used Gas determines the reward miners receive for executing a smart contract. If the Used Gas is proportional to the cost of executing a smart contract, irrespective of the platform used, then all miners are incentivized identically. In this paper we propose OpBench, a platform-independent benchmark framework for Ethereum, as a lightweight approach to determine if for operational code (opcodes) the rewarded Used Gas is proportional to the invested CPU time. We implement OpBench for PyEthApp (in Python), Go-Ethereum (in GoLang) and Parity (in Rust). From the experiments we conclude that Used Gas is not always proportional to the required CPU, with up to an order of magnitude difference between opcodes. We also conclude that for most opcodes Parity performs the best of the three clients and that preference for Linux or Windows depends on the chosen Ethereum client software.