Reviewerly/exHarmony huggingface dataset:
Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: A mini review

Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision-making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. Also,a framework for the integration of the two concepts in wind and solar energy studies was presented. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies that apply GIS-based method in site suitability were presented with the criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria in site suitability process for wind and solar resource exploration and the analytical hierarchical process (AHP) has been commonly used for criteria ranking, thus, leaving other MCDM techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning.
X-Fields

We suggest to represent an X-Field ---a set of 2D images taken across different view, time or illumination conditions, i.e., video, lightfield, reflectance fields or combinations thereof---by learning a neural network (NN) to map their view, time or light coordinates to 2D images. Executing this NN at new coordinates results in joint view, time or light interpolation. The key idea to make this workable is a NN that already knows the "basic tricks" of graphics (lighting, 3D projection, occlusion) in a hard-coded and differentiable form. The NN represents the input to that rendering as an implicit map, that for any view, time, or light coordinate and for any pixel can quantify how it will move if view, time or light coordinates change (Jacobian of pixel position with respect to view, time, illumination, etc.). Our X-Field representation is trained for one scene within minutes, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination.
A hybrid feature selection method based on Binary Jaya algorithm for micro-array data classification

Micro-array technology generates high-dimensional data. The high dimensionality of data hampers the learning capability of machine learning algorithms. Dimensionality can be reduced using feature selection (FS) techniques, which is an important and essential pre-processing step to process high dimensional data. In this work, a hybrid filter–wrapper approach is proposed for feature selection. The multi-attribute decision-making method called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used as a filter for informative feature extraction. Further, Binary Jaya algorithm with time-varying transfer function is proposed as a wrapper feature selector to find the optimal subset of features. The proposed approach is tested on 10 benchmark micro-array datasets and compared with state-of-the-art methods. Experimental results suggest that the proposed approach performs better in terms of classification accuracy and it is 10 times faster than existing approaches.
Federated Learning for intrusion detection system: Concepts, challenges and future directions

The rapid development of the Internet and smart devices trigger surge in network traffic making its infrastructure more complex and heterogeneous. The predominated usage of mobile phones, wearable devices and autonomous vehicles are examples of distributed networks which generate huge amount of data each and every day. The computational power of these devices have also seen steady progression which has created the need to transmit information, store data locally and drive network computations towards edge devices. Intrusion detection systems (IDS) play a significant role in ensuring security and privacy of such devices. Machine Learning (ML) and Deep Learning (DL) with Intrusion Detection Systems have gained great momentum due to their achievement of high classification accuracy. However the privacy and security aspects potentially gets jeopardized due to the need of storing and communicating data to centralized server. On the contrary, Federated Learning (FL) fits in appropriately as a privacy-preserving decentralized learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system. In order to establish the need for FL, various types of IDS, relevant ML approaches and its associated issues are discussed. The paper presents detailed overview of the implementation of FL in various aspects of anomaly detection. The allied challenges of FL implementations are also identified which provides idea on the scope of future direction of research. The paper finally presents the plausible solutions associated with the identified challenges in FL based intrusion detection system implementation acting as a baseline for prospective research.
RBFNN-Based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems

This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems, which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using a radial basis function neural network, while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated, while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design, which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given, which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be summarized as follows: 1) an output entropy model is presented using a neural network; 2) a nonlinear filter design algorithm is developed as the main result; and 3) a solution of the entropy assignment problem is obtained, which is an extension of the presented framework.
Faster, Higher, Stronger: E 2.3

E 2.3 is a theorem prover for many-sorted first-order logic with equality. We describe the basic logical and software architecture of the system, as well as core features of the implementation. We particularly discuss recently added features and extensions, including the extension to many-sorted logic, optional limited support for higher-order logic, and the integration of SAT techniques via PicoSAT. Minor additions include improved support for TPTP standard features, always-on internal proof objects, and lazy orphan removal. The paper also gives an overview of the performance of the system, and describes ongoing and future work.
Generative Participatory Design Methodology to Develop Electronic Health Interventions: Systematic Literature Review

Background Generative participatory design (PD) may help in developing electronic health (eHealth) interventions. PD is characterized by the involvement of all stakeholders in creative activities. This is different from the traditional user-centered design, where users are less involved. When looking at PD from a research through design perspective, it is important to summarize the reasons for choosing a certain form of generative PD to further develop its methodology. However, the scientific literature is currently unclear about which forms of PD are used to develop eHealth and which arguments are used to substantiate the decision to use a certain form of generative PD. Objective This study aimed to explore the reporting and substantiation of generative PD methodologies in empirical eHealth studies published in scientific journals to further develop PD methodology in the field of eHealth. Methods A systematic literature review following the Cochrane guidelines was conducted in several databases (EMBASE, MEDLINE Ovid, Web of Science, and CINAHL EBSCOhost). Data were extracted on the recruitment and management of stakeholders, the use of tools, and the use of outcome measures. Results Of the 3131 studies initially identified, 69 were selected for qualitative synthesis. The reporting was very variable, depending to a large extent on whether the study stated that reporting on the PD process was a major aim. The different levels of reporting and substantiation of the choices of a recruitment strategy, stakeholder management, and tools and outcome measures are presented. Only a few authors explicitly used arguments directly related to PD guiding principles such as democratic, mutual learning, tacit and latent knowledge, and collective creativity. Even though PD principles were not always explicitly discussed in the method descriptions of the studies, they were implicitly present, mostly in the descriptions of the use of PD tools. The arguments used to substantiate the choices made in stakeholder management, PD tools, and the type of outcome measures adopted point to the involvement of PD principles. Conclusions Studies that have used a PD research methodology to develop eHealth primarily substantiate the choice of tools made and much less the use of stakeholders and outcome measures.
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.
OPUS-MT – Building open translation services for the World

This paper presents OPUS-MT a project that focuses on the development of free resources and tools for machine translation. The current status is a repository of over 1,000 pre-trained neural machine translation models that are ready to be launched in on-line translation services. For this we also provide open source implementations of web applications that can run efficiently on average desktop hardware with a straightforward setup and installation.
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance

If the same neural network architecture is trained multiple times on the same dataset, will it make similar linguistic generalizations across runs?To study this question, we finetuned 100 instances of BERT on the Multigenre Natural Language Inference (MNLI) dataset and evaluated them on the HANS dataset, which evaluates syntactic generalization in natural language inference.On the MNLI development set, the behavior of all instances was remarkably consistent, with accuracy ranging between 83.6% and 84.8%.In stark contrast, the same models varied widely in their generalization performance.For example, on the simple case of subject-object swap (e.g., determining that the doctor visited the lawyer does not entail the lawyer visited the doctor), accuracy ranged from 0.0% to 66.2%.Such variation is likely due to the presence of many local minima in the loss surface that are equally attractive to a low-bias learner such as a neural network; decreasing the variability may therefore require models with stronger inductive biases.
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

Joint detection of drivable areas and road anomalies is very important for mobile robots. Recently, many semantic segmentation approaches based on convolutional neural networks (CNNs) have been proposed for pixelwise drivable area and road anomaly detection. In addition, some benchmark datasets, such as KITTI and Cityscapes, have been widely used. However, the existing benchmarks are mostly designed for self-driving cars. There lacks a benchmark for ground mobile robots, such as robotic wheelchairs. Therefore, in this article, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating existing state-of-the-art (SOTA) single-modal and data-fusion semantic segmentation CNNs using six modalities of visual features. Furthermore, we propose a novel module, referred to as the dynamic fusion module (DFM), which can be easily deployed in existing data-fusion networks to fuse different types of visual features effectively and efficiently. The experimental results show that the transformed disparity image is the most informative visual feature and the proposed DFM-RTFNet outperforms the SOTAs. In addition, our DFM-RTFNet achieves competitive performance on the KITTI road benchmark.
A Partition Filter Network for Joint Entity and Relation Extraction

In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first.Or they encode entity features and relation features in a parallel manner, meaning that feature representation learning for each task is largely independent of each other except for input sharing.We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter.In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition.The shared partition represents inter-task information valuable to both tasks and is evenly shared across two tasks to ensure proper two-way interaction.The task partitions represent intra-task information and are formed through concerted efforts of both gates, making sure that encoding of taskspecific features is dependent upon each other.Experiment results on six public datasets show that our model performs significantly better than previous approaches.In addition, contrary to what previous work has claimed, our auxiliary experiments suggest that relation prediction is contributory to named entity prediction in a non-negligible way.
Gödel’s incompleteness theorems

Kurt Godel, the greatest logician of our time, startled the world of mathematics in 1931 with his Theorem of Undecidability, which showed that some statements in mathematics are inherently "undecidable." His work on the completeness of logic, the incompleteness of number theory, and the consistency of the axiom of choice and the continuum theory brought him further worldwide fame. In this introductory volume, Raymond Smullyan, himself a well-known logician, guides the reader through the fascinating world of Godel's incompleteness theorems. The level of presentation is suitable for anyone with a basic acquaintance with mathematical logic. As a clear, concise introduction to a difficult but essential subject, the book will appeal to mathematicians, philosophers, and computer scientists.
Dual-Transfer Face Sketch–Photo Synthesis

Recognizing the identity of a sketched face from a face photograph dataset is a critical yet challenging task in many applications, not least law enforcement and criminal investigations. An intelligent sketched face identification system would rely on automatic face sketch synthesis from photographs, thereby avoiding the cost of artists manually drawing sketches. However, conventional face sketch-photo synthesis methods tend to generate sketches that are consistent with the artists'drawing styles. Identity-specific information is often overlooked, leading to unsatisfactory identity verification and recognition performance. In this paper, we discuss the reasons why conventional methods fail to recover identity-specific information. Then, we propose a novel dual-transfer face sketch-photo synthesis framework composed of an inter-domain transfer process and an intra-domain transfer process. In the inter-domain transfer, a regressor of the test photograph with respect to the training photographs is learned and transferred to the sketch domain, ensuring the recovery of common facial structures during synthesis. In the intra-domain transfer, a mapping characterizing the relationship between photographs and sketches is learned and transferred across different identities, such that the loss of identity-specific information is suppressed during synthesis. The fusion of information recovered by the two processes is straightforward by virtue of an ad hoc information splitting strategy. We employ both linear and nonlinear formulations to instantiate the proposed framework. Experiments on The Chinese University of Hong Kong face sketch database demonstrate that compared to the current state-of-the-art the proposed framework produces more identifiable facial structures and yields higher face recognition performance in both the photo and sketch domains.
Non-monotonic sequential text generation

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy's own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.
Stability of stochastic impulsive reaction–diffusion neural networks with S-type distributed delays and its application to image encryption

In this paper, we study stochastic impulsive reaction–diffusion neural networks with S-type distributed delays, aiming to obtain the sufficient conditions for global exponential stability. First, an impulsive inequality involving infinite delay is introduced and the asymptotic behaviour of its solution is investigated by the truncation method. Then, global exponential stability in the mean-square sense of the stochastic impulsive reaction–diffusion system is studied by constructing a simple Lyapunov–Krasovskii functional where the S-type distributed delay is handled by the impulsive inequality. Numerical examples are also given to verify the effectiveness of the proposed results. Finally, the obtained theoretical results are successfully applied to an image encryption scheme based on bit-level permutation and the stochastic neural networks.
Continual Unsupervised Representation Learning.

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning representations without any knowledge about task identity, and we explore scenarios when there are abrupt changes between tasks, smooth transitions from one task to another, or even when the data is shuffled. The proposed approach performs task inference directly within the model, is able to dynamically expand to capture new concepts over its lifetime, and incorporates additional rehearsal-based techniques to deal with catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised learning setting with MNIST and Omniglot, where the lack of labels ensures no information is leaked about the task. Further, we demonstrate strong performance compared to prior art in an i.i.d setting, or when adapting the technique to supervised tasks such as incremental class learning.
Quantity doesn’t buy quality syntax with neural language models

Recurrent neural networks can learn to predict upcoming words remarkably well on average; in syntactically complex contexts, however, they often assign unexpectedly high probabilities to ungrammatical words.We investigate to what extent these shortcomings can be mitigated by increasing the size of the network and the corpus on which it is trained.We find that gains from increasing network size are minimal beyond a certain point.Likewise, expanding the training corpus yields diminishing returns; we estimate that the training corpus would need to be unrealistically large for the models to match human performance.A comparison to GPT and BERT, Transformer-based models trained on billions of words, reveals that these models perform even more poorly than our LSTMs in some constructions.Our results make the case for more data efficient architectures.