Reviewerly/exHarmony huggingface dataset:
Strategies for helium pycnometry density measurements of welded ignimbritic rocks

This study is aimed at recognizing possible sources of dispersion of density measurements of welded ignimbritic rocks by helium pycnometer. The values of bulk density and solid fraction volume, useful for volcanological studies as well as for a complete definition of the mechanical properties of these rocks, commonly used for present or historical buildings, are often tricky to obtain, due to the presence of micropores and the high tortuosity of the porous network. Three samples representative of different lithofacies of Miocene ignimbrites from San Pietro Island (SW Sardinia, Italy), have been used here to tune up a density measurement method. Basing on a statistical study of the density data derived from different analytical strategies, we propose a specific procedure for determining to measure density of welded ignimbritic rocks. Several measurements (runs) are performed on the undisturbed and on powered specimens of the three samples, using two different methods of gas-purging of the pycnometer: flow and pulse purging modes, respectively. The main factors that can produce a source of accidental errors are: uncontrolled fluctuations in temperature of the instrument, pollution of the sample (e.g., moisture), intrinsic petrophysical characteristics (e.g., pore tortuosity) of ignimbritic rocks. Conversely, the main systematic errors introduced in the measurements revealed to be related to the selected time and mode of gas-purging of the instrument during the first steps of the analysis. Using an appropriate sequence of contiguous cycles, accuracy and precision of data can be optimized, thus excluding systematic errors. The results show that in the first cycle, regardless of the purge time (or pulses), the values ​​of density and standard deviations are always higher compared to the second and the third cycles. Basing on the analysis of our data, we propose that the better procedure to obtain an accuracy data is using 10-run cycles and considering the average value of the last seven measures.
Understanding static code warnings: An incremental AI approach

Knowledge-based systems reason over some knowledge base. Hence, an important issue for such systems is how to acquire the knowledge needed for their inference. This paper assesses active learning methods for acquiring knowledge for “static code warnings”. Static code analysis is a widely-used method for detecting bugs and security vulnerabilities in software systems. As software becomes more complex, analysis tools also report lists of increasingly complex warnings that developers need to address on a daily basis. Such static code analysis tools are usually over-cautious; i.e. they often offer many warnings about spurious issues. Previous research work shows that about 35% to 91 % warnings reported as bugs by SA tools are actually unactionable (i.e., warnings that would not be acted on by developers because they are falsely suggested as bugs). Experienced developers know which errors are important and which can be safely ignored. How can we capture that experience? This paper reports on an incremental AI tool that watches humans reading false alarm reports. Using an incremental support vector machine mechanism, this AI tool can quickly learn to distinguish spurious false alarms from more serious matters that deserve further attention. In this work, nine open-source projects are employed to evaluate our proposed model on the features extracted by previous researchers and identify the actionable warnings in a priority order given by our algorithm. We observe that our model can identify over 90% of actionable warnings when our methods tell humans to ignore 70 to 80% of the warnings.
Artificial Players in the Design Process: Developing an Automated Testing Tool for Game Level and World Design

Iterative user-centred design has become a standard approach for developing interactive products. This process relies on prototyping and usertesting as early as possible to deliver a positive user experience and ensure that final products align with designers' intentions. The video game industry has benefited greatly from this design approach, with games user researchers adapting and introducing various evaluation techniques. However, repeatedly creating game builds suitable for usertesting (i.e., high-fidelity prototypes) is time-consuming and expensive. Moreover, recruiting users and conducting evaluation sessions are labour-intensive tasks. These challenges are especially pressing in the evaluation of game level and world design, where designers may wish to evaluate many alternatives or rapidly measure the impact of many small design changes on a game's ability to deliver the intended experience. To support developers grappling with these challenges, we have developed PathOS, a novel tool for simulating testing sessions with agents that model player navigation. This paper reports on our development objectives, implementation, and a user study with professional game developers to assess PathOS' application in a level design context. Our results demonstrate the ability of an automated testing tool to enhance the workflow of practicing developers.
Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition

Beyond generating long and topic-coherent paragraphs in traditional captioning tasks, the medical image report composition task poses more task-oriented challenges by requiring both the highly accurate medical term diagnosis and multiple heterogeneous forms of information, including impression and findings. Current methods often generate the most common sentences due to dataset bias for the individual case, regardless of whether the sentences properly capture key entities and relationships. Such limitations severely hinder their applicability and generalization capability in medical report composition, where the most critical sentences lie in the descriptions of abnormal diseases that are relatively rare. Moreover, some medical terms appearing in one report are often entangled with each other and co-occurred, for example, symptoms associated with a specific disease. To enforce the semantic consistency of medical terms to be incorporated into the final reports and encourage the sentence generation for rare abnormal descriptions, we propose a novel framework that unifies template retrieval and sentence generation to handle both common and rare abnormality while ensuring the semantic coherency among the detected medical terms. Specifically, our approach exploits hybrid-knowledge co-reasoning: 1) explicit relationships among all abnormal medical terms to induce the visual attention learning and topic representation encoding for better topic-oriented symptoms descriptions and 2) adaptive generation mode that changes between the template retrieval and sentence generation according to a contextual topic encoder. The experimental results on two medical report benchmarks demonstrate the superiority of the proposed framework in terms of both human and metrics evaluation.
Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams

Examples of comparing the tweets of Hilary Clinton (pink) and Donald Trump (blue) during the presidential election of 2016 with the use of Co-Bridges.Representations of the discussions concerning issues "America", "families", "great", and "AmericaFirst" are juxtaposed for comparison.
A Robust Self-Adaptive Interval Type-2 TS Fuzzy Logic for Controlling Multi-Input–Multi-Output Nonlinear Uncertain Dynamical Systems

Recently, Type-2 fuzzy systems have become increasingly prominent as they have been applied to various nonlinear control applications. This article presents an adaptive fuzzy controller based on the sliding-mode control theory. The proposed self-adaptive interval Type-2 fuzzy controller (SAF2C) is based on the Takagi–Sugeno (TS) fuzzy model and it accommodates the “enhanced iterative algorithm with stop condition” type-reducer, which is more computationally efficient than the “Kernel–Mendel” type-reduction algorithm. We developed an integrated multi-input–multi-output (MIMO) SAF2C-controller to reduce the computation time so that we can expedite the learning process of our control algorithm by 80% compared to separate single-input–single-output (SISO) controllers. The stability of our controller is proven using the Lyapunov technique. To ensure the applicability of the presented control scheme, we implemented our controller on various nonlinear systems, including a hexacopter unmanned aerial vehicle (UAV). We also compare the accuracy of our controller with a conventional proportional–integral–derivative autopilot system. Our research indicates around 20% improvement in its transient response, in addition to achieving a better noise rejection capability with respect to a Type-1 fuzzy counterpart.
Fast Adapting Without Forgetting for Face Recognition

Although face recognition has made dramatic improvements in recent years, there are still many challenges in real-world applications such as face recognition for the elderly and children, for the surveillance scenes and for Near infrared vs. Visible light (NIR-VIS) heterogeneous scene, etc. Due to the existence of these challenges, there are usually domain gaps between training (source domain) and test (target domain). A common way to improve the performance on the target domain is fine-tuning the base model trained on source domain using target data. However, it will severely degrade performance on the source domain. Another way which jointly trains models using both source and target data, suffers from the heavy computations and large data storage, especially when we continue to encounter new domains. In response to these problems, we introduce a new challenging task: Single Exemplar Domain Incremental Learning (SE-DIL), which utilizes the target domain data and just one exemplar per identity from source domain data to quickly improve the performance on the target domain while keeping the performance on the source domain. To deal with SE-DIL, we propose our Fast Adapting without Forgetting (FAwF) method with three components: margin-based exemplar selection, prototype-based class extension and hard&soft knowledge distillation. Through FAwF, we can well maintain the source domain performance with only one sample per source domain class, greatly reducing the fine-tuning time-cost and data storage. Besides, we collected a large-scale children face dataset KidsFace with 12 K identities for studying the SE-DIL in face recognition. Extensive analysis and experiments on our KidsFace-Test protocol and other challenging face test sets show that our method performs better than the state-of-the-art methods on both target and source domain.
Proxy Signature-Based Management Model of Sharing Energy Storage in Blockchain Environment

Sharing energy storage (SES) is a novel business model in order to increase the profits and improve the utilization rate of idle energy storage facilities. On the other hand, blockchains can be competently applied in the transaction and operation of SES because of distributed network architecture, traceability and tamper proof. In this paper, a management model of SES based on proxy signatures in the blockchain environment is proposed. Many management models including the principal-agent model are analyzed for SES in terms of benefit, cost, resources, and so on. Moreover, a blockchain framework and a typical transaction process of SES is presented. Finally, a proxy signature mechanism based on the ElGamal algorithm is proposed in order to address the problem that the signature power of nodes cannot be transferred on blockchains. Simulation results show that the proposed proxy signature mechanism can achieve the delegation of digital signature power under the premise of security and reliability, which is suitable for the management model of SES on blockchains.
Statistical test criteria for sensitivity indexes of image cryptosystems

System sensitivity is an important security evaluation aspect of image cryptosystems. On the basis of probability and statistics this paper studies in detail the theoretical-value calculation methods of sensitivity evaluation indexes of image cryptosystems: the expected values and variances of NPCR (number of pixels change rate), UACI (unified average changing intensity), and BACI (blocked average changing intensity). Also, the acceptance domains of listed indexes are discussed using the central limit theorem, and five image cryptosystems are employed to test the acceptance domains. Finally, the following conclusion is drawn: the image cryptosystem is identified as sensitive when the passing rate of the BACI index of multiple tests is higher than 50% under the conditions that the number of samples is 100 and the significance level is α = 0.01, or when the maximum relative error of the average values of the BACI indexes of multiple tests is less than 0.1%.
A Personalized Classifier for Human Motion Activities With Semi-Supervised Learning

The motion activities performed by a mobile or wearable device user are characteristic of the user and the accuracy of detection of motion activity context can be improved by incorporating personalized samples of motion activity. But obtaining personalized samples of motion activity from device user is not practically feasible. In this article, a semi-supervised method is presented for improving the classification accuracy of the mobile or wearable device user's motion activities by deriving a classifier that is personalized to the user starting from a factory-set generalized classifier. The novelty lies in using information theoretic criterion to select personalized data samples from all the personalized data samples collected from the target user. This is useful in improving the motion activity detection accuracy, since each device user has a distinct gait personality as compared to the motion activity data patterns available in a generalized database that are used to train a generalized classifier. The average accuracy for 11 target users of a generalized classifier is 93.11% that increases to 96.50% using the proposed method when the device is used in in-hand mode. It is observed that the accuracy improvement with a personalized classifier over a generalized classifier is greater for those subjects whose accuracy is lower than others with the generalized classifier.
Pre-training Polish Transformer-Based Language Models at Scale

Transformer-based language models are now widely used in Natural Language Processing (NLP). This statement is especially true for English language, in which many pre-trained models utilizing transformer-based architecture have been published in recent years. This has driven forward the state of the art for a variety of standard NLP tasks such as classification, regression, and sequence labeling, as well as text-to-text tasks, such as machine translation, question answering, or summarization. The situation have been different for low-resource languages, such as Polish, however. Although some transformer-based language models for Polish are available, none of them have come close to the scale, in terms of corpus size and the number of parameters, of the largest English-language models. In this study, we present two language models for Polish based on the popular BERT architecture. The larger model was trained on a dataset consisting of over 1 billion polish sentences, or 135 GB of raw text. We describe our methodology for collecting the data, preparing the corpus, and pre-training the model. We then evaluate our models on thirteen Polish linguistic tasks, and demonstrate improvements over previous approaches in eleven of them.
Image edge detection with a photonic spiking VCSEL-neuron

We report both experimentally and in theory on the detection of edge features in digital images with an artificial optical spiking neuron based on a vertical-cavity surface-emitting laser (VCSEL). The latter delivers fast (&lt; 100 ps) neuron-like optical spikes in response to optical inputs pre-processed using convolution techniques; hence representing image feature information with a spiking data output directly in the optical domain. The proposed technique is able to detect target edges of different directionalities in digital images by applying individual kernel operators and can achieve complete image edge detection using gradient magnitude. Importantly, the neuromorphic (brain-like) spiking edge detection of this work uses commercially sourced VCSELs exhibiting responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the important telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-centre technologies.
Adaptive Multimodal Fusion for Facial Action Units Recognition

Multimodal facial action units (AU) recognition aims to build models that are capable of processing, correlating, and integrating information from multiple modalities (i.e., 2D images from a visual sensor, 3D geometry from 3D imaging, and thermal images from an infrared sensor). Although the multimodel data can provide rich information, there are two challenges that have to be addressed when learning from multimodal data: 1) the model must capture the complex cross-modal interactions in order to utilize the additional and mutual information effectively; 2) the model must be robust enough in the circumstance of unexpected data corruptions during testing, in case of a certain modality missing or being noisy. In this paper, we propose a novel A daptive M ultimodal F usion method (AMF ) for AU detection, which learns to select the most relevant feature representations from different modalities by a re-sampling procedure conditioned on a feature scoring module. The feature scoring module is designed to allow for evaluating the quality of features learned from multiple modalities. As a result, AMF is able to adaptively select more discriminative features, thus increasing the robustness to missing or corrupted modalities. In addition, to alleviate the over-fitting problem and make the model generalize better on the testing data, a cut-switch multimodal data augmentation method is designed, by which a random block is cut and switched across multiple modalities. We have conducted a thorough investigation on two public multimodal AU datasets, BP4D and BP4D+, and the results demonstrate the effectiveness of the proposed method. Ablation studies on various circumstances also show that our method remains robust to missing or noisy modalities during tests.
Resource provisioning in scalable cloud using bio-inspired artificial neural network model

Resource assignment is one of the emerging research area in the cloud scenario. Cloud computing provides a shared pool of resources in a distributed environment. It supports the features of utility-based computing. Efficient task provisioning on virtual machines is the major concern in an extensible cloud computing environment. The task provisioning minimizes the performance metrics total completion time (ms), average start time, average finish time, average execution time, scheduling time, and simulation time respectively. The scheduling is an important problem which becomes more complicated when various parameters consider. The key issue in virtual machine level scheduling is execution time overhead and scalability in a real-time scenario. Our objective is to make an optimal schedule of tasks on a virtual machine inside the datacenter using neural-bio inspired GA-ANN technique. This work presents a scheduler based on a genetic approach and an artificial neural network. The presented approach performs optimal scheduling of tasks on an appropriate virtual machine. The reliability of the system improves by reducing the number of tasks failed. The presented work uses a genetic algorithm to generated huge data sets and trains the neural model using the data set generated by using a genetic approach. The accuracy of the model is improved using back propagation with 98% accuracy. The set of experiments are performed using a scalable cloud computing environment. The presented bio-inspired technique is compared against nature-inspired, bio-inspired cost-aware BB–BC, GA-Cost, and GA-Exe based efficient task scheduling techniques. The results are obtained using real workload logs and synthetic data sets. Results indicate that the proposed GA-ANN bio-inspired predictive approach outperforms the considered nature-inspired scheduling approaches. The proposed algorithm is compared using various performance metrics total completion time, average start time, average finish time, and the fault rate, execution time, and scheduling time respectively. The proposed model reduces the fault rate by 82.63%, successfully completed tasks count improves by 26.81% and execution time improves by 10.66% and scheduling time improves by 69.94%. The scheduling time improves by 85.76% with an increasing number of iterations and constant numbers of tasks. Hence the presented GA-ANN scheduling technique outperformed the GA cost, GA EXE, and BB–BC COST scheduling approaches.
Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors

Recent image-to-image (I2I) translation algorithms focus on learning the mapping from a source to a target domain. However, the continuous translation problem that synthesizes intermediate results between two domains has not been well-studied in the literature. Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains. Existing I2I approaches are limited to either intra-domain or deterministic inter-domain continuous translation. In this work, we present an effectively signed attribute vector, which enables continuous translation on diverse mapping paths across various domains. In particular, we introduce a unified attribute space shared by all domains that utilize the sign operation to encode the domain information, thereby allowing the interpolation on attribute vectors of different domains. To enhance the visual quality of continuous translation results, we generate a trajectory between two sign-symmetrical attribute vectors and leverage the domain information of the interpolated results along the trajectory for adversarial training. We evaluate the proposed method on a wide range of I2I translation tasks. Both qualitative and quantitative results demonstrate that the proposed framework generates more high-quality continuous translation results against the state-of-the-art methods.
Large-Scale Few-Shot Learning via Multi-modal Knowledge Discovery

Large-scale few-shot learning aims at identifying hundreds of novel object categories where each category has only a few samples. It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results. To solve these problems, we propose a method based on multi-modal knowledge discovery. First, we use the visual knowledge to help the feature extractors focus on different visual parts. Second, we design a classifier to learn the distribution over all categories. In the second stage, we develop three schemes to minimize the prediction error and balance the training procedure: (1) Hard labels are used to provide precise supervision. (2) Semantic textual knowledge is utilized as weak supervision to find the potential relations between the novel and the base categories. (3) An imbalance control is presented from the data distribution to alleviate the recognition bias towards the base categories. We apply our method on three benchmark datasets, and it achieves state-of-the-art performances in all the experiments.