Reviewerly/exHarmony huggingface dataset:
The Impact of Noise on Evaluation Complexity: The Deterministic Trust-Region Case

Intrinsic noise in objective function and derivatives evaluations may cause premature termination of optimization algorithms. Evaluation complexity bounds taking this situation into account are presented in the framework of a deterministic trust-region method. The results show that the presence of intrinsic noise may dominate these bounds, in contrast with what is known for methods in which the inexactness in function and derivatives' evaluations is fully controllable. Moreover, the new analysis provides estimates of the optimality level achievable, should noise cause early termination. Numerical experiments are reported that support the theory. The analysis finally sheds some light on the impact of inexact computer arithmetic on evaluation complexity.
An Empirical Study of Korean Sentence Representation with Various Tokenizations

It is important how the token unit is defined in a sentence in natural language process tasks, such as text classification, machine translation, and generation. Many studies recently utilized the subword tokenization in language models such as BERT, KoBERT, and ALBERT. Although these language models achieved state-of-the-art results in various NLP tasks, it is not clear whether the subword tokenization is the best token unit for Korean sentence embedding. Thus, we carried out sentence embedding based on word, morpheme, subword, and submorpheme, respectively, on Korean sentiment analysis. We explored the two-sentence representation methods for sentence embedding: considering the order of tokens in a sentence and not considering the order. While inputting a sentence, which is decomposed by token unit, to the two-sentence representation methods, we construct the sentence embedding with various tokenizations to find the most effective token unit for Korean sentence embedding. In our work, we confirmed: the robustness of the subword unit for out-of-vocabulary (OOV) problems compared to other token units, the disadvantage of replacing whitespace with a particular symbol in the sentiment analysis task, and that the optimal vocabulary size is 16K in subword and submorpheme tokenization. We empirically noticed that the subword, which was tokenized by a vocabulary size of 16K without replacement of whitespace, was the most effective for sentence embedding on the Korean sentiment analysis task.
Demonstration of expert knowledge injection in Fuzzy Rule Interpolation based Q-learning

The learning phase of the traditional reinforcement learning methods can be started without any preliminary knowledge about the problem needed to be solved. The problem related knowledge-base is built based on the reinforcement signals of the environment during the trial and error style learning phase. If a portion of the a priori knowledge about the problem solution is available and if it could be injected into the initial knowledge of the reinforcement learning system, then the learning performance (and the learning ability of an agent) could be significantly improved. The goal of this paper is to highlight the effect of the external expert knowledge inclusion into the Fuzzy Rule Interpolation based Q-learning (FRIQ-learning) method, by briefly introducing a way for expert knowledge injection into FRIQ-learning and a discussion based on simulated runs of a practical benchmark example. The investigations presented here can aid in the designing of behaviour-based robot control systems, in such cases where the available expert knowledge is not enough by itself to construct a sufficiently working system.
The Communication Between Client-Developer in the Process of Requirements Elicitation for a Software Project

Communication is a fundamental part of the requirements elicitation process for a software project, but the constant changes in the requirements make it difficult to complete a project successfully, and sometimes the customer’s expectations are not met. The problem of communication has different reasons, among them, the shortcomings in the process by which the requirements engineer contacts the customer and the customer does not always have a clear idea of what he really needs. In order to identify the reasons for the communication problems between the requirements engineer and the customer, we have carried out a survey with the participation of employees from two software development companies. The results show the comparison and contrast of the effectiveness of the traditional processes against the current processes in the same way, we can highlight the preference of the experts when choosing a method of elicitation against another one and the advantages and disadvantages that each one represents. We also get results about how to improve the processes that present the greatest number of problems. In addition, it is worth mentioning that results were also obtained regarding how professionals recognize when there is a problem between the developer and the client or vice versa and the possible solution that should be applied to avoid some kind of conflict in the medium or long term in the software’s development project.
Semantic segmentation sample augmentation based on simulated scene generation—case study on dock extraction from high spatial resolution imagery

(2021). Semantic segmentation sample augmentation based on simulated scene generation—case study on dock extraction from high spatial resolution imagery. International Journal of Remote Sensing: Vol. 42, No. 13, pp. 4961-4984.
A Layout-Based Classification Method for Visualizing Time-Varying Graphs

Connectivity analysis between the components of large evolving systems can reveal significant patterns of interaction. The systems can be simulated by topological graph structures. However, such analysis becomes challenging on large and complex graphs. Tasks such as comparing, searching, and summarizing structures, are difficult due to the enormous number of calculations required. For time-varying graphs, the temporal dimension even intensifies the difficulty. In this article, we propose to reduce the complexity of analysis by focusing on subgraphs that are induced by closely related entities. To summarize the diverse structures of subgraphs, we build a supervised layout-based classification model. The main premise is that the graph structures can induce a unique appearance of the layout. In contrast to traditional graph theory-based and contemporary neural network-based methods of graph classification, our approach generates low costs and there is no need to learn informative graph representations. Combined with temporally stable visualizations, we can also facilitate the understanding of sub-structures and the tracking of graph evolution. The method is evaluated on two real-world datasets. The results show that our system is highly effective in carrying out visual-based analytics of large graphs.
End-to-End Whispered Speech Recognition with Frequency-Weighted Approaches and Pseudo Whisper Pre-training

Whispering is an important mode of human speech, but no end-to-end recognition results for it were reported yet, probably due to the scarcity of available whispered speech data. In this paper, we present several approaches for end-to-end (E2E) recognition of whispered speech considering the special characteristics of whispered speech and the scarcity of data. This includes a frequency-weighted SpecAugment policy and a frequency-divided CNN feature extractor for better capturing the high-frequency structures of whispered speech, and a layer-wise transfer learning approach to pre-train a model with normal or normal-to-whispered converted speech then fine-tune it with whispered speech to bridge the gap between whispered and normal speech. We achieve an overall relative reduction of 19.8% in PER and 44.4% in CER on a relatively small whispered TIMIT corpus. The results indicate as long as we have a good E2E model pre-trained on normal or pseudo-whispered speech, a relatively small set of whispered speech may suffice to obtain a reasonably good E2E whispered speech recognizer.
Pre-training Strategies and Datasets for Facial Representation Learning

What is the best way to learn a universal face representation? Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of interest (e.g. face recognition, facial landmark localization etc.) but has overlooked the overarching question of how to find a facial representation that can be readily adapted to several facial analysis tasks and datasets. To this end, we make the following 4 contributions: (a) we introduce, for the first time, a comprehensive evaluation benchmark for facial representation learning consisting of 5 important face analysis tasks. (b) We systematically investigate two ways of large-scale representation learning applied to faces: supervised and unsupervised pre-training. Importantly, we focus our evaluations on the case of few-shot facial learning. (c) We investigate important properties of the training datasets including their size and quality (labelled, unlabelled or even uncurated). (d) To draw our conclusions, we conducted a very large number of experiments. Our main two findings are: (1) Unsupervised pre-training on completely in-the-wild, uncurated data provides consistent and, in some cases, significant accuracy improvements for all facial tasks considered. (2) Many existing facial video datasets seem to have a large amount of redundancy. We will release code, and pre-trained models to facilitate future research.
Personalized Extractive Summarization for a News Dialogue System

In modern society, people's interests and preferences are diversifying. Along with this, the demand for personalized summarization technology is increasing. In this study, we propose a method for generating summaries tailored to each user's interests using profile features obtained from questionnaires administered to users of our spoken-dialogue news delivery system. We propose a method that collects and uses the obtained user profile features to generate a summary tailored to each user's interests, specifically, the sentence features obtained by BERT and user profile features obtained from the questionnaire result. In addition, we propose a method for extracting sentences by solving an integer linear programming problem that considers redundancy and context coherence, using the degree of interest in sentences estimated by the model. The results of our experiments confirmed that summaries generated based on the degree of interest in sentences estimated using user profile information can transmit information more efficiently than summaries based solely on the importance of sentences.
RSA and ECC universal, constant time modular inversion

Modular inversion is a crucial operation for performance and security of the public key cryptography. Unfortunately its use cases in the most popular algorithms like ECC and RSA are a bit different. It makes development of universal system/ algorithm for both of them difficult. This paper presents results of the constant time modular inversion algorithm development process which achieves high level of security in context of Timing Attacks and Simple Power Analysis which are basic but serious danger for cryptographic implementations.
A Performance-Stable NUMA Management Scheme for Linux-Based HPC Systems

Linux is becoming the de-facto standard operating system for today's high-performance computing (HPC) systems because it can satisfy the demands of many HPC systems for rich operating system (OS) features. However, owing to features intended for the general-purpose OS, Linux has many OS noise sources such as page faults or thread migrations that can result in the unstable performance of HPC application. Furthermore, in the case of the non-uniform memory access (NUMA) architecture, which has different memory access latencies to local and remote memory nodes, the performance stability of the application can be more exacerbated by the OS noise. In this paper, we address the OS noise caused by Linux in the NUMA architecture and propose a novel performance-stable NUMA management scheme called Stable-NUMA. Stable-NUMA comprises three techniques for improving performance stability: two-level thread clustering, state-based page placement, and selective page profiling. Our proposed Stable-NUMA scheme significantly alleviates OS noise and enhances the local memory access ratio of the NUMA system as compared to Linux. We implemented Stable-NUMA in Linux and experimented with various HPC workloads. The evaluation results demonstrated that Stable-NUMA outperforms Linux with and without its NUMA-aware feature by up to 25% in terms of average performance and 73% in terms of performance stability.
Supervised Hashing with Kernel Based on Feature Fusion for Remote Sensing Image Retrieval

Hashing has emerged as an influential solution to improve high-resolution remote sensing images retrieval (HRRSIR) performance. Most of the existing hashing methods adopt single feature to retrieve images. However, it is difficult for single feature to express the highly complex geometrical structures and spatial patterns of high-resolution remote sensing images. To address these issues, we propose a supervised hashing with kernel based on feature fusion method, which is called FKSH. FKSH mainly includes feature extraction, feature fusion and hash learning. Firstly, GoogLeNet and VGG16 are selected to learn features. In order to keep more spatial information, the features are extracted with the original input size and keep the output form of the three-dimensional tensor. Then max-pooling is performed on the tensor to retain the salient features. Secondly, the features from GoogLeNet are copied to uniform the dimension with that from VGG16. Thus, the features from GoogLeNet and VGG16 can be fused by element-wise addition. Finally, the high-dimensional fused features are mapped to compact binary codes by hashing, and the compact binary codes are adopted to retrieve remote sensing images. Experiments on the two benchmarked datasets clearly shows our superiority.
Optimizing Service Composition (SC) Using Smart Multistage Forward Search (SMFS)

Service Oriented Architecture (SOA) is a style of software design where Web Services (WS) provide services to the other components through a communication protocol over a network. WS components are managed, updated, and rearranged at runtime to provide the business processes as SCs, which consist of a set of WSs that can be invoked in a specific order to fulfill the clients’ requests. According to the Service Level Agreement (SLA) requirements, WS selection and composition are significant perspectives of research to meet the clients’ expectations. This paper presents an effective technique using SMFS that attempts to improve the WS selection as well as SC construction and ultimately optimize the WS resource utilization. The results show that the proposed SMFS technique enhances the WS resource utilization by 9.6% compared to the standard Multistage Forward Search (MFS) technique. Similarly, the number of constructed SCs using the proposed SMFS technique are increased by 36.97% compared to the number of constructed SCs with the standard MFS technique.
Language Model Using Differentiable Neural Computer Based on Forget Gate-Based Memory Deallocation

A differentiable neural computer (DNC) is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism. Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems. In this study, we apply a DNC to a language model (LM) task. The LM task is one of the reasoning problems, because it can predict the next word using the previous word sequence. However, memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory, which degrades performance. Therefore, we propose a forget gate-based memory deallocation (FMD) method, which searches for the minimum value of elements in a forget gate-based retention vector. The forget gate-based retention vector indicates the retention degree of information stored in each external memory address. In experiments, we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example. For LM tasks, we evaluated DNC using the Penn Treebank and enwik8 LM tasks. Although it does not yield SOTA results in LM tasks, the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character. For the speech recognition rescoring tasks, FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate.
Look Who’s Not Talking

The objective of this work is speaker diarisation of speech recordings `in the wild'. The ability to determine speech segments is a crucial part of diarisation systems, accounting for a large proportion of errors. In this paper, we present a simple but effective solution for speech activity detection based on the speaker embeddings. In particular, we discover that the norm of the speaker embedding is an extremely effective indicator of speech activity. The method does not require an independent model for speech activity detection, therefore allows speaker diarisation to be performed using a unified representation for both speaker modelling and speech activity detection. We perform a number of experiments on in-house and public datasets, in which our method outperforms popular baselines.
Structure of Multiple Mirror System from Kaleidoscopic Projections of Single 3D Point

This paper proposes a novel algorithm of discovering the structure of a kaleidoscopic imaging system that consists of multiple planar mirrors and a camera. The kaleidoscopic imaging system can be recognized as the virtual multi-camera system and has strong advantages in that the virtual cameras are strictly synchronized and have the same intrinsic parameters. In this paper, we focus on the extrinsic calibration of the virtual multi-camera system. The problems to be solved in this paper are two-fold. The first problem is to identify to which mirror chamber each of the 2D projections of mirrored 3D points belongs. The second problem is to estimate all mirror parameters, i.e., normals, and distances of the mirrors. The key contribution of this paper is to propose novel algorithms for these problems using a single 3D point of unknown geometry by utilizing a kaleidoscopic projection constraint, which is an epipolar constraint on mirror reflections. We demonstrate the performance of the proposed algorithm of chamber assignment and estimation of mirror parameters with qualitative and quantitative evaluations using synthesized and real data.
Evolutionary GAN–Based Data Augmentation for Cardiac Magnetic Resonance Image

Generative adversarial networks (GANs) have considerable potential to alleviate challenges linked to data scarcity. Recent research has demonstrated the good performance of this method for data augmentation because GANs synthesize semantically meaningful data from standard signal distribution. The goal of this study was to solve the overfitting problem that is caused by the training process of convolution networks with a small dataset. In this context, we propose a data augmentation method based on an evolutionary generative adversarial network for cardiac magnetic resonance images to extend the training data. In our structure of the evolutionary GAN, the most optimal generator is chosen that considers the quality and diversity of generated images simultaneously from many generator mutations. Also, to expand the distribution of the whole training set, we combine the linear interpolation of eigenvectors to synthesize new training samples and synthesize related linear interpolation labels. This approach makes the discrete sample space become continuous and improves the smoothness between domains. The visual quality of the augmented cardiac magnetic resonance images is improved by our proposed method as shown by the data-augmented experiments. In addition, the effectiveness of our proposed method is verified by the classification experiments. The influence of the proportion of synthesized samples on the classification results of cardiac magnetic resonance images is also explored.
Trading support system for portfolio construction using wisdom of artificial crowds and evolutionary computation

• Virtual Experts are used to develop crowd wisdom to outperform market indexes. • Wisdom of Crowd is developed in an artificial framework for portfolio selection. • Artificial Crowds are developed for Trading Support Systems. • Community of Virtual Experts improve stock investment decisions. Effective portfolio management requires vast quantities of information and accurate forecasts to make decisions that generate a profitable strategy. In this study, we propose a framework that extracts useful information from Virtual Experts, which are generated using Strongly-Typed Genetic Programming. Specifically, we created a Virtual Expert pool that provides different recommendations on selling or purchasing of a particular stock, and then applied a Wisdom of Artificial Crowds post-processing algorithm to decide which action to take. We call this framework Community of Virtual Expert Investors, and it is evaluated on different metrics of risk. Results show that this approach manages to outperform a Buy and Hold strategy in a long-term scenario, both in return and in Conditional Sharpe Ratio measures. To test the robustness of these results, a bootstrapping test was performed, in which the general findings of the results were maintained.
SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer

Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods in sustainable development and advanced manufacturing. To date, most manufacturing graph neural networks are mainly evaluated on social and information networks, which improve the quality of network representation y integrating neighbor node descriptions. However, previous methods have not yet been comprehensively studied on biomedical networks. Traditional techniques fail to achieve satisfying results, especially when labeled nodes are deficient in number. In this paper, a new semi-supervised deep learning method for the biomedical graph via sustainable knowledge transfer called SeBioGraph is proposed. In SeBioGraph, both node embedding and graph-specific prototype embedding are utilized as transferable metric space characterized. By incorporating prior knowledge learned from auxiliary graphs, SeBioGraph further promotes the performance of the target graph. Experimental results on the two-class node classification tasks and three-class link prediction tasks demonstrate that the SeBioGraph realizes state-of-the-art results. Finally, the method is thoroughly evaluated.