Reviewerly/exHarmony huggingface dataset:
Improved Hybrid Memory Cube for Weight-Sharing Deep Convolutional Neural Networks

In recent years, many deep neural network accelerator architectures are proposed to improve the performance of processing deep neural network models. However, memory bandwidth is still the major issue and performance bottleneck of the deep neural network accelerators. The emerging 3D memory, such as hybrid memory cube (HMC) and processing-in-memory techniques provide new solutions to deep neural network implementation. In this paper, a novel HMC architecture is proposed for weight-sharing deep convolutional neural networks in order to solve the memory bandwidth bottleneck during the neural network implementation. The proposed HMC is designed based on conventional HMC architecture with only minor changes. In the logic layer, the vault controller is modified to enable parallel vault access. The weight parameters of pre-trained convolutional neural network are quantized to 16 numbers. During processing, the accumulation of the activations with shared weights is performed and only the accumulated results are transferred to the processing elements to perform multiplications with weights. By using this proposed architecture, the data transfer between main memory and processing elements can be reduced and the throughout of convolution operations can be improved by 30% compared to using HMC based multiply-accumulate design.
Transductive Zero-Shot Learning via Visual Center Adaptation

In this paper, we propose a Visual Center Adaptation Method (VCAM) to address the domain shift problem in zero-shot learning. For the seen classes in the training data, VCAM builds an embedding space by learning the mapping from semantic space to some visual centers. While for unseen classes in the test data, the construction of embedding space is constrained by a symmetric Chamfer-distance term, aiming to adapt the distribution of the synthetic visual centers to that of the real cluster centers. Therefore the learned embedding space can generalize the unseen classes well. Experiments on two widely used datasets demonstrate that our model significantly outperforms state-of-the-art methods.
Action Space Learning for Heterogeneous User Behavior Prediction

Users' behaviors observed in many web-based applications are usually heterogeneous, so modeling their behaviors considering the interplay among multiple types of actions is important. However, recent collaborative filtering (CF) methods based on a metric learning approach cannot learn multiple types of user actions, because they are developed for only a single type of user actions. This paper proposes a novel metric learning method, called METAS, to jointly model heterogeneous user behaviors. Specifically, it learns two distinct spaces: 1) action space which captures the relations among all observed and unobserved actions, and 2) entity space which captures high-level similarities among users and among items. Each action vector in the action space is computed using a non-linear function and its corresponding entity vectors in the entity space. In addition, METAS adopts an efficient triplet mining algorithm to effectively speed up the convergence of metric learning. Experimental results show that METAS outperforms the state-of-the-art methods in predicting users' heterogeneous actions, and its entity space represents the user-user and item-item similarities more clearly than the space trained by the other methods.
Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit

An automatic machine learning (AutoML) task is to select the best algorithm and its hyper-parameters simultaneously. Previously, the hyper-parameters of all algorithms are joint as a single search space, which is not only huge but also redundant, because many dimensions of hyper-parameters are irrelevant with the selected algorithms. In this paper, we propose a cascaded approach for algorithm selection and hyper-parameter optimization. While a search procedure is employed at the level of hyper-parameter optimization, a bandit strategy runs at the level of algorithm selection to allocate the budget based on the search feedbacks. Since the bandit is required to select the algorithm with the maximum performance, instead of the average performance, we thus propose the extreme-region upper confidence bound (ER-UCB) strategy, which focuses on the extreme region of the underlying feedback distribution. We show theoretically that the ER-UCB has a regret upper bound O(K ln n) with independent feedbacks, which is as efficient as the classical UCB bandit. We also conduct experiments on a synthetic problem as well as a set of AutoML tasks. The results verify the effectiveness of the proposed method.
Thoughts on Course Construction of “New Energy Vehicle Lightweight Technology”

Based on the current situation that the course "New Energy Vehicle Lightweight Technology" is a new professional course, this paper expounds the construction objectives, theoretical teaching content and practical teaching content of the course, and also puts forward some teaching methods and means to improve the teaching effect of the course.
A Novel Data Hiding Method based on Edge Detection and 2k Correction with High Payload and High Visual Quality

In this paper a novel edge adaptive data hiding method is proposed. The proposed edge adaptive steganography method is improved version of the Bai et al.’s [31] (A high payload steganographic algorithm based on edge detection) method. The main aim of the proposed method is to achieve high payload with high visual quality. This method consists of pre-processing, edge detection, classification of the secret data, data embedding and data extraction phases. In the pre-processing phase 6 and 7 least significant bits (LSB) elimination are applied on the cover image to get a better edge detection in the next phase. As edge detection methods Sobel, Canny, Laplacian Of Gaussian (LOG), block based edge detection and hybrid edge detectors are used. After the edge detection, secret data is divided into 2 classes and then these are embedded into edge pixels and texture pixels of the image. In order to increase the visual quality 2k correction is applied on the stego images. Modulus operator is utilized in the data extraction phase. In the experimental results, payload and visual quality measurements demonstrated the success of the proposed method.
ATSIS: Achieving the Ad hoc Teamwork by Sub-task Inference and Selection

In an ad hoc teamwork setting, the team needs to coordinate their activities to perform a task without prior agreement on how to achieve it. The ad hoc agent cannot communicate with its teammates but it can observe their behaviour and plan accordingly. To do so, the existing approaches rely on the teammates' behaviour models. However, the models may not be accurate, which can compromise teamwork. For this reason, we present Ad Hoc Teamwork by Sub-task Inference and Selection (ATSIS) algorithm that uses a sub-task inference without relying on teammates' models. First, the ad hoc agent observes its teammates to infer which sub-tasks they are handling. Based on that, it selects its own sub-task using a partially observable Markov decision process that handles the uncertainty of the sub-task inference. Last, the ad hoc agent uses the Monte Carlo tree search to find the set of actions to perform the sub-task. Our experiments show the benefits of ATSIS for robust teamwork.
A comparative study on handwritten Bangla character recognition

Recognition of handwritten Bangla characters has drawn considerable attention recently. The Bangla language is rich with characters of various styles such as numerals, basic characters, and compound and modifier characters. The inherent variation in individual writing styles, along with the complex, cursive nature of characters, makes the recognition task more challenging. To compare the outcomes of handwritten Bangla character recognition, this study considers two different approaches. The first one is classifier-based, where a hybrid model of the feature extraction technique extracts the features and a multiclass support vector machine (SVM) performs the recognition. The second one is based on a convolution neural network (CNN). For recognition, we considered 10 Bangla numerals, 50 basic characters, and a subset of compound characters that are frequently used in the Bangla language. Experimental results demonstrate that the CNN model outperforms the traditional classifier-based approach, obtaining 98.04%, 99.68%, and 98.18% recognition accuracy for Bangla basic characters, numerals, and the subset of compound characters, respectively.
Structure Learning for Safe Policy Improvement

We investigate how Safe Policy Improvement (SPI) algorithms can exploit the structure of factored Markov decision processes when such structure is unknown a priori. To facilitate the application of reinforcement learning in the real world, SPI provides probabilistic guarantees that policy changes in a running process will improve the performance of this process. However, current SPI algorithms have requirements that might be impractical, such as: (i) availability of a large amount of historical data, or (ii) prior knowledge of the underlying structure. To overcome these limitations we enhance a Factored SPI (FSPI) algorithm with different structure learning methods. The resulting algorithms need fewer samples to improve the policy and require weaker prior knowledge assumptions. In well-factorized domains, the proposed algorithms improve performance significantly compared to a flat SPI algorithm, demonstrating a sample complexity closer to an FSPI algorithm that knows the structure. This indicates that the combination of FSPI and structure learning algorithms is a promising solution to real-world problems involving many variables.
The potential of semantic paradigm in warehousing of big data

Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases.
An Empirical Comparison of FAISS and FENSHSES for Nearest Neighbor Search in Hamming Space.

In this paper, we compare the performances of FAISS and FENSHSES on nearest neighbor search in Hamming space--a fundamental task with ubiquitous applications in nowadays eCommerce. Comprehensive evaluations are made in terms of indexing speed, search latency and RAM consumption. This comparison is conducted towards a better understanding on trade-offs between nearest neighbor search systems implemented in main memory and the ones implemented in secondary memory, which is largely unaddressed in literature.
Review of Unsupervised Adaptive Resonance Theory

Introduced by Stephen Grossberg in the year 1976 Adaptive Resonance Theory (ART), deals a wide variety of Artificial Neural Networks. Initially ART system was mainly for the unsupervised type learning but introduction of different ART systems have lead it deal the supervised learning as well. ART covers a huge range of neural networks for solving the problem of stability and plasticity. ART NN is appreciated for emerging mature clusters of random structures of input patterns by self-organizing capability. There are several types of architectures of ART neural network. This paper shows different type of ART architecture for unsupervised learning such as ART1, ART2, ART2A, ART3 and Fuzzy ART with parameters like choice, pre-processing and adaption rule. The motive of study is to support several researchers in ART architecture area in order for better understanding of suitable components while designing specific neural network classifier.
Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

The utilization of gradient information is a key issue in building Scale Invariant Feature Transform (SIFT)-like descriptors. In the literature, two types of gradient information, i.e., Gradient Magnitude (GM) and Gradient Occurrence (GO), are used for building descriptors. However, both of these two types of gradient information have limitations in building and matching local image descriptors. In our prior work, a strategy of combining these two types of gradient information was proposed to intersect the keypoint matches which are obtained by using gradient magnitude and gradient occurrence individually. Different from this combination strategy, this paper explores novel strategies of weighting these two types of gradient information to build new descriptors with high discriminative power. These proposed weighting strategies are extensively evaluated against gradient magnitude and gradient occurrence as well as the combination strategy on a few image registration datasets. From the perspective of building new descriptors, experimental results will show that each of the proposed strategies achieve higher matching accuracy as compared to both GM-based and GO-based descriptors. In terms of recall results, one of the proposed strategies outperforms both GM-based and GO-based descriptors.
Correction to: An Instance Theory of Semantic Memory

Figure 9 in the original version of the article contained an error. The corrected Fig. 9 is presented below. Conclusions from the Instance Theory of Semantics (ITS) are preserved. However, conclusions from LSA and BEAGLE are not.
Multilayered Narration in Electroacoustic Music Composition Using Nuclear Magnetic Resonance Data Sonification and Acousmatic Storytelling

Nuclear magnetic resonance (NMR) spectroscopy is an analytical tool to determine the structure of chemical compounds. Unlike other spectroscopic methods, signals recorded using NMR spectrometers are frequently in a range of zero to 20000 Hz, making direct playback possible. As each type of molecule has, based on its structural features, distinct and predictable features in its NMR spectra, NMR data sonification can be used to create auditory ‘fingerprints’ of molecules. This paper describes the methodology of NMR data sonification of the nuclei nitrogen, phosphorous, and oxygen and analyses the sonification products of DNA and protein NMR data. The paper introduces On the Extinction of a Species, an acousmatic music composition combining NMR data sonification and voice narration. Ideas developed in electroacoustic composition, such as acousmatic storytelling and sound-based narration are presented and investigated for their use in sonification-based creative works.
Voice verification and identification using i-vector representation

In the area of voice recognition, many methods have been proposed over time. Automatic speaker recognition technology has reached a good level of performance, but still needs to be improved. Signature verification (SV) is one of the most common methods of identity verification in the banking sector, where for security reasons, it is very important to have an accurate method for automatic signature verification (ASV). ASV is usually solved by comparing a test signature with a registration signature(-s) signed by the person whose identity is declared in two ways: online and offline. In this study, a new i-vector based method is proposed for SV online. In the proposed method, a fixed-length vector, called an i-vector, is extracted from each signature, and then this vector is used to create a template. Several methods, such as the nuisance attribute projection and the within-class covariance normalization, are also being investigated to reduce the intra-class variation in the i-vector space. At the stage of evaluation and decision-making, they also propose to apply the support vector machine with two classes. In this article, a new low-dimensional space, depending on the dynamics and the channel, is determined using a simple factor analysis, also known as i-vector. I-vectors have proven to be the most efficient functions for text independent speaker verification in recent studies.
A Label-Embedding Online Nonnegative Matrix Factorization Algorithm

Nonnegative matrix factorization is a widely used data processing method, which has been applied in many fields, such as data dimension reduction and feature extraction. Considering the label information of training samples is helpful to improve the performance of data dimension reduction and classification, and how to apply the subspace calculated by training samples to testing samples to improve the computation efficiency is also a problem worth considering. In this paper, an algorithm called label-embedding online nonnegative matrix factorization (LEONMF) is proposed for image dimensionality reduction and classification. First, the label is embedded in the training matrix, by which the base matrix and the weight matrix can be calculated. The data amount of the base matrix is greatly small than that of the input matrix data. Next, the base matrix is adjusted and connected with the testing matrix to get the new connection matrix. The new connection matrix can be expressed as the product of the new base matrix and the new weight matrix. The new weight matrix can be used for classification after adjustment. The experimental results demonstrated that the proposed LEONMF outperforms the state-of-the-art algorithms in classification accuracy and calculation speed.
Artificial Neural Network in the CATS Training System

This paper presents a variant of using an artificial neural network (ANN) for adaptive learning. The main idea of using ANN is to apply it for a specific educational material, so that after completing the course or its separate topic, the student can determine, not only his level of knowledge, without the teacher’s participation, but also get some recommendations on what material needs to be studied further due to gaps in the studied issues. This approach allows you to build an individual learning trajectory, significantly reduce the time to study academic disciplines and improve the quality of the educational process. The training of an artificial neural network takes place according to the method of back propagation of an error. The developed ANN can be applied to study any academic discipline with a different number of topics and control questions. The research results are implemented and tested in the CATS adaptive training system. This system is the author's development.
Monte Carlo Tree Search for Policy Optimization

Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free methods (e.g., genetic algorithms or evolution strategies) help mitigate these issues, poor initialization and local optima are still concerns in highly nonconvex spaces. This paper presents a method for policy optimization based on Monte-Carlo tree search and gradient-free optimization. Our method, called Monte-Carlo tree search for policy optimization (MCTSPO), provides a better exploration-exploitation trade-off through the use of the upper confidence bound heuristic. We demonstrate improved performance on reinforcement learning tasks with deceptive or sparse reward functions compared to popular gradient-based and deep genetic algorithm baselines.
Joining Together to Build More: The New England Software Carpentry Library Consortium

In 2017 a group of academic library and information technology staff from institutions across New England piloted a process of joining The Carpentries, an organization developed to train researchers in essential computing skills and practices for automating and improving their handling of data, as a consortium. The New England Software Carpentry Library Consortium (NESCLiC) shared a gold-level tier membership to become a Carpentries member organization. NESCLiC members attended a Software Carpentry workshop together and then participated in instructor training as a cohort, collaborating on learning the material, practicing, and beginning to host and teach workshops as a group. This article describes both the successes and challenges of forming this new consortium, suggests good practices for those who might wish to form similar collaborations, and discusses the future of this program and other efforts to help researchers improve their computing and data handling skills.
Theoretical Investigation of Generalization Bound for Residual Networks

This paper presents a framework for norm-based capacity control with respect to an lp,q-norm in weight-normalized Residual Neural Networks (ResNets). We first formulate the representation of each residual block. For the regression problem, we analyze the Rademacher Complexity of the ResNets family. We also establish a tighter generalization upper bound for weight-normalized ResNets. in a more general sight. Using the lp,q-norm weight normalization in which 1/p+1/q &gt;=1, we discuss the properties of a width-independent capacity control, which only relies on the depth according to a square root term. Several comparisons suggest that our result is tighter than previous work. Parallel results for Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) are included by introducing the lp,q-norm weight normalization for DNN and the lp,q-norm kernel normalization for CNN. Numerical experiments also verify that ResNet structures contribute to better generalization properties.
A Semantic Web Approach to Enable a Smart Route to Historical Archives

In this paper we show that an ontology-based approach can be beneficial for enhancing the access to cultural resources, and in particular historical documents. The paper starts with an overview of our approach, aimed at providing online archival systems with a semantic layer based on Semantic Web standards (OWL 2 and RDF). Two projects are introduced, namely Harlock900 and PRiSMHA, carried out in collaboration with local cultural institutions owning rich historical archives. In particular, the paper describes the computational ontologies supporting the approach, and then focuses on two case studies showing that our framework provides better results if compared with standard access systems. The case studies show the enhancement provided by a semantically rich representation of time intervals and a detailed formal description of events and their participants.