Reviewerly/exHarmony huggingface dataset:
Leveraging Multimodal Out-of-Domain Information to Improve Low-Resource Speech Translation

Speech translation (ST) is a bimodal conversion task from source speech to the target text. Generally, deep learning-based ST systems require sufficient training data to obtain a competitive result, even with a state-of-the-art model. However, the training data is usually unable to meet the completeness condition due to the small sample problems. Most low-resource ST tasks improve data integrity with a single model, but this optimization has a single dimension and limited effectiveness. In contrast, multimodality is introduced to leverage different dimensions of data features for multiperspective modeling. This approach mutually addresses the gaps in the different modalities to enhance the representation of the data and improve the utilization of the training samples. Therefore, it is a new challenge to leverage the enormous multimodal out-of-domain information to improve the low-resource tasks. This paper describes how to use multimodal out-of-domain information to improve low-resource models. First, we propose a low-resource ST framework to reconstruct large-scale label-free audio by combining self-supervised learning. At the same time, we introduce a machine translation (MT) pretraining model to complement text embedding and fine-tune decoding. In addition, we analyze the similarity at the decoder side. We reduce multimodal invalid pseudolabels by performing random depth pruning in the similarity layer to minimize error propagation and use additional CTC loss in the nonsimilarity layer to optimize the ensemble loss. Finally, we study the weighting ratio of the fusion technique in the multimodal decoder. Our experiment results show that the proposed method is promising for low-resource ST, with improvements of up to +3.6 BLEU points compared to baseline low-resource ST models.
Evolutionary Social Poisson Factorizationfor Temporal Recommendation

Abstract Preference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF $$\_$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>_</mml:mi> </mml:math> Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF $$\_$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>_</mml:mi> </mml:math> Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.
Cross-overlapping Hierarchical Reinforcement Learning in Humanoid Robots

In the RoboCup3D project, how to make the humanoid robot with faster running speed and more accurate kicking action is a popular research direction. In this paper, we extend the Overlapping Layered Learning method by proposing a cross-overlapping hierarchical reinforcement learning method, which is based on overlapping layered learning to smooth the action articulation by cross-learning the articulated action parameters or cross-learning the higher-level action parameters to obtain better action execution. The article also introduces the baseline-based optimization technique and elaborates the specific optimization strategy and optimization task. Finally, the effectiveness of cross-overlapping hierarchical reinforcement learning and baseline-based optimization techniques is demonstrated experimentally.
XMAP: eXplainable mapping analytical process

Abstract As the number of artificial intelligence (AI) applications increases rapidly and more people will be affected by AI’s decisions, there are real needs for novel AI systems that can deliver both accuracy and explanations. To address these needs, this paper proposes a new approach called eXplainable Mapping Analytical Process (XMAP). Different from existing works in explainable AI, XMAP is highly modularised and the interpretability for each step can be easily obtained and visualised. A number of core algorithms are developed in XMAP to capture the distributions and topological structures of data, define contexts that emerged from data, and build effective representations for classification tasks. The experiments show that XMAP can provide useful and interpretable insights across analytical steps. For the binary classification task, its predictive performance is very competitive as compared to advanced machine learning algorithms in the literature. In some large datasets, XMAP can even outperform black-box algorithms without losing its interpretability.
Quality enhancement for point cloud using morphology methods

Point cloud become a popular multimedia format. However, due to the massive amount of data, it is necessary to compress the point cloud. After compression, the number of points in the reconstructed point cloud will be reduced due to the quantization or downsampling in the compression process, which may introduce holes in the three-dimensional image. In this paper, we utilize the mathematical morphology to fill the holes in the reconstructed image. The results show that holes are filled with better subjective and objective quality metrics.
Empirical Comparision on Boosted Cascade of Haar-like Features to Histogram of Oriented Gradients for Person Detection

Person Detection can be used for various applications and consider one of the most challenging problems in computer vision. Many methods have been proposed to perform person detection, and two of the most popular form are Haar-Like features and Histogram of Oriented Gradients. Knowing which techniques work well is very important when making an application that uses person recognition and localization. This paper examines these two algorithms to determine which algorithm works best in a proposed dataset. In contrast to the default configuration, this study identifies and performs several essential characteristics of the algorithm pipeline to achieve an accurate comparison of the approaches. Then the classifier models are evaluated and compared for person detection. The result shows that Histograms of Oriented Gradients to be much more precise than Haar-like features. However, the Haar-like Features technique works much faster.
<i>Focus</i> : Function clone identification on cross‐platform

Automatic identification of function clones on cross-platform aims at determining whether two functions are identical or not without access to the source code, which is a fundamental challenge in vulnerability search, code plagiarism detection, and malware classification. With the rapid development of deep neural network in program analysis, the state-of-the-art neural network-based function clone identification methods propose to represent functions as embeddings by graph neural network (GNN). However, such a novel representation of functions brings in two challenges. (1) The feature engineering that accurately maps the raw data of binary code to machine learning features is complicated. (2) A highly accurate embedding of functions requires a customized GNN to focus on the most critical features to identify binary code. To the best of our knowledge, currently, a comprehensive work that can overcome the above challenges is still missing. In this paper, we propose a novel prototype named as Focus, which is designed to accurately and efficiently identify similar functions. Specifically, inspired by natural language processing techniques which effectively learns text semantic across natural languages, Focus can learn representative semantic features of functions by a customized learning model. To address the second challenge, a multi-head attention mechanism can be employed to capture the critical features of a function. Through extensive experiments, we demonstrate that Focus achieves high accuracy of function clone identification on a broad range of eight architectures. In particular, the identification performance (AUC value) of Focus is 97% and 99% for cross-platform and single-platform, respectively. Furthermore, the evaluation in real world applications shows that our Focus identifies 24 vulnerable functions among the top-30 candidates, which is one time higher than the baseline approaches.
Performance Evaluation of PoW Blockchain in Wireless Mobile IoT networks

There has been widespread adoption of blockchain in the wireless domain, especially with the growing interest and potential of Internet of Things (IoT). While the properties of blockchain namely trustworthy transactions and decentralization of authority have opened up numerous opportunities, new challenges pertinent to security and performance have emerged in the presence of wireless communications and mobility. Although current literature provides extensive analysis of blockchain networks when connectivity is offered by static and wired links, there has been little attention in the wireless space. Wireless blockchain analysis has been pursued under specific and simplified assumptions which are insufficient for generalized blockchain performance in real world scenarios. Our work tries to fill this gap by covering a large space of blockchain and network based parameters such as the block interval, the number of miners, node mobility, transmission power and network density and present comprehensive performance results obtained using the NS-3 simulator. Among a number of interesting results, our experiments reveal that transaction throughput is greatly influenced by node mobility patterns, a higher percentage of miners is detrimental for blockchain security and wireless network density and power transmission values beyond particular thesholds are required for block transmission behavior similar to the one observed in wired-static blockchain networks preventing randomness due to packet losses.
Design and Implementation of University Asset Management System Based on Discriminant Analysis and Decision Tree Model

research-article Design and Implementation of University Asset Management System Based on Discriminant Analysis and Decision Tree Model Share on Author: Xin Zhang Tianjin Vocational Institute, China Tianjin Vocational Institute, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2047–2052https://doi.org/10.1145/3482632.3484096Online:22 November 2021Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Blockchain: an EOM approach to reconciliation in banking

Purpose The study aim is to evaluate the contribution of Blockchain technology (Cryptobanking) using expected operating model (EOM) to address the pain points in reconciliation at middle and back-office operational levels in assessing the significance of this technology on return on investment. Design/methodology/approach A structured questionnaire was designed to collect primary data using a stratified sampling method from 120 respondents working in leading Investment banks operating in the geographical locality of urban Bangalore. Demographic variables, accounting variables, data reporting variables, approach variables, variables of EOM were considered to validate the hypothesis with the help of statistical tools, namely ANOVA, and Multiple Stepwise Regression Analysis. Findings The results obtained confirm that there is significant difference in reconciliation with implementation of an innovative business process. Financial analysis is the highest predictor of ROI when integrated with technology as the adapted Blockchain innovation in reconciliation is the most influencing factor in enhancing, improving ROI playing a pivotal role in the Investment banks. Originality/value Blockchain technology (Cryptobanking) facilitates in transforming the reconciliation process of these banks with improved operational efficiency. Blockchain and settlement platforms offer inter-organization solutions facilitating in the reconciliation of various transactions in real-time through a trust-based network in the form of digital settlements with better consortiums.
Research on Machine Translation and Computer Aided Translation Based on Cloud Computing

New breakthroughs have not been made in the principle and technology of machine translation, and high-quality automatic translation has not yet been realized. The core technology of computer-aided translation is translation memory technology. Although computer-aided translation has made considerable progress, the translation memory technology has not yet made a decisive breakthrough. Aiming at the problem that the translation time of machine-aided translation system is relatively long at present, the design of machine-aided translation system based on cloud computing is proposed. A new machine-aided translation system is designed by referring to the cloud computing model. The hardware of the system is divided into four layers: user layer, service layer, computing layer and storage layer. The storage structure, translation structure and retrieval structure are designed respectively. After that, the software and hardware of the system are designed. The test results show that the translation time of the machine-assisted translation system based on cloud computing is shorter than that of the traditional machine translation system.
Visual Analysis of Graph Algorithm Dynamics

In this paper we describe a visualization tool for representing the dynamics of graph algorithms. Toward this end, we designed a web-based framework which illustrates the dynamics as time-to-space mappings of dynamic graphs. Such static diagrams of dynamic data have the benefit of being able to display longer time spans in one view, hence supporting the observer with comparison tasks. The tool can show details about how an algorithm traverses a graph step-by-step in a static and animated fashion, for graph algorithm exploration as well as educational purposes. The animation together with the time-to-space mapping forms an overview-and-detail approach. By using flight carrier data from the U.S. Department of Transportation we show the usefulness of our interactive visualization for conveying graph algorithm dynamics.
Optimization of Breadth-First Search Algorithm for Path Solutions in Mazyin Games

A game containing elements of artificial intelligence, of course, requires an algorithm in its application. One example of a game that includes elements of artificial intelligence is the Labyrinth game. Maze is a simple educational game. This game is known as finding a way out of the maze to arrive at a predetermined goal. The labyrinth encounters numerous obstacles along the way, such as dead ends and parapets, to reach the target location. In this game, players are required to think logically about how to find the right maze path. The obstacle faced in this game is that sometimes players have difficulty finding a way out, especially if the game level has reached a high level in the process of finding a way out. To solve this problem, a graph tracing technique is needed. The Breadth-First Search (BFS) strategy can be used in conjunction with various graph search algorithms. An example of a broad search method is the Breadth-First Search Algorithm, which works by visiting nodes at level n first before moving on to nodes at level n+1. The advantage of the Breadth-First Search algorithm is that it can find a solution as the shortest path and find the minimum solution if there is more than one solution. This study will discuss how to find a path for the Labyrinth using the BFS algorithm. The result of applying this BFS algorithm is the shortest route solution raised so that the Labyrinth can arrive at the destination point through the route provided.
A modular RFSoC-based approach to interface superconducting quantum bits

Quantum computers will be a revolutionary extension of the heterogeneous computing world. They consist of many quantum bits (qubits) and require a careful design of the interface between the classical computer architecture and the quantum processor. Even single nanosecond variations of the interaction may have an influence on the quantum state. In this paper, we present the modular design of the FPGA firmware which is part of our qubit control electronics. It features so-called digital unit cells where each cell contains all the logic necessary to interact with a single superconducting qubit. The cell includes a custom-built RISC-V-based sequencer, as well as two signal generators and a signal recorder. Internal communication within the cell is handled using a modified Wishbone bus with custom 2-to-N interconnect and deterministic broadcast functionality. We furthermore provide the resource utilization of our design and demonstrate its correct operation using an actual superconducting five qubit chip.
Frequency Fitness Assignment: Optimization Without Bias for Good Solutions Can Be Efficient

A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection. Under frequency fitness assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in selection steps and is subject to minimization. FFA creates algorithms that are not biased toward better solutions and are invariant under all injective transformations of the objective function value. We investigate the impact of FFA on the performance of two theory inspired, state-of-the-art evolutionary algorithms, the Greedy (2+1) GA and the self-adjusting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1+(\lambda,\lambda))$ </tex-math></inline-formula> GA. FFA improves their performance significantly on some problems that are hard for them. In our experiments, one FFA-based algorithm exhibited mean runtimes that appear to be polynomial on the theory-based benchmark problems in our study, including traps, jumps, and plateaus. We propose two hybrid approaches that use both direct and FFA-based optimization and find that they perform well. All FFA-based algorithms also perform better on satisfiability problems than any of the pure algorithm variants.
Automatic Segmentation of Sweep Features of Constant Radius from Archaeological Artifacts

The sweep features of constant radius are particularly significant for the purposes of the historical-archaeological investigation and classification of ancient artifacts. The paper focuses on the automatic recognition of this specific class of features from triangulated 3D models experimentally acquired from cultural heritage objects. This is not a trivial problem. The ancient artifacts, although repeatable, are unique handmade pieces with a geometry commonly characterized by complex and non-analytical shapes. Their surfaces are also usually damaged and worn, so that the related geometric properties are altered or lost. The methodology proposed here is inspired by the one previously developed by the authors for the automatic segmentation of fillets, rounds and grooves from high-density triangulated models of mechanical components. The paper, in particular, focuses on the aspects of this methodology that must be tuned to allow the recognition of the sweep features of constant radius from archaeological finds. The methodology has been implemented and finally applied to an archaeological find acquired by a laser scanner.
Robust Optimized Structural Feature-Based Transformation Parameter Estimation for Image Registration

Hazra, Joydev Chowdhury, Aditi Roy Dasgupta, Kousik Dutta, ParamarthaAutomatic and proficient image registration is a very energizing task. In this article, we propose optimized structural feature-based robust prediction models to predict the transformation parameters toward image registration. Here, scale invariant feature transform (SIFT) is utilized as a feature extraction algorithm, and equilibrium optimization (EO) is utilized to optimize the number of features. Down-sized feature vectors are used as input datasets of the backpropagation neural network (BPNN) and random forest (RF) to fabricate the prediction model. The present investigation exhibits that the proposed technique can robustly estimate different transformational parameters. The comparative analysis of the proposed technique with other methods is depicted in experimental results.
Facial Expression Recognition with Flatcam Lensless Imaging

Gera, Darshan Balasubramanian, S.With the recent advancements in the field of artificial intelligence, Internet of things, and machine vision research, there is a great demand for low cost and miniature size cameras to be integrated in any device that can detect faces and recognize face and facial expressions. Current lens-based cameras have a major limitation of large weight, size, and expensive, thus making them difficult to be integrated into small devices. Lensless imaging cameras are compact and inexpensive compared to lens-based cameras due to the reduction in the form factor and use of simple semiconductor sensors. However, they come at the expense of low-quality images compared to lens-based imaging. In this work, we propose a first-of-its-kind evaluation of facial expression recognition (FER) in case of a specific lensless imaging camera called Flatcam which consists of a binary coded mask placed next to a sensor. We propose a deep learning-based FER framework to recognize ten different facial expressions on lensless face images captured in different variations of lightening, angle, and scale. Our framework has a UNet-based component for enhancing poor quality lensless face images, thereby improving the recognition performance comparable to that of lens-based cameras. Extensive experiments have been conducted on FCFD dataset captured using lens camera and Flatcam lensless camera. Improved performance w.r.t performance metrics of F1-score, precision, recall, and accuracy validate the effectiveness of proposed FER framework with lensless imaging.