An Exploratory Practice of Integration of Industry and Education in High-level Programming Language Course in the Background of New Engineering

This paper analyzes some problems of the current teaching situation in the course High-level Programming Language,such as the lagging content of the course compared with technology development,the emphasis on theory rather than on practice,the low enthusiasm of students for learning,and the weak practical ability of students.In response to the needs of enterprises for talents under the background of New Engineering,especially the cultivation of students’adaptability and practical abilities towards future industries to improve students’knowledge and problemsolving abilities to keep up with the modern technology,this paper proposes the ways and methods to reform and explore the teaching content and teaching methods by integrating industry and education,assisting teaching according to industrial demands,and lowering technology barriers of new technology.The practical effect is evaluated through a survey in class and the follow-up questionnaire after class,and the results show that the effect of the practice is effective.

Information Expression Oriented toward the Hearing-Impaired Based on Sign Language Video Synthesis

Among the human users of the Internet of Things,the hearing-impaired is a special group of people for whom normal information expression forms,such as voice and video are unaccessible,and most of them have some difficulty in understanding information in text form.The hearing-impaired are accustomed to receiving information expressed in sign language.For this situation,a new information expression form for the Internet of Things oriented toward the hearing-impaired is proposed in this paper,and the new expression is based on sign language video synthesis.Under the sign synthesis frame,three modules are necessary:constructing database,searching for appropriate sign language video units and transition units,and generating interpolated frames.With this method,text information could be transformed into sign language expression for the hearing-impaired.

Text-Driven Chinese Sign Language Synthesis

Sign language is visual-gestural language mainly used by healing-impaired people to communicate with each other. Gesture and facial expression ale important grammar parts of sign language. In this paper, a graphics method is taken to synthesize Chinese Sign Language, and a practical system is implemented. The input of the system is text. The output of the system is "graphics person" who can gesticulate sign language accompanied by facial expression that corresponds to text entered so as to realize automatic translation from text to sign language. It’s a meaningful exploration to computer multimedia data expression and fusion. It can play an important role in education of healingimpaired people and sign language interpretation.

Sign Language Synthesis in Multi-function Perception Machine

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Application of a Sign Language Synthesis System in Digital Library Services

Information Accessibility for disabled people is one of the most important design criteria for the China National Digital Library (CNDL) development. Sign language synthesis systems are effective to provide information services for people with hearing and speech impairments. This paper presents a framework of a sign language synthesis system application in CNDL, as well as discusses the relevant technologies applied in the system. CNDL has been a real practice area for the sign language synthesis research.

Towards Realizing Sign Language-to-Speech Conversion by Combining Deep Learning and Statistical Parametric Speech Synthesis

This paper realizes a sign language-to-speech conversion system to solve the communication problem between healthy people and speech disorders. 30 kinds of different static sign languages are firstly recognized by combining the support vector machine (SVM) with a restricted Boltzmann machine (RBM) based regulation and a feedback fine-tuning of the deep model. The text of sign language is then obtained from the recognition results. A context-dependent label is generated from the recognized text of sign language by a text analyzer. Meanwhile,a hiddenMarkov model (HMM) basedMandarin-Tibetan bilingual speech synthesis system is developed by using speaker adaptive training.The Mandarin speech or Tibetan speech is then naturally synthesized by using context-dependent label generated from the recognized sign language. Tests show that the static sign language recognition rate of the designed system achieves 93.6%. Subjective evaluation demonstrates that synthesized speech can get 4.0 of the mean opinion score (MOS).

ON THE OPTIMIZATION OF VLSI ALLOCATION IN HIGH-LEVEL SYNTHESIS

Allocation is one of main tasks in the high-level synthesis. It includes module , functional unit allocation, storage allocation and interconnection allocation. This paper models the allocation problem as cluster analysis and applies a new algorithm, neighbor state transition (NST) algorithm, for cluster optimization. It is proved that the algorithm produces an asymptotically global optimal solution with the upper bound on the cost function (1 + O(1/n)2-ε)F*, When F" is the cost of the optimum solution, n is the problem size and e is a positive parameter arbitrarily close to zero. The numerical examples show that the NST algorithm produces better results compared to the other known methods.

High-Level Portable Programming Language for Optimized Memory Use of Network Processors

Network processors (NPs) are widely used for programmable and high-performance networks;however, the programs for NPs are less portable, the number of NP program developers is small, and the development cost is high. To solve these problems, this paper proposes an open, high-level, and portable programming language called “Phonepl”, which is independent from vendor-specific proprietary hardware and software but can be translated into an NP program with high performance especially in the memory use. A common NP hardware feature is that a whole packet is stored in DRAM, but the header is cached in SRAM. Phonepl has a hardware-independent abstraction of this feature so that it allows programmers mostly unconscious of this hardware feature. To implement the abstraction, four representations of packet data type that cover all the packet operations (including substring, concatenation, input, and output) are introduced. Phonepl have been implemented on Octeon NPs used in plug-ins for a network-virtualization environment called the VNode Infrastructure, and several packet-handling programs were evaluated. As for the evaluation result, the conversion throughput is close to the wire rate, i.e., 10 Gbps, and no packet loss (by cache miss) occurs when the packet size is 256 bytes or larger.

A high-level synthesis based dual-module redundancy with multi-residue detection(DMR-MRD)fault-tolerant method for on-board processing satellite communication systems

On board processing(OBP) satellite systems have obtained more and more attentions in recent years because of their high efficiency and performance.However,the OBP transponders are very sensitive to the high energy particles in the space radiation environments.Single event upset(SEU)is one of the major radiation effects,which influences the satellite reliability greatly.Triple modular redundancy(TMR) is a classic and efficient method to mask SEUs.However,TMR uses three identical modules and a comparison logic,the circuit size becomes unacceptable,especially in the resource limited environments such as OBP systems.Considering that,a new SEU-tolerant method based on residue code and high-level synthesis(HLS) is proposed,and the new method is applied to FIR filters,which are typical structures in the OBP systems.The simulation results show that,for an applicable HLS scheduling scheme,area reduction can be reduced by 48.26%compared to TMR,while fault missing rate is 0.15%.

Synthesis of SrTiO_3 for immobilization of simulated HLW by SHS

Strontium titanate synroc samples were synthesized by self-propagating high-temperature synthesis （SHS）. Sr directly took part in the synthesis process. As a result, the loading content issue is basically resolved. The products were characterized by density, microhardness X-ray diffraction, and scanning electron microscopy （SEM/EDS）. The leaching rate was measured by the method of PCT （product consistency test）. The results indicate that the Sr^2＋-SrTiO3 compound is of high density, low leach rate and high stability and the synthesis process is feasible in technology and economy. It can be concluded that the strontium titanate synroc is a perfect material to immobilize HLW.

Resources for Development of Hindi Speech Synthesis System: An Overview

Most of the information in digital world is accessible to few who can read or understand a particular language. The speech corpus acquisition is an essential part of all spoken technology systems. The quality and the volume of speech data in corpus directly affect the accuracy of the system. However, there are a lot of scopes to develop speech technology system using Hindi language which is spoken primarily in India. To achieve such an ambitious goal, the collection of standard database is a prerequisite. This paper summarizes the Hindi corpus and lexical resources being developed by various organizations across the country.

语言学知识驱动的空间语义理解能力评测数据集研究

近20年来,深度学习技术显著提升了机器的自然语言处理能力,使之在诸多任务上接近甚至超过人类水平。机器学习的对象不再是直接来自人类语言学研究成果(知识),而是人类语言材料(数据)。在靠数据和算力驱动的大语言模型几近建成巴别塔的当下,语言学家通过深挖语言现象总结的语言学知识价值何在?本文提出从知识到数据的研究思路,设计了空间语义理解的6项任务:空间信息正误判别、异常空间信息识别、缺失参照成分补回、空间语义角色标注、空间表达异形同义判别、空间方位关系推理,以构建中文空间语义理解能力评测数据集为例,介绍从SpaCE2021到SpaCE2024数据集的设计思想、数据集制作概况以及机器在空间语义理解任务上的表现。总的来看,参加SpaCE赛事的大语言模型,在依赖表面分布特征(形式线索)的任务上容易获得好成绩,在依赖深层语义理解(认知能力)的任务上容易表现不好。因此,在人工智能高速发展使得语言学知识在计算机信息处理领域被动边缘化的当下,语言学知识的价值需要拓展,即用于指导小而精的高品质语言数据,以提升机器学习的效果和效率。为了计算应用的目的,语法研究应该在观察充分、描写充分、解释充分之上,追求更具挑战性的目标——生成充分。

结构化数据库查询语言智能合成技术研究进展

近年来,随着大数据、云计算等技术的飞速发展,大规模数据的产生使得各类应用对于数据库技术的依赖日益加深。然而,传统的数据库一般采用形式化的数据库查询语言SQL进行操作,对无编程经验或数据库使用经验的用户来说,复杂SQL语法难度较高,降低了各个领域数据库应用者的便捷程度。近年来,机器学习、深度神经网络等人工智能技术的飞速发展,尤其是ChatGPT横空出世引发的大语言模型技术热潮,驱动了数据库与人工智能的深度结合与技术变革。通过智能方法将用户输入语言自动化合成SQL语言,以满足不同程度数据库使用者的操作需求,提升数据库的智能性、环境适应性及用户友好性。为全面聚焦数据库查询语言智能合成技术的最新研究进展,从范例输入、文本输入及语音输入这3类用户输入切入,详细阐述各类智能合成模型的研究脉络、代表性工作及最新进展,同时对各类方法的技术框架进行归纳与对比,最后对全文进行全面性的总结,并针对现有方法存在的问题和挑战展望未来发展方向。

结合轻量卷积的非自回归语音合成方法

对如何有效捕捉音素之间的关联及如何合成韵律丰富的音频进行研究,提出一种结合轻量卷积的非自回归语音合成模型LCTTS。引入轻量卷积建立起音素之间的联系,解决发音出错问题。通过添加音高和能量预测器预测生成语音的韵律,解决音频韵律缺乏问题。训练模型获取梅尔频谱,结合预先训练好的声码器转化为音频。实验结果表明,提出的LCTTS模型优于先前提出的SpeedySpeech模型,在Emotional Speech Database数据集上平均意见得分获得2.8%的提升,梅尔倒谱失真测度下降0.15。

情感语音合成中的语义及韵律特征嵌入方法

针对当前的情感语音合成方法存在合成音频容易忽略文本语义信息的问题,在文本编码器中引入BERT预训练模型,辅助编码器捕获文本语义特征,并提出了语义及韵律特征嵌入方法。缅甸语情感语料的缺乏导致模型难以合成高质量情感语音,因此,文中通过微调各个网络模块参数的方法探索缅甸语情感语音合成模型的训练方法。实验结果表明,文中提出的特征嵌入方法以及训练方法在情感语料缺乏情况下仍能合成出高质量的情感语音,平均情感意见得分分别为4.16与4.18。

低资源非自回归壮语语音合成

基于FastSpeech2模型,文章提出了非自回归的壮语语音合成模型Zhuang-TTS。为了提升模型合成壮语语音的韵律,根据壮语特点及实地调查提出了一套新的壮语音系(声调、声母或辅音、韵母或元音),同时针对壮语声学特点进行了改进:(1)使用壮语音素序列表征壮语发音信息;(2)使用音素级的声学调节器(与FastPitch类似),使合成结果更加稳定;(3)使用Conformer代替FastSpeech2模型中的Transformer,同时构建了一个壮语语音合成语料库。实验结果表明,Zhuang-TTS在韵律方面的意见评分(Mean Opinion Score, MOS)达到3.90,合成实时率达8.65×10^(-2)。该模型在合成壮语语音的质量和速度方面获得了较大提升,优于Tacotron2和FastSpeech2基线模型,研究推动了壮语语音合成领域的发展。

基于文本知识的多跳推理综述

基于文本知识的多跳推理是一种从大规模文本知识库中选择和组合相关知识,以回答复杂问题或验证假设的过程。这种能力可以显著提高自然语言处理系统的实用性,因此基于文本知识的多跳推理已经成为自然语言处理领域的研究热点。然而,该任务面临两大挑战:1)如何从海量语料库中准确地选择推理所需的知识;2)如何通过多步语义合成进行有效推理。针对这些挑战,该领域出现了许多优秀的工作,但是尚缺乏对该领域的综述。因此对该领域的相关工作进行梳理和总结。首先给出多跳推理以及基于文本知识的多跳推理的定义;其次,分析、比较了当前流行和高质量的基于文本知识的多跳推理数据集;再次,对已有的知识选择方法进行分类,并分析各类方法的优缺点;继次,对已有的推理方法进行分类讨论;最后,探讨了该领域未来可能的发展方向。

人工智能语音新闻播报的缺陷与应对

人工智能语音新闻播报作为一种创新应用,以其独特的优势受到广泛关注。基于此,系统分析了人工智能语音新闻播报的主要缺陷,并探讨了人工智能语音新闻播报的发展策略,以期促进人工智能在新闻播报领域的进一步发展和优化。