Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristic Views Extraction Modal Based-on Deep Reinforcement Learning for 3D Model Retrieval

In this paper, we propose an effective framework for view-based 3D model retrieval based on reinforcement learning, which aims to extract the most important views from the view set of 3D models for model representation. Since the choice of the representative views can be treated as a Markov decision process (MDP), we model the view extraction as a progressive process through deep reinforcement learning. First, the visual tool developed by OpenGL is utilized to extract 12 rendered pictures in order from a 3D model. Second, these 12 views are fed into the network as a view-based sequence, and then we utilize characteristic views extraction model to extract the most informative views of the 3D model. Finally, a similarity measure between 3D models is performed to complete the last step pf the model retrieval problem. In the experiment section, the classic ModelNet40 is utilized to evaluate the performance of the proposed method. Extensive experiments and corresponding experimental results have demonstrated the superiority of our approach.
Machine Translation and the Evaluation of Its Quality

Machine translation has already become part of our everyday life. This chapter gives an overview of machine translation approaches. Statistical machine translation was a dominant approach over the past 20 years. It brought many cases of practical use. It is described in more detail in this chapter. Statistical machine translation is not equally successful for all language pairs. Highly inflectional languages are hard to process, especially as target languages. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This chapter also describes the evaluation of machine translation quality. It covers manual and automatic evaluations. Traditional and recently proposed metrics for automatic machine translation evaluation are described. Human translation still provides the best translation quality, but it is, in general, time-consuming and expensive. Integration of human and machine translation is a promising workflow for the future. Machine translation will not replace human translation, but it can serve as a tool to increase productivity in the translation process.
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification

Aspect-level sentiment classification is a crucial task for sentiment analysis, which aims to identify the sentiment polarities of specific targets in their context. The main challenge comes from multi-aspect sentences, which express multiple sentiment polarities towards different targets, resulting in overlapped feature representation. However, most existing neural models tend to utilize static pooling operation or attention mechanism to identify sentimental words, which therefore insufficient for dealing with overlapped features. To solve this problem, we propose to utilize capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm. Furthermore, interactive attention mechanism is introduced in the capsule routing procedure to model the semantic relationship between aspect terms and context. The iterative routing also enables encoding sentence from a global perspective. Experimental results on three datasets show that our proposed model achieves state-of-the-art performance.
Modeling Frames in Argumentation

In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the “right” frame(s) to convince the audience to adopt the author’s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience’s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates’ topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.
High Performance Heterogeneous Multicore Architectures: A Study

The significant increase in the need for high-performance and energy-efficient computing systems has introduced heterogenous computing. However, the incorporation of different architectures into one system complicates the distribution of the workload between architectures. To address this challenge while meeting the goals of high-performance computing systems, several research contributions have been made. This paper reviews some of the proposed workload partitioning approaches for GPU-based, DSP-based and FPGA-based heterogenous systems. This research also covers some comparison studies regarding the FPGA versus DSP and FPGA versus GPU debates, showing that sometimes collaboration between these architectures seems to be the key. The aim of this study is to provide academic and industrial researchers with an insight of techniques to achieve the workload balancing in heterogenous systems and motivate them for further research in the field.
Event Detection with Multi-Order Graph Convolution and Aggregated Attention

Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related entities than sentence representations. But, existing studies only use first-order syntactic relations (i.e., the arcs) in dependency trees to identify trigger words. For this reason, this paper proposes a new method for event detection, which uses a dependency tree based graph convolution network with aggregative attention to explicitly model and aggregate multi-order syntactic representations in sentences. Experimental comparison with state-of-the-art baselines shows the superiority of the proposed method.
Performance Evaluation of a Helper Initiated Distributed Cooperative Medium Access Control Protocol for Wireless Networks

Cross layer cooperative protocol which exploits the benefits of physical layer cooperative communication, is one of the widely recognized MAC layer protocol design strategies for future wireless networks. This paper presents performance analysis of a cooperative mac and these performance parameters are compared those of the legacy IEEE 802.11 DCF MAC. Appropriate relay station selection is the main hurdle in designing efficient cooperative MAC protocol for wireless networks. This cooperative mac demonstrated that intermediate relay nodes themselves can initiate cooperation for relaying data frame to the receiver on behalf of the sender. This procedure makes the selection process of a “helper node” more distributed in nature as well as it contributes to increase throughput of a wireless network by reducing the overheads that are usually incurred in the helper selection process. It has been shown by thorough analytical analysis that the proposed cooperative MAC protocol offers higher throughput and lower frame transmission delay in both ideal and error prone wireless environment. These performance metrics are also evaluated while the wireless nodes are mobile as well.
FeatherCNN: Fast Inference Computation with TensorGEMM on ARM Architectures

Deep Learning is ubiquitous in a wide field of applications ranging from research to industry. In comparison to time-consuming iterative training of convolutional neural networks (CNNs), inference is a relatively lightweight operation making it amenable to execution on mobile devices. Nevertheless, lower latency and higher computation efficiency are crucial to allow for complex models and prolonged battery life. Addressing the aforementioned challenges, we propose FeatherCNN – a fast inference library for ARM CPUs – targeting the performance ceiling of mobile devices. FeatherCNN employs three key techniques: 1) A highly efficient TensorGEMM (generalized matrix multiplication) routine is applied to accelerate Winograd convolution on ARM CPUs, 2) General layer optimization based on custom high performance kernels improves both the computational efficiency and locality of memory access patterns for non-Winograd layers. 3) The framework design emphasizes joint layer-wise optimization using layer fusion to remove redundant calculations and memory movements. Performance evaluation reveals that FeatherCNN significantly outperforms state-of-the-art libraries. A forward propagation pass of VGG-16 on a 64-core ARM server is 48, 14, and 12 times faster than Caffe using OpenBLAS, Caffe2 using Eigen, and NNPACK, respectively. In addition, FeatherCNN is 3.19 times faster than the recently released TensorFlow Lite library on an iPhone 7 plus. In terms of GEMM performance, FeatherCNN achieves 14.8 and 39.0 percent higher performance than Apple's Accelerate framework on an iPhone 7 plus and Eigen on a Samsung Galaxy S8, respectively. The source code of FeatherCNN library is publicly available at https://github.com/tencent/feathercnn.
Energy Efficient Protocol for Aggregation Head Selection in Wireless Sensor Network

—Data aggregation mechanism is employed to reduce the redundant messages and energy consumption by collecting and aggregating messages from different sensor nodes in wireless sensor networks. We present an energy efficient data aggregation protocol for selection of aggregation head on the basis of its residual energy and nearness to the base station as well as comparative threshold energy when an event is detected. The efficiency of this aggregation protocol is shown through simulation results in terms of network lifetime by estimating number of dead and idle sensor nodes after fixed number of rounds as well as number of packets transmitted to base station as compared with the existing approaches.
A comparative analysis of selected set of natural language processing (NLP) and machine learning (ML) algorithms for clinical coding using clinical classification standards

In Australia, hospital discharge summaries created at the end of an episode of care contain patient information such as demographic data, medical history, various diagnosis, interventions carried out, medications and drug therapies provided to the patient. These discharge summaries not only serve as a record of the episode of care, but later converted into a set of clinical codes for statistical analysis purposes. The process of clinical coding refers to assigning alphanumeric codes to discharge summaries. In Australia, clinical coding is done using International Classification of Diseases, version 10, Australian Modification (ICD-10-AM) and Australian Classification of Health Interventions (ACHI) as per the Australian Coding Standards (ACS), in an acute and subacute care setting, in both public and private hospitals. Clinical coding and subsequent analysis facilitate funding, insurance claims processing and research. The task of assigning codes to an episode of care is a manual process. This posed challenges in terms of ever-increasing set of codes in ICD-10-AM and ACHI, changing coding standards in ACS, complexity of care episodes, and large training and recruitment costs associated with clinical coders. In addition, the manual clinical coding process is time consuming and prone to errors, leading to financial losses. The use of Natural Language Processing (NLP) and Machine Learning (ML) techniques is considered as a solution to the above problem. In this thesis, four different approaches namely, pattern matching, rule based, machine learning and hybrid technique are compared to identify most efficient algorithm suitable for clinical coding. The ICD-10-AM and ACHI consists of 22 chapters based on human body organs, where each chapter describe diseases and interventions of a body system. The aforementioned, NLP and ML comparison is carried out only two chapters namely, diseases of the respiratory system and diseases of the digestive system. Initially, the dataset contained 190 clinical records of two chapters and named as Data190. Due to the limited number of clinical records, another 45 records were added to the existing dataset and this resultant dataset was named as Data235.
Visual Violence Rating with Pairwise Comparison

Children’s exposure to violence has become a severe problem with the rapid development of Internet. Recognizing violent video and estimating violence extent become crucial. Most researches focus on violent scene or violent action detection, lacking overall violence extent information. In this paper, we propose a violence rating prediction approach and build a novel violent video dataset. Our proposed method has two advantages: (1) videos are represented by features extracted from a learned two-stream network; (2) relationship between different violence extent can be learned and utilized to predict violence rating. To demonstrate the effectiveness of our method, we created a well-labelled dataset which contains 1, 930 violent videos. Each video is labelled with 6 objective violent attributes. Furthermore, we employ pairwise comparison method to obtain ground-truth violence rating for each video. Our proposed approach was evaluated on our dataset. Its results showed that our proposed method outperforms the state-of-art video classification methods.
Algebraic Fundamentals in Artificial Intelligence for the Purpose of Undergraduate Education and Training

College algebra syllabus setting is of utmost importance for the higher education developments and students education and training, especially for the newly opened bachelor majors. In this research, we investigated the traditional syllabus of college algebra in a nationwide manner, and discussed the algebraic fundamentals in artificial intelligence. Three case studies were proposed to address the fundamentals of abstract logic thinking, data observation and knowledge inference, and data-driven thinking. Eventually, a suggested syllabus for “Advanced Algebra and its Applications in AI” was introduced subsequently for the new major of “artificial intelligence”.
New Flexible Multiple-Precision Multiply-Accumulate Unit for Deep Neural Network Training and Inference

In this paper, a new flexible multiple-precision multiply-accumulate (MAC) unit is proposed for deep neural network training and inference. The proposed MAC unit supports both fixed-point operations and floating-point operations. For floating-point format, the proposed unit supports one 16-bit MAC operation or sum of two 8-bit multiplications plus a 16-bit addend. To make the proposed MAC unit more versatile, the bit-width of exponent and mantissa can be flexibly exchanged. By setting the bit-width of exponent to zero, the proposed MAC unit also supports fixed-point operations. For fixed-point format, the proposed unit supports one 16-bit MAC or sum of two 8-bit multiplications plus a 16-bit addend. Moreover, the proposed unit can be further divided to support sum of four 4-bit multiplications plus a 16-bit addend. At the lowest precision, the proposed MAC unit supports accumulating of eight 1-bit logic AND operations to enable the support of binary neural networks. Compared to the standard 16-bit half-precision MAC unit, the proposed MAC unit provides more flexibility with only 21.8 percent area overhead. Compared to a standard 32-bit single-precision MAC unit, the proposed MAC unit requires much less hardware cost but still provides 8-bit exponent in the numerical format to maintain large dynamic range for deep learning computing.
Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach

While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.
Transfer Learning of Wi-Fi Handwritten Signature Signals for Identity Verification based on the Kernel and the Range Space Projection

In this paper, we propose a system for identity verification based on the gesture signals of handwritten signature captured by the Wi-Fi CSI wave packets at different positions using transfer learning. Essentially, a ConvNet is first pretrained using the Wi-Fi signature signals collected from one position. Subsequently, the pretrained feature extractor is transferred to recognize signals collected from another position via a rapid retraining process. We utilize the kernel and the range space projection learning when we retrain the transferred model. Our experimental results on an in-house Wi-Fi handwritten signature signal dataset show that the signature signals from the new position can be effectively classified without needing to retrain the model from scratch.
SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence

We continue the study of generating se-mantically correct regular expressions from natural language descriptions (NL). The current state-of-the-art model SemRegex produces regular expressions from NLs by rewarding the reinforced learning based on the semantic (rather than syntactic) equivalence between two regular expressions. Since the regular expression equivalence problem is PSPACE-complete, we introduce the EQ_Reg model for computing the simi-larity of two regular expressions using deep neural networks. Our EQ_Reg mod-el essentially softens the equivalence of two regular expressions when used as a reward function. We then propose a new regex generation model, SoftRegex, us-ing the EQ_Reg model, and empirically demonstrate that SoftRegex substantially reduces the training time (by a factor of at least 3.6) and produces state-of-the-art results on three benchmark datasets.
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications. In existing ABSA datasets, most sentences contain only one aspect or multiple aspects with the same sentiment polarity, which makes ABSA task degenerate to sentence-level sentiment analysis. In this paper, we present a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two different aspects with different sentiment polarities. The release of this dataset would push forward the research in this field. In addition, we propose simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances. Experiments on our new dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods
DEFYING THE CERTIFICATION DIPLOMA FORGERY WITH BLOCKCHAIN PLATFORM: A PROPOSED MODEL

In the University setting, part of management recorded all education transaction since the students register until they graduate. In this point, the transaction raises several new and crucial challenges. At a closer look, the concerns appeared due to no data integration, verification, and validation from parties both inside or outside of the university. In this respect, the data is not centralized or integrated. As this backdrop, this causes repeated data confirmation, and no one can authenticate the data validity when distributed to other parties. In this point of entry, the diploma certification forgery has become one of the significant issues in education in Indonesia. As a framework that enables the data integration, Blockchain contains verified data on every single of a transaction, so the data is trustworthy. With this in mind, this paper aims to discuss the best blockchain model for the university level. This study is qualitative-based research. As a result, McRhys model is introduced as a potential and implementable blockchain model in university in Indonesia.
Asking Clarification Questions in Knowledge-Based Question Answering

The ability to ask clarification questions is essential for knowledge-based question answering (KBQA) systems, especially for handling ambiguous phenomena. Despite its importance, clarification has not been well explored in current KBQA systems. Further progress requires supervised resources for training and evaluation, and powerful models for clarification-related text understanding and generation. In this paper, we construct a new clarification dataset, CLAQUA, with nearly 40K open-domain examples. The dataset supports three serial tasks: given a question, identify whether clarification is needed; if yes, generate a clarification question; then predict answers base on external user feedback. We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation. Experiments show that the proposed model achieves better performance than strong baselines. The further analysis demonstrates that our dataset brings new challenges and there still remain several unsolved problems, like reasonable automatic evaluation metrics for clarification question generation and powerful models for handling entity sparsity.
Sequence Tagging for Fast Dependency Parsing

Dependency parsing has been built upon the idea of using parsing methods based on shift-reduce or graph-based algorithms in order to identify binary dependency relations between the words in a sentence. In this study we adopt a radically different approach and cast full dependency parsing as a pure sequence tagging task. In particular, we apply a linearization function to the tree that results in an output label for each token that conveys information about the word’s dependency relations. We then follow a supervised strategy and train a bidirectional long short-term memory network to learn to predict such linearized trees. Contrary to the previous studies attempting this, the results show that this approach not only leads to accurate but also fast dependency parsing. Furthermore, we obtain even faster and more accurate parsers by recasting the problem as multitask learning, with a twofold objective: to reduce the output vocabulary and also to exploit hidden patterns coming from a second parsing paradigm (constituent grammars) when used as an auxiliary task.
SOLVING THE HYBRID FLOW SHOP SCHEDULING PROBLEM USING HEURISTIC ALGORITHMS

A variant of the hybrid ﬂow shop (HFS) problem considering missing operations, transportation times and sequence-dependent setup times is investigated. Heuristic algorithms along with dispatching rulesand dispatching rules are used to solve the given problem. The objective function is minimization makespan. The computational experimets are conducted to test the performance of the heuristic algoirthms and dispatching rules. In order to depict the effect of the factors: number of jobs, number of machines, number of production stages, level of missing operations on the result, the additiol experimentes are carried out. The result of NEH heuristic with SPTF rule outperformed other heuristics for the proposed HFS problems.