2019 11th International Conference on Knowledge and Systems Engineering (KSE)最新文献

SharkTank Deal Prediction: Dataset and Computational Model SharkTank交易预测:数据集和计算模型

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919477

Thomas Sherk, M. Tran, Tam V. Nguyen

{"title":"SharkTank Deal Prediction: Dataset and Computational Model","authors":"Thomas Sherk, M. Tran, Tam V. Nguyen","doi":"10.1109/KSE.2019.8919477","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919477","url":null,"abstract":"SharkTank is a television show where start-ups pitch their idea to a panel of five investors (sharks) in hopes of striking a deal in the form of equity or royalties for money and other business perks. Since its inception, SharkTank has been a center of discussion and analysis for fans, statisticians, and business people alike in hopes of cracking the code to the start-up world and figuring out the formula for the next big ‘thing’. Most of these discussions and analyses have come in the form of blogs, articles, and academic research. However, there has been a lack of complete datasets and application of computational models for further analysis. In this paper, we investigate factors that play into the SharkTank deal. To this end, we first collect a new dataset, SharkTank Deal Dataset (STDD), by combining data from multiple public sources. The dataset includes descriptive features of each start-up such as product category, team composition, valuation, equity offering, specific sharks that appear on that episode, and state origin. For the computational model, we propose a new computational model to predict whether a start-up strikes a deal with a shark. We conduct experiments to demonstrate the superiority of our model over the baselines.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123039668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

End to end message encryption using Poly1305 and XSalsa20 in Low power and Lossy Networks* 在低功耗和有损网络中使用Poly1305和XSalsa20进行端到端消息加密*

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919479

Sonxay Luangoudom, Tuyen Nguyen, Duc Tran, Linh Giang Nguyen

引用次数: 2

Adversarial Examples Against Image-based Malware Classification Systems 针对基于图像的恶意软件分类系统的对抗性示例

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919481

Bao Ngoc Vi, H. Nguyen, N. Nguyen, Cao Truong Tran

引用次数: 3

Some Fuzzy Inference Processes in Picture Fuzzy Systems 图像模糊系统中的一些模糊推理过程

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919454

B. Cuong, Thanh-Huyen Pham, C. P. Van, Pham Van Hai

引用次数: 3

Vietnamese Span-based Constituency Parsing with BERT Embedding 基于BERT嵌入的越南语跨域选区分析

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919467

Thi-Thu-Hong Phan, Ngoc-Thanh-Tung Huynh, Thinh Hung Truong, Tuan-An Dao, Dinh Dien

{"title":"Vietnamese Span-based Constituency Parsing with BERT Embedding","authors":"Thi-Thu-Hong Phan, Ngoc-Thanh-Tung Huynh, Thinh Hung Truong, Tuan-An Dao, Dinh Dien","doi":"10.1109/KSE.2019.8919467","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919467","url":null,"abstract":"Syntactic structure of sentences obtained from Constituency Parsing is fundamental information in many Natural Language Processing tasks. However, due to the lack of available resources and the complex linguistic features of Vietnamese, the research into Constituency Parsing has not received enough attention in this language. To the best of our knowledge, the study presented in this paper is one of the first investigations to explore this task in Vietnamese. In this work, we present a Spanbased approach which focuses on representing spans through the use of contextualized pre-trained embeddings to obtain optimal parse trees for Vietnamese sentences. The conducted experiments indicate that our system achieved promising results on the VLSP Vietnamese Treebank dataset by significantly outperforming existing methods. The results of this study support the view that encoding context information into the representation of words is effective in improving the parsing performance of Vietnamese. Consequently, this idea can be generalized to apply to other tasks such as Dependency Parsing or other low-resource languages.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130191026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Marking Mechanism in Sequence-to-sequence Model for Mapping Language to Logical Form 语言到逻辑形式映射的序列到序列模型中的标记机制

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919471

Phuong Minh Nguyen, Khoat Than, Minh Le Nguyen

{"title":"Marking Mechanism in Sequence-to-sequence Model for Mapping Language to Logical Form","authors":"Phuong Minh Nguyen, Khoat Than, Minh Le Nguyen","doi":"10.1109/KSE.2019.8919471","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919471","url":null,"abstract":"Semantic parsing in Natural language processing (NLP) is a challenging task, which has been studied for many years. The main purpose is to model the language as a logical form like a machine translation task. Recently, an approach which uses a Neural network with Sequence to sequence model (Seq2seq) has achieved positive results. However, there are many challenges which have not been solved thoroughly yet, especially in the problem of rare words. Rare words in a natural sentence are usually the name of an object, a place or number, time, etc. Although these words are very various and difficult for the model to capture meaning, it holds a key information role in human communication (for example: name all the rivers in colorado ?). There are some methods to solve this problem such as using Attention or using Copy mechanism. However, these methods still difficult to copy phrase rare words, especially in case these phrases are variable in size. This paper proposes a novel approach to solve this problem, namely Marking mechanism in Seq2seq. The main idea is to label special words which are rare-words in a sentence by the encoder (marking step) and the decoder represents the logical form based on those labels (transforming step). Our experiments demonstrate that this approach works effectively, achieved a competitive result with old methods on all 3 datasets Geo, Atis, Jobs and special outperformed on our Artificial dataset.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"9 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131546175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Some Improvements of Using the NSGA-II Algorithm for the Problem of Resource Allocation and Scheduling and Its Applying to Inventory Management Strategies NSGA-II算法在资源分配与调度问题中的改进及其在库存管理策略中的应用

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919492

H. Thang, Doan-Cuong Nguyen, Thanh-Chung Dao, Thanh-Trung Vu, Thi-Huong-Giang Vu, Thi-Xuan-Hoa Nguyen

引用次数: 1

Fuzzy relational equations employing Dragonfly operations 采用蜻蜓运算的模糊关系方程

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919367

N. Cao, M. Štěpnička

引用次数: 1

Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children 基于机器学习的自闭症儿童自动语音对话分析

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919266

Anjana Wijesinghe, Pradeepa Samarasinghe, S. Seneviratne, P. Yogarajah, K. Pulasinghe

{"title":"Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children","authors":"Anjana Wijesinghe, Pradeepa Samarasinghe, S. Seneviratne, P. Yogarajah, K. Pulasinghe","doi":"10.1109/KSE.2019.8919266","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919266","url":null,"abstract":"Children with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"40 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129141978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Challenges of Testing Complex Internet of Things (IoT) Devices and Systems 测试复杂物联网(IoT)设备和系统的挑战

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919324

A. Gómez, Simi Bajaj

引用次数: 15