Int. J. Semantic Comput.最新文献_第6页

Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook 图形计算用于金融犯罪和欺诈检测:趋势、挑战和展望

Int. J. Semantic Comput. Pub Date : 2020-12-01 DOI: 10.1142/S1793351X20300022

Eren Kurshan, Hongda Shen

引用次数: 14

A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications 神经形态计算与人工智能应用的三维集成系统设计案例

Int. J. Semantic Comput. Pub Date : 2020-12-01 DOI: 10.1142/S1793351X20500063

Eren Kurshan, H. Li, Mingoo Seok, Yuan Xie

{"title":"A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications","authors":"Eren Kurshan, H. Li, Mingoo Seok, Yuan Xie","doi":"10.1142/S1793351X20500063","DOIUrl":"https://doi.org/10.1142/S1793351X20500063","url":null,"abstract":"Over the last decade, artificial intelligence has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, 3D integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefits the designs in the future.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125118913","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

A Review on Lexicon-Based and Machine Learning Political Sentiment Analysis Using Tweets 基于词典和机器学习的推文政治情绪分析综述

Int. J. Semantic Comput. Pub Date : 2020-12-01 DOI: 10.1142/S1793351X20300010

Alexandros Britzolakis, H. Kondylakis, N. Papadakis

{"title":"A Review on Lexicon-Based and Machine Learning Political Sentiment Analysis Using Tweets","authors":"Alexandros Britzolakis, H. Kondylakis, N. Papadakis","doi":"10.1142/S1793351X20300010","DOIUrl":"https://doi.org/10.1142/S1793351X20300010","url":null,"abstract":"Sentiment analysis over social media platforms has been an active case of study for more than a decade. This occurs due to the constant rising of Internet users over these platforms, as well as to the increasing interest of companies for monitoring the opinion of customers over commercial products. Most of these platforms provide free, online services such as the creation of interactive web communities, multimedia content uploading, etc. This new way of communication has affected human societies as it shaped the way by which an opinion can be expressed, sparking the era of digital revolution. One of the most profound examples of social networking platforms for opinion mining is Twitter as it is a great source for extracting news and a platform which politicians tend to use frequently. In addition to that, the character limitation per posted tweet (maximum of 280 characters) makes it easier for automated tools to extract its underlying sentiment. In this review paper, we present a variety of lexicon-based tools as well as machine learning algorithms used for sentiment extraction. Furthermore, we present additional implementations used for political sentiment analysis over Twitter as well as additional open topics. We hope the review will help readers to understand this scientifically rich area, identify best options for their work and work on open topics.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"87 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120882124","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: 5

Vulnerability Rating of Source Code with Token Embedding and Combinatorial Algorithms 基于令牌嵌入和组合算法的源代码漏洞评级

Int. J. Semantic Comput. Pub Date : 2020-12-01 DOI: 10.1142/S1793351X20500087

Joseph R. Barr, Peter Shaw, F. Abu-Khzam, Tyler Thatcher, Sheng Yu

{"title":"Vulnerability Rating of Source Code with Token Embedding and Combinatorial Algorithms","authors":"Joseph R. Barr, Peter Shaw, F. Abu-Khzam, Tyler Thatcher, Sheng Yu","doi":"10.1142/S1793351X20500087","DOIUrl":"https://doi.org/10.1142/S1793351X20500087","url":null,"abstract":"We present an empirical analysis of the source code of the Fluoride Bluetooth module, which is a part of standard Android OS distribution, by exhibiting a novel approach for classifying and scoring source code and vulnerability rating. Our workflow combines deep learning, combinatorial optimization, heuristics and machine learning. A combination of heuristics and deep learning is used to embed function (and method) labels into a low-dimensional Euclidean space. Because the corpus of the Fluoride source code is rather limited (containing approximately 12,000 functions), a straightforward embedding (using, e.g. code2vec) is untenable. To overcome the challenge of dearth of data, it is necessary to go through an intermediate step of Byte-Pair Encoding. Subsequently, we embed the tokens from which we assemble an embedding of function/method labels. Long short-term memory network (LSTM) is used to embed tokens. The next step is to form a distance matrix consisting of the cosines between every pairs of vectors (function embedding) which in turn is interpreted as a (combinatorial) graph whose vertices represent functions, and edges correspond to entries whose value exceed some given threshold. Cluster-Editing is then applied to partition the vertex set of the graph into subsets representing “dense graphs,” that are nearly complete subgraphs. Finally, the vectors representing the components, plus additional heuristic-based features are used as features to model the components for vulnerability risk.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"67 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124464191","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: 4

A Biofeedback Enhanced Adaptive Virtual Reality Environment for Managing Surgical Pain and Anxiety 生物反馈增强自适应虚拟现实环境管理手术疼痛和焦虑

Int. J. Semantic Comput. Pub Date : 2020-09-01 DOI: 10.1142/s1793351x20400152

Vishnunarayan Girishan Prabhu, L. Stanley, Robert Morgan

{"title":"A Biofeedback Enhanced Adaptive Virtual Reality Environment for Managing Surgical Pain and Anxiety","authors":"Vishnunarayan Girishan Prabhu, L. Stanley, Robert Morgan","doi":"10.1142/s1793351x20400152","DOIUrl":"https://doi.org/10.1142/s1793351x20400152","url":null,"abstract":"Pain and anxiety are common accompaniments of surgery, and opioids have been the mainstay of pain management for decades, with about 80% of the surgical population leaving the hospital with an opioid prescription. Moreover, patients receiving an opioid prescription after short-stay surgeries have a 44% increased risk of long-term opioid use, and about one in 16 surgical patients becomes a long-term user. Current opioid abuse and addiction now place the US in an “opioid epidemic,” and calls for alternative pain management mechanisms. To mitigate the preoperative anxiety and postoperative pain, we developed a virtual reality (VR) experience based on Attention Restoration Theory (ART) and integrated the user’s heart rate variability (HRV) biofeedback to create an adaptive environment. A randomized control trial among 16 Total Knee Arthroplasty (TKA) patients undergoing surgery at Patewood Memorial Hospital, Greenville, SC demonstrated that patients experiencing the adaptive VR environment reported a significant decrease in preoperative anxiety ([Formula: see text]) and postoperative pain ([Formula: see text]) after the VR intervention. These results were also supported by the physiological measures where there was a significant increase in RR Interval (RRI) ([Formula: see text]) and a significant decrease in the low frequency (LF)/high frequency (HF) ratio ([Formula: see text]) and respiration rate (RR) ([Formula: see text]).","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131772801","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: 8

Semantic Analysis for Conversational Datasets: Improving Their Quality Using Semantic Relationships 会话数据集的语义分析:利用语义关系提高其质量

Int. J. Semantic Comput. Pub Date : 2020-09-01 DOI: 10.1142/s1793351x2050004x

Maria Krommyda, Verena Kantere

引用次数: 3

Multi-Platform Expansion of the Virtual Human Toolkit: Ubiquitous Conversational Agents 虚拟人类工具箱的多平台扩展:无处不在的会话代理

Int. J. Semantic Comput. Pub Date : 2020-09-01 DOI: 10.1142/s1793351x20400127

Arno Hartholt, Edward Fast, Adam Reilly, W. Whitcup, Matt Liewer, S. Mozgai

引用次数: 9

Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality 圆柱全景视频深度和自我运动的无监督学习及其在虚拟现实中的应用

Int. J. Semantic Comput. Pub Date : 2020-09-01 DOI: 10.1142/S1793351X20400139

Alisha Sharma, Ryan Nett, Jonathan Ventura

引用次数: 24

Emergent Languages from Pretrained Embeddings Characterize Latent Concepts in Dynamic Imagery 基于预训练嵌入的涌现语言表征动态图像中的潜在概念

Int. J. Semantic Comput. Pub Date : 2020-09-01 DOI: 10.1142/s1793351x20400140

James R. Kubricht, A. Santamaría-Pang, Chinmaya Devaraj, Aritra Chowdhury, P. Tu

{"title":"Emergent Languages from Pretrained Embeddings Characterize Latent Concepts in Dynamic Imagery","authors":"James R. Kubricht, A. Santamaría-Pang, Chinmaya Devaraj, Aritra Chowdhury, P. Tu","doi":"10.1142/s1793351x20400140","DOIUrl":"https://doi.org/10.1142/s1793351x20400140","url":null,"abstract":"Recent unsupervised learning approaches have explored the feasibility of semantic analysis and interpretation of imagery using Emergent Language (EL) models. As EL requires some form of numerical embedding as input, it remains unclear which type is required in order for the EL to properly capture key semantic concepts associated with a given domain. In this paper, we compare unsupervised and supervised approaches for generating embeddings across two experiments. In Experiment 1, data are produced using a single-agent simulator. In each episode, a goal-driven agent attempts to accomplish a number of tasks in a synthetic cityscape environment which includes houses, banks, theaters and restaurants. In Experiment 2, a comparatively smaller dataset is produced where one or more objects demonstrate various types of physical motion in a 3D simulator environment. We investigate whether EL models generated from embeddings of raw pixel data produce expressions that capture key latent concepts (i.e. an agent’s motivations or physical motion types) in each environment. Our initial experiments show that the supervised learning approaches yield embeddings and EL descriptions that capture meaningful concepts from raw pixel inputs. Alternatively, embeddings from an unsupervised learning approach result in greater ambiguity with respect to latent concepts.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116135363","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

Accurate Lane Detection for Self-Driving Cars: An Approach Based on Color Filter Adjustment and K-Means Clustering Filter 自动驾驶汽车精确车道检测:基于颜色滤波调整和k均值聚类滤波的方法

Int. J. Semantic Comput. Pub Date : 2020-06-09 DOI: 10.1142/s1793351x20500038

Dongfang Liu, Yaqin Wang, Tian Chen, E. Matson

引用次数: 2