scholarly journals Simandro-plus: On computing similarity of android applications

2021 ◽  
pp. 36-36
Author(s):  
Masoud Hamedani ◽  
Sang-Wook Kim
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Similarity Score ◽  
The State ◽  
The Other ◽  
Android Applications ◽  
Similarity Computation ◽  
Real World Datasets

In this paper, we propose SimAndro-Plus as an improved variant of the state-of-the-art method, SimAndro, to compute the similarity of Android applications (apps) regarding their functionalities. SimAndro-Plus has two major differences with SimAndro: 1) it exploits two beneficial features to similarity computation, which are totally disregarded by SimAndro; 2) to compute the similarity score of an app-pair based on strings and package name features, SimAndro-Plus considers not only those terms co-appearing in both apps but also considers those terms appearing in one app while missing in the other one. The results of our extensive ex periments with three real-world datasets and a dataset constructed by human experts demonstrate that 1) each of the two aforementioned differences is really effective to achieve better accuracy and 2) SimAndro-Plus outperforms SimAndro in similarity computation by 14% in average.

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

A Novel Tagging Augmented LDA Model for Clustering

2019 ◽  
Vol 16 (3) ◽  
pp. 59-77
Author(s):  
Yi Zhao ◽  
Yu Qiao ◽  
Keqing He
Keyword(s):  
Transfer Learning ◽  
Real World ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Positive Influence ◽  
The State ◽  
Clustering Methods ◽  
Automatic Clustering ◽  
Real World Datasets ◽  
High Representation

Clustering has become an increasingly important task in the analysis of large documents. Clustering aims to organize these documents, and facilitate better search and knowledge extraction. Most existing clustering methods that use user-generated tags only consider their positive influence for improving automatic clustering performance. The authors argue that not all user-generated tags can provide useful information for clustering. In this article, the authors propose a new solution for clustering, named HRT-LDA (High Representation Tags Latent Dirichlet Allocation), which considers the effects of different tags on clustering performance. For this, the authors perform a tag filtering strategy and a tag appending strategy based on transfer learning, Word2vec, TF-IDF and semantic computing. Extensive experiments on real-world datasets demonstrate that HRT-LDA outperforms the state-of-the-art tagging augmented LDA methods for clustering.

scholarly journals Unifying Node Labels, Features, and Distances for Deep Network Completion

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 771
Author(s):  
Qiang Wei ◽  
Guangmin Hu
Keyword(s):  
Network Topology ◽  
Real World ◽  
State Of The Art ◽  
The State ◽  
Network Data ◽  
Convolutional Network ◽  
Deep Network ◽  
Network Recovery ◽  
Real World Datasets ◽  
Node Labels

Collected network data are often incomplete, with both missing nodes and missing edges. Thus, network completion that infers the unobserved part of the network is essential for downstream tasks. Despite the emerging literature related to network recovery, the potential information has not been effectively exploited. In this paper, we propose a novel unified deep graph convolutional network that infers missing edges by leveraging node labels, features, and distances. Specifically, we first construct an estimated network topology for the unobserved part using node labels, then jointly refine the network topology and learn the edge likelihood with node labels, node features and distances. Extensive experiments using several real-world datasets show the superiority of our method compared with the state-of-the-art approaches.

scholarly journals Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning

2020 ◽  
Author(s):  
Shoujin Wang ◽  
Liang Hu ◽  
Yan Wang ◽  
Quan Z. Sheng ◽  
Mehmet Orgun ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Sequence Data ◽  
Multiple Choice ◽  
Prediction Performance ◽  
The State ◽  
Psychological Theories ◽  
User Intentions ◽  
Real World Datasets ◽  
User Actions

User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.

scholarly journals MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation

2018 ◽  
Author(s):  
Sen Su ◽  
Li Sun ◽  
Zhongbao Zhang ◽  
Gen Li ◽  
Jielun Qu
Keyword(s):  
Social Networks ◽  
Real World ◽  
State Of The Art ◽  
The State ◽  
Effective Algorithm ◽  
Kkt Points ◽  
Real World Datasets ◽  
Significant Attention

Recently, reconciling social networks receives significant attention. Most of the existing studies have limitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address these three limitations, we rethink this problem and propose the MASTER framework, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In this framework, we first design a novel Constrained Dual Embedding model by simultaneously embedding and reconciling multiple social networks to formulate our problem into a unified optimization. To address this optimization, we then design an effective algorithm called NS-Alternating. We also prove that this algorithm converges to KKT points. Through extensive experiments on real-world datasets, we demonstrate that MASTER outperforms the state-of-the-art approaches.

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

The State of the Art in Management Education Applicable to Aerospace Activities

1967 ◽  
Vol 71 (677) ◽  
pp. 342-343
Author(s):  
F. H. East
Keyword(s):  
Research And Development ◽  
Management Education ◽  
State Of The Art ◽  
Development Project ◽  
The State ◽  
The Other ◽  
The Past ◽  
Defence Budget ◽  
The One ◽  
Almost All

The Aviation Group of the Ministry of Technology (formerly the Ministry of Aviation) is responsible for spending a large part of the country's defence budget, both in research and development on the one hand and production or procurement on the other. In addition, it has responsibilities in many non-defence fields, mainly, but not exclusively, in aerospace.Few developments have been carried out entirely within the Ministry's own Establishments; almost all have required continuous co-operation between the Ministry and Industry. In the past the methods of management and collaboration and the relative responsibilities of the Ministry and Industry have varied with time, with the type of equipment to be developed, with the size of the development project and so on. But over the past ten years there has been a growing awareness of the need to put some system into the complex business of translating a requirement into a specification and a specification into a product within reasonable bounds of time and cost.

scholarly journals Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

The State of the Art in Management Studies Applicable to Aerospace Activities

1967 ◽  
Vol 71 (677) ◽  
pp. 338-342
Author(s):  
G. P. Dollimore
Keyword(s):  
State Of The Art ◽  
Integrated Management ◽  
The State ◽  
The Other ◽  
Medium Size ◽  
General Management ◽  
Management Techniques ◽  
A Company

Perhaps I should start as earlier speakers have done with a disclaimer to the effect that I am not putting my thoughts forward as those of an expert in all fields of management. I am perhaps fortunate in having been concerned with projects which lend themselves to experiments in the use of integrated management techniques, and also in the operation of a company which, because of its medium size—a thousand or so strong—and its type of business, is just large enough on one hand to justify a reasonably sophisticated approach to general management and, on the other, small enough for one to see the effects of changes in approach. It is on this basis that I shall make my comments.

Review of Hill (2019): The Historical Phonology of Tibetan, Burmese, and Chinese

2020 ◽  
Vol 46 (2) ◽  
pp. 299-311
Author(s):  
Giorgio (Georg) Orlandi
Keyword(s):  
Significant Contribution ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
General Introduction ◽  
Historical Phonology ◽  
Shared History ◽  
Systematic Exploration ◽  
History Of ◽  
The One

Abstract The book under review serves as a significant contribution to the field of Trans-Himalayan linguistics. Designed as a vade mecum for readers with little linguistic background in these three languages, Nathan W. Hill’s work attempts, on the one hand, a systematic exploration of the shared history of Burmese, Tibetan and Chinese, and, on the other, a general introduction to the reader interested in obtaining an overall understanding of the state of the art of the historical phonology of these three languages. Whilst it is acknowledged that the book in question has the potential to be a solid contribution to the field, it is also felt that few minor issues can be also addressed.

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