Reviewerly/exHarmony huggingface dataset:
The Effect of Queries and Search Result Quality on the Rate of Query Abandonment in Interactive Information Retrieval

When a search result does not satisfy a user's needs, the user often abandons their query and submits a reformulated query in the hopes of receiving better search results. The action of abandoning search results is termed query abandonment, and previous research has indicated possible reasons for this action, such as dissatisfaction with the result or coming up with a better query. Query abandonment can be seen as a negative or positive signal. As we move closer to understanding when and what causes a user to abandon their query under different qualities of search results, we move forward in the development of an overall understanding of user behaviour with search engines. This can be helpful for developing more accurate evaluation measures and better methods of collecting relevance judgments. In this work project, we plan to study how the quality of both queries and search results can affect the rate and time users abandon their queries. Specifically, we discuss experiments to investigate the rate at which users abandon their query at different levels of search quality, and whether user examination is affected by the type of non-relevant results. User behaviour in our studies will be analyzed with a combination of eye-tracking and questionnaires. The use of eye-tracking will accurately measure user attention to different elements in the search engine results page (SERP) and how far in the search results a user examines before abandoning the query. Questionnaires will provide insights from users' perspectives into the form and quality of submitted queries.
Part Matching with Multi-Level Attention for Person Re-Identification

Person re-identification, which aims to match two instances across different cameras still remains challenging. One of the biggest obstacles is the misalignment problem, namely instances cannot be spatially aligned due to the large variations in scale, pose, etc. In this work, we proposed a Multi-level Attention Network associated with a Part Matching Distance to address this issue. Specially, the Multi-level Attention Network consists of both pixel-level and part-level attention, which together form a coarse-to-fine flow to extract discriminative features of each instance. The Part Matching Distance formulates the distance between two instances with a part searching scheme under the constrains of the spatial structures, which can further tackle the large-scale variations at the inference stage. Both quantity and quality results on three popular person Re-ID benchmarks and one person search benchmark show that our method outperforms all previous works by a notable margin.
Classification of Documents based on Local Binary Pattern Features through Age Analysis

A method for identifying the age of a document using local binary pattern (LBP) features is presented here. The paper documents are the most important source of information which has been using for communication, record maintenance and proof for smoothing. A paper document is a sheet used for writing or printing something on it. We have been using a large amount of paper documents in day today life. Watermarking, embedding signatures and printed patterns have been using to secure legal documents; however, due to the misuse of digital technology, several challenges related to document security is raised. To address this problem, document age identification is one of the significant steps used to identify document’s authenticity/originality. In this paper, 500 printed documents which are published during 1993–2013 are used for identifying as new or old documents based on their year of publication. We have considered whole document for study irrespective of the content like text, line, logo, noise, etc. Initially, we have segmented a document page into $$512 \times 512$$ blocks and retained those text blocks which are covered with full text. Later, applied LBP technique on these text blocks and extracted features. These features are fed to k-nearest neighbors (KNN) classifier and average classification accuracy achieved is 91.5%.
Dynamic Target Driven Trajectory Planning using RRT

In this paper, we focus on dynamic trajectory planning for an autonomous underwater vehicle (AUV). Specifically, we are interested in planning the trajectory of an AUV as it returns to a moving recovery vessel. To aid in this task, the AUV is equipped with a passive, angle-only, sensor to enable localization of the recovery vessel. Accordingly, we present an algorithm that is capable of dynamically updating the trajectory of the AUV given measurement data from the passive sensor. Our approach is based on adapting a static trajectory planning algorithm from robotics, known as Rapidly-exploring Random Tree (RRT&#x002A;), to allow for localization and tracking of a dynamic target (i.e. the recovery vessel). In contrast to dynamic programming or fixed grid trajectory planning, the RRT&#x002A; offers a computationally efficient method for long-term trajectory planning with probabilistic guarantees of optimality. In this framework, we explore two options: trajectory planning based on minimising the distance to the target; and trajectory planning based on maximising the tracking accuracy of the target using an information theoretic cost. Using AUV recovery as an evaluation scenario, we analyse and evaluate the proposed trajectory planning algorithm against traditional dynamic programming methods. In particular, we consider trajectory planning in noisy and obstructed environments.
Sequential Speaker Embedding and Transfer Learning for Text-Independent Speaker Identification

In this study, an approach to speaker identification is proposed based on a convolutional neural network (CNN)-based model considering sequential speaker embedding and transfer learning. First, a CNN-based universal background model (UBM) is constructed and a transfer learning mechanism is applied to obtain speaker embedding using a small amount of enrollment data. Second, considering the temporal variation of acoustic features in an utterance of a speaker, this study generates sequential speaker embedding to capture temporal characteristics of speech features of a speaker. Experiments were conducted on the King-ASR series database for UBM training, and the LibriSpeech corpus was adopted for evaluation. The experimental results showed that the proposed method using sequential speaker embedding and transfer learning achieved an equal error rate (EER) of 6.89% outperforming the method based on x-vector and PLDA method (8.25%). Furthermore, we considered the effect of speaker number for speaker identification. When the number of enrolled speakers was from 50 to 1172, the identification accuracy of the proposed method was degraded from 82.99% to 73.26%, which outperformed the identification accuracy of the method using x-vector and PLDA which was dramatically degraded from 83.17% to 60.95%.
The studies on Automated Road Safety Monitoring System and Real-Time Traffic Reporting System of an Automatic Vehicle

As technology develops and time progresses continuously, the automation technology is becoming increasingly significant in various fields. Automated road safety is an important issue nowadays. When the automatic vehicles driving on the road, it is very important to detect the surrounding objects. In this paper, we propose an automatic road safety monitoring system for detecting moving objects that will affect the safety of automatic vehicle. On the other hand, we propose a real-time traffic reporting system for determining the real-time traffic condition. Through above two systems, the safety and stability of the automatic vehicle can be improved.
Higher order 1-bit Sigma-Delta modulation on a circle

Manifold models in data analysis and signal processing have become more prominent in recent years. In this paper, we will look at one of the main tasks of modern signal processing, namely, at analog-to-digital (A/D) conversion in connection with a simple manifold model (circle). We will focus on Sigma-Delta modulation which is a popular method for A/D conversion of bandlimited signals that employs coarse quantization coupled with oversampling. Classical Sigma-Delta schemes would provide mismatches and large errors at the initialization point if the signal to be converted is defined on a circle. In this paper, our goal is to get around these problems for higher order Sigma-Delta schemes. Our results show how to design an update for the second and third order schemes based on the reconstruction error analysis such that for the updated scheme the reconstruction error is improved.
Specific category region proposal network for text detection in natural scene

Natural scene text usually carries considerable abstract semantic information, which is closely related to the surrounding environment. Thus, natural scene text detection plays a vital role in image content retrieval and understanding. In this study, the authors propose a novel specific category region proposal network (SCRPN) based on maximally stable extremal regions (MSER) and fully convolutional network (FCN) for natural scene text detection. First, FCN for pixel-level recognition is utilised to obtain the text saliency map and MSER is used to obtain oversegmented regions. Then, the multiple features of oversegmented regions and text saliency map are used for region aggregation. Next, single-linkage clustering method is adopted to cluster the segmentation regions to obtain a hierarchical structure of text region proposals. Finally, for the top-ranking region proposals, SCRPN built an end-to-end pipeline for scene text detection directly. Experiments on street view text and international conference on document analysis and recognition (ICDAR) 2013 have demonstrated the effectiveness of SCRPN for generating the text proposals. SCRPN could work with various two-stage text detection networks; thus, faster region convolutional neural network was used as the text detection framework to evaluate the performance of SCRPN in the ICDAR 2015 and MSRA-TD500 benchmarks. The experimental results confirmed that SCRPN makes text detection more robust in complex scenarios.
A Lightweight Channel-spatial Attention Network for Real-time Image De-raining

Image de-raining aims to eliminate rain streaks captured by outdoor equipment such as video surveillance, remote sensor and automatic pilot. Recently, a de-raining method called non-locally enhanced encoder-decoder network (NLEDN) has achieved reliability performance. Nevertheless, it is very time consuming (2.2571s per image) and takes up memory so that it cannot be applied to mobile devices to process image in real-time. To solve this problem, we design a lightweight channel-spatial attention network that is 55 times faster (41ms per image) and memory saving. The most advanced performances are achieved in most de-raining data sets. More specifically, we design a channel-spatial attention dense block (CSADB). The channel attention operation will be carried out together with the spatial attention. Our experiments demonstrate that the network can learn more effective features by this way. In order to make our proposed method more lightweight, the depthwise convolutions are adapted in each block to reduce parameters. We conduct experiments on four public synthetic datasets to demonstrate the effectiveness of our proposed method, which achieve excellent performance. And the real-world de-raining results are also tacked into comparison. Moreover, an additional experiment demonstrates that our method also works well on face hallucination task. The relevant code and trained models will be available in GitHub soon.
Future Direction of Traffic Classification in SDN from Current Patents Point-of-view

The variety and complexity of current Internet traffic has challenge the Internet Service Providers in providing quality service to the subscribers. Traffic classifications are devised in order to efficiently manage the traffic according to the demand and available resources. In contrast to traditional network, Software Defined Networking (SDN) has a centralized controller in which traffic classifier can take advantage. This paper has identified the state-of-the-art inventions that have been granted patents in the field of network traffic classification, both in traditional network and SDN. The implementation of traffic classification need to adapt; as more traditional network is migrating to SDN. Therefore, the limitation of the current traffic classification has been investigated in order to suggest for its future direction.
Lightweight models for weather identification

At present, the recognition of weather phenomena mainly depends on the weather sensors and the weather radar. However, large-scale deployment of meteorological observation equipment for intensive weather monitoring is difficult because it is expensive and difficult to maintain. Moreover, convolutional neural networks (CNNs) can also be used to identify weather phenomena, but existing methods require high computing power of equipment, making it difficult to deploy in practice. Therefore, designing a lightweight model that can be deployed in a small device with weak computing power is crucial for intensive weather monitoring. In this paper, we study the shortcomings of some existing lightweight models. By comparing the disadvantages of these models, a new lightweight model is proposed. In addition, considering the number of existing weather datasets are too small to meet real monitoring needs, so we produced a dataset with a more complex variety of weather phenomena. Through the experiments, the proposed method can save more than 25 times memory usage with only 1.55% accuracy lost compared with the best CNNs method which achieves state-of-the-art performance among the other lightweight models.
A Lightweight and Robust Face Recognition Network on Noisy Condition

Recently, deep learning has a significant breakthrough in face recognition research. Using the state-of-art convolutional neural network (CNN) model is continually improving the accuracy of recognition. However, it is difficult that the large CNN models deploy on mobile phones or embedded devices with limited computation resources and memory. At the same time, these face recognition networks show low performance in the complex environment, such as noise, shadow, illumination and so on. To address these problems, we propose a lightweight and robust face recognition network (LD-MobileFaceNet) to improve the traditional MobileFaceNet in noisy environment. In this paper, an efficient and flexible denoising block is proposed, which is an independent module to apply in MobileFaceNet. The proposed denoising block uses non-local means algorithm to denoise features that are extracted by convolutional layers. With the residual connection and the 1 × 1 convolution, it can remain more information and be combined with any layers in MobileFaceNet. Furthermore, we set fewer bottleneck layers, replace PReLU with swish nonlinearity to compensate for the loss accuracy. The experimental results demonstrate that LD-MobileFaceNet with swish is 21.35% more accurate on noisy LFW dataset while reducing parameters by 25 % compared to MobileFaceNet.
Genetic algorithm for RBF multi-model optimization for nonlinear system identification

This paper describes an optimization technique of neural networks radial basis function multi-model identification of nonlinear system. Indeed, this paper treats the multi-model approach based on parametric identification. The used architecture of the neural multi-model is radial basis function. We have used the descent gradient and the genetic algorithm technique and we have compared the found results. The effectiveness of the proposed approach is shown through a numerical simulation of a nonlinear system and we have confirmed that the genetic algorithm give a very good results.
Deep Learning Explorations in Wireless Channel Encoders

The modern wireless mobile network architectures are diverse and complex and face huge capacity demands. With increase in demand, monitoring and management of the multitude of network elements has become intractable. To meet the end-user requirements, Internet Service Providers (ISPs) are developing intelligent heterogeneous architectures and approaches that can offspring the 5th generation of mobile systems (5G). Thus, developing mathematical models and adapting them to modems has become far more difficult with 5G. Channel models are important because wireless PHYs must encode, transmit, receive, and decode data in such a way that bit-error-rates (BERs) stay acceptably low. Embedding versatile machine intelligence and deep learning to develop such mathematical models into future mobile networks is gaining much interest of researchers worldwide. In this paper, it is shown that Deep learning models can take transmitted and received baseband data and uses the results to optimize wireless channel encoders in mobile networks. The data fed as input to deep learning can come from channel measurements. Due to availability of data in large amount, deep learning can learn and produce coefficients in more accurate manner. The paper also shows that heterogeneous information received from 5G can be extracted efficiently for getting progressive abstract correlations from the data, while decreasing the effort done in pre-processing.
Rice Productivity Analysis by Province Using K-Means Cluster Algorithm

Abstract Rice (Oryza sativa L.) is a very important food crop in the world after wheat and corn. It is also a staple food for most of the world’s population, especially in Asia, like in Indonesia until now. In 2014 to 2018, rice productivity tended to change dynamically. In 2018, rice productivity in Indonesia was 51.92 (Ku/Ha). This research was conducted to classify rice productivity in 34 provinces in Indonesia in 2018. The data used were sourced from Statistics Indonesia. The method or approach used in this study is the K-Means cluster algorithm to classify rice productivity data by province in 2018. The results of the research are; (1) There are 19 provinces included in cluster 0 (Medium), (2) There are 4 provinces included in cluster 1 (High), and (3) There are 11 provinces that are included in cluster 2 (Low). Based on the results of the study, it was proven that there were 4 provinces in cluster 1 (High) they are West Java, Central Java, East Java and South Sulawesi, with the highest rice productivity. Three of them were on Java Island. It shows that Java still dominates the productivity of rice plants.
Adaptive BM3D Algorithm for Image Denoising Using Coefficient of Variation

Block matching 3D (BM3D) algorithm has shown powerful image denoising capability. This is achieved by block-matching, filtering and aggregating the three-dimensional arrays generated from noisy images. However, high computational cost, inadequate recovery of edge information, etc., limit its application. In this paper, we propose to reduce its high computational cost by an adaptive algorithm based on pre-classification using coefficient of variation. After pre-classification, we obtain two block subsets with different local structural information. In the subset with complex changes, called structural region, size-adaptive reference block matching is adopted for its blocks. In the subset with uniform variation, called flat region, the original size-fixed reference block matching procedure is applied. The adaptive algorithm can significantly reduce the traversal range of the BM3D algorithm for matching, and increase the similarity of the reference block size and the target block (the block to be processed) size if they are similar. This will lead to better removal of noise with lower computational cost. Experimental results show that computational cost of the adaptive algorithm is significantly reduced with close denoising performance to the original BM3D algorithm.
An Efficient Pipeline for Distant Person Detection and Identification in 4K Video using GPUs

The gradual advent of machine learning has been assisting to shift the field of computer vision from statistical methods to deep neural networks. These networks should be able to process high resolution video streams coming from the HD camera sources in real time. However, due to the fixed network size and to maintain the processing speed, high resolution frames need to be resized and down-sampled before feeding into the networks resulting in loss of feature information, hampering recognition accuracy. This motivated us to propose a methodology which focuses on creating and processing the active region of interests in the foreground image through an active region generator (ARG) module, eliminating the need to traverse the entire frame and down-sample the resolution before feeding it to the neural network. This resulted in saving 25x more image feature information, whilst maintaining a given person detection accuracy of 92 % mAP for longer distance up to 30~35 metre executing in real time w.r.t it's classical counterpart based on singleshot detector model. Besides, our proposed pipeline architecture utilizing multi-core TESLA GPU increases the execution throughput by a factor of 3X verified in NVIDIA DGX system.
Aplikasi Game Edukasi Tarian Daerah dan Rumah Adat Daerah Berbasis Android

The purpose of the research is as a medium for learning traditional dances and regional traditional houses and can be used as a debriefing for the community to better know the traditional dances and regional traditional houses properly. The research method used is the System Development Life Cycle (SDLC) waterfall method, a research method that has the characteristics of each phase in the waterfall must be completed before proceeding to the next phase The analytical aid used is Flowchart and Unified Modeling Language. Software used with the help of the Java programming language, Android Studio Editor, Android Standard Development Kit, and Android Development Tools. The conclusion is that this application was built so that it can provide convenience in learning regional dance education and local traditional houses.
Framing Situation Prediction as a Sequence Prediction Problem: A Situation Evolution Model Based on Continuous-Time Markov Chains

Following the acknowledged JDL data fusion model, a situation can be characterized as a set of objects in relations. Considering that this object-relational composition may change over time, as the monitored objects may alter their states (such as changing their event type and position), we can summarize a situation's evolution in a high-level fashion by the sequence of object-relational states it has evolved through, i.e., the sequence of its situation states. Thus, we propose to discretize the continuous evolution of the monitored real-world objects into a sequence of their different joint relational states (e.g., defined by the topological or qualitative distance relations holding between those objects), representing our alphabet, and consequently treat the problem of predicting a monitored situation's further qualitative evolution as a sequence prediction problem. We examine this approach on real-world data from the domain of road traffic incident management, in which situations are characterized by a changing aggregate of different event types, denoting the distinct phases of the monitored road traffic incidents. For the problem of predicting the monitored situation's next discrete state, we propose a predictive situation evolution model based on a first-order Continuous-Time Markov Chain. Our proposed structured situation prediction approach is applicable to all problem domains in which situations can be formulated as sequences of discretized states.