Reviewerly/exHarmony huggingface dataset:
Conditioners for Adaptive Regression Tracking

Given the input of the whole search area, the goal of regression tracking is to estimate the location of the target object by a calculated response map which usually generates by a Gaussian function. Due to object appearance variation caused by factors such as lighting change and deformation, the update is essential during tracking process. To this end, recent deep regression trackers usually fine-tune the model using hundreds of iterations of gradient descent. Although such trackers achieve pretty good performance, the fine-tuning process is inefficient and limits the tracker's speed which is a relatively important evaluation metric. To solve this problem, we propose an adaptive regression model that utilizes a single forward pass to adapt the model to the specific target object. During forward pass process, two additional networks are designed to adjust the parameters of intermediate layer of the regression model. Such two networks are referred to as conditioners which pay attention to the context and spatial information simultaneously. Extensive experiments illustrate that our approach is much faster than the fine-tuned trackers while achieving competitive performance on four public tracking benchmarks including OTB2015, TC128, UAV123 and VOT2018.
Safety Risks in Location-Based Augmented Reality Games

Location-based augmented reality games occur with a player moving through public spaces, which carries risks. The inattention of the player is one such hazard. Additionally, as seen in many news reports, players of these games have also engaged in trespassing, playing while driving, playing in places of worship, etc. These actions have led to public outrage, and in some well-known cases, arrest and legal consequences. These instances bring forward issues for developers to consider to ensure players’ ethical development and safety while using modern location-based AR games. To evaluate the current safety problems of these games, we provide a comparison of popular AR games.
Paperswithtopic: Topic Identification from Paper Title Only

The deep learning field is growing rapidly as witnessed by the exponential growth of papers submitted to journals, conferences, and pre-print servers. To cope with the sheer number of papers, several text mining tools from natural language processing (NLP) have been proposed that enable researchers to keep track of recent findings. In this context, our paper makes two main contributions: first, we collected and annotated a dataset of papers paired by title and sub-field from the field of artificial intelligence (AI), and, second, we present results on how to predict a paper’s AI sub-field from a given paper title only. Importantly, for the latter, short-text classification task we compare several algorithms from conventional machine learning all the way up to recent, larger transformer architectures. Finally, for the transformer models, we also present gradient-based, attention visualizations to further explain the model’s classification process. All code can be found online (Code available here: https://github.com/1pha/paperswithtopic ).
Comprehensive Evaluation of Blind Single Image Deblurring Algorithms on Real-world Blurs

In literature, several approaches have been used to evaluate the performance of deblurring algorithms on synthetically generated image datasets. The used image datasets only consist of motion-blurred images and do not consider depth variation. The challenge is that real-world blurred images consist of other types of blurs such as defocus blur and depth variation. This paper addresses this challenge by quantitatively evaluating the performance of recent optimization-based and learning-based deblurring algorithms on a real-world blurred image dataset. The used dataset consists of different types of blurs and considers depth variation. Experimental results show that deblurring algorithms do not perform well on real image blurs of natural images, people, and text. Also, optimization-based algorithms perform better than learning-based algorithms. However, optimization-based algorithms are slow. As such, they can be prohibitive for real-time applications. The experimental results also reveal the inconsistent performances of the algorithms on widely used benchmark deblurring image datasets.
Distance-Based Class Activation Map for Metric Learning

The interpretability of deep neural networks can serve as reliable guidance for algorithm improvement. By visualizing class-relevant features in the form of heatmap, the Class Activation Map (CAM) and derivative versions have been widely exploited to study the interpretability of softmax-based neural networks. However, CAM cannot be adopted directly for metric learning, because there is no fully-connected layer in metric-learning-based methods. To solve this problem, we propose a Distance-based Class Activation Map (Dist-CAM) in this paper, which can be applied to metric learning directly. Comprehensive experiments are conducted with several convolutional neural networks trained on the ILSVRC 2012 and the result shows that Dist-CAM can achieve better performance than the original CAM in weakly-supervised localization tasks, which means the heatmap generated by Dist-CAM can effectively visualize class-relevant features. Finally, the applications of Dist-CAM on specific tasks, i.e., few-shot learning, image retrieval and re-identification, based on metric learning are presented.
Identification of Paleotsunami Deposits Using XRF and Artificial Intelligence Methods on the Southern Coast of Lebak, Banten

Abstract The southern coastal area of Lebak, Banten is the southern region of Java which is prone to tsunami disasters. There is evidence of past tsunami events in the southern region of Java. However, not all tsunami deposits have identifiable sedimentological and micropaleontological traces. Geochemical proxies and artificial intelligence with machine learning methods can be used to identify paleotsunami deposits. Machine learning methods that can be used to cluster paleotsunami deposits are Agglomerative Hierarchical Clustering (AHC) and Support Vector Machine (SVM) with validation of model accuracy using the Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) methods. Input data are XRF analysis data and macroscopic core sample description data. The output from data processing is in the form of prediction of tsunami and non-tsunami deposits at each depth of core data sample. The data is correlated and interpreted to identify tsunami events that occurred in the past. The identification results show that the tsunami deposition in the research area, namely the area around the Bagedur coastal coast and Bolang village had tsunami deposit characteristics based on macroscopic description, XRF analysis, and artificial intelligence clusterization. It is also thought to be correlative with the tsunami deposition of previous research in the area around the Binuangeun coastal coast.
Meta-learning approach for implementation of AI methods in the context of CRISP-DM with case studies from master data management

Implementing artificial intelligence (AI) methods is often not a trivial task. Especially for beginners, it is a big challenge to find out a suitable AI method to a certain problem. Moreover, a heuristic approach to algorithm selection is often error-prone and suboptimal. As a solution to this problem, a process model was developed on the basis of a meta-learning approach in the context of CRISP-DM. The model uses a criteria grid to characterize the problem and, based on these characteristics, the process model is able to propose optimal AI methods as possible solutions. The model was evaluated using tasks from master data management and the observed results find support and recognition in the literature.
Picture semantic similarity search based on bipartite network of picture-tag type

Searching similar pictures for a given picture is an important task in numerous applications, including image recommendation system, image classification and image retrieval. Previous studies mainly focused on the similarities of content, which measures similarities based on visual features, such as color and shape, and few of them pay enough attention to semantics. In this paper, we propose a link-based semantic similarity search method, namely PictureSim, for effectively searching similar pictures by building a picture-tag network. The picture-tag network is built by "description" relationships between pictures and tags, in which tags and pictures are treated as nodes, and relationships between pictures and tags are regarded as edges. Then we design a TF-IDF-based model to removes the noisy links, so the traverses of these links can be reduced. We observe that "similar pictures contain similar tags, and similar tags describe similar pictures", which is consistent with the intuition of the SimRank. Consequently, we utilize the SimRank algorithm to compute the similarity scores between pictures. Compared with content-based methods, PictureSim could effectively search similar pictures semantically. Extensive experiments on real datasets to demonstrate the effectiveness and efficiency of the PictureSim.
Towards Nested and Fine-Grained Open Information Extraction

Open Information Extraction is a crucial task in natural language processing with wide applications. Existing efforts only work on extracting simple flat triplets that are not minimized, which neglect triplets of other kinds and their nested combinations. As a result, they cannot provide comprehensive extraction results for its downstream tasks. In this paper, we define three more fine-grained types of triplets, and also pay attention to the nested combination of these triplets. Particular, we propose a novel end-to-end joint extraction model, which identifies the basic semantic elements, comprehensive types of triplets, as well as their nested combinations from plain texts jointly. In this way, information is shared more thoroughly in the whole parsing process, which also lets the model achieve more fine-grained knowledge extraction without relying on external NLP tools or resources. Our empirical study on datasets of two domains, Building Codes and Biomedicine, demonstrates the effectiveness of our model comparing to state-of-the-art approaches.
You ask “Durian”, I gave “Musang King, Red Prawn and D24”: A Query Reformulation Behavior Experiment

The success of finding relevant information using search engines is highly dependent on the query used by the user. Therefore, an effective query formulation and reformulation is an important strategy in information search. The challenges become harder when finding for more in-depth information in a particular topic or domain. This paper aims to investigate the query behavior when searching for information in a given topic. We chose Durian as the topic of the study. To achieve the goal of the study, a user study was carried out using a task-driven experiment in which 12 scenario-based search tasks were designed for the user to complete using the Google search engine. A total of 4531 queries being analyzed, 3595 reformulation being made by 936 search tasks being submitted by 78 users who engaged in the experiment. The results reveal that 98% of the user performed query reformulation during the search and specification was the most frequent query reformulation strategy used by the users. In terms of the number of query words used, most of the users used 3 to 5 words in their query. In addition, majority of the users prefer to use natural language queries over keyword queries. This work provides insights to the query reformulation behavior in domain-specific information search, as well as the guidance for designing better query formulation assistance in search engine technology.
A Comprehensive Analysis on Question Classification Using Machine Learning and Deep Learning Techniques

The competence of any online Web site depends on the type of experience it gives to its users, which depends largely on the content they put up on their Web site. Hence, the content which is being put online should be really taken care. There are many Web sites that provide content to their user in terms of questions and answers, for example, the online Web site Quora, which has large scale of data in terms of questions and answers of users. Users are the one who put up questions and also provide answers to those questions. In this paper, a system is proposed by considering significant amount of data from Kaggle, where it is utilized for different approaches to predict the insincere questions. The goal of the proposed work is to develop a model that considers question as an input in English language and produces output as either 0 or 1. In that, 0 represents sincere type of questions and 1 represents insincere type of questions. Hence, the data are transformed into result including F1 score using preprocessing, TF-IDF and pre-trained word embedding. For each method, preprocessing tasks like tokenization, case normalization and punctuation removal are used. Each individual word after preprocessing is used as word vector in this work. These word vectors are then used for the insincere and sincere question classification which is given as input to the machine learning models like SVM, Naïve Bayes, logistic regression and deep learning model called RNN with word embedding. It is used to detect the toxic content in the reviews as 1 for insincere and 0 for sincere questions. The accuracy of these different methods is critically examined with the help of LSTM, word embedding and model ensembling.
Improved Distant Supervision Relation Extraction Based on Edge- Reasoning Hybrid Graph Model

Distant supervision relation extraction (DSRE) trains a classifier by automatically labeling data through aligning triples in the knowledge base (KB) with large-scale corpora. Training data generated by distant supervision may contain many mislabeled instances, which is harmful to the training of the classifier. Some recent methods show that relevant background information in KBs, such as entity type (e.g., Organization and Book), can improve the performance of DSRE. However, there are three main problems with these methods. Firstly, these methods are tailored for a specific type of information. A specific type of information only has a positive effect on a part of instances and will not be beneficial to all cases. Secondly, different background information is embedded independently, and no reasonable interaction is achieved. Thirdly, previous methods do not consider the side effect of the introduced noise of background information. To address these issues, we leverage five types of background information instead of a specific type of information in previous works and propose a novel edge-reasoning hybrid graph (ER-HG) model to realize reasonable interaction between different kinds of information. In addition, we further employ an attention mechanism for the ER-HG model to alleviate the side effect of noise. The ER-HG model integrates all types of information efficiently and is very robust to the noise of information. We conduct experiments on two widely used datasets. The experimental results demonstrate that our model outperforms the state-of-the-art methods significantly in held-out metric and robustness tests.
Model inspections in the engineering of collaborative cyber‐physical systems with instance‐level review diagrams

Abstract Model inspections are important to ensure high‐quality software and to satisfy legal obligations in model‐based engineering processes. As model‐based specifications are typically documented on type‐level, errors concerning the interactions between multiple system instances can go unnoticed. For collaborative cyber‐physical systems (CPS), a plethora of possible instance‐level configurations need to be taken into account. Therefore, we propose the definition of instance‐level review diagrams that show representative interactions of instance‐level configurations that help detect defects in the system specification. To evaluate the approach, we conducted a controlled experiment whose results indicate that instance‐level review diagrams have—compared with type‐level diagrams—important positive effects on reviewing processes for behavioral specifications of CPS. Specifically, the experiment provides empirical evidence that instance‐level review diagrams are significantly more expressive and effective than type‐level diagrams.
Research on the Construction of University Computer Service Platform Based on Cloud Computing Technology

Abstract Compared with the concept of campus network, the concept of digital campus is much richer in connotation, covering a wide range of contents, including not only the construction of hardware facilities and network systems, but also providing digital services for teaching and research, management, life services and other aspects. Traditional daily teaching and educational administration is mainly carried out by hand, which has disadvantages such as heavy workload and trivial things, and it is difficult to improve the management effect and level. The development of big data technology can more effectively guide teachers and students to learn independently. The cloud service teaching environment based on big data can not only monitor teachers’ teaching process, but also analyze students’ learning process and predict each student’s learning effect. This paper analyzes the problems existing in the traditional computer service environment, the common difficulties of working in different places, and the new situation faced by teaching organizations, and discusses the construction scheme of university information service platform based on cloud computing technology.
Interlink between Service Performance and Service Quality

The Quality has been a major weapon for the service industry in the phase of globalization with an intention to dominate the market through profitability. The Good Quality practices results in improved performance & Services which benefits the organization. Quality management ensures the organization in delivering a better quality of work in individual as well as collective operations. This research aims to investigate the good Quality service practices in the Private Tourist Companies based in U.P. and assessing its impact on the Visitors’ using the Performance Gap Model(Parasuraman et. Al 1985) in satisfying them.
A Montroyalite Mix-Up

The identification of rare minerals is a labor of love for impassioned mineralogists with access to analytical equipment. It is of no help that innumerable minerals are visually indistinguishable f...
Optically smooth and optically rough surfaces in 3D profilometry

The task of optical 3D profilometry is to measure the value of spatial coordinate of one or more surface points of an object. The value of measured coordinate is obtained from one or more intensity values that are recorded by a detector. The way in which the resulting intensity is generated depends on whether the surface is optically smooth or rough. The property of a surface to be optically smooth or rough depends on the mechanical properties of the surface and on the parameters of the optical system. We present a detailed analysis of the conditions on which a surface can be classified as optically smooth or optically rough. It is also discussed how the optically smooth surface and the resolved mode are related.
Underwater Target Motion Analysis using Product Beamformer and Bearing Match in Simulated Space

Target Maneuvers Analysis (TMA) is an indispensable part of the modern antisubmarine warfare system. A lot of research has gone into various passive TMA techniques. Some of these are batch processing methods, whereas many are iterative. Most iterative methods based on the Bayesian recursion framework had gained wide popularity in recent years. The method presented in this paper is a naïve method, based on inputs of possible values of range, and speed of the target. The method does a deterministic search on a possible discrete solution space. A set of possible target bearing sequences is constructed and the measured bearings are matched against these generated bearings to identify the best match. The dynamics of the best match give a robust solution to the TMA problem.
Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets

In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trades data from the most liquid Bitcoin markets, in order to understand the price formation process at sub-1second time scales.To achieve this goal, we construct a set of features that encapsulate relevant microstructural information over short lookback windows. These features are subsequently leveraged first to generate a leader-lagger network that quantifies how markets impact one another, and then to train linear models capable of explaining between 10% and 37% of total variation in 500ms future returns (depending on which market is the prediction target). The results are then compared with those of various PnL calculations that take trading realities, such as transaction costs, into account.The PnL calculations are based on natural taker strategies (meaning they employ market orders) that we associate to each model. Our findings emphasize the role of a market's fee regime in determining its propensity to being a leader or a lagger, as well as the profitability of our taker strategy. Taking our analysis further, we also derive a natural maker strategy (i.e., one that uses only passive limit orders), which, due to the difficulties associated with backtesting maker strategies, we test in a real-world live trading experiment, in which we turned over 1.5 million USD in notional volume. Lending additional confidence to our models, and by extension to the features they are based on, the results indicate a significant improvement over a naive benchmark strategy, which we also deploy in a live trading environment with real capital, for the sake of comparison.
Unitary Energy Transfer Between Coupled Cavities Using Temporal Switching

Tailored time variations can enable efficient control over signal flows, giving rise to exotic wave phenomena. In this work, we demonstrate how abrupt temporal switching of the coupling between two cavities can tailor the energy flow between them beyond the limitations of static scenarios, enabling unitary energy transfer. The proposed scheme is robust with respect to a wide range of non-idealities, with implications for classical and quantum phenomena, from computing to nanophotonic systems.
Bangla-German Language Translation Using GRU Neural Networks

Machine translation relates to highly autonomous software which is capable of translating source sentences into different languages. Previously some work was done in this sector where the result was comparatively low. Most of the researchers worked on common languages and none of them gave satisfactory Bilingual Evaluation Understudy (BLEU) score. Depending on these factors, we build a system of Bangla-German translator. This system can be used in various areas (i.e. reliable interpreters, business conduction, e-commerce merchandising, etc.). The system is built based on Gated Recurrent Unit (GRU) which is a gating mechanism of Recurrent Neural Network (RNN). Here, total five types of different RNN algorithms were used like Simple RNN, RNN with Embedding, Encoder-Decoder RNN, Bidirectional RNN, Hybrid RNN. All of them gave good accuracy. But the best result we got from the Hybrid model which was the combination of Embedded and Bidirectional algorithm. The accuracy was 85.69%. For further evaluation, BLEU score was used. The result of BLEU score of unigram to four gram was respectively increasing from 54.40% to 85.88%. Also the comparison between machine translated sentences and Google translated sentences showed that the system works very efficiently.