Miracle-dz/UniFAR-FTUs huggingface dataset:
Urban patterns: using spatial arrangement for vision-based place recognition and localisation

In this thesis, we investigate the impact of using visual landmarks with spatial knowledge to improve the performance of vision-based localisation. We separate our work into two parts. First, we introduce a new place recognition method based on a novel representation called landmark distribution descriptor (LDD) which combines landmark identification based on CNN features with their spatial distribution across a view. We use the representation to do matching within an image-to-image place recognition framework, which is to compare test images with single images taken at distinct locations in urban environments. Results on large datasets from 10 different cities obtained from Google StreetView and Bing Streetside demonstrate an average precision of around 70% (at 100% recall), compared with 58% obtained using whole image CNN features and 50% for a comparable landmark method without spatial information. Second, we investigate the problem of localisation in urban environments using only image data and a 2-D map of the area. We employ binary semantic descriptors (BSD): 4-bit binary descriptors indicating the presence or otherwise of salient landmarks at a given location which are indicated on the 2D map. On their own, these descriptors are not sufficiently distinctive to allow localisation. However, when combined sequentially over routes, the resulting concatenated descriptors prove to be highly discriminative, enabling robust localisation corresponds to the map. Performance can be further improved by incorporating the turn information along with a route. Landmark presence in 360-degree images taken at a given location is detected using a CNN binary classifier, trained using Google StreetView and OpenStreetMap data. Experiments with over 6,000 locations in an urban area show that the approach can give 95% of accuracy using an average route length of 200 metres. Thus, in both works, we demonstrate that landmarks combined with spatial knowledge provide an effective means of improving vision-based localisation.
Role of the scalar $f_0(980)$ in the process $D_s^+ \to \pi^{+} \pi^{0} \pi^{0}$

Based on the BESIII measurements on the reaction of $D_s^+\to \pi^+\pi^0\pi^0$, we investigate this process by considering the $S$-wave pseudoscalar-pseudoscalar interaction within the unitary chiral approach, and the contributions from the intermediate resonances $f_0(1370)$ and $f_2(1270)$. Our calculation could reasonably reproduce the experimental data, and our results imply that the $f_0(980)$, dynamically generated from the $S$-wave pseudoscalar-pseudoscalar interaction, plays an important role in this process, and the contributions from the intermediate resonances $f_0(1370)$ and $f_2(1270)$ are also necessary. The more precise measurements of this process in future \textcolor{red}{could shed light on} the nature of the $f_0(1370)$ and $f_2(1270)$. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Accelerating convex optimization in machine learning by leveraging functional growth conditions

In recent years, unprecedented growths in scale and dimensionality of data raise big computational challenges for traditional optimization algorithms; thus it becomes very important to develop efficient and effective optimization algorithms for solving numerous machine learning problems. Many traditional algorithms (e.g., gradient descent method) are black-box algorithms, which are simple to implement but ignore the underlying geometrical property of the objective function. Recent trend in accelerating these traditional black-box algorithms is to leverage geometrical properties of the objective function such as strong convexity. However, most existing methods rely too much on the knowledge of strong convexity, which makes them not applicable to problems without strong convexity or without knowledge of strong convexity. To bridge the gap between traditional black-box algorithms without knowing the problem’s geometrical property and accelerated algorithms under strong convexity, how can we develop adaptive algorithms that can be adaptive to the objective function’s underlying geometrical property? To answer this question, in this dissertation we focus on convex optimization problems and propose to explore an error bound condition that characterizes the functional growth condition of the objective function around a global minimum. Under this error bound condition, we develop algorithms that (1) can adapt to the problem’s geometrical property to enjoy faster convergence in stochastic optimization; (2) can leverage the problem’s structural regularizer to further improve the convergence speed; (3) can address both deterministic
Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network

Media sosial sebagai media informasi dan komunikasi mulai berkembang pesat sejak internet mudah diakses. Orang dengan mudah menyatakan pendapat, ekspresi, opini, dan informasi melalui tulisan pada media sosial. Opini atau informasi pada media sosial dapat digunakan untuk menilai baik atau buruk suatu brand perusahaan. Orang cenderung jujur dalam mengungkapkan perasaan terhadap sesuatu pada media sosial. Dengan menggunakan sentiment analysis terhadap opini dari pelanggan, analisis opini dapat dilakukan secara otomatis. Perusahaan dapat secara langsung mengetahui tingkat kepuasan pelanggan dan digunakan untuk meningkatkan kualitas pelayanan hingga menaikan brand perusahaan. Penggunaan metode classical machine learning yang sudah banyak diterapkan pada sentiment analysis , tetapi metode tersebut tidak memperhatikan pentingnya urutan kata pada suatu kalimat. Metode deep learning dengan algoritme Deep Convolutional Neural Network ditawarkan untuk menjawab permasalahan tersebut dengan melakukan operasi convolution menggunakan filter sebesar ukuran window untuk mendapatkan fitur berdasarkan urutan kata. Model Word2Vec untuk Bahasa Indonesia digunakan sebagai representasi kata dalam bentuk vektor. Penggunaan Word2Vec juga mempercepat proses pelatihan dan meningkatkan akurasi algoritme Deep Convolutional Neural Network. Data yang digunakan dalam makalah ini adalah data Twitter Bahasa Indonesia dengan jumlah 999 tweet . Hasil percobaan yang telah dilakukan dengan algoritme Deep Convolutional Neural Network memiliki nilai akurasi terbaik sebesar 76,40%. Abstract Social media as information media and communication is growing rapidly since the internet is easily accessible. People easily express opinions, expressions, and information by writing on social media. Opinion or information on social media can be used to assess how good or bad a companies is. People tend to be honest in expressing feelings towards something on social media. With sentiment analysis, analysis of the opinions of customers can be done automatically. The company will know the level of customer satisfaction and can be used to improve the quality of service to raise the company's brand. The use of classical machine learning methods that have been widely applied to sentiment analysis ignoring the importance of the word order in a sentence. Deep Convolutional Neural Network algorithm is offered to answer these problems by carrying out convolution operations using filters as large as window size to get features based on word order. Word2Vec model for Indonesian is used as a word vector representation. The use of Word2Vec also reduce the training time and improve the accuracy of the Deep Convolutional Neural Network algorithm. The data used in this paper is Indonesian Twitter data with 999 tweets. The results of experiments that have been carried out with the Deep Convolutional Neural Network algorithm have the best accuracy value of 76.40%.
Classification Approaches to Identify Informative Tweets

Social media platforms have become prime forums for reporting news, with users sharing what they saw, heard or read on social media. News from social media is potentially useful for various stakeholders including aid organizations, news agencies, and individuals. However, social media also contains a vast amount of non-news content. For users to be able to draw on benefits from news reported on social media it is necessary to reliably identify news content and differentiate it from non-news. In this paper, we tackle the challenge of classifying a social post as news or not. To this end, we provide a new manually annotated dataset containing 2,992 tweets from 5 different topical categories. Unlike earlier datasets, it includes postings posted by personal users who do not promote a business or a product and are not affiliated with any organization. We also investigate various baseline systems and evaluate their performance on the newly generated dataset. Our results show that the best classifiers are the SVM and BERT models.
Forecasting sales with micro-panels: Empirical Bayes approach. Evidence from consumer goods sector

Most of the existing non-Bayesian studies on dynamic panel models mainly focus on consistent estimation of the common auto-regressive parameters, while treating the heterogeneous intercept as a nuisance and removing it from the model, e.g. by first-differentiating. Bayesian literature proposes methods to obtain posterior distributions of the unit-specific intercept based on Markov Chain Monte Carlo sampling, which can be slow for large data sets. This paper investigates an empirical-Bayes procedure for obtaining the individual-specific intercept parameters without resorting to MCMC sampling. A set of simulation studies and empirical applications show that the proposed model outperforms benchmark models in some settings, especially for long-run forecasting of highly persistent data. Additionally, this paper proposes a bootstrap-based method for computing prediction intervals in the analysed modelling framework.
THE DATA SUBJECT’S RIGHTS AND LIABILITY FOR VIOLATIONS

With the development of information technology, data processing tools for individuals are increasingly used in various areas of life. Their accessibility and simplicity in circulation lead to more and more mass application of technologies in the lives of individuals, organizations, and society. Personal data of individuals are subject to turnover, inevitably there is a need to protect the carriers of this data from using the information received against their rights and interests. Despite the urgency of the problem, the concept of the subject of protection does not have a clear definition, as a result, the participants in legal relations will seek to interpret it depending on their needs. The law determines the administrative responsibility for the violation of the processing of data of individuals, but does not provide for special mechanisms for compensation for the damage caused. Consequently, there is an urgent need to specify the norms related to the protection of these individuals.
Offline Deep Importance Sampling for Monte Carlo Path Tracing

Although modern path tracers are successfully being applied to many rendering applications, there is considerable interest to push them towards ever‐decreasing sampling rates. As the sampling rate is substantially reduced, however, even Monte Carlo (MC) denoisers–which have been very successful at removing large amounts of noise–typically do not produce acceptable final results. As an orthogonal approach to this, we believe that good importance sampling of paths is critical for producing better‐converged, path‐traced images at low sample counts that can then, for example, be more effectively denoised. However, most recent importance‐sampling techniques for guiding path tracing (an area known as “path guiding”) involve expensive online (per‐scene) training and offer benefits only at high sample counts. In this paper, we propose an offline, scene‐independent deep‐learning approach that can importance sample first‐bounce light paths for general scenes without the need of the costly online training, and can start guiding path sampling with as little as 1 sample per pixel. Instead of learning to “overfit” to the sampling distribution of a specific scene like most previous work, our data‐driven approach is trained a priori on a set of training scenes on how to use a local neighborhood of samples with additional feature information to reconstruct the full incident radiance at a point in the scene, which enables first‐bounce importance sampling for new test scenes. Our solution is easy to integrate into existing rendering pipelines without the need for retraining, as we demonstrate by incorporating it into both the Blender/Cycles and Mitsuba path tracers. Finally, we show how our offline, deep importance sampler (ODIS) increases convergence at low sample counts and improves the results of an off‐the‐shelf denoiser relative to other state‐of‐the‐art sampling techniques.
Trust model genetic node recovery based on cloud theory for underwater acoustic sensor network

Underwater Acoustic Sensor Networks [UASNs] are becoming a very growing research topic in the field of WSNs. UASNs are harmful by many attacks such as Jamming attacks at the physical layer, Collision attacks at the data link layer and Dos attacks at the network layer. UASNs has a unique characteristic such as unreliable communication, mobility, and computation of underwater sensor network. Because of this the traditional security mechanism, e.g. cryptographic, encryption, authorization and authentications are not suitable for UASNs. Many trust mechanisms of TWSNs [Terrestrial Wireless Sensor Networks] had proposed to UASNs and failed to provide security for UASNs environment, due to dynamic network structure and weak link connection between sensors. In this paper, a novel Trust Model Genetic Algorithm based on Cloud Theory [TMC] for UASNs has been proposed. The TMC-GA suggested a genetic node recovery algorithm to improve the TMC network in terms of better network lifetime, residual energy and total energy consumption. Also ensures that sensor nodes are participating in the rerouting in the routing discovery and performs well in terms of successful packet delivery. Simulation result provides that the proposed TMC-Genetic node recovery algorithm outperforms compared to other related works in terms of the number of hops, end-to-end delay, total energy consumption, residual energy, routing overhead, throughput and network lifetime.
Classifying computer-mediated communication (CMC) interruptions at work using control as a key delineator

ABSTRACT Computer-mediated communication (CMC) interruptions are a common feature of people’s work activity. In studying interruptions, researchers can understand how people manage and co-ordinate their work when faced with multiple, often competing, demands. However, CMC interruptions are characteristically different from each other and impact people’s work performance in different ways. In this theoretical paper, we define and differentiate between computer-mediated communication (CMC) interruptions, according to the level of control people are able to exert over an interruption at different points in the delivery timeline. Informed by the extant interruptions literature and Action Regulation Theory, a classification framework is provided, to help researchers and work designers distinguish which types of real-world CMC interruption are more or less disruptive, based on levels of control. Using the developed framework, two key research propositions are made, which we encourage future research to attend to. Unique contributions and implications of this paper are discussed.
A SUPPORT VECTOR-BASED PREDICTIVE MODEL TO REVEAL THE RELATIONSHIPS AMONG ANTIBODY FEATURES AND THEIR EFFECTIVE FUNCTIONS AGAINST HIV

Despite 4 decades’ effort, an effective HIV-1 vaccine has not been produced owing to the inevitable antigenic diversity of the virus and millions of people around the world have lost their lives due to HIV. Increasing the knowledge of adaptive immune response to vaccination would ultimately lead to an effective HIV cure. Antibodies, which are responsible for protection and fighting against antigens, are vital parts of immune system response. In order to identify discriminative antibodies, which provide protection against HIV, and to disclose the associations between antibody features and their functional outcomes, computational methods, such as feature selection, regression and classification can be used to construct predictive models. Here we used our unsupervised K-Means Based Feature Selection (KBFS) method which is presented in our previous study, to identify functional antibodies that fight against HIV. The accuracy results for the proposed KBFS framework are compared with those presented in a recent study and are also compared with results from four different state-of-the-art unsupervised feature selection methods, namely MCFS, InFS, LapFS, and SPFS, along with the entire feature set. Then, support vector based systems are utilised to predict the associations between antibody features and their functional activities, namely gp120-specific antibody dependent cellular phagocytosis (ADCP), antibody dependent cellular cytotoxicity (ADCC) and cytokine release activities on RV144 vaccine recipients. Pearson Correlation Coefficient (PCC) metric is used to evaluate the prediction accuracy of the predictive models and to be consistent with the previous study. Our SVR based KBFS framework presented higher accuracy than the original study by improving prediction performance 16% for ADCP assay, 200% for the ADCC assay.
How does participation and browsing affect continuance intention in virtual communities? An integration of curiosity theory and subjective well-being

ABSTRACT Based on theories about curiosity and subjective well-being (SWB), this study proposes a research model for how participation and browsing initiate different routes to satisfying members’ diverse needs, thus increasing their SWB and their continuance intention in knowledge-based virtual communities (VCs). Two curiosity constructs, informational deprivation epistemic curiosity (D-EC) and interest-type epistemic curiosity (I-EC), moderate the wanting route and liking route, respectively. The research model is tested with data from 476 members of one knowledge-based VC using a web survey. Results show that member participation stimulates the wanting route to satisfaction by activating the need for reflective learning and uncertainty reduction, whereas browsing stimulates the liking route to member satisfaction by eliciting enjoyment. Both routes thus increase member SWB and, ultimately, continuance intention. Along the wanting route, D-EC reinforces relationships related to reflective learning but attenuates those related to uncertainty. I-EC alleviates the relationships along the liking route. Comparisons of the relative importance of hypothesised relationships between participants and lurkers indicate that participants feel more satisfied with knowledge sharing, enjoy greater SWB, and maintain higher continuance intention than lurkers. These results can therefore help managers of VCs leverage learning- or fun-oriented mechanisms, depending on member curiosity type.
Role of situational context in use continuance after critical exergaming incidents

Use continuance is crucial in terms of information systems (IS) success. Previous research has shown that situational context can be central for IS use continuance but has paid limited attention to its specific characteristics. Furthermore, the link between situational context and use continuance has remained unexplored in the novel area of “exergames,” which are defined as digital games requiring physical effort from the player that determines the outcome of the game. Studying exergames is deemed important due to their potential in providing health benefits for users, revenues for providers, and well‐being for societies. However, their potential remains unreached because users tend to discontinue usage after their initial experiences. To address these gaps, we investigate the relationships between specific situational characteristics and use continuance after critical exergaming incidents, in which the user has an exceptionally positive or negative experience. To do this, we quantitatively and qualitatively examined 461 actual critical exergaming incidents. Our findings provide a greater understanding of IS use continuance by revealing new knowledge about the relationships between specific situational characteristics (ie, purpose of use, type of gaming platform, social setting, place, and exertion level) and use continuance. We also offer explanations for these relationships, thus providing a first understanding of the previously unmapped area of how users behave situation‐dependently after critical exergaming incidents. Thus, we contribute both to the general IS continuance literature as well as to the specific area of exergaming. The context specificity of our study matches the calls for heavily contextualised IS research.
Altruistic and selfish communication on social media: the moderating effects of tie strength and interpersonal trust

ABSTRACT Individuals share a diversity of content on social media for a variety of reasons. Research has often described and explained disclosure via the application of a subjective cost–benefit analysis framed around reciprocity, suggesting that people communicate selfishly motivated by the expectation of receiving something in return. This paper investigates the moderating effects of tie strength and interpersonal trust on the relationship between expected reciprocity and intensity of communication between two social media connections. A Facebook application presented participants with a random set of their friends and asked them to rate their friendships in terms of these values. Overall, 90 participants rated 1728 friendships, while the application collected 11 activity variables depicting the actual communication that has taken place in each pair of connections. A principal component analysis was used to distinguish between text- and photograph-related communication, and two moderated multiple regressions were conducted to establish the moderating effects. The results show significant moderating effects of tie strength and trust on the communication around photographs, but not around text. This study contributes to communication research by explicating the ways that tie strength and trust affect patterns of communication on social media. Implications for social media researchers and designers are discussed.
Performance evaluation for outdoor wireless scenarios based on IEEE 802.11b/g in the presence of co-channel interference

Voice over Internet Protocol (VoIP) has evolved over the years, being a real-time service. VoIP has been coupled to different technologies, one of them is WiFi, which is one of the most used for wireless local area networks in domestic and commercial environments. In this paper, we evaluate the performance of wireless scenarios by considering VoIP traffic, based on WiFi technology in conformance with IEEE 802.11b/g in interfered outdoor scenarios, by considering an intrusive injection traffic technique, for codecs G711 (1 sample), G711 (2 samples), G723, G729 (2 samples), and G729 (3 samples), related to the main metrics associated to Quality of Service (QoS) parameters. Our results show the best performance was obtained with the codecs G723 and G729 (3 samples), obtaining up to 30 simultaneous voice connections with optimal values of delay, jitter and packet loss according to the recommendations given for VoIP by ITU-T, while the worst performance was obtained with the codec G711 (2 samples), obtaining only 5 simultaneous voice connections, reaching an efficiency loss of around 18% in a co-channel interference scenario.