Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Material for Domain Adaptation for Object Detection via Style Consistency

PASCAL VOC is used as a source dataset for most of our experiments. These are natural images of real scenes that can be considered an unbiased representation of typical consumer images. There are 20 object classes annotated with bounding boxes and labels. Figure C (left) shows some statistics e.g. the number of instances per image is typically below 7 and 94.7% of images are with less than 7 instances. The maximum number of different class labels in an image is 6, and there are 98.7% of the images with 3 or fewer class labels. Cross-domain dataset was proposed in [3], and we use it as target dataset for most of our experiments. Figure A (top) shows some image examples. There are three domains: Clipart1k, Watercolor2k and Comic2k. Clipart1k contains bounding boxes and labels for the 20 classes from PASCAL VOC, whereas Watercolor2k and Comic2k contain boxes and labels for only 6 of the PASCAL VOC classes. The datasets have large class imbalance as Table A shows, which brings additional challenges. In Clipart1k, the category person accounts for more than one-third of the instances, whereas in Watercolor2k it accounts for around 75% of the instances, and 86% in the case of Comic2k. In [3] there were extra images not annotated in instance-level for both Watercolor2k and Comic2k in a split called extra, which we do not use in any of the experiments. Figure B shows that each of the domains (clipart, watercolor and comic) contains instances with several different styles, with variations in the use of color, textures or patterns. Domain Transferred Images (DTI) were generated in [3] with CycleGAN [8] trained in an unsupervised setting. The adapted images from PASCAL VOC to the target domain were used to finetune the SSD detector [5] on those images, which gave significant improvements in mean Average Precision, in particular for the clipart domain. Figure A (bottom) shows some examples of DTI. Sim10k contains 10000 images collected from the video-game Grand Theft Auto V and annotated with instance-level boxes [4]. Some samples are given in Figure D. Cityscapes was proposed in [2] for semantic understanding. It does not contain bounding boxes annotations, so we follow the procedure in [1] given in the official code where the tightest rectangle around the instance is used as ground-truth label using the instance-level
What's past is prologue

Library leadership is one of the much‐ discussed subjects in library and information science. Yet, many librarians tend to shy away from using the word “leadership” and the authors wondered why. It seemed peculiar that librari - ans tend not to associate themselves with the context of leaders or leadership. Whether you are a driven librarian or an ambitious young librarian, or you are an ambitious and driven person, library leadership is a rather remote subject for most librarians and information professionals. Why is that the case? This paper will explore the world of library leaders and leadership by deconstructing the myths of leadership, examining whether there is a difference between a leader and a library leader, and guiding you to become a leader through seven stages of leadership development. One of our professional societies, Library Administra - tion and Management Association (LAMA) changed in 2008 to the Library Leadership and Management Associ - ation [LLAMA] in recognition of the growing importance of the issues of leadership and librarians. The LLAMA Annual Report (2008–2009) reported that the name change also presented an implicit challenge to LLAMA: “leadership” needs to move to the forefront of its activities, and resources need to be allocated to reflect and support this shift. Research has shown that millennials and new librarians have not aspired to become leaders. This paper is directed at early career librarians who are wondering about becoming a library leader. We assume that you are curious about leadership and someday you want to be a leader, if you are not one already. The goal is to offer encour - agement and guidance to young or newly minted librarians and offer much needed food for thought. The current library leaders who want to create a culture of entrepreneurship and nurture future library leaders are encouraged to join the session to share their views on leadership.
Joint Emotion Label Space Modelling for Affect Lexica

Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, vocabulary coverage issues, differences in construction method and discrepancies in emotion framework and representation result in a heterogeneous landscape of emotion detection resources, calling for a unified approach to utilising them. To combat this, we present an extended emotion lexicon of 30,273 unique entries, which is a result of merging eight existing emotion lexica by means of a multi-view variational autoencoder (VAE). We showed that a VAE is a valid approach for combining lexica with different label spaces into a joint emotion label space with a chosen number of dimensions, and that these dimensions are still interpretable. We tested the utility of the unified VAE lexicon by employing the lexicon values as features in an emotion detection model. We found that the VAE lexicon outperformed individual lexica, but contrary to our expectations, it did not outperform a naive concatenation of lexica, although it did contribute to the naive concatenation when added as an extra lexicon. Furthermore, using lexicon information as additional features on top of state-of-the-art language models usually resulted in a better performance than when no lexicon information was used.
Fine-Grained Geolocalization of User-Generated Short Text Based on a Weight Probability Model

Recently, the fine-grained geolocalization of User-Generated Short Texts (UGST) has become increasingly important. One challenge is that UGST contains relatively little location-indicative information due to such limitations as text length. Therefore, extract and effectively use the location-indicative information is the key issue for improving the effect of geolocalization. The existing works only consider the global weight of the terms and do not distinguish between the importance of identical terms in different locations. In addition, the existing add-one smoothing masks the difference between the features of different locations. In this paper, we propose a fine-grained geolocalization method to predict the PoI-level location of UGSTs based on a weight probability model (FGST-WP). The method mainly includes three parts: 1) Using the reverse maximum match algorithm to filter out UGSTs that do not contain any location-indicative information. 2) Building coupling of terms and locations and adopting a mixed weight strategy to assign weights to terms. 3) Calculating the probability of nongeotagged UGST posted from each location and selecting k locations according to the top- $k$ probabilities. The accuracy of FGST-WP on the three ground-truth datasets reaches 45%, 68%, and 72%, respectively. The results indicate the superior performance of FGST-WP.
Towards Hybrid Human-Machine Workflow for Natural Language Generation

In recent years, crowdsourcing has gained much attention from researchers to generate data for the Natural Language Generation (NLG) tools or to evaluate them. However, the quality of crowdsourced data has been questioned repeatedly because of the complexity of NLG tasks and crowd workers’ unknown skills. Moreover, crowdsourcing can also be costly and often not feasible for large-scale data generation or evaluation. To overcome these challenges and leverage the complementary strengths of humans and machine tools, we propose a hybrid human-machine workflow designed explicitly for NLG tasks with real-time quality control mechanisms under budget constraints. This hybrid methodology is a powerful tool for achieving high-quality data while preserving efficiency. By combining human and machine intelligence, the proposed workflow decides dynamically on the next step based on the data from previous steps and given constraints. Our goal is to provide not only the theoretical foundations of the hybrid workflow but also to provide its implementation as open-source in future work.
Studying SVM Method's Scalability Using Text Documents

In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining good results. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that increases exponentially are occurring. In this paper we are presenting a method that allows working with huge data sets into the training step without increasing exponentially the training time and without significantly decreasing the classification accuracy. 1. Introduction. While more and more textual information is available online, effective retrieval is difficult without good indexing and summarization of documents' content. Documents' categorization is one solution to this problem. The task of documents' categorization is to assign a user defined categorical label to a given document. In recent years a growing number of categorization methods and machine learning techniques have been developed and applied in different contexts. Documents are typically represented as vectors in a features space. Each word in the vocabulary is represented as a separate dimension. The number of word occurrences in a document represents the normalized value of the corresponding component in the document's vector. In this paper we are investigating how classification accuracy is influenced using only relevant selected input vectors subset belonging to a larger data training set. The answer will show if the classification method is scalable or not. We are using classifiers based on Support Vector Machine (SVM) techniques. They are less vulnerable to degrade when the dimensionality of the feature space is increasing, and have been shown effective in many classification tasks. The SVM is actually based on learning with kernels and support vectors. We are developing a seven steps strategy that trains the classifiers on a reduced data set without significant accuracy decrease comparing with training on the larger initial data set, but reducing the learning time [1]. In designing this strategy we were inspired by a method presented in [2] which uses a tree structure to hierarchically group similar databases articles, at different abstract levels. Despite this strategy was not recommended by the authors to be used on text documents, we modified it in order to group the similar text documents into a single level. In this experiment we are using an original implementation of SVM algorithm with some improvements presented by us …
Performance of Balanced Fairness in Resource Pools

Understanding the performance of a pool of servers is crucial for proper dimensioning. One of the main challenges is to take into account the complex interactions between servers that are pooled to process jobs. In particular, a job can generally not be processed by any server of the cluster due to various constraints like data locality. In this paper, we represent these constraints by some assignment graph between jobs and servers. We present a recursive approach to computing performance metrics like mean response times when the server capacities are shared according to balanced fairness. While the computational cost of these formulas can be exponential in the number of servers in the worst case, we illustrate their practical interest by introducing broad classes of pool structures that can be exactly analyzed in polynomial time. This extends considerably the class of models for which explicit performance metrics are accessible.
Computer-Aided Identification of Recognized Drugs as Pseudomonas aeruginosa Quorum-Sensing Inhibitors

ABSTRACT Attenuation of Pseudomonas aeruginosa virulence by the use of small-molecule quorum-sensing inhibitors (referred to as the antipathogenic drug principle) is likely to play a role in future treatment strategies for chronic infections. In this study, structure-based virtual screening was used in a search for putative quorum-sensing inhibitors from a database comprising approved drugs and natural compounds. The database was built from compounds which showed structural similarities to previously reported quorum-sensing inhibitors, the ligand of the P. aeruginosa quorum-sensing receptor LasR, and a quorum-sensing receptor agonist. Six top-ranking compounds, all recognized drugs, were identified and tested for quorum-sensing-inhibitory activity. Three compounds, salicylic acid, nifuroxazide, and chlorzoxazone, showed significant inhibition of quorum-sensing-regulated gene expression and related phenotypes in a dose-dependent manner. These results suggest that the identified compounds have the potential to be used as antipathogenic drugs. Furthermore, the results indicate that structure-based virtual screening is an efficient tool in the search for novel compounds to combat bacterial infections.
Application of fractals to the detection and classification of shoeprints

The most common clues left at a crime scene when a crime is committed are shoeprint impressions. These impressions are useful in the detection of criminals and the linking of crime scenes. A novel technique for use in the detection and classification of shoeprint impressions has been developed. The technique is based on fractal based feature extraction and pattern matching methods. The computerized system developed has been extensively tested on a large database of real shoeprint impressions and is robust to small variations of image orientations and/or translations.
Support Vector Machines for Design Space Exploration

To find a suitable model of a system it is usually necessary to conduct time consuming and expensive measurements. Design of Experiments (DoE) methods are then often used with the goal to find the best possible model using a minimum number of measurements by placing them optimally inside the design space. The design space is the set of values of the system’s actuating variables that do not violate any constraints imposed on the system. Unfortunately, the boundaries of the design space are often not (completely) known in advance, but are only discovered while performing the measurements. This paper proposes a method to find and describe these boundaries using a design space exploration method based on support vector machines (SVM). By means of this method not only convex, but also non-convex boundaries can be represented. The method described here is mainly motivated by the measurement tasks necessary for combustion engine development, but it is of course suitable for similar problems in other fields as well.
Optimal Placement of Fixed Series Capacitor in Distribution Networks

In this paper is present a method for the determining of fixed series capacitor location and of the optimal value of capacitive reactance so that the voltage profile in distribution networks to be improved. The optimal value of capacitive reactance is obtained through the minimization of the average of the squares of voltage deviations of the voltage from consumption node. To establish the capacitor location, the real network is replaced with a fictional equivalent line and it put the condition that the arithmetic average of the squares of voltage deviations for real network to be equal to the average of the squares of voltage deviations in the fictional node. Key-Words: series compensation, capacitor location, voltage profile, capacitive reactance, distribution network
Ageing with Parkinson’s: Identification of Personal Needs in the Northern Spanish Context

As individuals diagnosed with Parkinson’s disease enter older age, the myriad challenges and complications associated with this condition tend to escalate. Hence, there is a critical necessity to comprehensively discern the perceived needs of these individuals, along with their proposed remedies and essential support requisites. Additionally, understanding the perspectives of their families becomes imperative to formulate tailored interventions aimed at enhancing their overall development, progression, and quality of life. The study’s main objective is to assess the perceived needs of individuals with PD and their family members, propose necessary solutions, and suggest future perspectives. The study encompassed a cohort of 268 participants, comprising 179 individuals diagnosed with Parkinson’s disease and 89 of their relatives. A meticulously designed structured interview instrument consisting of 93 items was employed to assess various domains encompassing perceived needs, institutional support mechanisms, essential solutions, and future anticipations. Results: Statistically significant differences were found in health resources, social services resources, obstacles, solutions, and future outlook, with higher mean values from the relatives. Conclusions: The results highlight the most concerning needs in this context. Specifically, those needs related to health resources, social services resources, and future outlook present the greatest differences between the two subsamples, with the family members perceiving more needs. This alignment extended to both the categorization of unmet needs and the requisite solutions envisioned to address them. Suggested improvements include a sociosanitary strategy, stakeholder involvement, and prioritizing flexible home assistance to support older individuals with PD and their families.
Ubiquitin Ligase Parkin Regulates the Stability of SARS-CoV-2 Main Protease and Suppresses Viral Replication

The highly infectious coronavirus SARS-CoV-2 relies on the viral main protease (Mpro, also known as 3CLpro or Nsp5) to proteolytically process the polyproteins encoded by the viral genome for the release of functional units in the host cells to initiate viral replication. Mpro also interacts with host proteins of the innate immune pathways, such as IRF3 and STAT1, to suppress their activities and facilitate virus survival and proliferation. To identify the host mechanism for regulating Mpro, we screened various classes of E3 ubiquitin ligases and found that Parkin of the RING-between-RING family can induce the ubiquitination and degradation of Mpro in the cell. Furthermore, when the cells undergo mitophagy, the PINK1 kinase activates Parkin and enhances the ubiquitination of Mpro. We also found that elevated expression of Parkin in the cells significantly decreased the replication of SARS-CoV-2 virus. Interestingly, SARS-CoV-2 infection downregulates Parkin expression in the mouse lung tissues compared to healthy controls. These results suggest an antiviral role of Parkin as a ubiquitin ligase targeting Mpro and the potential for exploiting the virus–host interaction mediated by Parkin to treat SARS-CoV-2 infection.
A Taxonomy of DDoS Attacks and DDoS Defense Mechanisms

This paper proposes a taxonomy of distributed denial-ofservice attacks and a taxonomy of the defense mechanisms that strive to counter these attacks. The attack taxonomy is illustrated using both known and potential attack mechanisms. Along with this classification we discuss important features of each attack category that in turn define the challenges involved in combating these threats. The defense system taxonomy is illustrated using only the currently known approaches. The goal of the paper is to impose some order into the multitude of existing attack and defense mechanisms that would lead to a better understanding of challenges in the distributed denial-of-service field.
Domain adaptation for statistical machine translation

Statistical machine translation (SMT) plays more and more important role now. The performance of the SMT is largely dependent on the size and quality of training data. But the demands for translation is rich, how to make the best of limited in-domain data to satisfy the needs of translation coming from different domains is one of the hot focus in current SMT. Domain adaption aims to obviously improve the specific-domain performance by bringing much out-of-domain parallel corpus at the absence of in-domain parallel corpus. Domain adaption is one of the keys to get the SMT into practical application. This paper introduces mainstream methods of domain adaption for SMT, compares advantages and disadvantages of representative methods based on the result of the same data and shows personal views about the possible future direction of domain adaption for SMT.
Evaluation of Importance of Sentences based on Connectivity to Title

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