Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Time-Sensitive Strategies in Space Fortress

Although there has been remarkable progress and impressive performance on reinforcement learning (RL) on Atari games, there are many problems with challenging characteristics that have not yet been explored in Deep Learning for RL. These include reward sparsity, abrupt context-dependent reversals of strategy and time-sensitive game play. In this paper, we present Space Fortress, a game that incorporates all these characteristics and experimentally show that the presence of any of these renders state of the art Deep RL algorithms incapable of learning. Then, we present our enhancements to an existing algorithm and show big performance increases through each enhancement through an ablation study. We discuss how each of these enhancements was able to help and also argue that appropriate transfer learning boosts performance.
Monitoring makes a difference: quality and temporal variation in teacher education students’ collaborative learning

ABSTRACT The aim of this process-oriented video-observation study is to explore how groups that perform differently differ in terms of the number, quality, and temporal variation of their content-level (knowledge co-construction) and meta-level (monitoring) activities. Five groups of teacher education students (n = 22) were observed throughout a 3-month course. Video recordings (33 hours) of face-to-face group interaction (n = 12,931 speech turns) and pre- and post-tests of students’ knowledge were collected. The results show that the well-performing group was more engaged in high-level knowledge co-construction and monitoring activities. The well-performing group was also capable of maintaining a higher level throughout the tasks, whereas the lower performing groups’ knowledge co-construction and monitoring activities was reduced during the course.
A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments

eRulemaking is a means for government agencies to directly reach citizens to solicit their opinions and experiences regarding newly proposed rules. The effort, however, is partly hampered by citizens’ comments that lack reasoning and evidence, which are largely ignored since government agencies are unable to evaluate the validity and strength. We present Cornell eRulemaking Corpus – CDCP , an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. The corpus consists of 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB); the resulting dataset contains 4931 elementary unit and 1221 support relation annotations. It is a resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify what additional information is necessary for readers to understand and evaluate a given argument. Immediate applications include providing real-time feedback to commenters, specifying which types of support for which propositions can be added to construct better-formed arguments.
When ACE met KBP: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation

Building a Knowledge Base from text corpora is useful for many applications such as question answering and web search. Since 2012, the Cold Start Knowledge Base Population (KBP) evaluation at the Text Analysis Conference (TAC) has attracted many participants. Despite the popularity, the Cold Start KBP evaluation has several problems including but not limited to the following two: first, each year’s assessment dataset is a pooled set of query-answer pairs, primarily generated by participating systems. It is well known to participants that there is pooling bias: a system developed outside of the official evaluation period is not rewarded for finding novel answers, but rather is penalized for doing so. Second, the assessment dataset, constructed with lots of human effort, offers little help in training information extraction algorithms which are crucial ingredients for the end-to-end KBP task. To address these problems, we propose a new unbiased evaluation methodology that uses existing component-level annotation such as the Automatic Content Extraction (ACE) dataset, to evaluate Cold Start KBP. We also propose bootstrap resampling to provide statistical significance to the results reported. We will then present experimental results and analysis.
Current progress in discriminative object tracking

Recently, great progesses have been made in using discriminative classifiers in object tracking. More specifically, correlation filters (CFs) for visual tracking have been attractive due to t heir competitive performances on both accuracy and robustness. In this paper, the latest and representative approaches of CF based trackers are presented in detail. In addition, trackers used deep convolutional features are introduced and several famous tracking methods which fine-tune the pretrained deep network are presented. To evaluate the performances of different trackers, a detailed introduction of the evaluation methodology and the datasets is described, and all introduced trackers are compared based on the mentioned datasets. Finally, several promising directions as the conclusions are drawn in this paper.
Altmetrics: Present and future - panel

Scholars are increasingly incorporating social media tools like blogs, Twitter, and Mendeley into their professional communication. Altmetrics tracks usage of these and similar tools to measure scholarly influence on the social Web. Altmetrics researchers and practitioners have amassed a growing body of literature and working tools to gather and analyze altmetrics and there is growing interest in this emerging subfield of scientometrics. In this panel, sponsored by SIG/MET, we will present results demonstrating the utility of alternative metrics from a variety of stakeholders: researchers, librarians, publishers and those participating in academic social media sites. We will discuss and debate the value and validity of such metrics with strong degrees of participation from the audience encouraged. Metrics, for better or worse, have had a presence in the lives of scholars–we will discuss the challenges and opportunities of altmetrics for the future.
Updating the classification system for the secondary forests of Singapore

Singapore has undergone rapid deforestation since 1819 mainly for timber and plantations and subsequently, modernisation. Consequently, most of the spontaneous vegetation in Singapore is now secondary vegetation. Previous secondary vegetation classification systems for Singapore have been focused on the forests within the Bukit Timah Nature Reserve and Central Catchment Nature Reserve. Here, we update the classification system for secondary vegetation, focusing on dryland forest types in the whole of Singapore. Our classification system is based on past land-use history which shapes the floristic composition observed today. We recognise three forest types: nativedominated secondary forest, abandoned-land forest, and waste-woodlands. Native-dominated secondary forests are forests regrown on land cleared before the 1950s, and are dominated by native tree species. Abandoned-land forests are forests regrown from land that was previously used for plantations or kampungs which were abandoned with the mature trees largely intact. Waste-woodlands are forests regrown on land that was usually cleared after the 1960s, and are dominated by exotic tree species, and whatever species that are present is dependent on the seed source availability of the surroundings during the time of clearance, and succession. Reclaimed land forest, strictly considered, should be a product of primary and not secondary succession but is structurally similar to waste-woodlands and its plant species composition is likely dependent on the fill material used. Although secondary forests generally contain less species compared to primary forests, they can still be important for the conservation of biodiversity, especially in Singapore.
Augmenting OFDM wireless local networks with motion detection capability

Ubiquitous wireless local networks which use channel transfer function estimation to compensate for echoes on the wireless channel are uniquely suited for motion detection as a secondary use. In this way, added value can be provided without additional hardware. This paper presents a procedure with uses channel state information of an IEEE 802.11 High Throughput wireless link to reliably detect the presence of moving items, such as people crossing a room. By leveraging CSI instead of RSSI, the frequency-selective fading characteristic of indoor wireless channels can be taken into account while eliminating the effect of power level fluctuations in consumer-grade wireless hardware.
Human 5-HT receptors : the 5-HT receptor is functional but the 5-HT 5 5 A 5 B receptor was lost during mammalian evolution

We have isolated from a human genomic library the human 5-hydroxytryptamine 5-HT and 5-HT genes. The human 5-HT gene 5A 5B 5A encodes a protein with similar characteristics to its mouse homologue. When expressed in monkey COS-7 cells, the human 5-HT5A Žw x . receptor displayed a high affinity for tritiated 5-carbamidotryptamine H 5-CT; K s2.8 nM and iodinated lysergic acid diethylamide D Žw x . I LSD; K s187 pM . These binding sites displayed the following displacement profile: Ergotamine)Methiothepin)5-CT, D Ž . Ritanserin)5-HT. Reverse transcriptase polymerase chain reaction RT-PCR experiments revealed the presence of human 5-HT5A mRNA in the central nervous system but not in peripheral organs. When expressed in Xenopus oocytes, the 5-HT receptor was able to 5A couple to the inwardly rectifying K channel, GIRK . In contrast to the human 5-HT gene and the mouse 5-HT gene, the human 1 5A 5B 5-HT gene does not encode a functional protein because its coding sequence is interrupted by stop codons. Our results suggest, 5B therefore, that the 5-HT receptor has been lost during evolution after the divergence between rodents and primates. The 5-HT 5B 5B receptor is the first example of a brain-specific protein that is absent in human. q 2001 Elsevier Science B.V. All rights reserved. Ž .
Multichannel Distributed Local Pattern for Content Based Indexing and Retrieval

A novel color feature descriptor, Multichannel Distributed Local Pattern (MDLP) is proposed in this manuscript. The MDLP combines the salient features of both local binary and local mesh patterns in the neighborhood. The multi-distance information computed by the MDLP aids in robust extraction of the texture arrangement. Further, MDLP features are extracted for each color channel of an image. The retrieval performance of the MDLP is evaluated on the three benchmark datasets for CBIR, namely Corel-5000, Corel-10000 and MIT-Color Vistex respectively. The proposed technique attains substantial improvement as compared to other state-of-the-art feature descriptors in terms of various evaluation parameters such as ARP and ARR on the respective databases.
CLINIQA: A Machine Intelligence Based Clinical Question Answering System

The recent developments in the field of biomedicine have made large volumes of biomedical literature available to the medical practitioners. Due to the large size and lack of efficient searching strategies, medical practitioners struggle to obtain necessary information available in the biomedical literature. Moreover, the most sophisticated search engines of age are not intelligent enough to interpret the clinicians' questions. These facts reflect the urgent need of an information retrieval system that accepts the queries from medical practitioners' in natural language and returns the answers quickly and efficiently. In this paper, we present an implementation of a machine intelligence based CLINIcal Question Answering system (CLINIQA) to answer medical practitioner's questions. The system was rigorously evaluated on different text mining algorithms and the best components for the system were selected. The system makes use of Unified Medical Language System for semantic analysis of both questions and medical documents. In addition, the system employs supervised machine learning algorithms for classification of the documents, identifying the focus of the question and answer selection. Effective domain-specific heuristics are designed for answer ranking. The performance evaluation on hundred clinical questions shows the effectiveness of our approach.
Lexical and Semantic Features for Cross-lingual Text Reuse Classification: an Experiment in English and Latin Paraphrases

Analyzing historical languages, such as Ancient Greek and Latin, is challenging. Such languages are often under-resourced and lack primary material for certain time periods. This prevents applying advanced natural-language processing (NLP) techniques and requires resorting to basic NLP not relying on machine learning. An important analysis is the discovery and classification of paraphrastic text reuse in historical languages. This reuse is often paraphrastic and challenges basic NLP techniques. Our goal is to improve the applicability of advanced NLP techniques on historical text reuse. We present an experiment of cross-applying classifiers—that we trained for paraphrase recognition on modern English text corpora—on historical texts. We analyze the impact of four different lexical and semantic features, on the resulting reuse-detection accuracy. We find out that—against initial conjecture—word embeddings can help to drastically improve accuracy if lexical features (such as the overlap of similar words) fail.
Kinetic Basis of the Bifunctionality of SsoII DNA Methyltransferase

Type II restriction–modification (RM) systems are the most widespread bacterial antiviral defence mechanisms. DNA methyltransferase SsoII (M.SsoII) from a Type II RM system SsoII regulates transcription in its own RM system in addition to the methylation function. DNA with a so-called regulatory site inhibits the M.SsoII methylation activity. Using circular permutation assay, we show that M.SsoII monomer induces DNA bending of 31° at the methylation site and 46° at the regulatory site. In the M.SsoII dimer bound to the regulatory site, both protein subunits make equal contributions to the DNA bending, and both angles are in the same plane. Fluorescence of TAMRA, 2-aminopurine, and Trp was used to monitor conformational dynamics of DNA and M.SsoII under pre-steady-state conditions by stopped-flow technique. Kinetic data indicate that M.SsoII prefers the regulatory site to the methylation site at the step of initial protein–DNA complex formation. Nevertheless, in the presence of S-adenosyl-l-methionine, the induced fit is accelerated in the M.SsoII complex with the methylation site, ensuring efficient formation of the catalytically competent complex. The presence of S-adenosyl-l-methionine and large amount of the methylation sites promote efficient DNA methylation by M.SsoII despite the inhibitory effect of the regulatory site.
Simulating ASR errors for training SLU systems

This paper presents an approach to simulate automatic speech recognition (ASR) errors from manual transcriptions and describes how it can be used to improve the performance of spoken language understanding (SLU) systems. In particular, we point out that this noising process is very usefull to obtain a more robust SLU system to ASR errors in case of insufficient training data or more if ASR transcriptions are not available during the training of the SLU model. The proposed method is based on the use of both acoustic and linguistic word embeddings in order to define a similarity measure between words dedicated to predict ASR confusions. Actually, we assume that words acoustically and linguistically close are the ones confused by an ASR system. By using this similarity measure in order to randomly substitute correct words by potentially confusing words in manual annotations used to train CRF-or neural-based SLU systems, we augment the training corpus with these new noisy data. Experiments were carried on the French MEDIA corpus focusing on hotel reservation. They show that this approach significantly improves SLU system performance with a relative reduction of 21.2% of concept/value error rate (CVER), particularly when the SLU system is based on a neural approach (reduction of 22.4% of CVER). A comparison to a naive noising approach shows that the proposed noising approach is particularly relevant.
Natural Boundary Conditions for Smoothing in Geometry Processing

In geometry processing, smoothness energies are commonly used to model scattered data interpolation, dense data denoising, and regularization during shape optimization. The squared Laplacian energy is a popular choice of energy and has a corresponding standard implementation: squaring the discrete Laplacian matrix. For compact domains, when values along the boundary are not known in advance, this construction bakes in low-order boundary conditions. This causes the geometric shape of the boundary to strongly bias the solution. For many applications, this is undesirable. Instead, we propose using the squared Frobenius norm of the Hessian as a smoothness energy. Unlike the squared Laplacian energy, this energy’s natural boundary conditions (those that best minimize the energy) correspond to meaningful high-order boundary conditions. These boundary conditions model free boundaries where the shape of the boundary should not bias the solution locally. Our analysis begins in the smooth setting and concludes with discretizations using finite-differences on 2D grids or mixed finite elements for triangle meshes. We demonstrate the core behavior of the squared Hessian as a smoothness energy for various tasks.
Public Opinion Spamming: A Model for Content and Users on Sina Weibo

Microblogs serve hundreds of millions of active users, but have also attracted large numbers of spammers. While traditional spam often seeks to endorse specific products or services, nowadays there are increasingly also paid posters intent on promoting particular views on hot topics and influencing public opinion. In this work, we fill an important research gap by studying how to detect such opinion spammers and their micro-manipulation of public opinion. Our model is unsupervised and adopts a Bayesian framework to distinguish spammers from other classes of users. Experiments on a Sina Weibo hot topic dataset demonstrate the effectiveness of the proposed approach. A further diachronic analysis of the collected data demonstrates that public opinion spammers have developed sophisticated techniques and have seen success in subtly manipulating the public sentiment.
Contesting Tobacco-Control Policy in Indonesia

ABSTRACT Over the past decade and a half, the Indonesian government has progressed fitfully and inconsistently toward a stricter tobacco-control policy regime, albeit without much impact on the country's worsening tobacco epidemic. This article explains this pattern of reform in terms of the unequal but changing relationship of power between tobacco companies and tobacco farmers, on the one hand, and tobacco-control advocates based in NGOs, health professional organizations, universities, and international organizations, on the other. The first of these coalitions has had greater structural leverage, better political connections, stronger organizational capacity, greater ability to mobilize popular forces, and more capacity to cultivate a positive public image. But the second coalition has been able to exercise some influence over policy because of changes wrought by democratization. In this analysis, the author engages with the literatures on (1) Indonesia's political economy in the post–New Order period and (2) the politics of tobacco control in developing countries. With regard to the former, the author argues that we need to give greater attention to the role of actors previously excluded from the policy-making process than is currently the case. With regard to the latter, the author contends that more focus is needed on domestic actors and political institutions in shaping tobacco-control policy. Looking to the future, the author suggests that further progress in Indonesia's tobacco-control policies will be contingent upon an ongoing process of struggle; however, there are signs that the tide is turning in favor of the second coalition.
Granulomatous Lymphangitis Masquerading as Relapsed Hodgkin Disease on FDG PET/CT

A 38-year-old woman with Hodgkin lymphoma was referred for staging fludeoxyglucose (18F) positron emission tomography/computed tomography (FDG PET/CT) that showed widespread intensely FDG-avid disease in multiple nodal stations above the diaphragm and spleen and extranodal involvement in the lungs and vertebral bodies. She underwent chemotherapy and radiotherapy. Progress FDG PET/CT 5 months later showed significant metabolic and anatomic response. Repeat FDG PET/CT 1 month later was highly suspicious of recurrent disseminated FDG-avid lymphoma in multiple nodal stations above and below the diaphragm, spleen, multiple bones, and lungs. Subsequent bone marrow biopsy showed sarcoid-like granulomatous inflammation with no evidence of lymphoma. The patient was clinically well and no active treatment was instituted. Subsequent FDG PET/CT 2 months later showed complete resolution of metabolic activity.
Assessment of the generalization of learned image reconstruction and the potential for transfer learning

Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning.
SAI: Safety Application Identifier Algorithm at MAC Layer for Vehicular Safety Message Dissemination Over LTE VANET Networks

Vehicular safety applications have much significance in preventing road accidents and fatalities. Among others, cellular networks have been under investigation for the procurement of these applications subject to stringent requirements for latency, transmission parameters, and successful delivery of messages. Earlier contributions have studied utilization of Long-Term Evolution (LTE) under single cell, Friis radio, or simplified higher layer. In this paper, we study the utilization of LTE under multicell and multipath fading environment and introduce the use of adaptive awareness range. Then, we propose an algorithm that uses the concept of quality of service (QoS) class identifiers (QCIs) along with dynamic adaptive awareness range. Furthermore, we investigate the impact of background traffic on the proposed algorithm. Finally, we utilize medium access control (MAC) layer elements in order to fulfill vehicular application requirements through extensive system-level simulations. The results show that, by using an awareness range of up to 250 m, the LTE system is capable of fulfilling the safety application requirements for up to 10 beacons/s with 150 vehicles in an area of 2 × 2 km2. The urban vehicular radio environment has a significant impact and decreases the probability for end-to-end delay to be ≤100 ms from 93%–97% to 76%–78% compared to the Friis radio environment. The proposed algorithm reduces the amount of vehicular application traffic from 21 Mbps to 13 Mbps, while improving the probability of end-to-end delay being ≤100 ms by 20%. Lastly, use of MAC layer control elements brings the processing of messages towards the edge of network increasing capacity of the system by about 50%.