Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing image-based control systems considering workload variations

We consider the problem of designing an Image-Based Control (IBC) application mapped to a multiprocessor platform. Sensing in IBC consists of compute-intensive image processing algorithms whose execution times are dependent on image workload. The challenge is that the IBC systems have a high (worst-case) workload with significant workload variations. Designing controllers for such IBC systems typically consider the worst-case workload that results in a long sensing delay with suboptimal quality-of-control (QoC). The challenge is: how to improve the QoC of IBC for a given multiprocessor platform allocation? We present a controller synthesis method based on a Markovian jump linear system (MJLS) formulation considering workload variations. Our method assumes that system knowledge is available for modelling the workload variations as a Markov chain. We compare the MJLS-based method with two relevant control paradigms - LQR control considering worst-case workload, and switched linear control - with respect to QoC and available system knowledge. Our results show that taking into account workload variations in controller design benefits QoC. We then provide design guidelines on the control paradigm to choose for an IBC application given the requirements and the system knowledge.
Laser induced white emission of Sr 2 CeO 4 nanocrystals

Laser induced white emission of Sr2CeO4 nanocrystals W. Strek a, R. Tomala a*, L. Marciniak a, M. Lukaszewicz a, B. Cichy a, M. Stefanski, D. Hreniak a, A. Kedziorski b, M. Krosnicki c and L. Seijo d a Institute for Low Temperature and Structure Research, Polish Academy of Sciences, Wroclaw, Poland b Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Toruń, Poland c Institute of Theoretical Physics and Astrophysics, University of Gdańsk, Poland d Departamento de Quimica, Instituto Universitario de Ciencia de Materiales Nicolás Cabrera, and Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid, Spain *corresponding author: r.tomala@int.pan. wroc.pl
Project Report : Sarcasm Detection

Sentiment analysis is often used in NLP to understand people’s subjective opinions. However, the analysis results may be biased if people use sarcasm in their statements. In order to correctly understand people’s true intention, being able to detect sarcasm is critical. Many previous models have been developed to detect sarcasm based on the utterances in isolation, meaning only the reply text itself. Ghosh et al. [2017]’s research used both context and reply texts to detect sarcasm in reply and showed great improvement in performance. The main models used were SVMs and LSTMs with attention. In this project, we aim to improve accuracy of sarcasm detection by further exploring the role of contextual information in detecting sarcasm. We will investigate the performances of different models (various LSTM models and BERT) and compare the models using reply-only, context-only and context-and-reply data setups. BERT, short for Bidirectional Encoder Representations from Transformers [Devlin et al., 2018], has never been used for this specific task of sarcasm detection before. We show that a simple BERT classifier provides surprisingly good results for this task and analyze how the sarcasm detection task can benefit from the pretraining on other, related tasks that distinguishes BERT.
Improving the accuracy and efficiency of the Neural Network Classification with Auto-encoder And Genetic Algorithm

The common issues people struggle with when they are doing the work of classification are improving the efficiency and accuracy of training. Some of the reasons for this are there are too many unnecessary features and the limitation of original training dataset. This paper addresses the first issue by proposing an approach of the Genetic Algorithm (GA) to select essential features from original dataset to improve training efficiency. The second issue is dealt with in this paper by extending the original dataset with the blurred output data of an auto-encoder-decoder which can be considered as a drop-out measure to improve its classification accuracy. Therefore, my experiments can be divided into four parts as following, the realization of classification model, the efficiency improvement with the method of GA, the accuracy enhancement of auto-encoder, and the combination of the above two measures to achieve better accuracy with similar amount of training time. My results of these experiments confirm my assumption of the availability of these methods. The feature selection method can reduce the half number of features by achieving similar accuracy, while dataset extending measure can enhance the accuracy by almost 10%. Furthermore, the combination method of these two successfully achieve better accuracy and efficiency, and the improvement is remarkable.
CHAPTER 10 Crop pollination services

Crop pollination by bees and other animals is an ecosystem service of enormous economic value (Losey andVaughan 2006; Allsopp et al . 2008). Pollination can increase the yield, quality, and stability of crops as diverse as almond, cacao, canola, coffee, sunfl ower, tomato and watermelon. Indeed, Klein et al . (in press) found that 75% of globally important crops benefi t from animal pollination. The value of this service, while diffi cult to quantify properly, has been estimated several times over the past decade (Southwick and Southwick 1992; Costanza et al. 1997; Losey and Vaughan 2006; Allsopp et al . 2008) with a recent estimate of 195 billion Euros (~$200 billion) worldwide ( Box 10.1 ). While much research and policy attention has focused on managed bees (especially the honey bee, Apis mellifera ), wild bees and other insect species also contribute importantly to crop pollination. Our models focus on wild bees, because the pollination they deliver represents an ecosystem service from natural systems. In fact, for some crops (e.g., blueberry), wild bees are more effi cient and effective pollinators than honey bees (Cane 1997). Diverse bee communities potentially provide more stable pollination services over time, compared to single (managed) species (Greenleaf and Kremen 2006; Hoehn et al. . 2008). Finally, if alarming regional declines in honeybee populations continue (National Research Council of the National Academies 2006; Stokstad 2007; Klein et al ., in press), wild pollinators may become increasingly important to farmers. Maintaining pollinator habitats in agricultural landscapes, therefore, can help ensure food production, quality, and security. While other pollinators (e.g., bats and moths) also pollinate crops, bees are the most important crop pollinators (Free 1993) and are thus the focus of our models. The pollination models aim to quantify and map scores for relative pollinator abundance across an entire landscape, including farms requiring pollination. The models use these results to indicate areas supplying pollinators that increase crop yields. Alternatively, intermediate results can be integrated into our agricultural model (see Chapter 9 ) to estimate the economic value of pollination services as an input to crop yields in a more sophisticated manner. Either way, these models can inform agricultural and land management policies in several ways. First, land-use planners could predict consequences of different policies on pollination services and income to farmers (Priess et al. 2007). Second, farmers could use these tools to locate crops in places where their pollination needs are most likely to be met. Third, conservation organizations that guide land management and restoration could use the tool to optimize conservation investments for both biodiversity and crop productivity. Finally, governments or others proposing payment schemes for ecosystem services could incorporate the results into plans for who should pay whom, and how much.
Design and Implementation of an IFC Data Model to Unity Data Model Transformation Mechanism

The construction industry is moving towards the application of Building Information Modeling (BIM) for more efficient management and integration of engineering information. Today, engineering consultant companies, construction companies, and architectural companies are investing much research into the development of BIM to integrate and visualize engineering information for greater productivity. However, a lack of interactivity is a shortcoming of BIM applications. The core functionality typically provided by a game engine includes a rendering engine, a physics engine, animation, and so on, which can give a user the experience of an immersive virtual reality. By sharing BIM data to a game engine, a user can gain more operation and interaction capabilities Moreover, it can increase the range of possible applications. BuildingSMART has developed Industry Foundation Classes (IFC) that make it possible to hold and exchange relevant data between different BIM applications during a building’s life cycle. This research designs and implements a transformation mechanism for transforming an IFC Data Model to a Unity Data Model, so data can be imported into Unity to facilitate BIM visualization and manipulation. Moreover, a game engine can support cross-platform programming to enhance portability of BIM data for advancing the usage and application of BIM models.
Saccharomyces cerevisiae Saccharomyces bayanus Saccharomyces castellii Saccharomyces kluyveri Kluyveromyces lactis Debaryomyces hansenii Candida albicans Saccharomyces paradoxus Saccharomyces

Yeasts provide a powerful model system for comparative genomics research. The availability of multiple complete genome sequences from different fungal groups—currently 18 hemiascomycetes, 8 euascomycetes and 4 basidiomycetes—enables us to gain a broad perspective on genome evolution. The sequenced genomes span a continuum of divergence levels ranging from multiple individuals within a species to species pairs with low levels of protein sequence identity and no conservation of gene order. One of the most interesting emerging areas is the growing number of events such as gene losses, gene displacements and gene relocations that can be attributed to the action of natural selection.
1 . 10 DENSITY-FUNCTIONAL PERTURBATION THEORY

The calculation of vibrational properties of materials from their electronic structure is an important goal for materials modeling. A wide variety of physical properties of materials depend on their lattice-dynamical behavior: specific heats, thermal expansion, and heat conduction; phenomena related to the electron–phonon interaction such as the resistivity of metals, superconductivity, and the temperature dependence of optical spectra, are just a few of them. Moreover, vibrational spectroscopy is a very important tool for the characterization of materials. Vibrational frequencies are routinely and accurately measured mainly using infrared and Raman spectroscopy, as well as inelastic neutron scattering. The resulting vibrational spectra are a sensitive probe of the local bonding and chemical structure. Accurate calculations of frequencies and displacement patterns can thus yield a wealth of information on the atomic and electronic structure of materials. In the Born–Oppenheimer (adiabatic) approximation, the nuclear motion is determined by the nuclear Hamiltonian H: H = −∑
Removing Components from a Time-Frequency Representation

Time-frequency representations such as the spectrogram or short-time Fourier transform seem to be well suited to the task of removing certain components with approximately disjoint support in the time-frequency domain. For example, one might be interested in suppressing a certain instrument’s contribution from a music signal. Such approaches are used in Computational Auditory Scene Analysis by the name of Time-Frequency masks. However, the trivial approach of just deleting the corresponding component in the time-frequency representation leads to artifacts such as “ghost-tones”, musical noise and “phasing effects”. We compare different approaches to tackle this problem: To gain some insight in the nature of the problem, we consider the optimal separating mask for known signal components. A softthresholding procedure is applied and compared to a model promoting sparsity in the representation. The latter can lead to favorable results by yielding a feasible approximation of the optimal mask. The results are presented visually and acoustically during the presentation.
PAGES CLUSTER BASED ON THE RELATIONS OF MAPPING KEYWORDS TO ONTOLOGY CONCEPT HIERARCHY

In this paper, we present a related web page cluster method that not only considers the corresponding domain keywords on ontology but also analyzes semantic contents of web pages. First, the method embeds the corresponding domain ontology of search keyword to find web pages from the Internet. Next, consider the location of the keywords in the web pages, and relations between keywords and concepts in the domain ontology to find the features of the web pages. Then, the web pages were clustered based on the similarity values of mapping keywords concept-ontology level relations. Primary experimental results prove that our method is effective to find related web pages.
C L ] 8 O ct 2 01 9 Generating Highly Relevant Questions

The neural seq2seq based question generation (QG) is prone to generating generic and undiversified questions that are poorly relevant to the given passage and target answer. In this paper, we propose two methods to address the issue. (1) By a partial copy mechanism, we prioritize words that are morphologically close to words in the input passage when generating questions; (2) By a QA-based reranker, from the n-best list of question candidates, we select questions that are preferred by both the QA and QG model. Experiments and analyses demonstrate that the proposed two methods substantially improve the relevance of generated questions to passages and answers.
Greenspace Pattern and the Surface Urban Heat Island: A Biophysically-Based Approach to Investigating the Effects of Urban Landscape Configuration

Surface urban heat islands (SUHIs) are influenced by the spatial distribution of green space, which in turn can be influenced by urban planning. When studying the relationship between structure and function it is critical that the scale of observation reflects the scale of the phenomenon being measured. To investigate the relationship between green space pattern and the SUHI in the Kansas City metropolitan area, we conducted a multi-resolution wavelet analysis of land surface temperature (LST) to determine the dominant length scales of LST production. We used these scales as extents for calculating landscape metrics on a high-resolution land cover map. We built regression models to investigate whether–controlling for the percent vegetated area–patch size, fragmentation, shape, complexity, and/or proximity can mitigate SUHIs. We found that while some of the relationships between landscape metrics and LST are significant, their explanatory power would be of little use in planning for green infrastructure. We also found that the relationships often reported between landscape metrics and LST are artifacts of the relationship between the percent of vegetation and LST. By using the dominant length scales of LST we provide a methodology for robust biophysically-based analysis of urban landscape pattern and demonstrate that the contributions of green space configuration to the SUHI are negligible. The simple result that increasing green space can lower LST regardless of configuration allows the prioritization of resources towards benefiting neighborhoods most vulnerable to the negative impacts of urban heat.
NEWS RETRIEVAL BASED ON SHORT QUERIES EXPANSION AND BEST MATCHING

An information retrieval (IR) is a system that locates information in collections of unstructured or semistructured document which are relevant to the user query. Usually, the query represented by short query that have a few features or keywords that used to describe the user needed of information. These short queries containing only a few words can be vague and ambiguous. As a result, IR system returns documents that are generally not relevant to the user’s information needs. In this paper, an automatic approach of query expansion is proposed based on latent semantic indexing (LSI) method in order to enhance the performance of the information retrieval system. Development database of queries are used to find the best match between the user query and the development queries by using LSI algorithm. The best result that obtained from LSI will combine with the user query to create a new query that used later as new input in information retrieval system to retrieve the documents. To evaluation, the information retrieval system is compared before and after query expansion by using latent semantic indexing (LSI) method. The proposed approach improved the retrieval system performance from 70% to 75% F-measure with an average relative improvement of 7.2%, and it is better than the conventional LSI approach.
Spatio-temporal Relational Reasoning for Video Question Answering

Video question answering is the task of automatically answering questions about videos. Among query types which include identiﬁcation, localization, and counting, the most challenging questions enquire about relationships among different entities. Answering such questions, and many others, require modeling relationships between entities in the spatial domain and evolution of those relationships in the temporal domain. We argue that current approaches have limited capacity to model such long-range spatial and temporal dependencies. To address these challenges, we present a novel spatio-temporal reasoning neural module which enables modeling complex multi-entity relationships in space and long-term ordered dependencies in time. We evaluate our module on two benchmark datasets which require spatio-temporal reasoning: TGIF-QA and SVQA. We achieve state-of-the-art performance on both datasets. More signiﬁcantly, we achieve substantial improvements on some of the most challenging question types, like counting, which demonstrate the effectiveness of our proposed spatio-temporal relational module.
Managing intimate partner violence at the social services department of a Greek university hospital

• Summary: Although abused patients are thought to access the health system at a range of potential entry points, social services are usually the only referral option within the various hospital sectors. In Greece, little is known about the prevalence of intimate partner violence (IPV) and the quality of care provided to cases of abuse at the social services. The current study explored the department’s response to IPV cases in a Greek university hospital. Two different research methods were employed: qualitative and document research. Qualitative data were collected through personal interviews with the 10 social workers employed at the department. Document research involved the review of all registries and social history forms recorded between January 2005 and April 2009 at the department. Two predefined screening criteria were employed to identify IPV cases. Seven socio-demographic variables were abstracted from all IPV cases that met the inclusion criteria. • Findings: Four main themes emerged from the qualitative analysis: 1) knowledge gap and openness to education, 2) poor assessment and recording practices, 3) concerns about intervention in confirmed cases, and 4) mistrust in referral resources. The document research revealed a total of 42 records (3.8%) having an IPV screening record present, out of the 1117 initially reviewed. The majority of the IPV patients were women (n = 33, 78.6%), encountered at the age group of 28–37 years (n = 16, 38.1%) and presented mixed types of abuse (n = 32, 76.2%). • Applications: Developing training opportunities and introducing organizational changes could help social workers to better manage IPV cases in the hospital setting.
Multiple Transfer Net Based Region Ensemble Network for Deep Hand Pose Estimation

Deep hand pose estimation from single depth image plays an significant role in human-computer interaction. This paper proposes a novel method based on multiple transfer net to estimate hand pose with single-channel depth photos only. A channel extending process for original single channel depth image is implemented to extend hand and hand palm regions, match the input format of pre-trained network and fully utilize the parameters. A multiple transfer network refinement for the previous convolutional neural network is maken to obtain various different feature maps. And a region ensemble is used to merge all output feature maps and integrate the results. The experimental results demonstrate that proposed method outperforms state-ofart results by a considerable accuracy on NYU [1] and ICVL [2] datasets.
A Metadata Model for Data Goods

Digital transformation implies the development of data-driven business models and thus the management of data goods. While marketplaces for data are being established and platforms for the exchange of data are being created, companies have to adapt their data management to the increasing requirements. One central question can be deduced: How can data goods be described in a standardized way? This paper describes the development of the metadata model for data goods M4DG. The M4DG, based on an analysis of existing data marketplaces and metadata models of related topics, makes it possible to describe data sources with defined properties. This creates a unified understanding of the properties of data goods to facilitate selection and trading. We are convinced that the M4DG will contribute to the practical design of data management.
THERMOSTABLE FLAP ENDONUCLEASE DERVED FROM HYPERTHERMOPHILE BACTERIUM BELONGING TO THE GENUS PYROCOCCUS

The present invention relates to a thermostable Flap endo nuclease whose optimum temperature is 75 C. or more and DNA coding for (a) a protein consisting of the amino acid sequence shown in SEQ ID NO:2 or (b) a protein with Flap endonuclease activity, consisting of an amino acid Sequence where in the amino acid Sequence (a), one or more amino acids are deleted, Substituted or added. According to the present invention, there is provided a thermostable Flap endonuclease whose optimum temperature for reaction is 75 C. or more. Further, this enzyme is thermally stable, so it becomes possible to develop new techniques of conduct ing artificial homologous recombination or genetic shuffling highly efficiently by coupling the enzyme reaction with PCR.
Question Representation and Mapping In Transformers ( QeRMIT )

We introduce the Question Representation and Mapping In Transformers (QeRMIT) model that builds on the previous works of Bidirectional Encoder Representations from Transformers (BERT). We illustrate that QeRMIT is tailored towards improving performance on specific tasks such as reading comprehension, where the meaning of the query can be captured without a sequence. In this paper, we focus specifically on the popular Stanford Question Answering Dataset (SQuAD) v2.0 to objectively demonstrate the significance of better capturing the meaning of a question and mapping it onto the final BERT Pre-trained Contexual Embeddings (PCE) layer of the paragraph. We discuss the results and challenges in implementing QeRMIT, and successful approaches that increased EM and F1 scores on SQuAD v2.0.