bluuebunny/crossref_metadata_2025 huggingface dataset:
Structured Output Prediction for Semantic Perception in Autonomous Vehicles
Authors: Houthooft Rein , De Boom Cedric , Verstichel Stijn , Ongenae Femke , De Turck Filip
Published: June 2022

A key challenge in the realization of autonomous vehicles is the machine's ability to perceive its surrounding environment. This task is tackled through a model that partitions vehicle camera input into distinct semantic classes, by taking into account visual contextual cues. The use of structured machine learning models is investigated, which not only allow for complex input, but also arbitrarily structured output. Towards this goal, an outdoor road scene dataset is constructed with accompanying fine-grained image labelings. For coherent segmentation, a structured predictor is modeled to encode label distributions conditioned on the input images. After optimizing this model through max-margin learning, based on an ontological loss function, efficient classification is realized via graph cuts inference using alpha-expansion. Both quantitative and qualitative analyses demonstrate that by taking into account contextual relations between pixel segmentation regions within a second-degree neighborhood, spurious label assignments are filtered out, leading to highly accurate semantic segmentations for outdoor scenes.
Recognizing Actions in 3D Using Action-Snippets and Activated Simplices
Authors: Wang Chunyu , Flynn John , Wang Yizhou , Yuille Alan
Published: June 2022

Pose-based action recognition in 3D is the task of recognizing an action (e.g., walking or running) from a sequence of 3D skeletal poses. This is challenging because of variations due to different ways of performing the same action and inaccuracies in the estimation of the skeletal poses. The training data is usually small and hence complex classifiers risk over-fitting the data. We address this task by action-snippets which are short sequences of consecutive skeletal poses capturing the temporal relationships between poses in an action. We propose a novel representation for action-snippets, called activated simplices. Each activity is represented by a manifold which is approximated by an arrangement of activated simplices. A sequence (of action-snippets) is classified by selecting the closest manifold and outputting the corresponding activity. This is a simple classifier which helps avoid over-fitting the data but which significantly outperforms state-of-the-art methods on standard benchmarks.
Large Scale Similarity Learning Using Similar Pairs for Person Verification
Authors: Yang Yang , Liao Shengcai , Lei Zhen , Li Stan
Published: June 2022

In this paper, we propose a novel similarity measure and then introduce an efficient strategy to learn it by using only similar pairs for person verification. Unlike existing metric learning methods, we consider both the difference and commonness of an image pair to increase its discriminativeness. Under a pairconstrained Gaussian assumption, we show how to obtain the Gaussian priors (i.e., corresponding covariance matrices) of dissimilar pairs from those of similar pairs. The application of a log likelihood ratio makes the learning process simple and fast and thus scalable to large datasets. Additionally, our method is able to handle heterogeneous data well. Results on the challenging datasets of face verification (LFW and Pub-Fig) and person re-identification (VIPeR) show that our algorithm outperforms the state-of-the-art methods.
Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation
Authors: Yang Yang , Lei Zhen , Zhang Shifeng , Shi Hailin , Li Stan
Published: June 2022

A variety of encoding methods for bag of word (BoW) model have been proposed to encode the local features in image classification. However, most of them are unsupervised and just employ k-means to form the visual vocabulary, thus reducing the discriminative power of the features. In this paper, we propose a metric embedded discriminative vocabulary learning for high-level person representation with application to person re-identification. A new and effective term is introduced which aims at making the same persons closer while different ones farther in the metric space. With the learned vocabulary, we utilize a linear coding method to encode the image-level features (or holistic image features) for extracting high-level person representation. Different from traditional unsupervised approaches, our method can explore the relationship(same or not) among the persons. Since there is an analytic solution to the linear coding, it is easy to obtain the final high-level features. The experimental results on person re-identification demonstrate the effectiveness of our proposed algorithm.
Unsupervised Co-Activity Detection from Multiple Videos Using Absorbing Markov Chain
Authors: Yeo Donghun , Han Bohyung , Han Joon Hee
Published: June 2022

We propose a simple but effective unsupervised learning algorithm to detect a common activity (co-activity) from a set of videos, which is formulated using absorbing Markov chain in a principled way. In our algorithm, a complete multipartite graph is first constructed, where vertices correspond to subsequences extracted from videos using a temporal sliding window and edges connect between the vertices originated from different videos; the weight of an edge is proportional to the similarity between the features of two end vertices. Then, we extend the graph structure by adding edges between temporally overlapped subsequences in a video to handle variable-length co-activities using temporal locality, and create an absorbing vertex connected from all other nodes. The proposed algorithm identifies a subset of subsequences as co-activity by estimating absorption time in the constructed graph efficiently. The great advantage of our algorithm lies in the properties that it can handle more than two videos naturally and identify multiple instances of a co-activity with variable lengths in a video. Our algorithm is evaluated intensively in a challenging dataset and illustrates outstanding performance quantitatively and qualitatively.
Toward a Taxonomy and Computational Models of Abnormalities in Images
Authors: Saleh Babak , Elgammal Ahmed , Feldman Jacob , Farhadi Ali
Published: June 2022

The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset of abnormal images showing a wide range of atypicalities. We design human subject experiments to discover a coarse taxonomy of the reasons for abnormality. Our experiments reveal three major categories of abnormality: object-centric, scene-centric, and contextual. Based on this taxonomy, we propose a comprehensive computational model that can predict all different types of abnormality in images and outperform prior arts in abnormality recognition.
SentiCap: Generating Image Descriptions with Sentiments
Authors: Mathews Alexander , Xie Lexing , He Xuming
Published: June 2022

The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems. One such style is descriptions with emotions, which is commonplace in everyday communication, and influences decision-making and interpersonal relationships. We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. We propose a novel switching recurrent neural network with word-level regularization, which is able to produce emotional image captions using only 2000+ training sentences containing sentiments. We evaluate the captions with different automatic and crowd-sourcing metrics. Our model compares favourably in common quality metrics for image captioning. In 84.6% of cases the generated positive captions were judged as being at least as descriptive as the factual captions. Of these positive captions 88% were confirmed by the crowd-sourced workers as having the appropriate sentiment.
Optimizing Infrastructure Enhancements for Evacuation Planning
Authors: Kumar Kunal , Romanski Julia , Van Hentenryck Pascal
Published: June 2022

With rapid population growth and urbanization, emergency services in various cities around the world worry that the current transportation infrastructure is no longer adequate for large-scale evacuations. This paper considers how to mitigate this issue through infrastructure upgrades, such as the additions of lanes to road segments and the raising of bridges and roads. The paper proposes a MIP model for deciding the most effective infrastructure upgrades as well as a Benders decomposition approach where the master problem jointly plans the upgrades and evacuation routes and the subproblem schedules the evacuation itself. Experimental results demonstrate the practicability of the approach on a real case study, filling a significant need for emergencies services.
Predicting Spatio-Temporal Propagation of Seasonal Influenza Using Variational Gaussian Process Regression
Authors: Senanayake Ransalu , O'Callaghan Simon , Ramos Fabio
Published: June 2022

Understanding and predicting how influenza propagates is vital to reduce its impact. In this paper we develop a nonparametric model based on Gaussian process (GP) regression to capture the complex spatial and temporal dependencies present in the data. A stochastic variational inference approach was adopted to address scalability. Rather than modeling the problem as a time-series as in many studies, we capture the space-time dependencies by combining different kernels. A kernel averaging technique which converts spatially-diffused point processes to an area process is proposed to model geographical distribution. Additionally, to accurately model the variable behavior of the time-series, the GP kernel is further modified to account for non-stationarity and seasonality. Experimental results on two datasets of state-wide US weekly flu-counts consisting of 19,698 and 89,474 data points, ranging over several years, illustrate the robustness of the model as a tool for further epidemiological investigations.
Epitomic Image Super-Resolution
Authors: Yang Yingzhen , Wang Zhangyang , Wang Zhaowen , Chang Shiyu , Liu Ding , Shi Honghui , Huang Thomas
Published: June 2022

We propose Epitomic Image Super-Resolution (ESR) to enhance the current internal SR methods that exploit the self-similarities in the input. Instead of local nearest neighbor patch matching used in most existing internal SR methods, ESR employs epitomic patch matching that features robustness to noise, and both local and non-local patch matching. Extensive objective and subjective evaluation demonstrate the effectiveness and advantage of ESR on various images.
Social Emotion Classification via Reader Perspective Weighted Model
Authors: Li Xin , Rao Yanghui , Chen Yanjia , Liu Xuebo , Huang Huan
Published: June 2022

With the development of Web 2.0, many users express their opinions online. This paper is concerned with the classification of social emotions on varied-scale datasets. Different from traditional models which weight training documents equally, the concept of emotional entropy is proposed to estimate the weight and tackle the issue of noisy documents. The topic assignment is also used to distinguish different emotional senses of the same word. Experimental evaluations using different data sets validate the effectiveness of the proposed social emotion classification model.
A Comparison of Supervised Learning Algorithms for Telerobotic Control Using Electromyography Signals
Authors: Frasca Tyler , Sestito Antonio , Versek Craig , Dow Douglas , Husowitz Barry , Derbinsky Nate
Published: June 2022

Human Computer Interaction (HCI) is central for many applications, including hazardous environment inspection and telemedicine. Whereas traditional methods ofHCI for teleoperating electromechanical systems include joysticks, levers, or buttons, our research focuses on using electromyography (EMG) signals to improve intuition and response time. An important challenge is to accurately and efficiently extract and map EMG signals to known position for real-time control. In this preliminary work, we compare the accuracy and real-time performance of several machine-learning techniques for recognizing specific arm positions. We present results from offline analysis, as well as end-to-end operation using a robotic arm.
Structure Aware L1 Graph for Data Clustering
Authors: Han Shuchu , Qin Hong
Published: June 2022

In graph-oriented machine learning research, L1 graph is an efficient way to represent the connections of input data samples. Its construction algorithm is based on a numerical optimization motivated by Compressive Sensing theory. As a result, It is a nonparametric method which is highly demanded. However, the information of data such as geometry structure and density distribution are ignored. In this paper, we propose a Structure Aware (SA) L1 graph to improve the data clustering performance by capturing the manifold structure of input data. We use a local dictionary for each datum while calculating its sparse coefficients. SA-L1 graph not only preserves the locality of data but also captures the geometry structure of data. The experimental results show that our new algorithm has better clustering performance than L1 graph.
Business Event Curation: Merging Human and Automated Approaches
Authors: Wang Yiqi , Ma Huiying , Lowe Nichola , Feldman Maryann , Schmitt Charles
Published: June 2022

We present preliminary work to construct a knowledge curation system to advance research in the study of regional economics. The proposed system exploits natural language processing (NLP) techniques to automatically implement business event extraction, provides a user-facing interface to assist human curators, and a feedback loop to improve the performance of the Information Extraction Model for the automated parts of the system. Progress to date has shown that we can improve standard NLP approaches for entity and relationship extraction through heuristic means and provide indexing of extracted relationships to aid curation.
Evaluating the Robustness of Game Theoretic Solutions When Using Abstraction
Authors: Veliz Oscar
Published: June 2022

Game theory is a tool for modeling multi-agent decision problems and has been used to analyze strategies in domains such as poker, security, and trading agents. One method for solving very large games is to use abstraction techniques to shrink the game by removing detail, solve the reduced game, and then translate the solution back to the original game. We present a methodology for evaluating the robustness of different game-theoretic solution concepts to the errors introduced by the abstraction process. We present an initial empirical study of the robustness of several solution methods when using abstracted games.
Predicting Prices in the Power TAC Wholesale Energy Market
Authors: Chowdhury Moinul Morshed Porag
Published: June 2022

The Power TAC simulation emphasizes the strategic problems that broker agents face in managing the economics of a smart grid. The brokers must make trades in multiple markets and to be successful, brokers must make many good predictions about future supply, demand,and prices. Clearing price prediction is an important part of the broker’s wholesale market strategy because it helps the broker to make intelligent decisions when purchasing energy at low cost in a day-ahead market. I describe my work on using machine learning methods to predict prices in the Power TAC wholesale market, which will be used in future bidding strategies.
Image Privacy Prediction Using Deep Features
Authors: Tonge Ashwini , Caragea Cornelia
Published: June 2022

Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online, the development of effective and efficient prediction methods for image privacy settings are highly needed. In this study, we explore deep visual features and deep image tags for image privacy prediction. The results of our experiments show that models trained on deep visual features outperform those trained on SIFT and GIST. The results also show that deep image tags combined with user tags perform best among all tested features.
Pseudo-Tree Construction Heuristics for DCOPs with Variable Communication Times
Authors: M Tabakhi Atena
Published: June 2022

Empirical evaluations of DCOP algorithms are typically done in simulation and under the assumption that the communication times between all pairs of agents are identical, which is unrealistic in many real-world applications. In this abstract, we incorporate non-uniform communication times in the default DCOP model and propose heuristics that exploit these communication times to speed up DCOP algorithms that operate on pseudo-trees.
Robust Execution Strategies for Probabilistic Temporal Planning
Authors: Dietrich Sam , Lund Kyle , Boerkoel James
Published: June 2022

A critical challenge in temporal planning is robustly dealing with non-determinism introduced by the environment, e.g., the durational uncertainty of an action taken by a robot in the physical world due to slippage or other unexpected influences. Recent advances show that robustness, which accounts for uncertainty in predicting schedule success, is a better measure of solution quality than traditional metrics such as flexibility. This paper introduces the Robust Execution Problem (REP) for finding maximally robust dispatch strategies for general probabilistic temporal planning problems. While the REP is generally intractable in practice, we introduce approximate solution techniques—one that can be computed statically prior to the start of execution while providing robustness guarantees and one that dynamically adjusts to opportunities and setbacks during execution. We show empirically that dynamically optimizing for robustness improves the likelihood of execution success.
Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security
Authors: Fang Fei , Nguyen Thanh , Pickles Rob , Lam Wai , Clements Gopalasamy , An Bo , Singh Amandeep , Tambe Milind , Lemieux Andrew
Published: June 2022

Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, prior work introduced a novel emerging application called PAWS (Protection Assistant for Wildlife Security); PAWS was proposed as a game-theoretic (“security games”) decision aid to optimize the use of patrolling resources.This paper reports on PAWS’s significant evolution from a proposed decision aid to a regularly deployed application, reporting on the lessons from the first tests in Africa in Spring 2014, through its continued evolution since then, to current regular use in Southeast Asia and plans for future worldwide deployment. In this process, we have worked closely with two NGOs (Panthera and Rimba) and incorporated extensive feedback from professional patrolling teams. We outline key technical advances that lead to PAWS’s regular deployment: (i) incorporating complex topographic features, e.g., ridgelines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints.
Ontology Re-Engineering: A Case Study from the Automotive Industry
Authors: Rychtyckyj Nestor , Rama Venkatesh , Sankaranarayanan Baskaran , Kumar P Sreenivasa , Khemani Deepak
Published: June 2022

For over twenty five years Ford has been utilizing an AI-based system to manage process planning for vehicle assembly at our assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford’s manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. To preserve the viability of the GSPAS ontology and to make it easily usable for other applications within Ford, we needed to re-engineer and convert the KL-ONE ontology into a semantic web OWL/RDF format. In this paper, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed Semantic Web technology in our application.
Infusing Human Factors into Algorithmic Crowdsourcing
Authors: Yu Han , Miao Chunyan , Shen Zhiqi , Lin Jun , Leung Cyril , Yang Qiang
Published: June 2022

Algorithmic Crowdsourcing (AC) is an emerging field in which computational methods are proposed to automate cer- tain aspects of crowdsourcing. A number of AC methods have proposed recently in an attempt to address this problem. However, existing AC approaches are based on highly simplified models of worker behaviour which limit their practical applicability. To make efficient utilization of human resources for crowdsourcing tasks, the following tech- nical challenges remain open: Fairness of the solution, temporal changes in behaviour, optimizing wellbeing, and non-compliance by users.  For AI researchers to propose effective solutions to these challenges, labelled datasets reflecting various aspects of human decision-making related to task allocation in crowd-sourcing are needed. We construct an anonymized dataset based on player behavior trajectories captured by a multiagent game platform - Agile Manage. It allows players to demonstrate their task delegation strategies under different scenarios based on key characteristics involved in crowdsourcing task allocation. The game adopts implicit human computation  in which players contribute data which are valuable for research through informal games.
Data-Augmented Software Diagnosis
Authors: Elmishali Amir , Stern Roni , Kalech Meir
Published: June 2022

Software fault prediction algorithms predict which software components is likely to contain faults using machine learning techniques. Software diagnosis algorithm identify the faulty software components that caused a failure using model-based or spectrum based approaches. We show how software fault prediction algorithms can be used to improve software diagnosis. The resulting data-augmented diagnosis algorithm overcomes key problems in software diagnosis algorithms: ranking diagnoses and distinguishing between diagnoses with high probability and low probability. We demonstrate the efficiency of the proposed approach empirically on three open sources domains, showing significant increase in accuracy of diagnosis and efficiency of troubleshooting. These encouraging results suggests broader use of data-driven methods to complement and improve existing model-based methods.
Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior
Authors: Khadivi Pejman , Ramakrishnan Naren
Published: June 2022

Due to the economic and social impacts of tourism, both private and public sectors are interested in precisely forecasting the tourism demand volume in a timely manner. With recent advances in social networks, more people use online resources to plan their future trips. In this paper we explore the application of Wikipedia usage trends (WUTs) in tourism analysis. We propose a framework that deploys WUTs for forecasting the tourism demand of Hawaii. We also propose a data-driven approach, using WUTs, to estimate the behavior of tourists when they plan their trips.
Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography (OCT)
Authors: Shalev Ronny , Nakamura Daisuke , Nishino Setsu , Rollins Andrew , Bezerra Hiram , Wilson David , Ray Soumya
Published: June 2022

An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31% of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-ofthe-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed a frame at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming and prone to human error. We are building a system that, when complete, will provide interactive 3D visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk for thrombosis. In this paper, we describe our approach, focusing on machine learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.
Calories Prediction from Food Images
Authors: Chokr Manal , Elbassuoni Shady
Published: June 2022

Calculating the amount of calories in a given food item is now a common task. We propose a machine-learning-based approach to predict the amount of calories from food images. Our system does not require any input from the user, except from an image of the food item. We take a pragmatic approach to accurately predict the amount of calories in a food item and solve the problem in three phases. First, we identify the type of the food item in the image. Second, we estimate the size of the food item in grams. Finally, by taking into consideration the output of the first two phases, we predict the amount of calories in the photographed food item. All these three phases are based purely on supervised machinelearning. We show that this pipelined approach is very effective in predicting the amount of calories in a food item as compared to baseline approaches which directly predicts the amount of calories from the image.
Constraint-Based Verification of a Mobile App Game Designed for Nudging People to Attend Cancer Screening
Authors: Gotlieb Arnaud , Louarn Marine , Nygard Mari , Ruiz-Lopez Tomas , Sen Sagar , Gori Roberta
Published: June 2022

In Norway, cervical cancer prevention involves the participation of as many eligible women aged 25-69 years as possible. However, reaching and inviting every eligible women to attend cervical cancer screening and HPV vaccination is difficult. Using social nudging and gamification in modern means of communication can encourage the participation of unscreened people. Simula Research Laboratory together with the Cancer Registry of Norway have developed FightHPV, a mobile app game intended to inform adolescent and eligible women about cervical cancer screening and HPV vaccination while they play and, to facilitate their further participation to prevention campaigns. However, game design and health information transfer can be hard to reconcile, as the design of each game episode is more guided by the release of information than gameplay and playing difficulty. In this paper, we propose a constraint-based model of FightHPV to evaluate the difficulty of each episode and to help the game designer in improving the player experience. This approach is relevant to facilitate social nudging of eligible women to participate to cervical cancer screening and HPV vaccination, as shown by the initial deployment of FightHPV and tests performed in focus groups. The design of this mobile app can thus be regarded as a new application case of Artificial Intelligence techniques such as gamification and constraint programming.
Predicting Fuel Consumption and Flight Delays for Low-Cost Airlines
Authors: Horiguchi Yuji , Baba Yukino , Kashima Hisashi , Suzuki Masahito , Kayahara Hiroki , Maeno Jun
Published: June 2022

Low-cost airlines (LCAs) represent a new category of airlines that provides low-fare flights. The rise and growth of LCAs has intensified the price competition among airlines, and LCAs require continuous efforts to reduce their operating costs to lower flight prices; however, LCA passengers still demand high-quality services. A common measure of airline service quality is on-time departure performance. Because LCAs apply efficient aircraft utilization and the time between flights is likely to be small, additional effort is required to avoid flight delays and improve their service quality. In this paper, we apply state-of-the-art predictive modeling approaches to real airline datasets and investigate the feasibility of machine learning methods for cost reduction and service quality improvement in LCAs. We address two prediction problems: fuel consumption prediction and flight delay prediction. We train predictive models using flight and passenger information, and our experiment results show that our regression model predicts the amount of fuel consumption more accurately than flight dispatchers, and our binary classifier achieves an area under the ROC curve (AUC) of 0.75 for predicting a delay of a specific flight route.
Cracks Under Pressure? Burst Prediction in Water Networks Using Dynamic Metrics
Authors: Kaushik Gollakota , Manimaran Abinaya , Vasan Arunchandar , Sarangan Venkatesh , Sivasubramaniam Anand
Published: June 2022

Ranking pipes according to their burst likelihood can help a water utility triage its proactive maintenance budget effectively. In the research literature, data-driven approaches have been used recently to predict pipe bursts. Such approaches make use of static features of the individual pipes such as diameter, length, and material to estimate burst likelihood for the next year by learning over past historical data. The burst likelihood of a pipe also depends on dynamic features such as its pressure and flow. Existing works ignore dynamic features because the features need to be measured or are difficult to obtain accurately using a well-calibrated hydraulic model. We complement prior data-driven approaches by proposing a methodology to approximately estimate the dynamic features of individual pipes from readily available network structure and other data. We study the error introduced by our approximation on an academic benchmark water network with ground truth. Using a real-world pipe burst dataset obtained from a European water utility for multiple years, we show that our approximate dynamic features improve the ability of machine learning classifiers to predict pipe bursts. The performance (as measured by the percentage of future bursts predicted) of the best forming classifier improves by nearly 50% through these dynamic features.
Designing Better Playlists with Monte Carlo Tree Search
Authors: Liebman Elad , Khandelwal Piyush , Saar-Tsechansky Maytal , Stone Peter
Published: June 2022

In recent years, there has been growing interest in the study of automated playlist generation — music recommender systems that focus on modeling preferences over song sequences rather than on individual songs in isolation. This paper addresses this problem by learning personalized models on the fly of both song and transition preferences, uniquely tailored to each user’s musical tastes. Playlist recommender systems typically include two main components: i) a preferencelearning component, and ii) a planning component for selecting the next song in the playlist sequence. While there has been much work on the former, very little work has been devoted to the latter. This paper bridges this gap by focusing on the planning aspect of playlist generation within the context of DJ-MC, our playlist recommendation application. This paper also introduces a new variant of playlist recommendation, which incorporates the notion of diversity and novelty directly into the reward model. We empirically demonstrate that the proposed planning approach significantly improves performance compared to the DJ-MC baseline in two playlist recommendation settings, increasing the usability of the framework in real world settings.
Optimal Sequential Drilling for Hydrocarbon Field Development Planning
Authors: Torrado Ruben , Rios Jesus , Tesauro Gerald
Published: June 2022

We present a novel approach for planning the development of hydrocarbon fields, taking into account the sequential nature of well drilling decisions and the possibility to react to future information. In a dynamic fashion, we want to optimally decide where to drill each well conditional on every possible piece of information that could be obtained from previous wells. We formulate this sequential drilling optimization problem as a POMDP, and propose an algorithm to search for an optimal drilling policy. We show that our new approach leads to better results compared to the current standard in the oil and gas (O&G) industry.
Restoran Zincirlerinin Finansal Başarısızlık Riskleri ve Covıd-19’un Etkisi
Authors: ÖCEK Cemile , BEYAZ Fatma Senem , KARADENİZ Erdinç
Published: June 2022

Bu araştırmanın amacı marka değeri en yüksek restoran zincirlerinin finansal başarısızlık risk düzeylerini incelemek ve Covid-19 salgınının finansal başarısızlık riski üzerindeki etkisini ortaya koymaktır. Bu amaçla Brand Finance 2020 sıralamasına göre marka değeri en yüksek 17 restoran zincirinin, 2016-2020 yılları arasında finansal başarısızlık riskleri Altman Z Skor, Altman Z’ Skor, Altman Z’’ Skor, Springate, Fulmer ve Ohlson modelleriyle ölçülmüştür. Analiz sonucunda finansal başarısızlık riski taşıyan restoran sayısının tüm modellerde geçmişten günümüze yaklaştıkça artış eğiliminde olduğu belirlenmiştir. Bununla birlikte Covid-19 salgını öncesi (2016-2019) ve Covid-19 salgını dönemi (2020) arasındaki finansal başarısızlık risk skorları bağlamında istatistiksel olarak anlamlı bir fark olup olmadığı Wilcoxon İşaretli Sıra Testi ile test edilmiştir. Elde edilen sonuca göre Ohlson modeli dışındaki tüm modellerde Covid-19 salgını öncesi ve salgın dönemi arasında finansal başarısızlık değerleri bağlamında istatistiksel olarak anlamlı farklılık saptanmıştır.
The Effect of Gamma Irradiation on Ethanolic Extract of Temulawak (Curcuma xanthorrhiza Roxb.) Against Human Cancer Cell Lines
Authors: Susanto Susanto , Winarno Ermin Katrin , Winarno Hendig
Published: June 2022

The Effect of Gamma Irradiation on Ethanolic Extract of Temulawak (Curcuma xanthorrhiza Roxb.) Against Human Cancer Cell Lines. One of the medicinal plants that are widely used by the people of Indonesia is temulawak (Curcuma xanthorrhiza Roxb.) with the active ingredient is curcuminoid. The curcuminoid is a yellow compound that is believed to have anti-cancer properties, therefore its properties must be maintained in the post-harvest process. This research aimed to study the effect of gamma irradiation on the simplicia of C. xanthorrhiza rhizomes in inhibiting the proliferative activity against human cancer cell lines. The dry powder of C. xanthorrhiza rhizome was irradiated with gamma at a dose of 10 kGy in 2 replications, then extracted using ethanol solvent, evaporated, to obtain an ethanol extract. As a control, an un-irradiated sample was prepared and extracted in the same manner. Each ethanol extract from irradiated and control samples was then tested for their antioxidant activity by DPPH method and antiproliferative activity against human cancer cell lines (HUT78, A549, HeLa, and THP1). The results showed that the ethanol extract of irradiated C. xanthorrhiza rhizome with a dose of 10 kGy still had antioxidant activity and anticancer properties based on the bioassay against four those human cancer cell lines, although the antiproliferative activity decreased between 8-30% compared to the control sample. The highest antiproliferative activity was shown by the ethanol extract against HUT78 cancer cell line with IC50 values of 5.4 and 10.7 µg/ml for control and irradiated samples respectively. Based on the silica gel TLC plate, the ethanolic extracts of C. xanthorrhiza rhizome both unirradiated- and irradiated samples contained curcumin (Rf = 0.58) and demethoxycurcumin (Rf = 0.38).
THE STRATIFICATION BEHAVIOR OF REACTOR MATERIALS IN THE FRAMEWORK OF MOVING PARTICLE SEMI-IMPLICIT
Authors: Yulianto Y. , Mustari A. P. A. , Baliana A
Published: June 2022

In reactor research, the safety of the reactor plays an important role. Besides the experiment research, computational simulation can also provide important information about the behavior of materials inside the reactor core. The Moving Particle Semi-Implicit (MPS) method assists in providing information about the reactor material computationally. In this study, the stratification process of several materials, i.e. the melts of PbBi, PbLi, and Pb in combination with Al, have been investigated by simulation. The time to achieve stratified conditions for each material has been obtained where each paired material needs 0.5 seconds to reach the stratified condition. It is also obtained that the Pb-Al is the fastest in reaching the stratified condition among the other used materials.
ANALYSIS OF CS-137 RADIONUCLIDE ON THE EAST JAKARTA FLOOD CANAL WATER SAMPLES USING GAMMA SPECTROMETER
Authors: Cahyati Indah Dwi , Hendrawati Hendrawati , Amri Ghulam Fathul
Published: June 2022

Radionuclide Cs-137 is a radioactive element that is soluble in water, so its distribution in the environment is influenced by mixing and diffusion, so that Cs-137 radionuclide can cause harmful effects on biotic and abiotic components in the waters. The East Jakarta Flood Canal is a macro drainage system for the city of DKI Jakarta that drains water to Marunda beach, most of the water quality has been polluted with light to heavy levels of pollution. This study aims to determine the water quality of the East Jakarta Flood Canal including in situ and ex situ physical and chemical properties parameters (temperature, pH, salinity and TDS) and Cs-137 radionuclide concentration parameters and the distribution pattern of Cs-137 radionuclides at each sampling point. The samples used were water and kale, the samples were filtered and concentrated from the initial volume of 20 liter of water to 1 liter of water and 10 kilograms to 1 kilogram of Kale plant. The levels of Cs-137 were measured with a gamma spectrometer instrument for 3600 seconds, then data analysis was carried out. The results obtained are the concentration level of Cs-137 in Simplo KBT water ranging from 0-1.571 Bq/L, for duplo 0-0.424 Bq/L while for kale plant 0-3,228 Bq/Kg the overall results are within the quality standard limits set by regulations. the head of BAPETEN Number 7 of 2013 concerning the limit value of environmental radioactivity, which is 2.6×102 Bq/L.
Potentiometrie and spectroscopic study of isoniazid - an anti-tubercular drug
Authors: Tolulope D , Jackson G , Caira M , Hammouda A
Published: June 2022

The copper, nickel and zinc complexes of isoniazid (ISO, isonicotinic acid hydrazide), an anti-tubercular drug, have been investigated to determine whether these metal-ions improve the permeability and bioavailability of the drug. Since such properties of isonazid depend on its solution speciation, the latter was investigated by determining the equilibrium constants for the reaction of H+, Cu(II), Ni(II) and Zn(II) with isonazid in aqueous solution at 25.00 ± 0.01 °C and 0.15 M (NaCl), using glass electrode potentiometry. The structure of the copper complex of isoniazid was investigated using ultraviolet-visible spectroscopy. The results support the structures postulated from the potentiometric data. This study also considered membrane permeability using a Franz cell and octanol/water distribution coefficients. Distribution coefficient studies showed that ISO and its metal complexes are hydrophilic. The incorporation of a metal-ion improves the hydrophilicity of the ligand. The presence of a metal ion greatly enhanced the permeation of ISO through an artificial membrane in the order Cu(II) > Ni(II) > Zn(II) > ISO at pH 2 and Zn(II) > Ni(II) > ISO > Cu(II) at pH 4.
Awake Hypercapnic Ventilatory Response in Obstructive Sleep Apnea Syndrome
Authors: Sakuma Takashi , Shinomiya Shohei , Takahara Yutaka , Mizuno Shiro
Published: June 2022

Background and Objective Decreased ventilatory response to carbon dioxide or hypercapnic ventilatory response (HCVR) is a feature of pediatric obstructive sleep apnea (OSA) and is also known to diminish during sleep in obese adolescents (age, 12–16 years) with OSA. It reduces minute ventilation, air flow, and tidal volume during inspiration, as well as upper airway obstruction. The purpose of this study was to investigate awake HCVR in adult patients with OSA and to elucidate its association with sleep apnea.Methods HCVR was measured before performing polysomnography (PSG). PSG is performed as the evaluation method during sleep, and the severity of apnea is evaluated by apnea hypopnea index. Patient background, PSG data and HCVR were examined.Results Awake HCVR was greater in patients with severe OSA than in patients with mild and moderate OSA, and in severe OSA patients, the HCVR during awaking was higher in patients with larger changes in saturation of percutaneous oxygen during sleep. Awake HCVR did not differ by age, but it was greater in morbidly obese patients with OSA than in thin patients with OSA. The most frequent apnea pattern of OSA was obstructive, regardless of severity; although with an increasing severity of OSA, the central pattern decreased and the mixed pattern increased in frequency. The appearance of the mixed pattern increased in the augmented HCVR group.Conclusions This study suggested that awake HCVR could be used as an index of progression and a factor to determine the effects of treatment in patients with OSA.
Outcomes of Intranasal Corticosteroid Treatment and Associated Factors in Children With Clinically Diagnosed Obstructive Sleep Apnea
Authors: Iadprapal Trakarnta , Anuntaseree Wanaporn , Pruphetkaew Nannapat , Ruangnapa Kanokpan , Saelim Kantara , Prasertsan Pharsai
Published: June 2022

Background and Objective Many studies have supported the potential effect of anti-inflammatory medications treatment for childhood obstructive sleep apnea (OSA). This study aimed to evaluate the outcomes of intranasal corticosteroid treatment in children with OSA symptoms and adenoid hypertrophy (AH), and to identify factors associated with the treatment outcome.Methods This retrospective study included children aged 1–15 years who snored, had sleeprelated symptoms, and received intranasal corticosteroid treatment for 4–16 weeks. Treatment outcome was considered a success if all symptoms were completely resolved or if the child still snored without sleep-related symptoms; treatment was considered a failure if the child still snored and had concomitant sleep-related symptoms.Results Among the 328 eligible children, 109 (33.2%) experienced treatment failure. The age of the children was significantly higher in the failure group than in the success group (median [interquartile range], 5.5 [3.4, 8.4] vs. 4.2 [3.2, 6.3]; p = 0.004). The percentage of children with allergic rhinitis (AR) symptoms at follow-up visit was also significantly higher in the failure group than in the success group (44% vs. 27.9% with mild AR and 9.2% vs. 4.1% with moderateto-severe AR, p < 0.001). Multiple logistic regression revealed a significant association between treatment failure and older age (odds ratio [95% confidence interval], 1.13 [1.04–1.23]) and presence of mild AR (2.29 [1.39–3.77]) and moderate-to-severe AR (3.17 [1.20–8.39]).Conclusions Among children with OSA and AH, one-third experienced treatment failure, and the associated factors were older age and presence of AR symptoms.
[RETRACTED] Attention! Light Out CBD Gummies Reviews: Cost Side Effects Benefits Scam OR Legit? v1
Authors: not provided KetoXmedliquids
Published: June 2022

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Attention! Light Out CBD Gummies Reviews: Cost Side Effects Benefits Scam OR Legit?
In vitro germination of Austropuccinia psidii urediniospores v1
Authors: M Martino Alyssa
Published: June 2022

Optimisation of Austropuccinia psidii urediniospore germination for use in RNA extraction and cytogenetics.
[RETRACTED] Lights Out CBD Gummies – A Natural Pain Relief Formula With Full Spectrum CBD & CBN! v1
Authors: Out CBD Gummies Lights
Published: June 2022

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Lights Out CBD+CBN Gummies is a nutritional product that may only provide several benefits. This product may reduce all your chronic body aches and pain naturally.Buying:“Order Now! Voted #1 CBD+CBN Gummies in theUSA Only Visiting Official Website”
Fixation, Immunohistochemistry andin situHybridization of Human Lung Organoids v2
Authors: Baumgardt Morris
Published: June 2022

This protocol describes the fixation of infected human alveolar-like organoids, as well as the sample preparation for immunohistochemistry and immunohistology of infected tissue from alveolar-like organoids.
[RETRACTED] KetoXmed Liquid Fat Burn:-Really Work? A Detailed Review v1
Authors: not provided ketoxmedliquid
Published: June 2022

[RETRACTED]

That is where we come in. We will let you know everything to consider about KetoXmed before getting them! We need to cause explicit you to take full advantage of any enhancement you request. That infers being particularly educated prior to making an endeavor.
[RETRACTED] ProDentim *FAKE or REAL* The Components That Work Or a Fake? v1
Authors: A H
Published: June 2022

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ProDentim from the site, you have the opportunity to pick between three unique bundles with three distinct costs that are intended to suit everybody's ProDentim needs and their spending plans.
[RETRACTED] KetoXmed Liquid (2022) Siehe Schockbericht & Nebenwirkungen v1
Authors: not provided ketoxmed
Published: June 2022

[RETRACTED]

Die KetoXmed Liquid sollen den Körper 24 Stunden in einen Zustand der stetigen Fettverbrennung versetzen und Interessenten auf diese Weise schneller zum Traumgewicht verhelfen. In den letzten Jahren wurde immer mehr zur ketogenen Ernährung geforscht.
[RETRACTED] Attention! KetoXmed Liquid Bewertungen, Nebenwirkungen, Kosten? v1
Authors: not provided KetoXmedliquids
Published: June 2022

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Beware Of Fake Website Of KetoXmed Liquid
Sosyal Bilgiler Dersinde Öğretmenlerin Kullandıkları Filmlerin Eğitsel Açıdan İncelenmesi: Charlie ve Çikolata Fabrikası
Authors: ÇELİK Yaşar
Published: June 2022

Eğitimde belirlenen amaçlara ulaşabilmek için çeşitli öğretim materyallerine ihtiyaç duyulmaktadır. Bunlardan biri de eğitici filmlerdir. Eğitici filmler, okulda gerçekleştirilen öğrenmeleri desteklemek amacıyla, dersin amaç ve kazanımlarına uygun olarak belirlenmektedir. Eğitici filmlerin, ders materyali olarak kullanılabilecek özelliklere sahip olması gerekmektedir. Bu araştırmada öğretmenlerin sosyal bilgiler dersinde eğitsel amaçlı en çok kullandıkları “Charlie ve Çikolata Fabrikası” filminin, MEB tarafından belirlenen kriterlere göre incelenmesi amaçlanmıştır. Filmin incelenmesinde nitel araştırma desenlerinden olgubilim deseni ve doküman analizi yöntemi; elde edilen verilerin analizinde ise içerik analizi kullanılmıştır. Araştırmaya katılan 45 sınıf öğretmeni, sosyal bilgiler derslerinde en çok “Charlie ve Çikolata Fabrikası” filmini tercih ettiklerini belirtmişlerdir. Bu ‎film, MEB tarafından belirlenen "Taslak Ders Kitabı ve Eğitim Araçları İle Bunlara Ait E-‎İçeriklerin İncelenmesinde Değerlendirmeye Esas Olacak Kriterler Ve Açıklamaları" adlı ‎kriterlere göre incelenmiştir. İnceleme sonucunda filmin eşitlik, insan hakları ve ayrımcılığı reddetme gibi ilkelere uygun olduğu sonucuna ulaşılmıştır. Ayrıca aile, sabır, eşitlik, dürüstlük ve boş zamanı etkili değerlendirme gibi niteliklerin kazandırılması bakımından öğretim programının amaçlarına da uygun olduğu belirlenmiştir. Filmdeki görseller, ışıklandırma, anlaşılır olma, merak uyandırma ve renklendirme bakımından uygun özellikler taşımaktadır.