bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient-UCBV: An Almost Optimal Algorithm Using Variance Estimates
Authors: Mukherjee Subhojyoti , Naveen K. P. , Sudarsanam Nandan , Ravindran Balaraman
Published: June 2022

We propose a novel variant of the UCB algorithm (referred to as Efficient-UCB-Variance (EUCBV)) for minimizing cumulative regret in the stochastic multi-armed bandit (MAB) setting. EUCBV incorporates the arm elimination strategy proposed in UCB-Improved, while taking into account the variance estimates to compute the arms' confidence bounds, similar to UCBV. Through a theoretical analysis we establish that EUCBV incurs a gap-dependent regret bound which is an improvement over that of existing state-of-the-art UCB algorithms (such as UCB1, UCB-Improved, UCBV, MOSS). Further, EUCBV incurs a gap-independent regret bound which is an improvement over that of UCB1, UCBV and UCB-Improved, while being comparable with that of MOSS and OCUCB. Through an extensive numerical study we show that EUCBV significantly outperforms the popular UCB variants (like MOSS, OCUCB, etc.) as well as Thompson sampling and Bayes-UCB algorithms.
RelNN: A Deep Neural Model for Relational Learning
Authors: Kazemi Seyed Mehran , Poole David
Published: June 2022

Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recent studies. Many of the existing attempts, however, only focus on relations and ignore object properties. The attempts that do consider object properties are limited in terms of modelling power or scalability. In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression). We learn latent properties for objects both directly and through general rules. Back-propagation is used for training these models. A modular, layer-wise architecture facilitates utilizing the techniques developed within deep learning community to our architecture. Initial experiments on eight tasks over three real-world datasets show that RelNNs are promising models for relational learning.
Combining Experts’ Causal Judgments
Authors: Alrajeh Dalal , Chockler Hana , Halpern Joseph
Published: June 2022

Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts’ opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts’ causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being compatible, and show how compatible causal models can be combined. We then use it as the basis for combining experts causal judgments. We illustrate our approach on a number of real-life examples.
Optimal Approximation of Random Variables for Estimating the Probability of Meeting a Plan Deadline
Authors: Cohen Liat , Grinshpoun Tal , Weiss Gera
Published: June 2022

In planning algorithms and in other domains, there is often a need to run long computations that involve summations, maximizations and other operations on random variables, and to store intermediate results. In this paper, as a main motivating example, we elaborate on the case of estimating probabilities of meeting deadlines in hierarchical plans. A source of computational complexity, often neglected in the analysis of such algorithms, is that the support of the variables needed as intermediate results may grow exponentially along the computation. Therefore, to avoid exponential memory and time complexities, we need to trim these variables. This is similar, in a sense, to rounding intermediate results in numerical computations. Of course, to maintain the quality of algorithms, the trimming procedure should be efficient and it must maintain accuracy as much as possible. In this paper, we propose an optimal trimming algorithm with polynomial time and memory complexities for the purpose of estimating probabilities of deadlines in plans. More specifically, we show that our algorithm, given the needed size of the representation of the variable, provides the best possible approximation, where approximation accuracy is considered with a measure that fits the goal of estimating deadline meeting probabilities.
Information Acquisition Under Resource Limitations in a Noisy Environment
Authors: Soloviev Matvey , Halpern Joseph
Published: June 2022

We introduce a theoretical model of information acquisition under resource limitations in a noisy environment. An agent must guess the truth value of a given Boolean formula φ after performing a bounded number of noisy tests of the truth values of variables in the formula. We observe that, in general, the problem of finding an optimal testing strategy for φ is hard, but we suggest a useful heuristic. The techniques we use also give insight into two apparently unrelated, but well-studied problems: (1) rational inattention (the optimal strategy may involve hardly ever testing variables that are clearly relevant to φ) and (2) what makes a formula hard to learn/remember.
Anytime Anyspace AND/OR Best-First Search for Bounding Marginal MAP
Authors: Lou Qi , Dechter Rina , Ihler Alexander
Published: June 2022

Marginal MAP is a key task in Bayesian inference and decision-making. It is known to be very difficult in general, particularly because the evaluation of each MAP assignment requires solving an internal summation problem. In this paper, we propose a best-first search algorithm that provides anytime upper bounds for marginal MAP in graphical models. It folds the computation of external maximization and internal summation into an AND/OR tree search framework, and solves them simultaneously using a unified best-first search algorithm. The algorithm avoids some unnecessary computation of summation sub-problems associated with MAP assignments, and thus yields significant time savings. Furthermore, our algorithm is able to operate within limited memory. Empirical evaluation on three challenging benchmarks demonstrates that our unified best-first search algorithm using pre-compiled variational heuristics often provides tighter anytime upper bounds compared to those state-of-the-art baselines.
Generalized Adjustment Under Confounding and Selection Biases
Authors: Correa Juan , Tian Jin , Bareinboim Elias
Published: June 2022

Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditions for identifying causal effects by adjustment. We further design an algorithm for listing all admissible adjustment pairs in polynomial delay, which is useful for researchers interested in evaluating certain properties of some admissible pairs but not all (common properties include cost, variance, and feasibility to measure). Finally, we describe a statistical estimation procedure that can be performed once a set is known to be admissible, which entails different challenges in terms of finite samples.
Label Space Driven Heterogeneous Transfer Learning With Web Induced Alignment
Authors: Sukhija Sanatan
Published: June 2022

Heterogeneous Transfer Learning (HTL) algorithms leverage knowledge from a heterogeneous source domain to perform a task in a target domain. We present a novel HTL algorithm that works even where there are no shared features, instance correspondences and further, the two domains do not have identical labels. We utilize the label relationships via web-distance to align the data of the domains in the projected space, while preserving the structure of the original data.
Exploring Relevance Judgement Inspired by Quantum Weak Measurement
Authors: Wang Tianshu , Hou Yuexian , Wang Panpan , Niu Xiaolei
Published: June 2022

Quantum Theory (QT) has been applied in a number of fields outside physics, e.g. Information Retrieval (IR). A series of pioneering works have verified the necessity to employ QT in IR user models. In this paper, we explore the process of relevance judgement from a novel perspective of the two state vector quantum weak measurement (WM) by considering context information in time domain. Experiments are carried out to verify our arguments.
Decision Making Over Combinatorially-Structured Domains
Authors: Martin Andrea , Venable K.
Published: June 2022

We consider a scenario where a user must make a set of correlated decisions and we propose a computational modeling of the deliberation process. We assume the user compactly expresses her preferences via soft constraints. We consider a sequential procedure that uses Decision Field Theory to model the decision making on each variable. We test this procedure on randomly generated tree-shaped Fuzzy Constraint Satisfaction Problems. Our preliminary results showed that the time increases almost in the number of nodes. This is promising in terms of modeling decision over exponentially large domains. In the future, we plan to compare our results non-sequential approach and with behavioral data to asses our approach both in terms of modeling human decision making over complex domains, and adopting DFT as a means of incorporating a form of uncertainty into the soft constraint formalism.
StackReader: An RNN-Free Reading Comprehension Model
Authors: Jiang Yibo , Zhao Zhou
Published: June 2022

Machine comprehension of text is the problem to answer a query based on a given context. Many existing systems use RNN-based units for contextual modeling linked with some attention mechanisms. In this paper, however, we propose StackReader, an end-to-end neural network model, to solve this problem, without recurrent neural network (RNN) units and its variants. This simple model is based solely on attention mechanism and gated convolutional neural network. Experiments on SQuAD have shown to have relatively high accuracy with a significant decrease in training time.
Identifying Emotional Support in Online Health Communities
Authors: Khanpour Hamed , Caragea Cornelia , Biyani Prakhar
Published: June 2022

Extracting emotional support in Online Health Communities provides insightful information about patients’ emotional states. Current computational approaches to identifying emotional messages, i.e., messages that contain emotional support, are typically based on a set of handcrafted features. In this paper, we show that high-level and abstract features derived from a combination of convolutional neural networks (CNN) with Long Short Term Memory (LSTM) networks can be successfully employed for emotional message identification and can obviate the need for handcrafted features.
A Stratified Feature Ranking Method for Supervised Feature Selection
Authors: Chen Renjie , Chen Xiaojun , Yuan Guowen , Sun Wenya , Wu Qingyao
Published: June 2022

Most feature selection methods usually select the highest rank features which may be highly correlated with each other. In this paper, we propose a Stratified Feature Ranking (SFR) method for supervised feature selection. In the new method, a Subspace Feature Clustering (SFC) is proposed to identify feature clusters, and a stratified feature ranking method is proposed to rank the features such that the high rank features are lowly correlated. Experimental results show the superiority of SFR.
Enhancing RNN Based OCR by Transductive Transfer Learning From Text to Images
Authors: He Yang , Yuan Jingling , Li Lin
Published: June 2022

This paper presents a novel approach for optical character recognition (OCR) on acceleration and to avoid underfitting by text. Previously proposed OCR models typically take much time in the training phase and require large amount of labelled data to avoid underfitting. In contrast, our method does not require such condition. This is a challenging task related to transferring the character sequential relationship from text to OCR. We build a model based on transductive transfer learning to achieve domain adaptation from text to image. We thoroughly evaluate our approach on different datasets, including a general one and a relatively small one. We also compare the performance of our model with the general OCR model on different circumstances. We show that (1) our approach accelerates the training phase 20-30% on time cost; and (2) our approach can avoid underfitting while model is trained on a small dataset.
Adversary Is the Best Teacher: Towards Extremely Compact Neural Networks
Authors: Prabhu Ameya , Krishna Harish , Saha Soham
Published: June 2022

With neural networks rapidly becoming deeper, there emerges a need for compact models. One popular approach for this is to train small student networks to mimic larger and deeper teacher models, rather than directly learn from the training data. We propose a novel technique to train student-teacher networks without directly providing label information to the student. However, our main contribution is to learn how to learn from the teacher by a unique strategy---having the student compete with a discriminator.
Bayesian Optimization Meets Search Based Optimization: A Hybrid Approach for Multi-Fidelity Optimization
Authors: Hoag Ellis , Doppa Janardhan
Published: June 2022

Many real-life problems require optimizing functions with expensive evaluations. Bayesian Optimization (BO) and Search-based Optimization (SO) are two broad families of algorithms that try to find the global optima of a function with the goal of minimizing the number of function evaluations. A large body of existing work deals with the single-fidelity setting, where function evaluations are very expensive but accurate. However, in many applications, we have access to multiple-fidelity functions that vary in their cost and accuracy of evaluation. In this paper, we propose a novel approach called Multi-fidelity Hybrid (MF-Hybrid) that combines the best attributes of both BO and SO methods to discover the global optima of a black-box function with minimal cost. Our experiments on multiple benchmark functions show that the MF-Hybrid algorithm outperforms existing single-fidelity and multi-fidelity optimization algorithms.
Proposition Entailment in Educational Applications Using Deep Neural Networks
Authors: Bulgarov Florin , Nielsen Rodney
Published: June 2022

To have a more meaningful impact, educational applications need to significantly improve the way feedback is offered to teachers and students. We propose two methods for determining propositional-level entailment relations between a reference answer and a student's response. Both methods, one using hand-crafted features and an SVM and the other using word embeddings and deep neural networks, achieve significant improvements over a state-of-the-art system and two alternative approaches.
A Framework for Evaluating Barriers to the Democratization of Artificial Intelligence
Authors: Garvey Colin
Published: June 2022

The "democratization" of AI has been taken up as a primary goal by several major tech companies. However, these efforts resemble earlier "freeware" and "open access" initiatives, and it is unclear how or whether they are informed by political conceptions of democratic governance. A political formulation of the democratization of AI is thus necessary. This paper presents a framework for the democratic governance of technology through intelligent trial and error (ITE) that can be utilized to evaluate barriers to the democratization of AI and suggest strategies for overcoming them.
Adversarial Goal Generation for Intrinsic Motivation
Authors: Durugkar Ishan , Stone Peter
Published: June 2022

Generally in Reinforcement Learning the goal, or reward signal, is given by the environment and cannot be controlled by the agent. We propose to introduce an intrinsic motivation module that will select a reward function for the agent to learn to achieve. We will use a Universal Value Function Approximator, that takes as input both the state and the parameters of this reward function as the goal to predict the value function (or action-value function) to generalize across these goals. This module will be trained to generate goals such that the agent's learning is maximized. Thus, this is also a method for automatic curriculum learning.
Influence Maximization for Social Network Based Substance Abuse Prevention
Authors: Rahmattalabi Aida , Barman Adhikari Anamika , Vayanos Phebe , Tambe Milind , Rice Eric , Baker Robin
Published: June 2022

Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of reducing substance abuse. While effective, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. In this paper, we present GUIDE, an AI-based decision aid that leverages social network information to optimize the structure of the intervention groups.
Playing SNES Games With NeuroEvolution of Augmenting Topologies
Authors: Pham Son , Zhang Keyi , Phan Tung , Ding Jasper , Dancy Christopher
Published: June 2022

Teaching a computer to play video games has generally been seen as a reasonable benchmark for developing new AI techniques. In recent years, extensive research has been completed to develop reinforcement learning (RL) algorithms to play various Atari 2600 games, resulting in new applications of algorithms such as Deep Q-Learning or Policy Gradient that outperform humans. However, games from Super Nintendo Entertainment System (SNES) are far more complicated than Atari 2600 games as many of these state-of-the-art algorithms still struggle to perform on this platform. In this paper, we present a new platform to research algorithms on SNES games and investigate NeuroEvolution of Augmenting Topologies (NEAT) as a possible approach to develop algorithms that outperform humans in SNES games.
Learning Fast and Slow: Levels of Learning in General Autonomous Intelligent Agents
Authors: Laird John , Mohan Shiwali
Published: June 2022

We propose two distinct levels of learning for general autonomous intelligent agents. Level 1 consists of fixed architectural learning mechanisms that are innate and automatic. Level 2 consists of deliberate learning strategies that are controlled by the agent's knowledge. We describe these levels and provide an example of their use in a task-learning agent. We also explore other potential levels and discuss the implications of this view of learning for the design of autonomous agents.
Tracking Occluded Objects and Recovering Incomplete Trajectories by Reasoning About Containment Relations and Human Actions
Authors: Liang Wei , Zhu Yixin , Zhu Song-Chun
Published: June 2022

This paper studies a challenging problem of tracking severely occluded objects in long video sequences. The proposed method reasons about the containment relations and human actions, thus infers and recovers occluded objects identities while contained or blocked by others. There are two conditions that lead to incomplete trajectories: i) Contained. The occlusion is caused by a containment relation formed between two objects, e.g., an unobserved laptop inside a backpack forms containment relation between the laptop and the backpack. ii) Blocked. The occlusion is caused by other objects blocking the view from certain locations, during which the containment relation does not change. By explicitly distinguishing these two causes of occlusions, the proposed algorithm formulates tracking problem as a network flow representation encoding containment relations and their changes. By assuming all the occlusions are not spontaneously happened but only triggered by human actions, an MAP inference is applied to jointly interpret the trajectory of an object by detection in space and human actions in time. To quantitatively evaluate our algorithm, we collect a new occluded object dataset captured by Kinect sensor, including a set of RGB-D videos and human skeletons with multiple actors, various objects, and different changes of containment relations. In the experiments, we show that the proposed method demonstrates better performance on tracking occluded objects compared with baseline methods.
Scene-Centric Joint Parsing of Cross-View Videos
Authors: Qi Hang , Xu Yuanlu , Yuan Tao , Wu Tianfu , Zhu Song-Chun
Published: June 2022

Cross-view video understanding is an important yet under-explored area in computer vision. In this paper, we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes. Our key observations are that overlapping fields of views embed rich appearance and geometry correlations and that knowledge fragments corresponding to individual vision tasks are governed by consistency constraints available in commonsense knowledge. The proposed joint parsing framework represents such correlations and constraints explicitly and generates semantic scene-centric parse graphs. Quantitative experiments show that scene-centric predictions in the parse graph outperform view-centric predictions.
Learning Binary Residual Representations for Domain-Specific Video Streaming
Authors: Tsai Yi-Hsuan , Liu Ming-Yu , Sun Deqing , Yang Ming-Hsuan , Kautz Jan
Published: June 2022

We study domain-specific video streaming. Specifically, we target a streaming setting where the videos to be streamed from a server to a client are all in the same domain and they have to be compressed to a small size for low-latency transmission. Several popular video streaming services, such as the video game streaming services of GeForce Now and Twitch, fall in this category. While conventional video compression standards such as H.264 are commonly used for this task, we hypothesize that one can leverage the property that the videos are all in the same domain to achieve better video quality. Based on this hypothesis, we propose a novel video compression pipeline. Specifically, we first apply H.264 to compress domain-specific videos. We then train a novel binary autoencoder to encode the leftover domain-specific residual information frame-by-frame into binary representations. These binary representations are then compressed and sent to the client together with the H.264 stream. In our experiments, we show that our pipeline yields consistent gains over standard H.264 compression across several benchmark datasets while using the same channel bandwidth.
Learning a Wavelet-Like Auto-Encoder to Accelerate Deep Neural Networks
Authors: Chen Tianshui , Lin Liang , Zuo Wangmeng , Luo Xiaonan , Zhang Lei
Published: June 2022

Accelerating deep neural networks (DNNs) has been attracting increasing attention as it can benefit a wide range of applications, e.g., enabling mobile systems with limited computing resources to own powerful visual recognition ability. A practical strategy to this goal usually relies on a two-stage process: operating on the trained DNNs (e.g., approximating the convolutional filters with tensor decomposition) and fine-tuning the amended network, leading to difficulty in balancing the trade-off between acceleration and maintaining recognition performance. In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training. The two decomposed channels, in particular, are encoded to carry the low-frequency information (e.g., image profiles) and high-frequency (e.g., image details or noises), respectively, and enable reconstructing the original input image through the decoding process. Then, we feed the low-frequency channel into a standard classification network such as VGG or ResNet and employ a very lightweight network to fuse with the high-frequency channel to obtain the classification result. Compared to existing DNN acceleration solutions, our framework has the following advantages: i) it is tolerant to any existing convolutional neural networks for classification without amending their structures; ii) the WAE provides an interpretable way to preserve the main components of the input image for classification.
Unsupervised Deep Learning of Mid-Level Video Representation for Action Recognition
Authors: Hou Jingyi , Wu Xinxiao , Chen Jin , Luo Jiebo , Jia Yunde
Published: June 2022

Current deep learning methods for action recognition rely heavily on large scale labeled video datasets. Manually annotating video datasets is laborious and may introduce unexpected bias to train complex deep models for learning video representation. In this paper, we propose an unsupervised deep learning method which employs unlabeled local spatial-temporal volumes extracted from action videos to learn midlevel video representation for action recognition. Specifically, our method simultaneously discovers mid-level semantic concepts by discriminative clustering and optimizes local spatial-temporal features by two relatively small and simple deep neural networks. The clustering generates semantic visual concepts that guide the training of the deep networks, and the networks in turn guarantee the robustness of the semantic concepts. Experiments on the HMDB51 and the UCF101 datasets demonstrate the superiority of the proposed method, even over several supervised learning methods.
Dual-Reference Face Retrieval
Authors: Hu BingZhang , Zheng Feng , Shao Ling
Published: June 2022

Face retrieval has received much attention over the past few decades, and many efforts have been made in retrieving face images against pose, illumination, and expression variations. However, the conventional works fail to meet the requirements of a potential and novel task---retrieving a person's face image at a specific age, especially when the specific "age" is not given as a numeral, i.e. "retrieving someone's image at the similar age period shown by another person's image." To tackle this problem, we propose a dual reference face retrieval framework in this paper, where the system takes two inputs: an identity reference image which indicates the target identity and an age reference image which reflects the target age. In our framework, the raw images are first projected on a joint manifold, which preserves both the age and identity locality. Then two similarity metrics of age and identity are exploited and optimized by utilizing our proposed quartet-based model. The experiments show promising results, outperforming hierarchical methods.
CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual Generation
Authors: Hao Wangli , Zhang Zhaoxiang , Guan He
Published: June 2022

Visual and audio modalities are two symbiotic modalities underlying videos, which contain both common and complementary information. If they can be mined and fused sufficiently, performances of related video tasks can be significantly enhanced. However, due to the environmental interference or sensor fault, sometimes, only one modality exists while the other is abandoned or missing. By recovering the missing modality from the existing one based on the common information shared between them and the prior information of the specific modality, great bonus will be gained for various vision tasks. In this paper, we propose a Cross-Modal Cycle Generative Adversarial Network (CMCGAN) to handle cross-modal visual-audio mutual generation. Specifically, CMCGAN is composed of four kinds of subnetworks: audio-to-visual, visual-to-audio, audio-to-audio and visual-to-visual subnetworks respectively, which are organized in a cycle architecture. CMCGAN has several remarkable advantages. Firstly, CMCGAN unifies visual-audio mutual generation into a common framework by a joint corresponding adversarial loss. Secondly, through introducing a latent vector with Gaussian distribution, CMCGAN can handle dimension and structure asymmetry over visual and audio modalities effectively. Thirdly, CMCGAN can be trained end-to-end to achieve better convenience. Benefiting from CMCGAN, we develop a dynamic multimodal classification network to handle the modality missing problem. Abundant experiments have been conducted and validate that CMCGAN obtains the state-of-the-art cross-modal visual-audio generation results. Furthermore, it is shown that the generated modality achieves comparable effects with those of original modality, which demonstrates the effectiveness and advantages of our proposed method.
Learning Spatio-Temporal Features With Partial Expression Sequences for On-the-Fly Prediction
Authors: Baddar Wissam , Ro Yong
Published: June 2022

Spatio-temporal feature encoding is essential for encoding facial expression dynamics in video sequences. At test time, most spatio-temporal encoding methods assume that a temporally segmented sequence is fed to a learned model, which could require the prediction to wait until the full sequence is available to an auxiliary task that performs the temporal segmentation. This causes a delay in predicting the expression. In an interactive setting, such as affective interactive agents, such delay in the prediction could not be tolerated. Therefore, training a model that can accurately predict the facial expression "on-the-fly" (as they are fed to the system) is essential. In this paper, we propose a new spatio-temporal feature learning method, which would allow prediction with partial sequences. As such, the prediction could be performed on-the-fly. The proposed method utilizes an estimated expression intensity to generate dense labels, which are used to regulate the prediction model training with a novel objective function. As results, the learned spatio-temporal features can robustly predict the expression with partial (incomplete) expression sequences, on-the-fly. Experimental results showed that the proposed method achieved higher recognition rates compared to the state-of-the-art methods on both datasets. More importantly, the results verified that the proposed method improved the prediction frames with partial expression sequence inputs.
Using Syntax to Ground Referring Expressions in Natural Images
Authors: Cirik Volkan , Berg-Kirkpatrick Taylor , Morency Louis-Philippe
Published: June 2022

We introduce GroundNet, a neural network for referring expression recognition---the task of localizing (or grounding) in an image the object referred to by a natural language expression. Our approach to this task is the first to rely on a syntactic analysis of the input referring expression in order to inform the structure of the computation graph. Given a parse tree for an input expression, we explicitly map the syntactic constituents and relationships present in the tree to a composed graph of neural modules that defines our architecture for performing localization. This syntax-based approach aids localization of both the target object and auxiliary supporting objects mentioned in the expression. As a result, GroundNet is more interpretable than previous methods: we can (1) determine which phrase of the referring expression points to which object in the image and (2) track how the localization of the target object is determined by the network. We study this property empirically by introducing a new set of annotations on the GoogleRef dataset to evaluate localization of supporting objects. Our experiments show that GroundNet achieves state-of-the-art accuracy in identifying supporting objects, while maintaining comparable performance in the localization of target objects.
Jahwe und die Götter - Ein Vergleich der Gottesdarstellungen in den Fluterzählungen der Genesis, dem Gilgamesch-Epos und dem Atramhasïs-Mythos
Authors: Bellmann Raphael , Wünch Hans-Georg
Published: June 2022

The existence of striking similarities between the biblical flood narrative and the flood narratives of the Epic of Gilgamesh and the Atramhasïs myth has led to the widely accepted hypothesis that the biblical flood narrative depends on the Mesopotamian narratives. In this study, the representation of Yahweh in the biblicalflood narrative is compared with the representation of the gods in the Epic of Gilgamesh and the Atramhasïs myth by means of a synchronic juxtaposition of the texts in question. Since the flood narratives in question all originated in a common cultural sphere, the juxtaposition of the contents leads to the conclusion that the biblical text or the tradition on which it is based is not dependent on the Mesopotamian narratives, but playfully and sometimes also polemically contrasts itself to the prevailing Mesopotamian theology. By using or consciously avoiding certain formulations of the Mesopotamian flood narratives the biblicalflood narrative puts emphasis on the God of the biblical narrative over and against all the other gods.
Healthcare providers’ attitude towards abortion service provision in Gulu city, Northern Uganda
Authors: Pebalo Francis Pebolo , Auma Anna Grace , Obol James Henry
Published: June 2022

Background: Although induced abortion is legally allowed on various grounds in several sub-Saharan African countries, health care providers in these countries often persist in viewing induced abortion as immoral. Providers’ attitudes may conflict with the national abortion law or their personal and or religious values. Abortion services are severely restricted and highly contentious in Uganda. This study, therefore, is aimed at determining attitudes among healthcare providers on induced abortion service provision in Gulu City. Procedures: A cross-section survey was conducted among health workers about attitudes toward induced abortion between September and November 2019 using a modified abortion attitudinal score. The study was conducted in the Hospitals and Health centers in Gulu City, in Northern Uganda, the participants were drawn from Public, Private non-for-profit faith-based, Private for Profit and, Private non-for-profit Non-Government Organisation health centers. Findings: A total of 252 health care providers were surveyed. The mean attitudinal score for generally in support, generally not in support, conditional in support, personal attitude, and beliefs against and toward abortion provision were 2.80, 2.71, 2.86, 3.239, and 3.35 respectively. Factors that were positively associated with general support included age 40 years and above; and being employed in private non-for-profit non-governmental health facilities, with coefficients of 0.85 and 0.67 respectively. Factors that were positively associated with conditional support were; age 40years or above; being employed in a non for profit non-governmental health facility; private for-profit and private not-for-profit faith-based health facilities coefficients 0.55, 0.54, 0.40, and 0.37 respectively. However, being a born-again Christian was negatively associated with general support for induced abortion provision. Conclusion: Healthcare providers’ attitude is an important element in the provision of quality stigma-free post-abortion care services. A clear national effort to improve post-abortion and comprehensive abortion care training should include value clarification and attitude transformation among all healthcare providers.
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Published: June 2022

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[RETRACTED] Via Keto Apple Gummies Australia : Weight Loss Reviews, Price, and Official Store v1
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Published: June 2022

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Evaluation of light commercial vehicles operation process
in a transport company using the regression modelling method
Authors: Owczarek Paulina , Brzeziński Marian , Zelkowski Jarosław
Published: June 2022

This paper presents an analysis of the results of daily observations from the execution of
transport orders by three types of vehicles over a period of 2 years. The purpose of the
research was to evaluate the operation process and determine the influence of important
technical and operational variables on the economic efficiency of the operation process. A
set of 7 quantitative variables, previously not considered in the evaluation of the commercial
vehicle operation process, was subjected to statistical data analysis. An indicator analysis
and evaluation of the intensity of use of the following types of vehicles was conducted:
Renault Master, Fiat Ducato and Citroen Jumper. Based on the results of the research, the
vehicle with the highest efficiency was determined and possible assumptions of the strategy
applied in the company were indicated. The analysis and evaluation of vehicle efficiency
gave rise to the identification of independent variables determining the company's income.
Using the indicator method and the multivariate regression model, transport companies can
evaluate the efficiency of transport tasks undertaken.
influencia del uso del suelo en la vulnerabilidad de un acuífero en la cuenca hidrográfica San Juan, Cuba
Authors: González Trujillo Mayelin
Published: June 2022

El presente artículo aborda un problema ambiental en la cuenca hidrográfica San Juan, en la provincia Santiago de Cuba. El objetivo fue evaluar la vulnerabilidad del acuífero San Juan bajo las afectaciones del uso del suelo. Los métodos empleados son el análisis documental y el Groundwater confinment, Overlaying strata and Depth groundwater (GOD), la vulnerabilidad por exposición y fragilidad ambiental, representados en un Sistema de Información Geográfica (SIG). Los resultados evidencian una propuesta metodológica adaptada que permite determinar la vulnerabilidad de acuíferos y analizar las afectaciones por el uso del suelo, aportando una herramienta útil en la toma de decisiones. El mapa de vulnerabilidades muestra que el acuífero está sometido a vulnerabilidad alta y moderada, siendo el área acuífera una zona en conflicto de usos del suelo, con un crecimiento urbano desordenado, usos de contaminantes potenciales, como vertido de residuales domésticos, hospitalarios, industriales, porcinos y de cultivos varios.
Reflection of the Legitimate Interests of the Victim in his Right to Know about the Suspicions and Charges (Based on Russian and Kazakh Materials)
Authors: Fink D. A.
Published: June 2022

The object of the study is the legitimate interest of the victim and its reflection in the right of the victim to know about the suspicions and charges brought against him in a criminal case. The victim is interested in being informed in a timely manner about procedural decisions that determine the scope of suspicion and accusation, so that they are confirmed. This need of the victim is not sufficiently reflected in the criminal procedure law: they are not informed about the suspicions and charges formulated by the body conducting the criminal procedure. In fact, the victim receives relatively complete information about the charges against them only at the stage of completion of pre-trial proceedings, namely familiarization with the case materials. In this regard, the paper proposes to introduce into the Code of Criminal Procedure of the Republic of Kazakhstan the norms fixing the notification of the victim about the date, time and place of the announcement to the suspect of the decision on the qualification of the act, handing him a copy of this decision, a copy of the report on the criminal offense after its approval by the prosecutor. Insufficient awareness of the victim about the quality of the investigation at the stages following the bringing of the accused to trial by the prosecutor creates conditions for setting charges that do not meet the interests of the victim and leads to judicial errors. A change or waiver of charges in court is accepted by the public prosecutor without taking into account the position of the victim. These problems are proposed to be solved by postponing the consideration of the case and issuing by the court an order to the pretrial proceedings to eliminate the gaps in the investigation, and if it is not fulfilled, to make a decision based on the available evidence.
Territorial Zoning and Permitted Use of Lands as a Way to Determine the Legal Regime of Lands and Land Plots
Authors: Sutyagin M. D.
Published: June 2022

The paper is devoted to the study of problems related to the definition of the legal regime of land and land plots in Russia by establishing the types of permitted use in accordance with the territorial zoning. The issues raised in the paper are especially relevant in connection with the alleged exclusion from the Land Code of the Russian Federation of the principle of dividing land and land plots into categories according to their intended purpose. As a result, the main problems of establishing the legal regime are identified, such as the lack of legislatively fixed terminology in the field of establishing the legal regime and permitted use, the impossibility of establishing the types of permitted use for certain categories of land, and the underdevelopment of the institution of zoning territories. In this regard, there are problems in establishing the legal regime of land, since several types of zoning can be distributed to one territory, between which there is no relationship and subordination. The author concludes that the institutions of territorial zoning and permitted use require detailed revision. The paper suggests some ways to solve the identified problems. The necessity of legislative consolidation of the terminology associated with the establishment of the legal regime of land and the development of territorial zoning is emphasized.
Comparative study of biodiesel production from different waste oil sources for optimum operation conditions and better engine performance
Authors: AL DOORI Wadhah H. , ALI Obed M. , AHMED Ahmed H. , KOTEN Hasan
Published: June 2022

Biodiesel from waste sources can be considered as the cheapest and most viable fuel alternative to depleting mineral diesel. In this work, biodiesel has been produced from different sources of waste oil; grilled chicken waste oil (GCWO) which is animal based source and waste cooking oil (WCO) which is vegetable based using transesterification method. The impact of different variables such as temperature, transesterification time, and amount of catalyst on the conversion and yield of biodiesel were investigated. Produced biodiesel was characterizes using ASTM standard methods for biodiesel property testing to determine the fuel properties including; kinematic viscosity, specific gravity, flash point, pour point, cloud point and acid number. Engine test has been conducted at increasing speed and constant load to evaluate the engine performance using the produced fuel. The results obtained indicate that the yield and conversion of bio-diesel from grilled chicken waste oil are greater than waste cooking oil. Both produced biodiesel fuel properties are within the standard biodiesel fuel specifications ASTM D6751. The maximum conversion and yield of biodiesel obtained by 97.76% and 94.4% suing GWCO and WCO respectively at optimum operating variables of 600C temperature, 3 h reaction time, and 0.4 wt% potassium hydroxide. Engine test results show similar trends for both biodiesels compared to diesel in term of engine brake power (BP) and brake specific fuel consumption (BSFC) with increasing engine speed. The maximum reduction in BP is found to be about 19% at 2400 rpm and a maximum increase in BSFC of 17% is obtained at 1800 rpm engine speed with both biodiesels compared to diesel fuel. Accordingly, though both biodiesels show comparable engine performance, GWCO presents higher biodiesel yield.