Reviewerly/exHarmony huggingface dataset:
Learning explicitly transferable representations for domain adaptation

Domain adaptation tackles the problem where the training source domain and the test target domain have distinctive data distributions, and therefore improves the generalization ability of deep models. The very popular mechanism of domain adaptation is to learn a new feature representation which is supposed to be domain-invariant, so that the classifiers trained on the source domain can be directly applied to the target domain. However, recent work reveals that learning new feature representations may potentially deteriorate the adaptability of the original features and increase the expected error bound of the target domain. To address this, we propose to adapt classifiers rather than features. Specifically, we fill in the distribution gaps between domains by some additional transferable representations which are explicitly learned from the original features while keeping the original features unchanged. In addition, we argue that transferable representations should be able to be translated from one domain to the other with appropriate mappings. At the same time, we introduce conditional entropy to mitigate the semantic confusion during mapping. Experiments on both standard and large-scale datasets verify that our method is able to achieve the new state-of-the-art results on unsupervised domain adaptation.
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings

Language representations are known to carry certain associations (e.g., gendered connotations) which may lead to invalid and harmful predictions in downstream tasks.While existing methods are effective at mitigating such unwanted associations by linear projection, we argue that they are too aggressive: not only do they remove such associations, they also erase information that should be retained.To address this issue, we propose OS-CAR (Orthogonal Subspace Correction and Rectification), a balanced approach of mitigation that focuses on disentangling associations between concepts that are deemed problematic, instead of removing concepts wholesale.We develop new measurements for evaluating information retention relevant to the debiasing goal.Our experiments on genderoccupation associations show that OSCAR is a well-balanced approach that ensures that semantic information is retained in the embeddings and unwanted associations are also effectively mitigated. Quantifying Bias and Information RetainedKeeping in mind that removing bias and retaining information have to be done in synergy, we present how to obtain aggregated measurements for these two components.We will first describe the de-
Parrondo's paradox in quantum walks with time-dependent coin operators

We show that a Parrondo paradox can emerge in two-state quantum walks without resorting to experimentally intricate high-dimensional coins. To achieve such goal we employ a time-dependent coin operator without breaking the translation spatial invariance of the system.
AEGCN: An Autoencoder-Constrained Graph Convolutional Network

We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convolutional network operating directly on graphs, whose hidden layers are constrained by an autoencoder. Comparing with vanilla graph convolutional networks, the autoencoder step is added to reduce the information loss brought by Laplacian smoothing. We consider applying our model on both homogeneous graphs and heterogeneous graphs. For homogeneous graphs, the autoencoder approximates to the adjacency matrix of the input graph by taking hidden layer representations as encoder and another one-layer graph convolutional network as decoder. For heterogeneous graphs, since there are multiple adjacency matrices corresponding to different types of edges, the autoencoder approximates to the feature matrix of the input graph instead, and changes the encoder to a particularly designed multi-channel pre-processing network with two layers. In both cases, the error occurred in the autoencoder approximation goes to the penalty term in the loss function. In extensive experiments on citation networks and other heterogeneous graphs, we demonstrate that adding autoencoder constraints significantly improves the performance of graph convolutional networks. Further, we notice that our technique can be applied on graph attention network to improve the performance as well. This reveals the wide applicability of the proposed autoencoder technique.
Examining relationship between service quality, user satisfaction, and performance impact in the context of smart government in UAE

Governments attempt to use all forms of information technologies including Internet and mobile computing to be able to transform relationships with citizens. However, there is a clear gap between the indicator of the impact of technology innovation output and government’s vision in UAE (United Arab Emirates). In this regard, investigating the relationship between service quality, user satisfaction, and performance impact may help the government to mark its current progress and milestone achievement. This research proposed a model based on Delone &amp; McLean IS success model by considering the research context. The modeling of structural equations via PLS (Partial least squares) regression was applied to evaluate the model within the context of public sector in the UAE. The data was collected from a sample of 147 employees in public organizations using a questionnaire. Results demonstrated that the quality of service has a significant effect on user satisfaction. In addition, quality of service and user satisfaction positively influences the staff performance. The outcome of this research helps to enhance the understanding of the impact of smart government applications.
PyDL8.5: a Library for Learning Optimal Decision Trees

Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for learning DTs are heuristic in nature; they may produce trees that are of suboptimal quality under depth constraints. We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs). PyDL8.5 provides an interface for DL8.5, an efficient algorithm for inferring depth-constrained ODTs. The library provides an easy-to-use scikit-learn compatible interface. It cannot only be used for classification tasks, but also for regression, clustering, and other tasks. We introduce an interface that allows users to easily implement these other learning tasks. We provide a number of examples of how to use this library.
Personalized Federated Learning: An Attentive Collaboration Approach.

For the challenging computational environment of IOT/edge computing, personalized federated learning allows every client to train a strong personalized cloud model by effectively collaborating with the other clients in a privacy-preserving manner. The performance of personalized federated learning is largely determined by the effectiveness of inter-client collaboration. However, when the data is non-IID across all clients, it is challenging to infer the collaboration relationships between clients without knowing their data distributions. In this paper, we propose to tackle this problem by a novel framework named federated attentive message passing (FedAMP) that allows each client to collaboratively train its own personalized cloud model without using a global model. FedAMP implements an attentive collaboration mechanism by iteratively encouraging clients with more similar model parameters to have stronger collaborations. This adaptively discovers the underlying collaboration relationships between clients, which significantly boosts effectiveness of collaboration and leads to the outstanding performance of FedAMP. We establish the convergence of FedAMP for both convex and non-convex models, and further propose a heuristic method that resembles the FedAMP framework to further improve its performance for federated learning with deep neural networks. Extensive experiments demonstrate the superior performance of our methods in handling non-IID data, dirty data and dropped clients.
The Impact of e-Health Literacy on Health Promotion Behaviors of High School Students

Aim:The aim of this study was to investigate the effects of e-health literacy on health promotion behaviors on high school students.Materials and Methods: This descriptive, cross-sectional and relational study was conducted between May-June 2019 in a high school chosen by a random sampling method from the high schools of Narlıdere District of İzmir Province.Data were collected using a descriptive information form, the e-Health Literacy scale in Adolescents and the Adolescent Health Promotion scale.The data collected from the study was analyzed using numbers, percentages, average, Pearson correlation analysis and regression analysis in SPSS 22.0.Results: 57.1% of the students included in this study were male and their mean age was 16.52±0.92years.It was found that 47% of the students were studying in the 10 th grade.Of the students' mothers, 42.1% were primary school graduates, while 29.2% of the fathers were primary school graduates.It was determined that 95% of the students used the internet, 69.4% accessed the internet via mobile phone and 65.3% did not take health promotion lessons at school.It was also seen that there was a positive moderate relationship between the e-health literacy and mean Health Promotion scale scores of the high school students, and that 17% of the factors affecting adolescent health promotion behaviors were explained by the total e-health literacy mean scores (r=0.416;p<0.001). Conclusion:It was found that there was a statistically significant relationship between e-health literacy and the health promotion behaviors of the students, but it was found that the total score means of health promotion behaviors were not at the desired level.Starting from an early period, it is recommended to conduct interventional studies aimed at increasing health promotion behaviors in children and adolescents.
Adversarial Tri-Fusion Hashing Network for Imbalanced Cross-Modal Retrieval

Cross-modal retrieval has received increasing attentions for efficient retrieval across different modalities, and hashing technique has made significant progress recently due to its low storage cost and high query speed. However, most existing cross-modal hashing works still face the challenges of narrowing down the semantic gap between different modalities and training with imbalanced multi-modal data. This article presents an efficient Adversarial Tri-Fusion Hashing Network (ATFH-N) for cross-modal retrieval, which lies among the early attempts to incorporate adversarial learning for working with imbalanced multi-modal data. Specifically, a triple fusion network associated with zero padding operation is proposed to adapt either balanced or imbalanced multi-modal training data. At the same time, an adversarial training mechanism is leveraged to maximally bridge the semantic gap of the common representations between balanced and imbalanced data. Further, a label prediction network is utilized to guide the feature learning process and promote hash code learning, while additionally embedding the manifold structure to preserve both inter-modal and intra-modal similarities. Through the joint exploitation of the above, the underlying semantic structure of multimedia data can be well preserved in Hamming space, which can benefit various cross-modal retrieval tasks. Extensive experiments on three benchmark datasets show that the proposed ATFH-N method yields the comparable performance in balanced scenario and brings substantial improvements over the state-of-the-art methods in imbalanced scenarios.
Visual Hide and Seek

We train embodied agents to play Visual Hide and Seek to study the relationship between agent behaviors and environmental complexity. In Visual Hide and Seek, a prey must navigate in a simulated environment in order to avoid capture from a predator, only relying on first-person visual observations. By probing different environmental factors, agents exhibit diverse hiding strategies and even the knowledge of its own visibility to other agents in the scene. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, affect the learned policy. Our results suggest that, although agent weakness makes the learning problem more challenging, they also cause more useful features to be learned. Our project website is available at http://www.cs.columbia.edu/bchen/visualhideseek/.
Analysis of Face Recognition Algorithm: Dlib and OpenCV

In face recognition there are two commonly used open-source libraries namely Dlib and OpenCV. Analysis of facial recognition algorithms is needed as reference for software developers who want to implement facial recognition features into an application program. From Dlib algorithm to be analyzed is CNN and HoG, from OpenCV algorithm is DNN and HAAR Cascades. These four algorithms are analyzed in terms of speed and accuracy. The same image dataset will be used to test, along with some actual images to get a more general analysis of how algorithm will appear in real life scenarios. The programming language used for face recognition algorithms is Python. The image dataset will come from LFW (Labeled Faces in the Wild), and AT&amp;T, both of which are available and ready to be downloaded from the internet. Pictures of people around the UIB (Batam International University) is used for actual images dataset. HoG algorithm is fastest in speed test (0.011 seconds / image), but the accuracy rate is lower (FRR = 27.27%, FAR = 0%). DNN algorithm is the highest in level of accuracy (FRR = 11.69%, FAR = 2.6%) but the lowest speed (0.119 seconds / picture). There is no best algorithm, each algorithm has advantages and disadvantages.Keywords: Python, Face Recognition, Analysis, Speed, Accuracy.
Global Context Assisted Structure-Aware Vehicle Retrieval

In vehicle retrieval, the vehicle patch should first be localized to remove the irrelevant background information. Moreover, the negative samples are much more prevalent than the positive samples, and the information from the negative samples is not fully exploited in the triple loss. What we need is a way to incorporate global knowledge and structure information to address these two issues. Therefore, we introduce a local-global context network for landmark alignment to update the predicted results by using the semantic information and the local compatibility and propose a structure-aware quadruple loss to use multiple and diverse negative samples in retrieval. Experiments on the VehicleID and the ENJOYOR vehicle retrieval datasets demonstrate that our approach obtains accuracy comparable to state-of-the-art approaches in vehicle retrieval.
The ΔF method of vocal tract length normalization for vowels

Given the acoustic consequences of physiological differences between talkers, there is a practical need for effective and theoretically motivated procedures of vowel normalization to facilitate comparison of speech produced by people who differ by dialect or language. In addition, there is a question whether listeners might utilize a normalization procedure during speech perception. This paper reports the results of two studies that explore these questions—with particular focus on vocal tract length normalization. Drawing on research in speech engineering, where accurate estimates of vocal tract length are needed in some approaches to automatic speech recognition and speaker verification, a new model of vowel normalization is introduced. The model uses a direct measure of average formant spacing (the ΔF) which can be used to measure vocal tract length. The acoustic consequences of vocal tract length differences are removed from vowel measurements by scaling vowel formant measurements by ΔF. Study 1 found that this method is comparable to Nearey’s (1978) uniform normalization method, while providing an explicit vocal tract length interpretation, and a rationalized unit of measure. Study 2 found that uniform normalization measures (which let each formant serve as a noisy estimator of ΔF) improve vowel classification even with only a couple of randomly selected vowel tokens. This suggests that vocal tract length normalization could be involved in speech perception.
Bitcoin Transaction Forecasting With Deep Network Representation Learning

Bitcoin and its decentralized computing paradigm for digital currency trading are one of the most disruptive technology in the 21st century. This article presents a novel approach to developing a Bitcoin transaction forecast model, DLForecast, by leveraging deep neural networks for learning Bitcoin transaction network representations. DLForecast makes three original contributions. First, we explore three interesting properties between Bitcoin transaction accounts: topological connectivity pattern of Bitcoin accounts, transaction amount pattern, and transaction dynamics. Second, we construct a time-decaying reachability graph and a time-decaying transaction pattern graph, aiming at capturing different types of spatial-temporal Bitcoin transaction patterns. Third, we employ node embedding on both graphs and develop a Bitcoin transaction forecasting system between user accounts based on historical transactions with built-in time-decaying factor. To maintain an effective transaction forecasting performance, we leverage the multiplicative model update (MMU) ensemble to combine prediction models built on different transaction features extracted from each corresponding Bitcoin transaction graph. Evaluated on real-world Bitcoin transaction data, we show that our spatial-temporal forecasting model is efficient with fast runtime and effective with forecasting accuracy over 60 percent and improves the prediction performance by 50 percent when compared to forecasting model built on the static graph baseline.
Simultaneous Translation and Paraphrase for Language Education

We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE).Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce.This is motivated by the need to create and maintain large, high-quality sets of acceptable translations for exercises in a language-learning application, and synthesizes work spanning machine translation, MT evaluation, automatic paraphrasing, and language education technology.We developed a novel corpus with unique properties for five languages (Hungarian, Japanese, Korean, Portuguese, and Vietnamese), and report on the results of a shared task challenge which attracted 20 teams to solve the task.In our meta-analysis, we focus on three aspects of the resulting systems: external training corpus selection, model architecture and training decisions, and decoding and filtering strategies.We find that strong systems start with a large amount of generic training data, and then finetune with in-domain data, sampled according to our provided learner response frequencies.
Analyzing sentiment system to specify polarity by lexicon-based

Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
Generation of high dynamic range for enhancing the panorama environment

This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.
The role of organizational commitment in the relationship between human resource management practices and competitive advantage in Jordanian private universities

Human Resource Management (HRM) has the capacity of influencing the organizational behavior of the employees, thereby ensuring the achievement of the competitive advantage.This study proposes to assess the relationship between human resources (HR) practices and competitive advantage.It is also intended to test organizational commitment as a mediator, between HR practices and competitive advantage among the employees of the private universities in Jordan.The data were gathered from 232 individuals employed at ten private universities located in Jordan.PLS, SEM was performed on the data, using the SmartPLS 3 software.HRM practices result in a statistically significant variation in competitive advantage.Relationship of HRM practices and competitive advantage was partially mediated by organizational commitment.HRM practices resulted in a statistically significant variation in and organizational commitment.Our findings contribute to the existing body of literature of how organizational commitment can mediate the relationships among the HR practices adopted by organizations and competitive advantage.Jordanian private universities should improvise extra attention over HR practices that contribute positively toward the performance of employees.