Reviewerly/exHarmony huggingface dataset:
Stochastic Planning with Lifted Symbolic Trajectory Optimization

This paper investigates online stochastic planning for problems with large factored state and action spaces. One promising approach in recent work estimates the quality of applicable actions in the current state through aggregate simulation from the states they reach. This leads to significant speedup, compared to search over concrete states and actions, and suffices to guide decision making in cases where the performance of a random policy is informative of the quality of a state. The paper makes two significant improvements to this approach. The first, taking inspiration from lifted belief propagation, exploits the structure of the problem to derive a more compact computation graph for aggregate simulation. The second improvement replaces the random policy embedded in the computation graph with symbolic variables that are optimized simultaneously with the search for high quality actions. This expands the scope of the approach to problems that require deep search and where information is lost quickly with random steps. An empirical evaluation shows that these ideas significantly improve performance, leading to state of the art performance on hard planning problems.
Enhancing Cloud Storage Privacy (CSP) Based on Hybrid Cryptographic Techniques

Cloud computing has emerged as a new technology model for providing customized, high quality and reliable services for clients over the Internet. These clients can rent the services as long as they need rather than owning and maintaining large expensive IT infrastructure. One of the most used cloud services is the cloud storage as it provides storage on demand service. Nevertheless, due to the structure of cloud services which may not fully be managed by the cloud clients, the privacy and security of cloud storage become a significant challenge. Most of the previous works on the cloud security trying to solve the security challenges with data encryption techniques. However, these solutions do not protect data from being exposed or read by the server administrator or owner. In this paper, we have proposed a hybrid novel solution referred to as Cloud Storage Privacy (CSP) paradigm to enhance the privacy policies for cloud storage. This framework solution combines three of the superior cryptographic techniques: RSA, AES and Block-Chain. The solution not only secures the asset in the cloud but also protects the asset from unauthorized server access and improving user password protection. We have compared the proposed solution with the best based on our knowledge cloud storage services which are Google Drive and Dropbox in terms of performance and security. The result shows that the proposed solution outperforms existing services.
Rapid Performance Gain through Active Model Reuse

Model reuse aims at reducing the need of learning resources for a newly target task. In previous model reuse studies, the target task usually receives labeled data passively, which results in a slow performance improvement. However, learning models for target tasks are often required to achieve good enough performance rapidly for practical usage. In this paper, we propose the AcMR (Active Model Reuse) method for the rapid performance improvement problem. Firstly, we construct queries through pre-trained models to facilitate the active learner when labeled examples are insufficient in the target task. Secondly, we consider that pre-trained models are able to filter out not-very-necessary queries so that AcMR can save considerable queries compared with direct active learning. Theoretical analysis verifies that AcMR requires fewer queries than direct active learning. Experimental results validate the effectiveness of AcMR.
A Novel Feature Engineering Framework in Digital Advertising Platform

Digital advertising is growing massively all over the world, and, nowadays, is the best way to reach potential customers, where they spend the vast majority of their time on the Internet.While an advertisement is an announcement online about something such as a product or service, predicting the probability that a user do any action on the ads, is critical to many web applications.Due to over billions daily active users, and millions daily active advertisers, a typical model should provide predictions on billions events per day.So, the main challenge lies in the large design space to address issues of scale, where we need to rely on a subset of well-designed features.In this paper, we propose a novel feature engineering framework, specialized in feature selection using the efficient statistical approaches, which significantly outperform the state-of-the-art ones.To justify our claim, a large dataset of a running marketing campaign is used to evaluate the efficiency of the proposed approaches, where the results illustrate their benefits.
Governance and agricultural growth: Evidence from selected developing countries

Agriculture is a key sector for almost all developing countries. One of the factors influencing agricultural production improvement is government intervention and its important role in improving good governance indicators. This study examines the impact of governance on total agricultural output in developing nations. To address these issues, this paper estimates the panel data regression model. The data of Governance Indicators (GI) is provided by the World Bank. Findings/Originality: The main results suggest a reverse association between overall GI and agricultural growth. In addition, among the six individual GI, control of corruption has the highest impact. It implies that the governance has not addressed the problems in the agricultural sector. On the other hands, the development of agriculture sector is still mainly supported by the economic inputs. It is explained by the evidence that the inputs have positive and significant effect on the value of agricultural production.
Reinforcement Learning for Improved Low Resource Dialogue Generation

In this thesis, I focus on language independent methods of improving utterance understanding and response generation and attempt to tackle some of the issues surrounding current systems. The aim is to create a unified approach to dialogue generation inspired by developments in both goal oriented and open ended dialogue systems. The main contributions in this thesis are: 1) Introducing hybrid approaches to dialogue generation using retrieval and encoder-decoder architectures to produce fluent but precise utterances in dialogues, 2) Proposing supervised, semi-supervised and Reinforcement Learning methods for domain adaptation in goal oriented dialogue and 3) Introducing models that can adapt cross lingually.
A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation

Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy.
Customer Attitudes and Direct Sales Staffing Strategies for Direct Sales Agricultural Products

The use of fertilizer to nourish the soil will yield high quality and quantity. It will add value to farmers. This article aims to review the literature and conceptual framework related to customer attitudes and direct sales staffing strategies that influence the decision to buy direct sales agricultural products. The results of the study revealed that there were significant variables as the followings: 1) customer attitudes contained knowledge, understanding, feeling, and behaviour, 2) direct sales strategies using direct sales staff consisted of knowledgeable employees, the service staffs clean clothes, polite service, humble and friendly service, good human relations management, cheerful, fast, and continuous service. Moreover, they must understand the needs of customers, and 3) the decision to buy direct sales agricultural products of consumer fertilizer consisted of information perception, problem evaluation, data evaluation, purchase decision and feelings after the purchase. The results of this study will be used as a framework for quantitative data analysis in order to find guidelines for direct sales business which they can apply the tested model to use to determine the appropriate marketing strategy.
Utility-Preserving Privacy Mechanisms for Counting Queries

Differential privacy (DP) and local differential privacy (LPD) are frameworks to protect sensitive information in data collections. They are both based on obfuscation. In DP the noise is added to the result of queries on the dataset, whereas in LPD the noise is added directly on the individual records, before being collected. The main advantage of LPD with respect to DP is that it does not need to assume a trusted third party. The main disadvantage is that the trade-off between privacy and utility is usually worse than in DP, and typically to retrieve reasonably good statistics from the locally sanitized data it is necessary to have a huge collection of them. In this paper, we focus on the problem of estimating counting queries from collections of noisy answers, and we propose a variant of LDP based on the addition of geometric noise. Our main result is that the geometric noise has a better statistical utility than other LPD mechanisms from the literature.
Supervised Set-to-Set Hashing in Visual Recognition

Visual data, such as an image or a sequence of video frames, is often naturally represented as a point set. In this paper, we consider the fundamental problem of finding a nearest set from a collection of sets, to a query set. This problem has obvious applications in large-scale visual retrieval and recognition, and also in applied fields beyond computer vision. One challenge stands out in solving the problem---set representation and measure of similarity. Particularly, the query set and the sets in dataset collection can have varying cardinalities. The training collection is large enough such that linear scan is impractical. We propose a simple representation scheme that encodes both statistical and structural information of the sets. The derived representations are integrated in a kernel framework for flexible similarity measurement. For the query set process, we adopt a learning-to-hash pipeline that turns the kernel representations into hash bits based on simple learners, using multiple kernel learning. Experiments on two visual retrieval datasets show unambiguously that our set-to-set hashing framework outperforms prior methods that do not take the set-to-set search setting.
RefineText: Refining Multi-oriented Scene Text Detection with a Feature Refinement Module

Scene text detection is one of the most challenging tasks in many computer vision applications due to the large variety of scene text appearance and the complexity of scene context. In this paper, we propose an end-to-end trainable framework RefineText for multi-oriented scene text detection, which has a strong ability to detect different-scale texts and split them precisely. High-resolution semantic features are first generated by our designed Feature Refinement Module, which refines features progressively at multiple levels of abstraction. Then text regions are densely produced on the high-level semantic features and followed by Non-Maximum Suppression(NMS) to get final detection results. Experiments on benchmark datasets including ICDAR 2015, ICDAR 2013 and MSRA TD500 demonstrate that our proposed method has competitive performance and strong robustness.
Single Axis Solar Tracking System

This paper describes the analysis and implementation of solar tracking system to generate electricity from renewable energy.This is aim to recover the demand on electricity in the rural area.To get maximum energy, solar panel is tracked the path of sun using Arduino Uno, LDR sensors, stepper motor and solar panel.And the battery is used to store the energy from the solar panel through the charge controller and applied to the intended loads.Data analyzing is accomplished and compared to sunny day and cloudy on hour to hour.
A DBN-Based Ensemble Method for Resource Usage Prediction in Clouds

To accurately predict the dynamic changes on the usage of cloud computing resources so as to improve resource management efficiency, an ensemble method based on deep belief network (EMDBN) is proposed in this paper. Technically, it adopts the autoregressive model (AR) and the grayscale model (GM) as basic predictors. The results of the basic predictors are used together with historical data as training data for the DBN. Real data from the Google Cloud Data Center is used as experimental data. Back-propagation (BP) neural network, support vector regression (SVR), GM, AR and deep belief network (DBN) are chosen to carry out comparative experiments on cloud resource usage forecasting. Experimental results prove that EMDBN method can prominently enhance the precision in light of three current metrics for predict methods, i.e., mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE).
An Affordance-Based Online Review Analysis Framework

Abstract One of the main tasks of today's data-driven design is to learn customers' concerns from the feedback data posted on the internet, to drive smarter and more profitable decisions during product development. Feature-based opinion mining was first performed by the computer and design scientists to analyse online product reviews. In order to provide more sophisticated customer feedback analyses and to understand in a deeper way customer concerns about products, the authors propose an affordance-based online review analysis framework. This framework allows understanding how and in what condition customers use their products, how user preferences change over years and how customers use the product innovatively. An empirical case study using the proposed approach is conducted with the online reviews of Kindle e-readers downloaded from amazon.com. A set of innovation leads and redesign paths are provided for the design of next-generation e-reader. This study suggests that bridging data analytics with classical models and methods in design engineering can bring success for data-driven design.
A Similarity Measurement Method Based on Graph Kernel for Disconnected Graphs

Disconnected graphs are very common in the real world. However, most existing methods for graph similarity focus on connected graph. In this paper, we propose an effective approach for measuring the similarity of disconnected graphs. By embedding connected subgraphs with graph kernel, we obtain the feature vectors in low dimensional space. Then, we match the subgraphs and weigh the similarity of matched subgraphs. Finally, an intuitive example shows the feasibility of the method.
Correction to: Behavioral Economics

The original version of the book was revised: Author’s belated corrections have been incorporated.
Interactive HDR image-based rendering from unstructured LDR photographs

This paper proposes an interactive High Dynamic Range (HDR) image-based rendering system, dedicated to manually acquired photographs. It only relies on a point-based geometric proxy and the original photographs, calibrated using a standard structure-from-motion process. First, a depth map is estimated for each new rendered viewpoint, based on the point cloud. Second, pixel values are reconstructed from the original photographs, using a blending model that also handles occlusion. Our system can be used for producing HDR images from several series of unaligned photographs with different exposures. As shown in the results, it proves efficient with various types of objects, implemented in WebGL, making it practical for many purposes.
A Meta-Learning Approach for Custom Model Training

Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. when there are only a few classes and training examples available in the target task), meta-learning approaches that optimize for future task learning have outperformed the typical transfer approach of initializing model weights from a pretrained starting point. But as we experimentally show, metalearning algorithms that work well in the few-class setting do not generalize well in many-shot and many-class cases. In this paper, we propose a joint training approach that combines both transfer-learning and meta-learning. Benefiting from the advantages of each, our method obtains improved generalization performance on unseen target tasks in both few- and many-class and few- and many-shot scenarios.
Speech Companions: Evaluating the Effects of Musically Modulated Auditory Feedback on the Voice

Changing the way one hears one’s own voice, for instance by adding delay or shifting the pitch in real-time, can alter vocal qualities such as speed, pitch contour, or articulation. We created new types of auditory feedback called Speech Companions that generate live musical accompaniment to the spoken voice. Our system generates harmonized chorus effects layered on top of the speaker’s voice that change chord at each pseudo-beat detected in the spoken voice. The harmonization variations follow predeter-mined chord progressions. For the purpose of this study we generated two versions: one following a major chord progression and the other one following a minor chord progression. We conducted an evaluation of the effects of the feedback on speakers and we present initial ﬁndings assessing how different musical modulations might potentially affect the emotions and mental state of the speaker as well as semantic content of speech, and musical vocal parameters.
Pengaruh Fasilitas Belajar dan Motivasi Belajar Terhadap Prestasi Belajar Mahasiswa Jurusan Pendidikan Ekonomi 2017 Universitas Negeri Surabaya

This study aims to determine the effect of learning facilities and learning motivation on the learning achievement of 2017 Economics Education Department students at Surabaya State University. This research includes the type of associative research with a quantitative approach. Respondents in this study were 172 students from the Economics Education Department, consisting of four study programs, namely PAK, PTN, PE, and PAP. The analysis test used is multiple linear regression test. The results showed that learning facilities and learning motivation had a positive and significant effect simultaneously on student learning achievement based on the statistical F test with a significant value of 0.00, meaning the sig &lt;Î± (0.05) value, besides learning facilities had a positive and significant effect on achievement student learning partially with a significant value of 0.037 means the value of sig &lt; Î± (0.05). Learning motivation also has a positive and significant effect on student learning achievement partially with a significant value of 0.00, meaning the value of sig &lt;Î± (0.05) so that is rejected and is accepted. The coefficient of determination is based on the Adj R-Square table of 0.324.
The Development of Plant Encyclopedia Based on Datebase in Adiwiyata Senior High School

SMA Negeri 1 Cawas does not have a digital inventory of plants. Digital inventory can be stored for long time and updated. The study aims to develop an encyclopedia of-based plants database in adiwiyata schools. Research design with Research and Development (RnD). The steps include analysis of potential and problems; data collection; product design; design validation by media experts (lecturers and media experts BPMPK) and material experts (expert classification lecturers); revision 1; small-scale trials (for biology teachers, Wakakur and responses by class XI MIA students 2); revision 2; large-scale trials (all students of class XI MIA); revision 3; and the final product. Based on the results of the study there were 46 plant species. Encyclopedia design (Ensiya) includes templates, systems coding, and finalization program. Ensiya is used to facilitate the management of plant inventories. Revision 1, carried out on the program name, label, motivation, and location of the outbox. Revision 2 of the program name; label box size; motivation box and outbox. The results of a large-scale trial, Ensiya obtained a very feasible assessment by students and received positive responses. Revision 3, program name font; picture box; motivation, and the location of the outbox. Conclusion of this study, that the plant encyclopedia (Ensiya) in the database-based adiwiyata school was successfully developed using Microsoft Visual Basic Express. Ensiya digital documents, 165 MB file size, accessed offline, transferred between computers with data transfer, and can be changed by studying Microsoft Visual Basic Express programming, is very valid and very feasible.
Digital competences of teachers and associates at higher educational instiutions in the Republic of Croatia

One of the basic competences for lifelong learning is the development of digital competence, and especially at higher education institutions. There are long-term goals in economic and social progress in form of initiatives related to the ensuring the multiple information education. Due to the fact that there is not sufficient research aimed at assessing the initial state of information literacy within higher education institutions in Croatia, the goal of the present research is to determine the level of knowledge, more precisely the digital competence of teachers and educators in higher education institutions in the Republic of Croatia, the frequency of their usage of modern technologies in teaching, and to explain the necessity of implementing the multimedia and e-learning training programmes at different faculties. The research was conducted on a representative sample (N = 1800) of teachers and associates at higher education institutions in the Republic of Croatia working in different departments in Osijek, Split and Zagreb. An online questionnaire was used to examine the level of their digital competence, and the frequency and the method of using modern technologies in teaching. The results of the research show that teachers and associates working at technical faculties are more competent in conducting their research scientific work and use the modern technologies significantly more than teachers and associates working in the area of social sciences and humanities. The results show that more than 70% of teachers and associates are assessing themselves as being digitally competent in the use of computers and new technologies, and over 60% of them regularly use media in their teaching practice