Reviewerly/exHarmony huggingface dataset:
Sound engineering in the recording studio as creative practice

Sound engineering in the recording studio has historically been viewed as a technical rather than creative endeavor (Kealy 1979). Predominantly it is the artist and, more recently, the record producer who are regarded as creative individuals in the process (Thompson 2019). But from a sociological perspective, creativity is the act of bringing into being an idea or product that is original, valued and implemented (Csikszentmihalyi and Wolfe 2000, 81) and there is now growing evidence that creativity occurs through the convergence of multiple elements; an agent, a knowledge system (the domain) and a social organization that holds the domain knowledge (the field), through a dynamic system of interaction (Csikszentmihalyi 1988, 1997, 1999). Creativity can therefore include technical as well as artistic and musical contributions. Rather than focusing solely on the individual, Csikszentmihalyis creative system emphasizes a view of creativity that integrates all three elements of domain, field, and agent within a collaborative system. The following chapter illustrates the systems model of creativity in operation at various moments of making a record on both an individual and group scale, and the process of sound engineering is identified as a creative practice within the broader creative and collaborative systems of record production.
Supply arrangement of raw material and sugar stock to organize overstock risk in warehouse

Abstract Raw materials and production outcome play important role in supporting warehouse capacity. The performance of company's production process will not be bothered if the company can control raw materials inventory and production capacity so that this is important to control logistics in warehouse. The activities in company's warehouse are very important because they will affect the response level to goods collecting. Risk management is required to respond actions that affect problem in raw materials capacity and production stocks in warehouse. This study aims to determine the amount of raw material inventory and warehouse stock capacity in PG XYZ and to determine the schedule of sugarcane raw materials so that the raw materials intensity and stocks can be equally distributed during the milling season. This study obtained total inventory cost from inventory management analysis of Rp. 663.945.589,8 resulted in an efficiency of Rp 36.054.410,02 and there are 2 risks that are categorized into high risk category, namely the previous milled sugar is still available and refined sugar is circulating exceed capacity.
Curved Scene Text Detection Based on Mask R-CNN

Text detection in natural scenes has achieved good results in existing research methods. However, detecting the curved scene text is still a challenging task because of perspective distortion and variation of text scale. We proposed Mask-CSTD (Curved scene text detector based on Mask R-CNN), a detection model inspired by instance-aware semantic segmentation. Firstly, we utilized Mask instead of bounding box labels. Secondly, an integrated loss function was designed to detect curved text instances. The label loss satisfied the finer representation of curved text instances more efficiently. Finally, a Deformable NMS was proposed to improve heavily tilted, unnecessary overlap or severed text instances. Experimental result on curved benchmark dataset Total-Text achieves an F-measure of 82.41%, a highly-competitive performance, which exceeds the baseline by 2.46%. Moreover, results on MSRA-TD500 and ICDAR2015 verify the effectiveness of multi-oriented text detection.
Using Wearable Devices in Educational Assessment: Smartphone Exams

We are residing in a planet where technology is contemporary in our life routines. Today, smartphones are one of the vastest revolutions in individuals’ lifespans. Smartphones are becoming increasingly popular, both in formal and informal educational environments. This chapter discusses the benefits and obstacles in using smartphones as an assessment tools and it compares the achievement of exams delivered via smart phones to paper-based exams. The result of the study indicates that; there was a significant difference between three groups of English Paper Exams, however there was not any significant difference between these groups on English Language Mobile Exams.
A Shearlets-Based Method for Rain Removal from Single Images

This work focuses on the problem of rain removal from a single image. The directional multilevel system, Shearlets, is used to describe the intrinsic directional and structure sparse priors of rain streaks and the background layer. In this paper, a Shearlets-based convex rain removal model is proposed, which involves three sparse regularizers: including the sparse regularizer of rain streaks and two sparse regularizers of the Shearlets transform of background layer in the rain drops’ direction and the Shearlets transform of rain streaks in the perpendicular direction. The split Bregman algorithm is utilized to solve the proposed convex optimization model, which ensures the global optimal solution. Comparison tests with three state-of-the-art methods are implemented on synthetic and real rainy images, which suggests that the proposed method is efficient both in rain removal and details preservation of the background layer.
Don’t Forget Your Roots: Constant-Time Root Finding over $$\mathbb {F}_{2^m}$$ F 2 m

In the last few years, post-quantum cryptography has received much attention. NIST is running a competition to select some post-quantum schemes as standard. As a consequence, implementations of post-quantum schemes have become important and with them side-channel attacks. In this paper, we show a timing attack on a code-based scheme which was submitted to the NIST competition. This timing attack recovers secret information because of a timing variance in finding roots in a polynomial. We present four algorithms to find roots that are protected against remote timing exploitation.
Enterprise-Level Controlled Experiments at Scale: Challenges and Solutions

Online controlled experiments, known as A/B tests, have delivered tremendous value for businesses and customers. Traditionally, online controlled experiments running in search engines, websites, mobile apps, and other Business-to-Consumer (B2C) products use individual customers as randomization units. However, this may not be possible in Business-to-Business (B2B) products that are sold to, and used by, other enterprises. B2B products typically have constraints such as the need to provide identical experiences to all users within an enterprise. In other scenarios, such as features that need to be used by multiple users to be meaningful or the metrics of interest can be only obtained by showing the same experience under one enterprise, experimentation randomized by enterprise is needed. In this paper, based on experiences from Microsoft and Outreach, we introduce the concept of Enterprise Level Experiments (ELE) in software product development. We discuss the unique technical challenges in designing and analyzing ELEs and present solutions to overcome them. Furthermore, we present and discuss a real-world example of ELE run by Microsoft. Our aims in this paper are to provide practical guidance on running trustworthy experiments in B2B scenarios, and to stimulate further research in this new and important area of experimentation.
Geometric Verification Method of Best Score Increasing Subsequence for Object Instance Recognition

Weighted Longest Increasing Subsequence (WLIS) and its improvement, Best Increasing Subsequence (BIS) are two methods that has been proposed for pair verification in object instance recognition using local features. Tested on the Stanford Mobile Visual Dataset (SMVS), the BIS achieves better performance than WLIS on most categories, except for the “video frames” category. In this paper we propose several modifications to BIS which resulted in a better overall performance compared to the WLIS and the basic BIS approaches. On average, the proposed Best Score Increasing Subsequence (BSIS) performs 4.53% better than the BIS and 9.43% better than the WLIS.
RGB-D Depth Inpainting with Color Guide Inverse Distance Weight

This paper proposed a method to increase the precision of the depth inpainting method by using depth and color information from the RGB-D system in the same image frame. The procedure consists of 2 steps. The first part is color similarity checking, this part tries to select the same color in a window to collected neighbor depth information. The second step is weight calculation, for this step, The color weights of all selection points in a window are calculated and normalized. The predicted value of depth in a missing pixel is calculated by the relation of all color weight and their depth data. The experimental results showed that the proposed method improved the accuracy of depth inpainting.
Developing the physics magazine as an alternative of personal learning media

Abstract Recently, information technology literacy of the student in school is relatively high. Efforts to provide learning services that match the characteristics of the millennium generation will be one of the solutions to utilize this technology effectively in learning. Various open educational resources will significantly enhance personal learning activities; one of them is the physics magazine. This study is development research that aims to produce physics magazine on the subject of momentum, impulse, and collision. Learning features provided in this magazine include reading the concept, enrichment reading, crossword puzzles, multiple choice, essays, practicum, and so on. Various alternative activities are provided so that students can learn from anywhere they like. Physics experts and media experts validate this media. The result scores are 3.67 for the physics subject aspects and 4.18 for the media aspects out of 5. It shows a reasonable level of eligibility. Furthermore, this magazine was piloted on 26 students to see the level of interest in physics learning using this magazine. The result is a high level of interest (4.27 out of 5). It means that student can use this physics magazine to enhance the learning interest in physics.
A Constrained Self-adaptive Sparse Combination Representation Method for Abnormal Event Detection

Automated abnormal detection system meets the need of society for detecting and locating anomalies and alerting the operators. In this paper, we proposed a constrained self-adaptive sparse combination representation (CSCR). The spatio-temporal video volumes low-level features, which be stacked with multi-scale pyramid, can extract features effectively. The CSCR strategy is robust to learn dictionary and detect abnormal behaviors. Experiments on the published dataset and the comparison to other existing methods demonstrate the certain advantages of our method.
In uence of Stark shift and detuning on atomic entanglement induced by a thermal eld of one-mode cavity

Abstract We considered a quantum model consisting of two effective two-level atoms not resonantly interacting with a single-mode thermal cavity eld through two-photon transitions. We explored the entanglement dynamics between two atoms, and studied the effect of the Stark shift and detuning on the entanglement. The results showed that for a separable initial atomic state the Stark shift and detuning enhance the amount of atom-atom entanglement induced by the thermal eld. For entangled atomic state these reduce the atom-atom entanglement oscillations.
Optimization of GPU Memory Usage for Training Deep Neural Networks

Recently, Deep Neural Networks have been successfully utilized in many domains; especially in computer vision. Many famous convolutional neural networks, such as VGG, ResNet, Inception, and so forth, are used for image classification, object detection, and so forth. The architecture of these state-of-the-art neural networks has become deeper and complicated than ever. In this paper, we propose a method to solve the problem of large memory requirement in the process of training a model. The experimental result shows that the proposed algorithm is able to reduce the GPU memory significantly.
Improving Privacy in Graphs Through Node Addition

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users' identity. Since structure-based de-anonymization attacks can reveal users' identity's even when the graph is simply anonymized by employing naïve ID removal, recently, k- anonymity is proposed to secure users' privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides k- anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have k- anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.
The Algorithm for UAV Obstacle Avoidance and Route Planning Based on Reinforcement Learning

An algorithm for UAV collision avoidance based on the reinforcement learning is used for small fixed-wing unmanned aerial vehicles (UAVs). The proposed algorithm realized the obstacle avoidance of UAV in unknown environment and the result is close to the global optimal path. The simulation results show that the collision avoidance algorithm can adapt to various complex environments. Meanwhile, the UAV can quickly get close to the target while avoiding obstacles.
Visualization and Interaction Techniques in Virtual Reality for Guided Tours

This paper examines different techniques for visualization and interaction in virtual tours. It provides a case study and best-practice application that guides visitors through the Konzerthaus Berlin using virtual reality (VR) and conveys historical and architectural insights of the building. One of the key focus points of the research was the development of a natural 3D environment that would allow a visualization of historical photographs and video snippets. A preliminary user study was conducted to find the right balance between a static narrated part of the tour and an interactive part that offers free exploration by the user. Finally, we propose a revised user interaction in VR controlled by the user's head orientation.
Power Forecasting of Photovoltaic Generation Based on Multiple Linear Regression Method with Real-time Correction Term

This paper proposes a photovoltaic power generation forecasting model which improves Multiple Linear Regression method (MLRM) with real-time correction term (RCT). Firstly, a traditional day-ahead, hourly power generation prediction model is developed by MLRM based on qualitative variables (hour, month, weather type), quantitative variable (solar radiation intensity) and physical characteristics of interactions between the variables. Secondly, an improved model named MLRM+RCT is presented which adds a correction term based on shorter real-time measured power data to MLRM to reduce the hourly prediction errors of MLRM. MLRM+RCT is tested based on power generation data released by IEEE Energy Forecasting Group in 2014. The results show that the performance of MLRM+RCT is better than that of MLRM and a benchmark method called exponential smoothing method.
Information technology in the educational program design

Abstract The system analysis of the educational process of the university considers the possibilities of automation of its certain components while design and functioning. The educational process in Russian universities is implemented on the competence-based approach. Competence is a complex of knowledge required to solve a certain type of tasks within the framework of professional activity. For each type of professional activity tasks one can specify the actions of the specialists required to solve these problems. These actions are the basis for the formation of the required competencies. The task of training within the framework of the competence approach is formulated as follows: the transition from the input competencies A to the output competencies B . To automate the design of the educational program, it is required to use the automated information systems for decision-making support.
A State of Art Approaches on Handwriting Recognition Models

Presently, handwriting recognition (HWR) become an open research issue to recognize the handwritten words out of a scanned document which is unconstrained. In spite of the extensive implication of computer in office, handwriting still stays as a significant mode of annotating and apprehending textual data. In recent days, there is an exponential growth in the number of researches carried out in this field because of its benefits. On the past decade, great efforts are paid to the online hand-drawn content interpretation from handwritten text. Keeping this in mind, in this paper, we made a review of existing HWR model in different aspects have been reviewed.
Motivasi belajar Pengaruh Motivasi Berprestasi terhadap Prestasi Belajar Siswa pada Mata Pelajaran Ekonomi

This study aims to analyze the effect of achievement motivation on student achievement in economic subjects at SMK Imannuel Smart Rantepao, Toraja Utara Regency. In this study, the population was all 147 students of Imannuel Smart Rantepao, Toraja Utara Regency. Sampling in this study was taken by simple random sampling from each class that is 50% of the total population of 74 people. Analysis of the data in this study is multiple linear regression. The results of this study indicate that achievement motivation has a positive and significant effect of 0.217 on student achievement in economic subjects at SMK Imannuel Smart Rantepao, Toraja Utara Regency
LSPM: Joint Deep Modeling of Long-Term Preference and Short-Term Preference for Recommendation

In the era of information, recommender systems are playing an indispensable role in our lives. A lot of deep learning based recommender systems have been created and proven to be good progress. However, users' decisions are determined by both long-term and short-term preferences, and most of the existing efforts study these two requirements separately. In this paper, we seek to build a bridge between the long-term and short-term preferences. We propose a Long & Short-term Preference Model (LSPM), which incorporates LSTM and self-attention mechanism to learn the short-term preference and jointly model the long-term preference by a neural latent factor model. We conduct experiments to demonstrate the effectiveness of LSPM on three public datasets. Compared with the state-of-the-art methods, LSPM got a significant improvement in HR@10 and NDCG@10, which relatively increased by $$3.875\%$$ and $$6.363\%$$. We publish our code at https://github.com/chenjie04/LSPM/ .
Budget Cost Reduction for Label Collection with Confusability Based Exploration

Subjective judgment is an important manner of collecting labels which are used for training and evaluating models. Researchers always use a large enough and fixed number of workers to label an instance and then aggregate the labels of an instance from multiple workers into a single label. However, some easy instances only need a small number of workers and some difficult instances need more workers to reach stable aggregated labels. Using a fixed number of workers cannot efficiently use the limited budget. We thus propose an approach for reducing the cost of label collection by assigning a dynamic number of workers to each instance. We propose an Exploration-Focused Upper-Confidence Bound (EFUCB) approach which tends to explore the stable aggregated labels for all instances in the entire dataset. It iteratively selects an instance to ask the workers for one more label. To select the instances for labeling, in contrast to using the collected labels as the reward directly, it utilizes the disagreement of workers to measure the reward of collecting labels for an instance. The experiments based on real datasets verify that our approach can reduce the same budget cost with fewer influences on the aggregated labels so that the utility of the collected labels is preserved.
Causal Path Planning Graph Based on Semantic Pre-link Computation for Web Service Composition

The web has impacted development across different spheres of life by facilitating connection and communication between people and machines, with organizational productivity enhancement. Beyond connection and communication, access to functionalities via the same web for solving business tasks has increased its popularity. The idea of functionalities deployment on the web termed Service Oriented Computing (SOC) has been a major research area for some time. A key area of research focus in (SOC) is service composition. Service Composition deals with aggregating available services to address complex business processes or produce better functionalities. Due to explosion in the size of published web services, a need to improve the performance of web service composition arises. One of the key research issues in Service Composition is providing an efficient web discovery approach that contributes to an improved web service composition. This work proposes an efficient web service composition framework based on causal path pre-computation.
Exploring Artificial Neural Networks: A Data Complexity Perspective

Artificial Neural Networks perform a series of transformations to the input data, combining the outputs of multiple layers of neurons into more complex decision boundaries for separating the classes. This work presents a study on the processing performed at each layer of a Multilayer Perceptron neural network based on a data complexity perspective. The idea is to verify how the complexity of the classification problem evolves layer by layer. We show empirically that the separability of the classes is increased by each layer, as measured by feature overlapping and class separability measures, contributing to make the classification problem simpler for posterior layers.
Deep Grid Fusion of Feature-Level Sensor Data with Convolutional Neural Networks

This paper investigates neural network architectures that fuse feature-level data of radar and vision sensors in order to improve automotive environment perception for advanced driver assistance systems. Fusion is performed with occupancy grids, which incorporate sensor-specific information mapped from their individual detection lists. The fusion step is evaluated on three types of neural networks: (1) fully convolutional, (2) auto-encoder and (3) auto-encoder with skipped connections. These networks are trained to fuse radar and camera occupancy grids with the ground truth obtained from lidar scans. A detailed analysis of network architectures and parameters is performed. Results are compared to classical Bayesian occupancy fusion on typical evaluation metrics for pixel-wise classification tasks, like intersection over union and pixel accuracy. This paper shows that it is possible to perform grid fusion of feature-level sensor data with the proposed system architecture. Especially the auto-encoder architectures show significant improvements in evaluation metrics compared to classical Bayesian fusion method.