Reviewerly/exHarmony huggingface dataset:
Velody 2—Resilient High-Capacity MIDI Steganography for Organ and Harpsichord Music

A new method for musical steganography for the MIDI format is presented. The MIDI standard is a user-friendly music technology protocol that is frequently deployed by composers of different levels of ambition. There is to the author’s knowledge no fully implemented and rigorously specified, publicly available method for MIDI steganography. The goal of this study, however, is to investigate how a novel MIDI steganography algorithm can be implemented by manipulation of the velocity attribute subject to restrictions of capacity and security. Many of today’s MIDI steganography methods—less rigorously described in the literature—fail to be resilient to steganalysis. Traces (such as artefacts in the MIDI code which would not occur by the mere generation of MIDI music: MIDI file size inflation, radical changes in mean absolute error or peak signal-to-noise ratio of certain kinds of MIDI events or even audible effects in the stego MIDI file) that could catch the eye of a scrutinizing steganalyst are side-effects of many current methods described in the literature. This steganalysis resilience is an imperative property of the steganography method. However, by restricting the carrier MIDI files to classical organ and harpsichord pieces, the problem of velocities following the mood of the music can be avoided. The proposed method, called Velody 2, is found to be on par with or better than the cutting edge alternative methods regarding capacity and inflation while still possessing a better resilience against steganalysis. An audibility test was conducted to check that there are no signs of audible traces in the stego MIDI files.
Decomposed Collaborative Filtering: Modeling Explicit and Implicit Factors For Recommender Systems

Representation learning is the keystone for collaborative filtering. The learned representations should reflect both explicit factors that are revealed by extrinsic attributes such as movies' genres, books' authors, and implicit factors that are implicated in the collaborative signal. Existing methods fail to decompose these two types of factors, making it difficult to infer the deep motivations behind user behaviors, and thus suffer from sub-optimal solutions. In this paper, we propose Decomposed Collaborative Filtering (DCF) to address the above problems. For the explicit representation learning, we devise a user-specific relation aggregator to aggregate the most important attributes. For the implicit part, we propose Decomposed Graph Convolutional Network (DGCN), which decomposes users and items into multiple factor-level representations, then utilizes factor-level attention and attentive relation aggregation to model implicit factors behind collaborative signals in fine-grained level. Moreover, to reflect more diverse implicit factors, we augment the model with disagreement regularization. We conduct experiments on three public accessible datasets and the results demonstrate the significant improvement of our method over several state-of-the-art baselines. Further studies verify the efficacy and interpretability benefits bought from the fine-grained implicit relation modeling. Our Code is available on https://github.com/cmaxhao/DCF.
A Pilot Experience with Software Programming Environments as a Service for Teaching Activities

Software programming is one of the key abilities for the development of Computational Thinking (CT) skills in Science, Technology, Engineering and Mathematics (STEM). However, specific software tools to emulate realistic scenarios are required for effective teaching. Unfortunately, these tools have some limitations in educational environments due to the need of an adequate configuration and orchestration, which usually assumes an unaffordable work overload for teachers and is inaccessible for students outside the laboratories. To mitigate the aforementioned limitations, we rely on cloud solutions that automate the process of orchestration and configuration of software tools on top of cloud computing infrastructures. This way, the paper presents ACTaaS as a cloud-based educational resource that deploys and orchestrates a whole realistic software programming environment. ACTaaS provides a simple, fast and automatic way to set up a professional integrated environment without involving an overload to the teacher, and it provides an ubiquitous access to the environment. The solution has been tested in a pilot group of 28 students. Currently, there is no tool like ACTaaS that allows such a high grade of automation for the deployment of software production environments focused on educational activities supporting a wide range of cloud providers. Preliminary results through a pilot group predict its effectiveness due to the efficiency to set up a class environment in minutes without overloading the teachers, and providing ubiquitous access to students. In addition, the first student opinions about the experience were greatly positive.
Lower Density Selection Schemes via Small Universal Hitting Sets with Short Remaining Path Length

Universal hitting sets are sets of words that are unavoidable: every long enough sequence is hit by the set (i.e., it contains a word from the set). There is a tight relationship between universal hitting sets and minimizers schemes, where minimizers schemes with low density (i.e., efficient schemes) correspond to universal hitting sets of small size. Local schemes are a generalization of minimizers schemes which can be used as replacement for minimizers scheme with the possibility of being much more efficient. We establish the link between efficient local schemes and the minimum length of a string that must be hit by a universal hitting set. We give bounds for the remaining path length of the Mykkeltveit universal hitting set. Additionally, we create a local scheme with the lowest known density that is only a log factor away from the theoretical lower bound.
Autoencoding Improves Pre-trained Word Embeddings

Prior work investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given set of pre-trained word embeddings.However, theoretically this post-processing step is equivalent to applying a linear autoencoder to minimise the squared 2 reconstruction error.This result contradicts prior work (Mu and Viswanath, 2018) that proposed to remove the top principal components from pre-trained embeddings.We experimentally verify our theoretical claims and show that retaining the top principal components is indeed useful for improving pre-trained word embeddings, without requiring access to additional linguistic resources or labeled data.
School Staffing: Teachers’ Perceptions

Teachers play a key role in achieving the national policy goals in secondary education, in particular in improving students’ outcomes. The ongoing changes of the recent years inevitably affect the functions and working conditions of school teachers. Surveys of teachers’ perceptions of self and of the current situation and development prospects in secondary education should become a required element in the design of national education policies.This study was based on the results of the Monitoring of Secondary Education conducted by the Center for Lifelong Learning Economics (Institute of Applied Economic Research, Russian Presidential Academy of National Economy and Public Administration) in Novgorod Oblast, Nizhny Novgorod Oblast and Tula Oblast in 2020. The survey was administered in early March, prior to the emergency transition to remote online education due to the SARS-CoV‑2 pandemic. For this reason, the article reflects little to no transformations in teachers’ perceptions of their jobs and labor conditions caused by the rapid transition to a different format of teaching. At the same time, the monitoring results can serve as a baseline for analyzing the post-COVID developments in secondary education.Despite the government’s measures to refresh the teaching staff in schools, nothing has changed significantly in this regard so far. Teachers often resist massive refreshment initiatives, arguing that such changes would degrade the overall level of teaching competencies within the institution. Young teachers, in their turn, are increasingly more likely to be mismatched to their jobs, looking for additional sources of income within or outside the system, and ready to withdraw anytime. Attraction of youth to school teaching careers should thus be accompanied by managerial solutions for improving school effectiveness in the context of staff refreshment.
A Survey on Convolution Neural Networks

Major tools to implement any Artificial Intelligence and Machine Learning systems are Symbolic AI and Artificial Neural Network (ANN) AI. ANN has made a dramatic improvement in the versatile area of Machine Learning (ML). ANN is a gathering of vast number of weighted interconnected artificial neurons, initially invented with the inspiration of biological neurons. These models are much better than previous models implemented with Symbolic AI so far as their performance is concerned. One revolutionary change in ANN is Convolution Neural Network (CNN). These structures are mainly suitable for complex pattern recognition tasks within images. Here we would discuss basics of ANN as a tool for complex pattern recognition and image processing task. Also some applications of the CNN tool we will present OCR based text translation and biometric based uni modal and multimodal person identification systems.
Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.
ROS-based SLAM and Navigation for a Gazebo-Simulated Autonomous Quadrotor

Effective robot navigation is crucial for the proper implementation of a mobile robot. To navigate a robot in an indoor or outdoor environment, a map of the surrounding obstacles is required. In order to acquire this map, the robot uses sensors to collect information about its surroundings and its own location. This paper presents a robotic operating system based on autonomous simultaneous localization and mapping (SLAM), and robot navigation implementation of a Parrot AR. Drone 2.0 quadrotor, which is equipped with a laser scanner and inertial measurement unit. For autonomous mapping, SLAM along with frontier type exploration has been used to acquire a full 2D map of the environment in Gazebo. The A* search algorithm is utilized for the global planner, while the dynamic window approach has been implemented for the local planner. The Gazebo-simulated results validate the competent performance of the underlying structure of both the proposed SLAM as well as the provided navigation algorithm.
The Effects of the Content Elements of Online Banner Ads on Visual Attention: Evidence from An-Eye-Tracking Study

The aim of this paper is to examine the influence of the content elements of online banner ads on customers’ visual attention, and to evaluate the impacts of gender, discount rate and brand familiarity on this issue. An eye-tracking study with 34 participants (18 male and 16 female) was conducted, in which the participants were presented with eight types of online banner ads comprising three content elements—namely brand, discount rate and image—while their eye movements were recorded. The results showed that the image was the most attractive area among the three main content elements. Furthermore, the middle areas of the banners were noticed first, and areas located on the left side were mostly noticed earlier than those on the right side. The results also indicated that the discount areas of banners with higher discount rates were more attractive and eye-catching compared to those of banners with lower discount rates. In addition to these, the participants who were familiar with the brand mostly concentrated on the discount area, while those who were unfamiliar with the brand mostly paid attention to the image area. The findings from this study will assist marketers in creating more effective and efficient online banner ads that appeal to customers, ultimately fostering positive attitudes towards the advertisement.
On the geometry of generalization and memorization in deep neural networks

Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance. To examine the structure of when and where memorization occurs in a deep network, we use a recently developed replica-based mean field theoretic geometric analysis method. We find that all layers preferentially learn from examples which share features, and link this behavior to generalization performance. Memorization predominately occurs in the deeper layers, due to decreasing object manifolds’ radius and dimension, whereas early layers are minimally affected. This predicts that generalization can be restored by reverting the final few layer weights to earlier epochs before significant memorization occurred, which is confirmed by the experiments. Additionally, by studying generalization under different model sizes, we reveal the connection between the double descent phenomenon and the underlying model geometry. Finally, analytical analysis shows that networks avoid memorization early in training because close to initialization, the gradient contribution from permuted examples are small. These findings provide quantitative evidence for the structure of memorization across layers of a deep neural network, the drivers for such structure, and its connection to manifold geometric properties.
Pengembangan Media Boneka Jari Terhadap Keterampilan Berbicara Siswa Kelas IV SDN Saga VI Kabupaten Tangerang

This research and development aims to determine the steps to produce finger puppet media on the speaking skills of fourth grade students of SD Negeri Tangerang Regency and to determine the quality of finger puppet media based on validity and quality aspects in terms of the assessment of media expert validators, material experts, practical experts. and student response questionnaires. The development model used in this study uses the ADDIE model which consists of 5 stages, namely analysis, design, development, implementation, and evaluation. The results of this research and development show the level of validity of media experts and material experts with very good criteria and an average score of 4,3.The quality level of the expert practitioners' assessment and student response questionnaires with very good criteria and an average score of 4,9
Motion Planning for Mobile Robot with Modified BIT* and MPC

In this paper, a mobile robot motion planning method with modified BIT* (batch informed trees) and MPC (Model Predictive Control) is presented. The conventional BIT* was modified here by integrating a stretch method that improves the path points connections, to get a collision-free path more quickly. After getting a reference path, the MPC method is employed to determine the motion at each moment with a given objective function. In the objective function, a repulsive function based on the direction and distance of the obstacles is introduced to avoid the robot being too close to the obstacle, so the safety can be ensured. Simulation results show the good navigation performance of the whole framework in different scenarios.
Teaching style, learning motivation, and learning achievement: Do they have significant and positive relationships?

This study aims at determining whether there is a positive and significant relationship between three variables (teacher teaching style, learning motivation and learning achievement). The subjects in this research were 5th elementary school students in Saptosari sub-district, Gunung Kidul district, with a total of 141 students. This research is correlational quantitative research with data collection using a questionnaire in the form of a research questionnaire containing a scale. The hypothesis in this correlational study used partial correlation analysis and simultaneous regression analysis. The analysis used is data description analysis and multiple linear analysis. The results showed that there was a significant and positive relationship between learning achievement and teaching style got a value of 0.546 with a relative contribution of 23%. Meanwhile, learning motivation and learning achievement got a value of 0.462 with an effective contribution of 9% and the relation between teaching style and learning motivation on learning achievement show an r square value of 0.324 with a total contribution or effective contribution of 32%. Based on the results, it showed that teaching style and learning motivation have a significant and positive relationship with learning achievement.
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms

Estimating conditional mutual information (CMI) is an essential yet challenging step in many machine learning and data mining tasks.Estimating CMI from data that contains both discrete and continuous variables, or even discrete-continuous mixture variables, is a particularly hard problem.In this paper, we show that CMI for such mixture variables, defined based on the Radon-Nikodym derivate, can be written as a sum of entropies, just like CMI for purely discrete or continuous data.Further, we show that CMI can be consistently estimated for discrete-continuous mixture variables by learning an adaptive histogram model.In practice, we estimate such a model by iteratively discretizing the continuous data points in the mixture variables.To evaluate the performance of our estimator, we benchmark it against state-of-the-art CMI estimators as well as evaluate it in a causal discovery setting.
Fragments and structural identity on a direct interpretation approach

This paper examines the relationship between merger and sprouting fragments, which are typically taken to involve clausal ellipsis. We argue that structural identity constraints on fragments and their correlates should, where appropriate, make reference to the argument structure of lexical heads in the antecedent clauses. Our proposal is spelled out as part of a direct interpretation approach to clausal ellipsis, but, in addition, it incorporates processing-based preferences as a means to motivate the contrast between merger and sprouting fragments. We propose specifically that phrases which are available to serve as correlates for fragments are maximal categories derived from the argument structure of lexical heads in the antecedents. This proposal successfully predicts form-matching effects that surface under clausal ellipsis, as well as well-known limits on clausal ellipsis regarding the morphosyntactic form of fragments. We take advantage of the fact that fragments are not embedded in unpronounced structures, which allows us to articulate a proposal that avoids the difficulty of having to simultaneously relate a fragment to the structure of the antecedent and to its own unpronounced structure, a difficulty that current PF-deletion accounts face.
Network Security and Its Implications on Program Management

The Internet and related technologies have enabled companies to automate almost all of their operations resulting in enhanced efficiencies and cost-effectiveness. The technologies, however, have also introduced numerous security risks. Through security risks such as Electronic Hacking (EH), individuals and companies have lost a lot of valuable data and money. In this regard, there is a need to understand the extent of the threat of EH. A comprehensive thematic review and analysis of EH with a focus on developments, evolution, challenges, prognosis, and prevalence in select institutions was thus conducted. The research involved reviewing the literature on cybersecurity and its effect on organizations' operations. The result shows that cases of security breaches and associated costs continue to increase. Over five years, the healthcare and medical institutions were the most vulnerable. They were closely followed by corporations. The implications are that as institutions become more automated, their respective degrees of cybercrime vulnerability increase. The consequences of security breaches are normally dire for companies, as well as individuals. Millions, or possibly billions, of dollars worth of data, have been lost as a result of security breaches. This trend is expected to continue in the future, as computers and Internet technologies continue to advance. Through cybercrimes, numerous companies' operations have been sabotaged, and personal information from social media and email stolen. Long term, effective and sustainable strategies are therefore required. The paper is significant because it identifies the information security risks various organizations are exposed to and strategies that organizations can use to mitigate the risks.