Reviewerly/exHarmony huggingface dataset:
Comprehensive experimental characterization of violin vibration and radiation dynamics

Measuring only violin radiation cannot remove ambiguities in resonance peak identification arising from various minor substructure couplings, or whether the radiation arose from the f-holes or surfaces. Measuring only vibrations gives no clue as to how well each mode radiates or even how the vibrational energy was dissipated. To put these into a structural acoustics milieu, comprehensive measurements of all the violin’s energy loss paths were essential. To accomplish this a wide range of calibrated transducers (accelerometers, multiple microphones, near- and far-field microphone arrays, scanning laser vibrometers) were used in a wide range of experiments: cavity mode analysis using interior microphones; interior gas-exchange; EMA on a violin with and without a soundpost; waterfill experiments on a rigid violin-shaped cavity; automated zero-mass-loading, surface-normal (and 3-D) scanning laser EMA on violins supported in a near-zero-damping support fixture; automated far-field radiation measurements over a sphere in an anechoic chamber; bridge tuning effects; near-field acoustical holography over the f-holes. Each technique had limitations, some severe. Some experiments were “table top”; some required expensive associated facilities like an anechoic chamber. Costs of transducers and power-amplifier-control electronics, computer-based acquisition hardware/software varied dramatically; most operational problems occurred in the computer-based data acquisition/analysis systems.
Assignment Problem Based Deep Embedding

How to measure the similarity of samples is a fundamental problem in many computer vision tasks such as retrieval and clustering. Due to the rapid development of deep neural networks, deep metric learning has been widely studied. Some studies focus on the hard sample mining strategy for triplet loss. We observe that hard mining strategies are also vital for contrastive loss. But the hardest mining strategy for contrastive loss is sensitive to outliers. In this paper, based on combinatorial information of sample pairs, we propose a novel linear assignment problem based hard sample mining strategy for contrastive loss to learn feature embeddings. Specifically, our method can assign 0/1 weight to sample pairs for the hard sample selection by maximizing a linear assignment loss and ensure that each sample is only included by one pair for the optimization. Our method can obtain the state-of-the-art performance on the CUB-200-2011, Cars196, and In-shop datasets with the GoogLeNet network.
Utilizing Weak Supervision to Infer Complex Objects and Situations in Autonomous Driving Data.

While the detection and classification of simple objects encountered during autonomous driving sessions has been widely researched, the detection of complex objects and situations based on the combinations of objects in a scene remains relatively overlooked. This is especially difficult due to the cost of gathering labels for each complex scenario of interest before training a specialized model. To address this bottleneck of training data, we explore the applicability of weak supervision, or relying on higher level, noisier forms of supervision to label training data. Specifically, we use data programming, a paradigm that can learn the accuracy and dependency structure of these sources without using any ground truth labels and assign training labels accordingly. We focus on an example task of cyclist detection by comparing weak supervision, which relies on a set of user-defined rules over the outputs of detectors that identify people and bikes separately, to CyDet [1], which detects the cyclist as a complete object. We find that the weak supervision method can achieve a performance of 96.8 F1 points, 4.6 F1 higher than CyDet, without relying on any ground truth labels on the newly released Specialized Cyclist Dataset. We then discuss how heuristics can detect complex objects such as cyclists and by extension, situations, based on the output of existing object detection algorithms.
Electronic speckle pattern interferometry: applications to the structural acoustics of percussion instruments

Electronic speckle pattern interferometry (ESPI) provides real-time images of operating deflection shapes of acoustically driven structures. This technique is particularly useful in the study of musical percussion instruments containing bars, plates, and membranes. Musically relevant examples involving drumheads and vibraphone bars are described. In addition, a dual-ESPI system that produces simultaneous measurements of two surfaces of an object (e.g., the two heads of a drum) is illustrated. Imaging through water is also shown, as applied to the musical technique in which an orchestral crotale is submerged in water to varying depths. Finally, the interferometer is used to image the confined modes of the “musical saw” when bent into its characteristic “S”-shape. Practical issues of object mounting and image production are discussed.
Effects of Story Grammar Strategy on Korean EFL College Students’ Reading Comprehension Competence

This study examined the effectiveness of Story Grammar instruction to improve EFL college students’ reading competence. 64 participants were randomly selected and then assigned to the experimental (n=32) and control (n=32) groups. While the experimental group was taught using Story Grammar for 12 weeks (2 sessions per week), the control group was taught via regular reading classes. After training, the experimental group students showed a better improvement in reading comprehension test than did the control group. However the results indicated though there was no significant gain at the higher level, a statistically significant difference was noted in the lower level between the experimental and control groups.
A Study On Characteristic curriculum of publishing educational institutions

이 연구는 출판교육기관의 교육과정을 분석했다. 연구 방법으로 내용분석을 선택하여 전문대학교 교육과정 편성표, 특수대학원 홈페이지, 직업교육훈련기관 홈페이지의 교과목을 분석했다. 분석 결과, 전문대학교출판교육과정은 취업을 위한 출판 직무와 관련이 있다. 특수대학원 출판교육과정은 미디어 융합 추세와 미디어 이론에 대한 교과목이 다수이며, 미디어 이론의 일부로 출판전공교육을 실시하고 있다. 출판교육기관의 교육과정 특성은 각 교육기관의 교육 목표에 따라 달라진다. 전문직 업인 양성이 목표인 전문대학교는 출판 직무, 재직자 재교육이 목표인 특수대학원은 미디어 이론, 취업이 목표인 직업교육훈련기관은 실무위주의 단기 교육과정이다. 좋은 출판교육은 학문을 위한 교육과 직무교육이 균형을 이루는 것이다.This Study is written to survey curriculum about publishing educational institution. publishing education is carried out in 2-3 vocational Colleges, 6 graduated courses, 2 vocational training institutions. Publishing education of 2-3 vocational colleges and vocational training institution is focused on publishing education to get job. On the other hands. 6 publishing education of graduated courses subjects are composed of theory education on mass communication and digital media. The difference of publishing education come from educational goal among the three educational Institutions. It is very important to balance publishing practise subjects and theory subjects in curriculum of publishing educational institutions.
Generic sentences: Representativeness or Causality?

Many generic sentences express stable inductive generalizations. Stable inductive generalizations are typically true for a causal reason. In this paper we investigate to what extent this is also the case for the generalizations expressed by generic sentences. More in particular, we discuss the possibility that many generic sentences of the form ‘ks have feature e’ are true because (members of) kind k have the causal power to ‘produce’ feature e. We will argue that such an analysis is quite close to a probabilistic based analysis of generic sentences according to which ‘relatively many’ ks have feature e, and that, in fact, this latter type of analysis can be ‘grounded’ in terms of causal powers. We will argue, moreover, that the causal power analysis is sometimes preferred to a correlation-based analysis, because it takes the causal structure that gives rise to the probabilistic data into account. Unfortunately, there are problems for the causal power analysis too, and we will discuss them as well.
A Survey on Omnipresent HTTPS and Its Impact on Network Security Monitoring

HTTPS is gaining widespread popularity for performing secure transactions. Most popular sites have made default choice as HTTPS. Therefore, this paper makes a survey through various study done in the area and it has comprehensively explored the various tools, technologies, and mechanisms to deal with secured network in a robust way. We make a complete analysis and evaluation of HTTPS protocol–is it ensuring security or are we entering into a vicious cycle of finding weaknesses and trying to fill the gaps in Network security Monitoring. The gaps like Man In The Middle, active and passive attacks on device, compromising response time or accessibility at the cost of security are among the most important ones that has been explored and proposed solutions for various research studies. Data collected from couple of up to date research works and their conclusion has been discussed to provide a brief overview so as to provide the reader with global understanding of the research progress in this area.
Human behaviour recognition algorithm based on improved DMM and Fisher coding

Human behaviour recognition has become a key technology in intelligent sensing, location and tracking tasks. In view of the different speed of action execution and DMM loss of time dimension information, this paper proposes a human action recognition method based on improved DMM and Fisher coding. First, in consideration of the different action speeds of long and short video, this paper adopts two different video segmentation strategies. Second, in order to make video-based DMM contain more time dimension information, this paper proposes an improved DMM; then, in order to better express the texture information of the image, this paper improves the extraction of LBP features by DMM. Finally, due to the different feature lengths and high dimensions obtained by the long and short video, this paper adopts the Fisher vector for feature encoding and combines SVM to complete the action recognition. In the public action recognition database MSRAction3D and gesture recognition database MSRGesture3D, the accuracy rate of the algorithm is 96.25% and 96.00%, respectively, and it has higher recognition rate than many existing algorithms.
Decolonizing African music with visualizations and sonifications using beat-class theory

In a recent paper, we introduced mathematical music theory as a suitable new approach to analyze the music of Nigeria (Igbo) (Calilhanna et al., 2019). This paper revisits those materials and extends the ideas to further discuss why Nigerian music can be studied more suitably using mathematical music theory. We address a key issue facing music curriculum development: the perpetuation of traditional Western music theory to analyze and understand all music around the world. Our paper aims to demonstrate how Western music theory is inadequate for analyzing Nigerian music and we present some ways Western music theory has evolved. We explain how new music theory combines understandings of both music and mathematics and with a focus on musical meter we present a student-centred approach to learning. We achieve this through visualizations and sonifications of beat-class theory, using ski-hill graphs and circular cyclic graphs. This approach enables the listener to accurately articulate their subjective embodied psychoacoustic experience of music, meter and mathematics and incorporates research in neuroscience, music education, music psychology and music acoustics, which consistently indicates the importance of music for the well-being of the individual, school, workplace, and community.
Chaotic Encryption based secure Transmission

Secure image communication occupies a noticeable position in the ongoing multimedia communication technologies. The open channel concern has triggered the need for developing a stringent image cipher schemes which has led to the proposals of Image Encryption with various confusion and diffusion approaches. This manuscript shows a method for encrypting agriculture information by utilizing the chaotic maps: ‘Logistic-Sine System’ (LSS) and ‘Logistic-Tent System’. Various performance metrices are analyzed to check the robustness of the proposed algorithm.
Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Interactive Driving Behavior Prediction.

Since prediction plays a significant role in enhancing the performance of decision making and planning procedures, the requirement of advanced methods of prediction becomes urgent. Although many literatures propose methods to make prediction on a single agent, there is still a challenging and open problem on how to make prediction for multi-agent systems. In this work, by leveraging the power of statistics and information theory, we propose a novel deep latent variable model based on Wasserstein auto-encoder, which is able to learn a complex probabilistic distribution. Models such as neural networks cannot guarantee the satisfaction of dynamic system constraints directly. Therefore, we also propose a novel generative model structure to enable our approach to satisfy the kinematic constraints automatically. We test our model on both numerical examples and a real-world application to demonstrate its accuracy and efficiency. The results show that the proposed model achieves a better prediction accuracy than the other state-of-the-art methods under common evaluation metrics. Moreover, we introduce statistics to evaluate if the generative model literally learns the interaction patterns between different agents in the environments.
Visualization of Molecular Structures Using Augmented Reality

A thorough appreciation of the three-dimensional arrangement of atoms in molecules is crucial for new students of chemistry and critical to students continuing their studies in chemistry. We discuss current use of molecular models in education and investigate a variety of techniques used over the years to introduce students to atomic orbitals. This chapter examines a technique for the production of augmented reality models for use in lower level chemistry classes. This allows for methods to produce many different models that can be used in lectures without major cost incurred. The method allows for scaling up easily between large classes, multiple sections, and over many years. How this technique can be used in other chemistry classes and in other disciplines will also be discussed.
Il progetto Leganto alla LIUC

University libraries need to cooperate with faculty members to help students finding all the readings for their courses. New Reading list management systems have been developed to face the collaborative learning and teaching environment and the new digital publications. In 2018 LIUC University library decided to use Leganto, an ExLibris product integrated with Alma and Primo VE, to manage all the University reading lists. All courses and readings lists for A.Y. 2019/2020 have been created in Leganto by the library copying 2018/2019 lists. Professors have been enabled to update and complete them. Leganto is very easy to use. It makes simple to insert new readings starting from documents available in the Alma/Primo VE repository or to cite new resources importing metadata from many sources. It is possible to create new descriptions and to upload files created by professors. Leganto will be very useful for librarians too, because when professors modify a list, Leganto alerts them, so they could check the metadata or buy the new resources. The article underlines strengths and the possible improvements of the product.
The Role of E-Learning to Improve Staff Performance in Bahrain Airport Services

This research entitled the role of E-learning to improve staff performance in Bahrain Airport Services (BAS), where the research was limited to Bahrain Airport Services and targeted the employees and students who are attending courses at training in BAS Aircraft Engineering Training Center and Bahrain Airport Services Learning and development Centers. E-learning is a suitable term to cover the range of uses by the technology for learning and teaching, which is suit with the vision 2030 for Kingdom of Bahrain and industry 4.0 in Gulf Cooperation Council (GCC) by enhancing the education with modern technology, and it will help to increase staff performance by using the technology with modern technology. Hence therefore the based on the vision of industry 4.0, the E-learning system will be smarter to select the proper training courses to the employees according to their requirement and position. Research methodology used Solvin’s formula for the sample of the respondents which are selected from the population by using the probability technique and the random sample technique, and the result of the survey was that employees agreed on the E-learning and will improve the staff performance, SPSS software version 23.0 for data analysis, the Reliability Analysis by SPSS were used and founded the survey questionnaire reliable by having all the values more than 0.7, and the result of the questioners –survey-ware that employees agreed on the E-learning and will improve the staff performance.
Brokers, Clouds and Databases—The Good, the Bad and the Ugly

This chapter examines some of the technical issues that are useful for the discussion of this book. The chapter begins with the suggestion that over recent decades, information technology in the form of the Internet is at the apex of technological development and cloud computing is going to revolutionize the Internet world as it is known today.
Cross-Domain Face Recognition Using Dictionary Learning

Cross-domain face recognition refers to the matching of face images between different domains. It has many applications in night time surveillance, border security surveillance and law-enforcement. However, this is a difficult task because of the non-linear intensity pixel values between the images which occur due to the domain gap. Recently, dictionary learning methods such as coupled dictionary learning and domain adaptive dictionary learning methods are used to solve this problem. In this paper, we propose a dictionary learning based method to learn the common subspace in order to reduce the gap between domains. Initially, we separate the domain specific representation and identity related representation by using commonality and particularity dictionary learning. In the next step, we remove the domain specific representation and get the common subspace. Thereafter, in order to get the more discriminate representation, we use metric learning. The proposed method is tested on RGB-D-T data set and the experimental results show that the proposed method is performing better even when there is no person common between training and testing sets.
Organizing Unyang County’s Catholic Women Association in the 1920s and its Meaning

이 연구는 식민지기 ‘언양천주공교부인회’의 결성과 활동을 언양성당에서 전승되는 기록물을 통하여 밝힘으로써 이 시기 언양지역의 가톨릭부인회가 가지는 의미를 고찰하는데 있다. 이를 통해 지역사회 가톨릭부인회 조직의 목적과 활동 및 이것을 운영한 구성원을 구체적으로 알 수 있었다. ‘언양천주공교부인회’는 1923년 천주교의 발전을 목적으로 결성되었으며 ‘언양지방천주공교청년회’와 연동되어 전개되었다. 1920년대 초반 ‘신여성’ ‘신여자’ ‘부인’으로 표상되는 집단을 중심으로 전개된 여성운동과 ‘부인’단체의 결성은 당시 청년운동과 연동되어 전개되는데, 언양지역 가톨릭부인회도 이러한 흐름과 같이 한 사실을 구체적으로 알 수 있었다. 이러한 언양천주공교부인회의 결성은 1920년대 여성운동과 청년운동의 교착점을 보여준다는 점에서도 역사적 의미가 있을 것이 다. 특히 1920년대 말 1930년대 초 즈음 한국 가톨릭부인회는 중년부인회와 여자청년회로 분화되고, 여자청년회 단체는 그 하부에 있던 ‘소년소녀’를 지도하는 역할을 담당한다. 이것은 여자청년회의 하부에 소년소녀 단체가 조직되었음을 말해주는 것으로, 개신교와 천도교 계열의 청년운동에서 소년운동이 분화해 나가는데 대해 적극적으로 조응한 사실을 알 수 있었다. 도회지가 아닌 주변부 지역인 언양에서 다른 지역에 비해 청년회와 부인회 등 평신도 조직들이 빨리 결성된 배경에는 언양지역이 천주교수용초기부터 이후 이어져 온 많은 신앙공동체의 형성과 함께 부인운동과 청년운동을 선점한 천도교의 영향력이 강했던 지역사회 분위기도 작용하였던 것으로 보인다. 이러한 사회적 분위기에 천주교에서도 청년회, 부인회를 조직하여 적극적으로 대응한 모습을 보여준다. 이렇듯 언양의 천주공교부인회의 결성과 의미를 살펴보는 이 연구는 1920년대 초반 개신교와 천도교의 청년운동과 이것의 하부운동으로 전개된 부인운동을 의식한 가톨릭의 조응을 드러낸다는 점에서 그 역사적 의미가 있을 것이다. 또한 1920∼30년대 청년운동과 여성운동 나아가 가톨릭 운동사에서 가지는 의미를 밝히는데 기여할 수 있을 것이다.This research aims to study meaning of Unyang ‘Catholic women association’ of colonial era by defining organize and activities of Unyang ‘Catholic women association’ through records descended in Unyang Catholic church. We could identify a purpose and activities of community Catholic women association organization and member who run the organization specifically by this research. Unyang ‘Catholic women association’ was found in 1923 for growth of Catholic, and shared activities with ‘Unyang Catholic youth association.’ A women’s movement developed by groups that characterized by ‘new women’, ‘madam’ in 1920 was co-worked with youth movement, and Unyang Catholic women association was also identical with this stream. Organize of Catholic women association has an historical meaning by showing intersection of women’s movement and young men movement in 1920s. In late 1920’s to early 1930’s, Korean Catholic women association was separated into middle aged woman organization and young woman organization, and young woman organization took charge of guide boys and girls. This shows that Catholic organization corresponded with division of protestantism and Chundogyo’s youth movement to boy movement. The reason why Unyang, which is not a metropolitan, could have organizations like Catholic women association and youth association organization was that there were a lot of faith community since early accommodation of Catholic. And another reason was that there were powerful influences of Chundogyo, which preoccupied women movement and youth movement in Unyang. In this social mood, Catholic also dealt with this mood by organizing youth association organization and women association. As a result, this research has an historical meaning by revealing action of catholic dealing with youth movement and its’ lower part, women movement of protestantism and Chundogyo in early 1920s. Also, this research can contribute to identifying meanings of youth movement and women’s movement in history of Catholic movement.
DEVELOPMENT OF PROMOTIONAL SKILLS OF NIRLABA COMMUNITY IN BOGOR

Komunitas Hidroponik Bogor is a community of society who are active in planting activities through water media. Kelas Bogor is a community that accommodates sharing experiences that are useful to others. In practice, this community brings together people with certain knowledge or experience to share with people who need it for free or for free. This community service is in the form of content creation training in video form for promotional needs. The training activities consist of three sessions, the first session explaining that in the creation of publication content in the form of video requires special allocation, either in time or need to have a handling man. The second session, delivered how technical video content creation. What tools are used and what skills are required. The third session, the material presented is how to increase followers in social media, especially instagram.
Multi-Channel Convolutional Neural Network for Targeted Sentiment Classification

In recent years, targeted sentiment analysis has received great attention as a fine-grained sentiment analysis. Determining the sentiment polarity of a specific target in a sentence is the main task. This paper proposes a multi-channel convolutional neural network (MCL-CNN) for targeted sentiment classification. Our approach can not only parallelize over the words of a sentence but also extract local features effectively. Contexts and targets can be more comprehensively utilized by using part-of-speech information, semantic information and interactive information so that diverse features can be obtained. Finally, experimental results on the SemEval 2014 dataset demonstrate the effectiveness of this method.