nils-herrmann/clean_scirepeval huggingface dataset:
An exhaustive review of automatic music transcription techniques: Survey of music transcription techniques

The main objective of this paper is to review the technologies and models used in the Automatic music transcription system. Music Information Retrieval is a key problem in the field of music signal analysis and this can be achieved with the use of music transcription systems. It has proven to be a very difficult issue because of the complex and deliberately overlapped spectral structure of musical harmonies. Generally, the music transcription systems branched as automatic and semi-automatic approaches based on the user interventions needed in the transcription system. Among these we give a close view of the automatic music transcription systems. Different models and techniques were proposed so far in the automatic music transcription systems. However the performance of the systems derived till now not completely matched to the performance of a human expert. In this paper we go through the techniques used previously for the music transcription and discuss the limitations with them. Also, we give some directions for the enhancement of the music transcription system and this can be useful for the researches to develop fully automatic music transcription system.
Building loyalty to online communities through bond and identity-based attachment to sub-groups

Researchers and theorists have proposed that feelings of attachment to subgroups within a larger online community or site can increase users' loyalty to the site. They have identified two types of attachment, with distinct causes and consequences. With bond-based attachment, people feel connections to other group members, while with identity-based attachment they feel connections to the group as a whole. In two experiments we show that these feelings of attachment to subgroups increase loyalty to the larger community. Communication with other people in a subgroup but not simple awareness of them increases attachment to the larger community. By varying how the communication is structured, between dyads or with all group members simultaneously, the experiments show that bond- and identity-based attachment have different causes. But the experiments show no evidence that bond and identity attachment have different consequences. We consider both theoretical and methodological reasons why the consequences of bond-based and identity-based attachment are so similar.
Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder

In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images.



We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into.



We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively.



The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs.
Isolation Enhancement Between Two Closely Packed Antennas

This paper introduces a coupling element to enhance the isolation between two closely packed antennas operating at the same frequency band. The proposed structure consists of two antenna elements and a coupling element which is located in between the two antenna elements. The idea is to use field cancellation to enhance isolation by putting a coupling element which artificially creates an additional coupling path between the antenna elements. To validate the idea, a design for a USB dongle MIMO antenna for the 2.4 GHz WLAN band is presented. In this design, the antenna elements are etched on a compact low-cost FR4 PCB board with dimensions of 20times40times1.6 mm3. According to our measurement results, we can achieve more than 30 dB isolation between the antenna elements even though the two parallel individual planar inverted F antenna (PIFA) in the design share a solid ground plane with inter-antenna spacing (Center to Center) of less than 0.095 lambdao or edge to edge separations of just 3.6 mm (0.0294 lambdao). Both simulation and measurement results are used to confirm the antenna isolation and performance. The method can also be applied to different types of antennas such as non-planar antennas. Parametric studies and current distribution for the design are also included to show how to tune the structure and control the isolation.
The Frankfurt Airport Attack : A Case Study on the Radicalization of a Lone-Actor Terrorist

The case of the Frankfurt Airport attack in 2011 in which a 21-year-old man shot several U.S. soldiers, murdering 2 U.S. airmen and severely wounding 2 others, is assessed with the Terrorist Radicalization Assessment Protocol (TRAP-18). The study is based on an extensive qualitative analysis of investigation and court files focusing on the complex interconnection among offender personality, specific opportunity structures, and social contexts. The role of distal psychological factors and proximal warning behaviors in the run up to the deed are discussed. Although in this case the proximal behaviors of fixation on a cause and identification as a “soldier” for the cause developed over years, we observed only a very brief and accelerated pathway toward the violent act. This represents an important change in the demands placed upon threat assessors.
QUALITATIVE METHODOLOGY AND GROUNDED THEORY IN PROPERTY RESEARCH

Much of the research published in the property discipline consists of work utilising quantitative methods. While research gained using quantitative methods, if appropriately designed and rigorous, leads to results which are typically generalisable and quantifiable, it does not allow for a rich and in-depth understanding of a phenomenon. This is especially so if a researcher’s aim is to uncover the issues or factors underlying that phenomenon. Such an aim would require using a qualitative research methodology, and possibly an interpretive as opposed to a positivist theoretical perspective. The purpose of this paper is to provide a general overview of qualitative methodologies with the aim of encouraging a broadening of methodological approaches to overcome the positivist methodological bias which has the potential of inhibiting property behavioural research.
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients

Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on stationary problems, and permitting learning rates to grow appropriately in nonstationary tasks. Here, we extend the idea in three directions, addressing proper minibatch parallelization, including reweighted updates for sparse or orthogonal gradients, improving robustness on non-smooth loss functions, in the process replacing the diagonal Hessian estimation procedure that may not always be available by a robust finite-difference approximation. The final algorithm integrates all these components, has linear complexity and is hyper-parameter free.
Wearable seizure detection using convolutional neural networks with transfer learning

The ability to accurately and robustly detect seizures in an ambulatory setting using scalp-based EEG has been the focus of much research over the last several decades. However, its numerous challenges and obstacles have impeded the realization of a definitive solution. This work aims to build upon our existing research and apply newer advanced machine learning and hardware techniques to this issue. The novelty proposed is two-fold. First, we utilize max-pooling convolutional neural networks (MPCNN) to perform end-to-end learning. Second, we demonstrate that transfer-learning can be used to teach MPCNNs generalized features of both normal and epileptiform patterns from a pool of subjects' raw EEG data. Using this hybrid approach, the system is able to detect all 184 seizure onsets from 24 cases with average latency of 1.47 seconds and 3.2 false-alarms/day. To demonstrate the full system, the entire design is efficiently implemented onto a highly parallel, highly granular embedded SoC (NVIDIA Jetson TK1). When utilizing the GPU, the system is able to classify 15-second segments in 308 μs and last over 80 hours.
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization

Semantic specialization is a process of finetuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with a adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three languages and on three tasks: word similarity, dialog state tracking, and lexical simplification. We report consistent improvements over distributional word vectors and vectors specialized by other state-of-the-art specialization frameworks. Finally, we also propose a cross-lingual transfer method for zero-shot specialization which successfully specializes a full target distributional space without any lexical knowledge in the target language and without any bilingual data.
Short-term plasticity of the human auditory cortex

Magnetoencephalographic measurements (MEG) were used to examine the effect on the human auditory cortex of removing specific frequencies from the acoustic environment. Subjects listened for 3 h on three consecutive days to music "notched" by removal of a narrow frequency band centered on 1 kHz. Immediately after listening to the notched music, the neural representation for a 1-kHz test stimulus centered on the notch was found to be significantly diminished compared to the neural representation for a 0.5-kHz control stimulus centered one octave below the region of notching. The diminished neural representation for 1 kHz reversed to baseline between the successive listening sessions. These results suggest that rapid changes can occur in the tuning of neurons in the adult human auditory cortex following manipulation of the acoustic environment. A dynamic form of neural plasticity may underlie the phenomenon observed here.
“It Don’t Mean a Thing if It Ain’t Got that Swing”– an Alternative Concept for Understanding the Evolution of Dance and Music in Human Beings

The functions of dance and music in human evolution are a mystery. Current research on the evolution of music has mainly focused on its melodic attribute which would have evolved alongside (proto-)language. Instead, we propose an alternative conceptual framework which focuses on the co-evolution of rhythm and dance (R&D) as intertwined aspects of a multimodal phenomenon characterized by the unity of action and perception. Reviewing the current literature from this viewpoint we propose the hypothesis that R&D have co-evolved long before other musical attributes and (proto-)language. Our view is supported by increasing experimental evidence particularly in infants and children: beat is perceived and anticipated already by newborns and rhythm perception depends on body movement. Infants and toddlers spontaneously move to a rhythm irrespective of their cultural background. The impulse to dance may have been prepared by the susceptibility of infants to be soothed by rocking. Conceivable evolutionary functions of R&D include sexual attraction and transmission of mating signals. Social functions include bonding, synchronization of many individuals, appeasement of hostile individuals, and pre- and extra-verbal communication enabling embodied individual and collective memorizing. In many cultures R&D are used for entering trance, a base for shamanism and early religions. Individual benefits of R&D include improvement of body coordination, as well as painkilling, anti-depressive, and anti-boredom effects. Rhythm most likely paved the way for human speech as supported by studies confirming the overlaps between cognitive and neural resources recruited for language and rhythm. In addition, dance encompasses visual and gestural communication. In future studies attention should be paid to which attribute of music is focused on and that the close mutual relation between R&D is taken into account. The possible evolutionary functions of dance deserve more attention.
Exponential expressivity in deep neural networks through transient chaos

We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. Our results reveal an order-to-chaos expressivity phase transition, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially with depth but not width. We prove this generic class of deep random functions cannot be efficiently computed by any shallow network, going beyond prior work restricted to the analysis of single functions. Moreover, we formalize and quantitatively demonstrate the long conjectured idea that deep networks can disentangle highly curved manifolds in input space into flat manifolds in hidden space. Our theoretical analysis of the expressive power of deep networks broadly applies to arbitrary nonlinearities, and provides a quantitative underpinning for previously abstract notions about the geometry of deep functions.
Rotary Endodontics or Reciprocating Endodontics : Which is New and Which is True ?

In the past, shaping of root canals was done using stainless steel (SS) hand files. The introduction of rotary instrumentation has revolutionized the art and science of endodontic practice in the last decade with predictable success. The rotary files have been subjected to constant evolution in the form of metallurgy, design features, and the manner in which these instruments are driven (rotary/reciprocation), etc., resulting in revolution, both within the canal and in the area of contemporary endodontics. The purpose of this review is to identify publications regarding the evaluation, to present comprehensive and critical summaries of current knowledge, and to provide an update of the rotary and reciprocating concept, which is new and which is true.
Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization

The symbiotic organisms search (SOS) algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.
The Influence of the Avatar on Online Perceptions of Anthropomorphism, Androgyny, Credibility, Homophily, and Attraction

It has become increasingly common for websites and computer media to provide computer generated visual images, called avatars, to represent users and bots during online interactions. In this study, participants (N=255) evaluated a series of avatars in a static context in terms of their androgyny, anthropomorphism, credibility, homophily, attraction, and the likelihood they would choose them during an interaction. The responses to the images were consistent with what would be predicted by uncertainty reduction theory. The results show that the masculinity or femininity (lack of androgyny) of an avatar, as well as anthropomorphism, significantly influence perceptions of avatars. Further, more anthropomorphic avatars were perceived to be more attractive and credible, and people were more likely to choose to be represented by them. Participants reported masculine avatars as less attractive than feminine avatars, and most people reported a preference for human avatars that matched their gender. Practical and theoretical implications of these results for users, designers, and researchers of avatars are discussed.
Real-Time Re-Textured Geometry Modeling Using Microsoft HoloLens

We implemented live-textured geometry model creation with immediate coverage feedback visualizations in AR on the Microsoft HoloLens. A user walking and looking around a physical space can create a textured model of the space, ready for remote exploration and AR collaboration. Out of the box, a HoloLens builds a triangle mesh of the environment while scanning and being tracked in a new environment. The mesh contains vertices, triangles, and normals, but not color. We take the video stream from the color camera and use it to color a UV texture to be mapped to the mesh. Due to the limited graphics memory of the HoloLens, we use a fixed-size texture. Since the mesh generation dynamically changes in real time, we use an adaptive mapping scheme that evenly distributes every triangle of the dynamic mesh onto the fixed-size texture and adapts to new geometry without compromising existing color data. Occlusion is also considered. The user can walk around their environment and continuously fill in the texture while growing the mesh in real-time. We describe our texture generation algorithm and illustrate benefits and limitations of our system with example modeling sessions. Having first-person immediate AR feedback on the quality of modeled physical infrastructure, both in terms of mesh resolution and texture quality, helps the creation of high-quality colored meshes with this standalone wireless device and a fixed memory footprint in real-time.
VR-CAD integration: Multimodal immersive interaction and advanced haptic paradigms for implicit edition of CAD models

This paper presents an approach for the integration of Virtual Reality (VR) and Computer-Aided Design (CAD). Our general goal is to develop a VR–CAD framework making possible intuitive and direct 3D edition on CAD objects within Virtual Environments (VE). Such a framework can be applied to collaborative part design activities and to immersive project reviews. The cornerstone of our approach is a model that manages implicit editing of CAD objects. This model uses a naming technique of B-Rep components and a set of logical rules to provide straight access to the operators of Construction History Graphs (CHG). Another set of logical rules and the replay capacities of CHG make it possible to modify in real-time the parameters of these operators according to the user's 3D interactions. A demonstrator of our model has been developed on the OpenCASCADE geometric kernel, but we explain how it can be applied to more standard CAD systems such as CATIA. We combined our VR–CAD framework with multimodal immersive interaction (using 6 DoF tracking, speech and gesture recognition systems) to gain direct and intuitive deformation of the objects' shapes within a VE, thus avoiding explicit interactions with the CHG within a classical WIMP interface. In addition, we present several haptic paradigms specially conceptualized and evaluated to provide an accurate perception of B-Rep components and to help the user during his/her 3D interactions. Finally, we conclude on some issues for future researches in the field of VR–CAD integration.
Intelligent Bayesian Asset Allocation via Market Sentiment Views

The sentiment index of market participants has been extensively used for stock market prediction in recent years. Many financial information vendors also provide it as a service. However, utilizing market sentiment under the asset allocation framework has been rarely discussed. In this article, we investigate the role of market sentiment in an asset allocation problem. We propose to compute sentiment time series from social media with the help of natural language processing techniques. A novel neural network design, built upon an ensemble of evolving clustering and long short-term memory, is used to formalize sentiment information into market views. These views are later integrated into modern portfolio theory through a Bayesian approach. We analyze the performance of this asset allocation model from many aspects, such as stability of portfolios, computing of sentiment time series, and profitability in our simulations. Experimental results show that our model outperforms some of the most successful forecasting techniques. Thanks to the introduction of the evolving clustering method, the estimation accuracy of market views is significantly improved.