Reviewerly/exHarmony huggingface dataset:
Avleddha: an example of Griko in popular music

Abstract This paper is part of a wider study about a corpus of songs, collected in the three different languages spoken in Salento (Puglia- South Italy): Italian (with regional variety), Griko and Salentino dialect. We analyze all the discography (texts and music of songs) of Avleddha, one of the most famous popular bands, founded in the early ‘90s in the Grecìa Salentina. This corpus is compared to other 80 songs, that includes English, Italian and popular music. We use Principal Component Analysis and techniques of sound analysis (spectrograms) we demonstrate that songs of the four bands are well separated from a point of view of complexity of the texts and music. The aim of this study is to characterize Avleddha’s music with reference to the other group of songs. We show that it is closer to the tradition of Salento popular music, but is able to maintain an original fingerprint, that makes it unique in its field. In this research the description of language systems at morphosyntactic level is not present, but we present a novel quantitative approach to reveal features both generic and specific in corpora of popular music.
Smooth Coupled Tucker Decomposition for Hyperspectral Image Super-Resolution

Hyperspectral image processing methods based on Tucker decomposition by utilizing low-rank and sparse priors are sensitive to the model order, and merely utilizing the global structural information. After statistical analysis on hyperspectral images, we find that the smoothness underlying hyperspectral image encoding local structural information is ubiquity in each mode. Based on this observation, we propose a novel smooth coupled Tucker decomposition scheme with two smoothness constraints imposed on the subspace factor matrices to reveal the local structural information of hyperspectral image. In addition, efficient algorithms are designed and experimental results demonstrate the effectiveness of selecting optimal model order for hyperspectral image super-resolution due to the integration of the subspace smoothness.
Relating National Intellectual Capital with Resilience, Reliability, Sustainability, and Reputation of Countries

Resilience has been associated with the development of competences in many areas and is considered a determining factor in situations that involve major challenges. In recent years, with the emergence of sustainability issues, particularly the climate issue, resilience has been associated with countries’ sustainability. Furthermore, many resilience attributes seem to be founded and reinforced through intellectual capital components. Nowadays, resilience has become one of the most used words by decision-makers in all areas of society. Economic and social crises have forced countries to face numerous challenges that have tested their decision-making capacity and adaptation in a short period and a turbulent context. However, it appears that some countries have more agility than others in these processes of response and adaptation. This chapter aims to explore, at a national level, the possible relation between the concepts of Intellectual Capital, Resilience, Reliability, Sustainability, and Reputation. The research is supported by a data-driven approach, oriented by a path analysis model. This study contributes to the literature on resilience, and it can serve as a support to decision-makers, allowing to identify the determinants of resilience in terms of the management of intellectual capital, sustainability, and countries’ reputation.
Bayesian clustered coefficients regression with auxiliary covariates assistant random effects

In regional economics research, a problem of interest is to detect similarities between regions, and estimate their shared coefficients in economics models. In this article, we propose a mixture of finite mixtures clustered regression model with auxiliary covariates that account for similarities in demographic or economic characteristics over a spatial domain. Our Bayesian construction provides both inference for number of clusters and clustering configurations, and estimation for parameters for each cluster. Empirical performance of the proposed model is illustrated through simulation experiments, and further applied to a study of influential factors for monthly housing cost in Georgia.
The design of a portable seismograph used in aftershock seismic array observation

This paper introduces the design of a portable wireless-linked seismograph used in aftershock ultra-dense seismic array observation, which features easy-to-install, low-power consumption, real-time data transmission and remote control capability. Compared to traditional instruments, such as the combination of Reftek 130s data recorder and Guralp CMG-40T seismometer for China Seismic Array Program, it integrates the seismic sensor, seismic data recording unit, GPS timing service and wireless data link into a compact sealed housing. To verify the system's functionality, both laboratory testing and preliminary field experiments were conducted. The exceptional feature of this newly-developed portable wireless-linked seismograph makes it very suitable for aftershock seismic array observation.
Siamese Region Proposal Tracker with Feature Pyramid Network

Visual object tracking is a fundamental task in computer vision, and numerous tracking algorithms are proposed to improve both accuracy and robustness. However, recent trackers often adopt the feature from the last layer to track the target, which ignores the detailed information from the shallow layers. This paper aims to aggregate both shallow and deep features to improve the tracker's performance. Inspired by Feature Pyramid Network (FPN) in object tracking, we add a bottom-up pathway in the Siamese-RPN to obtain a more representative performance. We test our method on OTB-100, and we obtain an overall 0.617 maximum success rate. The experimental results show the strength of the shallow feature to improve the tracking accuracy.
Face Verification in Practice: The Case of Greek Artist Leonidas Arniotis

In this paper we investigate a specific case of face verification: Given some confirmed photos and video frames of Leonidas Arniotis, a Greek artist of late 1800 and early 1900s, we have examined two photos, of very low resolution, of an unknown person that could be Leonidas Arniotis. The widespread of deep learning and the availability of ResNet-50 model, which was trained on VGGFace2 dataset, allowed us to apply transfer learning for face verification purposes. At the same time an alternative technique which is based on facial points matching was applied. The main aim of the paper is to show that despite the progress made in the fields of face recognition and face verification, actual systems that can be used by non-experts for examining real world cases, like the one we deal in the current paper, remain impractical. In the framework of the current work, we also examine and discuss, in a critical way, the effectiveness of transfer learning for real face verification cases through specific examples.
Learning Where to Cut from Edited Videos

In this work we propose a new approach for accelerating the video editing process by identifying good moments in time to cut unedited videos. We first validate that there is indeed a consensus among human viewers about good and bad cut moments with a user study, and then formulate this problem as a classification task. In order to train for such a task, we propose a self-supervised scheme that only requires pre-existing edited videos for training, of which there is large and diverse data readily available. We then propose a contrastive learning framework to train a 3D ResNet model to predict good regions to cut. We validate our method with a second user study, which indicates that clips generated by our model are preferred over a number of baselines.
Latency-Aware Dynamic Server and Cooling Capacity Provisioner for Data Centers

Data center operators generally overprovision IT and cooling capacities to address unexpected utilization increases that can violate service quality commitments. This results in energy wastage. To reduce this wastage, we introduce HCP (Holistic Capacity Provisioner), a service latency aware management system for dynamically provisioning the server and cooling capacity. Short-term load prediction is used to adjust the online server capacity to concentrate the workload onto the smallest possible set of online servers. Idling servers are completely turned off based on a separate long-term utilization predictor. HCP targets data centers that use chilled air cooling and varies the cooling provided commensurately, using adjustable aperture tiles and speed control of the blower fans in the air handler. An HCP prototype supporting a server heterogeneity is evaluated with real-world workload traces/requests and realizes up to 32% total energy savings while limiting the 99th-percentile and average latency increases to at most 6.67% and 3.24%, respectively, against a baseline system where all servers are kept online.
An ICT-Based Teaching Strategy to Promote Communicative Interactions: An Online Learning Approach During the COVID-19 Pandemic

The COVID-19 pandemic has positioned the issue of the virtual environments in the "new normal" of the teaching and learning processes. During these last times, the rapid transition from traditional face-to-face learning to online learning or ICT-based learning has been viewed as a paradigm shift in higher education. Based on the theoretical fundamentals of communicative interactions, the present study describes their adaptation to an ICT-based teaching strategy considering the basic organizational needs and the pursuing of engagement and motivation of students and teachers. The present approach aims to contribute from two perspectives: (i) in a theoretically way to the literature with the fundamentals about communicative interactions, specifically from online learning or ICT-based learning, and (ii) in an empiric way with the practical implications of formulating strategies and processes of digital transformation in academic context and the relevant pondering about the teachers and students needs inside the virtual learning environments.
A Dataset for Answering Time-Sensitive Questions

Time is an important dimension in our physical world. Lots of facts can evolve with respect to time. For example, the U.S. President might change every four years. Therefore, it is important to consider the time dimension and empower the existing QA models to reason over time. However, the existing QA datasets contain rather few time-sensitive questions, hence not suitable for diagnosing or benchmarking the model's temporal reasoning capability. In order to promote research in this direction, we propose to construct a time-sensitive QA dataset. The dataset is constructed by 1) mining time-evolving facts from WikiData and aligning them to their corresponding Wikipedia page, 2) employing crowd workers to verify and calibrate these noisy facts, 3) generating question-answer pairs based on the annotated time-sensitive facts. Our dataset poses challenges in the aspect of both temporal understanding and temporal reasoning. We evaluate different SoTA long-document QA systems like BigBird and FiD on our dataset. The best-performing model FiD can only achieve 46\% accuracy, still far behind the human performance of 87\%. We demonstrate that these models are still lacking the ability to perform consistent temporal reasoning. Therefore, we believe that our dataset could serve as a benchmark to develop NLP models more sensitive to temporal shifts. The dataset and code are released in~\url{https://github.com/wenhuchen/Time-Sensitive-QA}.
Why-Type Question to Query Reformulation for Efficient Document Retrieval

Understanding the actual need of user from a question is very crucial in non-factoid why-question answering as Why-questions are complex and involve ambiguity and redundancy in their understanding. The precise requirement is to determine the focus of question and reformulate them accordingly to retrieve expected answers to a question. The paper analyzes different types of why-questions and proposes an algorithm for each class to determine the focus and reformulate it into a query by appending focal terms and cue phrase ‘because’ with it. Further, a user interface is implemented which asks input why-question, applies different components of question , reformulates it and finally retrieve web pages by posing query to Google search engine. To measure the accuracy of the process, user feedback is taken which asks them to assign scoring from 1 to 10, on how relevant are the retrieved web pages according to their understanding. The results depict that maximum precision of 89% is achieved in Informational type why-questions and minimum of 48% in opinionated type why-questions.
Opinion Mining for Curriculum Enrichment Using Self-Organizing Maps

Recently social networks have become a valuable source of information where tastes, preferences and opinions of users around the world come together. This information is an interesting challenge from the perspective of natural language processing (NLP) but is also an aspect of deep interest and great value not only as a marketing strategy for companies and political campaigns but also as an indicator for measuring consumer satisfaction with a product or service. In this paper, we present an opinion mining system that uses text mining techniques and artificial neural networks to automatically obtain useful knowledge about opinions, preferences and user trends. Making use of the Self-Organizing Maps (SOM), we train a neural network that is capable of depending on what is expressed by users in social networks, discern their mood, tastes and experiences in order to help a personnel selection company to find customers and employees necessities. The analysis of these results will make it possible to undertake corrective actions to improve the opinion of the user in relation to their work development. In all experiments, using SOM, we achieve a quantization error below 0.02. In addition, taking into account the evaluation metrics, It can be said that the model has been able to learn and relate the input context values and the results, which proves that the training has been successful and therefore the classification.
A Dynamic Data and Information Processing Model for Unmanned Aircraft Systems

Dynamic Data and Information Processing (DDIP) involves symbiotic control feedback through sensor reconfiguration to integrate real-time data into a predictive method for system behavior. Fundamentally, DDIP presents opportunities to advance understanding and analysis of activities, operations, and transformations that contribute to system performance, thereby aiding in decision-making and event prediction. Previously examined DDIP application domains include weather monitoring and forecasting, supply chain system analysis, power system and energy analysis, and structural health monitoring. Currently, there is limited existing work that implements DDIP in support of Unmanned Aircraft Systems (UAS). Developing a DDIP model for a UAS application could further demonstrate DDIP capabilities and provide information relevant to improving maintenance operations and resource management.
Feature Map Transfer: Vertical Federated Learning for CNN Models

Federated learning provides a privacy-preserving mechanism for multiple participants to collaboratively train machine learning models without exchanging private data with each other. Existing federated learning algorithms can aggregate CNN models when the dataset is horizontally partitioned, but cannot be applied to vertically partitioned datasets. In this work, we demonstrate the image classification task in the vertical federated learning setting where each participant holds incomplete image pieces of all samples. We propose an approach called VFedConv to solve this problem and achieve the goal of training CNN models without revealing raw data. Different from traditional federated learning algorithms sharing model parameters in each training iteration, VFedConv shares hidden feature maps. Each client creates a local feature extractor and transmits the extracted feature maps to the server. A classifier model at the server-side is constructed with extracted feature maps as input and labels as output. Furthermore, we put forward the model transfer method to improve final performance. Extensive experiments demonstrate that the accuracy of VFedConv is close to the centralized model.
Convolutional Neural Network Combined with Emotional Dictionary Apply in Chinese Text Emotional Classification

At present, convolutional neural network has achieved good results in text emotional classification, but in several common models, it does not make use of a large number of prior knowledge that human society has now acquired. This paper proposes a new CNN model based on emotional dictionary: emotional knowledge-CNN (EK-CNN), which uses emotional dictionary as additional knowledge to improve the performance of the model in emotional classification. The model has been validated on three real data sets, and is superior to the existing technology model for text emotion classification.
Federated Knowledge Graph Embeddings with Heterogeneous Data

Due to the problem of privacy protection, it is very limited to apply distributed representation learning to practical applications in the scenario of multi-party cooperation. Federated learning is an emerging feasible solution to solve the issue of data security. However, due to the heterogeneity of the data from multi-party platforms, it is not easy to employ federated learning directly to embed multi-party data. In this paper, we propose a new federated framework FKE for representation learning of knowledge graphs to deal with the problem of privacy protection and heterogeneous data. Experiments show that the FKE can perform well in typical link prediction, overcome the problem of heterogeneous data and have a significant effect.
Building an ASR System for Indian (Punjabi) language and its evaluation for Malwa and Majha dialect: Preliminary Results

Automatic Speech recognition (ASR), also referred to as voice recognition or speech-to-text, is one of the most developing field of Natural Language Processing (NLP) that recognizes speech. Speech recognition allowing the human voice to serve as the main interface between the human and the computer. Plenty of progress is to be done in the field of speech recognition for various popular languages and adult users with good recognition rates. But in case of Indian regional languages Punjabi, Telugu, Tamil etc, speech recognition is still at the infant level under disparate acoustic conditions. The objective of this research work is to demonstrate the effectiveness of the pitch acoustic features for improving the recognition performances of the ASR system for different Punjabi dialects. When our developed dialect-based Punjabi ASR system was evaluated for Malwa and Majha dialect speakers, the findings showed WER of 23.25 % and 25.91 %, respectively.
RGB Channel Analysis for Glomerulus and Proximal Tubule Detection in Kidney Histology Image

Segmentation in digital pathology, especially kidney histology images, is still rarely done. The detection of parts of the kidney is done manually by an experienced laboratory which takes a long time. This paper proposed method of extracting color features in glomerulus and proximal tubule as the initial stage of segmentation. Data were kidney histology images. RGB color feature extraction is done by utilizing histogram. Histogram give information about highest frequency of intensity. Data analysis was statistically using grouped frequency distributions to obtain range number of RGB. The result was RGB in kidney histology image can be extracted and comparison of color components in RGB show that all component overlap in the glomerulus while in proximal tubule, blue component can be used as feature differentiator. The conclusion was RGB channel cannot be used as a distinguishing feature in the segmentation of parts of the kidney, especially the glomerulus and proximal tubule.
Multi-objective Privacy-preserving Text Representation Learning

Private information can either take the form of key phrases that are explicitly contained in the text or be implicit. For example, demographic information about the author of a text can be predicted with above-chance accuracy from linguistic cues in the text itself. Letting alone its explicitness, some of the private information correlates with the output labels and therefore can be learned by a neural network. In such a case, there is a tradeoff between the utility of the representation (measured by the accuracy of the classification network) and its privacy. This problem is inherently a multi-objective problem because these two objectives may conflict, necessitating a trade-off. Thus, we explicitly cast this problem as multi-objective optimization (MOO) with the overall objective of finding a Pareto stationary solution. We, therefore, propose a multiple-gradient descent algorithm (MGDA) that enables the efficient application of the Frank-Wolfe algorithm [10] using the line search. Experimental results on sentiment analysis and part-of-speech (POS) tagging show that MGDA produces higher-performing models than most recent proxy objective approaches, and performs as well as single objective baselines.
An Evaluation of Screen Parallax, Haptic Feedback, and Sensory-Motor Mismatch on Near-Field Perception-Action Coordination in VR

Virtual reality ( VR ) displays have factors such as vergence-accommodation conflicts that negatively impact depth perception and cause users to misjudge distances to select objects. In addition, popular large-screen immersive displays present the depth of any target rendered through screen parallax information of points, which are encapsulated within stereoscopic voxels that are a distinct unit of space dictating how far an object is placed in front of or behind the screen. As they emanate from the viewers’ eyes (left and right center of projection), the density of voxels is higher in front of the screen (in regions of negative screen parallax) than it is behind the screen (in regions of positive screen parallax), implying a higher spatial resolution of depth in front of the screen than behind the screen. Our experiment implements a near-field fine-motor pick-and-place task in which users pick up a ring and place it around a targeted peg. The targets are arranged in a linear configuration of 3, 5, and 7 pegs along the front-and-back axis with the center peg placed in the same depth as the screen. We use this to evaluate how users manipulate objects in positive versus negative screen parallax space by the metrics of efficiency, accuracy, and economy of movement. In addition, we evaluate how users’ performance is moderated by haptic feedback and mismatch between visual and proprioceptive information. Our results reveal that users perform more efficiently in negative screen parallax space and that haptic feedback and visuo-proprioceptive mismatch have effects on placement efficiency. The implications of these findings are described in the later sections of the article.