Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPROVING MUSIC TAGGING FROM AUDIO WITH USER-TRACK INTERACTIONS

Automatic tagging of audio is an important research topic that can serve for organizing large collections of music. By tagging music, we can facilitate recommendations according to user interests and generate playlists [3]. Current state-of-the-art systems for music auto-tagging using audio are based on deep learning, in particular, convolutional neural networks (CNNs) [2]. Previous works have shown that it is possible to improve the tagging by combining other sources of information like biographies of the artists, images from the covers of the albums or reviews from users [4].
Security Extension of Automotive Communication Protocols Using Ethernet/IP

Current vehicles are increasingly depending on Electronic Control Units (ECUs) that control virtually every system of the vehicle. To enable more advanced features automotive embedded systems are opening to external world which raises security concerns. This work deals with the design of a novel approach to secure In-vehicle Systems by taking advantage of Ethernet/IP technology and proven security mechanisms from TCP/IP model. The focus is oriented mainly on the widespread Controller Area Network (CAN). The main goal is to design an efficient solution that meets requirements for latency without requiring high amounts of processing power and provides secure exchange of control signals. The presented solution is based on encapsulation of CAN traffic into UDP datagrams with added authenticity, integrity, and (if required) confidentiality of communication using IPsec protocol in transport mode which creates a âĂIJsecure tunnelâĂİ across backbone Ethernet network in a vehicle. Next part of the paper presents extensive tests both on hardware and in simulation in order to evaluate the characteristics of the designed security extension. The results indicate that using IPsec is a viable solution for securing in-vehicle communications.
DistributedOptimalPowerDispatchofOn/OffDevices via Newton-likeNeuralNetworkDynamics

This paper aims to solve a distributed optimization problem which naturally occurs in optimal dispatch of generators and flexible load devices that are limited to binary operational states, e.g. on and off. This problem is formulated as a binary program with a cost function defined by the summation of agent costs plus a global mismatch/penalty term. We propose a modification of the Hopfield Neural Network (HNN) dynamics in order to solve this problem while incorporating a novel Newton-like weighting factor. This addition lends itself to fast avoidance of saddle points, which the gradient-like HNN is susceptible to. Turning to a multi-agent setting, we reformulate the problem and develop a distributed implementation of the Newton-like dynamics. We show that if a local solution to the distributed reformulation is obtained, it is also a local solution to the centralized problem. A main contribution of this work is to show that the probability of converging to a saddle point of an appropriately defined energy function in both the centralized and distributed settings is zero under light assumptions. Finally, we enlarge our algorithm with an annealing technique which gradually “learns” a feasible binary solution. Simulation results demonstrate that the proposed methods are competitive with centralized greedy and SDP relaxation approaches in terms of solution quality, while the main advantage of our approach is a significant improvement in runtime over the SDP relaxation method and the distributed quality of implementation.
Moving beyond training packages: first steps

Training packages are central to the practice of vocational education and training (VET) in Australia. In the context of debate about the future of VET, questions must be asked about the role of training packages in any transformation. In this paper two basic questions are raised and directions for inquiry advanced. The first is about the processes and structures through which industry bodies and governments control training package design and use. If training packages are to evolve or be superseded, principles will need to be formulated concerning the roles and influence of government and industry. The paper argues for a set of principles that can guide shifts in the context around training packages as the latter change. The second question is about curricular form in future VET. Training packages are currently based on a competency-based training (CBT) model. The paper weighs up the case for CBT's influence in future VET and reviews alternative models and principles that might inform any shift away from a competency-based foundation. The principles and models considered in the paper are proposed to inform debate about changes to VET that are inevitable.
The Use of Rehearsal Strategies in Social Studies Education Textbooks by Prospective Classroom Teachers

School materials help both students and teachers to reach planned objectives. Textbooks are the pioneering ones that students use the most. The way that the students use the course books is as worth investigating as designing which should include learning strategies. Behaviors and thoughts that the learner performs during the learning process, which are expected to affect his/her coding process could be described as learning strategies. The kinds of them, rehearsal, organizing, comprehensive and affective strategies are preferred by the students in various circumstances. The purpose of this study is to describe how prospective classroom teachers use rehearsal strategies as they study social studies education textbook. In line with the purposes of the study, the textbooks belonging to 197 prospective teachers were analyzed. It was concluded that prospective classroom teachers mostly used the strategy of underlining with a pencil. The least-used rehearsal strategy was the use of question marks alongside text.
HIGHLY EXPRESSIVE PEKING OPERA SYNTHESIS WITH DURIAN SYSTEM

Jingju, also known as Peking or Beijing opera, is the most dominant form of Chinese opera which combines music, vocal performance, mime, dance and acrobatics. It arose in Beijing in the mid-Qing dynasty (1636–1912) and became fully developed and recognized by the mid-19th century. There are mainly four role types in Jingju including sheng (gentlemen), dan (women), jing (rough men), and chou (clowns). Each of the four roles has its own singing and performing style, which makes the music and vocal performance in Jingju highly expressive. In order to preserve this beautiful traditional performing art, this paper aims to model and synthesize Jingju singing from its lyrics and song score. Distinctly different from pop music, current challenges in Jingju synthesis involves:
BACHDUET: A HUMAN-MACHINE DUET IMPROVISATION SYSTEM

Back in the days of what today we refer to as Baroque period, improvisation was a key element of music performance and education. Great musicians such as J. S. Bach, were better known as improvisers than composers. Today, however, there is a lack of improvisation culture in classical music performance and education; classical musicians either do not know how to improvise, or cannot find other people to jam with. Motivated by this observation, we developed BachDuet, a system that enables real-time counterpoint improvisation between a human and a machine. This system uses a recurrent neural network to process the human musician’s monophonic performance on a MIDI keyboard and generates the machine’s monophonic performance in real time. We hope that it will serve as both an entertainment and practice tool for classical musicians to develop their improvisation skills.
Application of density-based propagation to fragment clouds using the Starling suite

The Starling suite estimates the non-linear evolution of densities in orbit. These densities can be probability density functions describing uncertainties about states or distributions of particle clouds modeled as continua. In this work, the Starling suite is applied to satellite fragmentations in a circular as well as highly eccentric orbit around Earth. The former case is challenging because the fragment distribution is bounded. Such boundedness is tackled using feature spaces defined relative to propagated reference hypersurfaces. The latter case is challenging as assumptions of the randomization in the node and argument of perigee do not hold over the mid-term. The suite also allows to estimate the impact rate from a fragment cloud on missions in any orbital region. Estimating the consequences of fragmentations is important as it can be used to rate planned space missions in terms of risks towards the future space environment.
Evaluation of phenolic compounds of two Lygeum

Lygeum spartum represents a natural barrier in the Algerian high plateaus against the advance of sand and the desertification. The plant is of interest due to its tolerance to environmental stress. There are two levels of ploïdy in this species; the diploid cytotype 2n = 16 which is confined in the locality of Ain Benkhllil and the polyploid cytotype 2n = 40 which thrives in semi-arid climates and has a large ecologic plasticity. The aim of this study was the evaluation of the phenolic compounds and flavonoids in both cytotypes. The antioxidant activity was also measured with results showing differences in the level of phenolic compounds; thus, these compounds could serve as biomarkers to distinguish the two cytotypes.
ALL-CONV NET FOR FRAME LEVEL MUSIC AND SPEECH SEGMENTATION

An all-convolutional neural network (all-conv net) [3] architecture designed for frame level music/speech classification with input as raw audio and processed using Kapre layers [2]. Proposed method is focused on music/speech segmentation using mirex 2018 dataset [1]. The proposed network comprises of Kapre layers followed by eight pairs of convolution and learned pooling layers, two (1,1) convolution layers. The input to network is raw audio files which is then processed to 40× 1 normalized melspectrogram by Kapre layers. In convolutional layers, we have used kernel size of 5× 5 with a stride of 1× 1 and only 16 filters. For pooling, convolution with kernel size of 5×1 and 2×1 with a stride of 5×1 and 2×1. In the end of the network, two 1 × 1 convolutional layers with 196 and 2 filters are used. The output of these layers is a two-dimensional feature map, converted into a probabilistic score vector using soft-max function. This vector signifies the probability of the presence of music or speech in a specified collection of frames (here we have considered 10 frames at a time) of any raw input of audio. Fig. 1. is code that we used to compute melspectrogram and normalize it per frequency.
Preconfiguring Robust Logical Topologies with Autonomous Routing for Data Center Networks

In recent years, online services such as cloud computing have become popular, and the amount of data, required to be processed by such online services, is increasing. In data centers, servers handle a large amount of data by cooperating with each other. Because the lack of the bandwidth between cooperating server pairs degrades the cooperation between servers, sufficient bandwidth between servers should be provided. However, the sufficient bandwidth between servers cannot be provided when the routes within a data center are fixed. The traffic patterns between servers vary according to the data and change drastically within 1 sec. The routes should be configured dynamically to follow such traffic changes so that a large number of traffic flows of the communicating server pairs do not concentrate on certain links. Moreover, failures are also common in data centers. If failures occur, the routes that do not include the failed links or devices should be configured. One approach to providing sufficient bandwidth between servers even in such cases is autonomous routing. In this thesis, we propose an autonomous routing method that can handle both of traffic changes and failures. In this method, we use an approach called multi-topology routing (MTR). In the MTR, multiple logical topologies, which are the subsets of the physical network topology, are preconfigured. When a node detects a failure, the node autonomously selects the logical topology that does not include the failed link or node, and relays the traffic using the selected logical topology by attaching the information of the selected logical topology to the header of the packets. In this thesis, we apply the MTR not only to handling the failures but also to avoiding the congestion even when traffic changes occur. In our method, each switch autonomously selects the logical topology based on the availability and the utilizations of the links connected to the switch. By considering the utilizations of the links, we avoid the concentration of the traffic on a certain 1 link. Even if failure or congestion occurs, the switch that detects it relays the packets so as not to pass the unavailable links by eliminating the logical topologies, where unavailable links are on the path to the destination, from the candidate logical topologies. Then, the information on the eliminated logical topologies is attached to the header of the packet to inform the unavailable logical topologies, and the other switches autonomously select the logical topology among the available logical topologies. In this thesis, we also propose a method to construct the logical topologies for our autonomous routing. In this method, the set of logical topologies is constructed so as to provide sufficient bandwidth between all communicating server pairs. We evaluate our routing method combined with the method to construct the logical topologies via simulation. The results show that our methods provide larger bandwidth between any server pairs than the ECMP even when failure occurs.
An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation

Data-to-text generation aims to generate descriptions given a structured input data (i.e., a table with multiple records). Existing neural methods for encoding input data can be divided into two categories: a) pooling based encoders which ignore dependencies between input records or b) recurrent encoders which model only sequential dependencies between input records. In our investigation, although the recurrent encoder generally outperforms the pooling based encoder by learning the sequential dependencies, it is sensitive to the order of the input records (i.e., performance decreases when injecting the random shuffling noise over input data). To overcome this problem, we propose to adopt the self-attention mechanism to learn dependencies between arbitrary input records. Experimental results show the proposed method achieves comparable results and remains stable under random shuffling over input data.
Implementation of Conservation Operationalisation

Conservation is the understanding, that, although the dimensions of a given object can change, the mass or quantity of that object remains constant. This concept of conservation is very difficult for young children yet feels intuitive to adults. Many studies have focused on when conservation starts, but little has been done to identify how the understanding of conservation develops or how children move through the various conservation strategies. This paper focuses on generating an ACT-R (Adaptive Control of Thought Rational) model that replicates previous studies on conservation by implementing an operationalised theoretical model by Siegler (Siegler, 1981). Results show that an operationalised model has to be more complex than previously thought to fully replicate the development of conservation strategies.
Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.
Learning Dispositif and Emotional Attachment: A Preliminary International Analysis

This research investigated the significance of learning dispositif (LD) and emotional attachment (EA) on perceived learning success (LS) across a diaspora of Western, Russian, Asian, Middle Eastern and Chinese student cohorts. Foucault’s LD captures the disparate socio-cultural contexts, institutional milieus and more or less didactic teaching styles that moderate learning. EA is a multi-dimensional notion involving affective bonds that emerged in child psychology and spread to marketing and other fields. The sequential explanatory research reviewed the learning and EA literatures and generated an LD–EA framework to structure the quantitative phase of its mixed investigations. In 2017 and 2018, the research collected 150 responses and used a range of statistical techniques for quantitative analysis. It found that LS varied significantly across cohorts, intimating that dispositifs influence learning. Nonparametric analysis suggested that EA also influenced learning, but regressions were inconclusive. Exploratory techniques hint at a dynamic mix of emotional or cognitive motivations during the student learning journey, involving structural breaks in student/instructor relationships. Cluster analysis identified distinct student groupings, linked to years of learning. Separately, qualitative analysis of open-ended survey questions and expert interviews intimates that frequent teacher interactions can increase EA. The synthesis of quantitative with qualitative results and pedagogical reflection suggests that LD and EA both influence learning in a complex, dynamic system. The key constituents for EA are Affection, Connection, Social Presence (SP), Teaching Presence (TP) and Flow but student emotional engagement is conditioned by the socio-cultural milieu (LD) and associated factors like relationships and trust. Unlike in the Community of Learning framework, in the EA framework Cognitive Presence (CP) is an outcome of the interaction between these EA constituents, associated factors and the socio-cultural milieu. Finally, whilst awareness of culture and emotions is a useful pedagogical consideration, learning mainstays remain inclusive educational systems that identify student needs and support well-designed programmes. Within these, scaffolded modules should include a variety of engaging learning activities with non-threatening formative and trustworthy summative feedback. We acknowledge some statistical study limitations, but its tentative findings make a useful preliminary contribution
A Qualitative Thematic Analysis of Faculty Engagement and Non-Engagement in Research

Teacher perceptions on the conditions facilitating research engagement and non-engagement were explored among faculty members of a private non-sectarian university in Manila. Teacher respondents in the college level (N=36) were asked to respond to a brief open-ended questionnaire and interview from which the qualitative research data were gathered. Qualitative analysis of data was conducted by identifying emergent thematic categories. The faculty perceived that conditions facilitating research engagement include: administrative support, time element, money matter, recognition, individual attributes, career advancement, team collaboration, topic, scope, and teacher responsibility.The outcomes on this paper shed some light on the idea that teachers are more likely to value research if they are given enough administrative support, time, money and recognition in the context of their own situation. If only teachers are provided with such and be encouraged to be more responsive to research, it is possible that the culture of research will be strengthened within and even outside the school community. As a result of these findings, the study recommends the following provisions: (a) improved research benefits and incentives, (b) faculty development program in the form of research trainings and seminars , (c) encouragement that starts in the departmental level, (d) clear research rules and guidelines set by the management, (e) professional research group affiliation, (f) university research institutes or centers.
Prognostic significance of initial electrocardiogram in patients with ST elevation acute myocardial infarction (STEMI): A study of 52 cases

Context: In acute myocardial infarction (AMI), patient′s surface electrocardiogram (ECG) allows risk management in individual patients by estimating the size of area at risk. This can be assessed from different waveforms of ECG. Settings and Design: In this prospective observational study we studied 52 consecutive patients of ST elevation AMI admitted into a teaching hospital. Materials and Methods: The electrocardiogram (ECG) of AMI patients which showed ST segment elevation of 1 mm in two or more limb leads or 2 mm in two or more chest leads with positive T waves in leads with ST segment elevation was analyzed in relation to ECG heart rate, number of leads showing ST segment deviation, ST segment deviation score, grades of ischemia as assessed by terminal portion of QRS complex. Out of 52 patients studied, mortality occurred in eight patients within 7 days of hospital stay. Thus we had two groups of patients, one who survived 7 days of hospital stay (n = 44) and other who did not survive 7 days of hospital stay (n = 8). The ECG parameters between these two groups were compared to determine significance of ECG in predicting short term mortality. Mean values with standard deviation (SD) were calculated for continuous variables. Chi-square tests were used to determine the significance of difference between proportions for discrete variables and P value of less than 0.05 was considered statistically significant. Results: Mean heart rate on admission was 84 beats per minute ranging from 32-136 beats per min. Tachycardia on admission was present in 17 patients who had high mortality than that in patients with normal heart rate ( 17.3 and 14.2% respectively). The mean ST segment deviation score was 22.69 ranging from 6-45. The high ST segment deviation score was associated with increased mortality at 7 days (P = 0.0209). Similarly in patients with ECG showing more number of leads showing ST segment deviation mortality was high (P = 0.009). Mortality in patients (n = 27) with grade III ischemia (terminal QRS distortion) was significantly more than that in patients (n = 25) with grade II ischemia (without distortion of terminal QRS complex, P = 0.0385). Conclusions: Tachycardia, high ST segment deviation score, more number of leads with ST segment deviation, distortion of terminal portion of QRS complexes on admission ECG denoted poor short-term prognosis.