Reviewerly/exHarmony huggingface dataset:
GLASS MUSIC OF THE TWENTIETH AND TWENTY-FIRST CENTURIES

Abstract This article serves as an introduction to the twentieth- and twenty-first-century musical practices that have made use of glass instruments and objects. Emphasis is placed on those practices that use glass in a raw, acoustic manner, and those that take advantage of the precision with which glass can be tuned. First, a general history of glass music is presented, followed by an overview of the physical and acoustic aspects pertaining to the material that are relevant to those composers wishing to integrate glass into their works. Finally, the composers, performers and instrument builders who have made significant use of glass in the late twentieth and early twenty-first centuries are surveyed.
Teaching Logic with Iltis

Iltis is an interactive, web-based system for teaching logic. It is designed to provide immediate and comprehensive feedback for exercises covering various aspects of the reasoning workflow. This poster presentation reports on new exercises and feedback mechanisms for modal and first-order logic.
Towards Automated Swimming Analytics Using Deep Neural Networks

Methods for creating a system to automate the collection of swimming analytics on a pool-wide scale are considered in this paper. There has not been much work on swimmer tracking or the creation of a swimmer database for machine learning purposes. Consequently, methods for collecting swimmer data from videos of swim competitions are explored and analyzed. The result is a guide to the creation of a comprehensive collection of swimming data suitable for training swimmer detection and tracking systems. With this database in place, systems can then be created to automate the collection of swimming analytics.
KBSET – Knowledge-Based Support for Scholarly Editing and Text Processing with Declarative Markup and a Core Written in SWI-Prolog

KBSET is an environment that provides support for scholarly editing in two flavors: First, as a practical tool KBSET/Letters that accompanies the development of editions of correspondences (in particular from the 18th and 19th century), completely from source documents to PDF and HTML presentations. Second, as a prototypical tool KBSET/NER for experimentally investigating novel forms of working on editions that are centered around automated named entity recognition. KBSET can process declarative application-specific markup that is expressed in Open image in new window notation and incorporate large external fact bases that are typically provided in RDF. KBSET includes specially developed Open image in new window styles and a core system that is written in SWI-Prolog, which is used there in many roles, utilizing that it realizes the potential of Prolog as a unifying language.
WAKAF LINGKUNGAN HIDUP DALAM RANGKA PELAKSANAAN PEMBANGUNAN BERKELANJUTAN DAN PENEGAKKAN KEADILAN ANTARGENERASI

This article provides an overview of environmental waqf and its relation with sustainable development and intergenerational equity. Waqf is one Islamic legal instrument to deal with the problems of the ummah, one of which is the environmental issue that is related to sustainable development and  intergenerational equity. This research is done qualitatively with the analytical descriptive design. The result shows that environmental waqf is able to balance the development as well as the sustainability of environment that lead to environmental sustainable preservation and intergenerational equity, where future generation is still in the same level of utilization with the present generation, in terms of the variety of choices, quality, and access to natural resources.
Security System Using A Robot Based On Speech Recognition

This study describes a security system using a humanoid robot by utilizing speech recognition. The robot has two main parts, namely, Raspberry Pi 3 and two Arduino UNO R3 as a slave. This robot is designed as a combination of speech recognition and voice biometric. The instruction given by a speaker must be obeyed by the robot using servo motor. Meanwhile, for voice biometric, robot may give access to an authorized person using speech recognition. Mel Frequency Cepstral Coefficients (MFCCs), their delta, and delta-delta are used as feature extraction which is fed to a classifier, Gaussian Mixture Model (GMM). Results of this study show that the robot may recognize the speaker with an accuracy of 99.4% and 99% for 50% of testing data and 20% of testing data, respectively. Thus, this suggests that the combination of MFCC and GMM can be implemented in speech recognition for security system performed by the robot.
21st Century Skills of ICT Professionals

Universities need to increasingly adapt their educational methods in order to prepare the new generation of experts with additional skills for the future of work. Especially in the field of ICT, besides proficiency in theoretical and subject-related ICT knowledge and practical programming skills, young graduates have to demonstrate mastery in different transversal and soft skills. Taking into account that the ICT sector is one of the most knowledge-intensive, export-oriented and globally recognized sectors in Bulgaria, the quality of the ICT training is substantially important for its future competitiveness and growth. Stepping on a theoretical framework for 21st century skills, the present research aims to outline what are the key 21st century skills of the young graduates, as evaluated by ICT employers and University lecturers. By presenting the outcomes of ten in-depth, semi-structured interviews with company owners, business leaders, HR managers, and academics, the paper outlines the main trends and rankings of the soft skills for ICT graduates, without taking in consideration the specific professional competences or subject-related knowledge. The first part introduces the context, the methodology and the scope of the research. The outcomes of the interviews are presented both as quantitative figures and as specific comments of the participants, highlighting different practical aspects. Finally, the current analysis provides fruitful insights and recommendations about the new types of methods for training relevant for the ICT sector 21st century skills.
A NOTE ON THE PROPERTIES OF ASSOCIATED BOOLEAN FUNCTIONS OF QUADRATIC APN FUNCTIONS

Let $F$ be a quadratic APN function of $n$ variables. The associated Boolean function $\gamma_F$ in $2n$ variables ($\gamma_F(a,b)=1$ if $a\neq{\bf 0}$ and equation $F(x)+F(x+a)=b$ has solutions) has the form $\gamma_F(a,b) = \Phi_F(a) \cdot b + \varphi_F(a) + 1$ for appropriate functions $\Phi_F:\mathbb{F}_2^n\to \mathbb{F}_2^n$ and $\varphi_F:\mathbb{F}_2^n\to \mathbb{F}_2$. We summarize the known results and prove new ones regarding properties of $\Phi_F$ and $\varphi_F$. For instance, we prove that degree of $\Phi_F$ is either $n$ or less or equal to $n-2$. Based on computation experiments, we formulate a conjecture that degree of any component function of $\Phi_F$ is $n-2$. We show that this conjecture is based on two other conjectures of independent interest.
Analysis of Measurements for Investigating Amplitude Modulation Using Interactive Methods

Teachers use different approaches to engage students with the learning process and make the material more interesting. Interactive tools and augmented reality (AR) applications are a great way to increase the students' participation. The paper demonstrates the use of AR and the interactive approach in the discipline “Radio Communication Technologies”. It describes what students need to do in the laboratory exercises on the topic of Amplitude Modulation.
Continual Leakage-Resilient Hedged Public-Key Encryption

Abstract Hedged public-key encryption (HPKE), introduced by Bellare et al. (ASIACRYPT 2009), provides useful security when the per-message randomness fails to be uniform due to faulty implementations or adversarial actions. The HPKE scheme achieves IND-CPA (chosen plaintext attack) security when the randomness they used is of high quality, but, when the randomness is poor quality, rather than breaking completely, it achieves a weaker but a useful notion of security called IND-CDA (chosen distribution attack) as long as the message and randomness together have sufficient min-entropy. However, little research on HPKE in the presence of key leakage was done. In this paper, we study HPKE featuring key leakage-resilience and formulate appropriate security notion for key leakage-resilient HPKE. We work in the continual key leakage model where the secret key is refreshed periodically and an adversary can learn arbitrary but bounded leakage on the secret key between the updates. We present two generic constructions of continual leakage-resilient HPKE in the standard model by using a continual leakage-resilient all-but-one lossy trapdoor function. Finally, we give an instantiation of leakage-resilient HPKE under the linear assumption in bilinear groups.
Improved QPSO with Selected Random Mean for Electromagnetic Problems

A new QPSO (Quantum-behaved Particle Swarm Optimization) with selected random mean is proposed in this paper for solving electromagnetic optimization problems. The selection procedure is a probabilistic mechanism indirectly based on the fitness measure of the solutions, solutions which are candidates for the QPSO mean term. The selection is controlled through a locality search parameter which is adapted during the optimization process to improve the overall performance of the algorithm. To test the performances two benchmark electromagnetic functions are considered, namely TEAM22 and Loney's solenoid.
A Satellite Overlapping Covert Communication Scheme Based on LDPC-DCSK Transmission Technology

Traditional satellite covert communications mostly use periodic short code direct sequence spread spectrum (DSSS) technology. The risk of DSSS being deciphered is increasing with the development of signal detection technology. As a technology that can replace traditional spread spectrum, chaos shift keying (CSK) not only has the characteristics of low power spectrum density, but also has good correlation and long-term unpredictability compared with traditional DSSS, so it is more suitable for covert communication. However, CSK has the problem of decision threshold drift, which is difficult to apply in practice. This article uses differential chaos shift keying (DCSK) technology, which not only has the advantage of high concealment of chaotic signals, but also overcomes the problem of CSK threshold drift. Then non-binary low-density parity-check (LDPC) code is introduced to DCSK (LDPC-DCSK) for high reliability. In order to improve the concealment of LDPC-DCSK signal further, a satellite overlapping communication scheme that the LDPC-DCSK covert signal is superimposed on civil satellite service signal is proposed by this paper. This scheme realizes the overlap of the covert signals and the satellite service signals at the transmitter, and it uses a separation algorithm based on auxiliary information at the receiver. In the end, the scheme realizes the separation of the covert signals and the satellite service signals from the mixed signals, and the reliability and spectrum characteristics are evaluated. The simulation results show that under the circumstance which the chaotic signals and the noise power are roughly similar, the scheme not only achieves that the bit error rate of the satellite service signals is almost unaffected after superimposing the covert signals, but also realizes the accurate separation and restoration of the covert signals.
Determinants of Firm Performance in Automotive Industry

This paper is centered towards examining whether innovation, social media marketing, and corporate social responsibility have any associations with firm performance in automotive sector. By reviewing the prior literature, it can be concluded that there are limited studies which focused on investigating the direct impacts of the above factors collectively on firm performance, particularly in automotive industry. Moreover, the performance of automotive industry in Malaysia has recently experienced slow growth. Therefore, a quantitative research method was employed for data collection and fulfilling the research objectives. In particular, the data were collected via survey instrument from many employees of automotive companies at the state of Pahang in Malaysia. The data was then anlayzed by SPSS Version 19 to verify and test the hypotheses. Overall, the outcomes revealed that innovation has a significant positive impact on firm performance. But the influence of social media marketing on firm performance was found insignificant. Finally, the outcomes showed that the practice of corporate social responsibility has a positive impact on firm performance.
An Introduction of Prediction Models From the View of Integration Between Basic Models

The combination model is a widely used method way in machine learning to design the more sophisticated and well-performed model, which can be applied for the field of the recommendation system and so on. This paper is aims to explore the prediction model The normal prediction models have to deal with neither dense numerical features or sparse categorical features. Decision tree is a popular way to deal with dense data, doing both regression and classification. It has various famous expanding algorithms, such as GBDT, XGBoost, and so on, which is designed by different ways of combing the basic decision tree. GBDT and XGBoost are both good at Large-scale machine learning and perform well with dense numerical features. As for sparse categorical features. FM is no doubt the best basic algorithm designed for it. By combing FM with other models, Wide&Deep and DeepFM are better for the recommended system because they perform well and can deal with high-order feature intersection. Furthermore, we can not only just add models together, Deep&Cross and DeepGBM are examples which use NN (natural network) to approximate other models, combing the different parts to an integration. And NN can not only substitute the model is approximated, but also is suited for the online training. This paper is aims to explore the development of the prediction models and the future trend for its improvement.
Elastic Deep Learning Using Knowledge Distillation with Heterogeneous Computing Resources

In deep neural networks, using more layers and parameters generally improves the accuracy of the models, which get bigger. Such big models have high computational complexity and big memory requirements, which exceed the capacity of small devices for inference. Knowledge distillation is an efficient approach to compress a large deep model (a teacher model) to a compact model (a student model). Existing online knowledge distillation methods typically exploit an extra data storage layer to store the knowledge or deploy the teacher model and the student model at the same computing resource, thus hurting elasticity and fault-tolerance. In this paper, we propose an elastic deep learning framework, EDL-Dist, for large scale knowledge distillation to efficiently train the student model while exploiting elastic computing resources. The advantages of EDL-Dist are three-fold. First, it decouples the inference and the training process to use heterogeneous computing resources. Second, it can exploit dynamically available computing resources. Third, it supports fault-tolerance during the training and inference processes within knowledge distillation. Our experimental validation, based on industrial-strength implementation and real datasets, shows that the throughput of EDL-Dist is up to 181% faster than the baseline method (online knowledge distillation).
Single-Pixel Image Classification via Nonlinear Optics and Deep Neural Network

We propose and experimentally demonstrate a hybrid system which utilizes a nonlinear mode-selective optical method to extract the features with single-pixel detection and subsequently recognize the high-resolution images from a deep neural network.
Exploring Partitioning Attacks on the Bitcoin Network

Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted third party. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to the network dynamics. We verify our observations through data-driven analyses and simulations, and discuss the implications of each attack on the Bitcoin network. We conclude with suggested countermeasures.
Federating for Learning Group Fair Models.

Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minmax group fairness in paradigms where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how this fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm -- FedMinMax -- for solving the proposed problem that provably enjoys the performance guarantees of centralized learning algorithms. We experimentally compare the proposed approach against other methods in terms of group fairness in various federated learning setups.
PhD Forum Abstract: Privacy-preserving Data Collection and Sharing in Smart Spaces

While the expanding Internet of Things (IoT) ecosystem provides services that improve space operation efficiency and benefit individuals, its pervasive monitoring ability also causes serious privacy concerns. Recently, an increasing number of privacy laws (e.g., GDPR, CCPA) have been proposed to regulate data controllers who own the space and collect data. The data controllers are mandated to enforce appropriate privacy-preserving mechanisms before the data is released or used for any purpose. However, facilitating the regulations in a smart space faces challenges like an untrusted execution environment, lack of a privacy-compliant data model, and limited computing resources at the edge. This extended abstract explores the issues by discussing our experience with instrumenting a real-world campus-scale IoT testbed. We then propose a system for attestable data collection, privacy-preserving data sharing, and efficient data processing at the edge.
Pre-train or Annotate? Domain Adaptation with a Constrained Budget

Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we view domain adaptation with a constrained budget as a consumer choice problem, where the goal is to select an optimal combination of data annotation and pre-training. We measure annotation costs of three procedural text datasets, along with the pre-training costs of several in-domain language models. The utility of different combinations of pre-training and data annotation are evaluated under varying budget constraints to assess which combination strategy works best. We find that for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, however, a combination of data annotation and in-domain pre-training yields better performance. Our experiments suggest task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
In Search of Probeable Generalization Measures

Understanding the generalization behaviour of deep neural networks is a topic of recent interest that has driven the production of many studies, notably the development and evaluation of generalization “explainability” measures that quantity model generalization ability. Generalization measures have also proven useful in the development of powerful layer-wise model tuning and optimization algorithms, though these algorithms require specific kinds of generalization measures which can probe individual layers. The purpose of this paper is to explore the neglected subtopic of probeable generalization measures; to establish firm ground for further investigations, and to inspire and guide the development of novel model tuning and optimization algorithms. We evaluate and compare measures, demonstrating effectiveness and robustness across model variations, dataset complexities, training hyperparameters, and training stages. We also introduce a new dataset of trained models and performance metrics, GenProb, for testing generalization measures, model tuning algorithms and optimization algorithms.
Investigating Students’ Experience of Instructional Videos with the UX Curve Method

Instructional videos are commonly used in both remote and in-campus curriculum. In order to investigate students’ experience when learning with online instructional videos, we adapted a method called “UX curve” from the user experience studies in industrial design. In this paper, we introduce the procedure and data processing method of using the UX curve method. We applied the UX curve method in a high school physics class while the students were watching an instructional video of kinematics. The results suggested that the UX curve method is practical, convenient, and effective in collecting students’ real-time experience.
Between neo-militant and quasi-militant democracy: restrictions on freedoms of speech and the press in Austria, Finland, and Sweden 2008–2019

After the economic collapse in 2008, we observed rapid changes among media markets, digital transformation of information, and the increasing role of social networks in communication, anti-democratic forces use the freedom of speech and the press to share illiberal rhetoric. Considering these phenomena, this study aims to verify how ruling elites use militant democracy means in consolidated democracies to combat anti-democrats by restricting citizens’ fundamental freedoms to express their thoughts and beliefs. The main argument is that different threats for the freedom of speech and the press have occurred in a consolidated democracy: abusing regulations about hate speech crimes and public incitement to hatred, political interventions in the media system, violating relations between the media market and political system, favouring public media and avoiding using restrictions against them. The scope of those threats is different, resulting from using neo-militant instruments or replacing them with quasi-militant democracy means. The methods employed for the analysis are the qualitative analysis of sources and the quantitative analysis of data in the comparative perspective. Austria, Finland, and Sweden – three EU member states recognised as consolidated democracies, with a democratic corporatist model of media systems – were selected for the study.
Multi-level anomaly prediction in Tier-0 datacenter

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. To solve large problems in science, engineering, and business, data centers provide High-Performance Computing (HPC) systems with aggregation of the computing capacity of thousand of computing nodes. Anomaly prediction is critical in order to preserve the continuity of the service of HPC systems and prevent hardware deterioration. In the datacenter, a thermal anomaly occurs when the balance of cooling capacity and computational demand is disturbed. Moreover, this is identifiable from a suspicious/abnormal pattern in the monitoring signals.