Reviewerly/exHarmony huggingface dataset:
Requirements for a File Format for Smart Musical Instruments

Smart musical instruments are an emerging category of musical instruments characterized by sensors, actuators, wireless connectivity, and embedded intelligence. To date, a topic that has received remarkably little attention in smart musical instruments research is that of defining an interoperable file format for the exchange of content produced by this class of instruments. In this paper we preliminary investigate the design of a format specific to smart musical instruments but that at the same time enables interoperability with other devices. We adopted a participatory design methodology consisting of a set of interviews with studio producers. The purpose of such interviews was that of identifying a set of use cases for a format encoding data generated by smart musical instruments, with the end goal of gathering requirements for its design.
Multidimensional Assessment of Open-Ended Questions for Enhancing the Quality of Peer Assessment in E-Learning Environments

In online learning environments, peer assessment activities lack the observation and supervision by the teacher or instructor. Therefore, students may be lacked full effort to assess their peers. There exist the students' hesitation about criticizing their peers and scoring their peers honestly, the likelihood for peer assessment to be occasionally unreliable and unfair. The present assessment methods focus only on the single-dimensional assessment of content rather than the activities and collaborations among the students. Students also have no chance to analyze and comment on their peer answers. This study explored the multidimensional assessment method on open-ended question to foster positive attitudes and full effort among students engaging in E-learning environments. The objectives are as follows: 1) To develop a process model for multidimensional assessment (M-DA) to enable effective learning 2) To develop free-text answers assessment by using vector space model and semantic extraction model 3) To develop an algorithm for evaluating students' based on multidimensional assessment. Two groups of parallel students taking an e-course were tested on the system. The results of experiment noted that the proposed method overcomes the method just focuses on student assessment only without consideration and evaluation of the quality of the peer assessment. In light of the findings, the proposed method actively impacted on the development and improvement of learning and the quality of peer assessment among students in E-learning environments.
Security Protection Scheme of Embedded System Running Environment based on TCM

Mobile embedded terminals widely applied in individual lives, but its security threats become more and more serious. Malicious attacker can steal sensitive information such as user’s phonebook, credit card information by instrumenting malicious programs, or compromising vulnerable software. Against these problems, this paper proposes a scheme for trusted protection system on the embedded platform. The system uses SM algorithms and hardware security chip as the root of trust to establish security mechanisms, including trusted boot of system image, trusted monitoring of the system running environment, disk partition encryption and verification, etc. These security mechanisms provide comprehensive protection to embedded system boot, runtime and long-term storage devices. This paper introduces the architecture and principles of the system software, design system security functions and implement prototype system for protection of embedded OS. The experiments results indicates the promotion of embedded system security and the performance test shows that encryption performance can meet the practical application.
IoT-based Access Management Supported by AI and Blockchains

This work specifies, implements, and evaluates access management based on face recognition. The system developed uses Internet-of-Things (IoT) for video surveillance, Artificial Intelligence (AI) for face recognition, and Blockchains (BC) for immutable permanent storage and provides excellent properties in terms of image quality, end-to-end delay, and energy efficiency.
Polarization-entangled photon pairs from a single molecule

Single organic dye molecules have been studied for almost three decades. A majority of experiments are now conducted under ambient conditions and aim towards material science and micro-biology. The experiments under cryogenic conditions are often based on highly rigid polycyclic hydrocarbons, which have excellent fluorescing properties when they are operated at temperatures below 2 K. Their photonic advantages integrate their high flux, their narrow-band nature and their tunability over the entire visible spectrum. Therefore the use of organic molecules as efficient and narrow-band single photon sources facilitated a number of quantum optical advances in the past years. The results mostly cover quantum sensing, the formation of quantum hybrid systems and the optical combination of multiple photons in all-optical experiments. Here we present all three fields and outline our own experiments in the combination of single molecule studies, atomic spectroscopy and entanglement generation. The brightness and the narrow-band nature of the molecules are outlined and a delayed-choice quantum eraser is presented. The mode-mixing of two photons on a beam-splitter allows for the generation of a degenerate photon pair, which is resonant to the sodium D<sub>2</sub>-line. Furthermore, its spectral width matches roughly to the natural linewidth of an isolated sodium atom, such that further experiments are feasible. The entanglement of the post-selected photon pair is testified by a violation of Bell's inequality. Even the raw collected clicks violate Bell's inequality by more than two standard deviations.
An Adaptive Edge-Based Steganography Algorithm for Hiding Text into Images

Steganography is one of the techniques for secure transformation of data which aims at hiding information inside other media in such a way that no one will notice. The cover media that can accommodate secret information include text, audio, image, and video. Images are the most popular covering media in steganography, due to the fact that, they are heavily used in daily applications and have high redundancy in representation. In this paper, we propose an adaptive steganography algorithm for hiding information in RGB images. To minimize visual perceptible distortion, the proposed algorithm uses edge pixels for embedding data. It detects the edge pixels in the image using the Sobel filter. Then, the message is embedded into the LSBs of the blue channel of the edge pixels. To resist statistical attacks, the distribution of the blue channel of the edge pixels is used when embedding data in the cover image. The experimental results showed that the algorithm offers high capacity for hiding data in cover images; it does not distort the quality of the stego image; it is robust enough against statistical attacks; and its execution time is short enough for online data transfer. Also, the results showed that the proposed algorithm outperforms similar approaches in all evaluation metrics.
Evaluating Trends and Issues in Distance Learning

Today's learners are intrinsically motivated in that they are apt to analyze, synthesize, internalize, rebuild, share, and disseminate knowledge not only with their instructors, but also their peers, and the greater community, including a plethora of learning communities from across the world. Web-based technologies play a key role in this learning process. Particularly online social media networks and mobile technologies, the two selected technology trends evaluated in this paper, are thriving in online learning/teaching settings, immensely helping students acquire valid learning gains. These technologies are not a fad, a myth, or talk-of-the-day in learning and teaching environments in any given discipline. With that said, there are also certain issues and challenges that accompany these media and technologies for learning and teaching practices, which will also be addressed in this paper. Finally, future implications on these trends and issues in DL will be further discussed with the hope that educational leaders can draw on them for further inquiry and development in the field.
Optimal Sensor Placement for Shooter Localization Using a Genetic Algorithm

This paper proposes a method to find an optimal set of sensor positions for the shooter localization task. Here, optimality is defined in terms of best possible state estimation accuracy given by the Cramér-Rao bound. We derive an optimality criterion, present an application specific genetic algorithm to solve the optimization problem and investigate different scenarios with complete and incomplete measurement data sets and varying number of sensors. As an intermediate step we assume that the shooter state is exactly known. The results show that depending on the available measurement data set, the recommended optimal sensor positions are often unexpected. For all considered scenarios, the applied optimization approach determines the optimal positions reliably.
Distribution Privacy Under Function $\rho$ -Recoverability

A user generates <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> independent and identically distributed data rvs with a pmf that must be guarded from a querier. The querier must recover, with a prescribed accuracy, a given function of the data from each of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> independent and identically distributed user-devised query responses. The user chooses the data pmf and the random query responses to maximize distribution privacy as gauged by the divergence between the pmf and the querier's best estimate of it based on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> query responses. Considering an arbitrary function, a basic achievable lower bound, that does not depend on n, is provided for distribution privacy. Next, upper (converse) and lower (achievable) bounds, dependent on n, are developed that converge to said basic bound as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> grows. Explicit strategies for the user and the querier are identified.
Coordinating<i>If</i>s

Abstract Accounting for the behavior of conjoined and disjoined if-clauses is not easy for standard theories of conditionals that treat if as either an operator or restrictor. In this paper, I discuss four observations about coordinated if-clauses, and motivate a semantics for conditionals that reorients the compositional structure of the restrictor theory. On my proposal, if-clauses provide restrictions on modal domains, but they do so by way of a higher type intermediary—a set of propositions—that is collapsed by the modal. I argue that combining this view with an independently plausible type-shifting operation applied to or and and predicts the range of data we find for conditionals with coordinated if-clauses.
Cloud Computing and Cybersecurity Issues Facing Local Enterprises

This chapter sets out to explore new trends in cyber and cloud security, and their implications for businesses. First, the terminology and assumptions related to cloud computing are stated. Next, the chapter reports on contemporary research around the awareness of security issues, and the security processes within the cloud computing realm. Cyber security poses a different challenge to local small and medium sized organizations, which may seem to have less at stake financially. However, they are more vulnerable, due to fewer resources dedicated toward prevention. A series of serious security incidents may even keep them out of business. Furthermore, security needs to be understood and handled differently in a cloud based environment. Therefore, the chapter identifies unique security practices and recommendations for these businesses to run their IT resources safely in the cloud.
Low-Rank Semantic Feature Reconstruction Hashing for Remote Sensing Retrieval

Remote sensing image retrieval (RSIR) is the main technology for automatic analysis and understanding of remote sensing big data, which has been widely concerned in recent years. The mainstream attention mechanism based on high order tensor plays an important role in computer vision, but the model complexity is high. Low-rank feature reconstruction can reconstruct high-order context semantics based on low-rank tensor, and the feature reconstruction layer can realize the fusion of high-order context semantics. In order to reconstruct high-order remote sensing semantics, we propose a novel low-rank semantic feature reconstruction hashing (LRSFRH) using a lightweight dual-attention mechanism and semantic reservation loss to capture remote contextual semantic information of remote sensing scenes for remote sensing retrieval. Its main contributions are as follows: 1) lightweight dual-attention mechanism is proposed based on effective channel attention (ECA) and high-order tensor reconstruction (HTR). Among them, HTR can explore high-order contextual semantic information of remote sensing with low-order constraints, and ECA’s cross-channel interaction can significantly reduce model complexity while maintaining performance; 2) in the remote sensing feature hashing space, we use second-order global covariance pooling (GCP) to accelerate model convergence and enrich remote sensing semantic representation; and 3) in metric learning, we propose a new multiple semantic reconstruction loss (MSRL) to optimize network parameters. Experimental results show that LRSFRH outperforms most existing hash algorithms on two public benchmark datasets (AID and UC Merced), and the proposed algorithm achieves state-of-the-art (SOTA) performance in RSIR tasks.
Network Risk Assessment Method Based on Asset Correlation Graph

In order to enhance the security of network operations, establish effective security measures, prevent the destruction of security incidents, and reduce or eliminate the losses caused by threats through network risk assessment is of important practical significance. However, most risk assessment methods focus on the research of threats and vulnerabilities. There are relatively few researches on risk based on network assets and there is a lack of accuracy in risk assessment. Therefore, this paper proposes a network risk assessment method based on asset association graphs. The method first describes the network from the perspective of asset interconnection and builds an asset association graph; secondly, it builds a threat scenario based on the asset association graph, identifies a threat event, and uses the probability of a threat event and the loss caused by the asset to obtain a quantitative description of the risk assessment; Different network risk levels and make decisions. Experiments show that the method of network risk assessment based on asset association proposed in this paper can realize the risk assessment of all assets, hosts and entire network system in the network, and provide effective guidance for network security protection.
Scientific and Methodological Approaches to Creating a System of Pedagogical Diagnostics of the Level of Training and Development of Primary School Graduates

The article discusses the problem of obtaining reliable information about the level of educational achievements of junior schoolchildren during&#x0D; their transition to the next stage of school education. The implementation of the development goals of students at the initial stage of schooling, set by the Federal State Educational Standard of Primary General Education, requires pedagogical diagnostics, which makes it possible to establish the cause of the difficulties that children have in the course of studying various subjects. This ensures the timely intervention of the teacher in the learning process to provide each younger student with pedagogical assistance and support, which makes it possible to eliminate the individual difficulties that arise in him.
Construction and Reasoning Method of Fault Knowledge Graph with application of Engineering Machinery

This paper studies the construction and reasoning method of engineering machinery fault knowledge graph, which provides a new idea for assisting the troubleshooting of engineering machinery. Aiming at the problems of low reuse rate of maintenance experience resources, scattered and non-standard naming of relevant maintenance documents, and low processing efficiency, this paper proposes a fault knowledge graph method based on deep learning, and embeds the knowledge graph into the multi-task learning framework to improve the generalization ability of the model and finally achieve the purpose of aid decision making (ADM). For the maintenance documents of engineering machinery, the natural language processing (NLP) technology is used to extract entities and relations. Then the convolution neural network model (CNN) is used to extract the features of text data and classify them. According to the preset rules, the resource description framework (RDF) triples are formed to generate fault knowledge graph. The fault knowledge graph embedding (KGE) task and the ADM task are two subtasks of multi-tasking learning framework, and the ADM model is obtained by alternating iterative train the two tasks. The effectiveness of the proposed fault diagnosis method is verified by using the maintenance documents of a domestic excavator.
Security and communication distance improvement in decoy states based quantum key distribution using pseudo-random bases choice for photon polarization measurement

This paper proposes a new quantum key distribution(QKD) protocol, namely the pseudo-random bases entangled photon based QKD (PRB-EPQKD) protocol. The latest mainly focuses on three properties, including the security of the protocol, the secure key size and the maximum communication distance between legitimate communication users (Alice and Bob). To achieve this, we first consider a spontaneous-parametric-down (SPDC) photon source located in a low-earth-orbit (LEO) type satellite capable of producing and distributing entangled photons pairs to Alice and Bob. Secondly, we assume that Alice’s and Bob’s photons state measurement bases are identically generated via a pseudo-random number generator (PRNG), namely the quantum logistic map (QLM). Finally, we also assume that in addition to their photons states, Alice and Bob intentionally share a set of decoy states at each pulse with randomly selected intensity, and with the goal to detect the presence of the eavesdropper (Eve). Under these considerations, the secure key rate upper bound is evaluated applying the Gottesman-Lo-Lutkenhaus-Preskill’s (GLLP) formula, for two different implementations, namely the non-decoy states and the infinite active decoy states based QKD. It is observed a significant improvement in the secure key size and the communication distance as well, compared to existing protocols, since we realize that under daylight, downlinks satellite conditions, a kindly selected light source, and good crystal’s properties, the maximum communication distance can reach up to 70000 km. In addition, using the combined type-I and type-II SPDC photons source as our entangled photons pairs generator, significantly improved the photon mean number and render our protocol more robust against photon number division attack and against attenuation-induced atmospheric propagation. Furthermore, the protocol is more secure as compared to existing ones, given that any eavesdropper must crack simultaneously the chaotic system used as PRNG and the QKD system, before getting any useful information as regards to the measurement bases used by Alice and Bob, and thus the secure key.
Dynamic mode of the mathematical model of an electric multipole with memresistive branches in conditions of interval uncertainty

Abstract This article discusses a method for obtaining a mathematical model of an electric multipole with memresistive branches in finite parameter increments for a large signal mode in time domain calculation when calculating a direct current based on a generalized model obtained earlier. A computational algorithm for calculating nanoelectronic circuits based on methods of interval arithmetic is also given.
Exploring feature interactions without specifications: a controlled experiment

In highly configurable systems, features may interact unexpectedly and produce faulty behavior. Those faults are not easily identified from the analysis of each feature separately, especially when feature specifications are missing. We propose VarXplorer, a dynamic and iterative approach to detect suspicious interactions. It provides information on how features impact the control and data flow of the program. VarXplorer supports developers with a graph that visualizes this information, mainly showing suppress and require relations between features. To evaluate whether VarXplorer helps improve the performance of identifying suspicious interactions, we perform a controlled study with 24 subjects. We find that with our proposed feature-interaction graphs, participants are able to identify suspicious interactions more than 3 times faster compared to the state-of-the-art tool.
Confocal Microscopy for Surface Profilometry

The application of automated optical inspection (AOI) to advanced manufacturing processes with tight takt time and specifications is critical in winning today’s global competition. In the past decades, great effort had been devoted to developing novel solutions for in-line optical inspection of surfaces and the dynamic characteristics of tested components or devices. Conventional approaches to microscale 3D surface profilometry have adopted novel optics or concepts in confocal microscopy for measuring 3D surface characteristics with high speed and precision. One-shot measurement capability is demanded to minimize measured uncertainty from environmental vibration or system instability. Nevertheless, extremely high-speed microscopic 3D confocal profilometric methodologies for 100% full-field inspection are yet to be developed. This chapter intends to provide the measurement principle and development of confocal optical profilometry in overcoming bottlenecks and developing feasible solutions for novel manufacturing technologies, such as roll-to-roll nanoimprinting or semiconductor processes. It contains the basic optics, the measuring algorithms and industrial applications in optical confocal microscopy required for surface profilometry.
Public Policies for Providing Cloud Computing Services to SMEs of Latin America

SMEs are considered the backbone of the Latin American economic region as they represent an overwhelming majority of private enterprises in the region and account for 99% of businesses and employ 67% of employees. One of the elements in which SMEs need assistance is their involvement in the IT-enabled services (ITES) both for greater efficiency and optimization of the economic data. Cloud computing as a modern concept has commercialized the use of ITES along with sharing data at a wider scale. Tacit knowledge in the form of data can be easily shared at large scale with the help of cloud computing. While MNCs have started using cloud computing to benefit the organizations, SMEs are far behind in truly understanding the relevance of cloud computing in benefitting business operations. The chapter analyzes the existing opportunities and then formulates relevant public policies for providing cloud computing services to the SMEs of Latin America.