Reviewerly/exHarmony huggingface dataset:
And She Will Sound The Alarm: Performance and Demo of the Body Sample Player

Kinesthetic empathy, in the context of interactive design, is defined as the ability to encode and decode the input of other users of the system or to sense a shift in the system itself [7]. Achieving this between two players or between players and observers in a virtual performance system involving sound is difficult to achieve and requires that players and audience have an existing connection with the system that marries the sonic and kinesthetic sense known as “auditory kinesthesia” [11]. These sensibilities can be facilitated through the design of the system itself following the principles of usability and kinesthetic interaction, or through performer training [9]. In traditional music and dance, kinesthetic empathy exists both between players and between players and audience. It is much more difficult to achieve this in the context of interactive performance as the audience does not have the same culturally specific empathetic response when observing digitally mediated performance as they do when viewing forms of music and dance they are already familiar with. The Body Sample Player is a digital musical instrument and performance platform that uses real-time data gathered by body tracking cameras to control the volumes of looping sound samples, turning the body itself into a sound controller. The system has evolved over the years, facilitating several different musical works ranging from solo ”DJ” controller style musical performances to large scale interactive music and dance works for multiple performers. It has also been used to create interactive installations that are accessible to the audiences of these large-scale works. It is our contention that by giving audience members first-hand experience engaging with this instrument before witnessing a performance, they will more easily be able to recognize expressivity in the performance, and in doing so, will experience a higher level of kinesthetic empathy. Therefore, we propose a performance of And She Will Sound the Alarm, a piece for a single performer created for the Body Sample Player system, to be preceded by a demonstration in which conference attendees can interact with the system itself.
Combining Logical and Probabilistic Reasoning to Improve a home care platform

Several areas have evolved with the evolution of technology. Telemedicine and home care are one of them. While there are many studies on home care, the overall problem of decision-making is not sufficiently addressed. In previous research, we proposed an ontological-based intervention system. Despite its abilities, reasoning on an ontology is currently limited to logic. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or assess the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In this article, we propose two approaches to combining probabilistic and logical reasoning within a home-based medical intervention system. The first, using probabilistic ontology PR-OWL. The second, providing a Jena API (Application Programming Interface).
SmoothOperator : A Device for Characterizing Smoothness in Body Movement

In our research we faced the problem of characterizing smoothness in human movement. Even though ’smooth’ is a common way to describe and conceptualize motion in the performing arts as well as in informal speech, we realized the need for a tool to differentiate between various degrees and modes of smoothness. We propose that smoothness operates at different interlocking orders. These appear only in aggregation, intertwined with other qualities of body movement. Akin to how a spectrometer splits light into a spectrum of frequencies, we developed a method to measure the degree of smoothness in each order, as an epistemic tool for dance practitioners to investigate the quality of body movement from a fresh perspective. To this end we have implemented a device that provides dancers with aural, haptic and visual feedback in real time, taking into account the constraints of a dance practice session.
Fabrication and evaluation of high-quality and low-cost quantum random number generators

In this paper, we have fabricated two quantum random number generators (QRNGs) based on different mechanisms. The first one is based on the photon time of arrival and produces high-quality random numbers without the need for post-processing but using expensive equipment. The second one is based on the tunneling effect in a Zener diode and produces random strings with comparable quality but using low-cost equipment. We then evaluated the random sequences from these QRNGs using a set of statistical tests and showed that they are suitable for special applications such as quantum technologies.
Research on Intelligent Classification Method of Seismic Information Text Based on BERT-BiLSTM Optimization Algorithm

With the development of science and technology, it is possible to quickly obtain massive disaster information after the earthquake, but because the earthquake information not only has the characteristics of strong timeliness, but also is always in the process of dynamic change, it can quickly classify and analyze the earthquake information, which is of great significance for earthquake emergency decision-making. In this paper, an earthquake news text intelligent classification model based on the BERT-BiLSTM optimization algorithm is proposed. First, based on the BERT (Bidirectional Encoder Representation from Transformers) pre-trained model, the algorithm performs a sentence-level feature vector representation of the seismic news text, and enters the feature vector into the BiLSTM layer to extract the global features of the seismic news text, and then enters the SoftMax classifier for classification. Finally, the control experiment of earthquake news text data in Qinghai and Yunnan was passed. Experimental results show that the model is improved by 2 percentage points compared with the traditional Bert model method. Therefore, the intelligent classification model of earthquake information text proposed in this paper can effectively and accurately determine the category of earthquake news and help earthquake emergency rescue decision-making.
Triviality and the logic of restricted quantification

This paper clarifies the relationship between the Triviality Results for the conditional and the Restrictor Theory of the conditional. On the understanding of Triviality proposed here, it is implausible—pace many proponents of the Restrictor Theory—that Triviality rests on a syntactic error. As argued here, Triviality arises from simply mistaking the feature a (e.g. conditional-involving) claim has when that claim is logically unacceptable for the feature a claim has when that claim is unsatisfiable (in a model). Triviality rests on a semantic confusion—one which some semantic theories, but not others, are prone to making. On the interpretation proposed here, Triviality Results thus play a theoretically constructive role in the project of natural language semantics.
A low-cost and verifiable sealed bid auction protocol based on smart contracts

Sealed-bid auctions are a widely used transaction method in today’s society. The traditional implementation scheme relies too much on third-party platforms, and there are severe problems in the auction protocol’s security and users’ privacy protection. At present, some sealed bid auction schemes based on blockchain technology have been proposed, but these schemes generally do not consider the storage cost of users. We propose a sealed-bid auction protocol based on Ethereum smart contracts that remove third parties in traditional protocols to address these issues. We have significantly reduced the storage cost of users by introducing the Interplanetary File System (IPFS). In particular, we use a commitment mechanism to protect bid privacy and combine it with a zero-knowledge proof algorithm to achieve publicly verifiable bidders, guaranteeing the protocol’s security, privacy, and transaction fairness. The experimental results show that the proposed sealed bid auction protocol can reduce the storage cost of users in Ethereum while ensuring the security of the protocol and the privacy of users’ bids.
Specifications for Textbook Materials at Madrasah Ibtidaiyah

This study aims to find the specification of textbook material for grade 1 Madrasah Ibtidaiyah (MI) in Salatiga City. This study used descriptive qualitative and descriptive quantitative approaches. A qualitative descriptive approach was employed to describe the data obtained through the interview method, with the type of guided interview. The interview method used an interview guide instrument, analyzed through the stages of analyzing data, reducing data, categorizing data, interpreting data, and drawing conclusions. Meanwhile, a quantitative descriptive approach was utilized to describe the data obtained through a questionnaire, which used a filling type questionnaire. Questionnaire data were then analyzed using descriptive statistical formulas. The study results found that the material specifications for the MI grade 1 textbook in Salatiga City had been adjusted to the core competencies (KI) and basic competencies (KD) set by the government; the development of the material was sourced from information technology that can be accounted for and from similar books; the material was adapted to the students' abilities; the material was adapted to the conditions of the student's cultural environment; the material paid attention to the condition of student diversity; the material contains factual truths and concepts; supporting learning; the material begins with question and command sentences. The theoretical implication of this finding is that it can be used as a reference in assessing and developing textbooks at MI.
SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs

Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental building blocks of complex graph algorithms and its implementation is included in graph libraries for large-scale graph processing. In this paper, we propose a novel direction selection method, SURF (Selecting directions Upon Recent workload of Frontiers) to enhance the performance of BFS on GPU. A direction optimization that selects the proper traversal direction of a BFS execution between the push and pull phases is crucial to the performance as well as for efficient handling of the varying workloads of the frontiers. However, existing works select the direction using condition statements based on predefined thresholds without considering the changing workload state. To solve this drawback, we define several metrics that describe the state of the workload and analyze their impact on the BFS performance. To show that SURF selects the appropriate direction, we implement the direction selection method with a deep neural network model that adopts those metrics as the input features. Experimental results indicate that SURF achieves a higher direction prediction accuracy and reduced execution time in comparison with existing state-of-the-art methods that support a direction-optimizing BFS. SURF yields up to a 5.62× and 3.15× speedup over the state-of-the-art graph processing frameworks Gunrock and Enterprise, respectively.
New approach of smoothing to extend language model in Lucene

This paper focuses on extending Information Retrieval (IR) API named Lucene by implementing various smoothing techniques. Lucene is a free API written entirely in JAVA. It allows to create an indexing and search engine for textual files. Our contribution has two parts: first, we propose a smoothing approach that we integrated into Lucene’s language model in order to improve its search abilities. The suggested approach is based on a combination of algorithms already implemented in Lucene. Next, we have implemented the Absolute Discount smoothing approach and integrated it into Lucene’s language model. Our proposed approaches have been evaluated in information retrieval on test collection. Our contribution yielded very good search results in some cases compared to other approaches implemented in Lucene.
Virtual Reality Simulation of a Robotic Laparoscopic Surgical System

Virtual reality simulation of robotic-assisted min-imal invasive procedures reveals interesting issues related to the perception, control and manipulation of laparoscopic tools with emphasis given to the pivot trajectories and the Remote-Center-of-Motion (RCM) constrained motion planning. In this paper, the Gazebo simulator under Robot Operating System (ROS) allows the inclusion of hardware-in-the-loop for Minimally Invasive Surgery (MIS) procedures. The RCM constraint is addressed through the transformation of the surgical task space into the robot's taskspace, while addressing the robot's manipulability. Emphasis is given in calculating various geometric paths to be followed by the robot during surgery. Simulations were conducted using the ROS framework and the MoveIt kinematic planner using the RRTConnect path planning algorithm to evaluate the efficacy proposed scheme.
A Data-Efficient Method for One-Shot Text Classification

In this paper, we propose BiGBERT (Binary Grouping BERT), a data-efficient training method for one-shot text classification. With the idea of One-vs-Rest method, we designed an extensible output layer for BERT, which can increase the usability of the training data. To evaluate our approach, we conducted extensive experiments on four celebrated text classification datasets, and reform these datasets into one-shot training scenario, which is approximately equal to the situation of our commercial datasets. The experiment result shows our approach achieves 54.9% in 5AbstractsGroup dataset, 40.2% in 20NewsGroup dataset, 57.0% in IMDB dataset, and 33.6% in TREC dataset. Overall, compare to the baseline BERT, our proposed method achieves 2.3% $\sim$ 28.6% improved in accuracy. This result shows BiGBERT is stable and have significantly improved on one-shot text classification.
An Effective Hybrid Stochastic Gradient Descent for Classification of Short Text Communication in E- Learning Environments

Nowadays, various media platforms play an inevitable role in regular life in such a way that we couldn't even think of living without these digital platforms. These digital platforms including e-learning systems are excellent for disseminating knowledge such as text, photos, and other pieces of data. Topic classification analysis on these systems have recently attracted a great deal of attention and this is expected to continue. It is the use of non-traditional media platforms that distinguishes them from other forms of communication. As a result, there is an urgent need for efficient methods of assessing the vast quantity of token variants that occur on a regular basis in the digital world. A piecewise Stochastic Gradient Descent (SGD) classification-based algorithm for categorization is proposed in this study. For the representation of textual features, the TF -IDF term weighting system with unigram, bigram, and trigram is used. To boost the effectiveness of the proposed system, partially ordered microword representations of tweets with changing look ahead distances are used. The proposed model is simulated with partial order microwords representation of tweets having lookahead distance 1 and it achieved an enhanced accuracy of 90.73%.
A New Financial Group Lending Based on Smart Contracts: An Agent-based Simulation

The aim of this study is to propose a new financial group lending system based on smart contracts, Under blockchain technology and Islamic jurisprudence point of view. The model proposes a new way that Islamic bank can finance projects and attract more investors who are looking for safe, profitable and Shariah compliant investment, this new model attempt to reduce antiquated and inefficient processes, settlement delays, fraud and concentration of risks by digitizing traditional lending paper contracts into a self-executed code line. We also propose an agent-based simulation using Netlogo as a multi-agent environment to experiment the new financial group lending system based on Smart contract.
Applying machine learning techniques to localize quadcopter sensor failures

The paper describes an algorithm for localization and classification of quadcopter sensor (accelerometer and gyroscope) failures. Based on the obtained attributes; the conclusion is made about the serviceability of a quadcopter-type unmanned aerial vehicle (UAV). Two machine learning methods are used: logistic regression and random forest method. Their performance evaluated and compared using the data obtained by simulating the physical model of a quadcopter. A computer simulation of position sensor failure detection is presented. The proposed approach has shown its effectiveness and, therefore, it can be used to build a fault-tolerant adaptive motion control subsystem for UAVs.
Information Visualization Design of Web under the Background of Big Data

With the rapid development of the Internet, the information on the Internet presents an explosive growth. Cloud computing and big data analysis technology based on Internet information rise accordingly. However, all web pages contain not only important information but also the noise information irrelevant to the subject information. They seriously affect the accuracy of information extraction, so the research of web page information extraction technology arises at the historic moment and becomes the research hotspot. The quality of web page text information will directly affect the accuracy of later information processing and decision-making. If we can accurately evaluate the information of the web pages captured from the Internet and classify the extracted web pages according to the corresponding characteristics, we can not only improve the efficiency of information processing, but also improve the practical value of the information decision-making system. From the practical application requirements and user-friendly operation point of view, the information visualization of web design based on big data is studied in this paper. Specifically, the system designed in this paper improves the traditional template-based web information extraction method, establishes a web information extraction rule scheme combined with templates, and achieves the goal of web information extraction rule selection and template generation in the visual environment. Finally, the visualization algorithm based on T-SNE verifies the effectiveness of the web page information visualization algorithm designed in this paper.
Complete analytical solution to the energy-optimal problem of multivariable perfect control systems based on the generalized H-inverse

A complete analytical solution to the complex energy-optimal design problem of the LTI MIMO perfect control systems is established in this paper. It is proven here that the application of the generalized SVD-based right H-inverse can successfully be engaged in analytical solving the compos-ite minimum-energy scheme for the multivariable nonsquare plants described in the discrete-time state-space framework. Moreover, the mentioned inverse clearly outperforms the non-unique o-inverse in the context of quality and computational complexity. Consequently, the advantageous peculiarity of the SVD-originated inverse canon will expressly contribute to the establishment of the new optimal unified inverse model control (IMC) design theory.
Gender Bias in Password Managers

For the first time, we report gender bias in people’s choice and use of password managers, through a semi-structured interview (n = 18) and a questionnaire-based survey (n = 200, conducted ‘in the wild’). Not only do women and men prefer different types of password managers, but software features that women and men frequently use also differ. These differences are statistically significant. The factors that women and men consider the most important or influential in choosing their password managers differ, too. Choice of convenience and brand are on the top of the women’s consideration, whereas security and the number of features top the list for men. This difference is statistically significant.
Optimized L2 Norm Loss for Adversarial Robustness

Although adversarial training is the most common method to make models obtain better adversarial robustness, its drawback of leading to reduced accuracy has been plaguing the academic community. In recent years, many articles have pointed out that good Lipschitz continuity helps models obtain better robustness and standard accuracy, and argued that models that are both robust and accurate exist. However, many methods still perform less well with models even with the addition of Lipschitz continuity constraints. Therefore, we discuss the drawbacks of existing Lipschitz continuity metric in deep learning in terms of Lipschitz continuity, and propose a counteracting Lipschitz continuity metric that is more suitable for deep learning. We demonstrate theoretically and experimentally that Mixup can significantly enhance the local Lipschitz continuity of the model. Using this property, we generate a large number of mix confrontation samples using Target attack to fill the entire neighborhood space. Our method gives the model a smoother localization and significantly improves the adversarial robustness of the model beyond most existing adversarial training methods.
A Jena API for combining ontologies and Bayesian object-oriented networks

Reasoning on an ontology is presently limited to the logical one. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or measure the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In these cases the users must integrate the probabilistic information of Bayesian networks (BN) in ontology manually. We propose a new approach to integrate the BN (more particularly Object-Oriented Bayesian Networks (OOBn)) with OWL by providing a Jena API (Application Programming Interface). OOBNs is an extension of the standard BN using the paradigm object. Our approach (i) uses the BN knowledge base for ontological enrichment, (ii) allows the adjustment of BN structures through structural and parametric learning based on ontological knowledge bases, (iii) integrate probabilistic information into SPARQL queries. In addition, we propose a solution in which we use a selection of instances according to the needs of the user described by a set of SPARQL requests.