Reviewerly/exHarmony huggingface dataset:
Feeling movement in live electronic music: An embodied autoethnography

Movement-based interaction relies on the measurement and abstraction of human motion. Yet reducing physical experience to quantitative and visual representations overlooks the inner perceptions and sensations that accompany movement. Rather than treating the body as an object to be observed and analysed, soma-based design approaches instead reflect first-person perspectives of felt experience, providing insight into how the body experiences and interacts with the world. Applying these methods to the musical realm, this paper offers a personal account of working with motion-sensing interfaces, exploring the underlying qualities and meanings of performance actions in live and recorded contexts. The embodied authethnographic study serves as an opportunity to question normative approaches to technology that favour techno-scientific narratives of external control and surveillance. Alternative values of agency, autonomy, empathy and transparency are investigated using first-person methodologies.
A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation

Recently, research on open domain dialogue systems have attracted extensive interests of academic and industrial researchers. The goal of an open domain dialogue system is to imitate humans in conversations. Previous works on single turn conversation generation have greatly promoted the research of open domain dialogue systems. However, understanding multiple single turn conversations is not equal to the understanding of multi turn dialogue due to the coherent and context dependent properties of human dialogue. Therefore, in open domain multi turn dialogue generation, it is essential to modeling the contextual semantics of the dialogue history rather than only according to the last utterance. Previous research had verified the effectiveness of the hierarchical recurrent encoder-decoder framework on open domain multi turn dialogue generation. However, using an RNN-based model to hierarchically encoding the utterances to obtain the representation of dialogue history still face the problem of a vanishing gradient. To address this issue, in this article, we proposed a static and dynamic attention-based approach to model the dialogue history and then generate open domain multi turn dialogue responses. Experimental results on the Ubuntu and Opensubtitles datasets verify the effectiveness of the proposed static and dynamic attention-based approach on automatic and human evaluation metrics in various experimental settings. Meanwhile, we also empirically verify the performance of combining the static and dynamic attentions on open domain multi turn dialogue generation.
Anomaly Detection with Selective Dictionary Learning

In this paper we present new methods of anomaly detection based on Dictionary Learning (DL) and Kernel Dictionary Learning (KDL). The main contribution consists in the adaption of known DL and KDL algorithms in the form of unsupervised methods, used for outlier detection. We propose a reduced kernel version (RKDL), which is useful for problems with large data sets, due to the large kernel matrix. We also improve the DL and RKDL methods by the use of a random selection of signals, which aims to eliminate the outliers from the training procedure. All our algorithms are introduced in an anomaly detection toolbox and are compared to standard benchmark results.
A Novel Sub-Pixel-Shift-Based High-Resolution X-ray Flat Panel Detector

In this paper, we describe a novel sub-pixel shift (SPS)-based X-ray flat panel detector (FPD), which can achieve high resolution while maintaining a high SNR (signal-to-noise ratio). In the proposed architecture, an XY precision shift stage is applied to complete the sub-pixel shift process. In addition, image acquisition and high-resolution image composition are integrated in the FPD hardware. According to the relevant standards for detector image quality evaluation, we tested and evaluated some image quality indicators. The results show that the proposed FPD with SPS outperforms the original FPD without SPS technology. More specifically, the measured pixel size of the proposed FPD was reduced from 162 to 140 μm for 2 × 2 sub-pixel shift mode, and 132 μm for 4 × 4 sub-pixel shift mode, that is, the basic spatial detector resolution was improved by 13.6% for the simplest 2 × 2 sub-pixel shift mode, and by 18.5% for 4 × 4 sub-pixel shift mode. With this method, a lower-price FPD is elevated both in resolution and SNRn to meet imaging quality requirements.
Analysis of Different Types of Digital Signature

A signature scheme is a method of signing a message stored in electronic form. Such signed message can be transmitted over a computer network. Digital signatures have many applications in information security, related to authentication, data integrity, and non-repudiation. One of the most important applications of digital signatures is the public keys certification in large networks. This paper is dedicated to analyze different digital signature schemes term of the domain of the group used and the analysis three categories are proposed to increase the development directivity of new efforts toward producing new digital signature algorithms.
Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

Abstract In this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.
Investigation of Sensor Bias and Signal Quality on Target Tracking with Multiple Radars

The tracking of airborne targets from low-cost Internet of Things (IoT) sensors, such as radars, is a problem of increasing interest due to the proliferation of drones. Such low-cost IoT sensors have problems with sensor bias and poor signal quality which may impact detection and tracking performance. In this paper, we investigate the impact of sensor imperfections on tracking performance. In particular, we consider the scenario of two ground-based sensors measuring the elevation/bearing/range of three airborne targets in clutter. The measurement uncertainty of the second sensor was altered between test cases to emulate sensor bias, then the results of the multi-target tracking algorithm were compared using the Generalized Optimal Sub-Pattern Assignment, Single Integrated Air Picture, and uncertainty metrics. Results indicate that bias in the range measurement tends to decrease the track’s robustness against clutter. A sensor with equally poor performance in the elevation/bearing/range measurements scored the lowest in all investigated metrics. The clutter density parameter of the investigated multi-target tracking problem, the Joint Probabilistic Data Association filter, was altered and found to have nearly negligible effect on the track quality.
Chinese-Korean Weibo Sentiment Classification Based on Pre-trained Language Model and Transfer Learning

Korean is the native and official language spoken by Chinese-Korean people, and Weibo is a social media software with a huge number of users in China. Currently, there is few studies related to sentiment analysis of Korean-language Weibo texts posted by Chinese-Korean users. In this paper, we propose a sentiment classification method for Chinese-Korean Weibo based on pre-trained language model and transfer learning. Firstly, we crawled the Chinese-Korean Weibo data from Sina Weibo and label them with sentiment to get the Chinese-Korean Weibo sentiment analysis (CKWSA) dataset. Secondly, to solve the problem of few training samples of the Chinese-Korean Weibo sentiment analysis dataset, we fine-tune the classifier based on the pre-trained Korean language model on the Korean Twitter sentiment analysis dataset to obtain the Korean Twitter sentiment classification model; and further fine-tune the model on CKWSA dataset to get Chinese-Korean Weibo sentiment classification model. The experiments show that the proposed classification method based on pre-trained language model and transfer learning has great performance, and there is an improvement compared other baselines on the Chinese-Korean Weibo sentiment analysis dataset.
A Batch Processing Technique for Wearable Health Crowd-Sensing in the Internet of Things

The influx of wearable sensor devices has influenced a new paradigm termed wearable health crowd-sensing (WHCS). WHCS enables wearable data collection through active sensing to provide health monitoring to users. Wearable sensing devices capture data and transmit it to the cloud for data processing and analytics. However, data sent to the cloud is vulnerable to on-path attacks. The bandwidth limitation issue is also another major problem during large data transfers. Moreover, the WHCS faces several anonymization issues. In light of this, this article presents a batch processing method to solve the identified issues in WHCS. The proposed batch processing method provides an aggregate authentication and verification approach to resolve bandwidth limitation issues in WHCS. The security of our scheme shows its resistance to forgery and replay attacks, as proved in the random oracle (ROM), while offering anonymity to users. Our performance analysis shows that the proposed scheme achieves a lower computational and communication cost with a reduction in the storage overhead compared to other existing schemes. Finally, the proposed method is more energy-efficient, demonstrating that it is suitable for the WHCS system.
Towards Internet of Things (IoT) Forensics Analysis on Intelligent Robot Vacuum Systems

With the rapid advancement of information tech-nology, the Internet of Things (IoT) has significantly impacted people&#x0027;s daily life. IoT devices not only bring comfort and convenience to every aspect of the world, but also appear to be a new target of cybercrimes. Thus, IoT forensics becomes a critical step in forensics investigation. Intelligent robot vacuums are one of the most popular IoT devices. As robot vacuums can connect to the Internet and be operated through mobile apps, a large amount of data may be stored and transmitted among the vacuums, mobile apps, and the network. The data may include the history of the robot&#x0027;s operation, network and user credentials, and layouts of the floor plan of a house. From the perspective of digital forensics, these data can be critical while collecting necessary evidence, investigating suspects and victims, and reconstructing crime scenes. To this end, this paper makes an initial attempt to conduct a digital forensic analysis on intelligent robot vacuum systems. Specifically, this paper retrieves and analyzes a robot vacuum&#x0027;s operation log, the installation details of the robot vacuum&#x0027;s control system, and the usage record of the application from the memory of a smartphone.
Recognition System Of Human Activities Based On Time-Frequency Features Of Accelerometer Data

With the development of Micro-Electro-Mechanical System, wearable sensor-based human activity recognition systems have important applications in various fields such as health management, motion analysis, military and industry. In this paper, we propose a time-frequency features extraction method based on wavelet transform, which extracts 5 time-frequency features, namely wavelet entropy, wavelet energy, wavelet waveform length, wavelet coefficient variance and wavelet coefficient standard deviation. The experimental results are evaluated on the publicly available benchmark WISDM dataset including accelerometer data. Our model achieves 99.2%, 99.1% and 95.6% test accuracy on Subspace KNN, Bagged tree and Gaussian SVM respectively.
Blockchain Technology for Biomedical Engineering Applications

The healthcare industry is one of the largest and fastest-growing industries involving more than 10% of the gross domestic product (GDP) of several countries globally. The industry is under strict regulatory norms, involves a complex system of different interconnected entities leading to fragmented patient data and enhanced costs due to system inefficiencies leading to lack of complete transparency in data transactions, data traceability issues, and security concerns for the patient. To overcome this problem, blockchain technology has gained considerable attention in several domains, including the healthcare industry, to ensure secure and reliable management of real-time clinical healthcare patient data. This would help in reducing healthcare costs, accurately report all clinical trials to overcome substandard and fake drug usage, which are still major areas of concern in the healthcare industry. The technology could be employed to support patient data management, hospital supply chain management (SCM), pharmaceutical research, online patient monitoring through hospital telemedicine set-up, and hospital billing 246system. The blockchain follows peer to peer integrated network where any transaction request made is broadcasted to all nodes and needs to be approved by all nodes to be added to the blockchain network. The blockchain system comprises of a decentralized database with open access, transparency, and autonomy to all users who are logged into the network to access information and regularly update after approval when required. Once the records are updated in the blockchain network, it cannot be deleted or removed easily without authentication and approval from more than half (51%) the number of users on the network.
Telecom Operator’s Approach to QoE

Telecommunication networks are ever more frequently relying on artificial intelligence and machine learning techniques to detect specific use patterns or potential errors and to take automated decisions when these are encountered. This concept requires that methods be employed to measure the level of quality of a given telecommunication service, i.e. to verify quality of service (QoS) metrics. In a broader context, methods assessing the entire user experience (quality of experience – QoE) are required as well. In this article, various approaches to assessing QoS, QoE and the related metrics are presented, with a view to implement these at an FTTH network operator in Poland. Since this article presents the architecture of the system used to analyze QoE performance based on a number of QoS metrics collected by the operator, we also provide a comprehensive introduction to the QoS and QoE metrics used herein.
New Framework of Self-Embedding Fragile Watermarking Based on Reference Sharing Mechanism

We proposed in this paper a new self-embedding framework based on a reference sharing mechanism. The framework has high flexibility; it can not only estimate the optimal recovered image quality based on a given tampering rate but also estimate the largest tampering rate that the framework can resist based on the given peak signal-to-noise ratio (PSNR) of the recovered image. When the tampering rate is given, we first calculate the largest number of character bits and then allocate an appropriate number of character bits according to the complexity of the image block to achieve the optimal recovered image quality. When the PSNR of the recovered image is given, the number of character bits is minimized by satisfying the corresponding constraints to achieve the largest tolerable tampering rate. Experimental results show the flexibility, effectiveness, and superiority of the proposed scheme compared with some reported schemes.
Automatically Generating Scenarios from a Text Corpus: A Case Study on Electric Vehicles

Creating ‘what-if’ scenarios to estimate possible futures is a key component of decision-making processes. However, this activity is labor intensive as it is primarily done manually by subject-matter experts who start by identifying relevant themes and their interconnections to build models, and then craft diverse and meaningful stories as scenarios to run on these models. Previous works have shown that text mining could automate the model-building aspect, for example, by using topic modeling to extract themes from a large corpus and employing variations of association rule mining to connect them in quantitative ways. In this paper, we propose to further automate the process of scenario generation by guiding pre-trained deep neural networks (i.e., BERT) through simulated conversations to extract a model from a corpus. Our case study on electric vehicles shows that our approach yields similar results to previous work while almost eliminating the need for manual involvement in model building, thus focusing human expertise on the final stage of crafting compelling scenarios. Specifically, by using the same corpus as a previous study on electric vehicles, we show that the model created here either performs similarly to the previous study when there is a consensus in the literature, or differs by highlighting important gaps on domains such as government deregulation.
A Comparative Evaluation of Deep Learning Methods in Automated Classification of White Blood Cell Images

The identification and characterization of a patient&#x2019;s blood sample are required for the diagnosis of blood-related disorders. As a result, the medical implications of automated methods for identifying and categorizing various kinds of blood cells are considerable. However, deep convolutional neural networks (CNN) and standard machine learning algorithms have performed well in the categorization of blood cell pictures. Red, White, and Platelets are all types of blood cells. Leucocyte, commonly known as the immune cell, is a type of blood cell that plays a vital part in human immune function. Depending on shape info and granulated data in leukocytes, white blood cells are usually split by hematologists into two different categories: non-granular cells (lymphocytes and monocytes) and granular cells (eosinophils, basophils, and neutrophils). The CNN portion receives the pre-trained weight parameters from the image dataset using the transfer learning approach. Also, We have used two different scenarios, the first scenario of using CNN directly gave us pictures. used SVM in the second scenario. Then we compare the best category results. The classification results demonstrated that the accuracy of CNN is 98.4 %, whereas the accuracy of Support Vector Machine (SVM) is 90.6 %. Other classifiers can be added to the suggested system to improve its performance.
A Multi-Convolutional Stream for Hybrid network for Driver Action Recognition at Nighttime

Driver monitoring at nighttime is a tremendous area of research because of its crucial role to save lives and decrease traffic crashes injuries. However, this task is highly complex because of the high amount of naturalistic driving issues and the low visibility. In the gist of this paper, a novel nighttime driver action recognition network named multi-convolutional stream for hybrid network are proposed, which effectively fuses multimodal data to efficiently classify driver's actions in low visibility and a cluttered driving scene. Using the unique public driver action dataset recorded at nighttime, up to our knowledge, in two separate viewpoints, our proposed methodology beats state-of-the-art methodologies in classification performance, with an advancement of up to 10% over the best-practice methods.
Organizational Climate Assessment of Trust, Knowledge, Learning, and Motivation of Agile Teams - A Case Study

Context: Organizational climate management is challenging for organizations developing Management Information Systems (MIS). Turnover, legacy code maintenance, delivery of fixed deadlines, lack of domain knowledge influences team trust, learning, and member motivation. Organizational climate surveys can provide concrete evidence of how the process, project activities, people, and culture work in practice. Problem: Not assessing specific human factors that contribute to the formation of agile teams’ climate inhibits the capacity for an assertive diagnosis, entailing difficulties for the analysis of possible causes of problems and the execution of corrective actions within climate management. Solution: We present the preliminary evaluation of TACT, an instrument to assess the organizational climate of agile teams, considering the Trust, Knowledge, Learning, and Motivation. IS Theory: Agile Software Development Methods (ASDM) and their effects. Method: TACT was developed using Design Science Research. We executed a case study in a Brazilian and a Canadian organization. We evaluated TACT using quantitative and qualitative methods. Summary of Results: TACT captured that the product owner’s lack of knowledge and experience probably influenced the adverse climate in team trust and that unrealistic deadlines may have generated a lack of team motivation due to an absence of autonomy to plan the iteration. The team leaders reported intention of future use. Contributions and Impact in the IS area: TACT dimensions presented high levels of reliability and can be used to better identify issues and improve actions aligned with the agile values, principles, and practices while developing Information Systems.
BitSQL: A SQL-based Bitcoin Analysis System

As cryptocurrency markets such as Bitcoin and Ethereum increase worldwide, cryptocurrency exchanges, service providers, and governments need to monitor and analyze cryptocurrency transactions. There are a few studies to analyze Bitcoin data in open-source projects. However, under massive data volume in the blockchain structure, we still meet the challenges of data processing or analysis speed and efficient data structure. For this purpose, this paper introduces BitSQL, which minimizes significantly database construction time and analysis performance using well-known RDBMS. BitSQL maintains the compact database due to a data mapping technique and a transaction split technique for Bitcoin analysis and it achieves the fast response time due to the index of the database. In addition, as BitSQL is based on the well-known RDBMS, we can easily support web integration or visualization tools. Through experiments, we demonstrate that the data mapping technique can decrease the database size by 95% compared to the simple Bitcoin data storing method. We also show that BitSQL can perform easily analysis regarding balances, transactions, and Bitcoin address clustering heuristics through SQL query statements. This study contributes to the scalable Bitcoin analysis software construction method for analyzing and tracking cryptocurrency blockchains and creating services.