Reviewerly/exHarmony huggingface dataset:
Analysis and Practice of Innovation and Entrepreneurship Activities in Higher Vocational Colleges Based on Information Technologies

In recent years, information technology has more and more influence on education mode. Flipped classroom, smart training and smart platform based on educational information technology provide a variety of possibilities for the design of innovation and entrepreneurship activities of higher vocational students. Based on information technology, professional characteristics and the integration of production and education, this paper creates innovative and entrepreneurial activities with productive training, maker business, college-enterprise collaborative technology innovation platform, competition platform and technology service platform as the carrier. Through the practice in a vocational college, the effect of student activities has been improved to a certain extent. This model is conducive to the promotion of similar colleges.
Organizational Characteristics Associated with Vulnerability to Social Engineering Deception: A Qualitative Analysis

Social engineering, the manipulation and deception of individuals to gain access to otherwise secure systems and information, has become a major vector to compromise the information security of organizations. Little research has explored characteristics associated with organizations vulnerable to social engineering, particularly from the perspective of persons experienced in such deceptions. To address this gap, the current study uses a qualitative, grounded theory-based approach to analyze interviews with both professional and nonprofessional social engineers (n = 37). Results reveals six themes corresponding to traits participants associated with organizations vulnerable to social engineering. These themes concern an organization’s value, structural controls, organizational efficacy, openness, size, and purpose. This study concludes by exploring directions for future research and policy implications.
Analysis of the Practices of Financial Intelligence Units (FIUs) and Other Anti-money Laundering Agencies Within EU

Increasingly, financial data is considered an important resource in the fight against money laundering and terrorism. In the name of pursuing terrorism financing, banks are required to report suspicious transactions, where Financial Intelligence Units create new databases and produce 'typologies reports. This work focuses on the chain of suspicious transactions analysis in Europe, from banks, to Financial Intelligence Units, to Courts and examine the way in which financial data are interpreted and shared across the security chain, leading to security decisions like frozen assets, closed accounts or criminal convictions. At present, there is NO comparative research on the work of European FIUs. Our ultimate goal is to make an independent conceptual contribution to the understanding of financial security by analyzing the implementation of different lows mechanisms and information systems models, in order to find better solutions that will ensure efficiency, effectiveness and easy access to the information required.
SymbioSinging: Robotically transposing singing experience across singing and non-singing bodies

In this paper we present our late-breaking work in leveraging a soft robotic fiber-based wearable system for the transposition of somatic knowledge and experience within the context of singing. We examine how the transposition of the physical nuances of singing from one body to another, or multiple other bodies, is possible by engaging with a soma design process. We share our findings in the context of experience transposition, resulting in a preliminary prototype: a pneumatically controlled soft robotic garment—called ADA (short for air-driven actuator) for re-enacting felt experiences of singing onto the human body. We contribute with 1) our initial findings in transposing singing experiences between and across bodies, and 2) a preliminary wearable robotic garment to mediate intersomatic experiences of singing.
Fully Decomposed Singular Value and Fixed Dictionary Extreme Learning Machine for Bogie Fault Diagnosis

As an essential part in the rail train, the bogie plays an important role in the safety of the train operation. However, the fluctuant wheel-rail connection, as well as the structure and complex operating environment of the bogie always lead to low signal-to-noise ratio condition and complicated wheel-rail dynamic coupling relationship. The existing fault diagnosis methods can hardly perform well in this scenario. Concerning this issue, a novel feature extraction method named fully decomposed singular value (FdSV) is proposed in this paper. FdSV can decompose singular value characteristics of signals completely and increase the divergence of features to extract weak fault features effectively. Then, inspired by the theory of compressed perception and Hierarchy-ELM, a fixed dictionary extreme learning machine (FD-ELM) is also proposed for fault identification. This method calculates the weight matrix by formulas without randomization and removes the bias matrix. Therefore, it can easily discover the internal laws of data and improve the running speed and accuracy rapidly. Finally, the proposed algorithms have been verified by actual bogie data collected from bogies under low SNR and variable working conditions. Compared with SVD, the FdSV features are 1%-6% higher in testing accuracies. The accuracies of FD-ELM are 2-20% higher than the conventional ELM, H-ELM and SVM.
Transparent I/O-Aware GPU Virtualization for Efficient Resource Consolidation

Graphics processing units (GPUs) are widely used in high performance computing (HPC) and cloud computing to accelerate workloads. Virtualization provides flexible access to resources while improving utilization and throughput. This is essential to resource disaggregation, which allows ubiquitous access to remote resources among nodes. However, remote GPU virtualization at scale suffers from severe performance degradation due to inter-node communication and resource consolidation overhead, especially for data-intensive workloads.We propose HFGPU, a GPU virtualization solution transparent to application code based on application programming interface (API) remoting. We define a virtual device manager that allows remote GPUs to be seen, managed, and used as though they were local. To perform at scale we combine multi-adapter InfiniBand networking with a novel distributed I/O forwarding mechanism that eliminates consolidation bottlenecks and reduces data movement. Experiments with up to 1024 NVIDIA V100 GPUs demonstrate overhead lower than 1% for data-intensive operations.
IA-CPS: Intelligent architecture for cyber-physical systems management

Nowadays, the emergence of large-scale and highly distributed cyber-physical systems (CPSs) in applications including Internet of things (IoT), cloud computing, mobility, Big Data, and sensor networks involves that architecture models have to work in an open and highly dynamic world. This fact increasingly highlights the importance of designing real-time and intelligent CPSs that are capable of decision making. In this paper, an Intelligent Architecture model for CPS management (IA-CPS) based on online processing is proposed for this purpose. Specifically, it can manage simple and complex events based on a service-oriented architecture and directed by an event-driven architecture, using event processing technology as Complex Event Processing (CEP). The novelty of our approach relies on the fact that the proposed architecture is service-oriented, which models the functionalities of the event-driven system. This gives us the possibility to offer a flexible service catalog, allowing us to connect to the system on any kind of device and interact in different scenarios. The model has been applied to two use cases: processing images from video surveillance cameras, and processing of consumption data captured by water and energy sensors installed in end-user environments.
Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.
Privacy-Utility Tradeoff with Nonspecific Tasks: Robust Privatization and Minimum Leakage

Privacy-preserving data release mechanisms aiming to minimize the privacy leakage under utility constraints of nonspecific tasks are studied through the lens of information theory. While the private feature to be protected is typically determined and known by the users who release their data, the specific task where the release data is utilized is usually unknown. To address the lack of information of the specific task, utility constraints laid on a set of multiple possible tasks are considered. The mechanism protects the privacy of a given feature of the to-be-released data while satisfying utility constraints of all possible tasks in the set. First, the single-letter characterization of the privacy-utility tradeoff region is derived. Characterization of the minimum privacy under log-loss utility constraints turns out to be a non-convex optimization problem involving mutual information in the objective function and the constraints. Second, focusing on the case where the raw data consists of multiple independent components, we show that the above optimization problem can be decomposed into multiple parallel privacy funnel (PF) problems [1] with different weightings. We explicitly derive the optimal solution to each PF problem when the private feature is a deterministic function of a data component. The solution is characterized by the leakage-free threshold, and the minimum leakage is zero while the utility constraint is below the threshold. Once the utility requirement is above the threshold, the privacy leakage increases linearly. Finally, we show that the optimal weighting of each privacy funnel problem can be found by solving a linear program (LP). Numerical results are shown to illustrate the robustness of our approach.
Agree to Disagree: Subjective Fairness in Privacy-Restricted Decentralised Conflict Resolution

Fairness is commonly seen as a property of the global outcome of a system and assumes centralisation and complete knowledge. However, in real decentralised applications, agents only have partial observation capabilities. Under limited information, agents rely on communication to divulge some of their private (and unobservable) information to others. When an agent deliberates to resolve conflicts, limited knowledge may cause its perspective of a correct outcome to differ from the actual outcome of the conflict resolution. This is subjective unfairness. As human systems and societies are organised by rules and norms, hybrid human-agent and multi-agent environments of the future will require agents to resolve conflicts in a decentralised and rule-aware way. Prior work achieves such decentralised, rule-aware conflict resolution through cultures: explainable architectures that embed human regulations and norms via argumentation frameworks with verification mechanisms. However, this prior work requires agents to have full state knowledge of each other, whereas many distributed applications in practice admit partial observation capabilities, which may require agents to communicate and carefully opt to release information if privacy constraints apply. To enable decentralised, fairness-aware conflict resolution under privacy constraints, we have two contributions: 1) a novel interaction approach and 2) a formalism of the relationship between privacy and fairness. Our proposed interaction approach is an architecture for privacy-aware explainable conflict resolution where agents engage in a dialogue of hypotheses and facts. To measure the privacy-fairness relationship, we define subjective and objective fairness on both the local and global scope and formalise the impact of partial observability due to privacy in these different notions of fairness. We first study our proposed architecture and the privacy-fairness relationship in the abstract, testing different argumentation strategies on a large number of randomised cultures. We empirically demonstrate the trade-off between privacy, objective fairness, and subjective fairness and show that better strategies can mitigate the effects of privacy in distributed systems. In addition to this analysis across a broad set of randomised abstract cultures, we analyse a case study for a specific scenario: we instantiate our architecture in a multi-agent simulation of prioritised rule-aware collision avoidance with limited information disclosure.
EVM-Perf: High-Precision EVM Performance Analysis

Smart contract execution engines are a central part of transaction processing in blockchains. One of the most widely used execution engines is the Ethereum Virtual Machine (EVM). EVM performance is a key determinant of the overall blockchain system. Being able to gather detailed insights into EVM performance characteristics is essential not only when implementing EVMs, but also when designing and dimensioning blockchain-based information systems. Today, there is no precise and easy-to-use solution to gather such information. To address this issue, we introduce EVM-Perf, a high-precision EVM evaluation, and analysis framework. Our framework allows detailed performance analysis of arbitrary EVM implementations. Unlike previous work, it leverages statical analysis methods to achieve higher levels of accuracy. We provide an open-source implementation of EVM-Perf and use it to conduct extensive performance analysis for different physical machines. It comprises both machines with x86_64 as well as ARMv8 instruction set architecture. We discuss how EVM-Perf supports system architects with machine selection as well as configurations of Ethereum deployments, e.g., determining Gas limits or block intervals.
Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous work has demonstrated the feasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP) perform well in our experiments. Our best models outperform previous comparable research.
BeamLite: Diminishing Ecological Fractures of Remote Collaboration through Mixed Reality Environments

Developing systems to support remote collaboration usually involves creating new environments in which non-co-located participants produce actions that are, at least in part, accessible to one another. However, this typically fractures the relationship between those actions and the sense of a shared environment, engendering difficulties that can render even the simplest of activities problematic. This becomes more pronounced as the activities become more complex and involve physical artifacts. Although mixed reality seems to offer promising ways of overcoming these troubles, there is still a risk of replicating the fractured ecology problem. We report on an empirical study and the development of a mixed reality prototype called BeamLite that seeks to bypass such issues by providing participants with the illusion of them sharing a single familiar place. Although our evaluation revealed possibilities for evading some troubles associated with artifact-focused remote collaboration, it exposed the need for virtual toolboxes that dynamically support specific work practices and the importance of virtual artifacts embedded within the physical environment to further diminish the sense of ecological fracture.
DDHH: A Decentralized Deep Learning Framework for Large-scale Heterogeneous Networks

Learning vector representations (i.e., embeddings) of nodes for graph-structured information network has attracted vast interest from both industry and academia. Most real-world networks exhibit a complex and heterogeneous format, enclosing high-order relationships and rich semantic information among nodes. However, existing heterogeneous network embedding (HNE) frameworks are commonly designed in a centralized fashion, i.e., all the data storage and learning process take place on a single machine. Hence, those HNE methods show severe performance bottlenecks when handling large-scale networks due to high consumption on memory, storage, and running time. In light of this, to cope with large-scale HNE tasks with strong efficiency and effectiveness guarantee, we propose Decentralized Deep Heterogeneous Hypergraph (DDHH) embedding framework in this paper. In DDHH, we innovatively formulate a large heterogeneous network as a hypergraph, where its hyperedges can connect a set of semantically similar nodes. Our framework then intelligently partitions the heterogeneous network using the identified hyperedges. Then, each resulted subnetwork is assigned to a distributed worker, which employs the deep information maximization theorem to locally learn node embeddings from the partition received. We further devise a novel embedding alignment scheme to precisely project independently learned node embeddings from all subnetworks onto a public vector space, thus allowing for downstream tasks. As shown from our experimental results, DDHH significantly improves the efficiency and accuracy of existing HNE models, and can easily scale up to large-scale heterogeneous networks.
Decreasing the Computational Demand of Unscented Kalman Filter based Methods

Computational load is a critical factor in sensor fusion applications especially in mobile devices (e.g., robots, drones, etc.) with limited resources onboard. The paper proposes a computational relaxation for the Unscented Transformation (UT) that is an essential part of the Unscented Kalman-filter based applications. The derivation for the most commonly used UT variant is presented and it is shown how the number of necessary sigma points is reduced. The practical merit of the proposed relaxation is demonstrated through a mobile robot localization example that clearly shows the benefit in terms of CPU time.
Creating a Mobile Application with the ESP32 Azure IoT Development Board Using a Cloud Platform

This paper describes the creation and deployment of an application that uses a development board with an ESP32 chip and its integrated sensors. The board is adapted for the implementation of applications that use the Microsoft Azure cloud platform. The paper describes an application that obtains data from a gyroscope and an accelerometer and these measured data are sent to a cloud service using an implemented IoT hub. The created mobile application displays the measured data, which can be monitored in an end device such as a smartphone or tablet.
Delegated attestation

Remote Attestation (RA) is an interaction between a trusted verifier (Vrf) and one or more remote and potentially compromised devices (provers or Prv-s) that allow the former to measure the software state of the latter. RA is particularly relevant to safety-critical cyber-physical systems (CPS) where a set of low-end micro-controllers (MCUs), operate under the control of a remote and more powerful controller. In such cases, RA is an effective and relatively efficient means to detect software compromise, e.g., malware infections, on these low-end MCUs that cannot support expensive security mechanisms.
Robust Universal Inference

Learning and making inference from a finite set of samples are among the fundamental problems in science. In most popular applications, the paradigmatic approach is to seek a model that best explains the data. This approach has many desirable properties when the number of samples is large. However, in many practical setups, data acquisition is costly and only a limited number of samples is available. In this work, we study an alternative approach for this challenging setup. Our framework suggests that the role of the train-set is not to provide a single estimated model, which may be inaccurate due to the limited number of samples. Instead, we define a class of “reasonable” models. Then, the worst-case performance in the class is controlled by a minimax estimator with respect to it. Further, we introduce a robust estimation scheme that provides minimax guarantees, also for the case where the true model is not a member of the model class. Our results draw important connections to universal prediction, the redundancy-capacity theorem, and channel capacity theory. We demonstrate our suggested scheme in different setups, showing a significant improvement in worst-case performance over currently known alternatives.
Level Identification of Soil pH and Macronutrients using Mobile Application through Image Processing

Soil pH and macronutrient (Nitrogen, Phosphorus and Potassium) levels are the primary parameters for crop and fertilizer recommendations given to farmers to achieve soil productivity. Soil Test Kits (STK) use a colorimetric process where results are dependent on the colors produced. This study aims to develop a mobile application that gives crop and fertilizer recommendations after determining the soil pH level and NPK content of the soil through image processing. The OpenCV library is used for the image processing component, while Eclipse is used for its Android development component. Using a t-test for the pH level and Wilcoxon signed ranks test for NPK levels, the results show the mobile application can adequately determine the soil's pH level and NPK content and give crop and fertilizer recommendations. The use of the mobile application can save time, and it is cost-efficient among its users.