Reviewerly/exHarmony huggingface dataset:
A Novel Few-Shot Action Recognition Method: Temporal Relational CrossTransformers Based on Image Difference Pyramid

Most current few-shot action recognition methods model temporal relationships on the basis of image classification and achieve satisfactory results.However, they focus on the extra temporal information of video data compared to images and use the frame tuple embedding representation of the query video for matching, but ignore the important information of "action changing feature" in action recognition.To use this information, we propose the Temporal Relational CrossTransformers Based on Image Difference Pyramid (TRX-IDP) method for few-shot action recognition.Based on TRX, we perform high-order image difference, sigmoid enhancement, resizing on the frame tuples which are directly used for query, and use the frame tuples to calculate the Motion History Image (MHI).Combined with the two, we construct the Image Difference Pyramid containing motion feature information.We also develop CrossTransformers query representation for IDP and restructure the linear mapping function of the model.We evaluate our model using four commonly used few-shot action recognition benchmark datasets.TRX-IDP achieves state-of-the-art performance on partial SSv2, HMDB51, and UCF101, while slightly lagging behind the current best models on Kinetics and SSv2.In addition, we perform detailed ablation experiments on TRX-IDP to prove the importance of each part of the model and to give the best hyperparameters of TRX-IDP.
Privacy-Aware Multiagent Deep Reinforcement Learning for Task Offloading in VANET

Offloading task to roadside units (RSUs) provides a promising solution for enhancing the real-time data processing capacity and reducing energy consumption of vehicles in the vehicular ad-hoc network (VANET). Recently, multi-agent deep reinforcement learning (MADRL)-based offloading approaches have been widely used for task offloading in VANET. However, existing MADRL-based approaches suffer from offloading preference inference (OPI) attack, which utilizes the vulnerability in the policy learning process of MADRL to mislead vehicles to offload tasks to malicious RSUs. In this paper, we first formulate a joint optimization of offloading action and transmitting power with the objective of minimizing the system cost, including local and edge costs, under the privacy requirement of protecting offloading preference during offloading policy learning process in VANET. Despite the non-convexity and centralized of this joint optimization problem, we propose a privacy-aware MADRL (PA-MADRL) approach to solve it, which can allow the offload decision of each vehicle to reach the Nash Equilibrium (NE) without leaking offloading preference. The key to resisting the OPI attack is to protect the offloading preference by 1)elaborately constructing the noise based on ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta,\Phi $ </tex-math></inline-formula> )-differential privacy mechanism and 2) adding it to the action selection and policy updating process of vanilla MADRL. We conduct a detailed theoretical analysis of the convergence and privacy guarantee of the proposed PA-MADRL, and extensive simulations are conducted to demonstrate the effectiveness, privacy-protecting capacity, and cost-efficiency of PA-MADRL approach.
Employee Promotion Prediction by using Machine Learning Algorithms for Imbalanced Dataset

Promotion processes are one of the most important processes in terms of human resources. A promotion process organized fairly within the organization is a managerial tool that motivates employees and contributes to business continuity. Promotion is an important extrinsic motivation for many employees. It ensures the employee’s engagement and commitment to the organization and contributes to the continuity of his current performance. It is also an important rewarding and performance control mechanism for the organization. Many factors such as seniority, performance level, competencies, age, awards, training score, organizational commitment of the personnel who will be promoted are taken into consideration. In this study, a prediction methodology will be studied based on the criteria evaluated for the employees in the promotion processes by Machine Learning algorithms such as Support Vector Machine, Artificial Neural Network, and Random Forest. Random Forest achieved the highest performance with 98% accuracy, 96% precision, 1.0% recall and 98% f1-score values with ROS approach. This study could be used by HR and manager to predict the probability of promotion so that managers can find the right parameters for someone to get promoted.
Compliance SSI System Property Set to Laws, Regulations, and Technical Standards

A self-sovereign identity (SSI) system is a new identity management system that builds a trust model for service authentication and authorization using blockchain technology.Commonly, SSI systems adhere to a set of SSI principles and system properties.As a system that manipulates personally identifiable information (PII), compliance with applicable laws, regulations, and technological standards is recommended or required for the SSI system.These source documents' security measures and controls should be aligned with the SSI principles and system properties.However, we find that current proposals for SSI principles and system properties continue to be inconsistent, demanding substantial effort to reconcile them.This article proposes a compliance SSI system property set (CSSPS) that ensures conformance with applicable laws, regulations, and technical standards through systematic review and analysis.The CSSPS reports increased consistency and facilitates implementors to develop SSI systems correctly while complying with those source documents.
Exploring the Application of RFID for Designing Augmented Virtual Reality Experience

Recent technical advancement has driven the boundary between the physical reality and digital virtuality to diminish significantly.As part of the emerging trend, existing research leverages a synergized use of Radio Frequency IDentification (RFID) and virtual reality (VR) to create compelling hybrid user experience.However, current state-of-the-art literature indicates a lack of coherent architecture for seamlessly integrating these two siloed technology stacks, thus hindering full-fledged mixed and extended reality applications.In this article, we first conducted a comprehensive literature review and identified key design themes and different technical affordances of RFID within VR context; in reflection of our findings, we hence proposed an overarching architecture to facilitate swift and flexible composition of RFID and VR; Three use cases were further established using the proposed architecture to both demonstrate its technical feasibility and qualitatively assess RFID's augmentation over conventional VR applications.This exploratory research intends to offer some preliminary design knowledge and insights for designing and developing RFID-augmented VR applications, open up opportunities for further discussion and research interest in this area, thus ultimately contributing to a more immersive, interactive and informative user experience.
AI Accelerator Embedded Computational Storage for Large-Scale DNN Models

As the model size of Deep Neural Networks (DNN) is extremely expanding, especially in Natural Language Processing (NLP) field, it is not suitable for performing AI application on conventional DRAM-based systems that lack capacity. For such a reason, we propose a new computational storage by integrating a Neural Processing Unit (NPU) that could perform the DNN inference to the NAND Flash Memory Controller (FMC). Our NPU is able to use weight scheduling to efficiently load data from multiple NAND channels, and the optimal number of MAC units in NPU Core module is derived. We evaluate our proposed NPU architecture when using a very large size of DNN model. In contrast to DRAM-based memory system, ours show an equal inference performance, and total memory access power and latency are reduced by 22.6% and 36.3%, respectively. Also, by using the data buffer of NAND flash memory as shared SRAM, the area overhead could be reduced by 82% compared to when NPU using dedicated SRAM.
An Attention-Based Cycle-Consistent Generative Adversarial Network for IoT Data Generation and Its Application in Smart Energy Systems

The availability of Internet of Things (IoT) data is essential for the operation of intelligent systems, such as smart energy systems. Unfortunately, information sensitivity and the lack of observations tend to impact the availability of IoT data. To solve this problem, this article proposes an attention-based cycle-consistent generative adversarial network (ABC-GAN) to generate IoT data. By efficiently learning the distribution among different data patterns and sufficiently capturing temporal features, ABC-GAN can effectively reproduce the IoT data collected from different devices and regions. Various experimental results in smart energy systems demonstrate that ABC-GAN excels at capturing the temporal features, distribution, and latent manifolds of the original data when compared to the baselines and that the prediction models trained with the data generated by ABC-GAN can achieve performances similar to models trained with the real data.
Secure Images Transmission Through OFDM System

OFDM (Orthogonal Frequency Division Multiplexing) Next-generation wireless communication networks rely heavily on the OFDM system to transmit digital information in a more effective method as well as other traditional methods. Using mobile devices, digital document data such as videos, images, and texts can be easily shared and copied without the owner's permission. In this case, information security is a primary consideration. There are several advantages to using chaotic systems, such as their randomness, which makes them far more secure than traditional encryption methods, such as RSA (AES, DES). An efficient method for transmitting secure images across an AWGN channel has been developed because of this research. The dual-sided encryption and scrambling method for an OFDM system proposed in this paper is described in detail. Here, MATLAB is used to design an OFDM system that employs high-efficiency scrambling and a chaotic map for encryption to achieve high-security image transfer. Signal to noise ratio as well as bit error rate measurements are utilized to gauge the clear of image transmission. Using entropy, histogram analysis, NPCR, and UACI for testing encryption quality.
Configurable CNN Accelerator in Speech Processing based on Vector Convolution

In speech applications, both input feature maps (IFMs) and kernels of neural networks are greatly diverse in shapes and sizes, which poses significant challenges to hardware acceleration. In this paper, a configurable CNN accelerator is introduced to make a good balance between the flexibility and efficiency for various neural network models in speech processing. The vector convolution scheme is first proposed by re-arrangement of IFM rows and weight values in vectors, by which the element convolution is converted into vector operations to break the limit of kernel-centric processing. The structure of vector processing element (VPE) is introduced to fit the continuous scaling down of IFMs with little control overheads, and the architecture of the CNN accelerator is proposed accordingly. FPGA implementation results demonstrate that the throughput is increased by 86% by the proposed architecture compared to state-of-the-art FPGA accelerators for the VGG16 network, while high DSP utilization is guaranteed for both 1D and 2D CNNs with various input sizes.
Meta Navigation Functions: Adaptive Associations for Coordination of Multi-Agent Systems

In this paper, we introduce a new class of potential fields, i.e., meta navigation functions (MNFs) to coordinate multiagent systems.Thanks to the MNF formulation, agents can contribute to each other's coordination via partial and/or total associations, contrary to traditional decentralized navigation functions (DNFs).In particular, agents may stimulate each other via their MNFs.Moreover, MNFs need to be confined which is a weaker condition compared to the Morse condition of DNFs.An MNF is composed of a confined function and an attraction kernel.The critical points of the former can be confined in a safe region around a target critical point.The collision-free trajectory of an agent and its associations to its peers are governed by a confined function before reaching its safe region.Then, the attraction kernel drives the agent to its target in the safe region.MNFs provide faster coordination compared to DNFs.We illustrate how MNFs may exhibit some social behaviors in the course of partial and total associations among agents.Our simulations verify the efficiency of MNFs to coordinate complex swarms of agents.
Motion Planning with Homotopy Class Constraints via the Auxiliary Energy Reduction Technique

We introduce the so-called Auxiliary Energy Reduction (AER) technique, which is a gradient-based approach to solving motion planning problems with homotopy class constraints for system models with full-scale nonholonomic dynamics. The hallmark of our approach is that we first introduce virtual control terms to the original system dynamics that ensure that any preset state trajectory is dynamically feasible with respect to the new extended system. We then gradually shift the contribution of the artificial inputs to the actual original inputs by solving a sequence of associated quadratic programs. When the contribution of the artificial inputs has been fully removed, the preset trajectory will have been deformed to a trajectory of the same homotopy class that is now also feasible with respect to the original system. The practicality of our method is demonstrated in simulation examples for the Brockett integrator, the unicycle, and a 12-dimensional nonlinear quadcopter model.
Duality-based Convex Optimization for Real-time Obstacle Avoidance between Polytopes with Control Barrier Functions

Developing controllers for obstacle avoidance between polytopes is a challenging and necessary problem for navigation in tight spaces. Traditional approaches can only formulate the obstacle avoidance problem as an offline optimization problem. To address these challenges, we propose a duality-based safety-critical optimal control using nonsmooth control barrier functions for obstacle avoidance between polytopes, which can be solved in real-time with a QP-based optimization problem. A dual optimization problem is introduced to represent the minimum distance between polytopes and the Lagrangian function for the dual form is applied to construct a control barrier function. We validate the obstacle avoidance with the proposed dual formulation for L-shaped (sofa-shaped) controlled robot in a corridor environment. We demonstrate real-time tight obstacle avoidance with non-conservative maneuvers on a moving sofa (piano) problem with nonlinear dynamics.
Pupa: Smart Contracts for BPMN with Time-Dependent Events and Inclusive Gateways

The digital transformation of business processes faces a major hindrance due to the lack of trust and transparency. As blockchain and other distributed ledger (DLT) are considered key enabling technologies, there is a need for supporting tools which can deploy business models over smart contracts in order to leverage these decentralized platforms. Existing blockchain-based Business Process Management (BPM) solutions support various blockchain platforms and different types of modelling language, i.e., Ethereum and Business Process Model Notation (BPMN). However, the majority of these methods do not support processes with time events and inclusive gateways due to severe limitations imposed by smart contract programming languages. In other words, mainstream blockchain platforms do not offer a straightforward way to execute a transaction at a later time. To overcome these aforementioned issues, we propose an engine called Pupa, a blockchain-based decentralized protocol to translate business processes with time events and inclusive gateways to smart contracts. Pupa accomplishes this by adding task feature to time events, check function on top of activities succeeding time events and, listening variables to sequence flow forking or joining inclusive gateways. We implemented Pupa by extending Caterpillar, an existing BPMN solution using Solidity and Ethereum, and evaluated the performance of our proposed engine and its generated smart contracts with a baseline solution. Our results show that Pupa is competitive with baseline solutions in terms of cost and performance, while offering additional advantages in terms of decentralization and supporting additional BPMN semantics.
Fine-Grained Data Access Control for Collaborative Process Execution on Blockchain

Multi-party business processes are based on the cooperation of different actors in a distributed setting. Blockchains can provide support for the automation of such processes, even in conditions of partial trust among the participants. On-chain data are stored in all replicas of the ledger and therefore accessible to all nodes that are in the network. Although this fosters traceability, integrity, and persistence, it undermines the adoption of public blockchains for process automation since it conflicts with typical confidentiality requirements in enterprise settings. In this paper, we propose a novel approach and software architecture that allow for fine-grained access control over process data on the level of parts of messages. In our approach, encrypted data are stored in a distributed space linked to the blockchain system backing the process execution; data owners specify access policies to control which users can read which parts of the information. To achieve the desired properties, we utilise Attribute-Based Encryption for the storage of data, and smart contracts for access control, integrity, and linking to process data. We implemented the approach in a proof-of-concept and conduct a case study in supply-chain management. From the experiments, we find our architecture to be robust while still keeping execution costs reasonably low.
Dual Branch Network Towards Accurate Printed Mathematical Expression Recognition

Over the past years, Printed Mathematical Expression Recognition (PMER) has progressed rapidly. However, due to the insufficient context information captured by Convolutional Neural Networks, some mathematical symbols might be incorrectly recognized or missed. To tackle this problem, in this paper, a Dual Branch transformer-based Network (DBN) is proposed to learn both local and global context information for accurate PMER. In our DBN, local and global features are extracted simultaneously, and a Context Coupling Module (CCM) is developed to complement the features between the global and local contexts. CCM adopts an interactive manner so that the coupled context clues are highly correlated to each expression symbol. Additionally, we design a Dynamic Soft Target (DST) strategy to utilize the similarities among symbol categories for reasonable label generation. Our experimental results have demonstrated that DBN can accurately recognize mathematical expressions and has achieved state-of-the-art performance.
Exploring diversity in data complexity and classifier decision spaces for pool generation

This paper introduces a novel method for classifier pool generation in which a two-level strategy explores diversity in both data complexity and classifier decision spaces. The rationale is to induce pool members using data subsets representing subproblems with different difficulties while promoting diversity in classifiers' decisions. Two possible variants of the proposed method with a focus on maximum dispersion and maximum accuracy are presented. These differ in the property used to define the best pool of classifiers provided by an optimization process. A robust experimental protocol encompassing 28 classification datasets shows that the proposed pool generation provided the best accuracy on 327 over 336 experiments (97.3%) when compared to well-known pool generation methods to provide multiple classifier systems with and without dynamic selection.
A Dashboard to Support Teachers During Students’ Self-paced AI-Supported Problem-Solving Practice

Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced learning. To address this challenge, we conducted design-based research with 20 middle school teachers to create a novel real-time dashboard, Tutti, that helps a teacher monitor a class and decide which individual students to help, based on analytics from students' tutoring software. Tutti is fully implemented and has been honed through prototyping and log replay sessions. A partial implementation was piloted in remote classrooms. Key design features are a two-screen design with (1) a class overview screen showing the status of each student as well as notifications of recent events, and (2) a deep dive screen to explore an individual student's work in detail, with both dynamic replay and an interactive annotated solution view. The project yields new insight into effective designs for a real-time analytics-based tool that may guide the design of other tools for K-12 teachers to support students in self-paced learning activities.
Can Source Code Analysis Indicate Programming Skills? A Survey with Developers

Background: Both open-source and proprietary software systems have become increasingly complex. Despite their growing complexity and increasing size, software systems must satisfy strict release requirements that impose quality, putting significant pressure on developers. Therefore, software projects’ success depends on the identification and hiring of qualified developers. Several approaches aim to address this problem by automatically proposing models and tools to automatically identify programming skills through source code. However, we still lack empirical knowledge on the applicability of these models in practice. Aims: Our goal is to evaluate and compare two models proposed to support programming skill identification. Method: This paper presents a survey with 110 developers from GitHub. This survey was conducted to evaluate the applicability of two models for computing programming skills of developers based on the metrics Changed Files and Changed Lines of Code. Results: Based on the survey results, we conclude that both models often fail to identify the developer’s programming skills. Concerning precision, the Changed Files model obtained 54% to identify programming languages, 53% for back-end & front-end profiles, and 45% for testing skills. About the Changed Lines of Code model, we obtained 36% of precision to identify programming languages, 45% for back-end & front-end profiles, and 30% for testing. Conclusion: Practitioners can use our survey to refine the practical evaluation of professional skills for several purposes, from hiring procedures to the evaluation of team.