Miracle-dz/UniFAR-FTUs huggingface dataset:
Modification of Alpha++ for Discovering Collaboration Business Processes Containing Non-Free Choice

A business process is a series of activities or work that is structured and interrelated to solve a problem. Business Process Model Notation (BPMN) is a standard that often use to create a series of business processes. There are not only one or two processes in a business process, but many processes which cross each other. Processes which have activities related to one another are called collaborative business processes. Process mining techniques analyze a process model that is generated based on actual processes with a process model as the Standard Operational Procedure (SOP). Existing process mining techniques depict activities of each process without consider the linkages between activities in a process with another process. In addition, there is an issue that should be handled by process mining techniques, i.e. non-fee choice. This paper proposes a Modification of Alpha++ for discovering linkages of activities in collaboration business processes. Alpha++ is chosen because this algorithm can form non-free choice relationship. In the evaluation, the proposed Modification of Alpha++ will be compared with Alpha++, Inductive Miner, and Heuristic Miner. The evaluation shows the proposed Modification Alpha++ algorithm can model event log data correctly based on the proposed business process. Modification Alpha++ can find non-free choice constructs on the event log data. Modification Alpha++ gets the best calculation results for fitness, precision, and f-measure with a value of 1.
Understanding the User Interactions on GitHub: A Social Network Perspective

As one of the biggest online developer communities, GitHub supports interactions between millions of developers around the world. This paper focuses on detecting and analyzing the interactions on GitHub from several perspectives. First, from a global viewpoint, we build an interaction graph based on GitHub Events to investigate the general structure and attributes of the GitHub interaction network. Second, from the perspective of important users on GitHub, we pay particular attention to those who bridge social circles by spanning across communities. Concretely, we apply the structural hole theory to identify and explore the structural hole spanners in this interaction network. Last but not least, we compare structural hole spanners with ordinary users in several aspects and excavate distinctions between them. To our best knowledge, this is the first study that applies structural hole theory to the interaction graph of a leading social network. It not only provides a unique standpoint on user interactions, but also gives a comprehensive inspection of structural hole spanners.
Processing influences of resin-based insulation materials for wireless power transfer applications

Due to the electrification of the whole mobility sector, the demand for new insulation materials and processes is constantly increasing. Beside the electric traction drives, new technologies such as inductive charging are becoming more and more important in electric vehicles. Especially for wireless power transfer, high-frequency litz wires are used to generate the required electromagnetic field. While semi-finished products and insulation systems are continuously developing, insulation processes like potting remain unchanged. The consequence is an inappropriate insulation of the coil structure resulting in increasing eddy current losses or breakdowns of the inductance. Additionally, high mechanical and thermal loads, occurring in a typical operation environment for electric vehicles, result in accelerated aging of the insulation material. This increases the challenges in the design and manufacturing of insulation systems for inductive power transfer devices. The aim of this work is to identify and evaluate all relevant process parameters for potting high-frequency litz wires with varying primary insulation systems. To ensure the required mechanical and electrical properties, a high degree of impregnation of the litz wires is targeted.
Empirically Analysing Knowledge Generation Framework in Student Homophily Through Crowdsourcing

Integrating different perspectives in the generation of knowledge is essential for understanding learning problems. And crowdsourcing provides new approaches to harvest distributed knowledge. However, students are more inclined to connect with people with similar characteristics, and the identity of a group has an impact on cooperative learning performance. Therefore, there is an attempt to integrate the homogeneous students in crowdsourcing and to explore the influence of student homophily knowledge generation framework through teaching experiments for more meaningful and effective participation in the process of knowledge generation. The results demonstrate that homophily can improve students' academic performance, and the knowledge generation framework in crowdsourcing has a positive effect on innovation generation.
In-Network Caching for the Green Internet of Things

By caching multiple copies of the sensed data and suppressing repeated content deliveries from the sensor nodes, the in-network caching exhibits strong potentials to reduce energy consumption and avoid service loss in the Internet of Things (IoT). In this paper, we review the fundamentals of the in-network caching in IoT, especially the deployment principles of the cache-enabled brokers. Since in-network caching is not the optimal choice in some cases, we propose the dynamic in-network caching scheme to decide whether the sensed data should be cached in brokers or not, where the location and delay requirement of the clients, traffic load of the brokers as well as the energy level of the sensor nodes are encapsulated in the proposed scheme. Next, we give a practical case study to demonstrate the effectiveness of the proposed in-network caching scheme in the IoT scenario. It shows that the proposed caching scheme is superior to the traditional sensor-to-client scheme in terms of energy saving. Finally, we conclude this paper, and summarize the future research directions to achieve the in-network caching for implementing the green IoT.
Usability Evaluation of Distance Education Tools Used in Jordanian Universities

During the current Covid-19 pandemic, the long-distance education mode has been the foundation stone in education around the globe and is the only method to continue the education process. Jordanian universities and schools have also fully utilized long-distance education during this hard time. Furthermore, educational experts have emphasized that it will be used even when this pandemic is over, at least by integrating it into a traditional classroom method. In Jordan, tools such as E-learning platform namely Moodle, and meeting tools such as Zoom and Microsoft Teams have been widely used. It is a great necessity to evaluate the usability of these tools. This study aims to evaluate and compare the usability of the mentioned tools. We conducted this study using the System Usability Scale (SUS) questionnaire, which mainly focuses on ease of use, efficiency, and easy to learn. The finding of this study indicates that Zoom has better usability results compared to Microsoft Teams and E-learning platform.
Coordination in Crowdsourced Software Development: A Systematic Mapping Study

Global Software Development (GSD) team members face barriers due to different location, time or culture that can directly influence the coordination of these distributed teams. As social problem solving approach to software development, Crowdsourcing is also facing coordination issues and these challenges must be overcome. For better understanding the issues, barriers and proposed solutions we conducted a systematic mapping. We identified 532 papers regarding coordination in crowdsourced software development, aiming to classify the most frequent features concerning crowdsourcing pillars and highlight the best practices to crowdsourcing coordination.
Efficient Query Refinement for View Recommendation in Visual Data Exploration

The need for efficient and effective data exploration has resulted in several solutions that automatically recommend interesting visualizations. The main idea underlying those solutions is to automatically generate all possible views of data, and recommend the top-k interesting views. However, those solutions assume that the analyst is able to formulate a well-defined query that selects a subset of data, which contains insights. Meanwhile, in reality, it is typically a challenging task to pose an exploratory query, which can immediately reveal some insights. To address that challenge, in this work we propose utilizing query refinement as one technique that allows to automatically adjust the analyst’s input query to discover such valuable insights. However, a naive query refinement, in addition to generating a prohibitively large search space, also raises other problems such as deviating from the user’s preference and recommending statistically insignificant views. In this paper, we address those problems and propose a novel suit of schemes, which efficiently navigate the refined queries search space to recommend the top-k insights that meet all of the analyst’s pre-specified criteria.
A Cluster Representative Selection Method for Stock Portfolio Based on Efficient Frontier

Portfolio is a financial concept to combine several stocks to reduce the risks and improve the profits. To choose the basic members of portfolio, we can group similar stocks into one cluster and then choose representative stock from each cluster. In this paper, we focus on the method of choosing representative stocks in clusters. The ordinary representative of a cluster is often the center of that cluster. We propose a new cluster representative method MDR (maximum distance representatives). In our method MDR, we choose the stocks which has maximum distance with other representatives. MDR can construct a more diverse portfolio than center method. The effectiveness of cluster representative selection methods can be evaluated by an index IBEF based on the concept of efficient frontier. Our experiments show that MDR can effectively improve the efficient frontier, which means MDR can bring more profits than center representative method at the same risk level.
Research on the Impacts of Feedback in Instructional Videos on College Students' Attention and Learning Effects

This study explored the impacts of feedback in instructional videos by different knowledge types on attention and learning outcomes. 120 college students were randomly divided into four groups (procedural knowledge with feedback, procedural knowledge without feedback, declarative knowledge with feedback, and declarative knowledge without feedback) for the corresponding videos for self-study. The attention span, learning performance, and learning experiences of each group were measured. The results showed that: (1) The learning performance, attention throughout video-watching, and learning satisfaction were significantly higher in the group with feedback than in the group without feedback. (2) There were significant differences in the impacts of different types of knowledge on learning performance and no significant differences in the impacts on attention retention. (3) Attention when watching the video was positively correlated with learning performance, learning satisfaction, and mental effort. (4) In the subjective interview, participants expressed a preference for instructional videos with feedback. Finally, this study provided recommendations for the design and production of instructional videos.
A Model-Based Test Script Generation Framework for Embedded Software

The abstract test cases generated through model-based testing (MBT) need to be concretized to make them executable on the software under test (SUT). Multiple re-searchers proposed different solutions, e.g., by utilizing adapters for concretization of abstract test cases and generation of test scripts. In this paper, we propose our Model-Based Test scrIpt GenEration fRamework (TIGER) based on GraphWalker, an open source MBT tool. The framework is capable of generating test scripts for embedded software controlling functions of a cyber physical system such as passenger trains developed at Bombardier Transportation AB. The framework follows some defined mapping rules for the concretization of abstract test cases. We have evaluated the generated test scripts using an industrial case study in terms of fault detection. We have induced faults in the model of the SUT based on three mutation operators to generate faulty test scripts. The aim of generating faulty test scripts is to produce failed test steps and to guarantee the absence of faults in the SUT. Moreover, we have also generated the test scripts using the correct version of the model and executed it to analyse the behaviour of the generated test scripts in comparison with manually-written test scripts. The results show that the test scripts generated by GW using the proposed framework are executable, provide 100% requirements coverage and can be used to uncover faults at software-in-the-loop simulation level of sub-system testing.
TEA-RNN: Topic-Enhanced Attentive RNN for Attribute Inference Attacks via User Behaviors

Obtaining demographic attributes of online users is of great significance for retail marketing, targeted advertisement and many other scenarios. Users' wanderings on various websites and applications contains user preference on different items, and can be leveraged to infer one's private attributes. Existing studies usually focus on manually defined features, relationships in online social networks, or modeling global user preferences. However, attribute inference from the most common behavioral data (e.g., browsing history, shopping cart) is recently overlooked, and still requires further research. In this work, we propose a Topic-Enhanced Attentive Recurrent Neural Network (TEA-RNN) model to capture both local neighborhood-based features (with attentive RNN) and global patterns (with topic model) within user behaviors, and apply multi-task learning mechanism with weighted losses to further leverage the latent relationships within demographics. Experimental results on real-world datasets demonstrates the effectiveness of TEA-RNN by comparing with several commonly used baselines.
Wideband channel estimation for THz massive MIMO

Terahertz (THz) communication is considered to be a promising technology for future 6G network. To overcome the severe attenuation and relieve the high power consumption, massive multiple-input multiple-output (MIMO) with hybrid precoding has been widely considered for THz communication. However, accurate wideband channel estimation, which is essential for hybrid precoding, is challenging in THz massive MIMO systems. The existing wideband channel estimation schemes based on the ideal assumption of common sparse channel support will suffer from a severe performance loss due to the beam split effect. In this paper, we propose a beam split pattern detection based channel estimation scheme to realize reliable wideband channel estimation in THz massive MIMO systems. Specifically, a comprehensive analysis on the angle-domain sparse structure of the wideband channel is provided by considering the beam split effect. Based on the analysis, we define a series of index sets called as beam split patterns, which are proved to have a one-to-one match to different physical channel directions. Inspired by this one-to-one match, we propose to estimate the physical channel direction by exploiting beam split patterns at first. Then, the sparse channel supports at different subcarriers can be obtained by utilizing a support detection window. This support detection window is generated by expanding the beam split pattern which is determined by the obtained physical channel direction. The above estimation procedure will be repeated path by path until all path components are estimated. Finally, the wideband channel can be recovered by calculating the elements on the total sparse channel support at all subcarriers. The proposed scheme exploits the wideband channel property implied by the beam split effect, i.e., beam split pattern, which can significantly improve the channel estimation accuracy. Simulation results show that the proposed scheme is able to achieve higher accuracy than existing schemes.
Modeling Attackers Based on Heterogenous Graph through Malicious HTTP Requests

As modern computer attacks are growing more and more complicated, there is a need for defenders to detect malicious activities and analyze which attacker or organization these attacks came from. It is a challenge to model an attacker from malicious web logs. In this paper, we modeled attacker activities based on malicious HTTP requests collected from kinds of websites, which recorded the behavior of IP addresses and provided the possibility to describe the attacker based on HTTP requests. First, we propose a novel method to get the IP address embedding through two aspects: we designed a heterogeneous graph, named IP-Domain-Graph, to capture the relation between the IP address and the domain it has sent malicious requests, and we designed an embedding method of requests content to capture the behavioral characteristics of the IP address. Then we use a similarity calculation method to cluster IP addresses to describe an attacker. The experimental results demonstrate the effectiveness of the proposed method.
Channel measurement and path loss modeling from 220 GHz to 330 GHz for 6G wireless communications

Terahertz (THz) communication has been envisioned as a key enabling technology for sixth-generation (6G). In this paper, we present an extensive THz channel measurement campaign for 6G wireless communications from 220 GHz to 330 GHz. Furthermore, the path loss is analyzed and modeled by using two single-frequency path loss models and a multiple-frequencies path loss model. It is found that at most frequency points, the measured path loss is larger than that in the free space. But at around 310 GHz, the propagation attenuation is relatively weaker compared to that in the free space. Also, the frequency dependence of path loss is observed and the frequency exponent of the multiple-frequencies path loss model is 2.1. Moreover, the cellular performance of THz communication systems is investigated by using the obtained path loss model. Simulation results indicate that the current inter-site distance (ISD) for the indoor scenario is too small for THz communications. Furthermore, the tremendous capacity gain can be obtained by using THz bands compared to using microwave bands and millimeter wave bands. Generally, this work can give an insight into the design and optimization of THz communication systems for 6G.
Ontology-based Semantic Similarity in Generating Context-aware Collaborator Recommendations

Nowadays, the number of collaborative tools has increased significantly. This makes it difficult for users to find the collaborators that are most relevant to their needs among these tools. Besides, their needs can also be influenced by the context of collaboration (e.g., workplace, tools, and resources). This raises an issue: how to help users find their collaborators within the collaboration context. In our research, we propose an ontology-based semantic similarity and employ it in a collaboration context ontology to generate context-aware collaborator recommendations for users. In this paper, we present how to calculate and apply the semantic similarity in context-aware recommendation algorithms.
Three-dimensional (3D) Non-volatile SRAM with IWO Transistor and HZO Ferroelectric Capacitor

We propose a 3D non-volatile SRAM (nvSRAM) design with the experimentally demonstrated BEOL transistors with W doped In2O3 (IWO) channel as isolation/access transistors and ferroelectric (FE) Hf0.5Zr0. 5O2 (HZO) capacitors as non-volatile memory (NVM) elements. The added 4 BEOL transistors and 2 Fe capacitors are integrated on top of a 6 transistor SRAM cell without any area overhead. By decoupling the storage nodes from the FE capacitors, our design can show better retention of the FE capacitors as well as less impact on SRAM read and write static noise margin. The proposed nvSRAM is compatible with advanced technology node (e.g. 28nm or beyond) by delivering the write voltage (Vw) for the FE capacitors (~2V) from BEOL transistor while maintain the SRAM at VDD <=1V. Based on the experimental data from HZO capacitors and IWO transistors, we run SPICE simulation to extract the STORE and RECALL energy. The simulated results suggest that an application with average idle time > 10ms can benefit from the nvSRAM.
An Efficient Method Based on Label Propagation for Overlapping Community Detection

In this paper we focus on a research of the overlapping community structures. Towards this goal, we propose DOCLPA (Detecting Overlapping Community based Label Propagation Algorithm), a fast label propagation method is designed to detect overlapping community in GraphChi (a parallel sliding windows method) with only network topology. Here the label initialization is designed to reduce labels redundancy by local maximal cliques. We focus in particular on propagation characteristics between nodes, as far as possible, let the label propagation process be similar to the real information dissemination. Crucially, the DOCLPA method can detect high quality communities adaptively and does not require limited parameter. The experimental findings on real-world networks show that DOCLPA is effectiveness and accuracy in comparison with GCE, COPRA and LPA. Furthermore, by adjusting the overlapping coefficient to reveal the behavior characteristics of overlapping nodes, we prove that overlapping communities on the real-world networks are very naturally social phenomenon. In addition, our experiments on social, product, collaboration and communication networks demonstrate that overlapping nodes often represent active users or products who involve multiple different communities.
Optimization of Dual-Channel Near-Infrared Non-Invasive Glucose Level Measurement Sensors Based On Monte-Carlo Simulations

Non-invasive glucose monitoring sensors are promising techniques in diabetes management. In particular, optical-based near-infrared glucose sensors are label-free, compact, user-friendly, and inexpensive. They require no daily calibration and can provide continuous glucose level monitoring. However, these sensors are still in the development stage since their accuracy is still not widely accepted by medical professionals. Here, we introduce an optimized dual-channel approach for this kind of sensor where four optodes for short and long channels are employed. The long channel signal that contains useful information about the glucose level can be utilized to calculate the glucose contents in the dermis layer of the skin while the short channel is utilized to measure the interference “noise” signal originating from the epidermis layer. So, the latter can then be removed from the long channel signal. This work investigates the optimal source-detector separation (SDS) of both short and long channels for a wide range of wavelengths from 1200 nm to 1900 nm. Based on the sensitivity distributions of both dermis and epidermis layers and the signal-to-noise ratio, it is suggested that a 2 mm SDS distance with a wavelength of 1450 nm is a potential choice for the short channel. Moreover, 6 mm SDS at the wavelength of 1750 nm is the optimal choice for the long channel. This work may pave the way for further optimization of non-invasive near-infrared glucose sensors that will be widely adopted in the healthcare sector.