Reviewerly/exHarmony huggingface dataset:
An inquiry on the effectiveness of the teaching internship program based on pre-service teachers’ appraisal

Based on various published scholarly works, teaching internship is an essential component of professional development in the field of teacher education. Additionally, relevant literature have accentuated the importance of students conducting reflective assessments of the internships they participate in so that educational practices can be refined. In this regard, this present study was conducted to examine the implementation of the teaching internship program of the Institute of Education, Arts, and Sciences at City College of Angeles, specifically, students from the Bachelor of Physical Education and Bachelor of Performing Arts programs. Additionally, it assessed the association and direct influence of Institutional (IE) and Company Evaluation (CE) on Program Effectiveness (PE). After obtaining data from 84 pre-service teachers (NMale = 38, NFemale = 46), it was unraveled that the perception of pre-service teachers based on Institutional Evaluation and Company Evaluation are fully implemented. Concerning PE, it was perceived by the pre-service teachers as very effective. Results revealed that the internship program of the Teacher Education department is very effective since it is responsive to the needs of the stakeholders, and at the same time effective in helping students develop the skills needed for their teaching career. Lastly, a significant association and direct influence were observed between IE, CE, and PE. In the following, recommendations and potential next steps for further research related to this topic are discussed.
Pengenalan Manipulasi Data dengan Menggunakan Bahasa Pemrograman R pada Guru SMK Sumber Ilmu Tulangan

Data processing is one of the basics that must be mastered by researchers and people who work with data. One of the people who work with data is a teacher. The data owned by the teacher can be utilized by a teacher in carrying out the classroom action research process. However, the contents of the data that are owned may not necessarily be directly processed. So, it is necessary to have treatment carried out on data such as data manipulation. In the data manipulation process this can be done with the help of software R. Based on this, dedication is carried out in the form of an introduction to data manipulation given to teachers at SMK Sumber Ilmu Reinforcement by using software R. Activities After the activities are carried out, the teacher can better understand how the process correct data manipulation with the help of R software
Mobilography is a new phenomenon in the media

Today's media not only spread information, but also create interesting news that we can watch, listen to and read by viewing our audiovisual creations using our small mobile devices. This trend usually coincides with modern searches in the media sphere. Therefore, mobile journalism is gaining momentum not only abroad, but also in Kazakhstan. Young journalists who are passionate about the possibilities of new media, and those who want to work in the field of media in the future, react especially to this. They prove that, as before, there is no need to sit in front of large portable computers, monitors. Through their small mobile devices in their hands, they can shoot, edit and create high-quality media products. Our article says that mobilography is one of such modern phenomena. We can say that this is a new direction in electronic journalism. At first it appeared as a genre of photography and in a short time gained popularity among millions of amateur photographers. And this was followed by skeptical interest, creating an image among photography professionals. Now, in this process, television filming has also begun to be done.
The Forgotten Model – Validating the Integrated Behavioral Model in Context of Information Security Awareness

The behavior of employees has a strong influence on the information security of a company. Whether humans behave information security compliant depends on a large extent on their information security awareness (ISA). Social psychology provides an understanding about factors that influence awareness and thus gives relevant insights on how to increase an employee‘s ISA. A promising theory from health psychology is the Integrated Behavioral Model (IBM). To validate the significance of the IBM for ISA, a structured literature review about models that explain ISA has been conducted. The analysis of the found ISA models and their constructs showed that the IBM indeed includes all found factors. Based on the findings, the paper presents an extended model of the IBM within the ISA context with a higher level of detail. The model can be used to analyze individualized ISA and help companies to enhance ISA in a systematic way.
Uncovering Situations of Cargo Cult Behavior in Agile Software Development Method Use

Misinterpretations and faulty use of Software Development Method (SDM) practices and principles are identified pitfalls in Software Development (SD). Previous research indicates cases with method adoption and use failures; one reason could be the SDM Cargo Cult (CC) behavior, where SD organizations claim to be agile but not doing agile. Previous research has suggested the SDM CC framework as an analytical tool. The aim of this paper is to refine the SDM CC framework and empirically test this version of the framework. We use data from an ethnographical study on three SD teams’ Daily Scrum Meetings (DSM). The empirical material was collected through observations, interviews, and the organization’s business documents. We uncovered twelve CC situations in the SD teams’ use of the DSM practice, structured into seven categories of SDM deviations: bringing irrelevant information, canceling meetings, disturbing the team, receiving unclear information, bringing new requirements, problem-solving, and task distribution.
A Qualitative Study on Project Failure in Agile Teams Using Socio- technical Systems Theory

Companies are adopting agile methodology in response to fulfill a growing demand for agility. Specifically, the scrum method gained popularity, despite having no guarantee of success. This study employs socio-technical systems interaction as a framework to study the reasons for agile project failure. It reflects on misalignments as an outcome of socio- technical systems interaction. In this study, we identify the events that are most commonly responsible for imbalance by using socio-technical systems theory as a diagnostic tool. We conducted a qualitative interpretive study interviewing twenty-seven individuals in agile information systems delivery (ISD) team settings. Our findings reveal how an individual's lack of belief about being agile, doing agile, and both result in events of failure in IT projects. This study provides a nuanced understanding of the challenges and reasons for failure in agile teams.
Text sentiment analysis from students' comments using various machine learning and deep learning techniques: a comparative analysis

The method of analysing text and sentiments from the data is commonly known as sentiment analysis. In this paper, the text-based dataset of students' comments has been considered. This automated model of text sentiment analysis has been trained on various machine learning techniques like random forest, MultinomialNB, and linear SVC. In this paper, we proposed a text sentiment analysis model for evaluating the performance matrix and accuracy of distinct machine learning and deep learning approaches. In this regard, the deep learning techniques that have been incorporated in this research are recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), and we compared these techniques with the state-of-the-art algorithms of machine learning, respectively. The experimental result of the proposed model shows that the model architecture was designed and created to easily analyse the sentiments from students' comments in the form of happy, angry, sad, and relaxed. The proposed model of the sentiment analysis approach has been more focused on business firms' customer reviews and comment analysis for future decisions and the development of their businesses.
A review on learning-based algorithms for human activity recognition

Human activity recognition (HAR) is an essential system that helps to identify, track and recognise human activities with the help of learning-based approaches in diverse areas of HCI, surveillance, health monitoring system, etc. The performance of the system is limited due to several factors such as placement of sensors, overfitting and underfitting of machine learning models, privacy issues, tracking GPS-based activities, cluttered background, etc. However, several comprehensive review papers have been published focusing on the state-of-the-art approaches using learning-based algorithms either in sensor-based HAR, vision-based HAR or both. This paper focuses on the research work to improve performance of the system using learning-based techniques focusing on video-based, sensor-based and Wi-Fi-based HAR. In addition, we have discussed the current state-of-the-art approaches, their merits and limitations, focused on the public datasets used for the performance evaluation, challenges and future directions for HAR.
How Cover Images Represent Video Content: A Case Study of Bilibili

User generated videos are the most prevalent online products on social media platforms nowadays. In this context, thumbnails (or cover images) serve the important role of representing the video content and attracting viewers’ attention. In this study, we conducted a content analysis of cover images on the Bilibili video-sharing platform, the Chinese counterpart to YouTube, where content creators can upload videos and design their own cover images rather than using automatically generated thumbnails. We extracted four components – snapshot, background, text overlay, and face – that content creators use most often in cover images. We found that the use of different components and their combinations varies in cover images for videos of different duration. The study sheds light on human input into video representation and addresses a gap in the literature, as video thumbnails have previously been studied mainly as the output of automatic generation by algorithms.
Towards the Design of Hybrid Intelligence Frontline Service Technologies – A Novel Human-in-the-Loop Configuration for Human-Machine Interactions

Rapid adoption of innovative technologies confront IT-Service-Management (ITSM) to incoming support requests of increasing complexity. As a consequence, job demands and turnover rates of ITSM support agents increase. Recent technological advances have introduced assistance systems that rely on hybrid intelligence to provide support agents with contextually suitable historical solutions to help them solve customer requests. Hybrid intelligence systems rely on human input to provide high-quality data to train their underlying AI models. Yet, most agents have little incentives to label their data, lowering data quality and leading to diminishing returns of AI systems due to concept drifts. Following a design science research approach, we provide a novel Human-in-the-Loop design and hybrid intelligence system for ITSM support ticket recommendations, which incentivize agents to provide high-quality labels. Specifically, we leverage agent’s need for instant gratification by simultaneously providing better results if they improve labeling automatically labeled support tickets.
Proposal and Evaluation of Intermediate Content for the Transition from Visual to Text-Based Programming Languages

Beginners learning to program learn visual-based programming languages, such as Scratch, whereas experts use text-based programming languages, such as C and Java. However, no seamless transition from visual to text-based programming languages has been established. In this paper, a transition method was established between both language types. In particular, emphasis was placed on the features that an intermediate language between visual and text-based programming languages should have. Additionally, learning with the proposed intermediate content enhanced the subsequent comprehension of text-based languages. The proposed intermediate content was evaluated using questionnaires to ensure that it had intermediate characteristics between both language types.
Kernel-Segregated Transpose Convolution Operation

Transpose convolution has shown prominence in many deep learning applications. However, transpose convolution layers are computationally intensive due to the increased feature map size due to adding zeros after each element in each row and column. Thus, convolution operation on the expanded input feature map leads to poor utilization of hardware resources. The main reason for unnecessary multiplication operations is zeros at predefined positions in the input feature map. We propose an algorithmic-level optimization technique for the effective transpose convolution implementation to solve these problems. Based on kernel activations, we segregated the original kernel into four sub-kernels. This scheme could reduce memory requirements and unnecessary multiplications. Our proposed method was 3.09(3.02)× faster computation using the Titan X GPU (Intel Dual Core CPU) with a flower dataset from the Kaggle website. Furthermore, the proposed optimization method can be generalized to existing devices without additional hardware requirements. A simple deep learning model containing one transpose convolution layer was used to evaluate the optimization method. It showed 2.2× faster training using the MNIST dataset with an Intel Dual-core CPU than the conventional implementation.
Software Technology to Develop Large-Scale Self-Adaptive Systems: Accelerating Agent-Based Models and Fuzzy Cognitive Maps via CUDA

Agent-Based Models (ABMs) have long served to study self-adaptive systems and the emergence of population-wide patterns from simple rules applied to individuals. Recently, the rules for each agent have been expressed using a Fuzzy Cognitive Map (FCM), which is elicited from a subject-matter expert. This provides a transparent and participatory process to externalize the `mental model' of an expert and directly embed it into agents. However, software technology has been lacking to support such hybrid ABM/FCM models at scale, which has drastically limited the scope of applications and the ability of researchers to study emergent phenomena over large populations. In this paper, we designed and implemented the first open-source library that automatically accelerates ABM/FCM models by leveraging the CUDA cores available in a Graphical Processing Unit. We demonstrate the correctness and scaling of our library on a case study as well as across different networks representing agent interactions.
Source code protection against unauthorised copying and analysis in IoT devices

One problem for manufacturers of IoT devices is protecting intellectual rights to the software. Unprotected software can easily be copied or analysed and used on other devices. Proving another party that the source code has been illegally copied is difficult. One of the solutions is code obfuscation, i.e., modifying the code, so it works the same way, but its structure is complicated to understand and analyse. The paper presents a solution dedicated to IoT devices that combines code obfuscation techniques and uses the trusted platform module to decrypt part of the data during execution. A dedicated obfuscation method is described. Experiments show that the method increases the time needed to change the code at least several times, and some junior programmers cannot understand an obfuscated code. Test results show almost no similarity between the code in clear form and obfuscated form. The obfuscated code is more complicated but takes slightly longer to execute.
A Lite Hierarchical Model for Dialogue Summarization with Multi-Granularity Decoder

dialogue summarization generation has recently attracted considerable research attention, especially in using hierarchical models to accomplish abstract dialogue summarization tasks successfully. However, problems in recent studies often include an excessive amount of model parameters and long training time mainly because existing dialogue summaries of hierarchical models are typically generated by adding extra encoders and attention layers in the decoder to enhance learning and summarization generation ability of the model. Hence, designing an increasingly lightweight hierarchical model is necessary. A lightweight hierarchical model named ALH-BART is proposed in this study to generate high-accuracy dialogue summaries rapidly. The proposed hierarchical model includes word and turn encoders, which enhance the ability of the model to understand dialogue. A multigranularity decoder in the model is also proposed to decode word- and turn-level information in the decoder at the same time. Encoder parameters in multihead self-attention are provided for each corresponding multihead self-attention to reduce the number of model parameters and improve the speed of model learning effectively. Finally, the effectiveness of the model is verified on SAMSum and DialogSum datasets.
Have the Agile Principles Endured? An Empirical Investigation Post 20th Anniversary of the Agile Manifesto (2001)

This study investigates whether the Agile principles introduced in the Agile Manifesto (2001) have endured today two decades later and whether they are still relevant to software developers. Further, are they positively correlated with work and affective outcomes of software development projects? We find out by conducting a survey with team members of 58 software development project in one of the largest global IT firms. To our surprise we find that overall, the Agile principles have endured and were positively correlated with team motivation, project effectiveness and project innovation. However, they were negative correlated with project efficiency. As expected, projects using Agile and plan-driven methodologies showed differential findings.
Conversational Agent as a Black Hat: Can Criticising Improve Idea Generation?

The Ideate phase of Design Thinking is the source of many idea creations. In this context, criticism is considered a creativity killer, yet recent studies show that criticism can be beneficial. An example of this is the black hat of one creativity method: Six Thinking Hats. It points out the weaknesses of an idea so that they are eliminated by further refining. Previous research shows that conversational agents have an advantage over humans when criticizing because of their perceived neutrality. To investigate this, we developed and implemented a conversational agent and evaluated it using an A/B test. The results of the study show that the prototype is perceived as less neutral when it criticizes. Criticizing by the conversational agent can lead to higher quality ideas. This work contributes to a better understanding of conversational agents in the black hat role as well as of their neutrality.
Mechanisms of Common Ground in Human-Agent Interaction: A Systematic Review of Conversational Agent Research

Human-agent interaction is increasingly influencing our personal and work lives through the proliferation of conversational agents in various domains. As such, these agents combine intuitive natural language interactions by also delivering personalization through artificial intelligence capabilities. However, research on CAs as well as practical failures indicate that CA interaction oftentimes fails miserably. To reduce these failures, this paper introduces the concept of building common ground for more successful human-agent interactions. Based on a systematic review our analysis reveals five mechanisms for achieving common ground: (1) Embodiment, (2) Social Features, (3) Joint Action, (4) Knowledge Base, and (5) Mental Model of Conversational Agents. On this basis, we offer insights into grounding mechanisms and highlight the potentials when considering common ground in different human-agent interaction processes. Consequently, we secure further understanding and deeper insights of possible mechanisms of common ground in human-agent interaction in the future.
Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments

The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The machine learning models significantly outperform a Naive classifier based on Euclidean distances from class means. Various configurations produce balanced accuracy scores between 26.08% and 96.89%.
Affordances of Augmented Reality Systems for Co-Located Collaboration

This paper aims to identify relevant affordances towards an augmented reality system for organizational practices of co-located collaboration. As augmented reality is a means to visualize and author information, such an artifact could enable intuitive sharing of information. It can be shown that single aspects are reflected in literature, missing so far is a comprehensive description of relevant affordances for such an artifact. The concept of affordances is increasingly used in information research, as it enables to define design principle based on the action goals of the user. Addressing the lack of clear formulation guidelines of affordances, a proposal is presented of how affordances can be formulated to reduce uncertainty and ambiguity. This paper makes two major contributions: (1) it introduces how augmented reality systems can contribute to organizational practices of co-located collaboration and (2) illustrates how affordances can be utilized to derive requirements for such systems from interviews.
How the Perception of Agile Software Development Affects Beta Users’ Stress and Satisfaction

Agile software development has been shown to alleviate stress and improve satisfaction levels in development teams. Since this development approach relies on strong user involvement, these effects might carry over to the users themselves. If users have a positive perception of the agile approach, they might be more receptive to the produced software. However, users are rarely aware of the underlying development methodology and are, therefore, only partly affected by it. Hence, this study develops a new construct to measure users’ perception of the development methodology and to investigate the effects on technostress and user satisfaction. A survey with 117 beta users was conducted showing that perceiving a development process as agile lowers users’ technostress and elevates their satisfaction levels. Our Results highlight the essential role of user communication in the development phase. We discuss our implications for theory and practice, and conclude with promising future research avenues.