Reviewerly/exHarmony huggingface dataset:
RSSI Data Preparation for Machine Learning

In this study, we prepared unstructured raw RSSI (Received Signal Strength Indication) data to prepare it for use in machine learning predictive modeling. RSSI data preparation is very important in predictive modeling machine learning projects, such as classification and regression, so that the algorithm can model it based on the right conditions, because the data has a more specific part but does not necessarily affect the algorithm performance. RSSI data were collected from the Dinamika Bangsa University Campus building, namely 225 reference points. The two main processes involved in data compilation, namely construction data and data cleaning. From the process of identifying a column containing a single RSSI value, several unique values were found in each column in the RSSI data table. Since no column has a Single Value, no columns are deleted. Then in the process of deleting columns that have low RSSI variance, the input is 5969 rows and 18 columns. The output is 5969 rows and 3 columns. However, the number of columns that passed the Low Variance selection was 5969 rows and 18 columns, meaning that all columns in the dataset have high variance. Additionally, no duplicate RSSI data were found. It can be said that the result of the overall preparation of this data set is the RSSI data used in good condition and can be used for machine learning needs.
A study on exercise recommendation method using Knowledge Graph for computer network course

With the vast applications of massive online learning platforms during the coVID-19 outbreak, the personalized exercise recommendation methods play an import role on computer aided instruction(CAI). Most existing methods generates the exercises according to the contents and knowledge system structure, lacking semantic relationships between exercises and its knowledge. Knowledge graph is widely used to represent the semi-structured and schemaless information (nodes) and their relation (edges), and indicate the sentence embedding grammatical structure and semantic relations, thus it can be applied on computer aided instruction to automatically generate the personalized exercises. Aiming to improve the efficiency of exercise recommendation, this paper studies the feature information of computer network course, and proposes a content and knowledge graph based personalized exercise recommendation method. More specifically, knowledge graph is firstly constructed from entities and relations of computer network course, and the information vectors of exercises are generated by combining the knowledge with the exercises content. And then the learner's historical log data is analyzed, and the semantic similarity between exercises and their knowledge are generated for the wrong answers. According the semantic similarity of knowledge, the final exercises are recommended for the learners. Experimental results show that the proposed method can improve the efficiency of exercises recommendation.
Penalized Fuzzy C-Means Coupled Level Set Based Biomedical Image Segmentation

The biomedical image segmentation is one of the challenging task and an interesting topic of research since a long time. In the field of biomedical image based disease analysis, automated segmentation methods plays a crucial role in fast and accurate disease analysis. It is also helpful to treat the at the early stage of a disease which often saves many lives. In this work, a new biomedical image segmentation method is proposed based on penalized fuzzy c-means coupled with level set method. The proposed method is tested on different types of biomedical images and some inspiring results are observed. Experimental results makes it suitable for the real life deployment in the diagnostic field to save some precious time for diagnostics.
Application of NLP to determine the State of Issues in Bug Tracking Systems

The amount of semi-structured textual data being generated all over the world wide web has placed the limelight on the fields of natural language processing and text mining. With the continuous growth of IT companies, there are a lot of new and diverse projects getting introduced. Hence, the need for project management tools that provide robust, efficient, and forthcoming services is rising. A bug tracking system is one of such systems that help in making the job of every stakeholder easier. This research utilizes the textual data provided by bug tracking systems in the form of problem summaries, descriptions, and comments to determine the state of project issues. The unlabeled textual data from the bug tracking system is subjected to a state of the art class-expansion stage, that automates the process of labeling issues based on severity level and clustering (constraint-based and density-based). During this stage, 40 percent of the total data could be labeled. Semi-supervised learners are introduced to consider labeled as well as unlabeled data for learning, and the results significantly outclass the results of a supervised learner. However, for the semi-supervised learners, the recall for the critical class is still not desirable at 53 percent. Since identifying critical tickets is of utmost importance for project managers, it becomes essential to improve the recall for the critical class. In order to deal with this, sentiment analysis' insights are introduced into the semi-supervised learners. As a result, the recall for the critical class improves up to 85 percent, which makes the proposed solution useful in cases where efficient classification among critical and non-critical classes is required.
Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations

Abstract Today’s deep learning systems deliver high performance based on end-to-end training but are notoriously hard to inspect. We argue that there are at least two reasons making inspectability challenging: (i) representations are distributed across hundreds of channels and (ii) a unifying metric quantifying inspectability is lacking. In this paper, we address both issues by proposing Semantic Bottlenecks (SB), which can be integrated into pretrained networks, to align channel outputs with individual visual concepts and introduce the model agnostic Area Under inspectability Curve (AUiC) metric to measure the alignment. We present a case study on semantic segmentation to demonstrate that SBs improve the AUiC up to six-fold over regular network outputs. We explore two types of SB-layers in this work. First, concept-supervised SB-layers (SSB), which offer inspectability w.r.t. predefined concepts that the model is demanded to rely on. And second, unsupervised SBs (USB), which offer equally strong AUiC improvements by restricting distributedness of representations across channels. Importantly, for both SB types, we can recover state of the art segmentation performance across two different models despite a drastic dimensionality reduction from 1000s of non aligned channels to 10s of semantics-aligned channels that all downstream results are based on.
A Multi Objective &amp; Trust-Based Workflow Scheduling Method in Cloud Computing based on the MVO Algorithm

The problem of task scheduling on VMs is selecting appropriate resources for a task so that its associated tasks have already been executed. Since the workflow contains a set of tasks, the likelihood of failure increases with the failure of a task throughout the workflow. The allocation of tasks on virtual machines with higher reliability improves workflow-scheduling efficiency. Therefore, Trust relationship is an important factor of resource allocation and job scheduling, and in this paper, we have presented a good method to estimate the trust of virtual machines on which the workflow is run. In addition to the trust, which is an important factor in the workflow scheduling, there are other criteria for the satisfaction of service providers and customers. By increasing the number of requests and the diversity of virtual machines as well as the contradiction between objectives, finding the optimal Pareto front is more challenging. Therefore, multi-objective evolutionary algorithms face a large space of permutations to find an optimal tradeoff of objectives. In this paper, we present a multi-objective workflow-scheduling algorithm using MVO algorithm with the aim of increasing diversity and convergence, so that the proposed method can consider QoS requirements for service providers and customers simultaneously.
Combining user-end and item-end knowledge graph learning for personalized recommendation

How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-paths for user-item pairs in KGs or use a graph convolution network on all KGs to obtain embeddings for users and items separately. Although both types of methods have respective effects, the former cannot fully capture the structural information of user-item pairs in KGs, while the latter ignores the mutual effect between the target user and item during the embedding learning process. To alleviate the shortcomings of these methods, we design a graph convolution-based recommendation model called Combining User-end and Item-end Knowledge Graph Learning (CUIKG), which aims to capture the relevance between users’ personalized preferences and items by jointly mining the associated attribute information in their respective KG. Specifically, we describe user embedding from a user KG and then introduce user embedding, which contains the user profile into the item KG, to describe item embedding with the method of Graph Convolution Network. Finally, we predict user preference probability for a given item via multilayer perception. CUIKG describes the connection between user-end KG and item-end KG, and mines the structural and semantic information present in KG. Experimental results with two real-world datasets demonstrate the superiority of the proposed method over existing methods.
A Deep Learning Framework for Viable Tumor Burden Estimation

Liver masses have become a common clinical challenge since they require to be defined and accurately categorized as neoplastic or nonneoplastic lesions. Hepatocellular carcinoma (HCC), the most common histologic type of primary liver malignancy, is a global health concern being the fifth most common cancer and the second cause of cancer mortality worldwide. Accurate diagnosis, which in some circumstances requires histopathology results, is necessary for appropriate management. Also, some tumor characteristics help in predicting tumor behavior and patient response to therapy. In this paper, we propose a deep learning framework for the segmentation of whole and viable tumor areas of liver cancer from whole-slide images (WSIs). To this end, we use Fast Segmentation Convolutional Neural Network (Fast-SCNN) as our network. We use the dataset from PAIP 2019 challenge. After data-augmentation on the training subset, we train the network with a multi-term loss function and SWA technique. Our model achieves 0.80 for the median of the Jaccard Index for the task of Viable Tumor Segmentation and 0.77 for the median of Weighted Absolute Accuracy for the task of Viable Tumor Burden Estimation on the whole-slide images of the test subset.
Interweaving vulnerability and everyday design: Encounters around an aquarium in a paediatric oncology ward

Contemporary understandings of vulnerability highlight its critical, relational and enabling aspects. Through leveraging these understandings, this article contributes to conceptualizing the notion of everyday design by interweaving it with that of vulnerability. A case study brings vulnerability into view by zooming out from and in on everyday practices around an aquarium in a paediatric oncology ward. Subsequently, we unravel the notion of vulnerability as used in design research and in anthropology and philosophy: while vulnerability grounds everyday design philosophically and ethically as a form of care, everyday design challenges the tendency to situate vulnerability in (relations between) human beings. Our article thus exemplifies design anthropology understood as bringing in dialogue theory and methods from both anthropology and design.
A Tree-based Fuzzy Average-Utility Mining Algorithm

Utility mining considers high-utility itemsets as useful by combining item quantities and item benefits. Its mining results do not, however, include the quantity information such as large amounts or small amounts. Fuzzy utility mining is thus proposed to fuzzify the results of utility mining and obtain linguistic high-utility itemsets. However, fuzzy utility measurement is not fair to evaluate itemsets because the fuzzy utility value of an itemset in a transaction may be higher than those of its subsets. In the past, we defined the fuzzy average utility mining to solve the above problem and proposed a two-phase method to solve the fuzzy average-utility mining problem and find high fuzzy average-utility itemsets. However, its execution is slow. In this paper, an efficient algorithm is proposed, which uses a tree structure to solve fuzzy average-utility mining. The proposed tree-structure method is compared with the previous two-phase approach. Experimental evaluation shows that the efficiency of the proposed method is better than that of the two-phase algorithm in execution time and numbers of candidates.
Automated Quality Assurance for Hand-Held Tools via Embedded Classification and AutoML

Despite the ongoing automation of modern production processes manual labor continues to be necessary due to its flexibility and ease of deployment. Automated processes assure quality and traceability, yet manual labor introduces gaps into the quality assurance process. This is not only undesirable but even intolerable in many cases.
The Role of the Mandamonic Games in Supporting Mandarin Learning at Elementry School

Abstract Foreign language education has begun to combine conventional education with mobile learning or “M-Learning”. M-Learning offers a modern way to support the learning process through mobile devices, such as handheld computers and tablets, MP3 players, smart phones and cellphones. This study explains Chinese learning through smart phones among elementary school students. Students’ interest in playing games, especially on smart phones, is the background of researchers designing Chinese learning applications by emphasizing game based learning. Before designing a smart phone game, researchers first conducted a survey of 129 elementary school students in grades 1-6 in two private primary schools in Jakarta about the use of smart phones and student interest in playing educational games. From the survey the researchers designed a game application called “Mandamonik”. This application consists of three types of games, namely memory games, puzzle games and word shoot games. The author asks 33 students to play the game. From student feedback, 85% of students consider Chinese learning, especially vocabulary mastery, can be fulfilled with the Chinese Character game.
Special issue on “self-improving self integration”

“Self-improving system integration” (SISSY) is a research initiative that aims to master the ever-changing demands of system organisation in the presence of highly-dynamic autonomous subsystems, evolving architectures, and unpredictable open environments. In contrast to traditional system modelling, design and development – typically performed by engineers, offline – the SISSY vision promotes automated integration-related decisions, performed during run-time. This implies a change of responsibilities, from engineers to the system itself, which must now find ways to restructure and reassemble itself so as to achieve its objectives. In addition to self-integrating and self-improving internally, such systems must equally be able to cooperate or co-exist with other autonomous systems, which may dynamically join a shared environment. In this editorial, we briefly introduce the ‘self-improving system integration’ initiative and summarise the contents of this special issue.
DiffTech: Differencing Similar Technologies From Crowd-Scale Comparison Discussions

Developers use different technologies for many software development tasks. However, when faced with several technologies with comparable functionalities, it is not easy to select the most appropriate one, as trial and error comparisons among such technologies are time-consuming. Instead, developers can resort to expert articles, read official documents or ask questions in Q&A sites. However, it still remains difficult to get a comprehensive comparison as online information is often fragmented or contradictory. To overcome these limitations, we propose the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DiffTech</small> system that exploits crowdsourced discussions from Stack Overflow, and assists technology comparison with an informative summary of different aspects. We first build a large database of comparable technologies in software engineering by mining tags in Stack Overflow. We then locate comparative sentences about comparable technologies with natural language processing methods. We further mine prominent comparison aspects by clustering similar comparative sentences and representing each cluster with its keywords and aggregate the overall opinion towards the comparable technologies. Our evaluation demonstrates both the accuracy and usefulness of our model, and we have implemented our approach as a practical website for public use.
Multi-Agent Path Planning Under Observation Schedule Constraints

We consider the problem of enhanced security of multi-robot systems to prevent cyber-attackers from taking control of one or more robots in the group. We build upon a recently proposed solution that utilizes the physical measurement capabilities of the robots to perform introspection, i.e., detect the malicious actions of compromised agents using other members of the group. In particular, the proposed solution finds multi-agent paths on discrete spaces combined with a set of mutual observations at specific locations to detect robots with significant deviations from the preordained routes. In this paper, we develop a planner that works on continuous configuration spaces while also taking into account similar spatio-temporal constraints. In addition, the planner allows for more general tasks that can be formulated as arbitrary smooth cost functions to be specified. The combination of constraints and objectives considered in this paper are not easily handled by popular path planning algorithms (e.g., sampling-based methods), thus we propose a method based on the Alternating Direction Method of Multipliers (ADMM). ADMM is capable of finding locally optimal solutions to problems involving different kinds of objectives and non-convex temporal and spatial constraints, and allows for infeasible initialization. We benchmark our proposed method on multi-agent map exploration with minimum-uncertainty cost function, obstacles, and observation schedule constraints.
Clicker Bot for Gacha Games Using Image Recognition

Gacha games has tedious activities need to be done by the players to progress the game besides using real money. The usage of bots and macros to automate the game are not uncommon nowadays with the high demand of grinding within the game. This research purposes is to create a bot that can replace the user task to click buttons on game stages, thus helping users in their grinding. The automatic clicker applied image recognition to find the area to be clicked. Firstly, the template image is manually taken by the user to be used as the matching. The screen image, which captured on the go, and the template image are pre-processed using three variations techniques to find the best precision of the coordinate area. Afterwards, the cursor will be moved to the coordinate position returned from the matching between template image and screen image. The coordinate returned from the matching is randomized using Gaussian Distributions but still within the matched positions between template image and screen image. The experiment shows that Canny Edges gives the good coordinate position. On the other hand, the grayscale gives a poor result on giving the coordinate position. Canny Edge and combination of Canny Edge and grayscale always return the target’s position. With this bot, user does not need to supervise the game every time the game stage has finished and restarting the stage.
Action Design Research Shaping University-Industry Collaborations for Wicked Problems

Research collaborations with external partners are becoming the norm in HCI. This tendency has been driven by the fact that HCI does research 'in the wild', engaging with societal problems, necessitating collaboration with external partners. Despite this interest, little work exists on which concrete approaches HCI researchers can take to make the different goals, resources, and responsibilities towards societal wicked problems align, throughout a research project. In this paper, we propose that design can be used as an approach to facilitate a strong and long-lasting inclusive collaboration with industrial as well as social partners. We do this by analyzing a research project with a major industrial partner on circularity and reflect on this using the theoretical constructs of action design research, concept design, and integrated research spaces.
Relating to the Environment Through Photography

Research on environmental sustainability in HCI is investigating the opportunities and hindrances technologies pose on living sustainably, beyond direct material impact of production, use and disposal. With this background, we focus on the smartphone camera as a tool that allows users to quickly and relatively effortlessly depict, save, share, access, augment or amplify information about the environment. Based on two years of participant observation studies, we present examples of how urban farmers use the smartphone camera as a tool in their practice. We discuss how the smartphone camera mediates human experiences of the environment and how certain uses of the camera may contribute to environmental sustainability. We highlight how the smartphone camera used as a tool in gardening was experienced to support (a) feelings of closeness or bonds towards the local environment and (b) the creation and sharing of knowledge.
A language model for Amdo Tibetan speech recognition

We built a language model which is based on Transformer network architecture, used attention mechanisms to dispensing with recurrence and convalutions entirely. Through the transliteration of Tibetan to International Phonetic Alphabets, the language model was trained using the syllables and phonemes of the Tibetan word as modeling units to predict corresponding Tibetan sentences according to the context semantics of IPA. And it combined with the acoustic model as the Tibetan speech recognition was compared with end-to-end Tibetan speech recognition.