Reviewerly/exHarmony huggingface dataset:
Geono-Cluster: Interactive Visual Cluster Analysis for Biologists

Biologists often perform clustering analysis to derive meaningful patterns, relationships, and structures from data instances and attributes. Though clustering plays a pivotal role in biologists' data exploration, it takes non-trivial efforts for biologists to find the best grouping in their data using existing tools. Visual cluster analysis is currently performed either programmatically or through menus and dialogues in many tools, which require parameter adjustments over several steps of trial-and-error. In this article, we introduce Geono-Cluster, a novel visual analysis tool designed to support cluster analysis for biologists who do not have formal data science training. Geono-Cluster enables biologists to apply their domain expertise into clustering results by visually demonstrating how their expected clustering outputs should look like with a small sample of data instances. The system then predicts users' intentions and generates potential clustering results. Our study follows the design study protocol to derive biologists' tasks and requirements, design the system, and evaluate the system with experts on their own dataset. Results of our study with six biologists provide initial evidence that Geono-Cluster enables biologists to create, refine, and evaluate clustering results to effectively analyze their data and gain data-driven insights. At the end, we discuss lessons learned and implications of our study.
Text recognition and classification of english teaching content based on SVM

When the English teaching text is regarded as the ontology, it must involve how to describe the attribute effectively. However, in the current research, the research on the automatic extraction of labels for English teaching texts is still insufficient. Intelligent English teaching has become an inevitable trend in the development of future English teaching models, so it is necessary to cooperate with intelligent text recognition technology. Based on SVM, this study applies convolutional neural network algorithm to text recognition of English teaching content, and effectively recognizes text features. After feature extraction, the original text content has been changed into data that the machine can directly identify and analyze, and semantic analysis is performed. In order to verify the performance of the algorithm, the performance of the algorithm was analyzed by example verification. It can be seen from the results that the proposed method has a certain accuracy rate and can be applied to the text recognition classification of English teaching content and can provide reference direction for related research.
Human-centered design for a sign language learning application

An animated sign language dictionary is a valuable resource for caregivers learning to communicate with residents who use sign language. This case study reports on the development of such a tool using the human-centered design methodology. Through interviews and observations with key stakeholders, we realized that our efforts could benefit a broader population that included the residents themselves. The result of this thoughtful approach is an application that is accessible across multiple platforms and features interaction modalities to accommodate the needs of various user groups.
OmniGlobeVR

In this paper, we present a novel collaboration tool, OmniGlobeVR, which is an asymmetric system that supports communication and collaboration between a VR user (occupant) and multiple non-VR users (designers) across the virtual and physical platform. OmniGlobeVR allows designer(s) to explore the VR space from any point of view using two view modes: a 360° first-person mode and a third-person mode. In addition, a shared gaze awareness cue is provided to further enhance communication between the occupant and the designer(s). Finally, the system has a face window feature that allows designer(s) to share their facial expressions and upper body view with the occupant for exchanging and expressing information using nonverbal cues. We conducted a user study to evaluate the OmniGlobeVR, comparing three conditions: (1) first-person mode with the face window, (2) first-person mode with a solid window, and (3) third-person mode with the face window. We found that the first-person mode with the face window required significantly less mental effort, and provided better spatial presence, usability, and understanding of the partner's focus. We discuss the design implications of these results and directions for future research.
Fingerprinting of Relational Databases for Stopping the Data Theft

The currently-emerging technology demands sharing of data using various channels via the Internet, disks, etc. Some recipients of this data can also become traitors by leaking the important data. As a result, the data breaches due to data leakage are also increasing. These breaches include unauthorized distribution, duplication, and sale. The identification of a guilty agent responsible for such breaches is important for: (i) punishing the culprit; and (ii) preventing the innocent user from accusation and punishment. Fingerprinting techniques provide a mechanism for classifying the guilty agent from multiple recipients and also help to prevent the innocent user from being accused of the data breach. To those ends, in this paper, a novel fingerprinting framework has been proposed using a biometric feature as a digital mark (signature). The use of machine learning has also been introduced to make this framework intelligent, particularly for preserving the data usability. An attack channel has also been used to evaluate the robustness of the proposed scheme. The experimental study was also conducted to demonstrate that the proposed technique is robust against several malicious attacks, such as subset selection attacks, mix and match attacks, collusion attacks, deletion attacks, insertion attacks, and alteration attacks.
Self-adaptation of XCS learning parameters based on learning theory

This paper proposes a self-adaptation technique of parameter settings used in the XCS learning scheme. Since we adaptively set those settings to their optimum values derived by the recent XCS learning theory, our proposal does not require any trial and error process to find their proper values. Thus, our proposal can always satisfy the optimality of XCS learning scheme, i.e. to distinguish accurate rules from inaccurate rules with the minimum update number of rules. Experimental results on artificial classification problems including overlapping problems show that XCS with our self-adaptation technique significantly outperforms the standard XCS.
The Role of Uncertainty as a Facilitator to Reflection in Self-Tracking

Self-trackers reflect on their personal data to understand their behaviour and plan accordingly. Often, this reflection involves uncertainty, which can affect decision-making. To better understand the role of uncertainty, we conducted an interview study to comprehend how uncertainty influences reflection and the resulting actions. Our findings suggest that, in addition to the conventional role of uncertainty as a barrier, uncertainty also manifests as a trigger and facilitator to reflection. We discuss functionalities to alleviate the negative effects of uncertainty (e.g. incorporating users' expectations in self-tracking), and leverage its positive effects through activity reconstruction mechanisms.
Tracking People Using Ankle-Level 2D LiDAR for Gait Analysis

People tracking is one of the fundamental goals of human behavior recognition. Development of cameras, tracking algorithms and effective computations make it appropriate. But, when the question is privacy and secrecy, cameras have a great obligation on it. Our fundamental goal of this research is to replace video camera with a device (2D LiDAR) that significantly preserve the privacy of the user, solve the issue of narrow field of view and make the system functional simultaneously. We consider individual movements of every moving objects on the plane and figure out the objects as a person based on ankle orientation and movements. Our approach calculates the number of frames of every moving object and finally create a video based on those frames.
An Analytical Study on Cross-Site Scripting

Cross-Site Scripting, also called as XSS, is a type of injection where malicious scripts are injected into trusted websites. When malicious code, usually in the form of browser side script, is injected using a web application to a different end user, an XSS attack is said to have taken place. Flaws which allows success to this attack is remarkably widespread and occurs anywhere a web application handles the user input without validating or encoding it. A study carried out by Symantic states that more than 50% of the websites are vulnerable to the XSS attack. Security engineers of Microsoft coined the term "Cross-Site Scripting" in January of the year 2000. But even if was coined in the year 2000, XSS vulnerabilities have been reported and exploited since the beginning of 1990’s, whose prey have been all the (then) tech-giants such as Twitter, Myspace, Orkut, Facebook and YouTube. Hence the name "Cross-Site" Scripting. This attack could be combined with other attacks such as phishing attack to make it more lethal but it usually isn’t necessary, since it is already extremely difficult to deal with from a user perspective because in many cases it looks very legitimate as it’s leveraging attacks against our banks, our shopping websites and not some fake malicious website.
3D Pedestrian tracking using local structure constraints

Tracking pedestrians based on visual sensors has many diverse applications, among them autonomous driving. Besides obtaining high recall, maintaining the consistency of tracked trajectories during data association is one of the most crucial issues of any tracker. This issue has been tackled in the literature for some time, taking advantage of geometry cues for improving the pairwise matching of detections across consecutive frames. However, this idea has only been employed in a simple way and not thoroughly leveraged in existing studies, i.e., only 2D information is utilized that cannot help to completely understand the real-world geometry in 3D space. Motivated by this observation, in this paper, we present a new method called 3D-TLSR (3D pedestrian tracking using local structure refinement). We use stereo images and expand the idea of geometry cues into 3D space to improve the association of existing trajectories and new detections. We divide the assignment optimization into two steps: (1) determining trajectories whose assignments are strongly believed to be correct, which we call anchors and (2) employing geometry constraints between the anchors and their nearby trajectories in 3D space to improve the matching of less reliable assignments of the first step. In addition, we suggest a simple approach to compute and correct the velocity of a tracked person so that we can better recover missed detections. Experimental results on the well known KITTI tracking benchmark, the ETHMS data set, as well as a self-generated dataset show that our tracker yields comparable results to other state-of-the-art methods with (for KITTI) multi object tracking accuracy (MOTA) of 54.00, which is the best online result among all investigated approaches, multi object tracking precision (MOTP) of 73.03, which is the best of all reported values, and mostly tracked (MT) of 29.55, being the second-best result. On the ETHMS dataset, our approach obtains best results with large margins for recall, precision, and MT, while maintaining a reasonable low number of Id switches (IDs) and fragmentation (FG). These findings confirms the effectiveness of our proposed association method and velocity estimation approach.
A Study on the Power Generation Prediction Model Considering Environmental Characteristics of Floating Photovoltaic System

The main contents of this paper are to verify the environmental factors affecting the power generation of floating photovoltaic systems and to present the power generation prediction model considering environmental factors by using regression analysis and neural networks studied during the last decade. This study focused on a comparative analysis of which model is best suited for the power generation prediction of the floating photovoltaic (PV) system. To compare the power generation characteristics of a floating and a land-based PV system, two identical 2.5 kW PV systems were installed—one on the water surface in the Boryeong Dam, Korea, and the other nearby on dry land—and their performances were compared. The solar irradiance of the floating PV system was 1.1% lower than that of the land-based PV. Nevertheless, the floating PV module temperature was 4.9% lower than that of the land-based PV, generating approximately 3% more power. Using the correlation analysis of data mining techniques, environmental factors affecting the efficiency of the floating PV system were investigated. The correlation coefficient between the module temperature and water temperature was r = 0.6317 which proves that the high efficiency and low module temperature characteristics of the floating PV system, when compared with that of the land-based PV, are due to the water evaporation effect. Considering environmental factors, power-generation prediction models based on regression analysis and neural networks are presented, and their accuracies are compared. This comparison confirms that the accuracy of the power generation prediction model using neural networks was approximately 2.59% higher than that of the regression analysis method. As a result of adjusting the hidden nodes in the neural network algorithm, it was confirmed that a neural network algorithm with ten hidden nodes was most suitable for calculating the amount of power generation.
Classifying Breast Cancer Based on Machine Learning

Breast cancer is the most prevalent cancer among Indian women and a prime cause of death due to cancer. Hence, an early detection and accurate diagnosis and staging of breast cancer are crucial in managing the disease. In this work, a comparative study of application of machine learning classifiers has been done for the classification of benign from malignant breast cancer. This paper investigates the performance of various supervised classification techniques like logistic regression, support vector machine, k-nearest neighbour and decision tree. These algorithms are coded in R and executed in R studio. For performance analysis, various parameters such as specificity, sensitivity and accuracy have been calculated and compared. The SVM classifier gives the accuracy of 99.82% indicating its suitability over other classification techniques. In this work, we have addressed the issue of distinguishing benign from malignant breast cancer.
The impact of using a domain language for an agile requirements management

Context: The development of software systems is a complex activity because of its nature and the management of its construction. It is challenging to create and follow a plan. Moreover, budget overrun is a common consequence of this situation. Agile methods, like Scrum, help to mitigate this problem using incremental and iterative development. Agile methods jump start new developments, but it is difficult to be agile after several months when the software has to deal with many requirements that are scattered and tangled across several User Stories written in different Sprints. Objective: In this paper, we propose a traceability approach anchored on an index structure to access specific User Stories from a large set. Our proposed strategy has the goal to consolidate the information dispersed in different User Stories into a particular lexicon: The Language Extended Lexicon (LEL). Method: The proposed approach consists of a set of rules which extract the information dispersed in the User Stories and organize it in symbols of the Lexicon. Thus, the Lexicon supplies a consolidated and organized structure to mitigate the problem of tangled information that generates lack of traceability among different sprints. Results: We assessed how the Lexicon built by our approach improves everyday activities related to requirement management. The assessment is based on a quantitative evaluation with 36 subjects. Conclusion: The approach presents benefits for requirement tracing in agile methodologies supported by the preliminary results of the evaluation. We have developed an application (a prototype) that automates the LEL derivation rules from a set of User Stories.
Golden parameter search

Automated algorithm configuration procedures such as SMAC, GGA++ and irace can often find parameter configurations that substantially improve the performance of state-of-the-art algorithms for difficult problems - e.g., a three-fold speedup in the running time required by EAX, a genetic algorithm, to find optimal solutions to a set of widely studied TSP instances. However, it is usually recommended to provide these methods with running time budgets of one or two days of wall-clock time as well as dozens of CPU cores. Most general-purpose algorithm configuration methods are based on powerful meta-heuristics that are designed for challenging and complex search landscapes; however, recent work has shown that many algorithms appear to have parameter configuration landscapes with a relatively simple structure. We introduce the golden parameter search (GPS) algorithm, an automatic configuration procedure designed to exploit this structure while optimizing each parameter semi-independently in parallel. We compare GPS to several state-of-the-art algorithm configurators and show that it often finds similar or better parameter configurations using a fraction of the computing time budget across a broad range of scenarios spanning TSP, SAT and MIP.
The Wooden Quilt

Women can face barriers to participation in universal makerspace environments and are consequently underrepresented within them. Further, women have historically been excluded from learning and working with particular types of materials, such as wood. To explore how we might address these inequalities in regards to both access to makerspaces and to diverse materials, we present the wooden quilt probe. Through this probe we aimed to 1) create a makerspace environment specifically for women where they could engage with materials and tools traditionally found in more male-dominated craft environments, and 2) facilitate the sharing of stories and experiences with other women within the community center where this work took place. We contribute a rich understanding of the stories and experiences of women from the community center, and discuss the implications of the work for Interaction Design: how designers can contribute towards diversifying makerspace environments that enable women's participation within them, the benefits of intertwining storytelling and making, and the boundaries around sharing personal narratives.
Towards a Generic Framework for Character-Based Chatbots

A sudden interest in chatbots occurred in 2016 due to the advancements in machine learning and artificial intelligence technologies. The increase of mobile messaging applications and the introduction of chatbot development frameworks allowed researchers to explore different paradigms for building chatbots. A major issue related to the user experience with chatbots was detected. Users with different personalities, preferences, and needs receive the same responses when they interact with a particular chatbot. This can create unpleasant experiences towards certain user groups. There were multiple efforts to create personalized chatbots in order to overcome such experiences. In this paper, a systematic review for personalized chatbots is conducted to investigate the attempts of building psychologically-driven personalized chatbots. Accordingly, we propose a generic framework for building character-based chatbots using the principles of Character Computing based on psychological theorizing. Our goal is to allow users to create their individual chatbot characters by selecting their preferred character traits. To this end, we introduce a retrieval-based approach for implementing our proposed framework.
Indian Currency Detection using Image Recognition Technique

The paper currency counterfeiting is a big problem for the world. Almost every country has been badly affected by this which has become a very acute problem. The main purpose behind this study is to recognize Indian paper currency with this hybrid approach which is portable and making an application used on the go. In this study, the Indian currency note features will be extracted and will be stored in MAT files and then these stored features will be matched with the input paper currency to recognize whether it is genuine or duplicate. With this system, easy to recognize the currency note anywhere, anytime. I have used the MATLAB image processing toolbox. The image processing is a way to improve the pictorial information of the image for the sake of machine or hardware perception. The currency notes will be recognized with the combination of both local binary patterns and principal component analysis. The LBP is significant progress in texture analysis and used for matching purpose. PCA is used for training purpose. Euclidian distance algorithm will be used for combining the metrics which has simple measure computations. Currency recognition has big challenges like watermark recognition, currency note resolution, dirty notes etc.
TrustCA: Achieving Certificate Transparency Through Smart Contract in Blockchain Platforms

Certificate Authorities (CAs) are important components for digital certificate issuances in Public Key Infrastructure(PKI). However, current CAs have some intrinsic weaknesses due to the CA-centric implementation. And when browser and operating system vendors contain a CA in the software, they place complete trust in the CA. In this paper, we utilize natural characteristics of tamper-proof and transparency of smart contracts in blockchain platforms to design an independent entity, named the CA proxy, to manage life cycle of digital certificates. This management will achieve the certificate transparency. We propose a new system architecture easy to integrate the CA proxy with current CAs through applying the blockchain oracle service. In this architecture, the CA proxy, CAs, and even professional identity verification parties can accomplish life cycle management of certificates, signature of certificates, identity verification for certificates correspondingly. The achievement of the certificate transparency through life cycle management of digital certificates in blockchain platforms, when compared with traditional CAs, solves traditional CAs' trust model weaknesses and improve the security.
SL-ReDu

We present SL-ReDu, a recently commenced innovative project that aims to exploit deep-learning progress to advance the state-of-the-art in video-based automatic recognition of Greek Sign Language (GSL), while focusing on the use-case of GSL education as a second language. We first briefly overview the project goals, focal areas, and timeline. We then present our initial deep learning-based approach for GSL recognition that employs efficient visual tracking of the signer hands, convolutional neural networks for feature extraction, and attention-based encoder-decoder sequence modeling for sign prediction. Finally, we report experimental results for small-vocabulary, isolated GSL recognition on the single-signer "Polytropon" corpus. To our knowledge, this work constitutes the first application of deep-learning techniques to GSL.