Reviewerly/exHarmony huggingface dataset:
Optical air-gap exfiltration attack via invisible images

In this paper, we evaluate an optical covert channel in which sensitive information (textual or binary) is exfiltrated from air-gapped computers through the LCD screen. Our experiments show that low contrast and fast blinking images which are invisible to human subjects, can be recovered from photos taken by a local camera. Consequentially, we show that malware on a compromised computer can obtain sensitive data (e.g., files, images, encryption keys, passwords), and project it onto a computer LCD screen, invisible and unbeknownst to users. An attacker can reconstruct the hidden data using a photo taken by a local camera. In order to demonstrate the feasibility of this type of attack and evaluate the channel's stealth, we conducted a battery of tests with 40 users. We also examined the channel’s boundaries under various parameters, with different types of encoded objects, at several distances, and using several kinds of cameras.
Building private blockchains over public blockchains (PoP)

Blockchain technology is increasingly being adopted as a trusted platform to support business functions including trusted and verifiable transactions, tracking, and validation. However, most business use-cases require privacy and confidentiality for data and transactions. As a result, businesses are forced to choose private blockchain solutions and unable to take full advantage of the capabilities, benefits and infrastructure of public blockchain systems. To address this issue, we present an Attribute-Based Encryption (ABE) security solution built on a private-over-public (PoP) blockchain approach. The policy based distributed operation of ABE conforms well to the blockchain concept. The cross-chain PoP approach provides the benefits from both public blockchains and private blockchains. Businesses will be able to restrict access, maintain privacy, and improve performance while still being able to leverage the distributed trust of public blockchains. This paper present the ABE-based security framework and protocol for securing data, transactions as well as smart contracts. Security analysis and performance evaluation show the proposed solution to be effective, efficient and practical. It can greatly reduce the cost and complexity for businesses compared to running isolated private blockchain solutions.
Clustering preference data in the presence of response‐style bias

Preference data, such as Likert scale data, are often obtained in questionnaire‐based surveys. Clustering respondents based on survey items is useful for discovering latent structures. However, cluster analysis of preference data may be affected by response styles, that is, a respondent's systematic response tendencies irrespective of the item content. For example, some respondents may tend to select ratings at the ends of the scale, which is called an ‘extreme response style’. A cluster of respondents with an extreme response style can be mistakenly identified as a content‐based cluster. To address this problem, we propose a novel method of clustering respondents based on their indicated preferences for a set of items while correcting for response‐style bias. We first introduce a new framework to detect, and correct for, response styles by generalizing the definition of response styles used in constrained dual scaling. We then simultaneously correct for response styles and perform a cluster analysis based on the corrected preference data. A simulation study shows that the proposed method yields better clustering accuracy than the existing methods do. We apply the method to empirical data from four different countries concerning social values.
Extended sammon projection and wavelet kernel extreme learning machine for gait-based legitimate user identification

Smartphones have pervasively integrated into our home and work environments managing confidential information but their owners still rely on as explicit as inefficient and insecure identification processes. Therefore, if a device is stolen, a thief can have access to the owner's personal information and services though the stored password/s. To avoid such situations, this work demonstrates the possibilities of legitimate user identification in a semi-controlled environment through the built-in smartphone motion dynamics captured by two different sensors. This is a two step process: sub-activity recognition followed by user/impostor identification. Prior to the identification, Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrated that time and frequency domain features, optimized by ESP to train the wavelet kernel based extreme learning machine classifier, implement an effective system to identify the legitimate user or an impostor with 97% accuracy.
Towards Better Security Decisions

Normal users are usually not good at making decisions about cybersecurity, being easily attacked by hackers. Quite a few tools have been devised and implemented to help, but they can not balance security and usability well. To solve the problem, this paper explores the application of prospect theory to security recommendations. We conducted online surveys (n=61) and a between-subjects experiment (n=106) in six conditions to investigate the issues. In the experiment, we provided different security recommendations about two-factor-authentication (2FA) to participants in different conditions and recorded their decisions about enabling it. Results show that participants in the condition "Disadvantage" were willing to adopt 2FA the most. The findings indicate that showing disadvantages can be useful to persuade users into better security decisions.
Word2Vec based spelling correction method of Twitter message

Twitter1 became popular owing to the devices like smartphones and tablets, with which short messages can be easily composed. Due to the popularity of Twitter, the volume of Twitter messages has increased rapidly. Accordingly, studies have been carried out to extract various data by analyzing Twitter messages. However, there is a limitation in mining accurate data from Twitter messages that are composed in short sentences because the misspelling problem is persisting. Although studies using Word2Vect are continuously conducted for spelling correction, it can be said that they are replacing the extracted words by using the Word2Vec rather than correcting the words. Furthermore, since characters of misspelled word are not taken into consideration, it does not fit the meaning of correction. This paper proposes a method of correcting misspelled words in Twitter messages by using an improved Word2Vec. Misspelled words in a Twitter message are selected through pre-processing process. For a selected misspelled word, candidate correction words are extracted through the improved Word2Vec. Among the extracted candidate words, the word that has the highest similarity value for the misspelled word replaces the misspelled word, thereby correcting the spelling error.
Irradiance modelling for bi-facial PV modules using the ray tracing technique

Bi-facial PV modelling software is scarce and existing software mainly implements the view factor model which may neglect a number of effects that greatly influence the rear side irradiance of bi-facial modules. This paper shows the results of bi-facial PV simulation software that is being developed by implementing the ray tracing technique to model the factors that lead to increased energy yields of bi-facial PV technology. A bi-facial PV plant is modelled by interfacing Radiance (ray tracing software) with python based simulation software. Simulation results are compared with real world irradiance measurements to determine the accuracy of the developed software model. The preliminary results of the simulation software are promising and show that ray tracing techniques can satisfy the shortcomings of existing bi-facial PV simulation software with more accurate modelling of the rear side irradiance of modules.
Designing an Informal Learning Curriculum to Develop 3D Modeling Knowledge and Improve Spatial Thinking Skills

We report on the design and implementation of a 3-week long summer academy introducing high school students to 3D modeling and 3D printing experiences. Supporting youth in developing 3D modeling knowledge can enhance their capacity to effectively use an array of emerging technologies such as Virtual Reality, Augmented Reality, and digital fabrication. We used tools and practices from both formal and informal education, such as storylining, to inform the design of the curriculum. We collected data through surveys, artifacts, observations, screen recordings, and group videos. Our findings suggest that (1) emphasizing curricular coherence as a design goal and (2) providing youth with multiple avenues for engaging in 3D modeling can help to: spark youth interest in 3D printing/modeling, maintain youth engagement in learning activities over the course of several weeks, and provide youth with opportunities to develop their spatial thinking skills.
A Comparative Study of Face Recognition Algorithms under Facial Expression and Illumination

Face recognition algorithms enable computational devices to automatically recognize human faces and have been adopted by many big tech companies. These algorithms have shown fairly acceptable performance in criminal identification, healthcare, advertisement, access and security, payments and other different areas. In this paper, we attempt comparatively to identify best algorithms in terms of accuracy when applied on datasets that have considerable facial expression and pose illumination challenges. We selected benchmark datasets and applied preprocessing techniques to suppress noise in the images. We then applied different algorithms for feature extraction and then feed these features to the classifier. Based on the conducted experiments, we observed that the Local Binary Pattern Histogram algorithm outperformed the other two selected algorithms by about 1% against Linear Discriminant Analysis (LDA) and about 9% against Principal Component Analysis (PCA). In addition, LDA outperformed the other two by obtaining about 99.532% of accuracy on JAFFE dataset.
On Interfaces to Support Agile Architecting in Automotive: An Exploratory Case Study

Practitioners struggle with creating and evolving an architecture when developing complex and safety-critical systems in large-scale agile contexts. A key issue is the trade-off between upfront planning and flexibility to embrace change. In particular, the coordination of interfaces is an important challenge, as interfaces determine and regulate the exchange of information between components, subsystems, and systems, which are often developed by multiple teams. In a fast-changing environment, boundary objects between teams can provide the sufficient stability to align software or systems, while maintaining a sufficient degree of autonomy. However, a better understanding of interfaces as boundary objects is needed to give practical guidance. This paper presents an exploratory case study with an automotive OEM to identify characteristics of different interfaces, from non-critical interfaces that can be changed frequently and quickly, to those that are critical and require more stability and a rigorous change process. We identify what dimensions impact how interfaces are changed, what categories of interfaces exist along these dimensions, and how categories of interfaces change over time. We conclude with suggestions for practices to manage the different categories of interfaces in large-scale agile development.
Visual content based video retrieval on natural language queries

With the emergence of video-sharing websites uploading thousands of videos per day, content based video retrieval has become an important research challenge. Existing approaches for content based video retrieval lag robustness and efficiency in terms of the dynamics and variations of videos and queries expressed in natural language. In this paper, we develop a novel approach for bi-directional retrieval of videos and its descriptions, represented through sentences in natural language, by combining various state-of-the-art learning and video/sentence representation techniques. We first develop a base model which is based on sentence embedding of captions generated from video content. Further we extend the base model with a joint learning of latent embedding space model, where we use a two-branch neural network to learn the shared embedding space of videos and corresponding sentences expressing its description. The network is trained using a maximum-margin loss and uses neighborhood information for structure-preserving constraints in joint embedding space. Our experiments with three different datasets show promising results for video and sentence retrieval in comparison with various baseline models.
Youth Concerns and Responses to Self-Tracking Tools and Personal Informatics Systems

Though some work has looked at the implementation of personal informatics tools with youth and in schools, the approach has been prescriptive; students are pushed toward behaviour change intervention or otherwise use the data for prescribed learning in a particular curriculum area. This has left a gap around how young people may themselves choose to use personal informatics tools in ways relevant to their own concerns. We gave workshops on personal informatics to 13 adolescents at two secondary schools in London, UK. We asked them to use a commercial personal informatics app to track something they chose that they thought might impact their learning. Our participants proved competent and versatile users of personal informatics tools. They tracked their feelings, tech activity, physical activity, and sleep with many using the process as a system for understanding and validating aspects of their own lives, rather than changing them.
Extracting partially ordered clusters from ordinal polytomous data

In practical applications of knowledge space theory, knowledge states can be conceived as partially ordered clusters of individuals. Existing extensions of the theory to polytomous data lack methods for building “polytomous” structures. To this aim, an adaptation of the k-median clustering algorithm is proposed. It is an extension of k-modes to ordinal data in which the Hamming distance is replaced by the Manhattan distance, and the central tendency measure is the median, rather than the mode. The algorithm is tested in a series of simulation studies and in an application to empirical data. Results show that there are theoretical and practical reasons for preferring the k-median to the k-modes algorithm, whenever the responses to the items are measured on an ordinal scale. This is because the Manhattan distance is sensitive to the order on the levels, while the Hamming distance is not. Overall, k-median seems to be a promising data-driven procedure for building polytomous structures.
Performance analysis of structural similarity-based backlight dimming algorithm modulated by controlling allowable local distortion of output image

This paper proposes a global backlight dimming algorithm based on structural similarity that controls the local distortion level of the output image for preserving overall image quality. Unlike existing methods, the proposed method non-iteratively determines a global clipping point that maintains structural similarity for each pixel above the desired level. The proposed method consists of three steps: region analysis, candidate clipping point calculation, and backlight luminance determination. In the first step, the structural similarity is analyzed based on various clipping points. In the second step, the proposed method estimates a candidate clipping point, which preserves the structural similarity index of each pixel above the desired level, based on the estimation of local statistics. Then, the proposed method determines a global clipping point based on a group of candidate clipping points. Finally, the output luminance is determined based on the selected global clipping point. Experimental results showed that the proposed method successfully maintained the structural similarity of each pixel above the desired level. In addition, the proposed method reduced the variation of minimum structural similarity by up to 94.7% compared to the benchmark methods.
WONDER -- Enhancing VR Training with Electrical Muscle Stimulation

Training employees on workplace procedures in virtual environments (VEs) is becoming popular since it reduces cost and risks. Although haptic enhancements with force feedback make such VEs more realistic and increase performance. Such enhancements are only available for 'spatial' scenarios. One potential enhancement for low-cost VEs is electrical muscle stimulation (EMS), but it remains open how EMS can be used to support trainees. Therefore we present WONDER: A virtual training environment with an EMS feedback enhancing layer. In an initial study, we show the feasibility of the approach and that it can successfully support trainees in remembering workflows. We test feedback that supports participants by pushing their hand towards a button or pulling their hand away from it. Participants preferred a combination of both feedback types.
Real-Time Multi-Camera Multi-Person Action Recognition using Pose Estimation

Action recognition possesses challenging issues in real-time multi-camera scenario when dealing with multi-person such as occlusion, pose variance and action interaction. In this paper, a real-time pipeline is proposed to address multi-person action recognition in multi-camera setup using joint key-points sequences from detected person. Joints trajectory is the important time-series information to identify actions. 14 key-points from human joints are scaled with relative to the Euclidean distance of neck-to-pelvis to obtain standard size of person, which is invariant to camera distance. Subsequently, 3D histogram correlation is applied to match multi-person identity. An indexed person with a series of action attribute are collected and fed into Long Short-Term Memory (LSTM) recurrent neural network. The proposed pipeline uses spatial-temporal feature of person's joint key-points trajectory for action recognition. Minimal single pass forward time through the LSTM network enables real-time multi-person action recognition in a video sequence. The proposed pipeline achieved up to 13 frames per second with 92% recognition rate with two camera setups.
Feasibility Analysis of Rainwater Harvesting System Implementation for Public Facilities in Palembang

Rainwater is one potential source of water that is often overlooked. Potential rainfall is often left discarded into runoff whereas with rainwater harvesting (RWH) system it could be harvested and used to meet the need for clean water and minimize the risk of flood by detaining the runoff. This study aims to analyze the feasibility of RWH systems implementation to meet the water need of public facilities in Palembang based on water balance concept. The behavioral model is used to simulate and mimic the RWH tank behavior. The analysis conducted on various scenarios based on RWH tank capacity and water demand. Two public facilities in the same area are analyzed, the office of Pengadilan Tinggi Agama (PTA) and SMAN 3 Palembang, along with 28 years of daily rainfall data. The financial analysis conducted using present worth analysis and payback period. For PTA, with 1050 L tank capacity, RWH is applicable and feasible. It can save the water up to 58% with an estimated payback period in 7, 5 years. Nonetheless, although SMAN 3 has a bigger catchment area, because of the higher demand (21 times than PTA), RWH is not feasible to implement. The water saving efficiency is just 29% for a system with 4 tanks of 5100 L. These results are expected to be used as the initial analysis to find the typology of RWH systems implementation in Indonesia.
Framework To Approximate Label Matching For Automatic Assessment Of Use-Case Diagram

E-learning plays a significant role in educating large number of students. In the delivery of e-learning material, automatic e-assessment has been applied only to some extent in the case of free response answers in highly technical diagrams in domains like software engineering, electronics, etc., where there is a great scope of imagination and wide variations in answers. Therefore, the automatic assessment of diagrammatic answers is a challenging task. In this article, algorithms that compute the syntactic and semantic similarities of nodes to fulfill the objective of automatic assessment of use-case diagrams are described. To illustrate the performance of these algorithms, students' use-case diagrams are matched with model use-case diagram. Results from 13,749 labels of 445 student answers based on 14 different scenarios are analyzed to provide quantitative and qualitative feedback. No comparable study has been reported by any other label matching algorithms before in the research literature.
Interactive Visualizer to Facilitate Game Designers in Understanding Machine Learning

Machine Learning (ML) is a useful tool for modern game designers but often requires a technical background to understand. This gap of knowledge can intimidate less technical game designers from employing ML techniques to evaluate designs or incorporate ML into game mechanics. Our research aims to bridge this gap by exploring interactive visualizations as a way to introduce ML principles to game designers. We have developed QUBE, an interactive level designer that shifts ML education into the context of game design. We present QUBE's interactive visualization techniques and evaluation through two expert panels (n=4, n=6) with game design, ML, and user experience experts.
CASA: A Convolution Accelerator using Skip Algorithm for Deep Neural Network

This paper proposes an accelerator that performs efficient convolution operations in the inference phase of a convolutional neural network (CNN). Convolution operation comprises the multiplication and accumulation (MAC) operation of weight and feature map data. If the neurons have zero as inputs, the neurons will not have a large effect on the performance of the neural network. Therefore, if convolution operations can be skipped by examining whether the neurons have zero value, the computational complexity can be greatly reduced. The proposed algorithm skips a convolution operation when the number of zeros in the values of input feature map is greater than or equal to the threshold value of 7 in the 3 × 3 filter size convolution operation. The threshold value was determined by considering the number of convolution operations skipped and the accuracy. The proposed convolution accelerator was composed at an operating frequency of 400 MHz using 65-nm technology. The performance of the proposed accelerator was 207 Giga Operation per second (GOp/s), which was 180% higher compared to when not skipped. Furthermore, the energy efficiency of 473 GOp/s/W was achieved in the core area of 1.2 mega gate equivalents (MGE).