Reviewerly/exHarmony huggingface dataset:
Zombies, Sirens, and Lady Gaga – Oh My! Developing a Framework for Coordinated Vulnerability Disclosure for U.S. Emergency Alert Systems

U.S. emergency alert systems (EAS) run on legacy software with aging hardware and limited cybersecurity. While EASs are an essential component of the U.S. critical infrastructure, they are often under-funded, and workers frequently lack the knowledge to protect these systems adequately. Recent compromises of various EASs have not inspired public confidence. We present a method for EAS authorities to engage with external cybersecurity researchers to find, recover from, and disclose vulnerabilities using coordinated vulnerability disclosure (CVD) policies. Clearly written CVD policies set guidelines and legal bounds for cybersecurity research, taking advantage of researcher expertise while working to strengthen the cybersecurity of the patchwork public-private-government networks comprising EASs. We intended to investigate the CVD policies of EASs in seven southeastern states; however, we could find no CVD policies through the entire supply chain. Instead, we investigated the CVD policies of the top 10 technology firms on the Fortune 500 list, analyzing best practices in terms of publication of a CVD policy, as well as: setting eligibility requirements, describing the submission process, delineating researcher restrictions, outlining agreements on sharing credit, and explaining bounties (if relevant). We recommend that EAS authorities develop CVD policies in line with suggested criteria, using policies from top technology organizations combined with the proposed framework, and using cybersecurity researchers as a valuable component of the EAS supply chain.
Whose Walkability?

The Walk Score is a patented algorithm for measuring the walkability of a given geographic area. In addition to its use in real estate, the accompanying API is used in a range of research in public health and urban development. This study explores how neighborhood residents differently understand the notion of walkability as well as the extent to which their personal definitions of neighborhood walkability are reflected in the Walk Score's underlying algorithm. We find that, while the Walk Score generally aligns with residents' priorities around walkability, significant subjective aspects that influence walking behavior are not reflected in the score, raising the need to consider implications for using algorithmic tools like the Walk Score in certain research contexts. We discuss the challenge of measuring subjective experience and how designers might begin to address it. We call for qualitative evaluations of algorithmic tools to help determine appropriate contexts of use.
Binary Autoencoder for Text Modeling

Variational Autoencoders play important role in text generation tasks, when semantically consistent latent space is needed. However, training VAE for text is not a trivial task due to mode collapse issue. In this paper, autoencoder with binary latent space trained using straight-through estimator is shown to have advantages over VAE on text modeling task. In our model, Bernoulli distribution is used instead of Gaussian (usual for VAE). The model can be trained with only reconstruction objective, without using any additional terms such as KL divergence. Experiments reported in this paper show binary autoencoder to have the main features of VAE: semantic consistency and good latent space coverage; while not suffering from the mode collapse and being a lot easier to train than VAE.
An Algorithm Based on Sparse Decomposition for Estimating Harmonic and Interharmonic Components of Stationary Signals in Power Systems

Many methods for estimating frequency components of stationary signals in power systems are based on the Discrete Fourier Transform. These methods have a fixed frequency resolution which depends on the sampling frequency and the number of samples of the signal, making it difficult to estimate interharmonics. This paper presents an algorithm for estimating harmonics and interharmonics of power system signals using the signal sparse decomposition technique with an overcomplete dictionary. Discrete Trigonometric Transforms have been analyzed for building this dictionary. The l-fold method has also been applied to the dictionary, which has allowed the adjustment of the frequency grid of the output spectrum. The algorithm proposed is called Harmonics and Interharmonics components Estimation based on Signal Sparse Decomposition, and it was assembled using a dictionary formed by atoms of Discrete Cosine and Discrete Sine Transforms of type II. Three synthetic signals containing harmonic and interharmonics distortions with different noise conditions were used to test the algorithm. The proposed method presented better results in the estimation of harmonic and interharmonics than Discrete Fourier Transform, Matrix Pencil Method and Fast Matching Pursuit algorithms. The results demonstrated robustness to noise and adequate estimation of the interharmonics when the frequency grid is adjusted correctly.
Teaching and Evaluating Collaborative Group Work in Large Visualization Courses

The growing number of students can be a challenge for teaching visualization lectures, supervision, evaluation, and grading. Moreover, designing a visualization course, matching the different experiences and skills of the students is one major goal in order to find a common solvable task for all of the students. However, the given task is important to follow a common goal, to collaborate in small project groups, but also to further experience, learn, or extend programming skills. In this paper we describe an approach to manage a large number of 272 students in a design-based active learning course who were relatively unexperienced first year bachelor students with a wide range of programming skills. We explain different subsequent stages to successfully handle the upcoming problems and describe how many and to which extent supervisors are involved in the development of the project. The project task description is given in a way that it has a minimal number of requirements but can be extended in many directions while most of the decisions are up to the students like programming languages, visualization approaches, or interaction techniques. Finally, we discuss the benefits and drawbacks of our teaching strategy.
Spontaneous Webcam Instance for User Attention Tracking

Eye tracking is one of the solutions applied in human-computer interaction research to study how users interact with user interfaces - where do they focus, what interests them and how do they browse through the content on the page. Eye tracking has also been used to detect user attention and even mind-wandering. Eye tracking is usually carried out by exploiting special hardware - expensive tools that cannot be applied to masses. Thereby, these trackers are only suitable for studies in lab conditions. The latest advancements in technology have enabled to use commercial grade webcams and special software for capturing user eye gaze. However, calibration still remains as a hurdle to overcome. Herein, we explore the applicability of spontaneous eye gaze tracking approach and its accuracy compared to a professional eye tracking solution in a controlled study. In particular, we focus on the applicability of eye tracking in a user-effortless way for focus area detection in web user interfaces.
Security awareness escape room - a possible new method in improving security awareness of users

Security awareness escape room is a new method of security awareness improvement. This program uses gamification elements to highlight information security risks and problems for users, for example bad habits of employees which could be exploited by a possible attacker. According to my experiences, users like the “learning-by-experience” better than classroom trainings, presentations or even online courses, because in this case participants can identify their own weaknesses and change their daily practices.
A streaming accelerator of Convolutional Neural Networks for resource-limited applications

Convolutional Neuronal Networks (CNN) implementation on embedded devices is restricted due to the number of layers of some CNN models. In this context, this paper describes a novel architecture based on Layer Operation Chaining (LOC) which uses fewer convolvers than convolution layers. A reutilization of hardware convolvers is promoted through kernel decomposition. Thus, an architectural design with reduced resources utilization is achieved, suitable to be implemented on low-end devices as a solution for portable classification applications. Experimental results show that the proposed design has a competitive processing time and overcomes resource utilization when compared with state-of-the-art related works.
Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval

One of the key challenges of deep learning based image retrieval remains in aggregating convolutional activations into one highly representative feature vector. Ideally, this descriptor should encode semantic, spatial and low level information. Even though off-the-shelf pre-trained neural networks can already produce good representations in combination with aggregation methods, appropriate fine tuning for the task of image retrieval has shown to significantly boost retrieval performance. In this paper we present a simple yet effective supervised aggregation method built on top of existing regional pooling approaches. In addition to the maximum activation of a given region, we calculate regional average activations of extracted feature maps. Subsequently, weights for each of the pooled feature vectors are learned to perform a weighted aggregation to a single feature vector. Furthermore, we apply our newly proposed NRA loss function for deep metric learning to fine tune the backbone neural network and to learn the aggregation weights. Our method achieves state-of-the-art results for the INRIA Holidays data set and competitive results for the Oxford Buildings and Paris data sets while reducing the training time significantly.
Automated Grading of Short Text Answers: Preliminary Results in a Course of Health Informatics

Students learning Health Informatics in the degree course of Medicine and Surgery of the University of L’Aquila (Italy) are required – to pass the exam – to submit solutions to assignments concerning the execution and interpretation of statistical analyses. The paper presents a tool for the automated grading of such a kind of solutions, where the statistical analyses are made up R commands and outputs, and the interpretations are short text answers. The tool performs a static analysis of the R commands with the respective output, and uses Natural Language Processing techniques for the short text answers. The paper summarises the solution regarding the R commands and output, and delves into the method and the results used for the automated classification of the short text answers. In particular, we show that through FastText sentence embeddings and a tuned Support Vector Machines classifier, we obtained an accuracy of 0.89, Cohen’s K = 0.76, and F1 score of 0.91 on a binary classification task (i.e. pass or fail). Other experiments including additional linguistically-motivated features, whose goal was to capture lexical differences between the students’ answer and the gold standard sentence, did not yield any significant improvement. The paper ends with a discussion of the findings and the next steps to be taken in our research.
Supporting the Cross-cultural Appreciation of Traditional Chinese Puppetry Through a Digital Gesture Library

In recent years, digital cultural heritage has attracted much attention in the HCI domain, but there are currently few studies that focus on enhancing the appreciation of intangible cultural heritage content amongst cross-cultural audiences. This article reports on the development of a Digital Gesture Library to support cross-cultural appreciation of traditional Chinese puppetry. We describe fieldwork with professional puppeteers to understand their practices and art form, which informed the development of the Digital Gesture Library, which uses a three-perspective archive of puppetry gestures and a tangible interface to support cross-cultural audiences’ appreciation of puppetry and encourages further exploration of Chinese culture. We present findings on the efficacy of the Digital Gesture Library from qualitative and quantitative user studies and, from this, discuss the opportunities and challenges for developing digital technology for cross-cultural appreciation of intangible heritage.
A Pedestrian Avoidance Method Considering Personal Space for a Guide Robot

Many methods have been proposed for avoiding obstacles in robotic systems. However, a robotic system that moves without colliding with obstacles and people, while still being mentally safe to the persons nearby, has not yet been realized. In this paper, we describe the development of a method for a mobile robot to avoid a pedestrian approaching from the front and to pass him/her by while preserving the “public distance” of personal space. We assume a robot that moves along a prerecorded path. When the robot detects a pedestrian using a laser range finder (LRF), it calculates the trajectory to avoid the pedestrian considering their personal space, passes by the pedestrian, and returns to the original trajectory. We introduce a virtual target to control the robot moving along the path, such that it can use the same control strategy as for human-following behavior. We carry out experiments to evaluate the method along three routes, in which the robot functioned without problems. The distance between the robot and the pedestrian was 9.3 m, on average, when the robot started to use avoiding behavior, which is large enough to keep a public distance from a pedestrian. When the robot passed by the pedestrian, the minimum distance between them was 1.19 m, which was large enough for passing safely.
A High-Assurance Evaluator for Machine-Checked Secure Multiparty Computation

Secure Multiparty Computation (MPC) enables a group of n</> distrusting parties to jointly compute a function using private inputs. MPC guarantees correctness of computation and confidentiality of inputs if no more than a threshold t</> of the parties are corrupted. Proactive MPC (PMPC) addresses the stronger threat model of a \emphmobile adversary that controls a changing set of parties (but only up to t</> at any instant), and may eventually corrupt all n</> parties over a long time. This paper takes a first stab at developing high-assurance implementations of (P)MPC. We formalize in \EasyCrypt, a tool-assisted framework for building high-confidence cryptographic proofs, several abstract and reusable variations of secret sharing and of (P)MPC protocols building on them. Using those, we prove a series of abstract theorems for the proactive setting. We implement and perform computer-checked security proofs of concrete instantiations of the required (abstract) protocols in \EasyCrypt. We also develop a new tool-chain to extract high-assurance executable implementations of protocols formalized and verified in \EasyCrypt. Our tool-chain uses \Why as an intermediate tool, and enables us to extract executable code from our (P)MPC formalizations. We conduct an evaluation of the extracted executables by comparing their performance to performance of manually implemented versions using \textsfPython -based \textsfCharm framework for prototyping cryptographic schemes. We argue that the small overhead of our high-assurance executables is a reasonable price to pay for the increased confidence about their correctness and security.
Development Of Web Based Application With Speech Recognition As English Learning Conversation Training Media

Needs of mastering English is very important in the current era. Among the four English language skills, English conversation skills are more difficult to learn than three other English language skills, namely, writing, listening, and reading. Nowadays, English conversation learning methods are often used only by practising the pronunciation of text or audio learning media when studying at school and outside the school. How to learn like just reciting something from a textbook or something that has just been heard has deficiencies such as quickly causing boredom, less effective in terms of time and cost and students tend to become silent readers. For this reason, there is a need for learning media that can provide effective ways of learning English conversation, which can save time. Web technology-based learning media is the choice, learning media in the form of web applications will be combined with speech recognition technology to train students to speak, not a silent reader anymore and will also provide experience in practising two-way conversations.
On Data Wastage in Viewport-Dependent Streaming

Omnidirectional videos are becoming more and more popular as display devices, such as Head Mounted Displays (HMD), become more user friendly. One of the biggest challenges with such video content is the great amount of bandwidth required for delivery at a good quality. The key difference between omnidirectional and the traditional content streaming is that with the former, users watch only part of the streamed content. Therefore, the non-watched areas of a streamed omnidirectional video are delivered unnecessarily. To reduce the network bandwidth requirements, the delivery mechanism could make use of Viewport-Dependent Streaming (VDS), according to which high-quality (HQ) video is maintained only in the current viewport, while lower quality video is streamed for the remaining part. In this paper, we examine the streaming data wastage, i.e., the parts of HQ video not watched by a user, in VDS when users linearly move from a generic viewport to another one within the 360 degrees space. We used both simulated trajectories as well real trajectories from 20 users. We show how the user's HMD exploration speed and the DASH transport segment lengths impact the streaming data wastage, leading up to 78% of wasted HQ content and up to 40% of total stream bit rate wasted due to users' motion. We also show how the users' trajectory prediction can decrease the streaming data wastage making the system up to 25% more efficient. This paper gives very useful hints that could lead to VDS optimizations, and it introduces quantitative estimates on how the data wastage characteristics change with respect to the streaming parameters.
Evaluation of a Virtual Reality (VR) Learning Tool for Fundamental Turbine Engineering Concepts

The education industry have evolved to meet the expectations of current technological trend. The field of mechanical engineering involves the study, design, development, construction, and testing of tools, engines, and machines. One of the critical equipment in the area of power plant engineering is the turbine, which are used to generate power. Students are taught to understand the drawings of these turbines through books or videos and occasionally visit the power plant site. This approach may not be appealing to the digital natives as they prefer to use technology to touch, feel and see the aspects of the turbine at any given time. The lack of engagement, shrinking attention span and obsession with digital devices amongst the younger generation have caused educators to experiment with various means of teaching techniques in ensuring that knowledge is successfully imparted. Hence, this paper provide results of a pilot study on a usability evaluation conducted on a group of undergraduate students to analyze the usability of a virtual reality (VR) application using the System Usability Scale (SUS) instrument. The study also aims to find out if there are any significant differences among gender and computer gamers in using the VR tool. Results indicate that the VR application requires further enhancement in terms of self-learning and interaction familiarity.
Forensic corpus data reduction techniques for faster analysis by eliminating tedious files

Digital Forensics, an emerging research field, became prominent due to cyber exploitations and cybercrimes. Forensic analysis plays an eminent role in finding and detecting cyber criminals. It is for this purpose, the compromised systems or targeted systems are seized, forensically validated and analyzed in the investigation labs. Globally, though there are many Forensic investigation agencies like Federal Bureau of Investigation (FBI), Department of Défense Cyber Crime Center (CC3), Central Bureau of Investigation (CBI) along with their central forensic labs, many digital evidence analysis (DEA) cases have been pending throughout the past two decades in these forensic labs. The significant and crucial reason contributing to this is the volume of the digital data to be analyzed forensically. Though there are handy tools and effective analysis algorithms, still forensic tools lack the capability of digging tons of data within polynomial time. One of the best methods to reduce time is to eliminate unwanted or uninteresting forensic files. Some limited papers and techniques define this problem, and this research proposes a methodology that gratifies the existing problem by using filtering techniques based on identified parameters and with massive hash sets. This work reduced the present study’s corpus to 29.8 million from 79.2 million by applying the proposed methodology.Abbreviations: AFF: Advanced Forensic Format; CBI: Central Bureau of Investigation; CC3: Department of Défense Cyber Crime Centre; CFReDS: Computer Forensic Reference Datasets; DEA: Digital Evidence Analysis; DRbSI: Digital Forensic Data Reduction by Selective Imaging; FBI: Federal Bureau of Investigation; HTML: Hyper Text Markup Language; KPMG: Klynveld Peat Marwick Goerdeler; LCS: Longest Common Substring; MD5: Message Digest Algorithm; NCRB: National Crime Records Bureau; NPS: Naval Postgraduate School; NSRL: National Software Reference Library; RDC: Real Drive Corpus; SHA-1: Secure Hash Algorithm; VMDK: Virtual Machine Disk File.
Semantic Segmentation using K-means Clustering and Deep Learning in Satellite Image

In this paper, a deep learning based method, aided by certain clustering algorithm for use in semantic segmentation of satellite images in complex background is proposed. The work considers the formation and training of SegNet in which the output of K-means clustering algorithm is used as input and the label of the particular region of interest (ROI) in the image are used as target. The method does not require any feature extraction, region growing or splitting methods to configure and train the SegNet, which is a deep convolutional Encoder-Decoder architecture trained with (error) Back Propagation learning. The method is tested with different satellite images. The method is also compared with the results obtained when trained with SegNet without selecting the ROI using K-means algorithm and evaluated using metrics such as accuracy, mean IoU and weighted IoU. The normalized confusion matrix is also plotted. The experimental results show that the method is reliable and suitable for real world situations.