Reviewerly/exHarmony huggingface dataset:
Investigating file use and knowledge with Windows 10 artifacts

Windows 10 operating system is the most widely used operating system today that contains many programs and mechanisms for managing computer hardware and software. Looking from a digital forensics point of view these produce valuable records of user activities. In a forensic world, such records are known as Windows artifact which can be described as a system generated records of the user activities that have forensic value. Gaining a deep understanding of how these records are created and what information they contain can help the examiner to acquire valuable data that can be used as evidence and support other evidence. The artifacts can be a great way to focus on relevant data and reduce the need for full examination of constantly increasing data storage that examiners encounter. Through this paper, the focus will be on analyzing different, fewer know artifacts, that aren't supported by mainstream forensic tools because they vary between versions of Windows, resulting in the need for manual analysis. Their deep understanding is necessary to avoid misinterpreting their content which can result in wrong conclusions. Additionally, the paper presents the results of testing Windows 10 artifacts and open-source tools used in the testing process.
Visual saliency object detection using sparse learning

In many applications in order to recognise the relationship between user and computer, the position at which the user looks should be detected. To this end, a salient object should be extracted that is attracted to the attention of the viewer. In this study, a new method is proposed to extract the object saliency map, which is based on learning automata and sparse algorithms. In the proposed method, after decomposition of an image to its superpixels, eight features (namely three features in red–green–blue colour space, coalition, central bias, rotation feature, brightness, and colour difference) are extracted. Then the extracted features are normalised to zero mean and unit variance. In this study, K-means singular-value decomposition is used to integrate the extracted features. The performance of the proposed method is compared with that of 20 other methods by applying four new databases, including MSRA-100, ECSSD, MSRA-10K, and Pascal-S. The obtained results show that the proposed method has a better performance compared to the other methods with regard to the prediction of the salient object.
DB3F &amp; DF-Toolkit: The Database Forensic File Format and the Database Forensic Toolkit

The majority of sensitive and personal user data is stored in different Database Management Systems (DBMS). For Example, Oracle is frequently used to store corporate data, MySQL serves as the back-end storage for most webstores, and SQLite stores personal data such as SMS messages on a phone or browser bookmarks. Each DBMS manages its own storage (within the operating system), thus databases require their own set of forensic tools. While database carving solutions have been built by multiple research groups, forensic investigators today still lack the tools necessary to analyze DBMS forensic artifacts. The unique nature of database storage and the resulting forensic artifacts require established standards for artifact storage and viewing mechanisms in order for such advanced analysis tools to be developed. In this paper, we present 1) a standard storage format, Database Forensic File Format (DB3F), for database forensic tools output that follows the guidelines established by other (file system) forensic tools, and 2) a view and search toolkit, Database Forensic Toolkit (DF-Toolkit), that enables the analysis of data stored in our database forensic format. Using our prototype implementation, we demonstrate that our toolkit follows the state-of-the-art design used by current forensic tools and offers easy-to-interpret database artifact search capabilities.
Which Instrument Should I Use? Supporting Decision-Making About the Evaluation of User Experience

User Experience (UX) has been intensively investigated lately, resulting in the proposal of several evaluation instruments, methods, and techniques. However, the definition of UX and its constructs is still a work in progress, making User Experience a concept open to various interpretations. Consequently, the development of UX evaluation methods and instruments rely on very different assumptions, often making professionals and beginning researchers uncertain about choosing the right methods to evaluate user evaluation aspects. Aiming to help fill in this gap, in this work we present the results of a systematic snowballing procedure conducted to investigate the characteristics of the UX evaluation instruments that have been proposed and used by HCI community in the last years. We compiled information about 116 instruments aiming to assist researchers and practitioners in making informed choices about which instruments can support UX data collection, according to their research goals. In addition to that, the data analysis provided a glance on the directions the research on UX evaluation instruments is taking.
Design and Implementation of Smart Contract: A use case for geo-spatial data sharing

In this paper, we propose to use the blockchain technology as a mechanism to store and share geospatial projects. Blockchain helps to improve efficiency and security. Smart contracts provide a secure, distributed and shared decentralized ledger of all assets and transactions. We will discuss a way to implement a platform on which scientists can share their studies. We propose a design methodology for the mentioned smart contracts, which enables the development of different use cases using blockchain technology. A detailed design of the smart contracts, functions and processes is presented. We will provide an outline of advantages and limitations of blockchain in general, and for the proposed platform.
Mask-Driven Mitosis Detection In Histopathology Images

Mitotic detection and counting are the primary diagnostic factors used for cancer detection and grading. In this paper, we introduce a method of automatically obtaining masks for the cells and using the generated masks for mitotic detection. In the first stage of processing, we use the Mask R-CNN network to obtain the masks for the cells and also classify the cells with a high recall and low precision. These cells detected in the first stage are further processed in a second stage to eliminate false positives. In the second stage the cell candidates are classified as mitotic or non-mitotic using a combination of hand-crafted features and features obtained from a deep convolutional neural network pre-trained on ImageNet. We have evaluated our method on the public ICPR 2012 and ICPR 2014 mitosis datasets and obtained improved performance over fully-supervised segmentation methods and also those using a combination of hand-crafted and learned deep features. We also show that masks learned from the small ICPR 2012 dataset can be effectively transferred to the ICPR 2014 dataset, which does not provide cell mask annotations.
An Exploratory User Study of Visual Causality Analysis

Abstract Interactive visualization tools are being used by an increasing number of members of the general public; however, little is known about how, and how well, people use visualizations to infer causality. Adapted from the mediation causal model, we designed an analytic framework to systematically evaluate human performance, strategies, and pitfalls in a visual causal reasoning task. We recruited 24 participants and asked them to identify the mediators in a fictitious dataset using bar charts and scatter plots within our visualization interface. The results showed that the accuracy of their responses as to whether a variable is a mediator significantly decreased when a confounding variable directly influenced the variable being analyzed. Further analysis demonstrated how individual visualization exploration strategies and interfaces might influence reasoning performance. We also identified common strategies and pitfalls in their causal reasoning processes. Design implications for how future visual analytics tools can be designed to better support causal inference are discussed.
Size Perception of Augmented Objects by Different AR Displays

Augmented reality (AR) has positioned itself as one of main media technologies, and its further proliferation depends on its usability, ergonomics and perceptual qualities with respect to the effective and correct convey of information. However, there exists no established guideline as how to visualize augmented information properly for various types of AR displays. We investigate for one important perceptual quality in AR, correct size perception of the augmented object, for three representative AR displays. The AR displays considered are: (1) small hand-held mobile device, (2) closed video see-through HMD, and (3) optical see-through HMD. The augmented object, a nominally sized box, is visualized in three different styles: (1) as a texture mapped simple polygonal model, (2) as a bump mapped polygonal model with shadow, and (3) as a detailed scanned mesh model. The size perception was assessed in two viewing angles: (1) sitting down and looking straight and (2) standing and looking down in 45 degrees (1.5 m distance). The experimental results revealed significant effects by the display type, rendering style and viewing angle. E.g. users tended to overestimate the object size and took longer to complete the task, when the small hand-held display is used. We believe the findings can serve as one guideline for developing effective AR applications.
Identity-Based Broadcast Encryption with Outsourced Partial Decryption for Hybrid Security Models in Edge Computing

Each layer of nodes and communication networks in edge computing, from cloud to the end device (i.e, often considered as resource-constrained IoT devices), exhibits a different level of trust for each stakeholder - e.g., edge nodes may not be fully trusted by IoT devices and the cloud. Moreover, asymmetric nature of resources between layers makes it hard to establish a balance between security and performance - e.g., lightweight cryptography may degrade security level against untrusted nodes while heavyweight ones may not be feasible for the light-weight end devices. An advanced encryption scheme such as the Identity-Based Broadcast Encryption (IBBE) is a popular technique to reduce storage and communication overhead. However, IBBE requires heavy computation to the end devices and still does not fully satisfy the security requirements that exist in the layers of edge computing. This paper presents a new IBBE with outsourced partial decryption for hybrid security models that each layer in edge computing requires. It balances the computational overhead based on asymmetric nature that nodes in each layer have. Particularly, with new schemes, the ciphertext can be transformed from its initial format. The cloud encrypts their data for multiple end devices and store them in the edge nodes, but those interim nodes can blindly transform the ciphertext from the cloud into a form which (i) is decryptable by only an authorized end device, and (ii) imposes smaller decryption and data transmission burden to end devices, regardless of the number of recipients. Our security analysis shows that new schemes are selectively and adaptively secure. We implement our solution and show that new schemes reduce the communication overhead from an edge node to end devices and the computation overhead on the end devices, compared to the original IBBE schemes.
Efficient Measurement of Key-Cap Flatness for Computer Keyboards With a Multi-Line Structured Light Imaging Approach

A computer keyboard is an important human-machine interaction device. A key-cap and a keyboard are flexibly connected, and its assembly quality, especially flatness, is crucial. In manufacturing quality control, the requirement for assembly quality of keyboards is quite strict, where the flatness measurement of key-caps is a key link. In this paper, an apparatus based on multi-line structured light imaging, and machine vision is designed for online rapid detection of key-cap flatness, is proposed. First, a novel 3D measurement scheme is presented to replace the complete 3D surface reconstruction of conventional methods. Then, an array of cameras with a perspective projection transformation matrix calibration approach is adopted to expand the scanning range of the 3D sensor for large-scale and high-resolution target imaging. In addition, a multi-step image processing is applied to accurately extract the centerline of the projected fringes on a key-cap with printed character, which indirectly helps to improve the measurement accuracy. Finally, a sparse sampling flatness estimation algorithm is proposed to improve the detection efficiency. According to experiments in real production lines, our apparatus can achieve a detection accuracy of 99.74% for various computer keyboards.
Towards Practical Microcontroller Implementation of the Signature Scheme Falcon

The majority of submissions to NIST's recent call for Post-Quantum Cryptography are encryption schemes or key encapsulation mechanisms. Signature schemes constitute a much smaller group of submissions with only 21 proposals. In this work, we analyze the practicability of one of the latter category – the signature scheme Falcon with respect to its suitability for embedded microcontroller platforms. Falcon has a security proof in the QROM in combination with smallest public key and signature sizes among all lattice-based signature scheme submissions with decent performance on common x86 computing architectures. One of the specific downsides of the scheme is, however, that according to its specification it is "non-trivial to understand and delicate to implement". This work aims to provide some new insights on the realization of Falcon by presenting an optimized implementation for the ARM Cortex-M4F platform. This includes a revision of its memory layout as this is the limiting factor on such constrained platforms. We managed to reduce the dynamic memory consumption of Falcon by 43% in comparison to the reference implementation. Summarizing, our implementation requires 682 ms for key generation, 479 ms for signing, and only 3.2 ms for verification for the $$n=512$$ parameter set.
Hierarchical Reinforcement Learning Approach for the Road Intersection Task

The task of building unmanned automated vehicle (UAV) control systems is developing in the direction of complication of options for interaction of UAV with the environment and approaching real life situations. A new concept of so called “smart city” was proposed and view of transportation shifted in direction to self-driving cars. In this work we developed a solution to car’s movement on road intersection. For that we made a new environment to simulate a process and applied a hierarchical reinforcement learning method to get a required behaviour from a car. Created environment could be then used as a benchmark for future algorithms on this task.
Alleviating sparsity by specificity‐aware ontology‐based clustering for improving web service recommendation

The rapid development of the Internet in recent years has led to a vast increase in the numbers of Web services, which challenge users' capability to find their favorite services quickly and accurately. There is thus an urgent demand for service recommendations that help discover applicable services. Although the collaborative filtering technique is one of the most successful recommendation system technologies, it suffers from data sparsity and cold‐start problems, which in turn lead to inaccurate results. In this article, we deal with these issues by applying a novel ontology‐based clustering approach that uses domain specificity and service similarity for ontology generation. This clustering approach can easily and effectively increase the data density of the user‐service dataset by assuming blank user preferences according to the history of user‐favored domain(s). Then, user ratings are predicted by calculating the trust value between users. The experimental results indicate that the proposed approach can effectively alleviate the data sparsity and cold‐start problems with lower prediction error with the best recommendation performance, and our method performed better than baseline approaches in terms of accuracy and novelty. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Development and Validation of Usability Heuristics for Evaluation of Interfaces in ATMs

ATM systems are part of the devices that have more managed approach and facilitate the use of financial services for people. However, on occasions, displayed interfaces can be frustrating to use. Usability is a principle that contemplates such situations, so it is necessary to have appropriate tools to evaluate ease of use on the products. For this reason, we present a revised version of eighteen usability heuristics proposed for ATM applications. In this paper, we describe the process that is followed from the collection of information, creation of the heuristic set, validation and refinement.
A Dynamic Hand Gesture Recognition Algorithm Based on CSI and YOLOv3

Abstract Object detection algorithms based on convolutional neural networks are generally suitable for static gesture recognition. For actual hand gesture scenes, dynamic gestures are also widely used. A dynamic hand gesture recognition algorithm based on Channel State Information (CSI) and You Only Look Once: Version 3 (YOLOv3) is proposed for continuous dynamic hand gesture recognition. The data acquisition adopts a CSI-based radio frequency method. The adaptive weighted fusion, Kalman filtering, threshold segmentation and data conversion are used to generate gray value images. Finally, the YOLOv3 object detection algorithm is used to train and identify the grayscale image which include the information of continuous dynamic hand gestures. The effectiveness of the proposed method is verified by the recognition confusion matrix. And the proposed method has an average recognition accuracy of 94% for four custom dynamic hand gestures.
Experimenting with a Fog-computing Architecture for Indoor Navigation

The present paper outlines the framework design of an Application Agnostic Architecture for Fog and Mist Computing systems. An analysis explains how the proposed architecture satisfies the primary characteristics of a Fog and Mist computing paradigm by using low power and low-cost components. It also introduces an Indoor Navigation System and a Notification service as a use-case, to assess the proposed framework as a Fog-Computing architecture while demonstrating how it can address Mobile and IoT challenges.
A Correction Algorithm of QR Code on Cylindrical Surface

Abstract When capturing a QR code on a cylinder, there may be geometric distortions due to the angle of the camera and the cylindrical deformation, which makes the QR code difficult to recognize. To solve this problem, a correction algorithm for QR codes is proposed in this paper. First, the boundary of the QR code on the cylinder is extracted by a morphological algorithm and geometry calculations. Then, the feature points on the image are accurately located. Next, the standard size of the QR code is determined by using the cross-ratio method in descriptive geometry. Finally, the image is corrected by perspective projection. It is proven that the algorithm can correct the distortion of the cylinder image effectively, and it has the ability to properly identify cylindrical QR codes.
Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data

Abstract The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large‐scale and up‐to‐date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up‐to‐date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal‐and‐task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back‐end and an interactive visual analytics interface. The monitoring component uses intelligent pre‐aggregation to allow dynamic queries in near real‐time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research.
Neural Networks for File Fragment Classification

File fragment classification is an important step in file forensics in which filetypes are assumed based on their available content fragments. Methods typically used for this task utilize machine learning techniques on features like byte frequency distributions and fragment entropy measures. In this paper, a contribution to this field is made through exploration of novel approaches to the problem including feedforward artificial neural networks and convolution networks. Feedforward neural networks were trained with byte histograms and with byte-pair histograms, while convolution neural networks were trained with blocks consisting of 512 bytes of data obtained from the GovDocs1 dataset. The results suggest convolution neural networks are not as promising for this problem as feedforward artificial neural networks, and feedforward artificial neural networks showing great results.
Several classes of optimal Ferrers diagram rank-metric codes

Four constructions for Ferrers diagram rank-metric (FDRM) codes are presented. The first one makes use of a characterization on generator matrices of a class of systematic maximum rank distance codes. By introducing restricted Gabidulin codes, the second construction is presented, which unifies many known constructions for FDRM codes. The third and fourth constructions are based on two different ways to represent elements of a finite field Fqm (vector representation and matrix representation). Each of these constructions produces optimal codes with different diagrams and parameters.