Reviewerly/exHarmony huggingface dataset:
BUILDING SOFT SKILLS OF STUDENTS AT A TECHNICAL UNIVERSITY

Taking into account a rapidly changing environment and the development priorities of TPU building over-professional competencies of students can be viewed as an essential tool for their successful integration into the global job market. Scientists from a variety of fields claim that the 21st century is a century of knowledge- based economy and drastic changes. All these changes inevitably result in the emergency of a variety of new jobs. The Atlas of Emerging Jobs compiled by Skolkovo is a vision of changes that may happen in the job market in the coming decades. The emergency of new jobs requires the building of new competencies. Soft skills are highly essential due to the fact that their development can increase the work performance significantly. The present paper introduces the concept of soft skills for the context of higher education. Soft skills are defined as an entirety of over-professional competencies and personal attributes that provide an opportunity to achieve the set goals through effective collaboration with others in the rapidly changing environment. The paper proves the increasing importance of soft skills development in the context of knowledge-based economy. The paper discusses the experience of National Research Tomsk Polytechnic University (TPU) School of Engineering Entrepreneurship in implementing an educational program aimed at soft skills development of students from various training programs. The educational program consists of three different modules: 1. Entrepreneurship education. 2. Extracurricular activity related to implementation of social and business projects. 3. Development of entrepreneurial environment at university. The authors state that the presented educational program supports soft-skills development of engineering students that are universal and highly demanded on the present job market as well as hard skills.
The Role of Foreign Directors in Corporate Risk Disclosure: Empirical Evidence From Jordan

The current study examined the role of foreign directors in enhancing the level of risk disclosure in the annual reports of Jordanian listed companies. The content analysis method was used to measure the level of risk disclosure by computing the number of risk-related sentences in annual reports. To achieve the study’s objective, random effect model have been applied on a sample of 376 firm-year observations of Jordanian non-financial companies for the period of 2014-2017. The findings are in line with the argument of agency theory and resource dependence theory, which posits that existence of foreign members on the board contributes in increasing the level of risk disclosure. The study aimed to fill the gap in the literature of risk disclosure regarding the relationship between foreign directors and risk disclosure. It is expected that the findings will be useful to researchers, authorities and investors alike in understanding the important role of foreign directors in improving practices of risk disclosure in Jordan.
Irrational Behavior of Youth When Taking Financial Decisions

Purpose:The article contains research on internal factors of irrational behaviour initiated by the objective to reveal correlation between individual and personal, psychological peculiarities and irrational behavior in life situations.Design/Methodology/Approach: The article is based on a hypothesis that there is a correlation between personal peculiarities and an irrational choice when students of economic HEIs make decisions of economic nature.Such life-purpose orientations as "Locus of control ─ Me" and "Locus of control ─ Life" are connected with irrational economic behavior. Findings: The study allows enriching the subject field of behavioral economics from the point of view of the specificity of economic and psychological characteristics, attitudes towards an economic choice, and the identification of emotional and personal indicators of irrational behavior. It has been revealed that the psychological characteristics of a person (mental processes, orientation of an individual, life-purpose orientations) influence students' irrational economic behavior. Practical Implications:The results of the study can be used by economic institutions (HEIs) in the selection of students for educational programs.The developed diagnostic game technique, the styles of irrational behavior described by the authors can be used in the practice of the educational process of economic HEIs or in assessing the propensity to make irrational economic decisions in other areas of human activity Originality/Value: The main contribution of this study is the clarification of factors of finacial behavior of youth.
On Compiling Away PDDL3 Soft Trajectory Constraints without Using Automata

We address the problem of propositional planning extended with the class of soft temporally extended goals supported in PDDL3, also called qualitative preferences since IPC-5. Such preferences are useful to characterise plan quality by allowing the user to express certain soft constraints on the state trajectory of the desired solution plans. We propose and evaluate a compilation approach that extends previous work on compiling soft reachability goals and always goals to the full set of PDDL3 qualitative preferences. This approach directly compiles qualitative preferences into propositional planning without using automata to represent the trajectory constraints. Moreover, since no numeric fluent is used, it allows many existing STRIPS planners to immediately address planning with preferences without changing their algorithms or code. An experimental analysis presented in the paper evaluates the performance of state-of-the-art propositional planners using our compilation of qualitative preferences. The results indicate that the proposed approach is highly competitive with respect to current planners that natively support the considered class of preferences, as well as with a recent automata-based compilation approach.
CHASE: A Configurable Hardware-Assisted Security Extension for Real-Time Systems

Real-time autonomous systems are becoming pervasive in many application domains such as vehicular ad-hoc networks, smart factories and delivery drones. The correct functioning of these real-time systems is timing-critical with hard deadlines. However, although they interact with other systems and exchange inputs/outputs with the physical world, they usually lack security mechanisms, which makes them susceptible to a wide range of attacks with critical consequences. Typically, this is because security mechanisms usually violate the real-time requirements of these systems and cannot be adjusted at runtime to provide the adequate security without compromising performance. In this paper, we propose a consolidated runtime-configurable hardware-assisted security extension called CHASE that supports different levels of security at runtime. Depending on the desired security level and the system real-time, availability or functionality requirements, CHASE can be configured accordingly at runtime, thus enabling the calibration of the security vs. performance trade-off. We analyze CHASE's effectiveness in providing different security guarantees against various adversarial capabilities, and show how this is achieved with reasonable logic overhead and minimal performance overhead.
A Recognition Method for Italian Alphabet Gestures Based on Convolutional Neural Network

Convolutional Neural Network(CNN) have achieved great success in image recognition and classification, but most of researches on gesture recognition are for English, there are very few identification studies for other small languages. An recognition method for Italian alphabet gestures based on CNN is proposed in this paper. First, an Italian alphabet gesture data set is created, including 22 letters, of which 4 letters (G, S, J, and Z) are excluded from the data set because of their dynamism, and a CNN model consisting of three blocks is established, convolutional and pooling layers are included in every block. Then weights and biases in the model are trained by the adaptive moment estimation algorithm called Adam to reduce the loss to the minimum step by step. Finally, we build a CNN model based on the proposed method on a deep learning platform named Keras, testing images are selected from the established static dataset randomly, the results of multiple experiments show that the recognition rate can reach up to 94%.
Hardware Friendly Robust Synthetic Basis Feature Descriptor

Finding corresponding image features between two images is often the first step for many computer vision algorithms. This paper introduces an improved synthetic basis feature descriptor algorithm that describes and compares image features in an efficient and discrete manner with rotation and scale invariance. It works by performing a number of similarity tests between the feature region surrounding the feature point and a predetermined number of synthetic basis images to generate a feature descriptor that uniquely describes the feature region. Features in two images are matched by comparing their descriptors. By only storing the similarity of the feature region to each synthetic basis image, the overall storage size is greatly reduced. In short, this new binary feature descriptor is designed to provide high feature matching accuracy with computational simplicity, relatively low resource usage, and a hardware friendly design for real-time vision applications. Experimental results show that our algorithm produces higher precision rates and larger number of correct matches than the original version and other mainstream algorithms and is a good alternative for common computer vision applications. Two applications that often have to cope with scaling and rotation variations are included in this work to demonstrate its performance.
Real-Time Trajectory Replanning for Quadrotor Using OctoMap and Uniform B-Splines

In this paper, we present a real-time local trajectory replanning method for quadrotors in unknown cluttered environment. In the process of following the global trajectory, an octree-based environment map is built using the onboard sensor. The map is stored in a fixed-size three-dimensional circular buffer to build a local map, which is centered on the quadrotor and updated in real time with the movement of the quadrotor. Based on the local map, we adopt a sampling-based path planning method to find the initial safe path passing through obstacles, and then use uniform b-spline to convert the path consisting of line segments into a smooth and dynamical feasible trajectory. The local trajectory replanning is performed at 0.5 s intervals until reaching the target point. Through simulation experiments and comparison with existing methods, we verify the effectiveness of the method we proposed.
Supporting Visual Annotation Cues in a Live 360 Panorama-based Mixed Reality Remote Collaboration

We propose enhancing live 360 panorama-based Mixed Reality (MR) remote collaboration through supporting visual annotation cues. Prior work on live 360 panorama-based collaboration used MR visualization to overlay visual cues, such as view frames and virtual hands, yet they were not registered onto the shared physical workspace, hence had limitations in accuracy for pointing or marking objects. Our prototype system uses spatial mapping and tracking feature of an Augmented Reality head-mounted display to show visual annotation cues accurately registered onto the physical environment. We describe the design and implementation details of our prototype system, and discuss on how such feature could help improve MR remote collaboration.
Securing the Device Drivers of Your Embedded Systems

Device drivers on Linux-powered embedded or IoT systems execute in kernel space thus must be fully trusted. Any fault in drivers may significantly impact the whole system. However, third-party embedded hardware manufacturers usually ship their proprietary device drivers with their embedded devices. These out-of-tree device drivers are generally of poor quality because of a lack of code audit. In this paper, we propose a new approach that helps third-party developers to improve the reliability and safety of device drivers without modifying the kernel: Rewriting device drivers in a memory-safe programming language called Rust. Rust's rigorous language model assists the device driver developers to detect many security issues at compile time. We designed a framework to help developers to quickly build device drivers in Rust. We also utilized Rust's security features to provide several useful infrastructures for developers so that they can easily handle kernel memory allocation and concurrency management, at the same time, some common bugs (e.g. use-after-free) can be alleviated. We demonstrate the generality of our framework by implementing a real-world device driver on Raspberry Pi 3, and our evaluation shows that device drivers generated by our framework have acceptable binary size for canonical embedded systems and the runtime overhead is negligible.
Virtual Reality Training with Passive Haptic Feedback for CryoEM Sample Preparation

We present an immersive virtual reality training system cryoVR with passive haptic feedback for training biological scientists preparing bio-sample for Cryo-Electron Microscopy (CryoEM). CryoEM requires the user to conduct careful operations on expensive delicate equipment. To minimize the risk and interruption of crucial research work, we tried to mimic the real lab using VR to let trainer practice in a virtual environment. We used 3D printed objects to provide passive haptic feedback in order to achieve a more realistic training experience. Participants are able to interact with the equipment in the virtual environment by moving or touching physical models. We developed all the necessary operations with haptic feedback, including moving, clicking, pouring, rotating, polling and pushing. By following instructions provided by our virtual reality simulator and interacting with physical objects, trainees will learn how to operate CryoEM with low cost and risk. By developing our training system, we explore the benefits, limitations and precautions of embedding haptic feedback to scientific VR training.
Does Augmented Reality Augment the Experience? A Qualitative Analysis of Enjoyment for Sports Spectators

This study examined users’ attitudes and opinions of using augmented reality technology in comparison to other non-augmented reality technologies. Broadly, there were differences between users when using different devices. These differences help shed light onto the process of implementation of such new technologies in various sports settings, from arenas and stadiums to at-home viewership. From the results of this study, it is likely that this technology may not lead to better fan experience, but instead might leave them feeling frustrated and potentially isolated. Particularly given the expense of technology like Google Glass, this study suggests such technology should be implemented selectively and carefully. Additionally, the effects of using Google Glass in stadiums and arenas may not simply be on the users, but also on those seated around them, who may be bothered by the deliberate actions of those wearing such augmented reality devices. Notably, this may shift as the devices become more prevalent.
Platforms in everything: Analyzing ground-truth data on the anatomy and economics of bullet-proof hosting

This paper presents the first empirical study based on ground-truth data of a major Bullet-Proof Hosting (BPH) provider, a company called Maxided. BPH allows miscreants to host criminal activities in support of various cybercrime business models such as phishing, botnets, DDoS, spam, and counterfeit pharmaceutical websites. Maxided was legally taken down by law enforcement and its backend servers were seized. We analyze data extracted from its backend databases and connect it to various external data sources to characterize Maxided's business model, supply chain, customers and finances. We reason about what the ``inside'' view reveals about potential chokepoints for disrupting BPH providers. We demonstrate the BPH landscape to have further shifted from agile resellers towards marketplace platforms with an oversupply of resources originating from hundreds of legitimate upstream hosting providers. We find the BPH provider to have few choke points in the supply chain amendable to intervention, though profit margins are very slim, so even a marginal increase in operating costs might already have repercussions that render the business unsustainable. The other intervention option would be to take down the platform itself.
Effect of Sensory Conflict and Postural Instability on Cybersickness

Sensory conflict theory and postural instability theory were often tested individually to explain cybersickness in VR systems, but they were seldom systematically compared. An earlier study evaluated them on a large screen using 2D videos. This study evaluated sensory conflict and postural instability on the discomfort in VR. Virtual visual locomotion were shown on an head-mounted display. A motion platform vibrated in low-frequency while the participant stood on top. Each factor was manipulated alone or in combination. Results showed that the visual motion only condition led to the highest miserable score, higher than the physical vibration only condition. This suggested that consistent with previous literature, sensory conflict may be a major contributing factor of cybersickness.
Path Planning of Slab Library Crane Based on Improved Ant Colony Algorithm

Due to the equipment characteristics (for example, the crane of each span cannot transfer products directly to other spans and path has less turning points and no slash lines) in a slab library, slab transportation is mainly realized by manually operating the crane. Firstly, the grid method is used to model the slab library. Secondly, an improved ant colony algorithm is proposed. The algorithm is used to solve the path planning of the slab library crane, which is improved by integrating the turning points, filtering the candidate solutions, dynamically evaporating pheromone, setting the dynamic region, etc. Finally, the algorithm is applied to plan the crane path of the slab library. The results show that the obstacle-free optimal path with fewer turning points, no slash lines, and short paths is found automatically.
Enhancing text recognition on Tor Darknet images

Text Spotting can be used as an approach to retrieve information found in images that cannot be obtained otherwise, by performing text detection first and then recognizing the located text.Examples of images to apply this task on can be found in Tor network images, which contain information that may not be found in plain text.When comparing both stages, the latter performs worse due to the low resolution of the cropped areas among other problems.Focusing on the recognition part of the pipeline, we study the performance of five recognition approaches, based on state-ofthe-art neural network models, standalone OCR, and OCR enhancements.We complement them using string-matching techniques with two lexicons and compare computational time on five different datasets, including Tor network images.Our final proposal achieved 39,70% precision of text recognition in a custom dataset of images taken from Tor domains.
Attacking Object Detectors Without Changing the Target Object

Object detectors, such as Faster R-CNN and YOLO, have numerous applications, including in some critical systems, e.g., self-driving cars and unmanned aerial vehicles. Their vulnerabilities have to be studied thoroughly before deploying them in critical systems to avoid irrecoverable loss caused by intentional attacks. Researchers have proposed some methods to craft adversarial examples for studying security risk in object detectors. All these methods require modifying pixels inside target objects. Some modifications are substantial and target objects are significantly distorted. In this paper, an algorithm which derives an adversarial signal placing around the border of target objects to fool objector detectors is proposed. Computationally, the algorithm seeks a border around target objects to mislead Faster R-CNN to produce a very large bounding box and finally decease its confidence to target objects. Using stop sign as a target object, adversarial borders with four different sizes are generated and evaluated on 77 videos, including five in-car videos for digital attacks and 72 videos for physical attacks. The experimental results show that adversarial border can effectively fool Faster R-CNN and YOLOv3 digitally and physically. In addition, the experimental results on YOLOv3 indicate that adversarial border is transferable, which is vital for black-box attack.
Aircraft Detection from Satellite Images Using ATA-Plane Data Set

Satellite image analysis is a field of study in the field of image processing for many civil and military applications. Satellite images have many civilian applications, including recognition, detection and classification of regions, buildings, roads, aircrafts and other man-made objects. Among these, especially aircraft detection is strategically important for military applications and forms the basis of this study. In the first phase of the study, a new dataset composed of airplanes at different airports on Google Earth's satellite images is prepared to eliminate the lack of data set in this field. In the second stage, the detection of aircraft was carried out by using algorithms based on Convolutional Neural Network. Transfer Learning was used to improve the performance of this process. The accuracy rate obtained as a result of Transfer Learning with previously trained network Alexnet, was 91%.
STBins: Visual Tracking and Comparison of Multiple Data Sequences Using Temporal Binning

While analyzing multiple data sequences, the following questions typically arise: how does a single sequence change over time, how do multiple sequences compare within a period, and how does such comparison change overtime. This paper presents a visual technique named STBins to answer these questions. STBins is designed for visual tracking of individual data sequences and also for comparison of sequences. The latter is done by showing the similarity of sequences within temporal windows. A perception study is conducted to examine the readability of alternative visual designs based on sequence tracking and comparison tasks. Also, two case studies based on real-world datasets are presented in detail to demonstrate usage of our technique.
Implementation Of Information Systems On E-commerce Websites As Media To Deliver Information

Attendance information conducted by students can now be easily accessed by a supervisor. However, there are 3 (three) difficulties faced by supervisors, one of which is presenting information with tables requiring considerable time and very high accuracy to measure the comparison of information contained within it. In order to facilitate the supervisor in recording and measuring attendance of student tutoring students handled, the Attendance Rating system will present information in the form of graphics using Highchart. Presentation of information in the form of a graph on Attendance Assessment will present information in the form of Nim (Student Registration Number), supervisor, and guidance time. Information on the guidance time in the graph can be used as a comparison to measure the level of student activity in following the guidance. The Attendance Rating System uses the YII Frameworkbased website because it is also easy to develop web applications and the YII Framework has a good level of security. In this study, there are 5 (five) advantages and 1 (one) deficiency in the Attendance Assessment system. With this research, it is expected that the Attendance Assessment system can improve the quality of student attendance in the tutoring process at Raharja College.