Reviewerly/exHarmony huggingface dataset:
Computer Vision in Control and Robotics for Educational Purposes

The paper is devoted to computer vision education for ITMO University students specializing in control systems and robotics. The set of laboratory works are attached. Several examples of finished works are provided. The topics of the laboratory course consists of histograms, profiles, projections, image geometric transformations, filtering and edges detection, images segmentation, Hough transformation, image morphologic analysis.
How to Choose Its Parents in the Tangle

The Tangle is a data structure mainly used to store transactions in the IOTA cryptocurrency. It has similarities with the blockchain structure of Bitcoin but in the Tangle, a block contains only one transaction and has not one, but two parents. The security and the stability of this distributed data structure is highly dependent on the algorithm used to select the parents of a new block. Previous work showed that the current parents selection algorithms are insecure, not stable or have low performances. And we propose a new algorithm that combines all these properties.
Breast Cancer: Segmentation of Mammograms using Invasive Weed optimization and SUSAN algorithms

Breast cancer is one of the most prevalent diseases among female populations worldwide. Because the cause and prevention remain unknown, early detection is considered the only way to increase the survival rate. X-ray imaging is currently the most reliable technique for detecting abnormalities in the breast and it is highly recommended for middle-aged women (40- 60) who are at higher risk of developing the disease. However, the efficiency of X-ray is dependent on radiologists' expertise as image interpretation is challenging and can lead to false positives and false negatives. Image processing algorithms are highly useful for providing reliable diagnosis that can help radiologists make a fast, reliable medical interpretation. Several techniques for isolating tumors from digital mammograms have been proposed, including histogram-based methods, region-based algorithms, and edge detection approaches. However, these techniques are not effective, as they use fixed threshold levels to isolate suspicious areas from the non-uniform image backgrounds. In this paper, we propose an approach based on Invasive Weed optimization (IWO) and Smallest Univalue Segment Assimilating Nucleus (SUSAN). The IWO algorithm determines the optimal threshold for the extraction of the suspicious regions in mammograms. The selected threshold is then used for the detection of dense abnormalities using the SUSAN algorithm. The results show that this technique is more accurate in detecting suspicious areas in breasts, and particularly dense breasts, than the existing techniques.
Optimally Indifferentiable Double-Block-Length Hashing Without Post-processing and with Support for Longer Key Than Single Block

Existing double-block-length (DBL) hash functions, in order to achieve optimal indifferentiable security (security up to $$O(2^n)$$ query complexity), require a block cipher with $$n$$ -bit blocks and $$k$$ -bit keys such that $$2n\le k$$ , and a post-processing function with two block cipher calls. In this paper, we consider the indifferentiability of $$\mathsf {MDPH}$$ , a combination of the MDP domain extender and Hirose's compression function. $$\mathsf {MDPH}$$ does not require the post-processing function (thus has better efficiency), and supports block ciphers with $$n< k$$ . We show that $$\mathsf {MDPH}$$ achieves (nearly) optimal indifferentiable security. To the best of our knowledge, this is the first result for DBL hashing with optimal indifferentiable security, with support for block ciphers with $$n< k$$ , and without the post-processing function.
Enhancement of FMEA risk assessment with SysML

Assuring the safety of workers and passengers is a primary concern that has to be addressed in transport systems such as Heavy Rail. Introduction of new technologies into existing operational environments requires the application of safety assurance techniques to ensure continued safe operation and maintenance. This paper describes a modelling approach (compliant with appropriate standards such as ISO 31000) which presents a set of mechanisms and guidelines to analyse transport systems from a safety point of view. This approach utilises SysML diagrams to depict hazards in their respective contexts for enhanced understanding when compared to simple table presentations with diagram variations that subsequently capture the hazard impacts and mitigation. Automatic routines generate FMEA tables thereby decreasing the effort to generate and maintain the tables while reducing the likelihood of missed content. These tables are linked back to the originating diagrams for greater clarity of how the risks have been reduced.
Improved LeNet-5 Model Based On Handwritten Numeral Recognition

Handwritten numeral recognition is a branch of character recognition, it is widely used in large-scale financial statistics, bank checks, financial statements and the like. In this paper, the traditional LeNet-5 model is improved, and the 1*1 convolutional layer in LeNet-5 is removed, and the moving average model is added. The experimental results on the MNIST dataset show that the improved model can identify handwritten numeral more effectively, and the recognition rate reaches 99.06%.
Testing for Integrity Flaws in Web Sessions

Web sessions are fragile and can be attacked at many different levels. Classic attacks like session hijacking, session fixation and cross-site request forgery are particularly dangerous for web session security, because they allow the attacker to breach the integrity of honest users' sessions by forging requests which get authenticated on the victim's behalf. In this paper, we systematize current countermeasures against these attacks and the shortcomings thereof, which may completely void protection under specific assumptions on the attacker's capabilities. We then build on our security analysis to introduce black-box testing strategies to discover insecure session implementation practices on existing websites, which we implement in a browser extension called Dredd. Finally, we use Dredd to assess the security of 20 popular websites from Alexa, exposing a number of session integrity flaws.
Automatic Lecture Video Content Summarizationwith Attention-based Recurrent Neural Network

This paper propose an automatic summarization method for a lecture video transcript that uses attention based Recurrent Neural Network (RNN) to capture the content of the lecture. Our fully data-driven model also utilize segmentation to split the input video transcript in order to increase topic coherency in each segment. We also use linguistic-based feature to help the model identify important word and key topic in the segment which improves the quality of the summary. Our model shows a significant improvement in the term of ROUGE score compared to the baseline models.
Privacy-Preserving Electronic Ticket Scheme with Attribute-Based Credentials

Users accessing services are often required to provide personal information, for example, age, profession and location, in order to satisfy access polices. This personal information is evident in the application of e-ticketing where discounted access is granted to visitor attractions or transport services if users satisfy policies related to their age or disability or other defined over attributes. We propose a privacy-preserving electronic ticket scheme using attribute-based credentials to protect users' privacy. The benefit of our scheme is that the attributes of a user are certified by a trusted third party so that the scheme can provide assurances to a seller that a user's attributes are valid. The scheme makes the following contributions: (1) users can buy different tickets from ticket sellers without releasing their exact attributes; (2) two tickets of the same user cannot be linked; (3) a ticket cannot be transferred to another user; (4) a ticket cannot be double spent. The novelty of our scheme is to enable users to convince ticket sellers that their attributes satisfy the ticket policies and buy discounted tickets anonymously. This is a step towards identifying an e-ticketing scheme that captures user privacy requirements in transport services. The security of our scheme is proved and reduced to a well-known complexity assumption. The scheme is also implemented and its performance is empirically evaluated.
Not Just About the User

The use of speech interaction in public spaces doesn't only affect the user experience, but also people being present in the same situation. Whether they like it or not, they are confronted and influenced by the interaction of the user. This study examined the subjective perception of speech interaction in public spaces from two different perspectives: user and bystanders. The scientific assumption is that speech interaction might be misunderstood and unpleasantly experienced by bystanders. Therefore, we further assume that users are aware of this effect and feeling uncomfortable in causing such perceptions to others. The expected consequence for the users is that they are inhibited in their interaction. An online survey has been conducted to examine subjective perceptions of the experience with speech interaction in public spaces from the perspective of bystanders and users. Three factors have been identified, which include 17 aspects that may influence the social acceptance of speech interaction. Furthermore, the location has a significant influence on the willingness to use speech interaction in public spaces.
Remote Guidance for Machine Maintenance Supported by Physical LEDs and Virtual Reality

Machines that are used in industry often require dedicated technicians to fix them in case of defects. This involves travel expenses and certain amount of time, both of which may be significantly reduced by installing small extensions on a machine as we describe in this paper. The goal is that an authorized local worker, guided by a remote expert, can fix the problem on the real machine himself. Our approach is to equip a machine with multiple inexpensive LEDs (light-emitting diodes) and a simple micro controller, which is connected to the internet, to remotely light up certain LEDs, that are close to machine parts of interest. The blinking of an LED can be induced on a virtual 3D model (digital twin) of the machine by a remote expert using a virtual reality application to draw the local worker's attention to certain areas. We conducted an initial user study on this concept with 36 participants and found significantly shorter completion times and less errors for our approach compared to only voice guidance with no visual LED feedback.
Gaussian Weighted Deep Modeling for Improved Depth Estimation in Monocular Images

Capturing RGB images and estimating their corresponding depth data for training deep models is a challenging task. Several deep network models have been recently reported to formulate the depth estimation process as an image reconstruction problem, in order to overcome the difficulty of scarcity of ground truth depth. These deep network models have multiple design decisions and parameters that are selected empirically, failing to capture the varying nature of the input and hence the adaptability is limited. In this paper, we propose an automatically Gaussian weighted deep model to achieve improved solutions for the problem of monocular depth estimation. In comparison with the existing state of the arts, our proposed very deep model is supported by novel components, including a hybrid and integrated loss function and a fine training strategy. The hybrid and integrated loss function maintains the balance between appropriate assessments of perceptual similarity and modest resilience for both small and large scale errors, where different loss terms are automatically weighted and hence their integration is optimized via a Gaussian distribution based modelling. The fine training strategy is proposed to adaptively screen all the training images via an error clustering mechanism to sustain an effective and efficient training process. Extensive experiments are carried out and the results show that our proposed outperforms the compared seven benchmarks, representative of the existing state of the arts, across all the assessment metrics.
Software Development Metrics Prediction Using Time Series Methods

The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it's declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software's release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.
Policy Needs to Go Hand in Hand with Practice: The Learning and Listening Approach to Data Management

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), “Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment”. However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-to-day research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.
Communication and Documentation Practices in Agile Requirements Engineering: A Survey in Polish Software Industry

Requirements engineering, system analysis and other analytical activities form the basis of every IT project. Such activities are not clearly defined in Agile development methods, but it does not mean that they are absent in an agile project. The aim of our work was to determine which practices related to requirements-related communication and which requirements documenting techniques are used in agile software projects. For this reason we carried out a survey study targeting agile practitioners from Polish IT industry. The paper presents survey results, discusses the noticed differences with respect to the general Agile values and principles and provides a comparison to results of similar studies described in the related work. The main observation about communication practices is that frequent, face to face communication is the most common, but many respondents also declare use of other, remote communication means or exchanging SRS documents. The investigation of requirements documentation techniques revealed differences between the techniques used while describing requirements for developers and those used to elicit requirements from stakeholders and to comprehend them.
Designing for reflection on shared HIV health information

People living with chronic conditions are increasingly turning to digital technologies to track their health, coupled with reaching out to their peers to make sense of fluctuations in their health. However, there is a lack of appropriate technologies to support reflecting on shared personal health information. This paper reports on a study investigating how technology could be designed to support people living with HIV in reflecting on shared personal health information. Participants used two design provocations to reflect on changes in their health. Results showed that the design provocations encouraged reflection, with higher levels of reflection appearing to require greater use of peer information. We contribute a new understanding of how reflection on shared health information takes place and consider the next generation of digital technologies for people living with HIV.
Tracing the intangible

This article describes the methodology and technology for data collection of hand gestures of master craftspeople, with a case study tracing the Intangible Cultural Heritage practice of Horse Tail Embroiderers of Shui ethnic minority in collaboration with GYPEC1 in Guiyang, Southwestern China. We describe unique technological design solutions to enable mobility to remote villages, and freedom of movement to capture the hand gestures of master craftspeople. The significance of this work is vitally important, as it outlines a technique to digitally record, analyse and archive the intricate dynamics of craft practices. We contextualise the research within a contemporary context, and describe the aims and objectives of the research for an interactive media museum interface to provide new insights into traditional practices. Finally, we propose future potential to investigate embodied knowledge and alternative pedagogical applications for innovative contemporary design.
Assessing Personality Traits in a Large Scale Software Development Company: Exploratory Industrial Case Study

Software development methodologies become increasingly more people and team-oriented. However, many software projects fail due to conflicts of team members. Formation of an effective software development team may be particularly challenging given the differences inherent across an individual’s personality. This paper explores personality traits of agile software development teams by using a context-specific interactive assessment. Concerning the assessment, we have conducted a questionnaire with 110 participants from a large-scale software development company. We have visualized personality traits with team radar charts showing the personality traits of 18 project teams and analyzed the results by validation interviews. Our preliminary results indicate that higher introversion is observed most commonly in isolated teams that has less contact with customers. Moreover, high levels of agreeableness and conscientiousness personality traits are observed in most of the agile software development teams.
How much training data is enough to move a ML-based classifier to a different network?

Analyzing and understanding network traffic is a crucial requirement for different network and security monitoring tools. The evolution of Internet services and protocols has caused traditional traffic analysis and classification approaches to be ineffective on traffic related to social media, streaming audio and video services. Key causes include: (i) the rise in the usage of dynamic port numbers by different applications causing port number based classification inaccurate, and (ii) the increase in encrypted traffic causing the payload to be opaque and therefore, deep packet inspection to fall short. This research aims to study how to leverage machine learning based network traffic analysis and classification work in the presence of encrypted services. In this work, we implement and evaluate a decision tree based machine learning classifier for encrypted social media, video, and audio traffic identification without using IP addresses, Port Numbers, application header fields, and payload. The extensive evaluations present high accuracy to classify not only the aforementioned services but also to investigate how much more training is necessary when such a classifier is moved to a new network in terms of location, time, and traffic volume.
Improved Particle Swarm Optimization By Fast Simulated Annealing Algorithm

This paper proposes a hybrid particle swarm optimization with the fast-simulated annealing (PSO-FSA). The proposed algorithm is meant to solve high dimensional optimization problems based on two strategies, which are utilizing the particle swarm optimization to define the global search area and utilizing the fast-simulated annealing to refine the visited search area. To evaluate its performance, we examined the algorithm on 14 benchmark functions. Based on the results, PSO-FSA has higher accuracy result compared with particle swarm, simulated annealing. We also apply the algorithm in clustering problem, and the results shows that the proposed method has better accuracy than the optimization methods.