Reviewerly/exHarmony huggingface dataset:
QAIC: Quality-assured image crowdsourcing via blockchain and deep learning

Recently, image crowdsourcing, a new trading mode, has been proposed to bridge the gap between the excess photos generated by intelligent devices and the great demand for images. However, traditional crowdsourcing methods often rely on centralized platforms, which risk data leakage and a single point of failure (SPOF). Moreover, due to the subjectivity of image quality assessment and the complexity of image data structure, image quality is difficult to control for traditional crowdsourcing frameworks without exposing data privacy. In this work, we propose a blockchain-based image crowdsourcing framework named QAIC to address these issues. Within the framework of QAIC, the transaction information is stored using a multichain structure, and the transaction process is implemented using smart contracts. We design an image selection and pricing mechanism for QAIC, where high-quality image sets can be spontaneously selected, and each image can be dynamically priced based on distortion degree and content relevance. Finally, to accurately obtain image quality, we design a dual output neural network model to evaluate the image quality, where a lightweight architecture is adopted, and piecewise outputs are designed to protect image privacy and reduce the on-chain computational cost Extensive analysis and experiments demonstrate that the quality of transaction data and reasonable pricing can be ensured using the QAIC without compromising image privacy.
Decentralized Application Identification via Burst Feature Aggregation

With the development of blockchain technology, de-centralized applications (DApps) are increasingly being developed and deployed on blockchain platforms. However, the complex data validation mechanism and strict encryption protocol settings of blockchain often lead to sparse traffic behavior of DApps. This sparsity poses a challenge for existing encrypted traffic identification methods to extract distinguishable DApps traffic features. In this study, we propose a novel approach for identifying DApps traffic features by observing the differences in burst timing features of DApps. We introduce a continuous burst feature matrix (CBFM) method based on burst feature aggregation that can aggregate sparse features and express the burst timing differences of DApps encrypted traffic. Additionally, we design a deep learning classifier to automatically extract the features contained in the CBFM. Our experimental results on real datasets demonstrate that the proposed CBFM method achieves a classification accuracy of 94%, outperforming state-of-the-art methods.
MK-FLFHNN: A Privacy-Preserving Vertical Federated Learning Framework For Heterogeneous Neural Network Via Multi-Key Homomorphic Encryption

The security and privacy of Vertical federated learning (VFL) deserve attention due to the computationally strong dependency between participants, which requires frequent and direct interactions. The existing Privacy-preserving (PP) VFL schemes are often limited by the model types supported, the communication cost and the number of participants. To alleviate this issue, we propose MK-FLFHNN, a novel PP framework for heterogeneous neural networks based on xMK-CKKS multi-key homomorphic encryption. The algorithm eliminates the limitation of traditional algorithms limited to generalized linear models and prevents the privacy leakage of shared information while solving the problem of potential leakage from the aggregated values of federated learning, adapting according to the number of participants and supporting flexible expansion to multi-party scenarios. Most importantly, for three-party and above scenarios, the framework is robust to collusion between K<N-1 participating devices and the cryptographic service provider. Our analysis and experiments demonstrate that compared with Paillier based scheme, MK-FLFHNN retains the model’s accuracy, significantly reducing the required interactions and information transmission by almost 2/3 and 30%, respectively, in the two-party scenario, providing strong security in three-party and above scenarios.
Who Are Querying For Me? Egress Measurement For Open DNS Resolvers

The dependencies and centralization in DNS infrastructure increase the risk of single-point failure and the scope of collateral damage. In the DNS recursive resolution, dependencies between different resolvers also exist due to situations such as forwarding. Currently, research on dependencies in recursive resolution is still insufficient. In this work, we take a deep insight into the recursive resolution implemented by open resolvers to investigate their dependencies, including the concentration of dependencies, and the influence of 3rd-party providers. We find that most open resolvers in the wild are dependent on a small number of egress resolvers to communicate with the authoritative name servers. 90% of the open resolvers are influenced by 8.41% of the egress resolvers. Besides, egress resolvers from 3rd-party providers are able to influence more than 44% of the open resolvers. The concentration makes a large amount of DNS traffic concentrated in a small number of egress resolvers/providers, which will reduce the redundancy of DNS and threaten user privacy.
Evaluating the Distinguishability of Tor Traffic over Censorship Circumvention Tools

Previous research has shown that Tor traffic can be easily identified, making Tor connections frequently blocked. In order to access the Tor network successfully, some censorship circumvention tools such as Shadowsocks and OpenVPN are utilized as front-proxy to connect to Tor entry nodes. However, the distinguishability of Tor traffic over these censorship circumvention tools has not yet been fully evaluated. By analyzing the equal-size segmentation mechanism of Tor and the transmission mechanisms of circumvention tools, we find that the payload length distribution of Tor traffic encrypted and encapsulated through these tools displays a distinct pattern, which makes such Tor traffic retain distinguishable from regular encrypted traffic. To verify this finding, we develop an automated, large-scale Tor traffic collection system to capture Tor traffic forwarded by various circumvention tools, and then design corresponding algorithms to extract traffic features in terms of payload length distribution. Finally, we perform the evaluation on the distin-guishability between the captured Tor traffic and the normal non-Tor traffic through extracted features. The F1-Score can achieve 0.99 with the false positive rate close to 0 when using Support Vector Machines for training and classification. The experimental results prove that circumvention tools cannot mask the inherent features of Tor traffic, and thus the Tor traffic forwarded by these tools can still be clearly distinguished from normal non-Tor traffic.
Deep Learning Based Model for Automatic Multimorbidity Pattern Prognosis

One of the main contemporary health issues in the healthcare system is multimorbidity, which is the co-existence of two or more diseases in the same patient. With the emerging of big data era, the need of analytical tools to extract actionable knowledge from high volumes of medical data is increasing to better understand Multimorbidity and manage health care system. In this work we propose to develop a recurrent neural network based model for the prediction of the multimorbidity sequences patterns. The proposed method is illustrated on real data and evaluated against Multi layer perceptron model and other baseline algorithms. The preliminary results show that recurrent neural model can outperform these baseline algorithmes according to F1 and precision scores which suggest recurrent neural network as a promising method when predicting multimorbid diseases.
SpoofingGuard: A Content-agnostic Framework for Email Spoofing Detection via Delivery Graph

Email spoofing is an effective attack vector for infiltrating companies and organizations. Traditional detectors are primarily based on the content of emails, but they ignore the frequent contextual changes. The blacklist-based solutions commonly used in the industry suffers from latency issues. Additionally, there are protocol-based solutions, such as SPF, DKIM, etc., but their adoption rates are unsatisfactory. To address these issues, this work presents a new framework named SpoofingGuard that detects email spoofing based on graph representation learning. As SpoofingGuard extracts important delivery path information related to the email service infrastructure from email headers, it is completely content-agnostic, and is expected to be more robust in the face of complex content variations. Finally, the evaluation results on two public datasets show that SpoofingGuard can achieve 99.51% precision and less than 0.5% false positive rate, demonstrating its effectiveness and advancement.
A Multi-Tenancy System Architecture for Online Examinations

Time-synchronous online examinations pose new demands on our infrastructure: lectures with more than 1000 students are common at TUM, and in case of time-synchronous examinations students concurrently download working instructions or submit solutions – with possibly multiple such examinations being conducted at the same time. Furthermore, the architecture has to withstand students that impatiently reload the download page multiple times as well as the sudden increase in traffic at the start and end of an exam. In addition, the architecture has to be resilient against both deliberate attacks of individuals and handling of unsuspecting users.In this paper we discuss our implementation of an examination management platform as multi-tenancy system based on operational requirements, demands on scalability, and bottlenecks we encountered in our attempt to roll out the infrastructure for as many examinations as needed.
Website UI Design Interactivity and Interface Visual Design Based on Improved DFA Algorithm

With the rapid development of the Internet, people rely more and more on network information. As a new means of communication, the interaction design of websites is particularly important. In this process, factors such as page layout, interface elements and colors should be considered. This paper has carried out a large number of related literature research on user browsing experience. By analyzing and summarizing the DFA algorithm model and calculation formula, determine the UI design scheme and implementation process based on the improved method. On this basis, the existing B/S architecture and functional modules are specifically optimized by using software engineering courses, and the overall effect of the website is evaluated by using the theoretical knowledge of interaction design. The evaluation test results show that the website UI design interactivity and interface visual design platform based on the improved DFA algorithm has high compression efficiency, and can design the website UI interface smoothly.
On the Advantages of Hand Gesture Recognition with Data Gloves for Gaming Applications

Recognizing hand gestures is a prerequisite for human-machine interaction, e.g., in gaming and surgery training applications. Compared to vision-based recognition, methods based on data gloves, e.g., using IMU sensors, have several advantages in flexibility and responsiveness. The goal is to quickly and accurately recognize hand gestures while minimizing the number of employed sensors. There are, however, several challenges, both in the combination of hardware and software solutions and in gesture design and respective recognition algorithms. In this study, we explore the advantages of using data gloves in gaming with a minimum number of sensors per finger. We demonstrate that users wearing the data gloves can accurately control an actor in a video game in perceived real-time.
ANALYTICAL REVIEW OF INFORMATION FOR TESTING THE OPEN PARAMETRIC SYSTEM OF AUTOMATED PROJECTION «VALENTINA»

The article analyzes proprietary systems, such as Lectra systems (France), Gerber Garment Technology (USA), Investronika (Spain), Assyst GmbH (Germany), Wild Ginger Software (USA) and open PatternViewer (USA), Valentina (Ukraine) automated design systems for sewing production. An analytical review of the information for testing the open parametric system of automated projection and design «Valentina» was performed. It was determined that the theoretical foundations of automated testing of automated design systems are currently an incompletely resolved problem. The minimum list of data for qualitative testing is presented, which contains 10 items, the main of which are: listings of source texts of software products, external specification, test plan and methods, test options and obtained results. The article provides a list of Internet sources of the «Valentina» software product distribution, a list of communication methods, as well as a list of external specification items required for testing the «Valentina» software product. Various versions of the «Valentina» were considered, as well as the changes made in the test version v0.7.52 compared to the previous version v0.7.51. Various types of errors that occur during the development of new functions of software products are analyzed, including the inability to perform one of the functions; inability to complete a function; the appearance on the screen of symbols or program primitives that do not correspond to the action; involuntary closing of the program window; closing the program window without warnings about saving data; other errors. The information obtained for testing the open parametric system of automated projection «Valentina» can be used in the future for the analysis of methods of testing software products and their improvement, as well as for the development of automated tests that can be implemented in the processes of creating and operating software products.
A Model-driven Approach to Design User Interaction on Smart Speakers

We are witnessing the wide spread of Vocal Assistants (VA) in our personal devices, such as smartphones, wearables and smart speakers. Such intelligent software services allow us to control IoT devices or trigger our preferred voice-based applications. Despite the amazing performances of the involved technologies (e.g. Natural Language Processing, speech-to-text and viceversa), the User Experience (UX) in using vocal apps is often poor. This is mainly due to the peculiar nature of the vocal channel, which is linear and transient. Mere porting of screen-based apps towards VAs without a deep re-thinking and re-designing of the whole app is not going to meet user expectations. In this paper we report on an experience we've had refactoring the UX of a popular music streaming app very used among the youngest: Spotify. The official Alexa skill for Spotify lacks a strong navigational model, which is fundamental for an application that should run without a display. Moreover, it also lacks a number of important features that are present on the Desktop and mobile counterparts. After refactoring the UX by using the Aural IDM methodology, we selected 20 beta-testers who used both the skills (the original and the refactored) for 1 day. The cohort had an average age of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu=30.25$</tex> years with a variance of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho^{\mathbf{2}}=41.2$</tex> , they were mainly students or junior workers, and the proportion between males and females were 60% vs. 40%. After being polled and interviewed, volunteers confirmed a clear preference in using the refactored version, by assigning it the maximum points twice and a half times with respect to the official version.
Identification of Local Neighbourhoods in a Network for Graph-based Signal Reconstruction

The Internet of Things (IoT) relies heavily on exchange of data collected by nodes in a sensor network. This network data can be represented as a signal on a graph allowing tools from graph signal processing (GSP) to analyse it. Here, we utilize graph signal reconstruction algorithms to extrapolate missing sensor data from partially sampled network data. An important class of graph signal reconstruction algorithms operate by partitioning the graph vertices into local sets to achieve lower reconstruction errors and faster convergence. However, methods for identification of these local neighbourhoods have not been clearly studied in the literature. Here, we propose two flexible algorithms to generate local sets on a graph. These algorithms are based on the distance between the sampled and unsampled vertices. Our algorithms are competitive with the methods in literature while offering flexibility in the number of sampled vertices. We carry out simulation-based analysis of sampling strategies and proposed local set generation algorithms using local-set-based reconstruction algorithms.
Intelligent Quotient Testing Using Stanford Binets Standardized Method

Abstract: We aim to help individuals, students, professionals or businesses produce quick and accurate mental skills estimates online, as well as to provide insights and advice on how these skills can be analyzed and fostered! In this paper we have promised to estimate an atmost accurate IQ of an individual with 15 set of questions which are based on important aspects such as logical mind, creative mind, spatial mind, verbal mind. This test estimates IQ till 170 that is highest IQ the test can estimate is 170. IQ stands for Intelligence Quotient, which is a parameter for measuring a human being’s ability to solved difficult problems and think. IQ tests have been designed and used to estimate this characteristic, and your IQ score tells you how smart you are in comparison to people your age. Your IQ test score actually reflects how well you do in various mental activities, and therefore, can be a good criterion to know if you can be successful in a specific field of study. The results from an IQ test should be analyzed carefully, but as core between 90 and 109 indicates average intelligence.
Perancangan Aplikasi Diagnosis dan Pemetaan 10 Penyakit Berbasis Android Pada Teaching Factory Nutrition Care Center

There has been an increase in non-communicable diseases in Indonesia in terms of morbidity and mortality. One of the risk factors for non-communicable diseases is obesity, high blood sugar and malnutrition in children. The need for public awareness and knowledge regarding the disease they have, because the increase in non-communicable diseases can cause a decrease in the activity of human resources in Indonesia and affect the quality of life. Usually non-communicable diseases are experienced by the elderly, but now they are starting to threaten the productive age group from 10 to 14 years old. Politeknik Negeri Jember has a TeFa NCC which is a Nutrition Service Center. TeFa NCC is intended for the community to carry out health consultations. In carrying out a health consultation, the patient is given a health record in the form of paper (manual) which the patient can take home. This sometimes makes it difficult for health cadres to see the track record of changes in a patient's health if the patient does not bring a consultation paper when he comes for a follow-up consultation. Proposed a design media design application for Diagnosing and Mapping 10 Android-Based Diseases in the Teaching Factory Nutrition Care Center. This application design is intended for the development of applications that can perform early diagnosis of the top 10 diseases in TeFa NCC. The method used in designing the application design is to use the waterfall model. The application design is designed for Android-based mobile devices. There is a splash screen menu, login menu, main display menu, patient data menu, added patient data menu, profile menu, most disease information menu, filtering, distribution map and diagnosis added menu. The designed design is based on the needs and requirements of the user. The design of the application design was successfully carried out
Prediction Of Project Schedule Performance Index for Trans South Java Road Project Lot 7 Blitar Regency Using Bayesian Network

The south coast of Indonesia has considerable potential and competitiveness in the field of tourism. This encourages the government to strive to improve tourism infrastructure through the construction of the South Coast Road. In its implementation, project reporting must be measured and controlled to assist project management in identifying problems and factors that affect project activities. This research aims to identify the most influential factors on Schedule Performance Index and develop a prediction model using the Bayesian Network. 5 main factors that affect project performance, such as Heavy Equipment, materials, Implementation and Work Relations, Labor, and Environment are used to are set 13 scenarios to detect the behavior of each factor appropriately. The factor is confirmed to the respondent to ensure that the factor occurs in the project. The study's results obtained Bayesian Network approach can be used to assess the condition of the JLS Lot 7 Blitar Schedule Performance Index (SPI) with an accuracy of around 80%. The dominant factor affecting SPI is the condition of the Heavy Equipment. The condition of the Heavy Equipment will affect the condition of SPI so closely that the contractor must maintain the performance of the heavy equipment so that the project performance is always in good condition. The identification results are expected to help in better decision-making and project risk management.
EDUCATIONAL MONITORING IS AN INFORMATION BASIS FOR EFFECTIVE MANAGEMENT OF EXTRACURRICULAR EDUCATION INSTITUTIONS

The article defines the concept of monitoring as a means of effective management of extracurricular educational institutions. Monitoring as a means of management is an effective information base that provides the head of the institution with data on the quality of conditions, the quality of the educational process and the quality of the results necessary for the implementation of the educational process in extracurricular educational institutions, ensures management decision-making, predicts trends in further development and identifies certain corrective measures, and it is believed that the effectiveness of management is influenced by external and internal factors. The success of effective management of extracurricular educational institutions is determined by two main factors. These are: the ability of the institution to clearly define the behavior of its teaching staff required to achieve the strategy, and the ability to apply effective management decisions to direct the behavior of the teaching staff in the right direction. Both tasks are equally important and challenging. The article describes the personal qualities, professional and managerial competencies of heads of educational institutions as an important aspect of the management of out-of- school educational institutions. Effective management of extracurricular educational institutions involves creating favorable conditions for the growth and development of pupils and students of different age groups, as well as for theself- realization of the team and the disclosure of their educational competencies, providing opportunities for experimental educational activities and the formation of advanced pedagogical experience. It is shown that a systematic approach plays an important role in the management of extracurricular educational institutions. The institution is characterized by the synergy of all potential subjects and entities, their subsystems and possible components, both in the internal environment of the extracurricular educational institution and in external influences.