Miracle-dz/UniFAR-FTUs huggingface dataset:
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

Mobile-application fingerprinting of network traffic is valuable for many security solutions as it provides insights into the apps active on a network. Unfortunately, existing techniques require prior knowledge of apps to be able to recognize them. However, mobile environments are constantly evolving, i.e., apps are regularly installed, updated, and uninstalled. Therefore, it is infeasible for existing fingerprinting approaches to cover all apps that may appear on a network. Moreover, most mobile traffic is encrypted, shows similarities with other apps, e.g., due to common libraries or the use of content delivery networks, and depends on user input, further complicating the fingerprinting process. As a solution, we propose FlowPrint, a semi-supervised approach for fingerprinting mobile apps from (encrypted) network traffic. We automatically find temporal correlations among destination-related features of network traffic and use these correlations to generate app fingerprints. Our approach is able to fingerprint previously unseen apps, something that existing techniques fail to achieve. We evaluate our approach for both Android and iOS in the setting of app recognition, where we achieve an accuracy of 89.2%, significantly outperforming state-of-the-art solutions. In addition, we show that our approach can detect previously unseen apps with a precision of 93.5%, detecting 72.3% of apps within the first five minutes of communication.
University of Birmingham Growth of two-dimensional C60 nano clusters within a propylthiolate matrix

Two-dimensional C 60 clusters embedded in a propylthiolate/Au(111) self-assembled monolayer (SAM) are produced by depositing C 60 molecules on top of the SAM at 110 K followed by thermal annealing to room temperature. The SAM acts as a spacer layer at 110 K preventing C 60 molecules from reaching the Au(111) substrate. As temperature is increased, molecular scale vacancies open up within the SAM allowing C 60 molecules to drop to the surface of the Au(111) substrate. This initial “rooting” is then followed by the formation of two-dimensional C 60 clusters. The clusters have a rather uniform orientation with a well-defined direction for the close-packed C 60 molecules.
An Agent-Based Model of Firm Size Distribution and Collaborative Innovation

ICT-based Collaborative innovation has a significant impact on the economy by facilitating technological convergence and promoting innovation in other industries. However, research on innovation suggests that polarization in firm size distribution, which has grown since the early 2000s, can interfere with collaborative innovation among firms. In this paper, I modelled firmsâ€™ decision-making processes that led to collaborative innovation as a spatial N-person iterated Prisonerâ€™s dilemma (NIPD) game using collaborative innovation data from Korean ICT firms. Using an agent-based model, I experimented with the effects of firm size heterogeneity on collaborative innovation. The simulation experiment results reveal that collaborative innovation in the industry increases as the size heterogeneity decreases. Findings suggest that policies promoting collaborative innovation should focus on mitigating structural inequalities in the industry.
Pangolin: Incremental Hybrid Fuzzing with Polyhedral Path Abstraction

Hybrid fuzzing, which combines the merits of both fuzzing and concolic execution, has become one of the most important trends in coverage-guided fuzzing techniques. Despite the tremendous research on hybrid fuzzers, we observe that existing techniques are still inefficient. One important reason is that these techniques, which we refer to as non-incremental fuzzers, cache and reuse few computation results and, thus, lose many optimization opportunities. To be incremental, we propose "polyhedral path abstraction", which preserves the exploration state in the concolic execution stage and allows more effective mutation and constraint solving over existing techniques. We have implemented our idea as a tool, namely Pangolin, and evaluated it using LAVA-M as well as nine real-world programs. The evaluation results showed that Pangolin outperforms the state-of-the-art fuzzing techniques with the improvement of coverage rate ranging from 10% to 30%. Moreover, Pangolin found 400 more bugs in LAVA-M and discovered 41 unseen bugs with 8 of them assigned with the CVE IDs.
A Constraint Based K-Shortest Path Searching Algorithm for Software Defined Networking

Software Defined Networking (SDN) is a concept in the area of computer networks in which the control plane and data plane of traditional computer networks are separated as opposed to the mechanism in conventional routers and switches. SDN aims to provide a central control mechanism in the network through a controller known as the SDN Controller. The Controller then makes use of various southbound Application Programming Interfaces (APIs) to connect to the physical switches located on the network and pass on the control information, which used to program the data plane. SDN Controller also exposes several northbound APIs to connect to the applications which can leverage the controller to orchestrate the network. The controller used with regard to this paper is the Open Network Operating System (ONOS), on which the algorithm in question is to be deployed. ONOS provides several APIs which is leveraged to connect the application to the network devices. The typical network path between any two endpoints is a shortest path fulfilling a set of constraints. The algorithm developed here is for optimal K-Shortest path searching in a given network satisfying specified constraints, controlled by ONOS.
HIV risk sexual behavior among MSM with different sex orientation in Chongqing

Objective To investigate sexual risk behavior of HIV among MSM with different sex orientation and provide evidence for intervention in MSM.Methods MSM were recruited using snowballing method from gay venue and volunteer workgroup from June to September 2006.Face-to-face interview was conducted anonymously to collect sexual behaviors by questionnaire.Results Of total 561 respondents,50.6% admitted gay and 45.5% bisexual(Bi). 20-29 age group of Gay and Bi had 50.4%,33.9%. Bi had more marriage status than Gay(P0.01). The proportion of first sex with male partner was higher in gay(67.3%) than in bisexual man(32.5%). First sex age with man of gay(25.5 years old ) was younger than bisexual man(28.2 years old ). In the last six months,the proportion of anal sex was higher in gay(87.3%) than in bisexual man(78.8%)(P0.05) and condom use rate was low with gay,22.9% and Bi,18.4%. 16.9% gay and 49.8% Bi had sex with female in the past 6 months(P0.01) and showed high proportion of never using condom,50.0% and 57.5%. 7.7% of gay and 4.1% of Bi bought sex with male sex partner,and consistent condom use rate were 63.6% and 25.0%(P0.05). conclusion There observed high proportion of marriage and female sex partner in Bi and male sex partner in gay,as well as frequent unprotective anal sex in two groups,and needed surveillance and intervention.
Image Compression Based on Neuroscience Models: Rate-Distortion Performance of the Neural Code

Image compression still remains one of the most challenging scientific fields as it is widely used in almost every kind of application. To design groundbreaking architectures the image processing community concentrated at first on visual perception. However, understanding how the visual system senses the semantics of the visual scene is still a big issue. Recently, a neuro-inspired compression (NICE) system was introduced, trying to encode the visual information by mimicking the firing mechanisms of neurons. The performance of this system was promising but inefficient when opposed to the JPEG standard. In this work, we aim at improving the architecture design of the NICE system. Thus, we propose 3 different architectures and we show that the quality of the reconstruction is substantially enhanced. In addition, the NICE system outperforms the JPEG standard according to the assessment of two full-reference metrics; the peak signal-to-noise ratio (PSNR) and the structure similarity index measure (SSIM), except for very low bits per pixel values.
Classical Checkers

For the last 60 years, games have been an important research subject in the ﬁeld of Artiﬁcial Intelligence. For some time the focus was mainly on creating strong computer Chess programs. With the rise of master-level game-playing programs the trend changed. Now, the focus is on determining which games can eﬀectively be solved and on solving them. The recent break-through with Monte-Carlo Tree Search (MCTS) techniques in complex games, such as Go and Amazons, also led the research away from alpha-beta. However, MCTS has not yet beaten alpha-beta in more simple games, such as Checkers and Chess. Inthis document, we describe a computer program created to play the game of Classical Checkers, Turska, and, for the ﬁrst time, we estimate and analyse the complexity of Classical Checkers. Additionally, some experiments were made with the program. The results are analysed in order to better understand how each one of the studied search algorithms and techniques behave in the domain under study, and how they can be improved. Turska was further evaluated by playing some matches against another Classical Checkers computer program.
The Role of Education in Long-run Earnings Inequality and Mobility

This paper uses survey-linked administrative earnings data to explore whether increases in cross-sectional earnings inequality since 1980 have translated into increasing long-run earnings inequality and declining intragenerational mobility, and to what extent changes in the distribution of education among workers drives these trends. We estimate trends in cross-sectional residual earnings inequality, long-run residual earnings inequality, and long-run residual earnings mobility since 1980 and estimate how inequality and mobility differ across education groups. We use a kernel reweighting method to assess the impact of the skills by gender composition on these trends. Consistent with other work, we find that cross-sectional residual earnings inequality increased for men but decreased for women. However, long-run within-group earnings inequality rose for both men and women. As long-run earnings inequality grew, intragenerational within-group earnings mobility fell. For men we find similarities in the level and trend in residual inequality and mobility across education groups and very little role for compositional change in explaining trends in long-run economic inequality and mobility. For women though, convergence in inequality across education groups, especially at the lower-tail of the earnings distribution, results in a role for compositional change which explains about 20% of the increase in inequality over the period.
Team Description Paper robOTTO RoboCup@Work 2021

Team robOTTO is the RoboCup@Work League team of the Otto-von-Guericke University Magdeburg, formerly participating in the Robocup Logistics League since its founding in 2010. In our team, we combine the expertise from Computer Science, Electrical and Mechanical Engineering to solve @Work’s unique challenges while fostering knowledge exchange between the diﬀerent leagues.
Dietary phytoestrogens enhance spatial memory and spine density in the hippocampus and prefrontal cortex of ovariectomized rats

Article history: Accepted 6 July 2006 Available online 1 September 2006 Long-term maintenance of ovariectomized rats (9weeks) on chow containing high phytoestrogen levels (Purina LabDiet® 5001) as compared to chow with minimal phytoestrogens (Harlan 2016 Teklad) was associated with better performance of the spatial memory task, object placement, increased dendritic spine density in CA1 and prefrontal cortex pyramidal neurons, and higher uterine weights. Object recognition memory, anxiety on an elevated plus maze and body weight were unaffected by phytoestrogen levels in the diet. © 2006 Elsevier B.V. All rights reserved.
The LINGBAN System for Blizzard Challenge 2019

This paper introduces a text-to-speech (TTS) system developed at LINGBAN research for the Blizzard Challenge 2019. The task for this year is to build a voice from about eight hours of highly expressive Mandarin speech data. We proposed a neural vocoder based parametric system that modeling speech wave-forms for this task. Firstly, A lightly-supervised speech recognition approach was adopted to select the clean speech data with accurate text. Moreover, a hybrid deep neural network (DNN) with long-short term memory (LSTM) built on multi-speaker speech data was applied for acoustic modeling and duration modeling. Finally, a WaveNet-based neural vocoder was used to generate speech waveforms from acoustic feature instead of the conventional vocoder. Subjective evaluation results show that our system performs good in all evaluation criteria
Applying Data Mining Techniques for Optimize MURA Graphic Process

The making of large area TFT-LCD mask starts from writing computer-aided design electronic circuit onto quartz blanks and then goes through developing, etching and cleaning processes for final products. MURA is a phenomenon that is caused by uneven brightness on monitors. This research focuses on the improvement of MURA issue solutions. This paper produces an integrated mask vector drawing which includes customer graphics, barcode, mask title, mask serial number, manufacturer name and manufactured date. According to this drawing, we can simulate the overlap areas of writing segments and then obtain a similar writing way of actual writers. It contributes to the shortening of MURA adjusting time during graphic processing procedure and creates an artificial intelligence module for forecasting MURA adjustment risk. By applying this particular module, one can predict the risk of MURA adjustment and prevent it from reaching high-risk graphic area so that the MURA issues can be reduced.
OcuLock: Exploring Human Visual System for Authentication in Virtual Reality Head-mounted Display

The increasing popularity of virtual reality (VR) in a wide spectrum of applications has generated sensitive personal data such as medical records and credit card information. While protecting such data from unauthorized access is critical, directly applying traditional authentication methods (e.g., PIN) through new VR input modalities such as remote controllers and head navigation would cause security issues. The authentication action can be purposefully observed by attackers to infer the authentication input. Unlike any other mobile devices, VR presents immersive experience via a head-mounted display (HMD) that fully covers users’ eye area without public exposure. Leveraging this feature, we explore human visual system (HVS) as a novel biometric authentication tailored for VR platforms. While previous works used eye globe movement (gaze) to authenticate smartphones or PCs, they suffer from a high error rate and low stability since eye gaze is highly dependent on cognitive states. In this paper, we explore the HVS as a whole to consider not just the eye globe movement but also the eyelid, extraocular muscles, cells, and surrounding nerves in the HVS. Exploring HVS biostructure and unique HVS features triggered by immersive VR content can enhance authentication stability. To this end, we present OcuLock, an HVS-based system for reliable and unobservable VR HMD authentication. OcuLock is empowered by an electrooculography (EOG) based HVS sensing framework and a record-comparison driven authentication scheme. Experiments through 70 subjects show that OcuLock is resistant against common types of attacks such as impersonation attack and statistical attack with Equal Error Rates as low as 3.55% and 4.97% respectively. More importantly, OcuLock maintains a stable performance over a 2month period and is preferred by users when compared to other potential approaches.
ELECTROCHEMICAL SYNTHESIS OF NICKEL HYDROXIDE FROM NITRATE SALT IN THE SLIT DIAPHRAGM ELECTROLYZER FOR POWER SOURCES APPLICATION

Ni(OH)2 is widely used as active material for Faradic electrode in super-capacitors. In this study, it was found that synthesis of nickel hydroxide synthesis in slit-diaphragm electrolyzer from nickel nitrate solution as catholyte is possible. It was discovered that synthesis only possible at low current densities (4-6 A/dm 2 ) due to overheating of catholyte. Analysis of synthesized samples revealed that have low crystallinity layered (α+β) Ni(ОН)2 structure, with increased content of αNi(ОН)2. A formation of phase that is intermediate between α-Ni(ОН)2 and β-Ni(ОН)2 was discovered. Cyclic voltammetry studies revealed high electrochemical activity and cyclic stability of synthesized samples. It was noted, that further studies are needed to refine the proposed method for industrial applications.
SVLAN: Secure & Scalable Network Virtualization

Network isolation is a critical modern Internet service. To date, network operators have created a logical network of distributed systems to provide communication isolation between different parties. However, the current network isolation is limited in scalability and flexibility. It limits the number of virtual networks and it only supports isolation at host (or virtualmachine) granularity. In this paper, we introduce Scalable Virtual Local Area Networking (SVLAN) that scales to a large number of distributed systems and offers improved flexibility in providing secure network isolation. With the notion of destination-driven reachability and packet-carrying forwarding state, SVLAN not only offers communication isolation but isolation can be specified at different granularities, e.g., per-application or per-process. Our proof-of-concept SVLAN implementation demonstrates its feasibility and practicality for real-world applications.
Visualization and Analysis of Customer Provided Forecasts

: Demand forecasts can either be provided by customers or manufacturing companies use own common prediction models to estimate the customer demand. This paper provides the first results of a research project with a focus on visualization of forecast uncertainty for customer provided forecasts. The visualization concept described in this paper is developed to analyse systematic and unsystematic forecasting behaviours with respect to periods before delivery. The aim of the developed visualisation concept is to provide support for practitioners in the area of production planning with a special attention on forecast uncertainty in the supply chain. Compared to a classical order analysis the paper shows the advantage of chronological forecast quality monitoring. In future research the visualization concept should be extended to an improved decision support tool in production and supply chain planning.
An Object Execution Model for Reactive Modules with a C ++ Implementation

We present an execution model that allows reactive modules that follow the synchronous paradigm (all computations are ended before a new ﬂow of data arrive) to be represented by objects that communicate synchronously or asynchronously. This model is intended to be used in the object-oriented design of complex applications that mix reactive and transformational parts. The reactive parts may be developed in any synchronous language, or even implemented in hardware. Our model allows synchronous communication between separately compiled parts, when there is no dependency loop. The object model allows dynamic creation and destruction of objects. This is supported by our model, along with dynamic changes in the synchronous communication network. This leads to the notion of reaction of a clock (a set of synchronous objects that communicate synchronously). A clock reacts to signals at instant t by producing outputs at t and determining what is going to be its state at t+1 (with a new set of objects and a new communication network). The nature of asynchronous communications is intentionally not imposed in order to be adapted to each application domain. This model is implemented by two tools and a C++class library. The ﬁrst tool, occ++ , translates oc code (automaton description) into a C++class. The second, MDLC , compiles descriptions of composite modules written in the Module Description Language . A library provides the execution machine for synchronous objects. The implementation works on any Unix machine, and has been ported to the real-time kernel VxWorks. This paper does not provide a methodology for dealing with reactive components, but presents tools for the application of Object Oriented methodologies, or object based software platforms, to systems with reactive components. Our system was integrated in the PTOLEMY environment which is used for the simulation and the prototyping of heterogeneous systems.
Russian Constructivism in a Prefascist Theory

The results from this paper are twofold. First, we give a purely syntactic presheaf model of CIC. Contrarily to similar endeavours, this variant both preserves conversion and interprets full dependent elimination. Using a particular instance of this model, we show how to extend CIC with Markov's principle, while preserving all good meta-theoretical properties like canonicity and decidability of type-checking. The resulting construction can be seen as a synthetic presentation of Coquand-Hofmann's syntactic model of PRAω + MP as the composition of Pédrot-Tabareau's exceptional model with our presheaf interpretation.
Multi-criteria food products identification by fuzzy logic methods

The paper deals with the theory of fuzzy sets as applied to food industry products. The fuzzy indicator function is shown as a criterion for determining the properties of the product. We compared the approach of fuzzy and probabilistic classifiers, their fundamental differences and areas of applicability. As an example, a linear fuzzy classifier of the product according to one-dimensional criterion was given and an algorithm for its origination as well as approximation is considered, the latter being sufficient for the food industry for the most common case with one truth interval where the indicator function takes the form of a trapezoid. The results section contains exhaustive, reproducible, sequentially stated examples of fuzzy logic methods application for properties authentication and group affiliation of food products. Exemplified by measurements of the criterion with an error, we gave recommendations for determining the boundaries of interval identification for foods of mixed composition. Harrington’s desirability function is considered as a suitable indicator function of determining deterioration rate of a food product over time. Applying the fuzzy logic framework, identification areas of a product for the safety index by the time interval in which the counterparty selling this product should send it for processing, hedging their possible risks connected with the expiry date expand. In the example of multi-criteria evaluation of a food product consumer attractiveness, Harrington’s desirability function, acting as a quality function, was combined with Weibull probability density function, accounting for the product’s taste properties. The convex combination of these two criteria was assumed to be the decision-making function of the seller, by which identification areas of the food product are established.