Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Scanning Behavior Detection Based on Distribution of Network Traffic Features Using Robust Autoencoders

In intrusion detection systems, the application of machine learning to detection methods has attracted interest from vendors and researchers, because most existing signature-based methods cannot cope with increasing incidents, and some machine learning methods, particularly neural networks (NNs), have achieved remarkable results in computer vision and other fields. In applying NNs to intrusion detection based on network traffic, it is very important to adopt unsupervised methods instead of supervised ones, because unsupervised methods are better for detecting unseen attacks, and network traffic behavior depends very much on each host or network, so that it is not realistic to prepare labels for each environment. In previous studies, however, the application of unsupervised methods has not been investigated much. A flow-based detection method using replicator NNs, or autoencoders, is one of a few unsupervised methods. The experimental results showed that unsupervised anomaly detection using autoencoders is certainly effective for detecting scanning behavior, however, the many false positives are still challenging. In this paper, flow-based intrusion detection using robust autoencoders (RAEs) is proposed. RAEs split the training data into benign and outlier features in an unsupervised manner, and the model learns low-dimensional representations only from the benign data, and splitting and learning are repeated alternately. We argue that RAEs reduce false positives in the test data by removing outliers in the training data. In this paper, a real-world traffic dataset, the MAWI (Measurement and Analysis on the WIDE Internet), was used for evaluations. Experiments showed that false positives were significantly reduced and attacks were detected more easily.
Prediction of Learner's Course Completion Probability Based on Learning Analytics

Based on learning analytics, this study investigated whether the learner’s time management data (TMD) accumulated in learning management system (LMS) could predict the probability for a student to complete a course in distance life-long education (DLE). It also attempted to determine the earliest learning progression point (LPP) that could predict the probability. The subjects of the study were 411 students who had taken the five courses offered by a typical DLE center located in Seoul, Korea in the spring semester of 2016. At the end of the semester, the regularity of connecting to the system (RCS), total learning time (TLT), and the number of accessing the system (NAS) were calculated and a stepwise multiple regression analysis was conducted to examine the relationship among the variables of RCS, TLT, and NAS and the total scores (TS). In order to decide LPP at which students’ TS can be predicted significantly, a multiple regression analysis was conducted each quarterly point of total 16 weeks for a semester. As a result, RCS and NAS were proved to predict the probability of completing a 1 course significantly from the first quarterly progression point.
Automatic Breast Cancer Detection Using Digital Thermal Images

Breast cancer is one of the most common and main reason of death for women all over the world. About one in eight of women is subject to breast cancer over the course of her lifetime. There is no effective method to prevent or know the reasons of growing these cancerous cells, however the number of deaths can be reduced by early detection. Breast cancer detection and classification is one of the most important fields that the researchers are working on. Thermal breast images are considered as an efficient type of screening strategies. The aim of this study is to develop an efficient system to detect breast cancer by using image processing techniques. The proposed system extracts the characteristic features of the breast from the region of interest that is segmented using a novel approach from the thermal input image. Then the image is classified based on these features to normal or abnormal using a neural network classifier. The system is evaluated on a benchmark dataset and a success rate of 96.51% is obtained.
FCTree: A Space Efficient FIB Data Structure for NDN Routers

Named Data Networking (NDN) is a future Internet architecture that replaces IP addresses with namespaces of contents that are searchable at the network layer. A challenging task for NDN routers is to manage forwarding-information bases (FIBs) that store next-hop routes to contents using their stored usually long names or name prefixes. In this paper, we propose FCTree, a compressed FIB data structure that significantly reduces the required storage space at the router and can efficiently meet the demands of having routes that are orders of magnitude larger than IP-based ones in conventional routing tables. FCTree employs a localized front-coding compression to buckets containing partitions of the routes. The top routes in these buckets are then organized in B-ary self-balancing trees. By adjusting the size of the buckets, the router can reach an optimal tradeoff between the latency of the longest prefix matching (LPM) operation and the FIB storage space. In addition, in contrast to existing hash and bloom-filter based solutions, the proposed FCTree structure can significantly reduce the latency required for range and wildcard searches (e.g., for latency sensitive streaming applications or network-layer search engines) where up to k routes are returned if they are prefixed by a requested name. Performance evaluation results demonstrate the significant space savings achieved by FCTree compared to traditional hash-based FIBs.
LSTM Language Model Adaptation with Images and Titles for Multimedia Automatic Speech Recognition

Transcription of multimedia data sources is often a challenging automatic speech recognition (ASR) task. The incorporation of visual features as additional contextual information as a means to improve ASR for this data has recently drawn attention from researchers. Our investigation extends existing ASR methods by using images and video titles to adapt a recurrent neural network (RNN) language model with a long-short term memory (LSTM) network. Our language model is tested on transcription of an existing corpus of instruction videos and on a new corpus consisting of lecture videos. Consistent reduction in perplexity by 5–10 is observed on both datasets. When the non-adapted model is combined with the image adaptation and video title adaptation models for n-best ASR hypotheses re-ranking, additionally the word error rate (WER) is decreased by around 0.5% on both datasets. By analysing the output word probabilities of the model, it is found that both image adaptation and video title adaptation give the model more confidence in the choice of contextually correct informative words
On the Effectiveness of Code Normalization for Function Identification

Information on the identity of functions is typically removed when translating source code to executable form. Yet being able to recognize specific functions opens up a number of applications. In this paper, we investigate normalization-based approaches for the purposes of aiding the reverse engineer and as an enabler for the rejuvenation of legacy binaries. We iteratively refine our methods and report on their effectiveness. Our results show that a naive approach can be surprisingly effective in both problem domains. Further, our evaluation looks into more advanced normalization techniques and finds that their practicality varies significantly with the problem domain.
Human-in-the-Loop Simulation Analysis of Conflict Resolution Maneuvers Using an Air Traffic Control Simulation

With rapid growth in the technologies and demand of Remotely Piloted Aircraft Systems (RPASs), integration of such systems into the existing airspace system is becoming an issue in many countries. RPAS have different flight performances, communication characteristics, separation assurance mechanisms, and human machine interfaces from manned aircraft. To establish rules and regulations for RPAS integration, it is important to understand the impacts of RPASs on the airspace system. A simulation system that integrates manned aircraft, air traffic control, and RPASs is developed in Inha University to investigate these impacts through Human-in-The-Loop (HiTL) simulations. Three conflict resolution scenarios between a manned aircraft and a Remotely Piloted Aircraft (RPA) were constructed and tested. Human factors such as the response times of pilots and controllers were measured and analyzed as well as the risk of each maneuver.
Extracting Knowledge For Product Recommendation From Social Network

In this paper, we propose a novel answer for a cross-webpage frosty begin item suggestion, which plans To prescribe objects from online business sites to customers at character to individual communication destinations in "could Begin" circumstances, an trouble which has once in a while been investigated a while currently. A noteworthy check is the way by way of which to apply data eliminated from Lengthy range informal verbal exchange locales for a pass-website online icy begin object thought. As of late, the bounds among webs based totally business and person to man or woman communication have become out to be step by step obscured. Numerous web primarily based commercial enterprise websites bolster the system of social login wherein customers can sign up the websites utilizing their casual community characters, as an example, their Facebook or Twitter bills. [1] Clients can likewise put up their recently offered items on micro blogs with connections to the net business item web page pages. We advise to utilize the connected clients crosswise over long variety interpersonal conversation locations and on-line enterprise web sites (clients who've casual communication accounts and have made buys on web based business sites) as a scaffold to delineate interpersonal interaction highlights to another element portrayal for the item suggestion. In particular, we propose learning the two clients' and items' component portrayals (called client embedding’s and item embedding’s, separately) From information amassed from internet business websites making use of repetitive neural systems and after that observe a changed perspective boosting trees approach to alternate clients' individual to person conversation highlights into purchaser embedding’s communication highlights into client embedding’s. We at that point build up an element Based lattice factorization approach that may use the educated client embedding’s for an icy begin item proposal. Trial comes about on an expansive informational collection built from the biggest Chinese micro blogging administration SINA WEIBO and the biggest Chinese B2C web based business site JINGDONG have demonstrated the viability of our proposed system.
Personalized Service Recommendation With Mashup Group Preference in Heterogeneous Information Network

In the service network, there exist various objects and rich relations among them. These various objects and rich relations naturally form a heterogeneous information network. Service recommendations can help users to locate their desired services. Previous service recommendation studies mainly aim at homogeneous networks or consider few kinds of relations rather than using the rich heterogeneous information. In this paper, we propose a mashup group preference-based service recommendation method in the heterogeneous information network for mashup creation. First of all, we analyze the historical invocation records between mashups and services and exploit the heterogeneous information to construct diverse meta paths with different semantic meanings. Then, we measure the similarity between the starting object and the ending object from different perspectives and integrate different similarity measures to obtain the hybrid similarity. Next, we introduce group preference to capture the rich interactions among mashups and apply a group preference-based Bayesian personalized ranking algorithm to learn the model. Finally, we recommend a list of personalized ranking services for mashup developers. A series of experiments conducted on a real-world dataset demonstrate the superiority of our proposed approach over other baseline approaches.
Learning Computer Graphics Utilizing Blocks on Web Environment

Computer graphics has been applied for generating digital contents in many fields such as virtual/augmented reality, as well as traditional applications such as visual effects. However, serious mathematical background obstructs people from learning computer graphics. The proposed paper focuses on computer graphics for generating an effective e-learning platform. We utilized a block-based system, which is implemented by WebGL and delivers educational interests and enhanced accessibility. Also, users could practice the theory of graphics using processing language and check their programming result directly.
Design and Construction of a Sensitive Electronic Control System for the Injection of Fast Transient Pulses into a Current Measurement System

This paper discusses the design and construction of a test station used in research to improve the high frequency response of optical current transformers (OCT’s). The system comprises of a short circuit high current busbar loop, two 5 kVA current transformers (CT’s), a standard CT and the current transformer under test. The standard CT is used to measure the main power supply current injected into the loop by driving the first 5 kVA CT via an 8 kVA Variac from the main electric network, whilst the second 5 kVA CT is used to inject high frequency current signals into the system. Apart from this electronic control to be in very close contact with the busbar, high voltage experiments are also done in the same laboratory, including corona discharge and other interferences that made it essential to employ electrostatic and electromagnetic screening of the electronic circuits. During this project, many special techniques have been developed and used to achieve this effective screening.
Short Text Topic Modeling to Identify Trends on Wearable Bio-Sensors in Different Media Type

The technology and techniques for bio-sensors are rapidly evolving. Accordingly, there is significant business interest to identify upcoming technologies and new targets for the near future. Text information from internet reflects much of the recent information and public interests that help to understand the trend of a certain field. Thus, we utilize Dirichlet process topic modeling on different media sources containing short text (e.g., blogs, news) which is able to self-adapt the learned topic space to the data. We share the observations from the domain experts on the results derived from topic modeling on wearable biosensors from multiple media sources over more than eight years. We analyze the topics on wearable devices, forecast and market analysis, and bio-sensing techniques found from our method.
Critical Incident Data and Their Importance in Remote Usability Evaluation

Although lab-based formative evaluation is effective for improving usability of user interfaces, it has limitations. Since most software has a life cycle extending well beyond the first release, the need for usability improvement does not end with deployment. To capture post-deployment usability data, we created the user-reported critical incident method, a method for remote, contemporaneous reporting of critical incidents by users for usability improvement. This method provides us with contextualized critical incident data - critical incident reports plus video and/or textual description of task context. Our studies of this self-reported critical incident method for remote usability evaluation show that users, located in their own working environment and with no background in software engineering or human-computer interaction and with the barest minimum of training, can identify, report, and rate the severity level of their own critical incidents during usage.
Multiple Cell Upsets Inside Aircrafts. New Fault-Tolerant Architecture

Multiple cell upsets (MCU) is an issue that has to be dealt with when designing electronics for working in a radiated environment. Furthermore, the constant evolution of ICs integration density causes an increment in the MCUs span. These issues are typical in aviation applications, where, additionally, fault-tolerant (FT) performance is required. FT systems are typically based on a redundancy concept for storing and retrieving healthy information, for example, with a triple modular redundancy (TMR) scheme. The main issue with redundancy is design oversizing. On the other hand, reconfiguration-based techniques allow error scrubbing with a limited overhead. The main drawback here is overhead vulnerability to radiation, which is invalid for FT requirements. This paper proposes a new hybrid architecture that takes advantage of the optimized performance of reconfiguration-based techniques supported on extremely compressed redundant information nonvulnerable to radiation, referred to in this paper as hardwired seed bits (HSB). It also includes different known techniques, such as interleaving, error detection and correction (EDAC) algorithms, etc., for optimizing the final architecture as much as possible. As a result, the proposed approach meets FT requirements thanks to nonvulnerable tiny redundant information combined with an optimized performance through EDAC-based implementation.
Application security through program obfuscation

Business models behind products such as iTunes and the Skype VoIP clients depend entirely on the secrecy of technical details of their product. Once the technical details are uncovered, a medium such as the Internet is extremely powerful to (anonymously) spread the sensitive information and it is shown that stopping the spread of such highly sensitive information is difficult. Therefore, program obfuscation recently attracted a lot of attention as a low cost approach to protect the inner workings of an application. However, when a new obfuscating transformation is proposed, it is unclear how to measure the quality of such transformation as there is no general agreement on this matter in this young domain. Collberg’s taxonomy [37] describes the quality of an obfuscating transformation in terms of cost, resilience and potency. The cost describes the execution penalty, the resilience measures how well a transformation withstands an attack while the potency measures how much more difficult the obfuscated code is to understand. Our work contributes by describing attacks that test the resilience of an obfuscating transformation and by the construction of a framework based on software complexity metrics to evaluate the potency of obfuscating transformations. In this dissertation, we bring together existing control flow obfuscating transformations and existing software complexity metrics. In particular, we consider three transformations: control flow flattening (CFF), branch procedures and opaque predicates together with two metrics: cyclomatic number and knot count. After applying the obfuscating transformations on a program, the complexity of the program increases. To measure this, our framework has to be capable of quantifying the obfuscating transformation independent of at which point in the development process the obfuscating transformation is applied. Therefore, our introduced framework works on the
Mutual Coupling Reduction between H Shaped Compact MIMO Antenna for WLAN Application

A simple compact H shaped MIMO antenna for WLAN application at resonating frequency 5.8 GHz is propose in this paper. The center to center spacing between closely placed H shaped MIMO antenna is 0. 311λo. The mutual coupling between the proposed antenna is reduced by using dumbbell shaped DGS at the center of the groundplane. These dumbbell shape defect act as bandstop filter because of its inductance and capacitance effect, helps to suppress the surface wave propagation of waves. The filter characteristic of the dumbbell shaped DGS is studied to achieve isolation over the 5.725-5.875 GHz frequency band. Further the parametric study such as length, width and the spacing of dumbbell shaped DGS is optimized and studied in order to achieve good isolation among the MIMO antenna elements. The proposed MIMO structure is fabricated on low cost FR4 substrate having thickness of 1.6mm and the overall dimension of MIMO antenna is 40mm x 24 mm leading to compact size of antenna. The mutual coupling reduces from 24 dB to 34 dB by placing dumbbell shaped DGS. The envelope correlation coefficient for the proposed antenna is below less than 0.04 dB for the operating frequency band. The proposed H shaped MIMO antenna is tested and the measured result follows the simulation results. Therefore the proposed antenna is a good candidate for WLAN application.
Design of Data Management for Multi SPMD Workflow Programming Model

As both the complexity of algorithms and architecture increase, development of scientific software becomes a challenge. In order to exploit future architecture, we consider a Multi-SPMD workflow programing model. Then, data transfer between tasks during computation highly depends on the architecture and middleware used. In this study we design an adaptive system for data management in a parallel programming environment which can express two level of parallelism. We show how the consideration of multiple strategies based on I/O and direct message passing can improve performances and fault tolerance in the YML-XMP environment. On a real application with a sufficiently large amount of local data, speedup of 1.36 for a mixed strategy to 1.73 for a direct message passing method are obtained compared to our original design.
Fast Comparison of High-Precision Time Scales Using GNSS Receivers

Comparison of high-performance time scales generated by atomic clocks in laboratories of time and frequency metrology is usually performed by means of the Common View method. Laboratories are equipped with specialized GNSS receivers which measure the difference between a local time scale and a time scale of the selected satellite. Every receiver generates log files in CGGTTS data format to record measured differences. In order to calculate time differences recorded by two receivers, it is necessary to obtain these logs from both receivers and process them. This paper deals with automation and speeding up of these processes.
High FOM Dual Band Wireless Power Transfer using Bow-tie Defected Ground Structure Resonators

This work presents a dual-band wireless power transfer (WPT) system with high figure of merit $(FoM)$. One band can be used for power transfer and another for data transfer in biomedical applications. A Bow-tie Defected Ground Structure (DGS) resonators with high quality (Q-) factor and coupling (k-) factor is utilized for ensuring high efficiency. An equivalent circuit model using the admittance (J-) inverters is utilized to design the admittance inversion and compensation networks. The measured result shows that the proposed WPT system is able to transfer the power at both of the bands with a highFoM, making a suitable candidate for compact near filed WPT applications.