Miracle-dz/UniFAR-FTUs huggingface dataset:
CA2 area detection from hippocampal microscope images using deep learning

In this study, we created a semantic segmentation model to recognize the Cornu Ammonis 2 (CA2) area inside the horizontal hippocampal slice section in a microscopic transparent-light image, which was only recognizable by biomarkers such as immunohistochemistry. The U-Net was modified so that we could incorporate the way how experts recognized the CA2 area. We achieved 100% accuracy and 84% precision. We built a system on an edge computing device and provided a practical microscope solution to assist neuroscientists.
Normalization Influence on ANN-Based Models Performance: A New Proposal for Features’ Contribution Analysis

Artificial Neural Networks (ANNs) are weighted directed graphs of interconnected neurons widely employed to model complex problems. However, the selection of the optimal ANN architecture and its training parameters is not enough to obtain reliable models. The data preprocessing stage is fundamental to improve the model’s performance. Specifically, Feature Normalisation (FN) is commonly utilised to remove the features’ magnitude aiming at equalising the features’ contribution to the model training. Nevertheless, this work demonstrates that the FN method selection affects the model performance. Also, it is well-known that ANNs are commonly considered a “black box” due to their lack of interpretability. In this sense, several works aim to analyse the features’ contribution to the network for estimating the output. However, these methods, specifically those based on network’s weights, like Garson’s or Yoon’s methods, do not consider preprocessing factors, such as dispersion factors, previously employed to transform the input data. This work proposes a new features’ relevance analysis method that includes the dispersion factors into the weight matrix analysis methods to infer each feature’s actual contribution to the network output more precisely. Besides, in this work, the Proportional Dispersion Weights (PWD) are proposed as explanatory factors of similarity between models’ performance results. The conclusions from this work improve the understanding of the features’ contribution to the model that enhances the feature selection strategy, which is fundamental for reliably modelling a given problem.
Insulator Surface Breakage Recognition Based on Multiscale Residual Neural Network

Insulators are one of the widely used important devices in power systems, and proposing an accurate and effective online monitoring method for insulator defects is an urgent need for intelligent and informative power systems. We propose a multiscale residual neural network (MSResNN) for insulator surface breakage recognition. The proposed multiscale convolution filter uses three different size convolution kernels for convolution filtering and performs feature maps fusion, which can enrich the spatial correlation and channel correlation of feature maps. We investigated the effects of different learning rates on the model performance and confirmed that the model performs best with learning rate of $10^{-6}$ . Compared with VGG16, Inception_v3, and Resnet50, the MSResNN model has significant advantages in detection capability and occupies less computational resources, and the detection speed can meet the industrial real-time detection requirements. Through dark–light and noise experiments, it is demonstrated that the proposed method is robust and can effectively deal with low-quality insulator images.
A Miniaturized Cavity backed Archemedian Spiral Antenna for mm-Wave Applications

Design of a UWB circularly polarized, cavity-backed Archimedean spiral antenna is presented in this paper. The antenna is designed for 18- 40 GHz frequency range with a binomially tapered parallel-strip to microstrip balun and RF foam absorbent for controlling the backscattering. The peak-measured gain is 4.5 dBi and is greater than 2 dBi for the entire band. The antenna is characterized by greater than 50 degrees 3 dB gain and axial ratio beam-widths which makes it highly suitable for airborne applications. Axial ratio is below 2 dB for the band of interest. The height of the antenna cavity is 19 mm and the diameter is 23 mm giving the antenna a low profile meeting the space requirements of any RF system.
Length of Pseudorandom Binary Sequence Required to Train Artificial Neural Network Without Overfitting

The artificial neural network (ANN) has been applied to the various fields due to its capability to process complicated nonlinear functions involving a large amount of data. A pseudorandom binary sequence (PRBS) is commonly used to train the ANN since the PRBS is easily generated by using a linear feedback shift register and has a correlation function which is peaked at zero delay but is almost zero at other delays. However, when the PRBS length is not sufficiently long (compared to the input size of the ANN), the ANN trained by the PRBS could suffer from the overfitting where the ANN describes the behavior of the training sequence very well, but does poorly on new data inputs. In this paper, we provide a minimum length of the PRBS required by the ANN to avoid the overfitting for a given input size of the ANN. For this purpose, we analyze the minimum length of the input sequence required to estimate the PRBS pattern through theoretical study. These analyses are confirmed by numerical simulation. The findings of this paper would be used to select the PRBS length for training the ANN.
A Qualitative Usability Evaluation of the Clang Static Analyzer and libFuzzer with CS Students and CTF Players

Testing software for bugs and vulnerabilities is an essential aspect of secure software development. Two paradigms are particularly prevalent in this domain: static and dynamic software testing. Static analysis has seen widespread adoption across the industry, while dynamic analysis, in particular fuzzing, has recently received much attention in academic circles as well as being used very successfully by large corporations such as Google, where for instance, over 20,000 bugs have been found and ﬁxed in the Chrome project alone. Despite these kinds of success stories, fuzzing has not yet seen the kind of industry adoption static analysis has. To get ﬁrst insights, we examine the usability of the static analyzer Clang Static Analyzer and the fuzzer libFuzzer. To this end, we conducted the ﬁrst qualitative usability evaluation of the Clang Static Analyzer [6] and libFuzzer [16]. We conducted a mixed factorial design study with 32 CS masters students and six competitive Capture the Flag (CTF) players. Our results show that our participants encountered severe usability issues trying to get libFuzzer to run at all. In contrast to that, most of our participants were able to run the Clang Static Analyzer without signiﬁcant problems. This shows that, at least in this case, the usability of libFuzzer was worse than of the Clang Static Analyzer. We make suggestions on how libFuzzer could be improved and how both tools compare.
Deterministic Approximate Counting of Polynomial Threshold Functions via a Derandomized Regularity Lemma

We study the problem of deterministically approximating the number of satisfying assignments of a polynomial threshold function (PTF) over Boolean space. We present and analyze a scheme for transforming such algorithms for PTFs over Gaussian space into algorithms for the more challenging and more standard setting of Boolean space. Applying this transformation to existing algorithms for Gaussian space leads to new algorithms for Boolean space that improve on prior state-of-the-art results due to Meka and Zuckerman [19] and Kane [13]. Our approach is based on a bias-preserving derandomization of Meka and Zuckerman’s regularity lemma for polynomials [19] using the [23] pseudorandom generator for PTFs.
Catching Phishers By Their Bait: Investigating the Dutch Phishing Landscape through Phishing Kit Detection

Off-the-shelf, easy-to-deploy phishing kits are believed to lower the threshold for criminal entrepreneurs going phishing. That is, the practice of harvesting user credentials by tricking victims into disclosing these on fraudulent websites. But, how do these kits impact the phishing landscape? And, how often are they used? We leverage the use of TLS certiﬁcates by phishers to uncover possible Dutch phishing domains aimed at the ﬁnancial sector between September 2020 and January 2021. We collect 70 different Dutch phishing kits in the un-derground economy, and identify 10 distinct kit families. We create unique ﬁngerprints of these kits to measure their prevalence in the wild. With this novel method, we identify 1,363 Dutch phishing domains that deploy these phishing kits, and capture their end-to-end life cycle – from domain registration, kit deployment, to take-down. We ﬁnd the median uptime of phishing domains to be just 24 hours, indicating that phishers do act fast. Our analysis of the deployed phishing kits reveals that only a small number of different kits are in use. We discover that phishers increase their luring capabilities by using decoy pages to trick victims into disclosing their credentials. In this paper, we paint a comprehensive picture of the tactics, techniques and procedures (TTP) prevalent in the Dutch phishing landscape and present public policy takeaways for anti-phishing initiatives.
Effects of Femtocell Ultradense Deployment on Downlink Performance in LTE Heterogeneous Networks

With huge number of smart gadgets and wireless devices being interconnected to each other, the demand for very high data bandwidth is becoming critically challenging. With such density of nodes inside wireless networks, providing high-quality service as well as wide coverage in indoor environment is a real challenge, which is due to the limited radio frequency and intense interference between nodes. As a one way to solve such problem and improve indoor service quality, femtocells have been introduced as an extension to the existing macrocell stations. Although femtocell is a promising technology, the pervasive deployment of huge number of femtocells without very tight network planning as well as coverage strategy may worsen the problem and degrade the service quality. One important problem that needs to be addressed when deploying femtocell technology in heterogenous networks (HetNets) is mitigating the various types of cross-tier and cotier interferences in between wireless cells. This study investigates the effect of unplanned ultradensity femtocell deployment in the downlink performance of two-tier heterogeneous networks in urban area based on LTE system. Instead of deploying femtocells one by one, grids of size either ( 3 × 3 ) or ( 5 × 5 ) of neighboring femtocell will be deployed inside each macrocell sector area. The simulation results show that femtocell deployment improves overall average user throughput in case of low and medium density scenarios. However, for ultradensity scenario, there is no enhancement in terms of fairness and throughput. The results confirm that this leads to high degradation for macrocell and femtocell user performance due to the severe interference between macrocells and femtocells, as well as among neighboring femtocells in each grid.
Precise and Scalable Detection of Use-after-Compacting-Garbage-Collection Bugs

Compacting garbage collection ( compact-gc ) is a method that improves memory utilization and reduces memory fragmentation by rearranging live objects and updating their references using an address table. A critical use-after-free bug may exist if an object reference that is not registered in the address table is used after compact-gc , as the live object may be moved but the reference will not be updated after compact-gc . We refer to this as a use-after-compact-gc ( use-after-cgc ) bug. Prior tools have attempted to statically detect these bugs with target-speciﬁc heuristics. However, due to their path-insensitive analysis and imprecise target-speciﬁc heuristics, they have high false-positives and false-negatives. In this paper, we present a precise and scalable static analyzer, named CGSan, for ﬁnding use-after-cgc bugs. CGSan detects use-after-cgc bug candidates by intra-procedural static symbolic taint analysis and checks their feasibility by under-constrained directed symbolic execution. To mitigate the in-completeness of intra-procedural analysis, we employ a type-based taint policy. For scalability, we propose using directed inter-procedural control-ﬂow graphs, which reduce search spaces by excluding paths irrelevant to checking feasibility, and directed scheduling, which prioritizes paths to quickly check feasibility. We evaluated CGSan on Google V8 and Mozilla SpiderMonkey, and we found 13 unique use-after-cgc bugs with only 2 false-positives while two prior tools missed 10 bugs and had 34 false-positives in total.
Fuzzy Labeled Private Set Intersection with Applications to Private Real-Time Biometric Search

The explosive growth of biometrics use (e.g., in surveillance) poses a persistent challenge to keep biometric data private without sacriﬁcing the apps’ functionality. We consider private querying of a real-life biometric scan (e.g., a person’s face) against a private biometric database. The querier learns only the label(s) of a matching scan(s) (e.g. a person’s name), and the database server learns nothing. We formally deﬁne Fuzzy Labeled Private Set Intersection (FLPSI), a primitive computing the intersection of noisy input sets by considering closeness/similarity instead of equality. Our FLPSI protocol’s communication is sublinear in database size and is concretely efﬁcient. We implement it and apply it to facial search by integrating with our ﬁne-tuned toolchain that maps face images into Hamming space. We have implemented and extensively tested our system, achieving high performance with concretely small network usage: for a 10K-row database, the query response time over WAN (resp. fast LAN) is 146ms (resp. 47ms), transferring 12.1MB; ofﬂine precomputation (with no communication) time is 0.94s. FLPSI scales well: for a 1M-row database, on-line time is 1.66s (WAN) and 1.46s (fast LAN) with 40.8MB of data transfer in online phase and 37.5s in ofﬂine precom-putation. This improves the state-of-the-art work (SANNS) by 9 − 25 × (on WAN) and 1 . 2 − 4 × (on fast LAN). Our false non-matching rate is 0.75% for at most 10 false matches over 1M-row DB, which is comparable to underlying plaintext matching algorithm.
A Centralized Win-Win Cooperative Framework for Wi-Fi and 5G Radio Access Networks

Cooperation to access wireless networks is a key approach towards optimizing the use of finite radio spectrum resources in overcrowded unlicensed bands and to help satisfy the expectations of wireless users in terms of high data rates and low latency. Although solutions that advocate this approach have been widely proposed in the literature, they still do not consider a number of aspects that can improve the performance of the users’ connections, such as the inclusion of (1) cooperation among network operators and (2) users’ quality requirements based on their applications. To fill this gap, in this paper we propose a centralized framework that is aimed at providing a “win-win” cooperation among Wi-Fi and cellular networks, which takes into account 5G technologies and users’ requirements in terms of Quality of Service (QoS). Moreover, the framework is supported by smart Radio Access Technology (RAT) selection mechanisms that orchestrate the connection of the clients to the networks. In particular, we discuss details on the design of the proposed framework, the motivation behind its implementation, the main novelties, its feasibility, and the main components. In order to demonstrate the benefits of our solution, we illustrate efficiency results achieved through the simulation of a smart RAT selection algorithm in a realistic scenario, which mimics the proposed “win-win” cooperation between Wi-Fi and cellular 5G networks, and we also discuss potential benefits for wireless and mobile network operators.
First Principle Study on the Adsorption of Hydrocarbon Chains Involved in Fischer–Tropsch Synthesis over Iron Carbides

The adsorption of n-alkyls, 1-alkenes, and n-alkanes has been studied on the χ-Fe5C2(010), Fe7C3(001), θ-Fe3C(001), and η-Fe2C(011) surfaces which have been reported as active phases for Fe-based Fischer–Tropsch catalysts. Spin-polarized density functional theory calculations with the vdW-DF2 functional were performed. The most stable adsorption configuration for n-alkyls is a bridge position for χ-Fe5C2(010), Fe7C3(001), and θ-Fe3C(001), whereas for η-Fe2C(011) the most stable configuration is on-top. The surfaces with the larger surface carbon content present a higher affinity for the adsorption of n-alkyls. For adsorption of 1-alkenes, the χ-Fe5C2(010) surface shows the strongest adsorption, with a di-σ adsorption mode. For n-alkanes, the strongest physisorption was found for the smoothest surface, i.e., the θ-Fe3C(001). The adsorption energies are found to be independent of the length of the hydrocarbon chain from a chain length of three carbon atoms for n-alkyl species, five carbon atoms for 1-alkene.
Network Threat Detection Based on Group CNN for Privacy Protection

The Internet of Things (IoT) contains a large amount of data, which attracts various types of network attacks that lead to privacy leaks. With the upgrading of network attacks and the increase in network security data, traditional machine learning methods are no longer suitable for network threat detection. At the same time, data analysis techniques and deep learning algorithms have developed rapidly and have been successfully applied to a variety of tasks for privacy protection. Convolutional neural networks (CNNs) are typical deep learning models that can learn and reconstruct features accurately and efficiently. Therefore, in this paper, we propose a group CNN models that is based on feature correlations to learn features and reconstruct security data. First, feature correlation coefficients are computed to measure the relationships among the features. Then, we sort the correlation coefficients in descending order and group the data by columns. Second, a 1D group CNN model with multiple 1D convolution kernels and 1D pooling filters is built to address the grouped data for feature learning and reconstruction. Third, the reconstructed features are input to shadow machine learning models for network threat prediction. The experimental results show that features reconstructed by the group CNN can reduce the dimensions and achieve the best performance compared to the other present dimension reduction algorithms. At the same time, the group CNN can decrease the floating point of operations (FLOP), parameters, and running time compared to the basic 1D CNN.
An Effective Algorithm for Intrusion Detection Using Random Shapelet Forest

Detection of abnormal network traffic is an important issue when builds intrusion detection systems. An effective way to address this issue is time series mining, in which the network traffic is naturally represented as a set of time series. In this paper, we propose a novel efficient algorithm, called RSFID (Random Shapelet Forest for Intrusion Detection), to detect abnormal traffic flow patterns in periodic network packets. Firstly, the Fast Correlation-based Filter (FCBF) algorithm is employed to remove irrelevant features to decrease the overfitting as well as the time complexity. Then, a random forest which is built upon a set of shapelet candidates is used to classify the normal and abnormal traffic flow patterns. Specifically, the Symbolic Aggregate approXimation (SAX) and random sampling technique are adopted to mitigate the high time complexity caused by enumerating shapelet candidates. Experimental results show the effectiveness and efficiency of the proposed algorithm.
Mobile Intercloud System for Edge Cloud Computing

Recent years have seen considerable interest in mobile cloud computing and edge cloud computing. This paper presents a mobile Intercloud system for supporting mobile cloud computing in general and edge cloud computing in particular. In essence, a mobile user with a mobile terminal can set up a virtual mobile terminal with applications and data in a central/home cloud. The virtual mobile terminal can facilitate task and computation offloading and other functions. Moreover, when a mobile terminal joins an edge cloud, the virtual mobile terminal (including required applications and data) can be migrated to enhance system efficiency and the user experience (e.g., shorter access delays). An experimental prototype has been developed for evaluating certain basic object transfer functions. To support the application transfer function, we formulate both finite- and infinite-horizon Markov decision models to determine decision policies (i.e., should an application be transferred to an edge cloud). The transfer decision depends on various factors, including transfer cost, duration associated with the edge cloud, usage probability, and usage cost in the central cloud and edge cloud. Based on the models, we obtain closed-form solutions for the decision policies, which can be expressed in meaningful formulas to provide useful insights for edge cloud computing in general. To evaluate the mobile Intercloud system for edge cloud computing, we conducted extensive evaluations, including experimental evaluation for testing the basic functions and protocols, analytical evaluation for studying the analytical models, and simulation evaluation for analyzing performance in a multiuser and multicloud environment in particular. The experimental, simulation, and analytical results provide useful insights into the design and development of the mobile Intercloud system for edge cloud computing as well as decision policies for application transfer.
Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net

Accurate particle detection is a common challenge in particle field characterization with digital holography, especially for gel secondary breakup with dense complex particles and filaments of multi-scale and strong background noises. This study proposes a deep learning method called Mo-U-net which is adapted from the combination of U-net and Mobilenetv2, and demostrates its application to segment the dense filament-droplet field of gel drop. Specially, a pruning method is applied on the Mo-U-net, which cuts off about two-thirds of its deep layers to save its training time while remaining a high segmentation accuracy. The performances of the segmentation are quantitatively evaluated by three indices, the positive intersection over union (PIOU), the average square symmetric boundary distance (ASBD) and the diameter-based prediction statistics (DBPS). The experimental results show that the area prediction accuracy (PIOU) of Mo-U-net reaches 83.3%, which is about 5% higher than that of adaptive-threshold method (ATM). The boundary prediction error (ASBD) of Mo-U-net is only about one pixel-wise length, which is one third of that of ATM. And Mo-U-net also shares a coherent size distribution (DBPS) prediction of droplet diameters with the reality. These results demonstrate the high accuracy of Mo-U-net in dense filament-droplet field recognition and its capability of providing accurate statistical data in a variety of holographic particle diagnostics. Public model address: https://github.com/Wu-Tong-Hearted/Recognition-of-multiscale-dense-gel-filament-droplet-field-in-digital-holography-with-Mo-U-net.