Miracle-dz/UniFAR-FTUs huggingface dataset:
Application Areas of Community Detection: A Review

In the realm of today's real world, information systems are represented by complex networks. Complex networks contain a community structure inherently. Community is a set of members strongly connected within members and loosely connected with the rest of the network. Community detection is the task of revealing inherent community structure. Since the networks can be either static or dynamic, community detection can be done on both static and dynamic networks as well. In this study, we have talked about taxonomy of community detection methods with their shortages. Then we examine and categorize application areas of community detection in the realm of nature of complex networks (i.e., static or dynamic) by including sub areas of criminology such as fraud detection, criminal identification, criminal activity detection and bot detection. This paper provides a hot review and quick start for researchers and developers in community detection area.
Chemical Characterization of Interacting Genes in Few Subnetworks of Alzheimer’s Disease

A number of genes have been identified as a key player in Alzheimer’s disease (AD). Topological analysis of co-expression network reveals that key genes are mostly central or hub genes. The association between a hub gene and its neighbour genes can be derived easily using relative abundance of their expression levels. However, it is still unexplored fact that whether any hub and its neighbour genes within a sub-network exhibits any kind of proximity with respect to their chemical properties of the DNA sequences or not, that code for a sequence of amino acids. In this work, we try to make a quantitative investigation of the underlying biological facts in DNA sequential and primary protein level in mathematical paradigm. It may gives a holistic view of the interrelationships existing between hub genes and neighbour genes in few selective AD subnetworks. We define a mapping model from physicochemical properties of DNA sequence to chemical characterization of amino acid sequences. We use distribution of chemical groups present in a sequence after decoding into corresponding amino acids to investigate the fact that whether any hub genes are associated closely with its neighbour genes chemically in the subnetworks. Interestingly, our preliminary results confirm the fact the dependent genes that are coexpressed with its hub gene are also having proximity with respect to their amino acid chemical group distributions. CCS Concepts •Applied computing → Computational genomics;
Deep Homography Based Localization on Videos of Endoscopic Capsules

Endoscopic capsules are vitamin-sized devices that create 8 to 10 hour videos of the digestive tract. They are the leading diagnosing method for the small bowel, a region not easily accessible with traditional endoscopy techniques. However, these capsules do not provide localization information, even though it is crucial for the diagnosis, follow-ups and surgical interventions. Currently, the capsule localization is either estimated based on scarce gastrointestinal tract landmarks or given by additional hardware that causes discomfort to the patient and represents a cost increase. Current software methods show great potential, but still need to improve in order to overcome their limitations. In this work, a visual odometry method for capsule localization inside the small bowel is proposed.
The Subsynchronous Oscillations Caused by an LCC HVDC Line in a Power System Under the Condition of Near Strong Modal Resonance

This paper derives a multi-input multi-output closed-loop model of a power system with an LCC HVDC line. The derived model is comprised of the HVDC subsystem and the subsystem of rest of power system (ROPS). Analysis in the paper indicates that the closeness of open-loop subsynchronous oscillations (SSO) modes of two subsystems on the complex plane is a special condition of near strong modal resonance (NESMOR). Under the condition of NESMOR, strong dynamic interactions are introduced by the LCC HVDC line to degrade the SSO stability of the power system. Theoretical derivation is presented in the paper to estimate the degree of SSO stability degradation caused by the NESMOR. Based on the derivation, a method of open-loop modal analysis is proposed to detect the SSO instability risk brought about by the multivariable dynamic interactions introduced by the LCC HVDC line. An example of multimachine power system with an LCC HVDC line is presented to demonstrate and evaluate analysis and conclusions made in the paper. Dynamic interactions of the LCC HVDC line with a VSC-based HVDC line or a PMSG wind farm occurred in the example power system under the condition of NESMOR, which caused growing SSOs.
Performance Investigation of RPL Routing in Pipeline Monitoring WSNs

In this paper, the adoption of WSN and IEEE 802.15. 4 standard for the linear WSN networks were considered. The interconnection of WSNs with the Internet is possible by assigning IPv6 addresses to low-power devices. The 6LoWPAN adaption layer enables the IPv6 addresses assignment with low levels of overhead. To facilitate maximum efficiency of WSN, integrated with IoT, the routing protocol RPL was developed to be compatible with 6LoWPAN networks. The main contribution of this paper is on identifying problems at the level of routing in WSN, and a study of specific metrics used to calculate the forwarding cost between nodes in a Multi Segment Linear Pipeline Monitoring WSNs. The RPL protocol was implemented on OMNeT++ simulator with the objective of analysing the behaviors of ETX, HOP-COUNT and RSSI routing metrics. ETX achieved the best results, while RSSI has shown poor results. However, HOP-COUNT, has shown good results in terms of routing stability with fast convergence and lower delay.
Patchwork-Based Audio Watermarking Robust Against De-Synchronization and Recapturing Attacks

Watermarking is a solution for copyright protection and forensics tracking, but recapturing and de-synchronization attacks may be used to effectively remove audio watermarks. Although much effort has been made in recent years, the robustness of audio watermarking against recapturing and de-synchronization attacks is still a challenging issue. Specifically, we first construct the frequency-domain coefficients logarithmic mean (FDLM) feature of digital audio. By theoretical analysis, we conclude that the residual of the two groups’ FDLM feature is robust against recapturing attack. We then propose a robust audio watermarking method based on this feature using the patchwork framework. Compared with the method having the best robustness performance against recapturing attack, the BER value of our method is decreased by 7%. Besides that, the proposed method outperforms the state-of-the-art patchwork-based watermarking methods notably, under recapturing and post-processed with signal processing operations and de-synchronization attacks.
External Content-dependent Features for Web Credibility Evaluation

Unreliable web pages such as fake news has become a global problem in big data era. The motivation to publish fake news is often for profit; for example, earning advertisement income by putting ads on their web pages. In this paper, we focus on different usage of HTML source tags between reliable and unreliable web pages, then propose new features for predicting their credibility. The experimental result shows that our proposed features increase accuracy when used together with previously proposed Contents features.
Identification of Important Images for Understanding Web Pages

People have become increasingly dependent on the Web. However, it is difficult for visually impaired individuals to understand the content, especially if the web page contains images. The images can be more accessible by adding alt text, but it is known that alt text added through the current automation techniques are not necessarily helpful. Crowdsourcing is a promising approach for it, but adding alt text to all the images on the Web requires a tremendous amount of effort. In addition, too many alt texts of images also increase the difficulty of reading. Therefore, it is crucial to select important images for understanding web pages. This paper presents the results of our preliminary experiments to identify important images for understanding web pages. We adopted a crowdsourcing approach with two microtask designs. The results of our study demonstrated that (1) there are several types of images that are difficult to automatically assess the importance; thus, human-in-the-loop can be a promising approach to identify important ones in the types of images; and (2) there are microtask designs that can result in the similar results but different to each other in terms of other criteria. The results suggested that the identification of important images for understanding web pages is an interesting problem to address.
XLBIST: X-Tolerant Logic BIST

Logic Built-In Self-Test (LBIST) is becoming a requirement for high-complexity, high-reliability ICs which are increasingly used in the automotive field. Traditionally, LBIST can only be applied when there are no unknown simulation values (Xs) which would render the LBIST signature unusable. Eliminating all Xs in large industrial designs, especially when containing third party hard IP blocks, can be difficult, and anticipating all possible X-sources can be impractical. We present an X-tolerant LBIST solution (XLBIST) which uses compressor/decompressor structures, including Xcontrol logic, that have already been inserted in the design for scan-compression deterministic patterns. Automatic test pattern generation (ATPG) leverages these structures to generate efficient XLBIST patterns. Patterns can be generated for any number (or density) of Xs, with resulting test coverage tradeoff. Results on industrial designs with high X densities demonstrate consistent XLBIST coverage.
An AMP-Based Low Complexity Generalized Sparse Bayesian Learning Algorithm

In this paper, an approximate message passing-based generalized sparse Bayesian learning (AMP-Gr-SBL) algorithm is proposed to reduce the computation complexity of the Gr-SBL algorithm, meanwhile improving the robustness of the GAMP algorithm against the measurement matrix deviated from the independent and identically distributed Gaussian matrix for the generalized linear model (GLM). According to expectation propagation, the original GLM is iteratively decoupled into two sub-modules: the standard linear model (SLM) module and the minimum mean-square-error module. For the SLM module, we apply the SBL algorithm, where the expectation step is replaced by the AMP algorithm to reduce the computation complexity significantly. The numerical results demonstrate the effectiveness of the proposed AMP-Gr-SBL algorithm.
PriBioAuth: Privacy-Preserving Biometric-Based Remote User Authentication

Biometric-based remote user authentication (BRUA) is a useful primitive that allows an authorized user to remotely authenticate to a cloud server using biometrics. However, the existing BRUA solutions in the client-server setting lack certain privacy considerations. For example, authorized user's multiple sessions should not be linked while his identity remains anonymous to cloud server. In this work, we introduce an identity-concealed and unlinkable biometric-based remote user authentication framework, such that authorized users authenticate to an honest-but-curious server in an anonymous and unlinkable manner. In particular, we employ two non-colluding cloud servers to perform the complex biometrics matching. We formalize two new security models, including biometrics privacy and user privacy, for our proposed framework, and prove the security of the proposed framework in the standard model.
An Autonomous Developmental Cognitive Architecture Based on Incremental Associative Neural Network With Dynamic Audiovisual Fusion

Developing cognition is difficult to achieve yet crucial for robots. Infants can gradually improve their cognition through parental guidance and self-exploration. However, conventional learning methods for robots often focus on a single modality and train a pre-defined model by large datasets in an offline way. In this paper, we propose a hierarchical autonomous cognitive architecture for robots to learn object concepts online by interacting with humans. Two pathways for audio-visual information are devised. Each pathway has three layers based on the self-organizing incremental neural networks. Visual features and names of objects are incrementally learned and self-organized in an unsupervised way in sample layers, respectively, in which we propose a dynamically adjustable similarity threshold strategy to allow the network itself to control cluster rather than using a pre-defined threshold. Two symbol layers abstract the cluster results from the corresponding sample layer to form concise symbols and transmit them to an associative layer. An associative relationship between two modalities can be built in real time by binding activated visual and auditory symbols simultaneously in the associative layer. In this layer, a top-down response strategy is proposed to let robots autonomously recall another associative modality, solve conflicting associative relationships, and adjust learned knowledge from the top down. The experimental results on two objects datasets and a real task show that our architecture is efficient to learn and associate object view and name in an online way. What is more, the robot can autonomously improve its cognitive level by utilizing its own experience without enquiring with humans.
Autoencoders as Weight Initialization of Deep Classification Networks Applied to Papillary Thyroid Carcinoma

Cancer is one of the most serious health problems of our time. One approach for automatically classifying tumor samples is to analyze derived molecular information. Previous work by Teixeira et al. compared different methods of Data Oversampling and Feature Reduction, as well as Deep (Stacked) Denoising Autoencoders followed by a shallow layer for classification. In this work, we compare the performance of 6 different types of Autoencoder (AE), combined with two different approaches when training the classification model: (a) fixing the weights, after pre-training an AE, and (b) allowing fine-tuning of the entire network. We also apply two different strategies for embedding the AE into the classification network: (1) by only importing the encoding layers, and (2) by importing the complete AE. Our best result was the combination of unsupervised feature learning through a single-layer Denoising AE, followed by its complete import into the classification network, and subsequent fine-tuning through supervised training, achieving an ${F}_{1}$ score of 99.61% ±0.54. We conclude that a reconstruction of the input space, combined with a deeper classification network outperforms previous work, without resorting to data augmentation techniques.
Comparison of dimensionality reduction methods for TCM symptom information

Objectives: To research a better method for reducing the dimensions of Traditional Chinese Medicine(TCM) symptom information by comparing Principal Component Analysis (PCA), Logistics Principal Component Analysis (LPCA) and Deep Belief network Autoencoder(DBA). All methods were applied to the pneumonic symptom dataset. Method: Mean-Square Error(MSE) was used to measure the performance of models. Heat maps were used to show the weight distributions of models which contained 10, 20 or 30 features. And Scatter plots were used to show the sample distributions which are described by 2 features. Results: In the 5-layer DBA network, MSE do not always decrease as the neuronal number of the 2nd layer increase, if the number is larger than 40. 5-layer DBA models have the best performance if the number of feature $ \lt 8$. Else LPCA models have the minimal MSE. If the number of feature $ \gt 16$, 3-layer DBA models are similar to 5-layer DBA models at MSE. Compared to PCA and LPCA, 3-layer DBA has the maximum range of weights and only a few variables in each feature have weights which significantly higher or lower than the other. In samples distributions, the features of 3-layer DBA and 5-layer DBA can divide the samples into different clusters more clearly than PCA and LPCA. Conclusion: LPCA and DBA can instead of PCA on dimensionality reduction for TCM symptom information. Compared with LPCA, DBAs features represent better with the same number of features and DBAs performance is better by building a multi-layer network when the feature number is small. Index Terms-symptom information, dimensionality reduction, traditional Chinese medicine, machine learning
Hardware Accelerated DNA Sequencing

Basecalling is a core function in DNA sequencing. It is responsible for the conversion of measured date to a text representation of the DNA’s molecular make-up. Recent advances in sequencing machinery have greatly accelerated the rate at which DNA data can be gathered using miniaturized platforms. To keep up, the basecalling function requires sub-stantial computing power. To ease this burden we demonstrate an FPGA-based hardware accelerator for basecalling.
Enforcing Policy-Based Security Models for Embedded SoCs within the Internet of Things

Within complex IoT ecosystems and network structures, hard to find vulnerabilities have potential to cause significant disruption and damage. In addition, device tampering and re-purposing can threaten business models of service providers. The vulnerability surface area of the ecosystem ranges across the entire system architecture, from the cloud to the IoT device. These can be introduced at any stage of the device life-cycle, including design, programming, manufacturing, integration, operation and maintenance of the device. While threat modelling during the design phase can alleviate some potential vulnerabilities, it is more difficult or even impossible to mitigate problems for devices already in the market. A policy-based device security model is proposed as an approach, that can be enforced using hardware and software security architectures. This paper reflects on existing literature on threat modelling and how derived security models can influence the design phase. This contribution proposes that by using the threat modelling to define specific use case security policies within the security model, OEMs will be able to tailor their solution to conform to the user's security requirements. Platform vendors, on the other hand, will have reduced design costs as they can offer generic solutions for differing levels of criticality. An example scenario is provided using an industrial PLC as the attack target. While threat modelling can establish countermeasures for both the design process and policy defining, the policy can be introduced quickly, whereas the design method approach requires extensive modification to the system firmware.
Scheduling Coflows with Incomplete Information

In recent years, the coflow abstraction has received significant attentions, for its prominent ability to capture application semantics. On this basis, multiple coflow scheduling mechanisms have been proposed to minimize the coflow completion time (CCT). Currently, existing coflow scheduling mechanisms mainly belong to two categories: information-omniscient and information-agnostic. However, in data center applications, there are still quite a few cases in between where incomplete coflow information is known, and such incomplete information makes great contributions to improving the CCT performance. To address such cases, we propose IICS, a coflow scheduling algorithm based on incomplete coflow information. IICS leverages information of a coflow's arrived parts to deduce the coflow's remaining transmission time, and uses it to approximate the Minimum Remaining Time First (MRTF) heuristic. Besides, IICS allocates bandwidth by monopolization and in a maximal manner, which achieves high bandwidth utilization. Extensive simulations under realistic settings show that IICS achieves the average CCT comparable to that of the information-omniscient algorithm and the 99th percentile CCT much smaller than both information-omniscient and information-agnostic algorithms. Furthermore, IICS holds observably higher throughput and is robust to algorithm parameters.
A New Model based on Fuzzy integral for Cancer Prediction

Cancer prediction models provide an important approach to assess risk and prognosis by identifying individuals and enabling estimates of the population burden and cost of cancer. Models also may aid in the evaluation of treatments and interventions. A number of statistical and machine learning techniques have been employed to develop various cancer prediction models. Meanwhile, gene selection is very important for cancer classification. We need to deal with high-dimensional gene space and few samples. But the epistasis means that some genes maybe cover or affect other genes. Fuzzy measure can describe the interaction between genes very well. In this article, we proposed one new model based on fuzzy integral with respect to fuzzy measure for cancer prediction with sparse genes. We can obtain a group of combinations of genes with the highest fuzzy measure values. The new method is applied to two cancer data for testifying the performance. Experimental results show that the proposed model has the highest testing accuracy and F-score by comparing with several state-of-the-art methods.
A 2:8 GHz to 12:8 GHz UWB LNA Using Transformer Wide-Band Input Matching for IR-UWB Radar Applications

A cascode ultra-wideband (UWB) low noise amplifier (LNA) with a transformer for input matching was implemented in 45 nm SOI CMOS technology. The transformer is applied with high coupling (k = 0.7 to 0.8) for acquiring a stable wide band input impedance and so that a flat gain, return loss, noise figure, etc. in the whole UWB band is provided. The LNA affords a maximal power gain (S21) of 12.5 dB and a -3dB bandwidth of 10 GHz from 2.8 GHz to 12.8 GHz. It possesses low noise $(\mathrm {NF} _{\min }=2.9\mathrm {d}\mathrm {B})$ and a high 1dB input compression point (IP1dB is -6dBm to 2 dBm). The LNA occupies a small chip area of 0.14mm2 without bond pads and consumes 11.7mW from a 1 V supply.
Gripper with Thumb Adduction/Abduction Joint for Enhanced In-Hand Orientation Manipulation

A new gripper with a Thumb Adduction/Abduction joint (TAA joint) is presented in this study. The purpose of the proposed gripper is to enable the orientation control of an object along all axes using in-hand manipulation and the last wrist joint. Using TAA joint motion, it was possible to achieve not only stable grasping but also in-hand orientation control. The gripper can produce 13 out of 16 human grasp motions according to hand taxonomy, including four of the most used hand postures [1] [2]. The grasping performance was evaluated using various objects. Some of them were tools for specific tasks such as electric drill, screwdriver, and hammer. The others were objects in daily life such as ball, CD, pen, cup, wallet, and card. To evaluate the capability of in-hand orientation control, the maximum ranges of rotations were also estimated using circular cross-sectional objects. The gripper can consecutively rotate the object along the roll and yaw directions, and this was demonstrated using a spherical object.