Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive control for a class of uncertain nonlinear pure-feedback systems based on the DSC technique

An adaptive control problem is studied for pure-feedback nonlinear systems with input saturation in this paper. Combining dynamic surface control based back-stepping technique with fuzzy approximation, the recursive controller design procedure is given for pure-feedback nonlinear systems. The problems of repeated differentiation of the virtual controller are avoided in the control presented scheme. It's proved that the ultimately bounded convergence of all the variables in the closed-loop system is guaranteed via stability analysis, and the tracking error can be made arbitrary small by adjusting the design parameters. Simulation results are shown to verify the effectiveness of the given approach.
SLADE: A Smart Large-Scale Task Decomposer in Crowdsourcing

Crowdsourcing has been shown to be effective in a wide range of applications, and is seeing increasing use. A large-scale crowdsourcing task often consists of thousands or millions of atomic tasks, each of which is usually a simple task such as binary choice or simple voting. To distribute a large-scale crowdsourcing task to limited crowd workers, a common practice is to pack a set of atomic tasks into a task bin and send to a crowd worker in a batch. It is challenging to decompose a large-scale crowdsourcing task and execute batches of atomic tasks, which ensures reliable answers at a minimal total cost. Large batches lead to unreliable answers of atomic tasks, while small batches incur unnecessary cost. In this paper, we investigate a general crowdsourcing task decomposition problem, called the Smart Large-scAle task DE composer (SLADE) problem, which aims to decompose a large-scale crowdsourcing task to achieve the desired reliability at a minimal cost. We prove the NP-hardness of the SLADE problem and propose solutions in both homogeneous and heterogeneous scenarios. For the homogeneous SLADE problem, where all the atomic tasks share the same reliability requirement, we propose a greedy heuristic algorithm and an efficient and effective approximation framework using an optimal priority queue (OPQ) structure with provable approximation ratio. For the heterogeneous SLADE problem, where the atomic tasks can have different reliability requirements, we extend the OPQ-based framework leveraging a partition strategy, and also prove its approximation guarantee. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments on representative crowdsourcing platforms.
Computational analysis of Plasmodium falciparum RNA-Seq data reveals PPIs that might be implicated in the invasion of the RBCs

In this study, differentially expressed genes for the trophozoite and schizont stages of Plasmodium falciparum's life cycle were extracted from a time series RNA-Seq gene expression experiment. About 28% of the 5,270 genes used in the experiment were found to show significant expression at these stages. Enrichment analysis using Gene Ontology implicated a total of 62 functions as highly enriched from the list of differentially expressed genes (DEGs). Some include; protein targeting to membrane, protein import, establishment of proteins localization to organelle, ribonucleic protein complex, nucleotide-excision repair and processes related to the mitochondria. A protein interaction network (PIN) for the DEGs at the schizont stage was extracted from experimental data of protein-protein interactions and supplemented with data from a protein interaction database. We predicted a number of protein-protein interactions in Plasmodium falciparum that may be implicated in invasion of the human red blood cells (RBCs). Some of these predictions are consistent with those from previous studies while quite a number of them are novel. We also identified 16 protein complexes from the PIN using the Molecular Complex Detection (MCODE) algorithm. The functional enrichment of the identified protein complexes showed functions related to gene expression, translation, RNA transport and metabolic/biological processes which have been identified to be important in the invasion process. The result from this study is meant to provide better insight into disease at hand.
Efficient 3D scan registration for hand modeling and reconstruction

In this paper, we propose a new method for 3D hand scan registration. This method includes two stages: liner blending skinning(LBS) and non-rigid ICP. A template hand mesh model is used to register to the 3D hand scans, which have large pose and shape variations. To fit different types of 3D hand scans, the template model is driven by the skeletons follow the principle of LBS to eliminate pose variations, and then non-rigid ICP is used to deform the template so that shape variations can be eliminated. Finally, template and scan model completely overlap together, and the vertices of template and scan can be matched one by one. We believe that the ability to align a template to each scan is the basis for more in-depth research in future. For example, this work can be used as basis for training pose and shape parameters. With the help of these parameters a template can be used to model any 3D hand in real life just like SMPL[7] for human and SMAL[14] for animals.
Delay analysis of new-flow setup time in software defined networks

Software Defined Networking (SDN) provides network engineers with a high-level of abstraction to manage network traffic and control the associated network resources. Unfortunately, the data-plane devices communicate with the controller for every new flow, which adds an extra overhead and causes excessive delays. Such communication relies on the probability called matching probability. In this work, we propose a mathematical model for the SDN flow-setup process with the consideration of all factors that contribute into the matching probability, such as proactive/reactive flow setup modes. Finally, we attempt at deriving the overall system capacity and blocking probability.
IL-23 enhances the malignant properties of hepatoma cells by attenuation of HNF4α

Chronic infection with hepatitis B virus (HBV) is one of the major risk factors for hepatocellular carcinoma. HBV infection can induce the expression of IL-23. However, the effects of IL-23 on carcinogenesis are rare and contradictory. To investigate the potential role of IL-23 on malignant properties of hepatoma cells, in the present study, first, we confirmed that HBV drove infected hepatoma cells to produce more IL-23. And then we found that at low concentration, human recombinant IL-23 (hrIL-23) enhanced malignant properties of hepatoma cells through increasing the proportion of stem/progenitor cells, promoting proliferation and colony formation, reducing apoptosis and inducing motility and invasivity of them. Hepatocyte nuclear factor 4 alpha (HNF4α), which is essential for liver development and hepatocyte function, was found to be downregulated in HBV integrated or transiently transfected hepatoma cells. Its expression was also decreased in cells treated by hrIL-23 or by HepG2.215 culture supernatant and this decrease could be abolished by supplementation of anti-IL-23p19 antibody. Hence, it is speculated that HBV related IL-23 can enhance malignant properties of hepatoma cells through attenuation of HNF4α. The findings identified a potential target of interventional strategies for treating hepatitis B patients through manipulation of the IL-23.
Turkish verbal multiword expressions corpus

In this study, a Turkish corpus with labeled verbal multiword expressions was built. The verbal multiword expressions in the corpus were annotated according to their subcategories. The Turkish train and test corpora that was published in PARSEME Shared Task 1.0 were updated as train and development corpora based on PARSEME Annotation Guidelines. Additionally, a new Turkish test corpus was created by following the guidelines. The corpus consists of newspaper articles on politics, world, life, art and columns. The corpus will be released in PARSEME Shared Task 1.1. The corpus will be an important source to be used in many Turkish natural languages processing applications such as syntactic parsing, machine translation and n-gram language modeling.
Approaches for reducing the computational budget of the metropolis hastings algorithm

In this study, we focus on methods for reducing the running time of the Metropolis-Hastings algorithm by computing the acceptance probability approximately only on a small subset of the available data. We introduce a simple approach coined as divide and use. With this simple approach, we demonstrate that we can achieve comparable results to that of existing approaches.
An enhanced RC5 (ERC5) algorithm based on simple random number key expansion technique

RC5 algorithm is lighweight in nature because it has low memory and low power requirement which makes it suitable to be implemented in devices with limited power and memory supply. However, it suffers from slow encryption speed compared to other encryption algorithms. The main purpose of this paper is to enhance this algorithm to increase its encryption speed through a simple yet fast random number addition-then-append key expansion technique. The enhancement includes the generation of random number to be added to the generated key which will be repeated for two rounds and later to be appended, to produce a key material. Additional blocks and bitwise operations are also included in the enhancement. Results show that the enhanced RC5 (ERC5) algorithm positively outperforms the traditional RC5 algorithm and successfully increased its encryption speed.
Security analysis of hamming codes in relay aided networks

In near future, industrial wireless sensor networks will be commonly deployed, so that the security improvements in these networks will gain much importance. With respect to the physical layer solutions, the standard system model that includes an eavesdropper is updated by adding trustful intermediate relays between legitimate nodes. This updated model is operated with a scheme based on the working principle of McEliece cryptosystem and the error correction capability of legitimate receiver. As a performance metric, the decoding error probability of eavesdropper is obtained analytically and with simulations based on the effects of noisy channels between eavesdropper and relays.
DLCP: A protocol for securing light client operation in blockchains

In blockchain, full nodes (FNs) are peers that store and verify entire chains of transactions, and light clients (LCs) are those which outsource chain verification to FNs (as they lack computing resources required to do so). In general, LCs perform simpler verification protocols, e.g. Simple Payment Verification (SPV), by offloading the execution of blockchain operations to FNs. To cope with byzantine faults (like malicious behavior), a current approach for blockchain transaction verification is requiring that LCs outsource their requests to multiple FNs, and compare received results. This approach, however, requires that LCs establish secure connections to each FN, which leads to client-side complexity and slower verification. To tackle this issue, we propose Distributed Lightweight Client Protocol (DLCP), a protocol for secure verification in blockchain. In summary, DLCP requires LCs to encrypt a request once, allowing a pre-determined set of FNs to access and process it. Through DLCP, LCs become able to verify whether FNs have agreed on the operation outcome. From some preliminary evaluation, we observed that DLCP decreased computing and communication overhead in LCs, while providing lower latency.
Using a cluster analysis method for grouping classes according to their inferred testability: An investigation of CK metrics, code coverage and mutation score

Software testing is one of the most time and resource consuming activities in every software development process. The effort to test a software is given by its testability, a quality indicator that can be indirectly measured to indicate how easily a software can be tested. Researchers have been studying the influence of object oriented metrics, such as the CK metrics, on the testability of a software. To do so, they correlate the CK metrics with the test size and also with test quality indicators, such as code coverage and mutation testing. However, those studies provide only evidence of the correlation between each metric but they do not provide any information regarding the range of values for which a reasonable amount of effort is spent or the quality of the test set considered. In this paper, we present an analysis in which we split the classes of four open source software into several clusters according to their CK metrics and show the ranges for which they have high or low testability according to their code coverage and mutation score.
State estimation for nonlinear switched systems under constrained switching

In this paper, the problem of state estimation for nonlinear discrete-time switched system is investigated. At first, the nonlinear switched system is represented as Takagi-Sugeno (T-S) fuzzy switched system, i.e., T-S fuzzy models are employed to approximate nonlinear parts. After that, a mode-dependent Luenberger-type observers under synchronous switching is employed to estimate the state of discrete-time T-S fuzzy switched system. By constructing multiply Lyapunov functions (MLFs) and common Lyapunov functions (CLFs), several sufficient conditions are established, in which the actual states of the estimated system under constrained switching (average dwell time (ADT) and mode-dependent average dwell time (MDADT) switching) and arbitrary switching can be tracked by the state of received observers, respectively. Finally, to testify the effectiveness of the proposed solution, one numerical example is provided.
Sum-Product Networks for Sequence Labeling

We consider higher-order linear-chain conditional random fields (HO-LC-CRFs) for sequence modelling, and use sum-product networks (SPNs) for representing higher-order input- and output-dependent factors. SPNs are a recently introduced class of deep models for which exact and efficient inference can be performed. By combining HO-LC-CRFs with SPNs, expressive models over both the output labels and the hidden variables are instantiated while still enabling efficient exact inference. Furthermore, the use of higher-order factors allows us to capture relations of multiple input segments and multiple output labels as often present in real-world data. These relations can not be modelled by the commonly used first-order models and higher-order models with local factors including only a single output label. We demonstrate the effectiveness of our proposed models for sequence labeling. In extensive experiments, we outperform other state-of-the-art methods in optical character recognition and achieve competitive results in phone classification.
Demonstration of ocean target detection by Tiangong-2 interferometric imaging radar altimeter

The interferometric imaging radar altimeter (InIRA) onboard Chinese Tiangong-2 Space Laboratory is a Ku-Band two-channel interferometric radar, which aims to test and validate the mechanism of wide-swath ocean topography measurement through interferometry with short baseline and at small incidence angles, as well as some key technologies, e.g. high-power solid state power amplifier (SSPA), from which accurate sea surface height (SSH) can be retrieved. The Tiangong-2 was launched on Sept. 15, 2016, and currently the InIRA works very well. In this paper, we present some typical observation results over ocean to demonstrate the capability of ocean target detection both directly and indirectly.
Two-degree-of-freedom visual tracking system with image stabilization for mobile platforms

A two-degree-of-freedom frame-based visual tracking system with image stabilization function and target tracking function is proposed and designed in this paper. By applying MEMS sensor signal processing technology, micro controller-based stepper motor control and electronic image stabilization technology based on grayscale projection, the system has good stability against large angle changes and high-frequency small-amplitude jitter. By combining the Kalman filter in the CamShift framework, the target tracking performance in the similar background is improved. The designed visual tracking device has good image stabilization performance and can perform continuously stable tracking when the target color is similar to the background.
Soft wearable device for lower limb assistance: Assessment of an optimized energy efficient actuation prototype

There is a shared trend, in engineering fields related to robotics, to privilege soft systems on behalf of hard ones. This direction stems from the desire of designing and implementing energetically efficient systems that are light, soft, wieldy, reconfigurable and with augmented dexterity. Soft wearable devices will also improve very important characteristics such as wearability and comfort. The design and implementation of a wearable system meeting these requirements is being developed in the XoSoft EU project. In this work we present the design of a soft modular energy-efficient lower limb exoskeleton. It is based on an energy efficiency analysis optimization. Novelty of this work is given by the integration of quasi-passive actuations in a soft exoskeleton. The goal is to assist-during daily tasks-subjects with low to moderate mobility impairments reducing by 10% to 30% the mechanical energy requirements and improving the gait (stability, tiredness, etc). Assistance is meant to be on hip, knee and ankle, in a unilateral or bilateral configuration. A first modular single-joint prototype is described, developed and assessed on a post-stroke patient gait. The comparison between the validation and the simulation showed a similar behavior and an energy reduction of 7.8%. The overall measured assistance given by the exoskeleton on the user's hip segment in terms of power is 9.3%.
A new family monitoring alarm system based on improved YOLO network

The traditional home remote monitoring has many problems. For example, the system can not independently identify the invading species, the system can not have targeted automatic alarm, and the accuracy of the system identification target is greatly affected by the external environment and other issues. About these problems, this paper presents a method based on deep learning, which can accurately find and distinguish the types of invaders by using the machine to train the characteristics of multiple species. This system improves the parameters of YOLO Network, it improves the processing speed, it also complete the real-time alarm, local storage and real-time remote playback. The function of the system is not affected by the external environment. In a complex environment for multiple experiments, the experimental results show that the intelligent monitoring system based on the improved YOLO network has high detection precision and fastness, and the false detection rate is 0 in the background of home remote monitoring. The program is feasible.
Fishbone-inspired soft robotic glove for hand rehabilitation with multi-degrees-of-freedom

Soft robotic gloves have shown great potential in accelerating the rehabilitation process of individuals with hand pathologies. However, most of the existing soft assistive devices allow a single degree-of-freedom (DoF) movement for each finger while independent motion of finger joints plays a crucial role in hand rehabilitation. Trying to address these challenges, a novel fishbone-inspired soft actuator with multi-DoFs is proposed in this article for the first time, to the best knowledge of the authors, and a preliminary soft glove is developed. With the assistance of the glove, the Metacarpophalangeal (MCP) and the proximal interphalangeal (PIP) joints of human fingers can bend or extend independently. In this paper, the basic concept of the actuators is illustrated in detail and the structural parameters are determined with FEM models. Additionally, several groups of experiments are conducted to demonstrate the varied motion patterns of the actuators and the bending curvature of each segment in different patterns is calculated. Lastly, the efficacy as well as the dexterity of the proposed soft glove is further validated by performing complicated gestures and conducting functional grasping tests.
Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network

Real-world networks feature weights of interactions, where link weights often represent some physical attributes. In many situations, to recover the missing data or predict the network evolution, we need to predict link weights in a network. In this paper, we first proposed a series of new centrality indices for links in line graph. Then, utilizing these line graph indices, as well as a number of original graph indices, we designed three supervised learning methods to realize link weight prediction both in the networks of single layer and multiple layers, which perform much better than several recently proposed baseline methods. We found that the resource allocation index (RA) plays a more important role in the weight prediction than other topological properties, and the line graph indices are at least as important as the original graph indices in link weight prediction. In particular, the success application of our methods on Yelp layered network suggests that we can indeed predict the offline co-foraging behaviors of users just based on their online social interactions, which may open a new direction for link weight prediction algorithms, and meanwhile provide insights to design better restaurant recommendation systems.
Research Progress and Strategy on Stand-alone DC Micro-grid with Multi-sources and Multi-loads

Compared to AC micro-grid, DC micro-grid has some advantages in improving power quality, decreasing energy consumption and other cost. Firstly, DC micro-grid with multi-sources and multi-loads are put forward. Secondly, its energy management and coordinated control structure are studied. Lastly, we focus on hybrid energy storage system (HESS), especially the HESS topology.
Gene regulatory network inference from gene expression dataset using autoencoder

Genes, specific stretches of DNA sequences, encode for gene products to perform a number of biological functions required for life. Gene expression is the process of synthesizing gene products from the genetic code. Complex biological functions are controlled by a tight regulation of these interdependent gene expressions. High-level organisms with large genomes shows perplexing patterns of regulatory interactions between large number of genes, hence, inferring the gene regulatory network from small number of gene expression profiles is highly challenging task. In this study, we propose an autoencoder architecture to explore regulatory relationships among genes from a gene expression dataset consisting of a large number of experiments. Autoencoder is trained by a gene expression dataset prepared by NIH LINCS program which consist of 100,000 mRNA measurements for the 12,320 genes. We observed that Autoencoder is capable of reproducing gene expressions fairly well (average MSE 0.0045) with only 50 hidden states. We conjecture that learned autoencoder weights (input to hidden and hidden to output) can be used to predict regulatory interactions between genes. Furthermore, we will investigate hidden state representations to check whether they conform to biological states which dictates certain gene expression responses.