Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault-tolerant control method of robotic arm based on machine vision

We proposed a fault-tolerant control method based on machine vision to enhance the reliability ofthe robotic arm in the radiation environment. Firstly, we introduced the joint angle visual detection method as an angle detection backup solution, which was used to timely detect the joint angle of the robotic arm in case of encoder fault. Next, we introduced an encoder fault diagnosis mechanism, and the difference between the machine visual feedback value and corresponding encoder feedback values was used as the basis of encoder fault. When the encoder failed, it could be switched to the joint angle visual detection in real time. With the Canny edge detection and the Probabilistic Hough transform, the method could extract the identification tag of robotic arm joint image from the front-end camera, and hence the joint angle value was acquired. Finally, in the fault-tolerance experiment, the proposed approach found the encoder failure and switched detection method, and the scheduled task continued to the end. The error about the angle detection based on the machine vision would not exceed ± 0.008.
Rifaximin as a rare cause of rhabdomyolysis in cirrhosis

Rifaximin has only been rarely reported to cause rhabdomyolysis. When physical and nonphysical etiologies have been excluded, thorough review of the patient’s medication list is necessary. In cirrhotics, the potential harm of rifaximin in treatment or prophylaxis of hepatic encephalopathy should be recognized.
Information System Assurance for Enterprise Resource Planning Systems: Unique Risk Considerations

Enterprise Resource Planning (ERP) systems inherently present unique risks due to tightly linked interdependencies of business processes, relational databases, and process reengineering. Knowledge of such risks is important in planning and conducting assurance engagements of the reliability of these complex computer systems. Yet, there is little empirical evidence on this issue. To examine this topic, a semi‐structured interview study was conducted with 30 experienced information systems auditors (from 3 of the Big 5 firms) who specialize in assessing risks for ERP systems. This approach allowed us to obtain detailed information about participants' views and client experiences. The results indicate that the implementation process of ERP systems has an important impact on system reliability. Further, interviewees identified a number of common implementation problems (e.g., improperly trained personnel and inadequate process reengineering efforts) that result in heightened risks. Interviewees also reported .
Energy profile representation in vector space

Word2vec is a computationally efficient way to represent words in vector space. Word embeddings are now considered an integral part of many Natural Language Processing problems. Its applicability has been successfully extended to other sequential problems, mainly in the discrete space. In this paper, the applicability of word2vec in time-series is examined, specifically for energy related problems. A novel framework, called Energy2vec, is presented and future improvements are discussed.
A New Simple Chaotic Lorenz-Type System and Its Digital Realization Using a TFT Touch-Screen Display Embedded System

This paper presents a new three-dimensional autonomous chaotic system. The proposed system generates a chaotic attractor with the variation of two parameters. Analytical and numerical studies of the dynamic properties to generate chaos, for continuous version (CV) and discretized version (DV), for the new chaotic system (NCS) were conducted. The CV of the NCS was implemented by using an electronic circuit with operational amplifiers (OAs). In addition, the presence of chaos for DV of the NCS was proved by using the analytical and numerical degradation tests; the time series was calculated to determine the behavior of Lyapunov exponents (LEs). Finally, the DV of NCS was implemented, in real-time, by using a novel embedded system (ES) Mikromedia Plus for PIC32MX7 that includes one microcontroller PIC32 and one thin film transistor touch-screen display (TFTTSD), together with external digital-to-analog converters (DACs).
Multipopulation evolution framework for multifactorial optimization

Multifactorial 1 Optimization (MFO) has been attracting considerable attention in the evolutionary computation community. In this paper, we propose a general multi-population evolution framework (MPEF) for MFO, wherein each population has its own random mating probability (rmp) and is used for its own task. The benefits of using MPEF are twofold: 1) Various well-developed evolutionary algorithms (EAs) can be easily embedded into MPEF for solving the task(s) of MFO problems; 2) Different populations can implement different genetic material transfers. Moreover, for instantiation, we embed a powerful differential evolution algorithm, namely SHADE, into MPEF to form a multipopulation DE algorithm (MPEF-SHADE) for solving MFO problems. The experimental results on nine MFO benchmark problems show that MPEF-SHADE is significantly better than or at least competitive with other multifactorial evolution algorithms, such as MFEA, MFDE, MFPSO and AMA.
Exploring the Limitations of Peripheral Blood Transcriptional Biomarkers in Predicting Influenza Vaccine Responsiveness

Systems biology has been recently applied to vaccinology to better understand immunological responses to the influenza vaccine. Particular attention has been paid to the identification of early signatures capable of predicting vaccine immunogenicity. Building from previous studies, we employed a recently established algorithm for signature-based clustering of expression profiles, SCUDO, to provide new insights into why blood-derived transcriptome biomarkers often fail to predict the seroresponse to the influenza virus vaccination. Specifically, preexisting immunity against one or more vaccine antigens, which was found to negatively affect the seroresponse, was identified as a confounding factor able to decouple early transcriptome from later antibody responses, resulting in the degradation of a biomarker predictive power. Finally, the broadly accepted definition of seroresponse to influenza virus vaccine, represented by the maximum response across the vaccine-targeted strains, was compared to a composite measure integrating the responses against all strains. This analysis revealed that composite measures provide a more accurate assessment of the seroresponse to multicomponent influenza vaccines.
Adaptive Neural Network Control of Serial Variable Stiffness Actuators

This paper focuses on modeling and control of a class of serial variable stiffness actuators (SVSAs) based on level mechanisms for robotic applications. A multi-input multi-output complex nonlinear dynamic model is derived to fully describe SVSAs and the relative degree of the model is determined accordingly. Due to nonlinearity, high coupling, and parametric uncertainty of SVSAs, a neural network-based adaptive control strategy based on feedback linearization is proposed to handle system uncertainties. The feasibility of the proposed approach for position and stiffness tracking of SVSAs is verified by simulation results.
Implementation of Deep Deterministic Policy Gradients for Controlling Dynamic Bipedal Walking

A control system for bipedal walking in the sagittal plane was developed in simulation. The biped model was built based on anthropometric data for a 1.8 m tall male of average build. At the core of the controller is a deep deterministic policy gradient (DDPG) neural network that was trained in GAZEBO, a physics simulator, to predict the ideal foot placement to maintain stable walking despite external disturbances. The complexity of the DDPG network was decreased through carefully selected state variables and a distributed control system. Additional controllers for the hip joints during their stance phases and the ankle joint during toe-off phase help to stabilize the biped during walking. The simulated biped can walk at a steady pace of approximately 1 m/s, and during locomotion it can maintain stability with a 30 kg·m/s impulse applied forward on the torso or a 40 kg·m/s impulse applied rearward. It also maintains stable walking with a 10 kg backpack or a 25 kg front pack. The controller was trained on a 1.8 m tall model, but also stabilizes models 1.4–2.3 m tall with no changes.
FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis

The epistasis is prevalent in the SNP interactions. Some of the existing methods are focused on constructing models for two SNPs. Other methods only find the SNPs in consideration of one-objective function. In this paper, we present a unified fast framework integrating adaptive ant colony optimization algorithm with multiobjective functions for detecting SNP epistasis in GWAS datasets. We compared our method with other existing methods using synthetic datasets and applied the proposed method to Late-Onset Alzheimer’s Disease dataset. Our experimental results show that the proposed method outperforms other methods in epistasis detection, and the result of real dataset contributes to the research of mechanism underlying the disease.
Extracting Linearization Equations from Noisy Sources

This note was originally written under the name On the Security of HMFEv and was submitted to PQCrypto 2018. The author was informed by the referees of his oversight of an eprint work of the same name by Hashimoto, see eprint article /2017/689/, that completely breaks HMFEv, rendering the result on HMFEv obsolete. Still, the author feels that the technique used here is interesting and that, at least in principal, this method could contribute to future cryptanalysis. Thus, with a change of title indicating the direction in which this work is leading, we present the original work with all of its oversights intact and with minimal correction (only references fixed). At PQCRYPTO 2017, a new multivariate digital signature based on Multi-HFE and utilizing the vinegar modifier was proposed. The vinegar modifier increases the Q-rank of the central map, preventing a direct application of the MinRank attack that defeated Multi-HFE. The authors were, therefore, confident enough to choose aggressive parameters for the Multi-HFE component of the central map (with vinegar variables fixed). Their analysis indicated that the security of the scheme depends on the sum of the number of variables k over the extension field and the number v of vinegar variables with the individual values being unimportant as long as they are not “too small. ” We analyze the consequences of this choice of parameters and derive some new attacks showing that the parameter v must be chosen with care.
Cryptographic Constructions Supporting Implicit Data Integrity

We study a methodology for supporting data integrity called ‘implicit integrity’ and present cryptographic constructions supporting it. Implicit integrity allows for corruption detection without producing, storing or verifying mathematical summaries of the content such as MACs and ICVs, or any other type of message expansion. As with authenticated encryption, the main idea behind this methodology is that, whereas typical user data demonstrate patterns such as repeated bytes or words, decrypted data resulting from corrupted ciphertexts no longer demonstrate such patterns. Thus, by checking the entropy of some decrypted ciphertexts, corruption can be possibly detected. The main contribution of this paper is a notion of security which is associated with implicit integrity, and which is different from the typical requirement that the output of cryptographic systems should be indistinguishable from the output of a random permutation. The notion of security we discuss reflects the fact that it should be computationally difficult for an adversary to corrupt some ciphertext so that the resulting plaintext demonstrates specific patterns. We introduce two kinds of adversaries. First, an input perturbing adversary performs content corruption attacks. Second an oracle replacing adversary performs content replay attacks. We discuss requirements for supporting implicit integrity in these two adversary models, and provide security bounds for a construction called IVP, a three-level confusion diffusion network which can support implicit integrity and is inexpensive to implement.
A Hybrid Constraints Handling Strategy for Multiconstrained Multiobjective Optimization Problem of Microgrid Economical/Environmental Dispatch

Microgrid (MG) economical/environmental dispatch (MGEED) problem is a complex multiobjective optimization topic. Since the generators are diversified and the operation mode changes frequently, the MGEEED problem always has different types of constraints, such as the load balance constraints and the ramp rates constraints, which make it a nonlinear, nonconvex optimization problem. In this paper, the mathematical model of a typical MG system applied in northwest China is established. Then, a hybrid constraints handling strategy (HCHS) based on nondominated sorting genetic algorithm II (NSGAII) is proposed to deal with the typical constraints, by which the constraints violations can be removed in several steps during the evolutionary process. A dimensionality reduction method is introduced to simplify the optimization model. And an individual repair approach is designed for the violations of ramp rates constraints. In order to balance the weights of various types of constraints, the process of constraints handling in standard NSGAII is revised. Thereafter, HCHS-NSGAII is applied to some typical MGEED problems, considering all kinds of typical constraints. The results show that HCHS-NSGAII can obtain feasible Pareto sets with satisfactory convergence and distribution, which is efficient in handling complex practical industrial MGEED problems with the change of constraints combinations.
Stairways detection based on approach evaluation and vertical vanishing point

Stair region detection and distance estimation from a stair image are challenging activities to support visually impaired navigation safely in unknown environments. In this paper, a framework is proposed for detecting stair region from a stair image utilising some natural and unique property of a stair. One unique property of them is, every stair step's beginning and ending horizontal edge point intersects with two vertical edge points creating three connected point (TCP). The TCPs are used to validate the stair edge segments and calculate the vertical vanishing point to justify the stair edges. This justification ensures that validated edge segments are arranged in an increasing parallel order which is the other unique property of a stair. These increasing edge segments are verified by utilising the y coordinate value of the vanishing point and the detection of stair candidate region is confirmed by these properties. In addition, the triangular similarity is used for distance estimation from camera to stair. The proposed framework is tested using various stair images under a variety of conditions and results are presented to demonstrate the efficiency and effectiveness.
On Consistency of Cross-Approximate Entropy in Cardiovascular and Artificial Environments

Cross-approximate entropy (XApEn) quantifies the mutual orderliness of simultaneously recorded time series. Despite being derived from the firmly established solitary entropies, it has never reached their reputation and deployment. The aim of this study is to identify the problems that preclude wider XApEn implementation and to develop a set of solutions. Exact expressions for XApEn and its constitutive parts are derived and compared to values estimated from artificial data. This comparison revealed vast regions within the parameter space that do not guarantee reliable probability estimation, making XApEn estimates inconsistent. A simple correction to one of the XApEn procedural steps is proposed. Three sets of formulae for joint parameter selection are derived. The first one is intended to maximize threshold profile. The remaining ones minimize XApEn instability according to the strong and weak criteria. The derived expressions are verified using cardiovascular signals recorded from rats (long signals) and healthy volunteers (short clinical signals), proposing a change of traditional parameter guidelines.
Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN) is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.
Klick Labs at CL-SciSumm 2018

The CL-SciSumm 2018 shared task is designed to further the state-of-the-art in constructing summaries of research papers. Participating systems extract focused information from reference papers given their citing papers in order to construct a summary. To that end, given a citing sentence, we would need to know what concept (a result, method, or inference) is the citing sentence referring to in the reference paper. We explore the efficacy of word embedding based similarity functions to find relevant sentences from reference papers that are likely being referred to by citing sentences (citances). We also classify the sentences into appropriate discourse facets. Our preliminary findings indicate rank optimization for returning top sentences is useful for this task, and using term-IDF weights for averaging term embeddings improves performance.
Effective image retrieval based on hybrid features with weighted similarity measure and query image classification

An effective image retrieval needs efficient extraction of low-level features, and for this various methods have been recently proposed. Most of these methods use the histogram or some variation for representing colour and other descriptors which require significant amount of space and extra similarity calculation. Here, an efficient content based image retrieval (CBIR) system is proposed, which is based on the fusion of chromaticity-colour moments, and colour co-occurrence-based small dimension features using inverse variance weighted similarity measure. In this measure, property of the varying weights reduces the effect of redundancy and effectively retrieves relevant images. In addition, this paper also proposes a supervised query image classification and retrieval model by filtering out irrelevant class images using multiclass SVM classifier. Basically, this model recovers the category of query images, and this successful categorisation of images significantly enhances the performance and searching time of retrieval system. Descriptive comparative analyses confirm the effectiveness of this work. We obtained 83.83% and 76.9% average precision for 12 and 20 images retrieval using weighted similarity measure together with 85.6% average precision and 84.4% recall for classification framework.
An efficient method for batch updates in OPTICS cluster ordering

DBSCAN is one of the popular density-based clustering algorithms, but requires re-clustering the entire data when the input parameters are changed. OPTICS overcomes this limitation. In this paper, we propose a batch-wise incremental OPTICS algorithm which performs efficient insertion and deletion of a batch of points in a hierarchical cluster ordering, which is the output of OPTICS. Only a couple of algorithms are available in the literature on incremental versions of OPTICS. This can be attributed to the sequential access patterns of OPTICS. The existing incremental algorithms address the problem of incrementally updating the hierarchical cluster ordering for point-wise insertion/deletion, but these algorithms are only good for infrequent updates. The proposed incremental OPTICS algorithm performs batch-wise insertions/deletions and is suitable for frequent updates. It produces exactly the same hierarchical cluster ordering as that of classical OPTICS. Real datasets have been used for experimental evaluation of the proposed algorithm and results show remarkable performance improvement over the classical and other existing incremental OPTICS algorithms.
Glycemic Control, Hand Activity, and Complexity of Biological Signals in Diabetes Mellitus

Both glycemic control and handgrip strength affect microvascular function. Multiscale entropy (MSE) of photoplethysmographic (PPG) pulse amplitudes may differ by diabetes status and hand activity. Of a middle-to-old aged and right-handed cohort without clinical cardiovascular disease, we controlled age, sex, and weight to select the unaffected (no type 2 diabetes, the well-controlled diabetes (HbA1c < 8%, ), and the poorly controlled diabetes (HbA1c ≥ 8%, ) groups. MSEs were calculated from consecutive 1,500 PPG pulse amplitudes of bilateral index fingertips. The and large-scale MSEs were defined as the average of scale 1 (), scales 2–4 (), and scales 5–10 (), respectively. Intra- and intergroups were compared by one- and two-sample -tests, respectively. The dominant hand was lower in the poorly controlled diabetes group than the well-controlled diabetes and the unaffected (1.28 versus 1.52 and 1.56, and 0.001, resp.) groups, whereas the nondominant hand was lower in the well- and poorly controlled diabetes groups than the unaffected group (1.35 and 1.29 versus 1.58, and 0.005, resp.). The of dominant hand was higher than that of nondominant hand in the well-controlled diabetes (1.35 versus 1.10, ). In conclusion, diabetes status and hand dominance may affect the MSE of PPG pulse amplitudes.
Blocking Music Metadata from Heterogenous Data Sources

Entity resolution or object matching describes the assignment of different objects to each other that describe the same object of the real world. It is used in a variety of technical systems, e.g. systems that fuse different data sources. Blocking is used in this context as an approach to reduce the total amount of comparisons by grouping similar objects in the same cluster and dissimilar objects in different clusters. As a result only the objects of the same clusters have to be compared to each other. To deal with noise, for instance spelling errors, that can result from different heterogeneous data sources, various blocking approaches exist that may add or remove redundancy to the data. In this paper we propose a system that utilizes a derivative of the standard blocking technique to compute correspondences between objects as starting points for a graph matching process. The blocking technique, which usually relies on identity of blocking keys derived from attributes, is modified to cope with heterogenous source data with few attributes suitable for matching. A common criticism of standard blocking is low efficiency, since the block sizes are unbalanced with regard to the number of contained entities. We take precautions to keep the efficiency high by reducing the size and amount of large partitions.