Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Diagnosis of Automobile Rear Axle Based on Wavelet Packet and Support Vector Machine

This paper presents a fault diagnosis method of automobile rear axle based on wavelet packet analysis (WPA) and support vector machine (SVM) classifier. By Fourier transformation we find out the frequency band that can mostly reflect the rear axle failure state and use wavelet packet to decompose and reconstruct the vibration signals of rear axle, then extract each band’s energy and the variance, standard deviation, skewness, kurtosis of the specific frequency band to constitute a feature vector. We use the feature vectors which are come from some pieces of normal and abnormal samples to train support vector machine classifier for obtaining the best classification，at the same time, discuss the optimization of SVM parameters. Application shows that the method is effective in real time fault diagnosis for the automobile rear axle and has a strong anti-interference ability in different working conditions.
Moving bounding boxes and incremental synthesis for dynamic obstacles

—While the use of formal synthesis for robotics problems in which the environment may act adversarially provides for exact—rather than probabilistic—correctness of controllers, such methods are impractical when the adversary can move freely in a large portion of the workspace. As is well-known, this is due to exponential growth in the state space with the addition of each new problem variable. Furthermore, such an approach is overly conservative because most conﬁgurations will not be reached in typical runs. Rather than entirely abandon the discrete game view, we propose a combined method that ensures exact satisfaction of a given speciﬁcation, expressed in linear temporal logic, while providing a lower bound on robot-obstacle distance throughout execution. Our method avoids explicit encoding of the moving obstacle and thus substantially reduces the reactive synthesis problem size, while allowing other nondeterministic variables to still be included in the speciﬁcation. Our approaches centers on modeling obstacle motion as changes in the presence of a virtual static obstacle, and performing incremental synthesis in response. The algorithm is tested in application to a planar surveillance task.
Survey of Approaches for Postprocessing of Static Analysis Alarms

Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the postprocessing approaches is, however, missing. In this article, we review 130 primary studies that propose postprocessing of alarms. The studies are collected by combining keywords-based database search and snowballing. We categorize approaches proposed by the collected studies into six main categories: clustering, ranking, pruning, automated elimination of false positives, combination of static and dynamic analyses, and simplification of manual inspection. We provide overview of the categories and sub-categories identified for them, their merits and shortcomings, and different techniques used to implement the approaches. Furthermore, we provide (1) guidelines for selection of the postprocessing techniques by the users/designers of static analysis tools; and (2) directions that can be explored by the researchers.
A Generalised Signature Method for Multivariate Time Series Feature Extraction

The ‘signature method’ refers to a collection of feature extraction techniques for multivariate time series, derived from the theory of controlled differential equations. There is a great deal of ﬂexibility as to how this method can be applied. On the one hand, this ﬂexibility allows the method to be tailored to speciﬁc problems, but on the other hand, can make precise application challenging. This paper makes two contributions. First, the variations on the signature method are uniﬁed into a general approach, the generalised signature method , of which previous variations are special cases. A primary aim of this unifying framework is to make the signature method more accessible to any machine learning practitioner, whereas it is now mostly used by specialists. Second, and within this framework, we derive a canonical collection of choices that provide a domain-agnostic starting point. We derive these choices as a result of an extensive empirical study on 26 datasets and go on to show competitive performance against current benchmarks for multivariate time series classiﬁcation. Finally, to ease practical application, we make our techniques available as part of the open-source [redacted] project.
An exploratory research analysis on Brain Machine/Computer Interface

— BCI and its potential to enhance human cognitive abilities are investigated in this article. The findings indicate that BCI is one among multiple paths to super intelligence. BCI’s have potential to help humans with various neuro and mental functional disorders when utilized as neuroprosthetic. BCI ’ s provide humans with communication channels that do not depend on peripheral nerves and muscles [1], BCI converts intention-driven neural activity into a control signal that performs useful tasks. BCI paves path for Brain Driven Robot Controls [2] based on brain activity. Brain-actuated/assisted robot control brings benefits especially in difficult scenarios where robots cannot perform all functions successfully without human assistance, For eg. , Dangerous missions with possible sensors and algorithms failure. Computer translation algorithms are used in modeling experiments to translate Brain activity signal to Device Command and controls to perform an activity, Transfer functions part of translation algorithms are classified into linear and non-linear. Linear Transfer functions are Linear Equations whereas Non-Linear Transfer functions are neural Networks. The idea of BCI may seem absurd at first glance, but it is based on decades of experimental study and is supported by scholars, scientists, and entrepreneurs such as Stephen Hawking and Elon Musk [3].
Self-Regulation among Students with Learning Disabilities in English Language and its Relationship to Some Variables

The present study aimed at identifying the level of self-regulation among students with learning disabilities in English language in Irbid Governorate and its relationship with the variables of gender and school grade. The study sample consisted of (380) male and female students in the elementary stage. The study used the descriptive approach and developed the self-regulation scale as an instrument to collect data. The content validity and reliability of the instrument were ensured using the Cronbach Alpha and repetition methods. The results showed that there were statistically significant differences in the level of self-regulation among those with learning disabilities due to the gender variable in favor of males, and to the school grade variable in favor of the second primary grade.
Unsupervised Synonym Extraction for Document Enhancement in E-commerce Search

The vocabulary gap between search queries and product descriptions is an important problem in modern e-commerce search engines. Most of the existing methods deal with the vocabulary gap issues by rewriting user-input queries. In this work, we describe another way to address vocabulary gap issues in the e-commerce search systems. In particular, we propose an unsupervised synonym extraction framework for document enhancement. Comparing to existing methods, the purposed framework has two main differences: 1) instead of extending and rewriting user-input queries, we make the synonym tokens searchable by adding them into the text descriptions of products; 2) the whole process is unsupervised and doesn’t require any human labels. A two-phase unsupervised synonym discovery framework is proposed for extracting synonym rules from the search log data. We demonstrate the effectiveness of our approach by two experiments: 1) we do online A/B testing experiments in multiple countries, which show significant improvements in key business metrics; 2) we conduct a human audit to evaluate the quality of the extracted synonym rules, which indicates that 85.5% of the extracted rules are high quality pairs.
Research on MIN-WLI Interference Alignment Algorithm Based on Symbol Detection Assistance

In order to mitigate inter-cell interference (ICI) in cellular networks, now the academic community have been discussed an algorithm to eliminate inter-cell interference namely interference alignment (IA). Interference alignment is an interference cancellation technique that achieves good system capacity at high signal-to-noise ratios. However, in order to achieve interference alignment, the transmitter and receiver must be jointly designed, which is often difficult to achieve. In addition, a large number of iterative transceiver design methods have been proposed and studied on the basis of different standards optimizing. This paper mainly studies the Minimum Weighted Leakage Interference (MIN-WLI). Here, we propose a symbol detection technique that assists in the alignment of minimum leakage interference[1-3]. It is a symbol detection algorithm based on the joint design of iterative transceivers with minimum leakage interference. The system simulation shows that the interference alignment method has better system performance than the traditional minimum leakage interference method.
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection

While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on several slot-filling, question-answering (QA), fact verification, and entity-linking tasks. In this paper, we implement an efficient, data-programming technique that enriches training data with KB-derived context and improves transformer utilization of encoded knowledge when fine-tuning for a particular QA task, namely answer sentence selection (AS2). Our method outperforms state of the art transformer approach on WikiQA and TrecQA, two widely studied AS2 benchmarks, increasing by 2.0% p@1, 1.3% MAP, 1.1% MRR, and 4.4% p@1, 0.9% MAP, 2.4% MRR, respectively. To demonstrate our improvements in an industry setting, we additionally evaluate our approach on a proprietary dataset of Alexa QA pairs, and show increase of 2.3% F1 and 2.0% MAP. We additionally find that these improvements remain even when KB context is omitted at inference time, allowing for the use of our models within existing transformer workflows without additional latency or deployment costs.
Noise exposure of opera musicians

A previous noise exposure survey involving the Canadian Opera Company (COC) orchestra followed several musicians over the course of two operas (Lee, Behar, Wong, and Kunov, 2005) and found that the musicians were not at risk. Since then, the COC has moved to a new building. Thus, a new study was conducted to examine whether the new venue would have an effect on noise exposure. Measurements were taken during three performances of five different operas using five dosimeters attached to music stands located throughout the orchestra pit. While the exposure levels were found to be different across operas and instrument sections, these effects were independent. In general, the exposure levels were slightly lower in the new building for all musicians with woodwinds showing a large decrease. These decreases are likely due to a larger and less enclosed orchestra pit along with fewer brass musicians playing under the pit roof. While the present study did not find evidence for a risk of hearing loss for work performed in the new venue, the musicians engage in a variety of activities outside the COC that when added to their COC work may pose a risk of noise induced hearing loss.
Eight Weeks of Swimming Training and CBD Oil Consumption Downregulates the Expression of MAPK and PPARα Genes in the Heart Tissue of Myocardial Infarction Rats

1. PhD student, Department of Physical Education and Sport Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran 2. Assistant Professor, Department of Physical Education and Sport Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran 3. Associate Professor, Department of Physical Education and Sport Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
RECOVERING DELETED DATA FROM ANDROID DEVICE USING ADB COMMAND

: The smart phone market is growing very rapidly. Now a days technologies are updating everyday which are becoming direct targets of criminals and hackers. Mobile contains a lot of important, sensible and sensitive data which is of an investigator’s attention. There are following internal and external data available on Android devices like SMS, MMS, emails, call logs, contacts, gallery photos, calendars, notes, browser history, GPS locations, passwords, and data stored on SD cards, and internal memory data etc. Thus, it is necessary that with the help of open and commercial tools Investigators are aware of all the techniques and method used for extracting data from Android Device. ADB (USB Debugging) method which utilizes a built-in protocol within the operating system. This paper proposed the recovering of deleted data extraction of android based smartphones iball Andi4.5M enigma version 4.4. 2 specified with adb command tools, and paper highlights various commands available in terms of physical acquisition.
Equity Pledge and Innovation Performance Grouping Research Based On Equity Structure

Innovation transformation, as a major issue for Chinese enterprises at present, is a key issue that the China continues to pay attention to and attach importance to. This paper tries to take the individual behavior—equity pledge as an entry point, pays attention to the impact of shareholder's equity pledge on the investment of enterprise research and development after the equity pledge, and also examines how the impact of equity pledge on enterprise research and development investment is different under different equity structures. In this paper, the A-share manufacturing listed companies were selected for 20142018 as a research sample, the empirical research are carried out by the method of multiple linear regression. It is found that equity pledge has a negative impact on the research and development of enterprises, compared with non-state-owned enterprises, the r&d investment of state-owned enterprises is more affected by equity pledge, and If the enterprise's equity concentration is higher, the less its research and development investment is affected by the equity pledge. Keywords—Innovation transformation; equity pledge; ownership structure; r&d investment
Real Time Twitter Sentiment Analysis

— Sentiment analysis of social media data includes attitudes, evaluations, and emotions, which can be thought of as a way of thinking. It is extremely tough to comprehend and classify the enormous collection of papers into good and bad elements. Twitter, Facebook, and Instagram, for example, give a forum for gathering information on people's feelings and opinions. Social media platforms are increasingly being used by businesses to provide various services and communicate with customers. Attitude Analysis (SA) divides the polarity of a set of tweets into positive and negative categories in order to better understand public sentiment. The goal of this work is to do sentiment analysis on real-time 2019 election twitter data using the feature selection model word2vec and the sentiment classification technique random forest. When compared to traditional methods like BOW and TF-IDF, Word2vec plus Random Forest enhances sentiment analysis accuracy dramatically.
Biotechnological Advances in Luciferase Enzymes

This chapter explores the history of the bioengineering advances that have been applied to common luciferase enzymes and the improvements that have been accomplished by this work. The primary focus is placed on firefly luciferase (FLuc), Gaussia luciferase (GLuc), Renilla luciferase (RLuc), Oplophorus luciferase (OLuc; NanoLuc), and bacterial luciferase (Lux). Beginning with the cloning and exogenous expression of each enzyme, their step-wise modifications are presented and the new capabilities endowed by each incremental advancement are highlighted. Using the historical basis of this information, the chapter concludes with a pro-spective on the overall impact these advances have had on scientific research and provides an outlook on what capabilities future advances could unlock.
Low power techniques for modular processors

Discusses highly specialized, modern electronics including powerful microcontroller devices with specialized peripherals. Without this technology, products would not have been manufacturable in a form that would be accepted by the general public. The imaginative use of technology and its rapid developments will continue to enable new products and new opportunities for old products. <<ETX>>
DESIGNED ANISOTROPY IN BRITTLE CERAMICS: MICA AS A MODEL SYSTEM

The contents of this chapter are based on work from the journal article "Obreimoff revisited: Controlled heterogeneous fracture through the splitting of mica" by M.T. Johnson, N.R. Brodnik, T. Ekeh, K. Bhattacharya, and K.T. Faber. This article has been accepted to the journal Mechanics of Materials and is pending publication. [1] M.T. Johnson and N.R. Brodnik were both listed as first authors on this publication, as they collaborated on both the experimental design as well as the testing of samples and production of figures.
Development of rapid-hardening ultra-high strength cementitious composites using superzeolite and N-C-S-H-PCE alkaline nanomodifier

It is shown that high operational reliability of structural materials, in particular at high temperatures, is achieved through the use of ultra-high strength cement composites. Studies of various types of Portland cements with mineral additives of the CEM II/A type have established that a stone based on Portland cement with superzeolite is the most resistant to high temperatures. It has been proven that due to the "self-autoclaving" effect, the strength of a stone based on CEM II/A-P 42.5 R is 1.2–1.3 times higher than a stone based on other types of CEM II/A. To obtain fast-hardening cement composites, a nanotechnological approach based on the use of sol-gel technology has been implemented. Using the methods of IR spectroscopy, electron microscopy, the fact of obtaining, by the chemical method of synthesis, an alkaline nanomodifier N-C-S-H-PCE, which is a nano–liquid based on nano-core seeds of sodium/calcium hydrosilicates, has been proved. It has been confirmed that the introduction of the alkaline nanomodifier N-C-S-H-PCE provides a significant intensification of the early structure formation processes in the paste based on Portland cement with superzeolite (after 12 hours, 24 hours and 28 days, the strength is 16.9; 30.5 and 104.1 MPa). It has been established that the complex combination of Portland cement with superzeolite, corundum aggregate, basalt fiber and alkaline nanomodifier provides rapid-hardening of ultra-high strength cement composites (T=400 °C) with improved operational properties. Thus, there is reason to assert the feasibility of developing rapid-hardening ultra-high strength cementitious composites. This solves the problems associated with the need to increase their early strength and performance. As a result, it is possible to carry out repair work to protect equipment from abrasive wear at elevated temperatures
Asymptotic Analysis of Meta-learning as a Recommendation Problem

Meta-learning tackles various means of learning from past tasks to perform new tasks better. In this paper, we focus on one particular statement of meta-learning: learning to recommend algorithms. We focus on a ﬁnite number of algorithms, which can be executed on tasks drawn i.i. d. according to a “meta-distribution”. We are interested in generalization performance of meta-predict strategies, i.e., the expected algorithm performances on new tasks drawn from the same meta-distribution. Assuming the perfect knowledge of the meta-distribution ( i.e., in the limit of a very large number of training tasks), we ask ourselves under which conditions algorithm recommendation can beneﬁt from meta-learning, and thus, in some sense, “defeat” the No-Free-Lunch theorem. We analyze four meta-predict strategies: Random , Mean , Greedy and Optimal . We identify optimality conditions for such strategies. We also de-ﬁne a notion of meta-learning complexity as the cardinal of the minimal clique of complementary algorithms. We illustrate our ﬁndings on experiments conducted on artiﬁcial and real data.
Estimation of Image Features Representing Facial Emotions for Emotion Synthesis

We develop a method to estimate emotion-speciﬁc features on human face. Application of such a method include characterizing an emotion class and synthesis of emotions. The emotion-speciﬁc features can also be used to study the statistical diﬀerences between two clusters, one facial expression images with no expressions and two facial expression images with some or maximum emotional content. Once the feature vectors are extracted from the input data, we classify the data and use the normal to the classiﬁer to trace the changes that a facial expression image may undergo in diﬀerent stages of an emotion. We use Support Vector Machines learning algorithm to construct an optimal classiﬁer. In the result section we show that we are able to reduce the number of features by 66.36% as compared to the total number of pixels. We show that using these features and state-of-the-art methods to synthesize images, we improved SNR of the synthesized image by 13.20% and also improved the cluster measures between a cluster of no-expression images and a cluster of with-expression images.