Miracle-dz/UniFAR-FTUs huggingface dataset:
The Sage Sourcebook Of Advanced Data Analysis Methods For Communication Research

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Analysis of Extreme Hip Motion in Professional Ballet Dancers

The aim of the present work is to analyze the consequences of repetitive extreme hip motion in professional ballet dancers. The motivation for this study is that dancers’ activities can be at the origin of hip osteoarthritis. To verify this hypothesis, bone poses of patient-specific hip joint are obtained using an optical motion capture system, while soft tissue artifacts are reduced by applying a global minimization. Subsequently, collision detections are performed among the joint tissues during motion. Results show a strong correlation between the observed collisions and the diagnosed lesions. The estimation of the hip kinematics was successfully validated with data collected from a dynamic MRI protocol. Keywords-hip joint; impingements; skin artifacts; extreme motion; dancing;
Studies on the Characterization and Machinability of Duplex Stainless Steel 2205 during Dry Turning

The present investigation is a study of the effect of advanced Physical Vapor Deposition (PVD) coatings on cutting temperature residual stresses and surface roughness during Duplex Stainless Steel (DSS) 2205 turning. Austenite stabilizers like nickel, manganese, and molybdenum reduced the cost of DSS. Surface Integrity (SI) plays an important role in determining corrosion resistance and fatigue life. Resistance to various types of corrosion makes DSS suitable for applications with critical environments like Heat exchangers, Desalination plants, Seawater pipes and Marine components. However, lower thermal conductivity, poor chip control and non-uniform tool wear make DSS very difficult to machine. Cemented carbide tools (M grade) were used to turn DSS in a dry environment. AlTiN and AlTiCrN coatings were deposited using advanced PVD High Pulse Impulse Magnetron Sputtering (HiPIMS) technique. Experiments were conducted with cutting speed of 100 m/min, 140 m/min and 180 m/min. A constant feed and depth of cut of 0.18 mm/rev and 0.8 mm were used, respectively. AlTiCrN coated tools followed by AlTiN coated tools outperformed uncoated tools due to properties like lower thermal conductivity, higher adhesion strength and hardness. Residual stresses were found to be compressive for all the tools used for dry turning, increasing the fatigue life of the machined component. Higher cutting temperatures were observed for coated tools due to its lower thermal conductivity, which results in very less tool wear than uncoated tools. Surface roughness with uncoated tools was found to be three times higher than coated tools due to lower coefficient of friction of coating used. Keywords—Cutting temperatures, DSS2205, dry turning, HiPIMS, surface integrity.
Anti Android Emulator Detection Accurate

Nowadays, mobile applications are highly demanded by increasing smartphone users. Android, which is the biggest mobile market, attracts many hackers to develop malware in order to exploit users. To encounter this situation, developing a trustworthy and fast malware analysis method is necessary. However, emulator detection in mobile has block the automated malware analysis development. Therefore, this project introduces a more flexible and feasible anti-emulator detection solution which aims to be applied in an automated analysis tool. In this project, the design will cooperate with existing automated malware analysis framework called Cuckoodroid. We will create a new module by using Frida, an open-source dynamic instrumentation toolkit. Recently, the research study has been finished. The next step will be the code implementation. Furthermore, malware analysis is a different research area in computer science hence there are many technical problems encountered. We will discuss them and attempt to explore if there exists alternative which can solve the problems.
Intrusion Detection System Using Neural Network

Almost of them doing job through internet like, online business , shopping, transfer money, voice call, online application filling data in hospital, booking bus ticket, train, apply jobs etc., such way may attack intruder easily if not secure. Example mobile apps getting updated daily, intrusion also being upgraded rapidly. Intruder can monitor or detection with the help of IDS. Two type of IDS are software or hardware and it helpful in safe guard the entire network or local network, the activities of the system and also helpful in finding the harmful operations. As the hacking techniques getting stronger daily so for improve the IDS but still not having strong intrusion detection system. In this paper we present IDS’s various types of attacks and it’s features to detect the intrusions on a network and possible ways to secure those intrusions. International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 1517-1525 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl. eu/hub/ Special Issue http://www.acadpubl. eu/hub/
Genetic Relationships among Some Stevia ( Stevia Rebaudiana Bertoni ) Accessions Based on ISSR Analysis

Stevia (Stevia rebaudiana) has many uses beside its traditional use as a sweetener. It has also been used as a medicine, cosmetic ingredient, pickling agent, and as a dentifrice. As a flavor enhancer, stevia helps to bring out the true flavors in cereals, breads, juices, candies, yogurt, and ice cream. Intersimple sequence repeats (ISSR) molecular marker have been employed to determine the phylogenetic relationships among six stevia accessions using 7 primers. A total of 179 DNA fragments were amplified, of which 127 DNA fragment were polymorphic (71%). Thirty-three amplified fragments found to be positive specific markers, while 6 fragments were negative specific markers. ISSR primers S4, S5 and S11 showed 7, 2 and 5 specific markers, respectively and could be most specific for the accession St1. Genetic similarities among the six stevia accessions based on the ISSR data revealed that the accessions St4 and St6 were the most closely related (76.7% similarity). On the other hand, the two most distantly related accessions were St1 and St6 (30.1%). The results of the consensus tree indicated that the accession St1 was genetically distant from all studied accessions. The results suggests that the molecular genetic analysis using ISSR technique among different stevia accession provides accurate results as it revealed high degree of polymorphism. In conclusion, the present study shows that, the ISSR technique can be used in the breeding programs to determine and fingerprint the stevia accessions with high content of stevioside.
Genome-wide identification and analysis of the SGR gene family in Cucumis melo

Chlorophyll (CHL) is present in many plant organs, and its metabolism is strongly regulated throughout plant development. Understanding the fate of CHL in senescent leaves or during fruit ripening is a complex process. The stay-green (SGR) protein has been shown to affect CHL degradation. In this study, we used the conserved sequences of STAY-GREEN domain protein (NP_567673) in Arabidopsis thaliana as a probe to search SGR family genes in the genome-wide melon protein database. Four candidate SGR family genes were identified in melon (Cucumis melo L. Hetao). The phylogenetic evolution, gene structure, and conserved motifs were subsequently analyzed. In order to verify the function of CmSGR genes in CHL degradation, CmSGR1 and CmSGR2 were transiently overexpressed and silenced using different plasmids in melon. Overexpression of CmSGR1
Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder (ASD) (Extended Version)

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning algorithms.
The Development and Verification of a Computational Tool for the Evaluation of the Visual Accessibility of Architectural Spaces

The present paper presents a tool for the assessment of the visual performance of spaces, especially with regard to the requirements of visually impaired people. This tool was one of the key deliverables of a recently completed project (ViDeA) addressing the special architecturally relevant needs of the visually impaired building users. Implementation of building regulations related to visual accessibility in a reliable manner (during design process) requires expert knowledge and the use of sophisticated visual simulation software. Toward this end, the tool attempts to facilitate the accurate evaluation of visual conditions in the proposed designs. The paper describes the general structure and implementation details. It also documents the preliminary verification of the tool based on a case study of an underground metro station.
Different : Discursive Diversity and Team Performance

How does diversity among members of a team affect their performance? Prior research has found diversity to be a double-edged sword that sometimes boosts and in other cases dampens performance. Yet empirical evidence on the link between diversity and performance remains mixed, and theoretical progress in understanding the contingencies has begun to stall. To help reinvigorate research in this field, we propose a novel conceptualization of team cognitive diversity and introduce a language-based technique to measure it. We focus on a particular aspect of cognitive diversity—discursive diversity—that reflects realized, rather than potential, divergence among group members; expressed, rather than latent, differences in the way members construe and ultimately communicate about a given set of topics; and temporal variation in construals and expressions over a team’s life cycle rather than the assumption of stability. We use the tools of natural language processing to develop a time-varying measure of discursive diversity. Using data from 117 remote teams of freelance software developers who collaborate via an online communication tool, we find that discursive diversity is generally associated with better team performance. However, levels of discursive diversity fluctuate significantly over teams’ life cycles. In more fine-grained analyses, we find that discursive diversity’s effects on performance are contingent on time: it is positive when a team’s next milestone is distant but turns negative as the next milestone approaches. We discuss implications of this work for research on diversity and cultural heterogeneity and the potential for computational methods to inform the design and implementation of diversity and inclusion initiatives in organizations.
Learning Event-Based Height From Plane and Parallax

In this work, we propose a fast method to perform event-based structure estimation for vehicles traveling in a roughly 2D environment (e.g. in an environment with a ground plane). Our method transfers the method of plane and parallax to events, which, given the homography to a ground plane and the pose of the camera, generates a warping of the events which removes the optical flow for events on the ground plane, while inducing flow for events above the ground plane. We then estimate dense flow in this warped space using a self-supervised neural network, which provides the height of all points in the scene. We evaluate our method on the Multi Vehicle Stereo Event Camera dataset, and show its ability to rapidly estimate the scene structure both at high speeds and in low lighting conditions.
Classifiers for educational data mining

The idea of classification is to place an object into one class or category, based on its other characteristics. In education, teachers and instructors are all the time classifying their students for their knowledge, motivation, and behaviour. Assessing exam answers is also a classification task, where a mark is determined according to certain evaluation criteria. Automatic classification is an inevitable part of intelligent tutoring systems and adaptive learning environments. Before the system can select any adaptation action like selecting tasks, learning material, or advice, it should first classify the learner’s current situation. For this purpose, we need a classifier – a model, which predicts the class value from other explanatory attributes. For example, one can derive the student’s motivation level from her/his actions in the tutoring system or predict the students who are likely to fail or drop out from their task scores. Such predictions are equally useful in the traditional teaching, but computerized learning systems often serve larger classes and collect more data for deriving classifiers. Classifiers can be designed manually, based on expert’s knowledge, but nowadays it is more common to learn them from real data. The basic idea is the following: First, we have to choose the classification method, like decision
EFFECT OF PREVIOUS CROP NITROGEN APPLICATION ON YIELD OF FOLLOWING MAIZE UNDER DIFFERENT PLANTING PATTERNS

Nitrogen application could affect the crop growth and yield, and then affect the water use efficiency (WUE). This study aimed to determine the effects of previous winter wheat (Triticum aestivum L.) nitrogen application and following summer maize (Zea mays L.) planting pattern on the yield and WUE of maize in the North China Plain. The experiments consisted of the winter wheat 112.5 (N1) and 225.0 (N2) kg/ha nitrogen application, and summer maize flat planting (FP) and ridge tillage planting (RTP) treatments arranged in a split-plot design (4 m × 4 m) with three replications in 2014 and 2015. Results showed that planting pattern and previous crop nitrogen had significant effect on yield components and WUE. Compared with FP, the RTP increased leaf relative water content, soil water content, soil water storage, WUE, and yield by 1.9%, 2.8%, 2.0%, 3.8%, and 7.1%, respectively. The yield, harvest index, stem diameter, and ear diameter of N2 treatment were 13.0%, 11.9%, 5.5%, and 2.3% higher than those of N1, respectively. Nitrogen of the previous winter wheat and the RTP pattern improved the water status and yield component of summer maize. It may be concluded previous crop nitrogen and RTP pattern can improve population structure, increase the yield and WUE of summer maize and thus is a promising method for farmers in North China.
Tractable Probabilistic Reasoning Through Effective Grounding

Templated Statistical Relational Learning languages, such as Markov Logic Networks (MLNs) and Probabilistic Soft Logic (PSL), offer much of the expressivity of probabilistic graphical models in a compact form that is intuitive to both experienced modelers and domain experts. However, these languages have historically suffered from tractability issues stemming from the large size of the instantiated models and the complex joint inference performed over these models. Although much research has gone into improving the tractability of these languages using approximate or lifted inference, a relatively small amount of research has gone into improving tractability through efficient instantiation of these large models. In this position paper, we will draw attention to open research areas around efficiently instantiating templated probabilistic models.
Robot Hands : Past , Present , and Future

Robot hands have been one of the major research topics since the beginning of robotics because grasping and manipulation of various objects by robot hands are fundamental functionalities of various robotic systems. A brief survey of the history and current state of research on multifingered robot hands both from hardware and software aspects is presented. Some of our recent results in grasping unknown objects optimally and performing various manipulation tasks by robot hands with soft fingers are also presented. Some future research directions are discussed.
Private k-means clustering : algorithms and applications

Today is a new era of big data. We contribute our personal data for the common good simply by using our smart phones, searching the web and doing online transactions. Researchers, companies and governments use the collected data to learn various user behavior patterns and make impactful decisions based on that. Is it possible to publish and run queries on those databases without disclosing information about any specific individual? Differential privacy is a strong notion of privacy which guarantees that very little will be learned about individual records in the database, no matter what the attackers already know or wish to learn. Still, there is no practical system applying differential privacy algorithms for clustering points on real databases. This thesis describes the construction of small coresets for computing k-means clustering of a set of points while preserving differential privacy. As a result, it gives the first k-means clustering algorithm that is both differentially private, and has an approximation error that depends sub-linearly on the data’s dimension d. Previous results introduced errors that are exponential in d. This thesis implements this algorithm and uses it to create differentially private location data from GPS tracks. Specifically the algorithm allows clustering GPS databases generated from mobile nodes, while letting the user control the introduced noise due to privacy. This thesis also provides experimental results for the system and algorithms, and compares them to existing techniques. To the best of my knowledge, this is the first practical system that enables differentially private clustering on real data. Thesis Supervisor: Daniela Rus Title: Professor
Extending BPMN for Analysis of Human Physical Risk Factors in Manufacturing Processes

Within Business Process Management (BPM) process models are used to visualize, analyze and enact processes. A modeling language that is widely used for this is the Business Process Modeling and Notation (BPMN) (Gordijn et al., 2000; Ould, 1995). The notation is originally developed for processes in the administrative domain, but increasingly often BPMN practices are translated to other domains such as manufacturing (Zor et al., 2011) and healthcare (Müller & Rogge-Solti, 2011; Braun et al., 2014). For basic process modeling, this can easily be done as the abstract elements of the notation are widely applicable, but sometimes it is required to model more domain-specific information. For such cases, standard BPMN can be extended with customized elements (Object Management Group, 2011). Currently already many BPMN extensions exist in many different application domains (Braun & Esswein, 2014). A domain in which BPMN is limited in its extensions is that of human factors in manufacturing (Polderdijk, 2016). Still, literature has already indicated that it is important to combine human factors and operational management as the processes of many organizations rely on humans (Neumann & Dul, 2010). Consequently, the aim of this research is to develop an extension for BPMN to enable a human factors risk assessment in a process oriented way for the manufacturing industry. This research has achieved this by using a structured methodology. This methodology includes a well-described design process of the graphical notation and a method to validate it. Until now, BPMN extensions are often designed in an ad hoc manner and their graphical notation is rarely validated. In this research, three conceptual designs are developed and compared to each other based on their cognitive effectiveness and user experience. The cognitive effectiveness is determined by the use of the Physics of Notations theory and the user experience is evaluated by interviewing experts in this field. Combining the results of both evaluations has lead to the final design which is implemented in software. The resulting tool is used to model and analyze processes of two manufacturing companies, particularly Thomas Regout International and Omron. This is done by conducting several interviews. After performing the risk assessment, the interviewees are asked to fill in a survey in order to assess the perceived ease of use, perceived usefulness and intention to use. Based on the results, it is concluded that the BPMN extension is perceived as useful and usable. Additionally, some interesting directions for future research are provided.
FU-01 Team ’ s Classification of Fact-checkable Opinions in NTCIR-14 QA Lab-PoliInfo Task

This paper reports on the achievements of Classification subtask of the NTCIR-14 QA Lab-PoliInfo task of FU-01 team. We proposed two different methods, rule-based and MaxEnt based methods, for classifying pros and cons of a political topic and whether an utterance sentence includes fact-checkable reasons or not. The results of formal run of the subtask shows our MaxEnt based method achieved higher accuracy than the rule-based method. Team Name. FU-01 Subtasks. Classification Task (Japanese)
Cross-Device Authentication via Motion Co-analysis with a Smartwatch in a Multi-user Multi-device Environment

of thesis entitled: Cross-Device Authentication via Motion Co-analysis with a Smartwatch in a Multi-user Multi-device Environment Submitted by LEUNG, Ho Man Colman for the degree of Master of Philosophy at The Chinese University of Hong Kong in October 2018 Smart devices contain sensitive information that has to be guarded against unauthorized access through authentication. Existing authentication methods become obsolete as they are designed either for logging-in one device at a time or are ineffective in a multi-user multi-device environment. This thesis explores the possibilities of using an already-authenticated smartwatch as a token for fast access and control of other smart devices by analyzing their motion data. In essence, we look for synchronous rotations between devices to perform authentication. We propose TwistIn, a simple gesture which the user twists a device in a to-and-fro manner. This gesture rotates a smartwatch and a handheld device at the same time, which can be detected using our proposed algorithm. To log in a device, one simply needs to pick it up and twist it a few times. Then, by co-analyzing the motion data from the device and the watch, our method can extend the user authentication on the watch to the device. This is a simple and tangible interaction that takes only one to two seconds to perform. It is particularly useful for devices such as smartphones, smart glasses, and small IoT objects. Furthermore, to account for user variation in wrist bending, we decompose wrist and forearm rotations via an optimization