nils-herrmann/clean_scirepeval huggingface dataset:
Conservative schemes for the symmetric Regularized Long Wave equations

In this paper, we study the Symmetric Regularized Long Wave (SRLW) equations by finite difference method. We design some numerical schemes which preserve the original conservative properties for the equations. The first scheme is two-level and nonlinear-implicit. Existence of its difference solutions are proved by Brouwer fixed point theorem. It is proved by the discrete energy method that the scheme is uniquely solvable, unconditionally stable and second-order convergent for U in L1 norm, and for N in L2 norm on the basis of the priori estimates. The second scheme is three-level and linear-implicit. Its stability and second-order convergence are proved. Both of the two schemes are conservative so can be used for long time computation. However, they are coupled in computing so need more CPU time. Thus we propose another three-level linear scheme which is not only conservative but also uncoupled in computation, and give the numerical analysis on it. Numerical experiments demonstrate that the schemes are accurate and efficient.
Mining User Behavioral Rules from Smartphone Data Through Association Analysis

The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual information and mobile phone usage records through the device logs. This paper formulates the problem of mining behavioral association rules of individual mobile phone users utilizing their smartphone data. Association rule learning is the most popular technique to discover rules utilizing large datasets. However, it is well-known that a large proportion of association rules generated are redundant. This redundant production makes not only the rule-set unnecessarily large but also makes the decision making process more complex and ineffective. In this paper, we propose an approach that effectively identifies the redundancy in associations and extracts a concise set of behavioral association rules that are non-redundant. The effectiveness of the proposed approach is examined by considering the real mobile phone datasets of individual users.
Open source software for personal information managers and personal knowledge management

Technology is common in the domain of knowledge distribution, but it rarely enhances the process of knowledge use. Distribution delivers knowledge to the potential user's desktop but cannot dictate what he or she does with it thereafter. It would be interesting to envision technologies that help to manage personal knowledge as it applies to decisions and actions. The viewpoints about knowledge vary from individual, community, society, personnel development or national development. Personal Knowledge Management (PKM) integrates Personal Information Management (PIM), focused on individual skills, with Knowledge Management (KM). KM Software is a subset of Enterprise content management software and which contains a range of software that specialises in the way information is collected, stored and/or accessed. This article focuses on KM skills, PKM and PIM Open Sources Software, Social Personal Management and also highlights the Comparison of knowledge base management software and its use.
Intelligent Lighting Control for Vision-Based Robotic Manipulation

The ability of a robot vision system to capture informative images is greatly affected by the condition of lighting in the scene. This paper reveals the importance of active lighting control for robotic manipulation and proposes novel strategies for good visual interpretation of objects in the workspace. Good illumination means that it helps to get images with large signal-to-noise ratio, wide range of linearity, high image contrast, and true color rendering of the object's natural properties. It should also avoid occurrences of highlight and extreme intensity unbalance. If only passive illumination is used, the robot often gets poor images where no appropriate algorithms can be used to extract useful information. A fuzzy controller is further developed to maintain the lighting level suitable for robotic manipulation and guidance in dynamic environments. As carried out in this paper, with both examples of numerical simulations and practical experiments, it promises satisfactory results with the proposed idea of active lighting control.
Adaptive Human–Robot Interaction Control for Robots Driven by Series Elastic Actuators

Series elastic actuators (SEAs) are known to offer a range of advantages over stiff actuators for human–robot interaction, such as high force/torque fidelity, low impedance, and tolerance to shocks. While a variety of SEAs have been developed and implemented in initiatives that involve physical interactions with humans, relatively few control schemes were proposed to deal with the dynamic stability and uncertainties of robotic systems driven by SEAs, and the open issue of safety that resolves the conflicts of motion between the human and the robot has not been systematically addressed. In this paper, a novel continuous adaptive control method is proposed for SEA-driven robots used in human–robot interaction. The proposed method provides a unified formulation for both the robot-in-charge  mode, where the robot plays a dominant role to follow a desired trajectory, and the human-in-charge  mode, in which the human plays a dominant role to guide the movement of robot. Instead of designing multiple controllers and switching between them, both typical modes are integrated into a single controller, and the transition between two modes is smooth and stable. Therefore, the proposed controller is able to detect the human motion intention and guarantee the safe human–robot interaction. The dynamic stability of the closed-loop system is theoretically proven by using the Lyapunov method, with the consideration of uncertainties in both the robot dynamics and the actuator dynamics. Both simulation and experimental results are presented to illustrate the performance of the proposed controller.
TextLogger: inferring longer inputs on touch screen using motion sensors

Today's smartphones are equipped with precise motion sensors like accelerometer and gyroscope, which can measure tiny motion and rotation of devices. While they make mobile applications more functional, they also bring risks of leaking users' privacy. Researchers have found that tap locations on screen can be roughly inferred from motion data of the device. They mostly utilized this side-channel for inferring short input like PIN numbers and passwords, with repeated attempts to boost accuracy. In this work, we study further for longer input inference, such as chat record and e-mail content, anything a user ever typed on a soft keyboard. Since people increasingly rely on smartphones for daily activities, their inputs directly or indirectly expose privacy about them. Thus, it is a serious threat if their input text is leaked.
 To make our attack practical, we utilize the shared memory side-channel for detecting window events and tap events of a soft keyboard. The up or down state of the keyboard helps triggering our Trojan service for collecting accelerometer and gyroscope data. Machine learning algorithms are used to roughly predict the input text from the raw data and language models are used to further correct the wrong predictions. We performed experiments on two real-life scenarios, which were writing emails and posting Twitter messages, both through mobile clients. Based on the experiments, we show the feasibility of inferring long user inputs to readable sentences from motion sensor data. By applying text mining technology on the inferred text, more sensitive information about the device owners can be exposed.
A Test Automation Framework Based on WEB

Many software applications today are written as web-based applications to be run in an Internet browser. Selenium is a set of powerful different software tools working with many browsers, operating systems, programming languages, and testing frameworks each with a different approach to supporting automation test for testing web-based applications. JMeter is used to simulate a heavy load on a server, network or object to test its strength or to analyze overall performance under different load types. JMeter operates at the protocol-level, on the other hand, Selenium works at the user-level. In this paper, authors have designed an automatic software testing framework for web applications based on the Selenium and JMeter. With the use of the software framework, we efficiently improve the extensibility and reusability of automated test. The results show that the new software framework improves software products quality and develop efficiency. This paper also illustrates how to design web-based test automation framework in details.
Exploring the Application of Blockchain Technology to Combat the Effects of Social Loafing in Cross Functional Group Projects

Today, many multi-national organisations operate in a dispersed geographical environment. Teams consisting of members from around the globe can be assembled on an as-needed basis. However, this can prove to be a complex managerial task. Individuals, who believe that their efforts are not being effectively monitored by upper management, lose their motivation to fully contribute to the best of their abilities as they do not believe there is any correlation between the effort they exert and the reward they receive. With low levels of intrinsic involvement among employees, a lack of task visibility from upper management and limited social interaction among group members, many organisations struggle to combat the issue of social loafing in cross functional working groups. Blockchain technology, widely acknowledged as enabling openness, can facilitate the development of an immutable, transparent, secure and verifiable application for capturing individuals Intellectual Property as they work. This would motivate employees to more openly contribute to group work, safe in the knowledge that their contribution will be recognised, enabling management to maintain a high level of task visibility over their employees work without requiring their physical presence.
Segmentation of Touching , Overlapping , Skewed and Short Handwritten Text Lines

Text line segmentation is an inherent part of document recognition system and important preprocessing step for word and character segmentation. Presence of touching or overlapping text lines, short-lines, curvilinear or skewed lines and small or variant gaps between the text lines make the segmentation challenging. These variations cause errors in recognition phase. This paper describes the topdown approach of handwritten text line segmentation. The proposed method begins with core detection. To segment the overlapping components, run-length is used for obtaining the structural knowledge which classifies the components into upper and lower text lines. To segment the short lines and skewed lines, distance metrics and connected component are used recursively. The system was evaluated using 200 images from the IAM database and 100 documents collected from different writers. From the experiments conducted, it was observed that the system has 91.92% accuracy and imbibes in its reliability.
Sunlight, Vitamin D and Skin Cancer

Today, there is a controversial debate in many scientific and public communities on how much sunlight is appropriate to balance between the positive and negative effects of solar UV-exposure. UV exposure undoubtedly causes DNA damage of skin cells and is a major environmental risk factor for all types of skin cancers. In geographic terms, living in parts of the world with increased erythemal UV or high average annual bright sun results in increased risks of skin cancers, with the greatest increased risk for squamous cell carcinoma, followed by basal cell carcinoma and then melanoma. On the other hand, sunlight exerts positive effects on human health, that are mediated in part via UV-B-mediated cutaneous photosynthesis of vitamin D. It has been estimated that at present, approximately 1 billion people worldwide are vitamin D-deficient or -insufficient. This epidemic causes serious health problems that are still widely under-recognized. Vitamin D deficiency leads to well documented problems for bone and muscle function. There are also associations between vitamin D-deficiency and increased incidence of and/or unfavourable outcome for a broad variety of independent diseases, including various types of malignancies (e.g. colon-, skin-, and breast cancer), autoimmune diseases, infectious diseases, and cardiovascular diseases. In this review, the present literature is analyzed to summarize our present knowledge about the important relationship of sunlight, vitamin D and skin cancer.
Quantitative measures of hair cell loss in CBA and C57BL/6 mice throughout their life spans.

The CBA mouse shows little evidence of hearing loss until late in life, whereas the C57BL/6 strain develops a severe and progressive, high-frequency sensorineural hearing loss beginning around 3-6 months of age. These functional differences have been linked to genetic differences in the amount of hair cell loss as a function of age; however, a precise quantitative description of the sensory cell loss is unavailable. The present study provides mean values of inner hair cell (IHC) and outer hair cell (OHC) loss for CBA and C57BL/6 mice at 1, 3, 8, 18, and 26 months of age. CBA mice showed little evidence of hair cell loss until 18 months of age. At 26 months of age, OHC losses in the apex and base of the cochlea were approximately 65% and 50%, respectively, and IHC losses were approximately 25% and 35%. By contrast, C57BL/6 mice showed approximately a 75% OHC and a 55% IHC loss in the base of the cochlea at 3 months of age. OHC and IHC losses increased rapidly with age along a base-to-apex gradient. By 26 months of age, more than 80% of the OHCs were missing throughout the entire cochlea; however, IHC losses ranged from 100% near the base of the cochlea to approximately 20% in the apex.
The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment.

This study examined the process of how socioeconomic status, specifically parents' education and income, indirectly relates to children's academic achievement through parents' beliefs and behaviors. Data from a national, cross-sectional study of children were used for this study. The subjects were 868 8-12-year-olds, divided approximately equally across gender (436 females, 433 males). This sample was 49% non-Hispanic European American and 47% African American. Using structural equation modeling techniques, the author found that the socioeconomic factors were related indirectly to children's academic achievement through parents' beliefs and behaviors but that the process of these relations was different by racial group. Parents' years of schooling also was found to be an important socioeconomic factor to take into consideration in both policy and research when looking at school-age children.
Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce

We live in a world were data are generated from a myriad of sources, and it is really cheap to collect and storage such data. However, the real benefit is not related to the data itself, but with the algorithms that are capable of processing such data in a tolerable elapse time, and to extract valuable knowledge from it. Therefore, the use of Big Data Analytics tools provide very significant advantages to both industry and academia. The MapReduce programming framework can be stressed as the main paradigm related with such tools. It is mainly identified by carrying out a distributed execution for the sake of providing a high degree of scalability, together with a fault-
Speaker independent emotion recognition based on SVM/HMMS fusion system

Speech emotion recognition as a significant part has become a challenge to artificial emotion. It is particularly difficult to recognize emotion independent of the person concentrating on the speech channel. In the paper, an integrated system of hidden Markov model (HMM) and support vector machine (SVM), which combining advantages on capability to dynamic time warping of HMM and pattern recognition of SVM, has been proposed to implement speaker independent emotion classification. Firstly, all emotions are divided into two groups by SVM. Then, HMMs are used to discriminate emotions from each group. For a more robust estimation, we also combine four HMMs classifiers into a system. The recognition result of the fusion system has been compared with the isolated HMMs using Mandarin database. Experimental results demonstrate that comparing with the method based on only HMMs, the proposed system is more effective and the average recognition rate reaches 76.1% when speaker is independent.
PVM: A Framework for Parallel Distributed Computing

The PVM system is a programming environment for the development and execution of large concurrent or parallel applications that consist of many interacting, but relatively independent, components. It is intended to operate on a collection of heterogeneous computing elements interconnected by one or more networks. The participating processors may be scalar machines, multiprocessors, or special-purpose computers, enabling application components to execute on the architecture most appropriate to the algorithm. PVM provides a straightforward and general interface that permits the description of various types of algorithms (and their interactions), while the underlying infrastructure permits the execution of applications on a virtual computing environment that supports multiple parallel computation models. PVM contains facilities for concurrent, sequential, or conditional execution of application components, is portable to a variety of architectures, and supports certain forms of error detection and recovery.
Scalable Coherent Combining of Kilowatt Fiber Amplifiers Into a 2.4-kW Beam

We demonstrate coherent beam combining of multiple Yb-doped non polarization-maintaining fiber amplifiers operating at individual power levels above 1 kW. A 2.4-kW, M2 = 1.2 output beam was generated by combining three amplifiers with a diffractive optical element (DOE). A single low-power sample of the combined output beam provided error signals for active phase and polarization locking of all three fibers. The beam combining efficiency of 80% was nearly constant with amplifier power, indicating the absence of significant thermal or nonlinear effects. Modeling anchored by precise measurements of the beam characteristics indicates potential for this architecture to scale to higher fiber counts, higher combining efficiency, and higher power.
Analysis of structural mechanics of solid microneedle using COMSOL software

Microneedles are currently being extensively researched for therapeutic and diagnostic applications, as they are painless, cheaper than conventional needles, and reduce risk of infection. In this paper, we have selected 10 materials for solid microneedle and have performed the structural analysis of each material using COMSOL Multiphysics 4.3. The study was based on the factors affecting microneedle strength such as buckling and bending forces. The result indicated that the microneedle made of Silicon carbide was superior to the other selected materials and while considering the property of biodegradability for the same study, Silk was preferred. PLA and Polycarbonate experienced buckling and thus were not preferred from the selected materials.
A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces

Recent advancements in brain computer interfaces (BCI) have demonstrated control of robotic systems by mental processes alone. Together with invasive BCI, electroencephalographic (EEG) BCI represent an important direction in the development of BCI systems. In the context of EEG BCI, the processing of EEG data is the key challenge. Unfortunately, advances in that direction have been complicated by a lack of large and uniform datasets that could be used to design and evaluate different data processing approaches. In this work, we release a large set of EEG BCI data collected during the development of a slow cortical potentials-based EEG BCI. The dataset contains 60 h of EEG recordings, 13 participants, 75 recording sessions, 201 individual EEG BCI interaction session-segments, and over 60 000 examples of motor imageries in 4 interaction paradigms. The current dataset presents one of the largest EEG BCI datasets publically available to date.
Defect Management in Agile Software Development

Agile development reduces the risk of developing low quality software in the first place by minimizing defects. In agile software development formal defect management processes help to build quality software. The core purpose of defect management is to make the software more effective and efficient in order to increase its quality. There are several methods for handling defects like defect prevention, defect discovery and resolution which are used by software developers and testers. Refactoring keeps the system clean by identifying and removing quality defects. To gain the full confidence of the customer defect management should be involved at every stage of development. Agile methodologies focus on delivering the software in form of short iterations. Thus each iteration helps to overcome defects and leads better development and end user satisfaction. This study paints the picture of handling the software defects using agile based Software Development Process.
Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression

People can benefit from disclosing negative emotions or stigmatized facets of their identities, and psychologists have noted that imagery can be an effective medium for expressing difficult emotions. Social network sites like Instagram offer unprecedented opportunity for image-based sharing. In this paper, we investigate sensitive self-disclosures on Instagram and the responses they attract. We use visual and textual qualitative content analysis and statistical methods to analyze self-disclosures, associated comments, and relationships between them. We find that people use Instagram to engage in social exchange and story-telling about difficult experiences. We find considerable evidence of social support, a sense of community, and little aggression or support for harmful or pro-disease behaviors. Finally, we report on factors that influence engagement and the type of comments these disclosures attract. Personal narratives, food and beverage, references to illness, and self-appearance concerns are more likely to attract positive social support. Posts seeking support attract significantly more comments.  This paper includes some detailed examples of content about eating disorders and self-injury illnesses.
NEW UPPER BOUNDS FOR SOME SPHERICAL CODES

The maximal cardinality of a code W on the unit sphere in n dimensions with (x, y) ≤ s whenever x, y ∈ W, x 6= y, is denoted by A(n, s). We use two methods for obtaining new upper bounds on A(n, s) for some values of n and s. We find new linear programming bounds by suitable polynomials of degrees which are higher than the degrees of the previously known good polynomials due to Levenshtein [11, 12]. Also we investigate the possibilities for attaining the Levenshtein bounds [11, 12]. In such cases we find the distance distributions of the corresponding feasible maximal spherical codes. Usually this leads to a contradiction showing that such codes do not exist.