Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Evolution of Relative Frequencies of Single Nucleotide Polymorphisms

We present new methods for modeling sampling distributions of SNP frequencies in populations with time varying size. Computation based on our methods are more efficient than the Monte Carlo simulations and allow for analysis of large genealogies. We analyze some of available SNP data and we compare our results of demographic parameters estimation to those obtained in previous researches in population genetics. The analyzed data are not inconsistent with the hypothesis of past population growth of modern humans.
Scale Issues in Soil Hydrology

The soil profile is the critical scale for representation of soil hydrology also at larger scales. Natural soils do not follow well‐defined hydraulic properties. Concepts are needed to model hydraulic nonequilibrium and hysteresis. Spatial patterns of functional soil types need to be identified. These can account for vertical stratification of hydraulic properties and structural attributes.
Optimal State Feedback Design with Takagi-Seguno Techniques for the Torque Control of a Nonlinear Hydrostatic Transmission

In this paper, a decentralized optimal control approach is proposed for the motor torque provided by a hydrostatic transmission. As basis of the control design, a nonlinear control-oriented model of the hydrostatic transmission is derived. On the one hand, the decentralized structure consists of a flatness-based control of the normalized tilt angle of the hydraulic motor, on the other, of an optimal control design of the hydraulic motor torque based on a Takagi-Sugeno (TS) approach. Given a TS state-space model, the optimal state feedback follows from local optimal designs that are interpolated by exact membership functions. Closed-loop stability is ensured by solving a set of linear matrix inequalities (LMIs) that leads to a joint Lyapunov function. Furthermore, the feedback control is extended by feedforward control to increase the tracking accuracy. An extended Kalman filter estimates the unmeasured states as well as disturbances, which are used for a subsequent disturbance rejection. Finally, the benefits of the proposed control structure are pointed out by simulations using a validated model of a dedicated test rig.
GA Based Concurrent Optimization and Design of Flexible Manipulator System

Concurrent design method is adopted in this paper to design and integrally optimize a single-link robotic flexible manipulator system,whose mathematical model includes construction parameters,sensor parameters and controller parameters. An improved genetic algorithm(GA) is used for parameter global optimization. Simulation results demonstrate that the optimized manipulator beam is non-uniform cross-section beam. And with simple PD controller,the system shows improved performances of tip vibration suppression.
Understanding the Software Development Effort Estimation in Chilean Small Companies

This article presents an empirical study that identifies the most frequent effort estimation approaches used in operationally stable small software companies in Chile. Based on it, we intend to understand both, the reasons behind the selection of these approaches, and the opportunities to improve that activity. We found that all companies participating in this study use flavors of expert-based estimation techniques, which is not surprising. However, we identified that many companies have no other option, since they share human resources among their projects, which limits their capability to record trustworthy historical information. This aspect conditions not only the estimation technique that can be used, but also the capability of the company to perform quality control of their estimations and improve its
Student Project Exposé : Evaluating Review Helpfulness Prediction across Domains

When deciding whether to buy a product or use a service, a valuable source of information are other peoples experiences and opinions. In online marketplaces such as Amazon or iTunes, every user can read and write reviews about the offered products and services. Due to the huge amount of reviews and opinions, it is very time-consuming to build an informed opinion. It is impossible to read every single review before making a decision. One approach to circumvent the problem is to let customers vote on the helpfulness of reviews. The customer can answer the question "Was this review helpful? (yes/no)" and the respective vote count is displayed below the review (e.g. "7 of 8 people found this helpful.") All existing reviews for a product can then sorted by this score, resulting in the most "helpful" reviews being listed at the top. The described voting process, however, is not optimal. As noted by [1], the amount of votes per review ends up being unbalanced, with a small proportion of all reviews having the majority of all votes. As a consequence, only a small subset of reviews will have a high helpfulness score and thus be shown to the customers. Many, potentially helpful reviews will almost never be considered in the customers decision-making process. A review helpfulness prediction algorithm, applied in a system that automatically sorts all reviews by their predicted helpfulness score, can help to overcome the described problem. Furthermore, it could be used to predict the helpfulness of a review-draft in the review writing process and to gain an understanding
Online Learning with Maximal No-Regret ` 1 Regularization

This paper asks: what is the maximum amount of `1 regularization can we apply while preserving cumulative regret in online learning? We show that scaling the `1 penalty by √ T , rather than a fixed `1 or a linearly increasing `1 penalty, provides the maximum possible `1 regularization and corresponding model sparsity while preserving cumulative regret bounds, and provide supporting empirical results.
Additional File 1 : Supplemental Methods and Figures for Deep or Shallow Julia Fukuyama

In each of the simulations, we first create a matrix M representing the “true” abundance of each taxon in each sample, and we then create a matrix X, a noisy version of M, representing the “observed” abundance of each taxon in each sample. We then compute the branch contributions to the Unifrac distances in the observed taxon abundance matrix X and plot the branch contribution curves. The simulations differ in how the mean matrix M is created. The code used to perform the simulations and to generate the figures is available at https://github.com/jfukuyama/DeepOrShallow.
Building Common Ground in Visual Dialogue : The PhotoBook Task and Dataset

The past few years have seen an increasing interest in developing computational agents for visuallygrounded dialogue, the task of using natural language interaction to communicate about visual content. Current challenges include posing and answering questions about a visual scene (Das et al., 2017a; Das et al., 2017b) or about specific objects in it (De Vries et al., 2017). While these tasks and associated datasets provide a useful starting point to develop multimodal dialogue agents, they have several shortcomings regarding their dialogical properties: (i) the interaction consists of questions followed by answers, which makes the exchanges closer to visual question-answering (Antol et al., 2015) than to dialogue proper, where different dialogues acts can take place; (ii) the tasks are asymmetric: each agent has a predefined role (e.g., questioner or answerer), which determines their contribution in the conversation; and (iii) there are limited opportunities to model how agents accumulate shared information (common ground (Stalnaker, 1978; Clark, 1996)) about the visual content they discuss. We present ongoing work on a novel symmetric dialogue setting, the PhotoBook Task, which elicits dialogues that provide rich data for investigating and learning common ground and partner-specific dialogue features in visual environments.
Exploring the experiential impact of online propaganda using eye-gaze and pupil dilation : A comparison across three ideological groups ?

Our study explores the physiological reactivity of women to propaganda generated by far-right, far-left, and jihadist groups to promote radicalization and recruitment into organizations promoting extremist violence. The theoretical bases of the study derive from the writings of Chomsky and Ellul who explore the power of propaganda to engineer opinions according to specific doctrines while maintaining the illusion of independent thought. Our sample was comprised of 90 women aged 18 through 35 years who described themselves as being conservative, liberal, or Muslim. The first half of the sample participated in a series of three online search sessions designed to capture compelling images related to the radicalization of women. The second half of the sample underwent a single in-lab session which involved reviewing a sample of these materials while being monitored according to eye-gaze, heart rate, galvanic skin response, and emotional facial recognition.We found images of horrific violence prompted dilation of the pupils across all three study groups, suggesting the triggering of sympathetic and parasympathetic pathways to brain regions associated with cognitive and emotional processing. These findings are relevant to understanding the pathways through which violent propaganda stimulates specific areas of the brain possibly enhancing receptivity to further ideological content. In addition, our study provides a perspective for seeking to understand propaganda and the processes of recruitment scientifically using empirical measures of physiological response and reactivity.
Design principles for national cyber security sensor networks: Lessons learned from small-scale demonstrators

The timely exchange of information on new threats and vulnerabilities has become a cornerstone of effective cyber defence in recent years. Especially national authorities increasingly assume their role as information brokers through national cyber security centres and distribute warnings on new attack vectors and vital recommendations on how to mitigate them. Although many of these initiatives are effective to some degree, they also suffer from severe limitations. Many steps in the exchange process require extensive human involvement to manually review, vet, enrich, analyse and distribute security information. Some countries have therefore started to adopt distributed cyber security sensor networks to enable the automatic collection, analysis and preparation of security data and thus effectively overcome limiting scalability factors. The basic idea of IoC-centric cyber security sensor networks is that the national authorities distribute Indicators of Compromise (IoCs) to organizations and receive sightings in return. This effectively helps them to estimate the spreading of malware, anticipate further trends of spreading and derive vital findings for decision makers. While this application case seems quite simple, there are some tough questions to be answered in advance, which steer the further design decisions: How much can the monitored organization be trusted to be a partner in the search for malware? How much control of the scanning process should be delegated to the organization? What is the right level of search depth? How to deal with confidential indicators? What can be derived from encrypted traffic? How are new indicators distributed, prioritized, and scan targets selected in a scalable manner? What is a good strategy to re-schedule scans to derive meaningful data on trends, such as rate of spreading? This paper suggests a blueprint for a sensor network and raises related questions, outlines design principles, and discusses lessons learned from small-scale pilots.
Quality and Nugget Detection for Short Text Conversation ( STC-3 ) based on Hierarchical Multi-Stack Model with Memory Enhance Structure

In this paper, we consider the Nugget Detection (ND) and Dialogue Quality (DQ) subtasks for Short Text Conversation 3 (STC-3) using deep learning method. The goal of NQ and DQ subtasks is to extend the one-round STC to multi-round conversation such as customer-helpdesk dialogues. The DQ subtask aims to judge the quality of the whole dialogue using three measures: Task Accomplishment (A-score), Dialogue Effectiveness (E-score) and Customer Satisfaction of the dialogue (S-score). The ND subtask, on the other hand, is to classify if an utterance in a dialogue contains a nugget, which is similar to dialogue act (DA) labeling problem. We applied a general model with utterance layer, context layer and memory layer to learn dialogue representation for both DQ and ND subtasks and use gating and attention mechanism at multiple layers including: utterance layer and context layer. The result shows that BERT produced a better utterance representation than multi-stack CNN for both DQ and ND subtasks and outperform the baseline models proposed by NTCIR on Ubuntu customer helpdesk dialogues corpus.
Atom bond connectivity temperature index

In the study of QSPR/QSAR, topological indices such as Zagreb index, Randic index and atom-bond connectivity index are exploited to estimate the bioactivity of chemical compounds. Inspired by many degree based topological indices, we propose here a new topological index, called the atom bond connectivity temperature index ABCT(G) of a molecular graph G which shows good correlation with entropy, acentric factor, enthalpy of vaporization and standard enthalpy of vaporization of an octane isomers. In this paper, we compute the atom bond connectivity temperature index ABCT(G) of line graphs of subdivision graphs of 2D−lattice, nanotube and nanotorus of TUC4C8[p,q].
Insights from senior-secondary physical education students on teacher-related factors they perceive to influence academic achievement

This research aimed to explore student perceptions of teacher-related factors that may influence academic achievement in the context of Victorian Certificate of Education (VCE) Physical Education. This qualitative study involved 23 VCE Physical Education students from three government and one independent secondary school in Victoria, Australia. Focus groups utilising a semi-structured interview schedule explored student perceptions of teacher-related factors on academic achievement. The importance of teachers having a good 'attitude', a broad repertoire of teaching strategies, making real-world connections, developing positive student-teacher relationships and facilitating access to themselves outside of scheduled class time were perceived by students as important influences.
New avenues towards mobile brain computer interfaces

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Smart Healthcare Monitoring using IoT

IoT in healthcare is the key player in providing better medical facilities to the patients and facilitates the doctors and hospitals as well. The proposed system here consists of various medical devices such as sensors and web based or mobile based applications which communicate via network connected devices and helps to monitor and record patients’ health data and medical information. The proposed outcome of the paper is to build a system to provide world-class medical aid to the patients even in the remotest areas with no hospitals in their areas by connecting over the internet and grasping information through about their health status via the wearable devices provided in the kit using a raspberry pi microcontroller which would be able to record the patient’s heart rate, blood pressure. The system would be smart to intimate the patient’s family members and their doctor about the patient’s current health status and full medical information in case any medical emergency arises. The collected information can be used to analyze and predict chronic disorders or other diseases such as heart attacks in preliminary stage itself using the data mining techniques that will also provide the approach advantageous for decision making.
2-Leyer Security System for Hiding Sensitive Text Data on Personal Computers

High security system suitable to hide sensitive text-data on personal computer is proposed and implemented. The system hiding techniques involves AES cryptography followed by image based steganography as two layers to insure high security. The study involved several tests to increase the capacity within the steganography layer adopting 1 and 2 least significant bits stego methods. The study also explores the data dependency and its security effects by experimenting it on 30 different fixed size images showing interesting attractive results. 
The impact of Business Analytics on organisations: An Information Processing Theory perspective

Over the last few years, the number of organisations adopting business analytics (BA) has grown rapidly. Organisations adopt BA to gain better insights on their processes and structures that help them to make more informed business decisions. There are several studies that investigate how BA systems can improve decision-making, create competitive advantage and provide value. Mostly, the focus of these studies have been on the technical aspects of BA and its implementation in areas such as supply chain management, marketing, and finance. However, there is a lack of significant body of literature reporting on the impact of BA adoption on organisations. This study aims to develop a richer understanding of BA capabilities and organisational impact. Information processing theory (IPT) as a theoretical lens is used to investigate this phenomenon. This study will utilise an in-depth case study to provide rich explanations on the impact of BA adoption on organisations.
Loop Modeling in Proteins Using a Database Approach with Multi-Dimensional Scaling

Modeling loops is an often necessary step in protein structure and function determination, even with experimental X-ray and NMR data. It is well known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with sub-angstrom accuracy when compared with ab initio techniques, but the disadvantage of often being able to produce usable results as they depend entirely on the loop already being represented within the database. My contribution is the LoopWeaver protocol, a database method that uses multidimensional scaling to rapidly achieve better clash-free, low energy placement of loops obtained from a database of protein structures. This maintains the above-mentioned advantage while avoiding the disadvantage by permitting the use of lower quality matches that would not otherwise fit. Test results show that this method achieves significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper before refinement. With refinement, the results (LoopWeaver and Loopy combined) are better than ROSETTA’s, with 0. 53Å RMSD on average for 206 loops of length 6, 0. 75Å local RMSD for 168 loops of length 7, 0. 93Å RMSD for 117 loops of length 8, and 1. 13Å RMSD loops of length 9, while ROSETTA scores 0. 66Å, 0. 93Å, 1. 23Å, 1. 56Å, respectively, at the same average time limit (3 hours on a 2.2GHz Opteron). When ROSETTA is allowed to run for over a week against LoopWeaver’s and Loopy’s combined 3 hours, it approaches, but does not surpass, this accuracy.
Comparison of Recognition Methods for Emotions Involved in Speech

This paper investigates the characteristics of recognizing emotions contained in human speech. The concept of artificial neural network (ANN) is adopted for a recognition algorithm. An approach based on the support vector machine (SVM) or hidden markov model (HMM) is also investigated as an alternative recognition method. Using a large database of phoneme-balanced Japanese words, three recognition systems are trained and tested. To evaluate the emotion recognition results, emotion recognition testing is carried out with human subjects. The obtained average emotion recognition rates are 51% using ANN, 51% using SVM, 32% using HMM, and 55% with humans. Experimental results confirm that the emotion recognition rate achieved by using the ANN or SVM in the speakerand context-independent mode is feasible and that ANN and/or SVM are well suited to this task.
The Recombinant Human Secretory Leukocyte Protease Inhibitor ( SLPI ) protects cardiac fibroblasts injury against an in vitro ischemia / reperfusion injury

© 2018 Nitchawat Paiyabhroma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License -NonCommercialShareAlikeUnported License (http://creativecommons.org/licenses/by-nc-sa/3.0/). *Corresponding Author Sarawut Kumphune, Department of Medical Technology, Biomedical Research Unit in Cardiovascular Sciences, Faculty of Allied Health Sciences, Naresuan University, 99 Moo 9, Phitsanulok, Phitsanulok 65000, Thailand. E-mail: sarawutk @ nu.ac. th The Recombinant Human Secretory Leukocyte Protease Inhibitor (SLPI) protects cardiac fibroblasts injury against an in vitro ischemia/reperfusion injury
Semantic Jitter for Learning Binary Visual Attributes

Solving the classification task can allow us to make meaningful predictions about what properties, or attributes, an image exhibits. It can allow us to determine whether a shoe is fancy or not, or if a face is smiling. With advances in machine learning, our ability to make such predictions has seen significant improvement over the years. However, most successful methods in solving this problem are supervised, meaning that they require labeled data. Depending on the domain, acquiring such data can be difficult. Thus, it is worthwhile to be able to construct more training samples from existing data. Prior work has been done to this effect, often in the form of low-level “jitter,” which employs spatial and photometric changes to add variety to the original dataset. In this work, we train classifiers to learn to predict binary attribute labels by augmenting our training data with new, “semantically” jittered images. To do so, we use an attribute-conditioned generative model to create label-preserving changes to existing data. We show that, in some cases, our proposed techniques result in higher classification accuracy than the baselines on the challenging UT-Zap50k and CelebA datasets. Furthermore, preliminary experiments show that in the case where the original dataset contains a class imbalance, our technique provides more substantial improvements.
SeqL : Secure Scan-Locking for Sequential Circuits

Existing oracle-guided and oracle-less attacks are known to successfully decrypt a functionally correct key of a locked combinational circuit. It is possible to extend these attacks to sequential circuits through the scan-chain by selectively initializing the combinational logic and analyzing the responses. In this paper, we propose SeqL, which achieves functional isolation and locks selective functional-input/scan-output pairs, thus rendering the decrypted key functionally incorrect. We conduct a formal study of the scan-locking problem and demonstrate automating our proposed defense on any given sequential circuit. We show that SeqL hides functionally correct keys from the attacker, thereby increasing the likelihood of the decrypted key being functionally incorrect. When tested on sequential benchmarks (ITC’99) and pipelined combinational benchmarks (ISCAS’85, MCNC), SeqL gave 100% resilience to state-of-the-art oracle-guided as well as oracle-less attacks. Therefore, SeqL is generic and robust defense against logic locking attacks on sequential circuits.