Reviewerly/exHarmony huggingface dataset:
Bayesian Inference for a Mixture Model on the Simplex

The Flexible Dirichlet (Ongaro and Migliorati, J. Multivar. Anal. 114:412–426, 2013) is a distribution for compositional data (i.e., data whose support is the simplex), which can fit data better than the classical Dirichlet distribution, thanks to its mixture structure and to additional parameters that allow for a more flexible modeling of the covariance matrix. This contribution presents two Bayesian procedures—both based on Gibbs sampling—in order to estimate its parameters. A simulation study has been conducted in order to evaluate the performances of the proposed estimation algorithms in several parameter configurations. Data are generated from a Flexible Dirichlet with D = 3 components and with representative parameter configurations.
Residual Cigarette Recycle System Spectrum Switching Based on Markov Model

Abstract In the process of communication of residual cigarette recycle system, due to the residual cigarette recycling bin WIFI switch with the signal strength, signal quality, distance of WIFI and other reasons. In order to keep the residual cigarette recycling bin in a non-inductive switching state, WIFI needs to adopt seamless Switching technology [1]. In which cognitive radio automatic networking (CRAN) is one of the core technologies, in order to suppress cognitive users from interfering with authorized users, it is necessary to reduce WIFI switching times as much as possible. Advance a cognitive wireless dynamic WIFI switching method based on detection of the WIFI chanel idle time (PWCIT). The theory is based on a known-state sequence hidden Markov [2] model (KSS-HMM). The selection method of the alternative WIFI is given, and the optimal channel is selected for data transmission by comparing the date transmission throughput of each candidate WIFI. The simulation results show that compared with the traditional channel selection method and the random channel [3] selection, the method can significantly reduce the number of WIFI handovers and improve the throughput of cognitive [4] users.
Administrative and Judicial Decisions on Advanced Algorithms

This chapter constitutes the main chapter of the book, where landmark cases of algorithmic regulation are presented. In total, eight cases of algorithmic conduct are discussed. These involve both direct and indirect examples of regulation. Among others, examples from the ICT sector (data-protection, internet browsers, and ad-blockers) and the gig economy (i.e., Airbnb and Uber) are presented and discussed. Recent regulatory action, such as in the cases of the diesel emissions scandal and high-frequency trading are also analyzed.
Causes of bit error and error correction for quantum key distribution protocols

Quantum key distribution protocols (QKD) identify eavesdroppers on quantum channel based on laws of quantum mechanics. In the original BB84 protocol, unauthorized measurements on qubits are discovered after Alice and Bob sacrifice some random resulting key bits as a test and find out a mismatch rate. Decoy state protocol alerts photon number splitting attack when the decoy multiphoton pulses deliberately positioned by Alice are registered abnormally higher at Bob’s detectors than the single photon pulses are. E91 protocol is the first QKD protocol that ever brought about entangled atoms. Based on Bell’s theorem, a probability coefficient is constrained within a fixed range under the circumstances of interception. While in practice, bit error rate (BER) is not necessarily associated with vicious eavesdropping. Detection inefficiency, transmission loss, imperfect entanglement sources and many other factors contribute as false alarms. Error correction is an essential post-processing procedure to tolerate implementation limits, yet secure the accuracy of the transmitted key. Quantifying third party contaminations also helps setting an optimal BER threshold to decide if one should abandon all previous communications. In this work, we summarize major causes of bit error in various practical QKD protocols mentioned above and study whether error correction models, such as low-density parity-check code (LDPC), perform accordingly to different kinds of errors.
Determination of Critical Parameters of Some Models of Binary Gibbs Random Fields Based on Image Modeling

Parameters of the texture images described by binary Gibbs random fields when phase transition occurs are investigated. Ising model and its generalizations are considered. The phenomenon of phase transition in a model of a binary field with vertical, horizontal and diagonal cliques is evaluated.
On-line/Off-line Identity-based Signature Scheme with Strong Unforgeability in the Standard Model

To solve the problems of low efficiency of the strong unforgeable identity-based signature scheme, a new strong unforgeable on-line/off-line identity-based signature scheme in standard model is proposed. Our new scheme uses the chameleon hash function and divides the signature algorithm into two stages: on-line stage and off-line stage. Our scheme generates signatures in a very short time, which efficiently improves the performance of the signature. The analysis results show that the proposed scheme not only has higher security, but also has lower computation cost.
Gaussian Lifted Marginal Filtering

Recently, Lifted Marginal Filtering has been proposed, an efficient Bayesian filtering algorithm for stochastic systems consisting of multiple, (inter-)acting agents and objects (entities). The algorithm achieves its efficiency by performing inference jointly over groups of similar entities (i.e. their properties follow the same distribution). In this paper, we explore the case where there are no entities that are directly suitable for grouping, which is typical for many real-world scenarios. We devise a mechanism to identify entity groups, by formulating the distribution that is described by the grouped representation as a mixture distribution, such that the parameters can be fitted by Expectation Maximization. Specifically, in this paper, we investigate the Gaussian mixture case. Furthermore, we show how Gaussian mixture merging methods can be used to prevent the number of groups from growing indefinitely over time. We evaluate our approach on an activity prediction task in an online multiplayer game. The results suggest that compared to the conventional approach, where all entities are handled individually, decrease in prediction accuracy is small, while inference runtime decreases significantly.
ANN: Prediction of Per Capita Income Rural Community on Poverty Line Based on Province

Abstract The problem of poverty is a fundamental problem that is of concern to every country. The Indonesian state has established a poverty reduction program as the main program. Poverty occurs in urban and rural communities. This research raises the problem of poverty in rural communities. The contribution of research to the government is to predict the per capita income of rural communities according to the poverty line based on the future provinces. The data used is data from the National Statistics Agency. These data are 2015 semester 1 data up to 2018 semester 1. The algorithm for its completion uses the artificial neural network backpropagation method. Input data is 2015 data for the 1 to 2017 semester 2. The training and testing architecture model is 4, namely 6-2-1, 6-3-1, 6-2-3-1, and 6-3-2-1. Target data is 2018 semester 1 data. The best architectural model is 6-2-1 with 79 epoch, MSE 0,004801 and 100% accuracy rate. From this model, a prediction of rural income per capita in the poverty line is based on the provinces of each province in Indonesia.
Traffic sign detection using histogram of oriented gradients and max margin object detection

Abstract Traffic signs are important markers in two-wheeled and four-wheeled vehicles. However, there is a change in direction or arrangement on the road that cannot be opened on a map which can cause incorrect information, which can cause traffic jams. In this journal the author uses a camera mounted on a car that provides a solution for drivers who issue problems that occur on the road that show directions or arrangements that are not directly updated using the HOG and MMOD methods. HOGs and MMODs are methods that can refute objects well, and move images and will be recognized immediately. The information received can be sent directly to an electronic map so that it can be accessed automatically by the driver’s information and assistance and other information finds the right path, so that it can help the driver and can avoid traffic jams.
Improve methodology for tumor detection in mammograms image

Breast cancer is a serious and become common disease that affects thousands of women in the world each year. Early detection is essential and critical for effective treatment and patient recovery. This work gives an idea of extracting features from the mammogram image to find affected area, which is a crucial step in breast cancer detection and verification. We present the affected area identification through in which place the tumor cells are extracted directly from the grey scale mammogram image. To remove noise from the mammogram image this work presents a simple and efficient technique using fast average filter, to determine the pixel value in the noise less image. To contour detection used shearlet transform and classic filters as like Sobel, Prewitt, and others. To evaluate the quality of contour used SSIM measure. Our experimental results demonstrate that our approach can achieve the better performance in time duration of reduce noise and with shearlet transform select affected area with high efficiency.
DeltaNet: Differential Binary Neural Network

Energy-constrained neural network processing is in high demanded for various mobile applications. Binarized neural network (BNN) aggressively enhances the computational efficiency, and in contrast, it suffers from degradation of accuracy due to its extreme approximation. We propose a neural network model using a new activation function "Delta" based on binarization of differences between weighted-sums. The "Delta" retains the magnitude relation between numerical values, and conveys richer information than ordinary binarization. We can design the hardware architecture for the proposed model with almost the same elements as BNN. The evaluation shows that it achieves higher recognition accuracy than a conventional BNN with almost the same hardware configuration.
A Performance Analysis of the Data Gathering Model for Unslotted CSMA/CA in Home Automation Environment

The medium access control (MAC) protocol provided by the IEEE 802.15.4 standard (contention-based CSMA/CA scheme) is not tailored to supply the QoS support demanded by real-time applications because of collisions, especially under high loads. The Internet of Things (IoT) exemplifies a networking paradigm which requires the incorporation and interoperability of diversified wireless standards. Data delivery across such integrated frameworks encounters challenges in terms of scalability and latency among other aspects. As a comprehensive analysis addressing such constraints is not available, we have developed a new technique for data gathering and have performed experiments to explore its effect on scalable network efficiency. The experimental network consisted of Waspmotes, programmed in C++ in a scalable star topology, emulating a typical home IoT network. Performance parameters of the MAC layer were measured while varying the network size and sending technique in non-beacon transmission mode. Results show that network performance varies greatly with different delivery modes, providing a theoretical basis for improving network structure in smart home applications.
A Framework for Taxonomy Based Testing Using Classification of Defects in Health Software-SW91

Taxonomy based testing is an efficient approach to find software defects at earlier phases of medical device software development. It allows the creation of goal oriented test cases while brainstorming test ideas. This approach is adaptable into standard testing processes such as the ISTQB and the 29119-2. It uses a new standard, a defect taxonomy SW91 which is identified as a consensus standard by the US Food and Drug Administration (FDA). This paper presents steps to be followed in implementing taxonomy based testing at a medical device software organisation which follows the software development process explained in IEC 62304:2006+A1:2015. Finally, the future work section explains how this framework can be tailored to testing techniques and how its efficiency and benefits will be evaluated via expert reviews and implementation at medical device software organisations.
The Effect of Self Efficacy on Statistics Learning Achievement at Students’ of STIKOM Tunas Bangsa Pematangsiantar

Abstract The purpose of this study was to analyze the effect of self-efficacy on statistical learning outcomes. The research method by using the survey way. Collecting data by random sampling 50 students’ second semester Academic Year 2017/2018 STIKOM Tunas Bangsa. Technique data collection by using questionnaire and tests. The data analysis technique by using a simple linear regression analysis with SPSS 25. Meanwhile, hypothesis testing using t-test to indicate that self-efficacy influences learning outcomes statistics. The results of this study obtained t count value of 7.452 while, t table value with a significance level of 0.05 is 2.011 because the value of t count &gt; t table and a significant value of 0.000 &lt;0.05, self-efficacy (X) partially has an effect is significant to the dependent variable namely learning outcomes (Y).
Tracking via Enhanced Context-Aware Correlation Filter

The tracking method based on correlation filtering is widely used, because of its superior performance and high frame rate in large data sets. A significant amount of recent researchers focus on the incorporation of stronger features for a richer representation of the tracking target, but ignore the fitting accuracy between samples and regression target. In this paper, based on correlation filter model and context clues, more discriminant information is provided during the tracking, to solve the interference such as occlusion and in-plane rotation. In addition, as the introduction of contextual clues cause unnecessary loss, this paper analyzes the regression, establishes an optimized target regression model, and inhibits it through regression parameters to reduce information loss. Extensive experiments proved the advancement of the proposed algorithm.
Placing human values at the core of socio-technical systems development

Socio-technical systems such as software systems play a critical role in modern society. Our societies have rapidly transformed into information societies. At the same time, there is a distinction between system-centred design and human-centred design. Recent studies indicate the challenges concerning system architecture design and usability. Still, software architecture modeling techniques such as the UML are inconsistent with human-centredness. Users are no longer passive consumers but co-designers, co-creators, and content producers of interactive systems. Therefore, human-centred development has become a focus for many systems development ranging from medical devices, business support software to media and learning applications. We report our activities, preliminary findings, and lessons learned from an ongoing project detailing a unified media playout for audiovisual heritage items. Our approach follows the human-centred design methodology described in ISO 9241-210.
Toward the Flexible Automation for Robot Learning from Human Demonstration Using Multimodal Perception Approach

This study proposes a multi-modal perception approach to make a robotic arm perform flexible automation and further simplify the complicated coding process of controlling a robotic arm. The depth camera is utilized for detect face and hand gesture for recognizing operator identification and commands. In addition, the kinematics of the robotic arm associated with the position of manipulated objects can be derived based on the information through human demonstrations and detected objects. In the experiments, the proposed multi-modal perception system can firstly recognize the operator. Then, the operator can demonstrate a task to generate the learning data with the assistance of using gesture. Afterward, the robotic arm can perform the same task as human demonstration. During the process of imitating task, the robotic arm can also be guided by the gesture command of operator.
A Deep Recurrent Neural Network Based Predictive Control Framework for Reliable Distributed Stream Data Processing

In this paper, we present design, implementation and evaluation of a novel predictive control framework to enable reliable distributed stream data processing, which features a Deep Recurrent Neural Network (DRNN) model for performance prediction, and dynamic grouping for flexible control. Specifically, we present a novel DRNN model, which makes accurate performance prediction with careful consideration for interference of co-located worker processes, according to multilevel runtime statistics. Moreover, we design a new grouping method, dynamic grouping, which can distribute/re-distribute data tuples to downstream tasks according to any given split ratio on the fly. So it can be used to re-direct data tuples to bypass misbehaving workers. We implemented the proposed framework based on a widely used Distributed Stream Data Processing System (DSDPS), Storm. For validation and performance evaluation, we developed two representative stream data processing applications: Windowed URL Count and Continuous Queries. Extensive experimental results show: 1) The proposed DRNN model outperforms widely used baseline solutions, ARIMA and SVR, in terms of prediction accuracy; 2) dynamic grouping works as expected; and 3) the proposed framework enhances reliability by offering minor performance degradation with misbehaving workers.
Variational approach to semi-automated 2D image segmentation

The segmentation of 2D biomedical images is very complex problem which has to be solved interactively. Original MRI, CT, PET, or SPECT image can be enhanced using variational smoother. However, there are Regions of Interest (ROI) which can be exactly localized. The question is how to design human interaction with computer for user friendly biomedical service. Our approach is based on user selected points which determine the ROI border line. The relationship between point positions and image intensity is subject of variational interpolation using thin plate spline model. The general principle of segmentation is demonstrated on biomedical images of human brain.
Fast Structure Learning for Deep Feedforward Networks via Tree Skeleton Expansion

Despite the popularity of deep learning, structure learning for deep models remains a relatively under-explored area. In contrast, structure learning has been studied extensively for probabilistic graphical models (PGMs). In particular, an efficient algorithm has been developed for learning a class of tree-structured PGMs called hierarchical latent tree models (HLTMs), where there is a layer of observed variables at the bottom and multiple layers of latent variables on top. In this paper, we propose a simple unsupervised method for learning the structures of feedforward neural networks (FNNs) based on HLTMs. The idea is to expand the connections in the tree skeletons from HLTMs and to use the resulting structures for FNNs. Our method is very fast and it yields deep structures of virtually the same quality as those produced by the very time-consuming grid search method.
Structural Model of the Effects of Psychological Hardiness and Self-esteem on Students' Psychological Empowerment: Mediating Role of Academic Self-Concept

Structural Model of the Effects of Psychological Hardiness and Self-esteem on Students' Psychological Empowerment: Mediating Role of Academic Self-Concept
Science Leaves Clues

The elusive science of security. Science advances when research results build upon prior findings through the evolution of hypotheses and theories about the fundamental relationships among variables within a context and considering the threats and limitations of the work. Some hypothesize that, through this science of security, the industry can take a more principled and systematic approach to securing systems, rather than reacting to the latest move by attackers. Others debate the utility of a science of security.
Mind tricks for presence

As virtual and mixed reality (VR/MR) technology moves steadily towards general availability accessible descriptions of the surrounding theory is desirable. An initial focus on high-level concepts can provide common language for diverse teams, including artists, designers and engineers, helping them to quickly get a sense of basic principles and gain a familiarity with related research for further study.
Dual-Cursor: Improving User Performance and Perceived Usability for Cursor-Based Text Entry on TV Using Remote Control

Abstract Entering text with a general five-key TV remote is a laborious task. A strategy for entering text with two interconnected cursors is proposed, whereby a secondary cursor is employed to maneuver the main cursor through fast-tracks. The main cursor is maneuvered in a 2D full-size onscreen keyboard space, whereas the sub-cursor moves among all predictive candidates in a 1D subspace. Each cursor is operated by a specific interaction method, and the movement of either must be mapped from one to the other. Compared to single-cursor methods, the combination of the main cursor and the sub-cursor operations usually results in fewer manual loadings, even when the target character is out of the prediction list range. A computer simulation based on a corpus of 57 258 multimedia titles (in Chinese) demonstrated that the keystrokes per character, powered by a dual-cursor technique, could be predicted to be reduced by 38.6–69.9% with very few predictive candidates for various keyboard layouts (compared with those of conventional non-predictive method). The keyboard layout and the number of candidates were further investigated by means of a usability test. The results revealed that with only 10 min of practice, novice users could achieve a mean text entry speed of 33.3, 29.5 and 22.8 characters per minute for QWERTY, alphabetic square, and alphabetic two-row layouts, respectively, which is 31.6%, 14.3% and 67.6% faster than the corresponding conventional input method, and is 12.7%, 6.9% and 25.0% faster than the current version of popup dialog method. The dual-cursor can significantly improve perceived usability and offers the potential to be applied to numerous other cursor-based text entry contexts. RESEARCH HIGHLIGHTS A new interaction strategy for TV input with two interconnected cursors. This strategy employs long-pressing for jumping around predicted candidates and short-pressing for navigating through keyboard keys. The number of keystrokes could be predicted to be reduced by 38.6–69.9% when compared with that of the conventional non-predictive input method for various keyboard layouts. Few predicted candidates were required to achieve a substantial decrement of keystrokes. The user experiment showed that novice users’ TV input speed could be substantially increased with dual-cursor.