Reviewerly/exHarmony huggingface dataset:
Correction to Improved PHARM for JPEG Steganalysis: Making PHARM More Efficient and Effective [2019 50330-50346]

We would like to add funding information regarding our article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> “This work was supported by the Fundamental Research Funds for the Central Universities under Grant 328201905.”
Towards to virtual infrastructure allocation on multiple IaaS providers with survivability and reliability requirements

The diversity of services, prices, and geographical footprints has turned the clouds into a complex and heterogeneous environment. Moreover, the survivability and reliability aspects are often disregarded by tenants, eventually resulting in heavy losses due to unavailability of services that are hosted on Virtual Infrastructures (VIs). We present an alternative to improve Vis' survivability and reliability, which considers the use of replicas and the spreading of virtual resources atop providers, regions, and zones. We formulate the VI allocation with survivability and reliability requirements as a mixed integer program, and three strategies to solve the formulation are proposed. First, the binary constraints are relaxed to obtain a Linear Program (LP), and the LP solution is given as input for the simulated annealing technique. Then, two GPU-accelerated algorithms are proposed to speed up the allocation of large-scale scenarios. Simulation with different reliability requests indicate an increasing in survivability without inflating costs.
Agilität und TZI – Zur Kompatibilität zweier methodischer Ansätze

Summary The organization of software development projects has changed over the past two decades in many companies thanks to the development of agile methods such as scrum. The transfer of responsibilities from management level to development teams and novel methods such as adaptive planning that enable a quick response to changing requirements are essential for agile methods. This leads to new responsibilities for management since it calls for a different corporate culture. Other areas of business are taking on board more and more of these ideas and methods. The values and methods of TCI and agile approaches are an excellent fit. TCI can be applied successfully to agile projects, providing support for the team and management alike.
Chicken Swarm Optimization in Task Scheduling in Cloud Computing

In order to solve the problem of low efficiency in resource scheduling in cloud computing, an improved chicken swarm optimization (CSO) is proposed for task scheduling.Firstly, the concept of opposition-based learning is introduced to initialize the chicken population and improve the global search ability.Secondly, the concepts of the weight value and learning factor in particle swarm optimization (PSO) are introduced to improve the positions of chickens, and the individual positions of chickens are optimized.Thirdly, the overall individual positions of the CSO are optimized by the difference algorithm.Finally, the possible cross-boundary of individual positions in the algorithm is prevented as a whole by boundary processing.In the simulation experiment, the optimized CSO is compared with the basic CSO, PSO, and ant colony optimization (ACO) in terms of completion time, cost, energy consumption, and load balancing, and good results are achieved.
Baseball Player Behavior Recognition System using Multimodal Features with an Augmented Reality Display on a Smart Glass

In this paper, a real-time baseball player behavior recognition system is proposed. By analyzing the sensing signals from the wearable sensors and the skeletons from the depth channel belonging a Kinect camera, the behaviors can be recognized by the proposed system. When a body part is occluded or the depth frames is with motion blur effects in a depth camera, the sensing signals from worn sensors can compensate the recognition capability. In addition, by analyzing the multimodal features obtained from heterogeneous sensors, the recognized results can be displayed on a smart glass with an augmented reality displaying. In this prototype, a player's behavior can be monitored by a coach to assist the advising process in an on-field and off-field baseball playing environment.
Open Day at the TGM WRI

On June 20, 2019, the premises of the TGM Water Research Institute on the occasion of the celebration of the 100th anniversary of its foundation, were opened to the general public as part of the Open Day, both at the Prague and at the Brno and Ostrava branches.
Living with feature interactions (keynote)

Feature-oriented software development enables rapid software creation and evolution, through incremental and parallel feature development or through product line engineering. However, in practice, features are often not separate concerns. They behave differently in the presence of other features, and they sometimes interfere with each other in surprising ways.
Generalized Learning Models for Structured Data

Author(s): Agrawal, Rakshit | Advisor(s): de Alfaro, Luca | Abstract: Structures are present in almost everything around us. In most of the systems that we interact with, or the way we interact with them, some emergent structure can often be observed. A simple sentence is a sequence of words. A small classroom of interacting students can be depicted as a network with each student defining a node of it. The emergent structures, therefore, highlight the inter-relatedness of different entities within systems, where while each entity has a significant individuality, it is also a component of a larger structure. This structural information, combined with the individual knowledge, can assist the task of learning properties in such systems. On a social network, for instance, we can learn link related properties between users by learning from the users as well as the graph of several users on the same network. Similarly, in an interactive sequence of a click-stream on a system, using the ordered information of these click actions, we may be able to learn the intent of a user performing the clicks.In this dissertation, we present the concept, methodology, and experiments for performing generalized learning from structural data. We discuss the emergence of structures within datasets, and the entire approach to learn from those structures. We provide a methodology for capturing the structures and assembling the information hidden within the structures. We present a concept of neural aggregation which helps combine information from complex structures while ensuring the learning capability of the models. We present several neural network based architectures for learning different properties from sequential and graphical structures. The dissertation provides the general approach, as well as specific learning frameworks for problems and datasets across several application domains.
Mental Simulation of Causality: The Role of Social Class

The study investigated the joint and relative contributions of four predicting variables in predicting academic performance among secondary school students.The research design was descriptive survey while the study made use of 200 SS2 students chosen purposively from five secondary schools in Ijebu-Ode Local Government area of Ogun State, Nigeria.The two different instruments used for data collection were the accommodator cognitive style questionnaire and converger cognitive style questionnaire (ACSQ and CCSQ).Data were analysed using multiple regression.The results indicated an F value of 4.34 at a significant level of 0.05 which indicated that the four predictors could be used to predict the academic performance of students.Beta coefficients value for converger, accommodator, gender and area of specialisation were .014, .
Improving the Applicability of the Ranked Nodes Method to build Expert-Driven Bayesian Networks (S)

One challenge in constructing a Bayesian network (BN) is defining the node probability tables (NPTs), which can be learned from data or elicited from domain experts.In practice, for large-scale BN it is common not to have enough data for learning and elicitation from experts is unfeasible.Previous work proposed a solution to this problem: the Ranked Nodes Method (RNM).However, this solution needs to be applied by a RNM expert who, through the elicitation of expert judgement, identifies the necessary parameters for the RNM algorithm to generate the NPTs.Hence, this paper presents a novel approach to define NPT using the RNM with no ranked nodes-specific knowledge.The solution is named Simulated Bayesian Network Expert (SBNE).It consists of eliciting a subset of the NPT from the domain experts which is used as input to an algorithm that estimates the optimal parameters for the RNM to generate the NPTs.To validate our solution, we conducted an experiment with multiple domain experts and compared the results with other methods.Our solution outperformed the other methods (producing NPTs at least 12% more accurate) and is, therefore, a promising approach to apply RNM without relying on RNM experts.
UAV Autonomous Path Optimization Simulation Based on Multiple Moving Target Tracking Prediction

In the UAV path planning study, due to the relative movement of multiple targets and the drone, long-term and large-scale UAV autonomous tracking has not been achieved. Therefore, aiming at this problem, this paper uses multiple moving target tracking algorithm to provide a real-time feedback on target position, estimates the later motion state of the target according to its position, and then performs the dynamic path planning by combining the feedback data and the state estimation result. Finally, The UAV path is optimized in real time. Experiments show that the proposed scheme can better plan the UAV path when multiple targets are in motion, thus improving the intelligence of the drone and the capability of long-time tracking.
Python Modules and Packages

Modules and packages are two constructs used in Python to organise larger programs. This chapter introduces modules in Python, how they are accessed, how they are define and how Python finds modules etc. It also explores Python packages and sub-packages.
ANALISIS KESALAHAN EJAAN PADA MAKALAH MAHASISWA STKIP AL HIKMAH SURABAYA (KAJIAN MATA KULIAH BAHASA INDONESIA)

AbstractThe aim of this research is to describe: (1) The misuse of capital letter; (2) The misuse of prefix and affix; and (3) The misuse of punctuation on students’ paper at STKIP Al Hikmah Surabaya. This paper used descriptive qualitative. The sample of this research was 6 papers, which were: 2 papers from elementary teacher department, 2 papers from English teacher department, and 2 papers from Physics teacher department. The data collection technique used was documentation. The instrument used was document, that is the students’ paper. The result of the research showed that the total number of spelling error was 150, which consists: (1) the misuse of capital error was 25 numbers; (2) the misuse of prefix di- and ke- was 13 numbers, which was 11 misuse for prefix di- and 2 misuse for ke-, moreover there were no mistake on the use of prefix di-, ke-, and  dari; (3) the misuse of punctuation  was 112 total numbers, which consists 33 misuse of point, 67 misuse of comma, 3 misuse of hyphen, and 6 misuse of colon.Keywords: Error Analysis, Spelling, Papers
Holographic optics based interferometer for comparative studies of phase objects

In this paper a versatile holographic optics based interferometer is presented in which two different phase type objects can be studied simultaneously and also the same interferometer can study a single object in two different modes at the same time. In this configuration simultaneous phase shifting in both the interferograms is also possible by moving a single inbuilt element. The optical configuration for the realization of proposed interferometer utilizes two holographic optics and one diffraction grating in conjunction with a collimating optics.
Estimate of stochastic model parameter of exchange rate using machine learning techniques

In this paper, we present a new approach for estimating the stochastic model parameter of exchange rate using genetic algorithms and neural networks. This approach takes in consideration the minimisation of exchange rate risk that is measured by the conditional value at risk CVaR in the estimation procedure of this parameter. The objective of this approach is to provide a tool of decision for the exchange market managers.
A First Python Program

In this chapter we will return to the Hello World program and look at what it is doing. We will also modify it to become more interactive and will explore the concept of Python variables.
Mobile solutions to the mexican kidnapping epidemic (MAKE). Beyond elite counter-measures towards citizen-led innovation

This paper presents an overview of the project Mobile Solutions to the Mexican Kidnapping Epidemic (MAKE): Beyond Elite Counter-Measures towards Citizen-Led Innovation. This initiative has charted the shifting topography of the Mexican kidnapping epidemic and examined various ‘mobile solutions’ that have emerged to counter it. The purpose of this research intervention has been to shed new light on this illicit industry and its effects, to provide a deeper understanding of kidnapping that informs and innovates citizen-led responses. The project has two aims. First, to engage with activists, victims and their families to explore the potential of citizen-led counter-kidnapping. Second, to track the complex mobilities triggered by kidnapping, examining those ‘mobile solutions’ that have emerged in response to it. Aware that activist-citizens are developing their own strategies to address this threat, this initiative not only sets out to document and to understand their approaches, but also to explore how everyday practices and technologies can be adapted into them. It spotlights how kidnapping both reiterates and exacerbates social and security inequalities, our analysing the strategies deployed by elites and subalterns, as well as the transborder and migratory effects of kidnapping. The key research question at the core of this project is: how do you counter kidnapping when you cannot access private solutions or rely on the state? Answering this question, we are working to develop a portfolio of counter-kidnapping resources that build community resilience and strengthen civic action against this pervasive threat to Mexican society. Working collectively with civic-action groups, victims groups, human rights defenders and technology specialists, this initiative is currently co-producing a counter-kidnapping toolkit that we will make available to ordinary Mexican citizens. This project is designed to not just build counter-kidnapping capacity in Mexico from the perspective of citizen-led innovation. Its ultimate aim is to leave a legacy of empowered Mexican citizens who will continue to collectively confront this threat, bringing forward much needed social change, and contributing to more sustainable security across Mexican society.This initiative echoes ongoing Latin American debates about how social justice can be achieved ‘from below”, as well as how security can be achieved in states of impunity. In the realm of counter-kidnapping policy interventions, we are conscious of the ‘hard’ policy transfers of military tactics and security expertise that are flowing from Colombia to Mexico in relation to kidnapping. However, this project sets out to open new channels for ‘soft’ transfers; to facilitate and forge connections between civic activists that hold the potential to catalyse and inspire innovative counter-kidnapping approaches that are both citizenled but also tailored to context.
The Mastro Ecosystem: Ontology-Based Data Management from Theory to Practice

Ontology Based Data Management (OBDM) is the paradigm that enables accessing existing data by means of a comprehensible and semantically rich representation of the application domain expressed by an ontology. In this demonstration we present the Mastro Ecosystem, a suite of tools providing solutions for OBDM covering a vast array of features ranging from ontology design, data access and querying, data quality analysis, open data creation and publishing, and many more.
&amp;lt;i&amp;gt;Fault diagnosis of Motorized Spindle based on S- Transform&amp;lt;/i&amp;gt;

<b>Abstract.</b> As one of key functional components, the status of high-speed motorized spindle directly influences the machining precision and performance of the CNC machine tool. The research on fault diagnosis of motorized spindle not only obtains the fault position and pattern, but also provides data for reliability design in order to improve the reliability of motorized spindle. S-Transform was applied in this paper for fault diagnosis of motorized spindle. The acceleration signal was processed by S-Transform to search the fault feature frequency and diagnose the fault pattern. And the fault data of rotor friction and misalignment on motorized spindle were analyzed by S-Transform. Compared with FFT, STFT and HHT, S-Transform had a better performance in fault diagnosing of motorized spindle.
Recipe Recommendation Method Using Text Analytics and Ingredients Hierarchy

Even in the age of digital technology, food is still one of the most important part of everyday life for all of us. We can also easily access food-relevant data about the topic on social media, from trends like diet issues to various culinary recipes. In this study, we performed the text analytics on the natural language recipes, and proposed the recipe recommendation method considering our preference and ingredients at hand. The ingredient components were extracted from the natural language recipes using the Conditional Random Fields, and the popular or unexpected ingredient match was explored using the modified Pointwise Mutual Information and Inverse Document Frequency techniques. Finally, we provided the insights to design new recipe considering alternative ways in case we have insufficient ingredients, which is inferred by the statistical and linguistic analysis.
Software Modularity Coupling Resolution by the Laplacian of a Bipartite Dependency Graph

Software modularity by pinpointing and subsequent resolution of the remaining coupling problems is often assumed to be a general approach to optimize any software system design. However, software coupling types with differing specific characteristics, seemingly pose serious impediments to any generic coupling resolution approach. Despite the diversity of types, this work proposes a generic approach to solve any coupling type in three steps: a-obtain the Dependency graph for the coupled modules; b-convert the dependency graph into a Bipartite Graph; c-generate the Laplacian Matrix from the Bipartite Graph. Coupling problems to be resolved are then located, using Laplacian eigenvectors, in particular the Fiedler eigenvector. The generic approach is justified, explained in detail, and illustrated by a few case studies.
Summon over Alma: Getting the Best from Both for Your Users

Libraries that have opted to implement and migrate to a Summon over Alma (SoA) configuration are hoping to capture the power of both products, however they also face unique issues that may live in one or both products and how they interact. For example, Alma Course Reserves functionality is extensive and recently added Summon features attempt to capture this data and present it to users. Limitations in how the Summon features are configured require particular formatting on the Alma side. SoA libraries also face issues surrounding the matching and merging of records, the fine tuning of indexes and content type mappings and multiple link generation with unintended results. Additionally, because all SoA libraries represent recent migrations, there are also lingering migration issues that may impact performance. In this session, the presenters will address these issues and others that require configuration of and cooperation between both Alma and Summon.
Forecasting flutter echo: The role of largest local Lyapunov exponents

In this article, we theoretically established a method of mapping ray movement in three-dimensional (3D) enclosures and used a ray-tracing algorithm to describe the paths along which a ray propagates in a space with a slope or Sinai geometry. Time series points extracted from the paths were used to calculate the largest local Lyapunov exponents (LLLEs). We investigated the LLLE distributions for ray systems in the two spaces. The distributions contained considerable information on ray system dynamics; this information can be used to study the evolution of ergodic properties. A comparison of the LLLEs of the ray systems in the two spaces showed that the Sinai geometry produced a diffusive characteristic, whereas the slope geometry produced flutter echo. By applying the method to building acoustics, the acoustic fields in different rooms were described. A diffuse acoustic performance was indicated by the uniformity of the sound pressure level (SPL) by simulations and experiment. The results show that LLLEs have potential for use in acoustic performance description.
Ambient Intelligence for Rehabilitation: A Survey

Rehabilitation is defined as a set of measures that assist individuals who experience, or are likely to experience, disability to achieve and maintain optimal functioning in interaction with their environments.Ambient Intelligence (AmI) refers to a digital environment that proactively, but sensibly, supports people in their daily lives.AmI is useful in order to rehabilitate patients.In this paper, a survey of AmI for rehabilitation is presented.
MONITORING PHOTOVOLTAIC SYSTEMS LEVERAGING BIG DATA INFRASTRUCTURE

Photovoltaic (PV) systems require advanced monitoring and control activities for a stable operation during time.To be accurate, monitoring activities should be based on large volumes of data collected from the PV system by means of inverters, PV modules sensors, weather stations or web sites.In most cases data are coming continuous, in streaming and in high volumes and, for monitoring purposes, big data technologies are considered for processing and storage.
Collaborative learning activities and their substantial role in the cognitive development of children with Learning Disabilities

The present paper intends to gain a better understanding of the characteristics directly associated with collaborative learning given that it actually entails a cognitive development among students with Learning Disabilities. These students undoubtedly face difficulties in both developing cognitive abilities and acquiring new knowledge. They also live under a constant anxiety in relation to their behavior refinement, the strengthening of their communication skills and finally the establishment of solid relationships with their surroundings. This paper further provides the repertoire of types of collaborative learning activities which are considered suitable for learning purposes directed to students with impairments. Finally, this paper pinpoints that using Information and Communication Technologies and multimedia contents in collaborative learning environment raise students’ interest in learning and boosts attention spam.
Round Paper Tray Grabbing Detection Based on Hough Transform Optimization

For various problems such as poor precision, too long calculation time and too large storage amount in the detection of the target circle, an improved Hough transform based on target circle detection method is proposed. The method first uses connected component labeling on the target image, and obtains roundness value by getting on roundness calculation. Then we set the threshold to estimate the approximate location of the connected component of the target circle, and get on double detection on the target with external influence by performing Hough transform and fitting circle operation. We calculate the difference between the radius and the center of the circle, and the results improve the computational efficiency and detection accuracy.