Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning for Multi-robot Field Coverage Based on Local Observation

Field coverage is a representative exploration task that has many applications ranging from household chores to navigating harsh and dangerous environments. Autonomous mobile robots are widely considered and used in such tasks due to many advantages. In particular, a collaborative multirobot group can increase the efficiency of field coverage. In this paper, we investigate the field coverage problem using a group of collaborative robots. In practical scenarios, the model of a field is usually unavailable and the robots only have access to local information obtained from their on-board sensors. Therefore, a Q-learning algorithm is developed with the joint state space being the discretized local observation areas of the robots to reduce the computational cost. We conduct simulations to verify the algorithm and compare the performance in different settings.
Integrated Virtual Communities into User Group Management System (UGMS) for Smart Cities

Abstract Society as a collection of people who are connected together based on certain understanding and bind by the regulation is adapted to perform multiple functions that for the sake of engaging technology advancement to improve the quality of life. Thus, there are some opportunities for application development to cater to the needs in the complexity of interaction between the individual in the society, one of them is user group management. This study is aimed to design the business canvas and mobile application by integrating virtual communities based on national identity in the form of e-KTP through Scrum method. Therefore, there are some challenges should be considered in the process of identification such as the right resource to implement the smart platform and the alignment between project objective and the readiness in the society. This study present APLICOT API as the framework for UGMS together with business model canvas, use case diagram and prototype.
Overcoming Reserve - Supporting Professional Appropriation of Interactive Costumes

Deploying wearable technologies in the performing arts not only concerns costume wearers but affects further stakeholders whose work is impacted by the interactive effects or who help maintain the technology. Beyond the wearer, literature neglects how these other stakeholders engage with interactive costumes, though a performance production is based on the contribution of many parties. We conducted a longitudinal study to examine how stakeholders of a youth ballet production experience and appropriate interactive costuming. Our findings suggest that user experiences vary according to stakeholders' closeness to the costume, background and taste, the costume interaction mode and social environment. We expand existing models of technology appropriation with two novel technology relations: professional reserve and polite indifference. Based on these, we suggest integration into existing practices, to design for the show, and create positive experiences to incorporate interactive costumes in the performing arts and discuss relevance for other professional fields.
The Reuse of Digital Computer Data: Transformation, Recombination and Generation of Data Mixes in Big Data Science

Abstract This chapter is concerned with the relationship between the materiality of digital computer data and their reuse in scientific practice. It builds on the case study of a ‘data mash-up’ infrastructure for research with environmental, weather and population health data. I problematise the extent to which scientists reusing digital computer data heavily manipulate the sources through complex and situated calculative operations, as they attempt to re-situate data well beyond the epistemic community in which they originated, and adapt them to different theoretical frameworks, methods and evidential standards. The chapter interrogates the consequent relationship between derivative data and the data sources from which they originate. The deep relationality of scientific computer data is multi-layered and scaffolded , as it depends on relations between various kinds of data, computing technologies, assumptions, theoretical scaffoldings, hypotheses and other features of the situation at hand.
Acoustic Resonance Recognition of Coins

In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.
Real-Time Distributed Ensemble Learning for Fault Detection of an Unmanned Ground Vehicle

As the demand for mobile autonomous systems increases across various industries, fault diagnostic systems will need to become more intelligent and robust. In this paper we propose a distributed Long Short-Term Memory (LSTM)- based ensemble learning architecture for learning highly nonlinear, temporal fault classification boundaries for an Unmanned Ground Vehicle (UGV). The main goal of the architecture is to reduce classification bias by ensembling LSTM models as well as achieving near-real time processing time. This is done by parallelizing the deep learning models on Amazon Web Services (AWS) cloud instances via Apache Kafka, a real-time data pipelining infrastructure. An experiment is conducted on a UGV subjected to dislocated suspension faults and results showing the effectiveness of the approach are shown.
One property to rule them all?

Substitution boxes (S-boxes) are nonlinear mappings that represent one of the core parts of many cryptographic algorithms (ciphers). If S-box does not possess good properties, a cipher would be susceptible to attacks. To design suitable S-boxes, we can use heuristics as it allows significant freedom in the selection of required cryptographic properties. Unfortunately, with heuristics, one is seldom sure how good a trade-off between cryptographic properties is reached or if optimizing for one property optimizes implicitly for another property. In this paper, we consider what is to the best of our knowledge, the most detailed analysis of trade-offs among S-box cryptographic properties. More precisely, we ask questions if one property is optimized, what is the worst possible value for some other property, and what happens if all properties are optimized. Our results show that while it is possible to reach a large variety of possible solutions, optimizing for a certain property would commonly result in good values for other properties. In turn, this suggests that a single-objective approach should be a method of choice unless some precise values for multiple properties are needed.
Jointly detecting and multiple people tracking by semantic and scene information

In this paper, we propose a new method for online multiple people tracking, which combines the detection process and the single object tracking process, and establishes the interactions between them. The detector detects objects in the still images which ignores the sequential information. Meantime, the single object tracker does not use the category semantic information during tracking. To take both the sequential and semantic information into account, we exchange information among the detector and the trackers. More specifically, the trackers deliver sequential information to the detector by providing the detector with the extra proposals. The detector supplements each tracker with the robust semantic information by using bounding box regression to modify the tracking result. Besides, the interactions also happen among the trackers through the occlusion speculation, the perspective model interpretation and the trajectory merging process. The experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art MOT methods.
Detection of Entamoeba Histolytica using Canny Edge Detection and Watershed Transform

Integration of Detection of Entamoeba Histolytica Using Canny Edge and Watershed Transform enables laboratory persons to have an ease of classification if the bacteria in the stool is an Entamoeba Histolytica. The general objective of the study is to create a device using Raspberry PI, which will help medical technicians to examine the stools and to use Canny Edge Detection and Watershed Transform technique in determining the Entamoeba Histolytica. The stool used was prepared by a medical technologist for safety precautions. After the preparations were done, image was captured using an endoscopic camera placed above the microscope. The image captured first undergoes gray imaging for the canny edge to occur. Canny edge detects all edges in the picture. Then the area of the image was filtered in order to get the main circle, which is the cyst. Dilation and erosion process were used to form a clear circle. The circle-like form inside the cyst was detected using watershed transform. Then Hough transform verifies the circle-like forms inside the cyst if it's a nucleus by identifying its sizes. The final process was displayed through a monitor. The results obtained when placed on a confusion matrix, out of ten samples taken, 8 samples came out as a true positive value of having an amoeba and one came out as true negative in not having an amoeba while one sample came out as false positive.
Bayesian model selection approach for coloured graphical Gaussian models

We consider a class of coloured graphical Gaussian models obtained by imposing equality constraints on the precision matrix in a Bayesian framework. The Bayesian prior for precision matrices is given by the coloured G-Wishart which is the Diaconis-Ylvisaker conjugate. In this paper, we develop a computationally efficient model search algorithm which combines linear regression with a double reversible jump Markov chain Monte Carlo. The latter is to estimate Bayes factors expressed as a posterior probabilities ratio of two competing models. We also establish the asymptotic consistency property of the model determination approach based on Bayes factors. Our procedure avoids an exhaustive search in the space of graphs, which is computationally impossible. Our method is illustrated with simulations and a real-world application with a protein signalling data set.
Anomaly Detection in Public Procurements using the Open Contracting Data Standard

The detection of anomalies in public procurements can enable the improvement of the quality of purchases, and consequently enable a better quality of life in the country through the correct use of public funds. In this paper, we use as a case study the public procurement data from Paraguay, available in the open data format of the Open Contracting Data Standard, to train an unsupervised learning model for anomaly detection based on the Isolation Forest algorithm. The Open Contracting Data Standard allows the developed technique to be replicated in other countries that implement the same data standard, thus achieving an interoperable solution. The resulting classification enables the scoring of contracts and procurement processes which can be used to identify anomalies and make it possible to obtain an intelligent sampling of the data. This can be utilized as a support in the task of the government in its role to regulate and control the public procurements. The effectiveness of the model is validated with local known anomalous procurement processes, which are: a)processes protested by entities involved in the contracting process, which were determined in favor of the protestant, and b)complaints about the contracting process from external entities with the possibility of anonymity. The results show an accuracy of over 90 % in detecting these known anomalies as early as in the tender stage and during the contracting stage. Thus indicating a feasible approach for anomaly detection in public procurements.
OCR Post Processing Using Support Vector Machines

In this paper, we introduce a set of detailed experiment using Support Vector Machines (SVM) to try and improve accuracy selecting the correct candidate word to correct OCR generated errors. We use our alignment algorithm to create a one-to-one correspondence between the OCR text and the clean version of the TREC-5 data set (Confusion Track). We then extract five features from the candidates suggested by the Google web 1T corpus and use them to train and test our SVM model that will then generalize into the rest of the unseen text. We then improve on our initial results using a polynomial kernel, feature standardization with minmax normalization, and class balancing with SMOTE. Finally, we analyze the errors and suggest on future improvements.
A parallel and constraint induced approach to modeling user preference from rating data

Observed rating data in Web2.0 applications concerns user attributes and rating scores, which explicitly reflects users’ overall evaluation on events, products and various informative items. However, the unobservable user preference is critical for personalized services, precise marketing, accurate advertising, etc. In this paper, by adopting Bayesian network (BN) with a latent variable as the knowledge framework to describe user preference using the latent variable, we propose user preference Bayesian network (UPBN) to represent dependence relations among the latent and observed variables. By incorporating the classic expectation maximization (EM) algorithm and scoring & search idea for learning a BN, we focus on UPBN construction from rating data, i.e., the learning of probability parameters and graphical structure. To make UPBN fit the rating data, we first give the constraints of structure and parameters in terms of inherence dependencies among user preference, latent variable and characteristics of EM. Consequently, we present a parallel and constraint induced algorithm for UPBN construction based on EM, structural EM (SEM) and Bayesian information criterion. To deal with the large amount of iterations of probability computations and guarantee the efficiency of model construction, we implement our algorithms upon Spark for the massive intermediate results and large scale rating datasets. Experimental results show the expressiveness of UPBN for preference modeling and the efficiency of model construction, and also demonstrate that UPBN outperforms some state-of-the-art models for user preference estimation and rating prediction.
If unsure, shuffle

Despite significant advantages in theory of evolutionary computation, many papers related to evolutionary algorithms still lack proper analysis and limit themselves by rather vague reflections on why making a certain design choice improves the performance. While this seems to be unavoidable when talking about the behavior of an evolutionary algorithm on a practical optimization problem, doing the same for computational complexities of parts of evolutionary algorithms is harmful and should be avoided.
A cross-entropy based stacking method in ensemble learning

Stacking is one of the major types of ensemble learning techniques in which a set of base classifiers contributes their outputs to the meta-level classifier, and the meta-level classifier combines them so as to produce more accurate classifications. In this paper, we propose a new stacking algorithm that defines the cross-entropy as the loss function for the classification problem. The training process is conducted by using a neural network with the stochastic gradient descent technique. One major characteristic of our method is its treatment of each meta instance as a whole with one optimization model, which is different from some other stacking methods such as stacking with multi-response linear regression and stacking with multi-response model trees. In these methods each meta instance is divided into a set of sub-instances. Multiple models apply to those sub-instances and each for a class label. There is no connection between different models. It is very likely that our treatment is a better choice for finding suitable weights. Experiments with 22 data sets from the UCI machine learning repository show that the proposed stacking approach performs well. It outperforms all three base classifiers, several state-of-the-art stacking algorithms, and some other representative ensemble learning methods on average.
Quality of experience prediction in mobility scenarios based on recurrent neural networks

This paper evaluates the use of machine learning prediction models for assessing the quality of experience perceived by mobile network subscribers under mobility conditions. Input data are taken from a single measuring point available at commercial subscribers' smartphones. A sequence data classifier based on a type of recurrent neural network, the long shortterm memory network, has been implemented and compared with two simpler machine learning models. A set of nine relevant network parameters, mainly related to the signal level and the signal quality of the serving and the neighbour cells, has been considered as inputs for the prediction model. The prediction model has been applied to estimate the downlink TCP performance on a set of non-intrusive drive tests. The overall performance achieved on the test set is around 80%, a really good result considering the size of the dataset.
A Hybrid Book Recommendation Algorithm Based on Context Awareness and Social Network

The resources of university libraries are constantly enriched, and the needs of users are increasingly diversified and personalized. It is difficult to find the resources they want in time. To solve this problem, this paper proposes a hybrid book recommendation algorithm based on context awareness and social network. The algorithm firstly obtains the user's current task and context information, and then calculates the context entropy and context weight to represent the influence degree of different context factors on resource selection. Traditional Pearson similarity is combined with the similarity calculated by the neighborhood-based social network layer to get the hybrid user similarity. The final prediction results are obtained by the hybrid weighting process of the users' prediction scores in different context. Finally, the algorithm is tested by experiments. The results show that the proposed algorithm outperforms traditional collaborative filtering algorithm in precision and recall rate.
Impact of prior knowledge on privacy leakage in trajectory data publishing

The rapid growth in the usage of location-based services has resulted in extensive research on users’ trajectory data publishing. But, a key concern here is a potential breach of user privacy through various linkage attacks by an efficient adversary. There exist a few privacy preservation methods to defend against either single or combination of linkage attacks, namely Identity linkage attack, Attribute linkage attack, and Similarity attack. However, the Correlated-records linkage attack has not been studied in any previous privacy preservation methods, and there is no privacy preservation method to address all the above four linkage attacks. In this paper, a novel anonymization method is proposed to provide the privacy guarantee to users against all the four linkage attacks. The proposed method consists of two phases, namely virtualization and suppression. The virtualization method works as a replacement mechanism for the sensitive attribute and the suppression method works as anonymization mechanism for users trajectories, in order to anonymize the trajectory datasets for preserving users’ privacy from the above four linkage attacks. To validate the efficiency of the proposed method, it is also compared with existing methods, namely KCL-L, KCL-G and KCL-PPTD, considering both synthetic and real-time datasets. The experimental results exhibit that the proposed approach results in better performance with a significant reduction in the information loss when compared to other states of the art methods.
A new approach for service migration in cloud‐based e‐commerce using an optimization algorithm

Summary Service‐oriented architecture (SOA) has a crucial role in backing productive cloud services. Also, the vast spread of the theoretical notion of diverse businesses (like e‐commerce) into the actual use has been recently applied by cloud computing. The service functionality could be affected by overfilling of the network traffic because of the broadly dispersed nature of e‐commerce in clouds—a key challenge for immediate jobs. Throughout the last decade, a vast range of applications or large‐scale operators has increasingly attracted to migrate the services in clouds. An effective method for accessing the applications throughout standard business hours is continually moving virtual machine containers from one data center to another. Now, with the commonness of cloud computing, many applications have been moved to the cloud fully/partly. It can be handled through the migration of cloud services to diverse platforms in a way that minimizes the communication cost of e‐commerce. As this issue has an NP‐hard nature, in the present article, we present an automatic smart service migration outline through the ant colony optimization (ACO) algorithm on cloud‐oriented e‐commerce. In the presented model, we use the ACO algorithm to take the finest (near‐optimal) service migration decisions. Based on the obtained results, the proposed technique has the optimal number of migrations compared to the existing models.
Measuring Task Conflict and Person Conflict in Software Testing

Task-related conflict and person-related conflict in software testing are inevitable and can impact the effectiveness and efficiency of the software development process. The dimensionality of conflict in software testing is reasonably well understood, although in past research both types of conflict have frequently been modeled as reflective constructs that can obstruct the effectiveness of their use as organizational assessment and training tools. One contribution of this study is an empirical model of conflict sources in software engineering; such sources of conflict threaten to derail efficient software development outcomes in firms. A second contribution of this research is the development of a formative measurement model for purposes of development of assessing task conflict and person conflict in software teams. These validated measures can be utilized as training and development instruments for on-the-job remediation of development team conflict. As is indicated in the organizational behavior and software engineering literature, deploying valid measures of workplace stressors such as conflict can lead to the managerial application of effective strategies and tactics to improve workplace morale and satisfaction, to the great benefit of productivity and retention.