Reviewerly/exHarmony huggingface dataset:
Tracking Multiple Indistinguishable and Deformable Objects Based on Multi-Anchor Flow With Annular Sector Model

Most of current multi-object tracking (MOT) methods solve the task of identity assignment mainly by using the distinguishable features and predictable motions of nearly rigid objects. However, when the objects are indistinguishable, non-rigidly deformable and erratically motorial, it is challenging to differentiate the identities and regions of multiple objects in occlusions. In this paper, a novel method based on multi-anchor flow with annular sector model (ASM) is proposed to handle occlusions and complex motions for tracking multiple of these kinds of objects. The multi-anchor flow is built on anchor points which are extracted from the union contours of deformable objects. By optimizing the multi-anchor flow over frames, the proposed method solves the task of identity assignment when multiple objects are in clustered. Meanwhile, the annular sector model is also combined into the tracking method not only to rectify the results of identity assignment, but also to depict the poses or regions of deformable objects in occlusions. In our experiments, the proposed method is evaluated on two publicly challenging dataset for tracking multiple Drosophila Larvae. The results demonstrate that the proposed method has better performance on both of the accuracy of identity assignment and the computational time when compared to other multi-object tracking algorithms.
Morbid curiosity? Discussion of the disposal of amputated limbs on online question and answer sites

Amputation is a growing health issue with implications for the corporeal form and sense of bodily identity. Disposal of the removed limb (the amputate) has historically been suggested to impact on patient adaptation to amputation, although understandings of limb disposal are scarce within existing research. The growth of online question and answer sites has created opportunities for social actors to post and respond to a vast array of topic areas, including those that are seen as morbid or taboo. This paper then explores the discussion of amputate disposal within threads from two popular question and answer sites. Using thematic analysis, the paper examines how perceived ownership of limbs, understanding of the amputate as 'waste' and recourse to grotesque humour are key means by which limb disposal is discussed within these sites. Posters then create a new knowledge around the disposal of limbs, albeit one framed by uncertainty.
Towards a Network of Practices: Identifying Central Elements to Inform Design

For over a decade researchers from the HCI community are taking social practices as a unit of design. While the first generation focused on social practice in isolation, more recent work argues for the interrelatedness of mutually influencing practices as the primary unit of analysis. We discuss these current approaches to motivate the notion of a network of practices. We argue that network theory presents a promising method to create more detailed and sophisticated models of social practices, that raise awareness about central elements and their connecting characteristics. Further on, our work identifies open questions that should be addressed in future work, to increase the benefits of the method.
Reviewing the Inclusion of Artists’ Holograms in the Permanent Collections of Fine Art Museums

Opening in 1976 with the exhibition, “Through the Looking Glass”, the Museum of Holography (MOH) emphasized from the beginning the importance of artistic holography with the inclusion of several holograms by artists whose primary practice was holography, articulating for the first time a distinction between artists, scientists and technicians. While the scientific and engineering principles underlying the technology could educate a public, holograms made by artists provided the visual syntax for the creative possibilities holography could offer. The MOH continued to encourage and support artists’ work throughout its history, amassing a large collection of holograms representative of the most prolific period of artistic activity from the mid 1970s to the mid 1980s. The Massachusetts Institute of Technology Museum (MIT Museum) in Boston acquired the entire archive including artistic and technical holograms as well as all related materials when the MOH closed in 1992. This paper will seek to explore whether the medium of holography within the visual arts has led to fine art museum acquisitions in the intervening decades.
Digital spotlighting filtering optimization for SAR imaging

Abstract In this paper, a synthetic aperture radar (SAR) image formation simulator is used to objectively evaluate the parameter selection within the digital spotlighting process. Specifically, recommendations for the filter type and filter order of the low-pass filters used in the range and azimuth decimation processes within the digital spotlighting algorithm are determined to maximize image quality and minimize computational cost. Results show that a finite impulse response low-pass filter with a Taylor $(\overline {n}=5)$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>(</mml:mo><mml:mover><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mn>5</mml:mn><mml:mo>)</mml:mo></mml:math> window applied provides the highest image quality over a wide range of filter orders and decimation factors. Additionally, a linear relationship between filter length and decimation factor is found.
Repeated Trust Game – Statistical Results Concerning Time of Reaction

The paper presents basic results regarding probability distributions together with the parameters related to the decision-making time in the repeated trust game. The results obtained are of a general nature, related to the waiting time for a reaction in computer-aided systems, as well as a special one related to the characteristics of the decision-makers participating in the experiment.
Measuring Personal Values in Cross-Cultural User-Generated Content

There are several standard methods used to measure personal values, including the Schwartz Values Survey and the World Values Survey. While these tools are based on well-established questionnaires, they are expensive to administer at a large scale and rely on respondents to self-report their values rather than observing what people actually choose to write about. We employ a lexicon-based method that can computationally measure personal values on a large scale. Our approach is not limited to word-counting as we explore and evaluate several alternative approaches to quantifying the usage of value-related themes in a given document. We apply our methodology to a large blog dataset comprised of text written by users from different countries around the world in order to quantify cultural differences in the expression of person values on blogs. Additionally, we analyze the relationship between the value themes expressed in blog posts and the values measured for some of the same countries using the World Values Survey.
A Neural Based Approach to Evaluate an Answer Script

Assessment of answers with particular questions is a heavy task as in assumption that all the students answers have to be awarded. In this paper we give cue of the possibility to cut down the teacher's workload on open questions by a component managing a neural network-based model of the students' decisions, involved in a peer-assessment task. The answer is recognized by OCR and it converted by machine readable format. The network of constraints and relations established among the answers through the students' knowledge, allows us to compare and relate with set of possible keyword of the database. Convolution neural network plays a vital role in comparison of answer database and student database. The Receiver Operating Characteristic curve are constructed based on the accuracy of students marks. Based on the comparison result obtained, the accuracy of student answer is measured and awarded. Our computer system suggests that the subset of the answers is evaluated with database answer in which the performance of evaluation is measured. This is used to reduce workload of humans and it automatically evaluate the answer. It is mainly used in schools, colleges, university etc.
A Constrained Box Algorithm for Imbalanced Data in Satellite Images

Classification of imbalanced data is a challenging issue in the interpretation of remote sensing images. In a majority class (negatives), there are much more pixels than in a minority class (positives). This imbalanced data makes the classification extremely difficult to produce higher accuracy. The sampling technique is one of techniques, which work well for many pattern data, but not for remote sensing images. The Fast Box algorithm (FBA) which uses the clustering algorithm was then proposed to characterize and discriminate the minority class from the majority class by determining the decision boundaries of the minority data. The FBA performs well in most pattern datasets; however, it fails to recognize the minority class properly in remote sensing images. In this study, we propose a new Constrained Box Algorithm (CBA), which can effectively detect the minority class within remote sensing images. The FBA often misclassifies negatives as positives, CBA eliminates this issue by restricting the maximum number of allowed positives within a box. Our new algorithm finds the minority class by an iterative process of discovering appropriate boundaries using clustering and eliminating majority instances from initial boundaries. A threshold is used to guide the search process to find acceptable boundaries. The set of accepted boundaries are then used to discover the minority class. Experimental results demonstrates that the minority class was correctly detected in satellite images.
Big Social Data - Predicting Users' Interests from their Social Networking Activities

The amount of data produced by Social Network users, whether through direct content creation or as a byproduct of their Social Network usage, is ever increasing. This research presents an approach to predicting unknown user interest in entities based on Entity Extraction from User Generated Content and through the use of a Potential Link Prediction algorithm for recommendations. An algorithm was developed which is able to extract relevant entities from the microtext forming the metadata of Facebook pages liked by a user. These entities are then used in order to suggest other potentially interesting pages to the user. Additionally, crowd-sourced knowledge is used in order to automatically filter out entities which are likely to be irrelevant to future users based on past ratings. Using these filtered entities and by having at least 10 interests disclosed by a user, it is possible to predict further entities of interest to a user, with at least 80% confidence in the predictions.
K-nearest neighbor analysis to predict the accuracy of product delivery using administration of raw material model in the cosmetic industry (PT Cedefindo)

Abstract The purpose of this study is to apply the K-Nearest Neighbor (KNN) algorithm to predict the accuracy of product delivery using the raw material administration model, which has 3 attributes, namely administration of material purchases, administration of material readiness and processing work orders compared to existing targets. From this study, it was found that the KNN Algorithm was effective in predicting the accuracy of product delivery using the administration of raw material model applied to the production history data from July 2018 to December 2018 with a small error ratio. Thus, data mining with the KNN algorithm can be used in decision making within the company to predict the accuracy of product delivery.
Mobile Learning in the Era of IoT

Teaching is always affected by the advent of technology. Nowadays, mobile devices can offer an air of innovation in classrooms and multiple benefits in learning. On the other hand, IoT is expanding rapidly and promises to provide education with new dynamics: sensors and beacons may contribute to pervasive provision of educational content to students, whereas wearables can track the students' interaction with educational objects. As a result, learning is changing and may happen anywhere, anytime, and with any means. This evolution, described under the term Ubiquitous learning, promises to be the future of education for all ages and needs. This chapter presents the transformation of learning from traditional to e-learning, mobile learning, and Ubiquitous learning, and discusses the features and applications of the latter. Furthermore, authors describe the Experience API specification and investigate how it can be used to implement adaptive learning applications and make Ubiquitous learning a reality not only in typical but also in Early Childhood learning.
Probabilistic maximal frequent itemset mining methods over uncertain databases

Uncertain data are data accompanied with probability, which makes frequent itemset mining more challenging. This paper focuses on the problem of mining probabilistic maximal frequent itemsets. We redefine the concept of probabilistic maximal frequent itemset to be consistent with the traditional de finition and provide a better view on how to devise pruning strategies. A tree-based index called the probabilistic maximal frequent itemset tree is constructed to maintain the probabilistic frequent itemsets. We proposed a depth-first probabilistic maximal frequent itemset mining algorithm to bottom-up generate the exact results, in which support and expected support are used to estimate the range of probabilistic support, enabling the frequency of an itemset to be inferred with less runtime and memory usage. Also, superset pruning is employed to further reduce mining cost. Nevertheless, certain probabilistic supports have to be computed when the minimum support is low, which may result in highly increased mining speed. This problem is addressed in our approximate probabilistic maximal frequent itemset mining method, which uses the expected support to directly compute the probabilistic support. Theoretical analysis and experimental studies demonstrate that our proposed algorithms have high accuracy, expend less computational time and use less memory, and significantly outperform the TODIS-MAX [20] state-of-the-art algorithm.
The Cascade Improved Model Based Deep Forest for Small-scale Datasets Classification

It is very important to classify some small-scale datasets accurately in biology. With the rapid advancement of classification models, support vector machine(SVM), Random Forest(RF), Deep Forest, Convolutional Neural Networks(CNNs) are widely used. However, for small-scale datasets, CNNs always need massive datasets to train. Other methods usually can't achieve better effects. Therefore, in this paper, a new forest model is proposed to solve the problems in small-scale datasets. It improves the classification performance through integrated learning method. The improved model is different from the primitive model in two important aspects. Firstly, considering the fitting quality of every forest, the standard deviation of some most major features in every forest make up a new feature to be transport in the next cascade layer. Secondly, the sub-layer structure is adapted to the cascade layer to increase the training opportunities. Experiments on five datasets demonstrate that our method has better classification effect than other classification models in the small-scale datasets.
A Method to Reduce Transaction Time for Real-time IoT Applications

Due to the recent prevalence of IoT (Internet of Things) devices, streaming data such as video data or sensor data are collected and analyzed for real-time applications. The transaction time for data collection and analysis is one of the main factors to improve performance of some real-time IoT applications. For instance, in real-time surveillance camera systems, a shorter transaction time further reduces the delay to find thieves recorded by the cameras. However, computational and communication capacities of processing computers give lower limits to transaction time. To break this limitation, we propose an efficient data collection method using a progressive quality improvement approach. In our proposed method, each data source produces some content data of those qualities are lower than the quality of the original content data such as low resolution image data. Only the cases where higher quality data are needed for analyses, the processing computer progressively collects them. Thus, by reducing the average data amount of collections and for analyses, our proposed method reduces the average transaction time. We measured the transaction time of our proposed method in our developed simulator and confirmed that our proposed method can reduce the transaction time.
A Multi-User 360-Video Streaming System for Wireless Network

With the rapid development of Virtual Reality technology and its hardware, 360-degree video is becoming a new form of media which arouses the interest of public. In the past few years, many 360-degree video delivery schemes are proposed, but there hasn’t been a standard solution which can perfectly overcome the difficulties caused by network latency and bandwidth limit. In this paper, we consider the context of a wireless network consisting of a base station and several users. We proposed a 360-degree video delivery and streaming scheme which serves multiple users simultaneously while optimizing the global bandwidth consumption. The system will predict the head movement of users using machine learning algorithm, and extract the visible portion of the video frame for transmission. The core contribution of the scheme is that it will recognize the conjoint viewport of multiple users, and then optimize the global bandwidth consumption by arranging the transmission of conjoint viewport over the public channel of the wireless network. The results prove that the proposed scheme can effectively reduce global bandwidth consumption of the network with relatively simple configuration.
Photovoltaic power forecasting through temperature and solar radiation estimation

The Utilization of the photovoltaic power as a source of electricity has been strongly growing. The unpredictability of the PV power energy induces frequency fluctuations and power system instabilities. Thus, short term PV power prediction, from one hour to several hours, becomes very important to ensure grid stability. The photovoltaic power depends on different weather conditions mostly temperature and solar radiation. Therefore, weather data forecasting becomes highly recommended. This paper presents a comparison study between the adaptive neuro-fuzzy inference system and the feed forward neural network for one hour ahead temperature and solar radiation estimation using different input data. Two and four hours ahead forecasting of the metrological data are done using the feed forward neural network model. Using the forecasted weather data, the photovoltaic power is deduced. The accuracy of the topologies is based on the normalized root mean square error (NRMSE), and the mean absolute percentage error (MAPE) The simulation results show that the FFNN outperforms the ANFIS model.
Ordinal Equivalence Classes for Parallel Coordinates

We give a mathematical treatment to the concept of ordinal equivalence defined relative to all m! possible permutations of parallel axes. We prove that the ordinal equivalence is determined by the pair-wise co-monotonicity equivalence relations, thus leading to simple algorithmic procedures for finding the corresponding partition. Each ordinal equivalence class can be visualized as a profile of co-monotone polylines, in this way preventing any clutter at the image. We illustrate our approach with two datasets taken from the literature.
An Empirical Model for Thyroid Disease Diagnosis Using Data Mining Techniques

Utilization of data mining in healthcare sectors showing great role in effectiveness of treatment, healthcare administration, finding of fraud and abuse and customer relationship management but besides that it is also used for diagnosis of diseases. In this work, our focus is on diagnosis of thyroid diseases by using three classification models like K-Nearest Neighbor (K-NN), Decision Tree and Naïve bayes based on certain clinical thyroid attributes like Age, Gender, TSH, T3 and T4. The entire research work is to be conducted with RapidMiner version 8.1, an open source tool under Windows 10 environment. The Experimental study shows that decision tree outperformed over other models.
Maximizing the Availability of Composable Systems of Next-Generation Data Centers

The next-generation data center introduces the refactoring of the traditional data center in order to create pools of disaggregated resource units, such as processors, memory, storage, network, power, and cooling sources, named composable system (CSs) with the purpose of offering flexibility, automation, optimization, and scalability. In this paper, we solve an optimization problem to allocate CSs considering next- generation data centers. The main goal is to maximize the CS availability for the application owner, having its minimum requirements (in terms of CPU, memory, network and storage), and available budget as restrictions. This problem is modeled as a bounded multidimensional knapsack problem, and we solve it using Dynamic Programming (DP), and two Soft Computing approaches: Differential Evolution (DE) and Particle Swarm optimization (PSO). We consider two different scenarios in order to analyze heterogeneity and variability aspects when allocating CSs in a data center. Moreover, we also analyze the importance of system components to give directions and priorities of actions to upgrade the system design.
Towards Self-automatable and Unambiguous Smart Contracts: Machine Natural Language

A smart contract originally drafted by natural language is an essential task of many applications in blockchain technology. Firstly, natural language cannot be directly executed by computers, self-executing requires terms of the smart contract be computer-readable and executable. Secondly, in crossing environments or parties, contract translation needs the overall meaning of a sentence to have a meticulous precision, besides, low tolerance of mistakes for reducing a tedious process. Lastly, many kinds of templates of smart contracts need a common sense of agreement where each party agrees on the context of the contract. This paper explores the problems of the smart contract in natural language and self-executing to redefine the smart contract through an approach, which supports a human-readable, computer-understandable and self-executable contract representations with enabling semantic structural based on Machine Natural Language (MNL). Meanwhile, a common dictionary (CoDic) transfers natural languages into universal machine codes or languages without the ambiguity across parties.
Variable and situated user interfaces

Increasing computational capacity, small-size and small-cost sensors are leading the way towards widespread and pervasive presence of smart and IoT-based devices. The contribution presents an ongoing research conducted within DecoChrom, a Horizon 2020 project working on interfaces that have the potential to modify the functionality of a smart object by appearing and disappearing. Since 2018, we started developing interfaces that draw themselves accordingly to the user need. Drawing User Interfaces have the ability to react to external stimuli---as the information gathered from the physical world via sensors---, modifying their aesthetics and function accordingly. Following this logic, an object can display different interfaces depending on its positioning, physical variation of the environment, or even the presence of other smart objects.