Reviewerly/exHarmony huggingface dataset:
Blockchain and the Law: A Critical Evaluation

It is a high-risk, high-reward enterprise to write a scholarly monograph on an emerging technology when its societal use, economic worth, and even its technical design are still in flux. With little empirical material with which to work, one often has to resort to extrapolating the future developments from the myriad seed of possibilities of the present. Yet, there are moments in time when undertaking such an enterprise seems inevitable, because there is a rough consensus that the emerging technology represents more than just an incremental improvement of already existing routines, and promises—or threatens—a disruption of the status quo. Such is the case of blockchain or distributed ledger technologies. In that light, Primavera De Filippi and Aaron Wright’s Blockchain and the Law is a timely and valuable contribution.
Analyzing the Performance of Various Clustering Algorithms

Clustering is one of the extensively used techniques in data mining to analyze a large dataset in order to discover useful and interesting patterns.It partitions a dataset into mutually disjoint groups of data in such a manner that the data points belonging to the same cluster are highly similar and those lying in different clusters are very dissimilar.Furthermore, among a large number of clustering algorithms, it becomes difficult for researchers to select a suitable clustering algorithm for their purpose.Keeping this in mind, this paper aims to perform a comparative analysis of various clustering algorithms such as k-means, expectation maximization, hierarchical clustering and make densitybased clustering with respect to different parameters such as time taken to build a model, use of different dataset, size of dataset, normalized and un-normalized data in order to find the suitability of one over other.
Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal

In the era of Deep Learning and Big Data, the place of Genetic Programming (GP) within the Machine Learning area seems difficult to define. Whether it is due to technical constraints or conceptual barriers, GP is currently not a paradigm of choice for the development of state-of-the-art machine learning systems. Nonetheless, there are important features of the GP approach that make it unique and should continue to be actively explored and studied. In this work we focus on two aspects of GP that have previously received little or no attention, particularly in tree-based GP for symbolic regression. First, on the potential of GP to perform transfer learning, where solutions evolved for one problem are transferred to another. Second, on the potential of GP individuals to detect the true underlying structure of an input dataset and detect anomalies in the input data, what are known as outliers. This work presents initial results on both issues, with the goal of fostering discussion and showing that there is still untapped potential in the GP paradigm.
A Dynamic Partitioning Algorithm for Sip Detection using a Bottle-Attachable IMU Sensor

Hydration tracking technologies are a promising tool for improving health outcomes across a variety of populations.As a non-wearable solution that is reconfigurable across containers, bottle-attachable inertial measurement unit (IMU) sensors offer numerous advantages versus alternative tracking approaches.This paper proposes a novel dynamic temporal partitioning and classification algorithm for spotting drinks within the streaming data generated by such sensors.By exploiting the distinguishing characteristics of the container's estimated inclination during drinking, the algorithm identifies candidate drink intervals for subsequent classification using a Threshold-Merge-Discard framework.The proposed approach is benchmarked against a slight variation of a previously introduced sliding window classifier for a series of experiments replicating the intended use case of the device.The new algorithm is shown to increase the true-positive detection rate by 23.7%, while reducing the number of required classification operations by more than an order of magnitude.
Survival of the Fittest: Increased Stimulus Competition During Encoding Results in Fewer but More Robust Memory Traces

Forgetting can be accounted for by time-indexed decay as well as competition-based interference processes. Although conventionally seen as competing theories of forgetting processes, Altmann and colleagues (2002, 2012) argued for a functional interaction between decay and interference. They revealed that, in short-term memory, time-based forgetting occurred at a faster rate under conditions of high proactive interference compared to conditions of low proactive interference. However, it is unknown whether interactive effects between decay-based forgetting and interference-based forgetting also exist in long-term memory. We employed a delayed memory recognition paradigm for visual indoor and outdoor scenes, measuring recognition accuracy at two time-points, immediately after learning and after one week, while interference was indexed by the number of images in a semantic category. We found that higher levels of interference during encoding led to a slower subsequent decay rate. In contrast to the findings in working-memory, our results suggest that a “survival of the fittest” principle applies to long-term memory processes, in which stimulus competition during encoding results in fewer, but also more robust memory traces, which decay at a slower rate. Conversely, low levels of interference during encoding allow more memory traces to form initially, which however subsequently decay at a faster rate. Our findings provide new insights into the mechanism of forgetting and could inform neurobiological models of forgetting.
The Importance of Education in Peace Marketing

Peace defined means the absence of war. War and peace have always been not only military but also political issues advantage. Educational systems are confronted with exceptional challenges during violent conflict. During the conflict International humanitarian law is particularly important. Geneva Conventions a specific reference to protection related to education in wartime. Education peace is also related to the creation of values and skills that help students achieve fullness a life that embraces all people. Education peace relates to aid pupils to recognize many forms and causes violence and promote values and skills to live society. Peace marketing is trying to influence social behavior, change attitudes, habits, and not to the marketing person, but to the benefits of society in general. Article analyzes peace education concepts, role education in promoting peace marketing, universities peace initiative.
Mixture of Experts with Entropic Regularization for Data Classification

Today, there is growing interest in the automatic classification of a variety of tasks, such as weather forecasting, product recommendations, intrusion detection, and people recognition. “Mixture-of-experts” is a well-known classification technique; it is a probabilistic model consisting of local expert classifiers weighted by a gate network that is typically based on softmax functions, combined with learnable complex patterns in data. In this scheme, one data point is influenced by only one expert; as a result, the training process can be misguided in real datasets for which complex data need to be explained by multiple experts. In this work, we propose a variant of the regular mixture-of-experts model. In the proposed model, the cost classification is penalized by the Shannon entropy of the gating network in order to avoid a “winner-takes-all” output for the gating network. Experiments show the advantage of our approach using several real datasets, with improvements in mean accuracy of 3–6% in some datasets. In future work, we plan to embed feature selection into this model.
Blockchain Technology with Applications to Distributed Control and Cooperative Robotics: A Survey

As a disruptive technology, blockchain, particularly its original form of bitcoin as a type of digital currency, has attracted great attentions. The innovative distributed decision making and security mechanism lay the technical foundation for its success, making us consider to penetrate the power of blockchain technology to distributed control and cooperative robotics, in which the distributed and secure mechanism is also highly demanded. Actually, security and distributed communication have long been unsolved problems in the field of distributed control and cooperative robotics. It has been reported on the network failure and intruder attacks of distributed control and multi-robotic systems. Blockchain technology provides promise to remedy this situation thoroughly. This work is intended to create a global picture of blockchain technology on its working principle and key elements in the language of control and robotics, to provide a shortcut for beginners to step into this research field.
Deep Segmentation Refinement with Result-Dependent Learning

In this contribution, we propose a 2D deep segmentation refinement approach, that is inspired by the U-Net architecture and incorporates result-dependent loss adaptation. The performance of our method regarding segmentation quality is evaluated on the example of hip joint segmentation in T1-weighted MRI data sets. The results are compared to an ordinary U-Net implementation. While the segmentation quality of the proximal femur does not significantly change, our proposed method shows promising improvements for the segmentation of the pelvic bone complex, which shows more shape variability in the 2D image slices along the longitudinal axis.
The Effects of 3D Multi-User Virtual Environments on Collaborative Learning and Social Presence

Three-dimensional Multi-User Virtual Environments (MUVEs) are being increasingly used in many areas, and they are becoming more and more integrated with learning and teaching.MUVEs can be used in learning and teaching to facilitate student learning and collaboration.This study identified the effects of MUVEs on collaborative learning and social presence and investigated whether these effects varied by gender.The participants were sophomore students attending a programming language course.They used a MUVE for their group meetings as part of their collaborative work.They also held voice communication.A four-section questionnaire was administered to the students who attended the meetings, in which the researchers served as facilitators.Interviews were held with randomly selected students.In addition, the group project meetings were tape-recorded, and field notes were taken for each meeting.The results showed that the MUVE enabled the group members to exchange ideas in an authentic environment, and that the file-sharing platform used as a complement to the MUVE reinforced collaborative learning.As long as technical problems are prevented, effective collaborative learning can be achieved in these environments.
Investigating 3D Printer Residual Data

The continued adoption of Additive Manufacturing (AM) technologies is raising concerns in the security, forensics, and intelligence gathering communities.These concerns range from identifying and mitigating compromised devices, to theft of intellectual property, to sabotage, to the production of prohibited objects.Previous research has provided insight into the retrieval of configuration information maintained on the devices, but this work shows that the devices can additionally maintain information about the print process.Comparisons between before and after images taken from an AM device reveal details about the device's activities, including printed designs, menu interactions, and the print history.Patterns in the storage of that information also may be useful for reducing the amount of data that needs to be examined during an investigation.These results provide a foundation for future investigations regarding the tools and processes suitable for examining these devices.
PKCHD: Towards A Probabilistic Knapsack Public-Key Cryptosystem with High Density

By introducing an easy knapsack-type problem, a probabilistic knapsack-type public key cryptosystem (PKCHD) is proposed. It uses a Chinese remainder theorem to disguise the easy knapsack sequence. Thence, to recover the trapdoor information, the implicit attacker has to solve at least two hard number-theoretic problems, namely integer factorization and simultaneous Diophantine approximation problems. In PKCHD, the encryption function is nonlinear about the message vector. Under the re-linearization attack model, PKCHD obtains a high density and is secure against the low-density subset sum attacks, and the success probability for an attacker to recover the message vector with a single call to a lattice oracle is negligible. The infeasibilities of other attacks on the proposed PKCHD are also investigated. Meanwhile, it can use the hardest knapsack vector as the public key if its density evaluates the hardness of a knapsack instance. Furthermore, PKCHD only performs quadratic bit operations which confirms the efficiency of encrypting a message and deciphering a given cipher-text.
Multiscale Entropy Analysis of Page Views: A Case Study of Wikipedia

In this study, the Wikipedia page views for four selected topics, namely, education, the economy/finance, medicine, and nature/environment from 2016–2018 are collected and the sample entropies of the three years’ page views are estimated and investigated using a short-time series multiscale entropy (sMSE) algorithm for a comprehensible understanding of the complexity of human website searching activities. The sample entropies of the selected topics are found to exhibit different temporal variations. In the past three years, the temporal characteristics of the sample entropies are vividly revealed, and the sample entropies of the selected topics follow the same tendencies and can be quantitatively ranked. By taking the 95% confidence interval into account, the temporal variations of sample entropies are further validated by statistical analysis (non-parametric), including the Wilcoxon signed-rank test and the Mann-Whitney U-test. The results suggest that the sample entropies estimated by the sMSE algorithm are feasible for analyzing the temporal variations of complexity for certain topics, whereas the regular variations of estimated sample entropies of different selected topics can’t simply be accepted as is. Potential explanations and paths in forthcoming studies are also described and discussed.
Reading Preferences of ESL Students: Electronic Texts vs. Printed

The present study investigated the reading preferences of international students regarding their choices between electronic texts (e-texts) and printed texts (p-texts). The study also explored the influence of reading e-texts and p-texts on comprehending their contents, as well as the purpose of students using these electronic devices (e-devices). The data were collected using a questionnaire completed by non-native English speakers (36 males, 24 females) at a southwestern university in the United States. The findings indicated that the students preferred using p-texts over e-texts. Among these students, there was no gender difference in terms of reading preferences. Moreover, the results indicated a statistically significant difference between males and females regarding understanding the content in the printed format. The females understood the content better than the males when they read p-text. The findings also revealed that students preferred using electronic devices for personal uses rather than academic uses. Such personal uses were web browsing, listening to or watching media, and reading and writing emails. The study suggested several pedagogical implications for students and e-book developers and designers.
Mixed Region Covariance Discriminative Learning for Image Classification on Riemannian Manifolds

Covariance matrices, known as symmetric positive definite (SPD) matrices, are usually regarded as points lying on Riemannian manifolds. We describe a new covariance descriptor, which could improve the discriminative learning ability of region covariance descriptor by taking into account the mean of feature vectors. Due to the specific geometry of Riemannian manifolds, classical learning methods cannot be directly used on it. In this paper, we propose a subspace projection framework for the classification task on Riemannian manifolds and give the mathematical derivation for it. It is different from the common technique used for Riemannian manifolds, which is to explicitly project the points from a Riemannian manifold onto Euclidean space based upon a linear hypothesis. Under the proposed framework, we define a Gaussian Radial Basis Function- (RBF-) based kernel with a Log-Euclidean Riemannian Metric (LERM) to embed a Riemannian manifold into a high-dimensional Reproducing Kernel Hilbert Space (RKHS) and then project it onto a subspace of the RKHS. Finally, a variant of Linear Discriminative Analyze (LDA) is recast onto the subspace. Experiments demonstrate the considerable effectiveness of the mixed region covariance descriptor and the proposed method.
Solar PV nowcasting based on skycamera observations

Abstract. In recent years the installation of PV systems has increased dramatically in many countries. Annual global installed power has already reached more than 400 GW in 2017. A major challenge for operators is that PV system output is highly fluctuating due to cloud movements and other atmospheric influences. Forecasting of solar irradiation and PV power on different time scales will, therefore, become more and more important for different users. As part of the Austrian PV-go-Smart project, several skycameras have been installed in the region of Upper Austria. In this paper we show differences and advantages of two skycams and their image qualities. Algorithms for the detection of clouds, cloud movement, and GHI forecasting have been developed and validated with ground observation at the Wels site. This work focusses on image quality issues related to short-term irradiance forecasting using all-sky cameras, in particular the influence on raindrops on forecast performance.
The students� reflective writing manifestation of reader-response literary analysis

This article presents reflective writing manifestation in Reader-response literary analysis. The article is aimed at finding out how the reflective writing is manifested in the students� Reader-response of literary analysis. The writer carried out the study by analyzing the students� portfolio documents of their reflective essays responding to three literary works. Fourteen students enrolling Literary Criticism Subject at a private university in West Java took part in the present research. Literary Criticism subject was chosen as the object of the current study, considering that the students in enrolling the subject were not merely introduced to literature, but also were led to be able to evaluate, describe, and analyze literary works. The study revealed some findings. Firstly, Reader-response prompts were helpful in giving aids to the students that they give the students idea how to write down their ideas and thoughts in minds. However, as the second finding, most students tend to keep their focus on the feeling section. Some of them were proven to be more critical by questioning things happened in the stories, yet they still had no idea how to analyze and evaluate the questions they had. The last finding, the writer noticed that none of the students relate their reflective essays to relevant literature to support their opinion. The writer considered that this occurred due to their lack of knowledge and practice of critical thinking and critical writing.
Using LSTM for Automatic Classification of Human Motion Capture Data

Creative studios tend to produce an overwhelming amount of content everyday and being able to manage these data and reuse it in new productions represent a way for reducing costs and increasing productivity and profit. This work is part of a project aiming to develop reusable assets in creative productions. This paper describes our first attempt using deep learning to classify human motion from motion capture files. It relies on a long short-term memory network (LSTM) trained to recognize action on a simplified ontology of basic actions like walking, running or jumping. Our solution was able of recognizing several actions with an accuracy over 95% in the best cases.
Improving Diversity of Image Captioning Through Variational Autoencoders and Adversarial Learning

Learning translation from images to human-readable natural language has become a great challenge in computer vision research in recent years. Existing works explore the semantic correlation between the visual and language domains via encoder-to-decoder learning frameworks based on classifying visual features in the language domain. This approach, however, is criticized for its lacking of naturalness and diversity. In this paper, we demonstrate a novel way to learn a semantic connection between visual information and natural language directly based on a Variational Autoencoder (VAE) that is trained in an adversarial routine. Instead of using the classification based discriminator, our method directly learns to estimate the diversity between a hidden vector embedded from a text encoder and an informative feature that is sampled from a learned distribution of the autoencoders. We show that the sentences learned from this matching contains accurate semantic meaning with high diversity in the image captioning task. Our experiments on the popular MSCOCO dataset indicates that our method learns to generate high-quality natural language with competitive scores on both correctness and diversity.
A system for one-shot learning of cervical cancer cell classification in histopathology images

Convolutional neural networks (CNNs) have been popularly used to solve the problem of cell/nuclei classification and segmentation in histopathology images. Despite their pervasiveness, CNNs are fine-tuned on specific, large and labeled datasets as these datasets are hard to collect and annotate. However, this is not a scalable approach. In this work, we aim to gain deeper insights into the nature of the problem. We used a cervical cancer dataset with cells labeled into four classes by an expert pathologist. By employing pre-training on this dataset, we propose a one-shot learning model for cervical cell classification in histopathology tissue images. We extract regional maximum activation of convolutions (R-MAC) global descriptors and train a one-shot learning memory module with the goal of using it for various cancer types and eliminate the need for expensive, difficult to collect, large, labeled whole slide image (WSI) datasets. Our model achieved 94.6% accuracy in detecting the four cell classes on the test dataset. Further, we present our analysis of the dataset and features to better understand and visualize the problem in general.
Neuro-fuzzy-based Electronic Brake System Modeling using Real Time Vehicle Data

Electronic Brake System (EBS) is considered as one of the most complicated systems whose performance depends on the subsystems parameters. Usually these parameters are difficult to predict. Based on the task to improve the EBS performance, this article presents a mathematical modeling approach based on neuro-fuzzy network method to model a subsystem of EBS. For the model parameters identification, a neuro-fuzzy network has been implemented based on Least Square Error (LSE) and Levenberg- Marquardt Algorithm (LMA) as the optimization algorithms. Finally, the performance of identified model has been evaluated.