Reviewerly/exHarmony huggingface dataset:
Detection of physical activity using machine learning methods

In the case of diabetes mellitus physical activity does have a high effect on the glycemic state of the patients. This is especially regarding the patients with Type 1 diabetes mellitus, who need external insulin administration in their daily life. Nevertheless, physical activity - as one source of stress - is underrepresented in the decisions of patients and medical staff and in the decisions of the available automated glucose regulatory devices. The goal of the study was to build up a simulation framework for data generation and to assess which machine learning solution can be the most accurate in the identification of physical activity.
Open Social Systems

While normative systems have excelled at addressing issues such as coordination and cooperation, they have left a number of open challenges. The first is how to reconcile individual goals with community goals, without breaching the individual’s privacy. The evolution of norms driven by individuals’ behaviour or argumentation have helped take the individual into consideration. But what about individual norms that one is not willing to share with others? Then there are the ethical considerations that may arise from our interactions, such as, how do we deal with stereotypes, biases, or racism, or how to avoid the abuse of community resources. This paper is concerned with accounting for individual needs while respecting privacy and adhering to the community’s ethical code. We propose a decentralised architecture for normative systems that, along with the community norms, introduces individual’s requirements to help mediate the interaction between members.
NMT based Similar Language Translation for Hindi - Marathi

This paper describes the participation of team F1toF6 (LTRC, IIIT-Hyderabad) for the WMT 2020 task, similar language translation. We experimented with attention based recurrent neural network architecture (seq2seq) for this task. We explored the use of different linguistic features like POS and Morph along with back translation for Hindi-Marathi and Marathi-Hindi machine translation.
E-Transactional Fraud Detection Using Fuzzy Association Rule Mining

As the E-transactions have become integral part day-to-day life activities. Hence trust and security has become most crucial part of the financial institutions. We have proposed here fraud detection algorithm for E-transactional frauds. We used here fuzzy logic and Association Rule mining. The fuzzy logic is used to determine the cognitive properties of features, so that the authentic users are not blocked for e-transactions and fraudsters are not allowed and the association rules, we have used to filter out the fraud transactions from the dataset of financial transactions. The results obtained here and the analysis of the results shows that detection rate is high and efficient compared to the other recent works.
The Hate Speech Elimination Act

This chapter offers an outline of the Hate Speech Elimination Act and analyses some of its issues. When the Japanese Diet enacted the Hate Speech Elimination Act in 2016, it was the first law to directly tackle hate speech. The law is unusual because while it clearly declares hate speech to be impermissible, it imposes no penalties upon it. On the one hand, one might argue that the Act properly balances equality and freedom of speech; on the other hand, one might question its effect in combating hate speech. It should be emphasized that the Act requests the national and local governments to implement educational activities to eliminate unfair discriminatory speech and behaviour, as well as to raise awareness among the general public about the issue. Such government activities can be interpreted as a type of ‘government speech’, which can be used to discourage and deter hate speech while avoiding constitutional problems. As such, the Japanese Act may present a modest model that strikes an appropriate balance between freedom of speech and anti-racism.
A Bi-Directional Extensible Interface Between Lean and Mathematica

We implement a user-extensible ad hoc connection between the Lean proof assistant and the computer algebra system Mathematica. By reflecting the syntax of each system in the other and providing a flexible interface for extending translation, our connection allows for the exchange of arbitrary information between the two systems. We show how to make use of the Lean metaprogramming framework to verify certain Mathematica computations, so that the rigor of the proof assistant is not compromised. We also use Mathematica as an untrusted oracle to guide proof search in the proof assistant and interact with a Mathematica notebook from within a Lean session. In the other direction, we import and process Lean declarations from within Mathematica. The proof assistant library serves as a database of mathematical knowledge that the CAS can display and explore.
Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.
On the Transferability of Winning Tickets in Non-natural Image Datasets

We study the generalization properties of pruned neural networks that are the winners of the lottery ticket hypothesis on datasets of natural images. We analyse their potential under conditions in which training data is scarce and comes from a non-natural domain. Specifically, we investigate whether pruned models that are found on the popular CIFAR-10/100 and Fashion-MNIST datasets, generalize to seven different datasets that come from the fields of digital pathology and digital heritage. Our results show that there are significant benefits in transferring and training sparse architectures over larger parametrized models, since in all of our experiments pruned networks, winners of the lottery ticket hypothesis, significantly outperform their larger unpruned counterparts. These results suggest that winning initializations do contain inductive biases that are generic to some extent, although, as reported by our experiments on the biomedical datasets, their generalization properties can be more limiting than what has been so far observed in the literature.
Fusing appearance and motion information for action recognition on depth sequences

With the advent of cost-efficient depth cameras, many effective feature descriptors have been proposed for action recognition from depth sequences. However, most of them are based on single feature and thus unable to extract the action information comprehensively, e.g., some kinds of feature descriptors can represent the area where the motion occurs while they lack the ability of describing the order in which the action is performed. In this paper, a new feature representation scheme combining different feature descriptors is proposed to capture various aspects of action cues simultaneously. First of all, a depth sequence is divided into a series of sub-sequences using motion energy based spatial-temporal pyramid. For each sub-sequence, on the one hand, the depth motion maps (DMMs) based completed local binary pattern (CLBP) descriptors are calculated through a patch-based strategy. On the other hand, each sub-sequence is partitioned into spatial grids and the polynormals descriptors are obtained for each of the grid sequences. Then, the sparse representation vectors of the DMMs based CLBP and the polynormals are calculated separately. After pooling, the ultimate representation vector of the sample is generated as the input of the classifier. Finally, two different fusion strategies are applied to conduct fusion. Through extensive experiments on two benchmark datasets, the performance of the proposed method is proved better than that of each single feature based recognition method.
Estimating Bar Graph Averages: Overcoming Within-the-Bar Bias

Although most people are not aware of it, bias can occur when interpreting graphs. Within-the-bar bias describes a misinterpretation of the distribution of data underlying bar graphs that indicate an average or where the average estimation point moves inside the bar when the average of several graphs is estimated. This study proposes and tests two methods based on information processing to reduce within-the-bar bias. The first method facilitates bottom-up processing by changing various graph features, such as presenting confidence intervals, placing boundaries around the graph, and showing cumulative bars with different tones. The second method facilitates top-down processing by instructing participants to estimate the mean based on a dot at the end of each bar. Testing of the first method showed that cumulative bars reduced bias, but the other methods did not. The second method was found to reduce bias. Overall, our results demonstrate that the accurate interpretation of bar graphs can be facilitated through the manipulation of specific graph features and instruction.
Sufficient Condition for Rectifiability Involving Wasserstein Distance $$W_2$$

A Radon measure $\mu$ is $n$-rectifiable if it is absolutely continuous with respect to $\mathcal{H}^n$ and $\mu$-almost all of $\text{supp}\,\mu$ can be covered by Lipschitz images of $\mathbb{R}^n$. In this paper we give two sufficient conditions for rectifiability, both in terms of square functions of flatness-quantifying coefficients. The first condition involves the so-called $\alpha$ and $\beta_2$ numbers. The second one involves $\alpha_2$ numbers -- coefficients quantifying flatness via Wasserstein distance $W_2$. Both conditions are necessary for rectifiability, too -- the first one was shown to be necessary by Tolsa, while the necessity of the $\alpha_2$ condition is established in our recent paper. Thus, we get two new characterizations of rectifiability.
Application of genetic programming in the identification of tool wear

Purpose The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear. Design/methodology/approach In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management. Findings In result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise. Research limitations/implications The regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models. Originality/value In this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided.
Bayesian Zero-Shot Learning

Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the notion of meta-classes and implements a Bayesian hierarchy around these classes to effectively blend data likelihood with local and global priors. Local priors driven by data from seen classes, i.e., classes available at training time, become instrumental in recovering unseen classes, i.e., classes that are missing at training time, in a generalized ZSL (GZSL) setting. Hyperparameters of the Bayesian model offer a convenient way to optimize the trade-off between seen and unseen class accuracy. We conduct experiments on seven benchmark datasets, including a large scale ImageNet and show that our model produces promising results in the challenging GZSL setting.
Effects of Digital Writing Software as a Tool for Process Approach to Writing on Teacher Trainees’ Academic Writing Performance

Knowledge about academic writing is important to be promoted among the community in Institutes of Teacher Education Malaysia (ITEMs) as the teacher trainees will need to teach these academic writing skills to their students in the future.Additionally, the amalgamation of digital technologies and instructional practice is known to be able to ignite the change in the teacher trainees' learning of academic writing.As such, this study was conducted by adopting a single group quasi-experimental research design to investigate the effects of digital writing software as a tool for process approach to writing on teacher trainees' academic writing performance.The study was done on 10 teacher trainees at one ITEM using pre-and post-tests, observation, and reflective journal.Findings indicate that the use of digital writing software as a tool for process approach to writing does improve the teacher trainees' academic writing performance.The eclectic approach proposes an effective way of enhancing the writing process and increasing
On the Performance of the Nonsynaptic Backpropagation for Training Long-term Cognitive Networks

Long-term Cognitive Networks (LTCNs) are recurrent neural networks for modeling and simulation. Such networks can be trained in a synaptic or nonsynaptic mode according to their goal. Nonsynaptic learning refers to adjusting the transfer function parameters while preserving the weights connecting the neurons. In that regard, the Nonsynaptic Backpropagation (NSBP) algorithm has proven successful in training LTCN based models. Despite NSBP&apos;s success, a question worthy of investigation is whether the backpropagation process is necessary when training these recurrent neural networks. This paper investigates this issue and presents three nonsynaptic learning methods that modify the original algorithm. In addition, we perform a sensitivity analysis of both the NSBP&apos;s hyperparameters and the LTCNs&apos; learnable parameters. The main conclusions of our study are i) the backward process attached to the NSBP algorithm is not necessary to train these recurrent neural systems, and ii) there is a nonsynaptic learnable parameter that does not contribute significantly to the LTCNs&apos; performance.
An End-to-End Medical Emergency Response System to Support Elderly People

This paper proposes the concept and preliminary design of an end-to-end medical emergency response system (EEMERS) to support and help elderly people in the community who live alone. The system integrates the informal caregivers, like the neighbors, friends, and family, with the traditional formal caregivers, such as the paramedics, ambulance and medical professionals. The informal caregivers act as the first responders to attend a patient in case of a medical emergency situation before the arrival of an ambulance or other medical services. An overview of the different modules of the EEMERS, the technological details and the end-to-end process flow of the system are discussed in this work. Moreover, the selection of the most appropriate informal caregiver to attend a medical emergency situation depends on a list of pre-defined contexts and is an important part of EEMERS. This work also discusses the preliminary validation results of the informal caregiver selection based on three machine learning algorithms, namely, Logistic Regression, Support Vector Machine, Nave Bayes. Finally, the paper provides a brief overview of the basic proof-of-concept implementation of the system.
AI education matters

In this column, we briefly describe a rich dataset with many opportunities for interesting data science and machine learning assignments and research projects, we take up a simple question, and we offer code illustrating use of the dataset in pursuit of answers to the question.
A Robust Method for Gaussian Profile Estimation in the Case of Overlapping Objects

The precise stellar object identification is one of the major research fields in astronomy. In astronomical images, the 2D Gaussian function provides a good approximation of stellar objects. This paper proposes a robust method for the estimation of the 2D Gaussian profiles in astronomical images when the stellar components are overlapped. The proposed method efficiently separates the regions of interest of close components without knowing neither their exact number nor accurate center positions. Therefore, fainter objects can be identified, and outliers can be detected. The proposed method is an iteratively reweighted least squares method where weights are updated in each step using a robust measure of residual error dispersion. Soft thresholding based on robust statistics increases the accuracy of the estimated 2D profile parameters and efficiently solves the problem of outliers. The comparison of the proposed method with the conventional least squares method showed a significant modeling gain for one particular set of contributing factors; the difference of magnitudes of close objects, their center-to-center distance, and the widths of their semiaxes relative to the imaging system resolution.
Archival Study of Blockchain Applications in the Construction Industry From Literature Published in 2019 and 2020

Purpose: This paper aims to investigate proposed blockchain applications in the construction industry from contemporaryliterature.Methodology: Archival studies will be used to obtain academic content from secondary sources. An explorative strategywill be adopted with no preconception or biases on the preferred route of execution. Blockchain is a fast-evolving technologywith a high rate of yearly progression; therefore, this paper refines the search to recently published material in 2019 and2020. Data is collected in two stages, firstly, categories of research are extrapolated from secondary literature and recordedinto a table, and afterwards, the corresponding proposed application of blockchain is documented and reviewed.Findings: An adequate breadth and variety of categories are substantiated from archival literature, which effectivelycontributes to the extraction of proposed blockchain applications for construction. The data collection extracts 19 categoriesfrom the explorative study, in which 19 proposed solutions (one per category) is presented. All of the advisory content forthe proposed solutions were obtained from a deliberated selection of 21 academic study papers.Limitations: The study is limited to one proposed application per category, totalling 19 proposed solutions; however,assessing various approaches per category could not be researched comparatively due to voluminous information. Thus,recommendations incorporate a holistic case study of one subject category which incorporates a multitude of variousproposed applications.Originality: This paper contributes to new knowledge through extrapolating proposed blockchain applications fromacademic literature in 2019 and 2020.
On polyhedral realizations of Hurwitz's regular map {3, 7}_18 of genus 7 with geometric symmetries

In 2017 a first selfintersection-free polyhedral realization of Hurwitz’s regular map {3, 7}18 of genus 7 was found by Michael Cuntz and the first author. For any regular map which had previously been realized as a polyhedron without self-intersections in 3-space, it was also possible to find such a polyhedron with nontrivial geometric symmetries. So it is natural to ask of whether we can find for the above-mentioned regular map a corresponding version with some non-trivial geometric symmetry. The orientation-preserving combinatorial automorphism group of this Hurwitz map is the projective special linear group PSL(2,8) of order 504 = 23 ⋅ 32 ⋅ 7. All non-trivial subgroups of PSL(2,8) are candidates for such a geometric symmetry. Using the GAP software for exploring the subgroup structure, we found that it is sufficient to consider only four cyclic subgroups whose order is 9, 7, 3, and 2, respectively. We prove that there are obstructions for selfintersection-free polyhedral realizations of the Hurwitz map {3, 7}18 of genus 7 with geometric rotational symmetries of order 9 or 3. We provide new small integer coordinates within the realization space known from 2017, which are also suitable for making a 3D-printed model. We present Kepler–Poinsot type realizations, both with 7-fold and with 3-fold rotational symmetry, the latter with integer coordinates.
Optimized Deep Learning for Enhanced Trade-off in Differentially Private Learning

Privacy and data analytics are two conflicting domains that have gained interest due to the advancements of technology in the big data era. Organizations in sectors such as finance, healthcare, and e-commerce take advantage of the data collected, to help them enable innovative decision making and analysis. What is sidelined is the fact that the collected data have associated private data of the individuals involved, and may be exploited and used for unjustified purposes. Defending privacy and performing useful analytics are two sides of the same coin, and hence achieving a good balance between these is a challenging scenario. This paper proposes an optimized differentially private deep learning mechanism that enhances the trade-off between the conflicting objectives of privacy, accuracy, and performance. The goal of this paper is to provide an optimal solution that gives a quantifiable trade-off between these contradictory objectives.