Reviewerly/exHarmony huggingface dataset:
Eliciting Wrist and Finger Gestures to Guide Recognizer Design

While hand gestures, i.e. movements of the fingers and wrist, are a low-effort input modality, sensing and recognition of these smallscale gestures is challenging. In particular, while many authors have explored varying designs of hardware to support hand gesture input, each systems recognize their own gesture set, rendering challenging comparisons between different capture and recognition systems. In this paper, we explore the design of hand and finger gesture input by conducting an elicitation study to understand the tradeoffs between hand, wrist, and arm gestures. Alongside this, to evaluate the overall potential of wrist-worn recognition, we explore the design of hardware to recognize gestures by contrasting an IMUonly recognizer with a simple low-cost wrist-flex sensor. We discuss the implications of our work both to the comparative evaluation of systems and to the design of enhanced hardware sensing.
Voting-based edge detection

The edge detection algorithms stand on convolving the input image with a filter for a specific direction. With the present scenario, no solution can grant the best output for various types of input image. Then, diverse algorithms have been implemented for a specific approach and have a good result only on a specific dataset and a selected filter. We can evaluate the behavior of an algorithm based on different types of images and in this way, we could choose the best candidate from all the results. The voting algorithm uses weighted, majority and unanimous votes. The general propose of this voting mechanism is to increase the accuracy of the output. This paper proposes a voting mechanism that chooses the best candidates from all available ones and selects the relevant result from a pool of intermediate outputs from well-known techniques.
Method for reconstructing the real coordinates of an object from its plane image

Original method of reconstructing the real coordinates of moving objects from their plane images is presented. The method uses multidimensional test objects whose parameters are measures and which are known with high accuracy. In order to ensure the process of measuring movements in real space a test object must be connected with a real object and mathematical models of images of multicomponent movements of a test object must be formed. The parameters of test objects that are used in the named mathematical models are vectors. Such models are used in the construction of systems of measurement equations the solution of which gives the desired components movements of a moving object in 3D space. The method was experimentally tested on specially created stand.
Hybrid metric to reduce labour costs in assessing the complexity of the programs

Abstract The article deals with the problem of assessing the complexity of software products. A review of widely used metrics (number of lines of code, average number of lines for functions, Halstead’s metrics, Jilb’s metrics, ABC-metric) is being conducted, on the basis of which a conclusion is drawn about their deficiencies. Also, indicators are analyzed, subtracted when assessing the complexity of programs, such as: program saturation with conditional operators or cycle operators, complexity of program understanding, program coding complexity, program content. A hybrid metric based on a composition of already well-known ones is proposed. As a result of the experiment to assess the complexity of the program, it is shown that the proposed hybrid metric allows us to more effectively evaluate the program. The use of such a metric can significantly reduce the computation time.
How Undergraduate Students ‘Chat Language’in Whatsapp

This paper is based on Masters’ dissertation submitted to Vaal University of Technology, which investigated messaging service applications as an educational supporting tool. The use of social media applications to support learning is fairly new in most Higher education institutions in South Africa. The more technology contributions are embraced, it is fundamental that the education system takes advantage of incorporating technology in teaching and learning. This research paper investigates messaging service applications as an educational supporting tool in higher education (HE) institutions of South Africa. The methodology of textual interaction was adopted in order to analyse the textual interactions between students themselves and their lecturer. This paper reports on an on-going study that introduced the use of texting technology as a supporting tool. This paper discusses how undergraduate students tend to be freer when using the so called “chat language” texting in a digital space environment. Furthermore the paper provides an insight on the benefits of allowing undergraduate students to freely use the ‘chat language’ and how students understand the language. What was beneficial to the project was that students were already using WhatsApp among other social media applications, therefore, no training was required or extra cost was experienced. As example, students who find it difficult to interact with other students and the lecturer during face-to-face contact sessions are given the opportunity to make use of messaging service applications to ask questions and communicate with fellow students and lecturers. This paper states the argument that students and their lecturer(s) should be in a position to reach one another and have learning interactions through technology that enables instant communication through various messaging service applications. The use of messaging service applications affords students and lecturers an advantage to extend learning beyond class contact with minimal time constraints. …The way they text so fast, I wonder what language they are using:-)
Welfare Mapping of Region in Indonesia based on Health and Nutrition Indicators using Self Organizing Mapping (SOM) Method

Health and nutrition are important indicators of public welfare. Availability map of the region on those indicators is necessary for constructing an effective plan of welfare equal distribution in health and nutrition. This study aims to create a map of the welfare index on health and nutrition indicators over Indonesian provinces. This research employs a soft computing approach specifically a self-organizing mapping (SOM). SOM is a method based on the neural network concept. The data are drawn from the Statistics Indonesia. Sixteen variables are considered as factors to create the map. They could be grouped into six factors namely illness, mortality, life expectancy, nutritional conditions, breastfeeding and immunization, clean water facilities and latrines. A Davies Bouldin Index (DBI) is used to determine the optimal number of clusters. The best mapping is obtained by five clusters as corresponding to the least DBI value. Thirty-four provinces in Indonesia are mapped into five clusters. The characteristics of each cluster are described by six factors of health and nutrition indicators.
From Economization to Tokenization: New Forms of Economic Life On-Chain

The emerging field of token engineering aims to open “economy itself as design space,” using blockchains and other distributed ledger and smart contract tech- nologies in combination with game theory and behavioral economics. This paper describes the emergence of the Ethereum network as the principle site for the development of a cryptographically tokenized mode of economic life, and the re- construction of human economic agency along game-theoretic lines in the figure of Homo cryptoeconomicus. The paper draws together questions from the econo- mization and marketization, materiality and human/non-human agency programs in economic sociology: token engineering represents both a new field of practice for a self-consciously performative economics, and a potential point of intervention for a practice-oriented mode of social studies of finance.
Modeling over-dispersed datasets using generalized geometric distributions

Through this article we propose a class of over-dispersed distributions namely ‘the generalized geometric distribution (GGD)’ as an extension of the well-known ‘geometric distribution’. Also, we develop an extended version of it and named it as ‘the extended generalized geometric distribution (EGGD)’. Several properties of these classes of distributions are studied here. The maximum likelihood estimation of the parameters of the EGGD is discussed and fitted to certain real-life data sets for illustrating its usefulness compared to the existing models. Further, the generalized likelihood ratio test procedure is considered for testing the significance of the additional parameter of the EGGD model and a simulation study is carried out for assessing the performance of the estimators of each parameter of the model.
Sales prediction of Four Wheelers Unit (4W) with seasonal algorithm Trend Decomposition with Loess (STL) in PT. Astra International, Tbk

Abstract Corporate Information System and Technology (CIS&amp;T) division in PT Astra International Tbk is digitizing all business lines. Therefore, the company started to utilize machine learning to predict four wheelers (4W) sales in 2017. Data used were car sales data from 2014 to 2016. The data were obtained from company’s data warehouse (DW). Further, to develop application, firstly, Minitab was used to compare algorithm in modelling phase. Then, Azure Machine Learning Studio (Azure ML) was utilized to build prediction model based on algorithm chose in first phase. Finally, visualization was developed using Power Business Intelligence (Power BI). Time series algorithm used to solve the case is Seasonal Trend Decomposition with Loess (STL). While, machine learning model uses R library, and process flow module in Azure ML. The STL prediction model yield 9.38% error. Visualization of result is divided into two sections filtered by three parameters type, color and area. The first section in dashboard is sales data report showing percentage of type, color of cars and deployment of 4W units in Indonesia through ArcGIS Maps. The second section is forecast result containing chart of x axis to represent year and y axis to represent number of units. Further, the chart provide historical, current and forecast information. The research contributes to illustrate development of prediction using machine learning in industrial environment.
Many Universal Convolution Cores for Ensemble Sparse Convolutional Neural Networks

A convolutional neural network~(CNN) is one of the most successfully used neural networks and it is widely used for many embedded computer vision tasks. However, it requires a massive number of multiplication and accumulation (MAC) computations with high-power consumption to realize it, and higher recognition accuracy is desired for modern tasks. In the paper, we apply a sparseness technique to generate a weak classifier to build an ensemble CNN. There is a trade-off between recognition accuracy and inference speed, and we control sparse (zero weight) ratio to make an excellent performance and better recognition accuracy. We use P sparse weight CNNs with a dataflow pipeline architecture that hides the performance overhead for multiple CNN evaluation on the ensemble CNN. We set an adequate sparse ratio to adjust the number of operation cycles in each stage. The proposed ensemble CNN depends on the dataset quality and it has different layer configurations. We propose a universal convolution core to realize variations of modern convolutional operations, and extend it to many cores with pipelining architecture to achieve high-throughput operation. Therefore, while computing efficiency is poor on GPUs which is unsuitable for a sparseness convolution, on our universal convolution cores can realize an architecture with excellent pipeline efficiency. We measure the trade-off between recognition accuracy and inference speed using existing benchmark datasets and CNN models. By setting the sparsity ratio and the number of predictors appropriately, high-speed architectures are realized on the many universal covers while the recognition accuracy is improved compared to the conventional single CNN realization. We implemented the prototype of many universal convolution cores on the Xilinx Kintex UltraScale+ FPGA, and compared with the desktop GPU realization of the ensembling, the proposed many core based accelerator for the ensemble sparse CNN is 3.09 times faster, 4.20 times lower power, and 13.33 times better as for the performance per power. Therefore, by realizing the proposed ensemble method with many of universal convolution cores, a high-speed inference could be achieved while improving the recognition accuracy compared with the conventional dense weight CNN on the desktop GPU.
Application of the DeepSense Deep Learning Framework to Determination of Activity Context from Smartphone Data

Current methods of assessing health are infrequent, costly, and require advanced medical equipment. 92% of US adults carry mobile phones, and 77% carry smartphones with advanced sensors (Smith, 2017). Smartphone apps are already being used to identify disease (e.g., skin cancer), but these apps require active participation by the user (e.g., uploading images). The goal of this research is to develop algorithms that enable continuous and real-time assessment of individuals by leveraging data that is passively and unobtrusively captured by cellphone sensors. Our first step to accomplish this is to identify the activity context in which the device is used as this affects the accuracy and reliability of sensor data for measuring and inferring a user’s health; data should be interpreted differently when the user is walking or running versus on a plane or bus. To do this, we use DeepSense, a deep learning approach to feature learning first developed by (Yao, Hu, Zhao, Zhang, &amp; Abdelzaher, 2017). Here we present six experiments validating our model on: (1) a baseline implementation of DeepSense on the same data used by Yao et al., (2017) achieving a balanced accuracy (BA) of 95% over the six main contexts; (2) its ability to classify context using a different publically-available dataset (the ExtraSensory dataset) using the same 70/30 train/test split used by Vaizman et al. (2018), with a BA of 75%; (3) its ability to achieve improved classification when training on a single user, with a BA of 78%; (4) its ability to achieve accurate classification of a new user with a BA of 63%; (5) its improvement to 70% BA for new users when we considered phone placement to remove confounding information, and (6) its ability to accurately classify contexts over all 51 contexts collected by Vaizman et al, achieving a BA of 80% on 9 contexts, 75% on 12, and 70% on 17. We are now working to improve these results by adding other sensors available through smartphone data collection included in the ExtraSensory dataset (e.g., microphone). This will allow us to more accurately assess minor deviations in user behaviors that could indicate changes in health or injury status by accurately accounting for irrelevant, inaccurate, or misleading readings due to contextual effects that may confound interpretation.
On the Study of Childhood Medulloblastoma Auto Cell Segmentation from Histopathological Tissue Samples

Whole slide imaging in histopathology is one of the most important aspects of computational pathology. Nucleus identification and extraction can play a critical part in digital microscopic examination. This work is an extension of our previous published work on childhood medulloblastoma biopsy machine learning classification where the classifier was based on ground truth annotated data. However complete automation would entail automatic segmentation of the cells. The paper explores various segmentation techniques for cell identification from biopsy tissue samples of childhood medulloblastoma microscopic images based on conventional machine learning methods. The study is based on indigenous patient data collected from medical centers of the region. The performance of the segmentation algorithms was compared using Jaccard and Dice coefficient metric.
Machine Learning Methods for Ordinal Classification with Additional Relative Information

The ability to learn an accurate classification process is often limited by the amount of labeled data. Incorporating additional information into the learning process for overcoming this limitation has been a popular research topic. In this work, we focus on ordinal classification problems that are provided with limited absolute information and additional relative information. We modify some classical machine learning methods to combine both types of information. Moreover, we propose a new distance metric learning method to exploit both types of information for learning a suitable distance metric that can be incorporated into the augmented method of k nearest neighbors for ordinal classification. The experimental results show that our method is competitive with other modified machine learning methods and considering additional relative information leads to a better performance.
Algorithm for detecting spherulite marker in human blood serum facies

One of the effective methods of early medical diagnosis is the method of wedge dehydration. It is based on the analysis of facies images. Facia is a thin film of dried human biological fluids. The presence of special structures (markers) indicates various pathologies of the organism at their earliest stages. In this article, the algorithm for detecting spherulite marker on microscopic images of human serum facies is presented. The presence of spherulites on facies is the norm. However, the atypical form of spherulite is a marker of precancerous diseases: uterine fibroids, endometrial hyperplastic processes and the mammary gland. Due to the visual analysis of the marker, its characteristic features were identified. Then algorithmic detection methods for these features were developed. The decision on the probable presence of a marker was made if there was a combination of features of this marker. As a result of the application of the developed algorithm, most images of atypical spherulites were identified.
Object Detection for Chinese Traditional Costume Images Based GRP-DSOD++ Network

The image object detection methods based on deep learning have achieved remarkable results in recent years. However, as object sizes of Chinese Traditional Costume Images (CTCI-4) data set are smaller than that of natural images, and there are not enough training samples, the previous excellent object detection methods cannot achieve good detection result. To tackle this issue, mainly inspired by GRP-DSOD, we propose an effective network, namely GRP-DSOD++ network, to detect objects in the CTCI-4 data set. In order to collect multi-scale context information and capture a wider range of features, we introduce Dilated-Inception module (DI module) and applied it to object detection framework that is learned from scratch. We also applied other advanced components of several excellent object detectors to the proposed network architecture. The proposed detector in the CTCI-4 data set achieves 77.08% mAP, higher than the GRP-DSOD detector (75.33% mAP). And the detector (learning on VOC “07+12” trainval) also can achieve good performance on PASCAL VOC2007.
Analyzing the Variation Property of Contextualized Word Representations

Recent studies have shown that contextualized word representation models are effective on a variety of NLP tasks. However, how these models encode information into representations is not yet well understood because of the insufficient lack of analysis methodologies. To shed light on the behavior of these models, we explore the variation property of word representations produced by the ELMo encoder. More specifically, we analyze how the linguistic, statistical, and semantic features of each word are associated with the variation of its representations. We find that lexical category, word position, the diversity of the contexts, and the diversity of the word senses are the features most closely associated with variation, although the entanglement of word senses in the representation space is also important. In addition, detailed analysis using a randomized ELMo encoder indicates that the word position effect is not a characteristic acquired through the pre-training process but an inductive bias that arises from the ELMo encoder architecture.
Low complexity and large interactions are possible in Strategy logic

Strategy logic is an expressive multi-agent logic which extends ATL* (the multi-agent version of CTL*). It allows for powerful and interdependent branching (making it possible to express the existence of a dominant strategy, or a qualitative Nash equilibrium). However its model-checking is Non-Elementary. In their paper, Mogavero and al. conjectured that restricting the information strategies have of one another would bring the complexity back to 2-EXPTIME, the same as LTL or ATL* on games. This spurs a bunch of papers ranging over different semantics and fragments of Strategy Logic. As of this instant all papers supported the conjecture. However, so far a 2-EXPTIME model-checking is always obtained by restricting the interactions between the different goals (the interdependent branches in a formula); for example only using a single goal or a conjunction of goals. This severely limits the properties one can create, for example neither admissible strategies nor Nash equilibria can be expressed in these restrictions. In this paper, we prove that a 2-EXPTIME model-checking can be obtained for the fragment SL[BG] in the timeline semantic without restricting the interactions between the goals, greatly improving the expressiveness over other known fragments with 2-EXPTIME model-checking. This places SL[BG] in the timeline semantic as the largest extension of ATL* with similar complexity, yet capable of expressing global properties.
Weighted Multi-label Learning with Rank Preservation

As one of the central topic in the field of machine learning, multi-label learning gets widely applied in real life. The classical algorithm does not consider the relation of rank and weight between labels simultaneously, while correlation between labels own a certain impact on the quality of classification models, which makes the algorithm unable to be applied in some scenarios and the accuracy of the model is affected. To solve this problem, a new algorithm named weighted multi-label learning with rank preservation (abbrev. WMR) is proposed. WMR extends and optimizes the SVM-based multi-label learning algorithm by introducing two kinds of label pairs, which is called “related-unrelated” and “related-related” label pairs, to measure the rank and weight between labels. The experiment is based on the real datasets and compared to the RankSVM algorithm, and the experimental results show that WMR mines the correlation between labels fully and improve the quality of the classification model effectively.
Intelligent Text Location Based on Multi Model Fusion

In order to reduce the additional learning burden and efficiency of people when looking for information. This paper mainly combines the prediction results of bagging, random forest and linear regression models, and builds a model to understand natural language problems and retrieve related materials. In the process of building the model, the improved data processing method is used to process the data, and then the data is imported into different models for training. This paper uses the multi-model fusion algorithm to output the predicted results, which can prevent the prediction from over-fitting And improve the accuracy of positioning.