Reviewerly/exHarmony huggingface dataset:
Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning

Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms.
Constructing Low-latency Involutory MDS Matrices with Lightweight Circuits

MDS matrices are important building blocks providing diffusion functionality for the design of many symmetric-key primitives. In recent years, continuous efforts are made on the construction of MDS matrices with small area footprints in the context of lightweight cryptography. Just recently, Duval and Leurent (ToSC 2018/FSE 2019) reported some 32 × 32 binary MDS matrices with branch number 5, which can be implemented with only 67 XOR gates, whereas the previously known lightest ones of the same size cost 72 XOR gates.In this article, we focus on the construction of lightweight involutory MDS matrices, which are even more desirable than ordinary MDS matrices, since the same circuit can be reused when the inverse is required. In particular, we identify some involutory MDS matrices which can be realized with only 78 XOR gates with depth 4, whereas the previously known lightest involutory MDS matrices cost 84 XOR gates with the same depth. Notably, the involutory MDS matrix we find is much smaller than the AES MixColumns operation, which requires 97 XOR gates with depth 8 when implemented as a block of combinatorial logic that can be computed in one clock cycle. However, with respect to latency, the AES MixColumns operation is superior to our 78-XOR involutory matrices, since the AES MixColumns can be implemented with depth 3 by using more XOR gates.We prove that the depth of a 32 × 32 MDS matrix with branch number 5 (e.g., the AES MixColumns operation) is at least 3. Then, we enhance Boyar’s SLP-heuristic algorithm with circuit depth awareness, such that the depth of its output circuit is limited. Along the way, we give a formula for computing the minimum achievable depth of a circuit implementing the summation of a set of signals with given depths, which is of independent interest. We apply the new SLP heuristic to a large set of lightweight involutory MDS matrices, and we identify a depth 3 involutory MDS matrix whose implementation costs 88 XOR gates, which is superior to the AES MixColumns operation with respect to both lightweightness and latency, and enjoys the extra involution property.
Twenty Steps Towards an Adequate Inferential Interpretation of <i>p</i>-Values in Econometrics

Abstract We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer to the theoretical preconditions for using p -values. They furthermore include wording guidelines as well as structural and operative advice on how to present results, especially in research based on multiple regression analysis, the working horse of empirical economists. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p -values or completely replacing frequentist approaches by Bayesian statistics.
Application of fractal models to delineate mineralized zones in the Pulang porphyry copper deposit, Yunnan, southwestern China

Abstract. The aim of this study is to delineate and identify various mineralized zones and barren host rocks based on surface and subsurface lithogeochemical data from the Pulang porphyry copper deposit, southwestern China, utilizing the number–size (N-S), concentration–volume (C-V) and power-spectrum–volume (S-V) fractal models. The N-S model reveals three mineralized zones characterized by Cu thresholds of 0.28 % and 1.45 %: &lt;0.28 % Cu represents weakly mineralized zones and barren host rocks, 0.28 %–1.45 % Cu represents moderately mineralized zones, and &gt; 1.45 % Cu represents highly mineralized zones. The results obtained by the C-V model depict four geochemical zones defined by Cu thresholds of 0.25 %, 1.48 % and 1.88 %, representing nonmineralized wall rocks (Cu&lt;0.25 %), weakly mineralized zones (0.25 %–1.48 %), moderately mineralized zones (1.48 %–1.88 %) and highly mineralized zones (Cu&gt;1.88 %). The S-V model is used by performing a 3-D fast Fourier transformation of assay data in the frequency domain. The S-V model reveals three mineralized zones characterized by Cu thresholds of 0.23 % and 1.33 %: &lt;0.23 % Cu represents leached zones and barren host rocks, 0.23 %–1.33 % Cu represents hypogene zones, and &gt;1.33 % Cu represents supergene enrichment zones. All the multifractal models indicate that high-grade mineralization occurs in the central and southern parts of the ore deposit. Their results are compared with the alteration and mineralogical models resulting from the 3-D geological model using a log-ratio matrix. The results show that the S-V model is best at identifying highly mineralized zones in the deposit. However, the results of the C-V model for moderately and weakly mineralized zones are also more accurate than those obtained from the N-S and S-V models.
Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms.
An enhanced 3DCNN‐ConvLSTM for spatiotemporal multimedia data analysis

Summary At present, human action recognition is a challenging and complex task in the field of computer vision. The combination of CNN and RNN is a common and effective network structure for this task. Especially, we use 3DCNN in CNN part and ConvLSTM in RNN part. We divide the video into multiple temporal segments by average and compress each segment into one feature map by pooling layer. Adding the pooling layer, dropout layer, and batch normalization layer into ConvLSTM is our groundbreaking work. We test our model on KTH, UCF‐11, and HMDB51 datasets and achieve a high accuracy of action recognition.
Design and Assessment of Using Smartphone Application in the Classroom to Improve Students’ Learning

Teachers are always frustrated with students and their use of cell phones during classroom. Texting, tweeting, and snap chatting during in class are an incredible distraction, resulting in a difficult teaching environment. In this paper, we pro-pose an innovative approach to encourage students to use effectively their smartphone in classroom. The main idea behind this is to develop an easy and friendly application related to the course that may be accompanied by the used textbook. The proposed application can be adapted easily to any textbook, where no coding is required.
Combined classification and source apportionment analysis for trace elements in western Philippine Sea sediments

Trace elements have been widely used for classification (of variables and of samples) and source apportionment studies, but the comparison and combination of the two is uncommon in previous works. In this paper, the grouping of trace elements, clustering of samples, and source identification were merged for an integrated understanding of the origin and distribution of trace elements in western Philippine Sea sediments. The grouping and clustering studies were implemented by a nonlinear clustering method called a self-organizing map (SOM), and the source identification was accomplished by a nontraditional factor analysis method called positive matrix factorization (PMF). Through visualization and clustering techniques, the SOM simultaneously classified a database of 26 trace elements into four groups of trace elements and five clusters of samples. Each sample cluster occupies a certain geographic area and is characterized by high concentrations of trace elements that are classified within one or two groups. Five potential sources were identified by PMF, representing the land mass of Taiwan Island, anthropogenic emissions from Taiwan, nutrient exportation from the South China Sea, mineral attachment in the deep ocean, and biogenetic components and riverine inputs from the Luzon Islands. The spatial distributions of the sample clusters are comparable to the ranges of high contributions from the five sources distinguished by PMF. This conclusion was further supported by displaying the PMF outputs on the SOM plane. Furthermore, a corresponding relationship was observed between every factor profile and every trace element group. Our work tests the consistency of the classification (of the trace elements and of the samples) and source identification and improves the application of multiperspective methodology in environmental studies.
A Data-centric Approach to Unsupervised Texture Segmentation Using Principle Representative Patterns

Features that capture textural patterns of a certain class of images are crucial for texture segmentation tasks. This paper introduces a data-centric approach to efficiently extract and represent textural information, which adapts to a wide variety of textures. Based on the strong self-similarities and quasi-periodicity in texture images, the proposed method first constructs a representative texture pattern set for the given image by leveraging the patch clustering strategy. Then, pixel-wise texture features are designed according to the similarities between local patches and the representative textural patterns. Moreover, the proposed feature is generic and flexible, and can perform segmentation task by integrating it into various segmentation approaches easily. Extensive experimental results on both textural and natural image segmentation show that the segmentation method using the proposed features achieves very competitive or even better performance compared with the stat-of-the-art methods.
PENGARUH MEDIA PEMBELAJARAN GOOGLE CLASSROOM DALAM PEMBELAJARAN ANALISIS REAL TERHADAP MOTIVASI BELAJAR MAHASISWA

This study aims to describe the influence of google classroom learning on learning motivation Students Semester V of Mathematics Education Study Program FKIP Muslim University of Maros. The type of research used in this research was descriptive research with the design of One Shot Case Study. The subjects in this study were Semester V students who were programming the Real Analysis 1 Mathematics Education Study Program in the scope of 32 people in the FKIP of the Muslim University of Maros. The instrument that will be used in this study is in the form of learning outcomes tests and student learning motivation questionnaire sheets. Analysis of the data used is descriptive analysis and to test hypotheses used t-test using SPSS for windows. The results showed that there was a significant effect of student learning motivation after applying google classroom learning. This is indicated by the value of tcount = 43.116 with a p-value = 0,000 <α = 0.05. In addition, the average score of student learning outcomes and the percentage of student response values amounted to 78.31 in the high category and 83.72% in the very good category.
Net neutrality

Cloud-delivery networks could dramatically improve blockchains' scalability, but clouds must be provably neutral first.
An Automatic Threshold Segmentation and Mining Optimum Credential Features by Using HSV Model

In this present study a perfect outcome of skin lesion in the computerized image analysis is used to segment the abnormal layers on the skin. The dermatologist finds difficult for easy identification of skin lesion. A computational tool should be developed to assist the dermatologist for diagnosis. This paper reports the differentiation of segmentation with various techniques. The review is made with related works to the current proposed method as a comparative study with plenty of fundamental steps like image acquisition, pre-processing and segmentation. In this work, the asymmetric pattern extractions from the dermoscopic images are segmented by the HSV segmentation to find the contour image. An automatic segregation of RGB–HSV is incorporated in the masked threshold on the proposed system which segments the lesion. The techniques involved in each stage are perfectly explained. From the state of RGB input and handling of pre-processing and segmentation were evaluated effectively. The outcome of this result is compared with other segmentation techniques to improve the result. The proposed performance measures between Ground Truth image and Segmented Image provides best-offered values of accuracy up to 96% for PH2 dataset and 95% for ISIC 2016 Dataset.
WiseType: A Tablet Keyboard with Color-Coded Visualization and Various Editing Options for Error Correction

To address the problem of improving text entry accuracy in mobile devices, we present a new tablet keyboard that offers both immediate and delayed feedback on language quality through autocorrection, prediction, and grammar checking. We combine different visual representations for grammar and spelling errors, accepted predictions, and auto-corrections. and also support interactive swiping/tapping features and improved interaction with previous errors, predictions, and auto-corrections. Additionally, we added smart error correction features to the system to decrease the overhead of correcting errors and to decrease the number of operations. We designed our new input method with an iterative user-centered approach through multiple pilots. We conducted a lab-based study with a refined experimental methodology and found that WiseType outperforms a standard keyboard in terms of text entry speed and error rate. The study shows that color-coded text background highlighting and underlining of potential mistakes in combination with fast correction methods can improve both writing speed and accuracy.
AUTOMATIC LIP-READING OF HEARING IMPAIRED PEOPLE

Abstract. Inability to use speech interfaces greatly limits the deaf and hearing impaired people in the possibility of human-machine interaction. To solve this problem and to increase the accuracy and reliability of the automatic Russian sign language recognition system it is proposed to use lip-reading in addition to hand gestures recognition. Deaf and hearing impaired people use sign language as the main way of communication in everyday life. Sign language is a structured form of hand gestures and lips movements involving visual motions and signs, which is used as a communication system. Since sign language includes not only hand gestures, but also lip movements that mimic vocalized pronunciation, it is of interest to investigate how accurately such a visual speech can be recognized by a lip-reading system, especially considering the fact that the visual speech of hearing impaired people is often characterized with hyper-articulation, which should potentially facilitate its recognition. For this purpose, thesaurus of Russian sign language (TheRusLan) collected in SPIIRAS in 2018–19 was used. The database consists of color optical FullHD video recordings of 13 native Russian sign language signers (11 females and 2 males) from “Pavlovsk boarding school for the hearing impaired”. Each of the signers demonstrated 164 phrases for 5 times. This work covers the initial stages of this research, including data collection, data labeling, region-of-interest detection and methods for informative features extraction. The results of this study can later be used to create assistive technologies for deaf or hearing impaired people.
Sequence to sequence analysis with long short term memory for tourist arrivals prediction

Prediction is one of the very important elements in decision-making. In general, the effectiveness of a decision depends on several factors. These factors cannot be observed when the decision is taken. Decisions should be taken based on existing and past data. Predictions can be done with two approaches. A first approach is a time-series approach. Time series model does not show the tendency of past data available. The second approach is the approach of showing a cause-effects method. This approach explained the occurrence of a situation (an explanatory method) by specific causes. The initial problem is how to make predictions model. In the beginning, to make predictions used the forecasting methods such as Autoregressive Integrated Moving Average. But, this method has limitations on waiver possibility of non-linear relationship, stationary and homokedastitas residual. At this time, forecasting methods of data with time-series have evolved with Neural Network approach. This research examines the prediction of tourist visits with the Long Short Term Memory (RNN LSTM) Recurrent Neural Network approach. The results of the research carried out by constructing a prediction model for tourist visits with the LSTM RNN using three models. The three LSTM models that are carried out namely LSTM regression, LSTM with sliding window and LSTM with time steps. There are no model that provides optimal results in terms of training and testing at once. The best results in the training process for predicting tourist visits were obtained using a regression model with RMSE 6529.42. Meanwhile, for the testing process, the best RMSE value is 13512.34 with the Sliding Window model.
Comparison between Chunk-Based and Layer-Based Container Image Storage Approaches: an Empirical Study

Container management frameworks, such as Docker, pack applications and their complex software environment in self-contained images, which facilitates application deployment, distribution, and sharing. Currently, container images are organized in a multi-layer file-system manner. For example, Docker uses AUFS to organize its container image storage. However, when a new version is created, all the changes are stored in a new layer via a Copy-On-Write mechanism, which can trigger a heavy burden for the network and storage. There are also other image storage approaches such as chunk-based technology. In this paper, we suggest that the container image storage system should not use one storage technology for all images, but should choose different technology for concrete images according to the image's distinct features. We design some experiments to compare the layer-based and chuck-based technology. And based on the analysis of the collected data, this paper gives some principles for the selection of image management techniques and discusses some interesting potential research topics.
Robust appearance feature learning using pixel‐wise discrimination for visual tracking

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand‐crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking , according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel‐level agreement to the model learned from the detection phase is achieved. Our two‐phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.
Monitoring Haiti’s Quakes with Raspberry Shake

A network of “personal seismometers” is intended to complement Haiti’s national seismic network to engage and inform residents about earthquake hazards and preparation.
Extending Modular Semantics for Bipolar Weighted Argumentation

Weighted argumentation offers a tool for decision support and social media analysis. Arguments are evaluated by an iterative procedure that takes initial weights and attack and support relations into account. Mossakowski and Neuhaus recently unified different approaches and proved first convergence results in cyclic graphs. We build up on this work, simplify and generalize convergence results and add runtime guarantees. As it turns out, there is a tradeoff between convergence guarantees and the ability to move strength values away from the initial weights. We demonstrate that continuizing semantics can avoid divergence without this tradeoff. Semantically, we extend the framework with a Duality property that assures a symmetric impact of attack and support. We also present a Java implementation of modular semantics and explain the practical usefulness of the theoretical ideas.
Jupyter Notebook in CS1

Jupyter Notebook is currently used as the programming environment for labs and assignments in a CS1 course at the University of Victoria. Some motivation for this choice is given, and the paper then acts as an experience report with a specific focus on the way the environment helps and hinders teaching and learning of Python 3, specifically for new programmers.