Reviewerly/exHarmony huggingface dataset:
CM-NAS: Rethinking Cross-Modality Neural Architectures for Visible-Infrared Person Re-Identification.

Visible-Infrared person re-identification (VI-ReID) aims at matching cross-modality pedestrian images, breaking through the limitation of single-modality person ReID in dark environment. In order to mitigate the impact of large modality discrepancy, existing works manually design various two-stream architectures to separately learn modality-specific and modality-sharable representations. Such a manual design routine, however, highly depends on massive experiments and empirical practice, which is time consuming and labor intensive. In this paper, we systematically study the manually designed architectures, and identify that appropriately splitting Batch Normalization (BN) layers to learn modality-specific representations will bring a great boost towards cross-modality matching. Based on this observation, the essential objective is to find the optimal splitting scheme for each BN layer. To this end, we propose a novel method, named Cross-Modality Neural Architecture Search (CM-NAS). It consists of a BN-oriented search space in which the standard optimization can be fulfilled subject to the cross-modality task. Besides, in order to better guide the search process, we further formulate a new Correlation Consistency based Class-specific Maximum Mean Discrepancy (C3MMD) loss. Apart from the modality discrepancy, it also concerns the similarity correlations, which have been overlooked before, in the two modalities. Resorting to these advantages, our method outperforms state-of-the-art counterparts in extensive experiments, improving the Rank-1/mAP by 6.70%/6.13% on SYSU-MM01 and 12.17%/11.23% on RegDB. The source code will be released soon.
GEBCO and ETOPO1 gridded datasets for GMT based cartographic Mapping of Hikurangi, Puysegur and Hjort Trenches, New Zealand

The study focused on the comparative analysis of the submarine geomorphology of three oceanic trenches: Hikurangi Trench (HkT), Puysegur Trench (PT) and Hjort Trench (HjT), New Zealand region, Pacific Ocean. HjT is characterized by an oblique subduction zone. Unique regional tectonic setting consist in two subduction zones: northern (Hikurangi margin) and southern (Puysegur margin), connected by oblique continental collision along the Alpine Fault, South Island. This cause variations in the geomorphic structure of the trenches. PT/HjT subduction is highly oblique (dextral) and directed southwards. Hikurangi subduction is directed northwestwards. South Island is caught in between by the “subduction scissor”. Methodology is based on GMT (The Generic Mapping Tools) for mapping, plotting and modelling. Mapping includes visualized geophysical, tectonic and geological settings of the trenches, based on sequential use of GMT modules. Data include GEBCO, ETOPO1, EGM96. Comparative histogram equalization of topographic grids (equalized, normalized, quadratic) was done by module ’grdhisteq’, automated cross-sectioning – by ’grdtrack’. Results shown that HjT has a symmetric shape form with comparative gradients on both western and eastern slopes. HkT has a trough-like flat wide bottom, steeper gradient slope on the North Island flank. PT has an asymmetric V-form with steep gradient on the eastern slopes and gentler western slope corresponding to the relatively gentle slope of a subducting plate and steeper slope of an upper one. HkT has shallower depths &lt; 2,500 m, PT is &lt;-6,000 m. The deepest values &gt; 6,000 m for HjT. The surrounding relief of the HjT presents the most uneven terrain with gentle slope oceanward, and a steep slope on the eastern flank for PT, surrounded by complex submarine relief along the Macquarie Arc. Data distribution for the HkT demonstrates almost equal pattern for the depths from -600 m to ₋2,600 m. PT has a bimodal data distribution with 2 peaks: 1) -4,250 to -4,500 m (18%); 2) -2,250 to -3,000 m, &lt; 7,5%. The second peak corresponds to the Macquarie Arc. Data distribution for HjT is classic bell-shaped with a clear peak at -3,250 to -3,500 m. The asymmetry of the trenches resulted in geomorphic shape of HkT, PT and HjT affected by geologic processes.
Creating and Testing Specialized Dictionaries for Text Analysis

Practitioners in many domains–e.g., clinical psychologists, college instructors, researchers–collect written responses from clients. A well-developed method that has been applied to texts from sources like these is the computer application Linguistic Inquiry and Word Count (LIWC). LIWC uses the words in texts as cues to a person’s thought processes, emotional states, intentions, and motivations. In the present study, we adopt analytic principles from LIWC and develop and test an alternative method of text analysis using naïve Bayes methods. We further show how output from the naïve Bayes analysis can be used for mark up of student work in order to provide immediate, constructive feedback to students and instructors.&#x0D; References&#x0D; &#x0D; Blei, D. M., Ng, A. Y., &amp; Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022.&#x0D; Boot, P., Zijlstra, H., &amp; Geenen, R. (2017). The Dutch translation of the Linguistic Inquiry and Word Count (LIWC) 2007 dictionary. Dutch Journal of Applied Linguistics, 6(1), 65-76.&#x0D; Chung, C. K., &amp; Pennebaker, J. W. (2008). Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language. Journal of research in personality, 42(1), 96-132.&#x0D; Hsieh, H-F., &amp; Shannon, S. E. (2005).Three approaches to qualitative content analysis. Qualitative health research, 15(9), 277-1288.&#x0D; Kintsch, W. (1998). Comprehension: A paradigm for cognition. New York: Cambridge University Press.&#x0D; Landauer, T. K., Foltz, P. W., &amp; Laham, D. (1998). An introduction to latent semantic ana­lysis. Discourse processes, 25(2-3), 259-284.&#x0D; Lund, K., &amp; Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, &amp; Computers, 28(2), 203-208.&#x0D; Massó, G., Lambert, P., Penagos, C. R., &amp; Saurí, R. (2013, December). Generating New LIWC Dictionaries by Triangulation. In Asia Information Retrieval Symposium (pp. 263-271). Springer, Berlin, Heidelberg.&#x0D; Newman, M., Groom, C.J., Handelman, L.D., &amp; Pennebaker, J.W. (2008). Gender differences in language use: An analysis of 14,000 text samples. Discourse Processes, 45(3), 211-236.&#x0D; Pennebaker, J.W., Boyd, R.L., Jordan, K., &amp; Blackburn, K. (2015). The development and psychometric properties of LIWC 2015. Austin, TX: University of Texas at Austin.&#x0D; Tausczik, Y. R., &amp; Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology, 29(1), 24-54.&#x0D; Van Wissen, L., &amp; Boot, P. (2017, September). An Electronic Translation of the LIWC Dictionary into Dutch. In: Electronic lexicography in the 21st century: Proceedings of eLex 2017 Conference. (pp. 703-715). Lexical Computing.&#x0D;
Intelligent Arduino Based Automatic Solar Tracking System Using Light Dependent Resistors (LDRs) and Servo Motor

With the advancement of technology things are becoming Simpler and easier in every aspect of life.Automation is the use of control systems and information technologies to reduce the need for human work.Sun is an abundant source of energy and this solar energy can be harnessed successfully using solar photovoltaic cells and photovoltaic effect to convert solar energy into electrical energy.But the conversion efficiency of a normal Photovoltaic (PV) cell is low.One of the main reason for this is that the output of Photovoltaic (PV) cell is dependent directly on the light intensity and with the position of the sun in the sky changing continuously from time to time; the absorption efficiency of an immobile solar panel would be significantly less at certain time of the day and year; for the solar photovoltaic cells are maximum productive when they are perpendicular to the sun and less productive otherwise.So to maximize the energy generation and improve the efficiency; intelligent solar trackers come into play.This paper presents the design and construction of an intelligent Arduino Based solar tracking system using Light Dependent Resistors (LDRs) and Servo-motor for tracking the movement of the sun so as to get maximum power from the solar panels as they follow the sun.It uses Light Dependent Resistors (LDRs) to sense the position of the sun which is communicated to a Arduino Uno microcontroller which then commands a set of two servo-motors to reorient the panel in order to stay perpendicular to the sun rays.The design was constructed successfully and tested.
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study

This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation. We begin by introducing the behind motivation on how this incompatibility is raised, i.e., label smoothing erases relative information between teacher logits. We provide a novel connection on how label smoothing affects distributions of semantically similar and dissimilar classes. Then we propose a metric to quantitatively measure the degree of erased information in sample's representation. After that, we study its one-sidedness and imperfection of the incompatibility view through massive analyses, visualizations and comprehensive experiments on Image Classification, Binary Networks, and Neural Machine Translation. Finally, we broadly discuss several circumstances wherein label smoothing will indeed lose its effectiveness.
Logistic and Blockchain: A Strong Partnership

Economic growth is an important goal for governments. This growth is promoted by economic activities known to be a major emitter of greenhouses gases resulting in climate change. Most of those activities are related to logistics and supply chain sectors. Countries around the world implemented an initiative to control and reduce GHG especially CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> under a monitory system to trade carbon emission allowances. However, the CET suffered from shortages that degraded its effectiveness. Also, it was not comprehensive enough to cover all sources of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions. Many researchers proposed solutions to elevate the present CET scheme in which the technology of blockchain was utilized. Blockchain presented itself as an ideal solution for the current CET market due to its characteristics of transparency, immutability, security, and decentralization. However, non of the previous work was adequate or comprehensive enough. This research proposes a comprehensive blockchain-based framework to support and CET that integrates IoT devices and includes logistics and supply chain sectors. Our framework collects full data about CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions directly from all sources and shares them securely and immutably among stakeholders. It enhances making decisions on economic processes that reduce CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission as well as trading allowances.
#MeToo as Catalyst: A Glimpse into 21st Century Activism

The Twitter hashtag #MeToo has provided an accessible medium for users to share their personal experiences and make public the prevalence of sexual harassment, assault, and violence against women. This online phenomenon, which has largely involved posting on Twitter and “retweeting” to share other’s posts has revealed crucial information about the scope and nature of sexual harassment and misconduct. More specifically, social media has served as a central forum for this unprecedented global conversation, where previously silenced voices have been amplified, supporters around the world have been united, and resistance has gained steam.This Essay discusses the #MeToo movement within the broader context of social media activism, explaining how this unique form of collective action is rapidly evolving. We offer empirical insights into the types of conversations taking place under the hashtag and the extent to which the movement is leading to broader social change. While it is unclear which changes are sustainable over time, it is clear that the hashtag #MeToo has converted an online phenomenon into tangible change, sparking legal, political, and social changes in the short run. This Essay provides data to illustrate some of these changes, which demonstrate how posting online can serve as an impetus, momentum, and legitimacy for broader movement activity and changes offline more characteristic of traditional movement strategies.
A Survey of Blockchain Technology Applications and Consensus Algorithm

Blockchain technology applications are growing fast in today’s digital world. Blockchain technology is a decentralized and immutable data format. Blockchain consists of a distributed ledger of information called blocks. However, to use blockchain effectively, it is necessary to consider solving the security and privacy problems. This technology is applied in various fields, for example, finance, healthcare, cybersecurity to name a few. The consensus layer is the most important for the blockchain architecture, in which the consensus protocol is confirmed to how the new block is added to the blockchain network. A consensus algorithm is used to solve mathematical puzzles and increases the overall security for each transaction. Here, a survey is presented on the state-of-the-art of blockchain technology, its applications to different fields and classification of the consensus algorithms.
Children and the Internet: Learning, in the Times to Come

It is proposed that the purpose of education is to enable people to live happy, healthy and useful lives — now and in the future. A curriculum and framework for children’s education is derived from the above purpose. Using the results of over twenty years of research, the pedagogical and physical environments required that will enable children to learn are discussed in the post-pandemic world. Scenarios for schools and homes are presented with a special emphasis on the role of the Internet in children’s learning. New methods for assessment and certification are described to complete a framework for children’s education that is fit for purpose for our times.
SADG: Self-Aligned Dual NIR-VIS Generation for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) has aroused significant interest in recent years, with some challenging tasks such as misalignment problems and limited HFR data. Misalignment occurs among different modalities’ images mainly because of misaligned semantics. Although recent methods have attempted to settle the low-shot problem, they suffer from the misalignment problem between paired near infrared (NIR) and visible (VIS) images. Misalignment can bring performance degradation to most image-to-image translation networks. In this work, we propose a self-aligned dual generation (SADG) architecture for generating semantics-aligned pairwise NIR-VIS images with the same identity, but without the additional guidance of external information learning. Specifically, we propose a self-aligned generator to align the data distributions between two modalities. Then, we present a multiscale patch discriminator to get high quality images. Furthermore, we raise the mean landmark distance (MLD) to test the alignment performance between NIR and VIS images with the same identity. Extensive experiments and an ablation study of SADG on three public datasets show significant alignment performance and recognition results. Specifically, the Rank1 accuracy achieved was close to 99.9% for the CASIA NIR-VIS 2.0, Oulu-CASIA NIR-VIS and BUAA VIS-NIR datasets, respectively.
ArcStereoNet: A New ArcGIS® Toolbox for Projection and Analysis of Meso- and Micro-Structural Data

ArcStereoNet is a new ArcGIS® based toolbox for stereographic projections that we implement here using Python 2.7 programming language. The reason to develop another stereographic projection package arises from the recent use of Python as an exclusive programming language within the ArcGIS® environment. This permits a more flexible approach for the development of tools with very intuitive GUIs, and also allows the user to take full advantage of all potential GIS mapping processes. The core of this new projections toolbox is based on the capability to easily apply and compare most of the commonly used statistical methods for cluster and girdle analysis of structural data. In addition to the well-known Fisher, K-means, and Bingham data elaborations, a completely new algorithm for cluster analysis and mean vector extraction (Mean Extractor from Azimuthal Data), was developed, thereby allowing a more reliable interpretation of any possible structural data distribution. Furthermore, as in any other GIS platform, users can always precisely correlate each single projected data point with the corresponding geographical/locality position, thereby merging or subdividing groups of structural stations with a simple selection procedure. ArcStereoNet also creates rose diagrams, which may be applied not only to fault/joint planes orientation data, but also for the analysis of 2D microstructural fabric parameters. These include geometrical datasets derived from the minimum bounding approach as applied to vectorized grains in thin sections. Finally, several customization settings ensure high-quality graphic outputs of plots, that also allow easy vector graphics post-processing.
Analyses of statistical feature fusion techniques in breast cancer detection

ABSTRACTBreast cancer is one of the mortal diseases amongst women with increased incidences and mortality rate in every year globally. As its symptoms are not prominently noticeable in early stage, the early detection is difficult. Over the past four decades Mammography is used for diagnosing breast diseases. Most of CAD systems use either Cranio-Caudal or Medio-Lateral Oblique mammographic views. Radiologist will look at both the view for better diagnosis. To incorporate this perception with CAD, the detection performance of various statistical feature fusion in fusing the texture features of these two mammographic views are analysed in this work. The improved performance of accuracy: 97.5%, sensitivity: 100%, specificity: 97.2%, precision: 97.1%, F1 score: 96.23%, Mathews Correlation Coefficient: 0.952% and Balanced Classification Rate: 98.74% was achieved with Local Binary Pattern features fused through Canonical Correlation Analysis.
A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method

Three-phase induction motors are widely diffused in the industrial environment. Many times, the rated power of three-phase induction motors is not properly chosen causing incorrect operating conditions from an energetic point of view. Monitoring the mechanical dimension of a new motor is helpful, should an existing motor need to be replaced. This paper presents an IoT sensors network for monitoring the mechanical power produced by three-phase induction motors, adopting an indirect measuring method. The proposed technique can be easily adopted to monitor the mechanical power using only one line of current transducer, reducing the cost of the monitoring system. The proposed indirect measurement technique has been implemented on a low-cost IoT system, based on a Photon Particle SoC. The results show that the proposed IoT system can estimate the mechanical power with a relative error of within 8%.
The Reality of Applying Knowledge Management Practices and Its Impact on Achieving the Quality of Higher Education in Jordan

This research aims to identify the reality of applying knowledge management practices and reveal their impact on achieving the quality of higher education in Jordan, and to identify the concept of knowledge management and the quality of higher education, and uncover knowledge management practices, and determine the components of applying knowledge management in higher education institutions. To achieve the research objectives, the researcher used the descriptive and analytical approach, relying on the questionnaire as the main tool for collecting research data. The research was applied to a sample consisting of (142) workers and those in charge of the educational process at Al-Balqa Applied University, who were chosen randomly. Among the most prominent results of this research is that there is an effect of knowledge management on higher education quality at Al-Balqa Applied University at the significance level (α= 0.05). As the results showed, one of the essential practices of knowledge innovation and creativity is that knowledge management encourages brainstorming to create new ideas and reveal tacit knowledge. One of the most important practices of applying the knowledge is that the university conducts tests on knowledge before applying it, while the essential practice of storing and distributing knowledge is that the university has many methods and means used to preserve knowledge. In light of the study results, the research recommends the need to pay attention to those in charge of the educational process and those working in it, support them and develop their capabilities, and work to implement the knowledge that it possesses through a specialized work team, and the necessity for the knowledge management to work on developing the creative vision of the employees at the university. Received: 3 November 2020 / Accepted: 3 January 2021 / Published: 17 January 2021
GAN "Steerability" without optimization

Recent research has shown remarkable success in revealing `steering directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful image transformations (e.g., shift, zoom, color manipulations), and have the same interpretable effect across all categories that the GAN can generate. Some methods focus on user-specified transformations, while others discover transformations in an unsupervised manner. However, all existing techniques rely on an optimization procedure to expose those directions, and offer no control over the degree of allowed interaction between different transformations. In this paper, we show that ``steering trajectories can be computed in closed form directly from the generator's weights without any form of training or optimization. This applies to user-prescribed geometric transformations, as well as to unsupervised discovery of more complex effects. Our approach allows determining both linear and nonlinear trajectories, and has many advantages over previous methods. In particular, we can control whether one transformation is allowed to come on the expense of another (e.g., zoom-in with or without allowing translation to keep the object centered). Moreover, we can determine the natural end-point of the trajectory, which corresponds to the largest extent to which a transformation can be applied without incurring degradation. Finally, we show how transferring attributes between images can be achieved without optimization, even across different categories.