Reviewerly/exHarmony huggingface dataset:
PERAN RUMAH PINTAR UNIVERSITAS BRAWIJAYA DALAM MEWUJUDKAN GERAKAN LITERASI INFORMASI DI KOTA MALANG

Universitas Brawijaya Library  has one of the leading  work programs in the development and  cooperation section, namely  managing Smart Homes, because not all universities in Indonesia  have this facility as an effort to improve education and  community service in realizing an in- formation literate society. The research method is a case study by directly participating  in every activity in the Universitas Brawijaya smart house. The  results of this study are to describe in  detail  the activities of work programs that have been carried out in the Smart House of Brawijaya University  in  order to obtain  a clear picture of the importance  of the existence of the Smart House  in  Brawijaya University because it plays a very important  role in realizing information literacy in Malang  City.
Optimizing MRI Registration using Software/Hardware Co-Design Model on FPGA

The correct localization of brain tissue deformation and determination of the tumor growth relies majorly on the accuracy of the process known by image registration. Poor registration may lead to misclassified diseases and highly affect image-guided surgery and radiation therapies. Voxel-based morphometry (VBM) is an image analytical technique encompassing accurate registration but suffers from intensive time computations, similar to most of image registration techniques. Achieving the compromise between accuracy and computations is a challenging mission. Field programmable gate arrays have fast-evolving and customizable hardware acceleration capabilities that promise to help speed up computational tasks. This paper presents a software/hardware co-design model for accelerating the implementation of the diffeomorphic image registration algorithm ‘DARTEL’ as a part of VBM that analyzes MRI images. An optimized and pipelined hardware architecture is proposed and integrated into the Statistical Parametric Mapping (SPM) software tool that runs the DARTEL. Acceleration of the DARTEL registration algorithm resulted in a speedup factor of 114x on function-level, compared to the CPU with a contribution of 8x faster for the overall performance in the registration process of the SPM. The proposed model is successfully validated for the identification of Alzheimer’s disease based on T1-weighted MRI. A proposed software/hardware co-design model for VBM achieves remarkable acceleration while maintaining classification accuracy and proving proficiency against other CPU and GPU implementations.
Practical Application of Computer Assisted Cooperative Learning Mode in College English

Abstract To be honest, the emergence of computer-aided technology really makes our life and learning more convenient and casual. In the process of English language learning, computer-aided cooperative learning mode is a new school teaching mode which is widely used and has a significant effect. Only for teachers, this learning mode can be called an effective way to mobilize students’ enthusiasm and subjectivity. It is suitable for students with a certain amount of vocabulary accumulation [1] . For students, this mode can realize the integration of teaching, examination and review services. Therefore, students can save more learning practice and improve the efficiency of English learning as soon as possible. On a certain basis, it is necessary to study the practical application of computer assisted cooperative learning mode.
LAYANAN SIRKULASI PERPUSTAKAAN DI MASA NEW NORMAL: UPAYA PUSTAKAWAN UNNES DALAM MENINGKATKAN JASA LAYANAN KEPADA PEMUSTAKA

The  existence of a university library has a very important  role in  the process of transferring science and  technology, as well as in supporting the activities of the Tri Dharma of Higher Education; research, teaching, and  community service. In  the current Covid-19 pandemic, it can  be said that  almost all libraries are also affected in  providing  services to librarians. Not a few libraries in the early days of the pandemic  closed their services, as well as some that continued to open services virtually (digital / online). This situation  had stopped for quite a long time. Then the situation moved to the New Normal era, where in this period the library began to adjust its situation,  regarding its library service model. There are those who open services entirely online,  where libraries do not open services in the form of library visits, there are also those that open online and  offline with modifications  at some of their service points. This also happened  in  the Semarang  State University Library, which during  the New Normal  period opened its services gradually.  The following article attempts to describe the library services of Semarang  State University, more specifically in the section on circulation (borrowing and returning library materials (books).
A Discriminative Gaussian Mixture Model with Sparsity

In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space. The mixture model can address this issue, although it leads to an increase in the number of parameters. We propose a sparse classifier based on a discriminative GMM, referred to as a sparse discriminative Gaussian mixture (SDGM). In the SDGM, a GMM-based discriminative model is trained via sparse Bayesian learning. Using this sparse learning framework, we can simultaneously remove redundant Gaussian components and reduce the number of parameters used in the remaining components during learning; this learning method reduces the model complexity, thereby improving the generalization capability. Furthermore, the SDGM can be embedded into neural networks (NNs), such as convolutional NNs, and can be trained in an end-to-end manner. Experimental results demonstrated that the proposed method outperformed the existing softmax-based discriminative models.
“Nobody Considered Me a Fraudster Yet...”: S.M. Alyansky Letters to L.D. Blok (1922–1924)

The article, based on publication of S.M. Alyansky’s letters to L.D. Blok (1922, 1924), reconstructs the history of relations between the owner of “Alkonost” and the poet’s widow, who transferred the exclusive rights to all of Blok’s works to Alyansky. The article clarifies the details and circumstances of business negotiations between Alyansky and representatives of the “Slovo” publishing house in Berlin, which published Blok’s collected works (three volumes of poems and “The Theater”) in 1922, as well as the financial conditions for publishing a ten-volume collection of Blok’s works under the “Alkonost” brand in the publishing house “Epokha” (Berlin, 1923). The main subject of consideration is a fact unknown in Block studies — the fraud allegations made against Alyansky by L.D. Blok. The introduced archival materials do not only shed light on this incident, but also prove the unsoundness of the insinuations put forward against the owner of “Alkonost”, whose publishing and royalty policies were determined by professional enterprise and high — “knightly” — moral qualities.
Multiple Detection and Tracking of Multi Class Vehicles using Locality Sensitive Histogram

Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variations. Evaluation and comparison of the proposed method with various other techniques is performed using performance parameters. Results depict that the proposed technique outperforms with increased accuracy and is the top performer.
A experiência imersiva dos óculos de realidadevirtual

This article is about the comparative analysis of virtual reality glasses and their contributions to optimize the user's immersive experience.A remote survey carried out with people using the technology was essential, considering that two types of proposed artifacts, Oculus Rift or Playstation VR, would be more relevant in the case of immersion.The artifacts have the same possibility: promote a greater concentration of the player in the environment controlled by the game, making the syntax part of the story.Create a chronological and ergonomic evolution of artifacts that use virtual reality, such as variants of this technology, services to enrich the present study.
Supplemental Material: A new model for the optimal structural context for giant porphyry copper deposit formation

Tables S1 and S2, a summary of all the relevant data from mineral deposits and active volcanic systems compiled for testing the model presented in this work.&lt;br&gt;
Discrete Cosine Transform in JPEG Compression

Image Compression has become an absolute necessity in today's day and age. With the advent of the Internet era, compressing files to share among other users is quintessential. Several efforts have been made to reduce file sizes while still maintain image quality in order to transmit files even on limited bandwidth connections. This paper discusses the need for Discrete Cosine Transform or DCT in the compression of images in Joint Photographic Experts Group or JPEG file format. Via an intensive literature study, this paper first introduces DCT and JPEG Compression. The section preceding it discusses how JPEG compression is implemented by DCT. The last section concludes with further real world applications of DCT in image processing.
Fault Feature Recognition Method of Intelligent Manufacturing Equipment based on Virtual Instrument

Aiming at the problem of inaccurate fault feature identification in traditional fault feature identification methods of intelligent manufacturing equipment, a virtual instrument-based fault feature identification method of intelligent manufacturing equipment is proposed. Using virtual instrument technology, a data acquisition card for intelligent manufacturing equipment fault data is designed to achieve data acquisition. On this basis, the equipment fault features are extracted by gray level co-occurrence matrix, and the extracted fault features are taken as sample data to construct the feature matrix to achieve the purpose of identifying fault matrix. The experimental results show that the designed fault feature identification method for intelligent manufacturing equipment based on virtual instrument is more complete and accurate, and the sum of squared errors of the identification is lower than other methods, indicating that the reliability of the identification method has been improved.
A Two-Person Lift Image Detail Extraction Method based on Global Texture Feature Optimization

When traditional information extraction methods are used to extract image feature points, image denoising degree is not sufficient, and there are still small scale noise points in detail images, which leads to the spatial dimension error of information extraction. To solve this problem, a new method based on global optimization texture feature is proposed to extract the details of double lift image. Using laser scanning measurement technology, the data of double lift image is collected, the point cloud data of image is preprocessed, and the detail feature points are extracted. The texture sequence of feature points is obtained by using the global optimization texture feature technology, and the detail color and texture are mapped into the grid to obtain the detail information of the lifting image. The experimental results show that the spatial dimension error of this method is smaller than that of the two traditional methods, which makes the extracted information meet the requirements of high precision and increase the reliability of information extraction.
PROTOTYPE OF STREET LIGHT CONTROL SYSTEM USING FUZZY MAMDANI METHOD

Street light usually set to turn on at night based on fixed time setting. In rainy condition, it will be better that the street light turned on and the color of light is yellow instead of white for effectiveness of human sight. In this research, a system which could control the street light will be designed by considering environmental light intensity, rainy condition, and time setting. Fuzzy Mamdani method is used to adjust the level of the street light intensity. The experiment results show that the system work properly. The light intensity of the street lights can change automatically adapting the brightness of environmental light and rainy or non-rainy conditions through the fuzzy process with the highest light intensity about 248.93 (in 0-255 PWM range) with an average error between experiment results and simulation using Matlab about 0.05%. In rainy condition, the color of the street light will turn to yellow from default color (white).
Colour-Based Extraction Methods for the Classification of Breast Milk (ASI)

The development of cellular devices makes accessing information in the form of text or images more easier. In line with the growing field of computer vision, various processes in image/image processing continue to increase. Image processing can be done by increasing image quality (image enhancement) and image recovery (image restoration). Feature extraction is divided into three types, namely feature form extraction, texture feature extraction, and color feature extraction. The application of color-based feature extraction methods has been widely used by researchers in the process of classification of various objects. This paper aims to review the technology that can be applied to image processing in a CBIR system with the object of breast milk so that it can measure the quality of breast milk based on its color.
An Efficient Description with Halide for IIR Gaussian Filter

Recursive Gaussian filter is one of the constant-time algorithms fir Gaussian filtering to reduce the computational order. The recursive property makes programs hard for optimization because all resulting pixels in a recursive filter depend on all input pixels. Halide is a domain-specific language for image processing and can powerfully accelerate image processing with a concise description. However, it is also tricky to describe concise scheduling for recursive filters. As a solution to this problem, RecFilter has been developed as a domain-specific language for handling recursive filter, which internally generates the Halide code. The limitation of RecFilter is in approximation accuracy. Here, we extend RecFilter to suit the methods commonly used in recursive Gaussian filter to solve this problem. Also, we improve computational performance by changing computational scheduling. Experimental results showed that the proposed generator produces more computationally efficient codes, and the resulting images have higher approximation accuracy.
'That's somebody evil:' Search on for suspect randomly cutting brakes in south King County

Detectives have uncovered a rash of victims who had their break lines cut in the dead of night by a man detectives are calling extremely malicious.
Sustainable Federated Learning

Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introduce sustainable machine learning in federated learning settings, using rechargeable devices that can collect energy from the ambient environment. We propose a practical federated learning framework that leverages intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to a wide range of machine learning settings in networked environments, including distributed and federated learning in wireless and edge networks. Our experiments demonstrate that the proposed framework can provide significant performance improvement over the benchmark energy-agnostic federated learning settings.
Using uncertainty for multi-domain text classification

Multi-domain learning allows for joint feature detection to promote the performance on a learning task. The shared feature space, however, has limited capacity and should include only the most discriminative task-independent features that are useful for all the tasks. To this end, we proposed a global-local task uncertainty measure to monitor the usefulness of features for all tasks, increasing their effectiveness and generalizability while disentangling them from task-specific features that are not helpful for other tasks. Besides, this measure can utilize unlabeled domain data, tapping the vast reserves of unlabeled data to have even better features. An experiment on a multi-domain text classification shows that the proposed method consistently improves the baseline’s performance and improves the knowledge transfer of learned features to unseen data.
On the Issue of Topological Observability of Cognitive Maps while Maintaining Their Stability

The article considers the issue of optimizing the cognitive map of a complex system in compliance with the requirements for its full topological observability and stability, which allows cognitive modeling and analysis of the interaction of all vertices-variables and, in case of confirmation of complete controllability, -which of these variables. The information technology of analysis and optimization of topological observability of multiconnected geographic information systems created earlier by the authors is characterized. According to the system of rules it transforms the information or mathematical model of the system into the formalization of the system model in the form of a dichromatic graph with vertices-variables and vertices-dependences rule of topological observability analysis. It is suggested how any cognitive map can be transformed into such a dichromatic graph, which allows to extend the previous developments for this type of system models, i.e. to analyze and optimize the level of topological observability of information systems models in the form of a cognitive map. Special attention is paid to the stability of cognitive maps. A new method for the synthesis of fully topologically observable stable cognitive maps of the nth order of a certain type on the basis of a basic fully topologically observable stable cognitive map of a smaller order, constructed by experts, is proposed. The paper demonstrates the work of the method based on the basic second-order cognitive map. The basic cognitive map is transformed into a stable higher-order cognitive model by adding one new vertex incident to one of the vertices of this basic cognitive map. Structurally, the adjacency matrix of the cognitive map includes a contiguity matrix of the lower order cognitive map, to which is added another row and a column with one non-zero element and zeros in all other elements of this row. It is proved, using Vieta’s theorem and the rule of calculating the determinant of the matrix through algebraic additions that the cognitive maps of the nth order synthesized in this way will be both completely topologically observable and stable. An example of application of the created method to the analysis and optimization of the cognitive map, which takes into account the main components of the educational and professional program of a higher education institution, is given. It is shown that only such an educational process will be fully topologically observable, stable, which will provide intermediate final certification, similar to "Step-1" or "Step-2" for medical specialties. Such process will be more managed in the case of proving complete controllability, it allow to synthesize the law of quality management of higher education. It is noted that the obtained conclusions can be extended to multi-module or multi-semester disciplines (intermediate colloquia, etc. are required) and practice (intermediate tests).
Automatic Acquisition Method of Geotechnical Engineering Survey Data based on Artificial Intelligence

The process of collecting survey data, the traditional automatic acquisition method of geotechnical engineering survey data does not integrate enough information, resulting in high packet loss rate. Therefore, an automatic data acquisition method for geotechnical engineering survey based on artificial intelligence is proposed. The 3D laser scanning data can be obtained to represent the local characteristics of rock and soil. GPS control network is set up to collect d-level and e-level GPS data. The information state features are extracted by using artificial agent, and the above data are integrated to realize the automatic acquisition of survey data. Comparing this method with the two traditional methods to collect cross-section node information of rock-soil tunnels, the results show that this method reduces the data packet loss rate and ensures the integrity of survey data.
ScarlettH.. Neuroscience for organizational change: An evidence‐based practical guide to managing change. London: Kogan Page, 2019, 288 pages, $39.95 paperback.

Personnel PsychologyVolume 74, Issue 1 p. 199-201 BOOK REVIEW Scarlett, H.. Neuroscience for organizational change: An evidence-based practical guide to managing change. London: Kogan Page, 2019, 288 pages, $39.95 paperback. Julie Lister, Corresponding Author Julie Lister j.lister@westminster.ac.uk Senior Lecturer in Human Resource Management, Westminster Business School, University of Westminster, London Correspondence Email: j.lister@westminster.ac.ukSearch for more papers by this author Julie Lister, Corresponding Author Julie Lister j.lister@westminster.ac.uk Senior Lecturer in Human Resource Management, Westminster Business School, University of Westminster, London Correspondence Email: j.lister@westminster.ac.ukSearch for more papers by this author First published: 12 February 2021 https://doi.org/10.1111/peps.12438Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume74, Issue1Spring 2021Pages 199-201 RelatedInformation
Object classification of remote sensing images based on optimized projection supervised discrete hashing

Recently, with the number of large-scale remote sensing (RS) images increasing, the demand for large-scale RS image object classification is growing, and many researchers are interested. Hashing, as a result of its low memory requirements and high time efficiency, has widely solved the problem of large-scale RS images. Supervised hashing methods mainly leverage RS image label information to learn hashing function; however, the similarity of the original feature space cannot be well preserved, which cannot meet the accurate requirements of RS images object classification. To address the aforementioned analysis, we propose a method named optimized projection supervised discrete hashing (OPSDH), which jointly learns optimized projection constraint and discrete binary codes generation model. It uses an effective optimized projection method to further constraint on the supervised hashing learn, and generated hash codes preserve the similarity based on the data label while retaining the original feature space’s similarity. The experimental results show that OPSDH reaches improved performance compared with existing hashing methods and demonstrates that the proposed OPSDH is more efficient for operational applications.