Reviewerly/exHarmony huggingface dataset:
Latent reweighting, an almost free improvement for GANs

Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting different classes of images. In particular, the generator will necessarily sample some low-quality images in between the classes. Rather than modifying the architecture, a line of works aims at improving the sampling quality from pre-trained generators at the expense of increased computational cost. Building on this, we introduce an additional network to predict latent importance weights and two associated sampling methods to avoid the poorest samples. This idea has several advantages: 1) it provides a way to inject disconnectedness into any GAN architecture, 2) since the rejection happens in the latent space, it avoids going through both the generator and the discriminator, saving computation time, 3) this importance weights formulation provides a principled way to reduce the Wasserstein&#x2019;s distance to the target distribution. We demonstrate the effectiveness of our method on several datasets, both synthetic and high-dimensional.
Quantum public key encryption scheme with four states key

Abstract Asymmetric encryption has some important applications in the field of communication. In classical computers, most public key encryption schemes are designed based on computational assumption. In other words, they can ensure computational security. When the computing power is large enough, it will pose a threat to these encryption schemes. We propose a quantum public key encryption scheme which based on properties of quantum physics. Our scheme realizes encryption and decryption with few resources and does not rely on any computational assumption. The key in our scheme consists of classical bits and qubits. Where the private key are classical bit and public key are qubits. The public key are generated through private key. We analyze the performance and security of the scheme. Our scheme ensures the information-theoretic secure and can be implemented easily.
Real-Time Face Mask Detection Using Machine Learning Algorithm

Our world is going through a lot of misery due to the outbreak of coronavirus disease (COVID-19) from the end of 2019 affecting many people’s livelihood and causing deaths. Wearing a face mask has become very normal today as the whole world is trying to recover back to normalcy and continue their activities. Therefore, it has become crucial to implement this policy especially in public areas, offices, etc. Constant monitoring cannot be done on every individual. Hence, technology plays a prominent role for a steady implementation. The method consists of two stages of model creation using convolutional neural network (CNN) and model implementation using cascaded classifier (Haar cascade classifier). This model consists of an in-depth reading program. A rigid model is built using TensorFlow, Keras, and OpenCV. By varying the datasets and epoch numbers, we tend to maximize the accuracy and minimize the loss score. This model will be helpful for the authorities to track safety violations, promote the mask usage, and secure the work environment when integrated in various devices.
GEOAGRI: A Geospatial web-based decision support system to Farm-to-Market Road Network Plan

Geographic Information System (GIS) for Agricultural and Fisheries Machinery and Infrastructure (GEOAGRI) was developed by Caraga State University in partnership with the Department of Agriculture mainly to aid various agencies which intend to propose a farm-to-market road project. GEOAGRI provides visualization of the road and automatically cross-checks it to all existing proposed, ongoing, and completed road projects for possible duplication. The web-based GIS system requires the user to upload a shapefile or KML of the road; it then pinpoints the location based on its geometry and executes an intersection query to the existing road projects within the location. By having this mechanism, one can determine the extent of the intersection of the proposed road to the existing ones that users can also be view on the map. In addition, it can help the agency managers in deciding whether to approve or deny the project. The system also embedded other map layers such as rice production, public market, machinery, and infrastructures which is also one of the determining factors in the approval of the proposed road. Non-registered users may also view all the existing road projects and do basic map queries. The system is accessible at http://geoagri.da.gov.ph/, which is running and operational.
A Deep Learning Framework for Enhancer Prediction Using Word Embedding and Sequence Generation

Enhancers are non-coding DAN fragments that play key roles in gene regulations and can promote the transcription of structural genes, thereby affffecting the expression of structural protein catalytic enzymes and regulatory proteins. Accurate identifification of enhancers helps to understand the transcription of structural genes and the development of human tumorigenesis, diagnosis and treatment. The enhancer sequences have high position variations and dispersions, and the identifification of enhancers is more challenging than other genetic factors. Based on word embedding and sequence generative adversarial networks, a deep learning framework for enhancer identifification is proposed. Firstly, considering the small number of sequences in the benchmark dataset, RankGAN is used to amplify the dataset size while maintaining the data characteristics. Then, in view of the similarity between DNA sequence and natural language, DNA sequence is regarded as a sentence composed of multiple “words”, and the word embedding technology FastText is applied to transform it into a numerical matrix. To extract the dependencies and highly abstract features of nucleotides in DNA sequences, a Long Short-Term Memory Convolutional Neural network (LSTM-CNN) is constructed to perform the identifification task. On the cross-validation set, the prediction accuracy of enhancers and their types is as high as 0.940 and 0.913. On the independent test set, the accuracy and Matthew’s correlation coeffiffifficient (MCC) for enhancer prediction are 0.751 and 0.501, respectively. For the enhancer type prediction, the accuracy and MCC of this method are 0.696 and 0.408, respectively. Compared with existing methods, this method achieves more satisfactory results for the prediction of enhancers and their types. This study will further enrich the application of natural language processing in bioinformatics.
Using the Capabilities of the Common Digital Space of Scientific Knowledge for Educational Purposes

The role of the Common Digital Space of Scientific Knowledge (CDSSK) in the development of educational technologies is considered. CDSSK is being implemented by a number of Russian organizations as a digital environment of a new generation for the purpose of providing information for scientific research, education, popularization of science and preservation of scientific heritage. The CDSSK structure provides for the formation and support of special components related to secondary and higher education. One of the elements related to education in the structure of the CDSSK is a scientific virtual exhibition. Such an exhibition can be dedicated both to a specific person and a scientific event in general. Virtual exhibitions combine a variety of resources related to the presented topic (biographies of scientists, related museum and archival materials, copies of films, interactive multimedia resources). The paper presents the basic principles of constructing the CDSSK educational component, provides examples of implemented virtual exhibitions used in the educational process. Virtual exhibitions are implemented on the digital library “Scientific Heritage of Russia” platform.
Comment on “An enhanced and secured RSA public cryptosystem algorithm using Chinese remainder theorem (ESRPKC)”

In [1], Kumar et al. proposed an enhanced and secured RSA public key cryptosystem (ESRPKC) algorithm using Chinese remainder theorem. In their scheme, the public key is defined as (N,e,μ) where N is the product of four distinct large prime numbers, e is a public exponent, and μ is a parameter called encryption key to encrypt the message. Compared to the traditional RSA cryptosystem, the authors used the extra parameter μ, and claimed that security increased due to this extra parameter. They claimed that it is required to use a brute force attack to break the system even if the number N is factorized by an adversary to obtain the private parameters k1 and k2 which are components of μ. The authors claimed that ESRPKC is a highly secure and not easily breakable scheme compared to the traditional RSA scheme. In this paper, we do a cryptanalysis on Kumar et al.'s scheme given in [1] and demonstrate some major security weaknesses. We prove that if N is factorized, there is no need to use brute force to break the system. Additionally, choosing four prime numbers instead of two prime numbers decreases the security significantly and only increases the computation time. Therefore, the proposed system (ESRPKC) is not as efficient as the traditional RSA algorithm.
Image Processing Application for Lipa Saqbe Mandar

Seeing from today's technological developments, it is growing rapidly, but there are still many people, especially the younger generation today, whose understanding is still very lacking, in recognizing any motifs or patterns in Lipa Saqbe Mandar. From these problems, an image processing application is needed to recognize the Lipa Saqbe pattern that can be used to help facilitate the community or the younger generation, where the camera application will be directed to Lipa Saqbe and will then provide information or identify the name of the Lipa Saqbe pattern or motif. This system uses the template matching method, which functions to match each part of an image with the image that becomes the template (reference). One of the applied methods of the convolution technique. This method is often used to identify character images of letters, numbers, fingerprints, and other image matching applications. This application is built using the Java programming language, and database. The image management process uses the template matching method through a scanning process so that it can identify the name of the motif or pattern in Lipa Saqbe
How Well do Students Understand the All-Encompassing, Ubiquitous, and Interconnected Nature of IoT?

The Internet of Things (IoT) content and curricula is a recently emerged trend for computer science and software engineering educators. IoT as a paradigm is often described as an all-encompassing new phenomenon covering homes, industries, governments, and the environment. For that reason, IoT topics have become a part of computer science and software engineering programs. Existing research has been conducted on the development of IoT curricula but as this work is fairly recent the evaluation of those approaches requires further work. This paper presents a case study from a capstone course within a three-course IoT specialization. We evaluate student capstone projects using the thematic analysis method in order to assess our IoT specialization studies. As a result, we present an overview of how the students see the IoT, and what kind of IoT projects they design and implement. Most often the student projects implemented home automation projects. Often these prototypes lacked connectivity and communication capabilities to other systems. An IoT characteristic that is most rarely seen in student projects is 'physical actions in the environment.'
An Anonymous Auction Scheme Based on Blockchain and Cryptographic Techniques

Online auctions are convenient, time-saving and more preferable for participants with the advantages of low cost, wide range and high speed. In this paper, we present an anonymous auction transaction system on the consortium blockchain based on identity-based encryption, anonymous signature, order revealing encryption. In a nutshell, initially, the bidders submit encrypted bids to a transaction center. Subsequently, according to the auction policies, the transaction center determines the transaction match pairs and publish them. If a bidder is matched, he can offer a verification token to open his identity and prove he is the owner of the matched bid. When a participant complains about the result of the transaction, every participant can check the validity of complains. In addition, we provide the security and performance analyses of the proposed systems. Our system satisfies two-layer privacy (inner-auction privacy and inter-auction privacy), matching-pairs verifiability, and scalability. Compared with existing work, the performance of transaction generation algorithm improves by 87% and the performance of transaction check performance improves by 25%.
Sonic sense

Abstract This chapter aims to set up the context for novel engagements in notions of sense, knowledge, and meaning via sound. Sound is introduced as material as well as concept, and listening is considered at once as strategy and as expanded method to engage and challenge existing notions of prehension, interpretation, and significance, and to query their rationale. The suggestion is that the sonic offers itself to experience not as meaning but as a challenge to the making of sense, and that consequently through listening we can reach a different sense, that is plural and includes what we did not expect to know. These ideas are developed by listening to Christof Migone’s work HitParade (NewYork) (2012), a group performance which sounds bodies, surfaces, and microphones through their shared rhythm, and to Jana Winderen’s The Wanderer (2015), a field recording of zooplankton and phytoplankton between the North Pole and the Equator.
Towards reduction of expert bias on Gleason score classification via a semi-supervised deep learning strategy

Gleason Score (GS) is the principal histological grading system to support the quantification of cancer aggressiveness. This analysis is carried out by expert pathologists but with reported evidence of moderate agreement among pathologists (kappa values less than 0.5). This fact can be prone to errors that directly affect the diagnosis and subsequent treatment. Current deep learning approaches have been proposed to support such visual pattern quantification but there exist a remarked on expert annotations that overfit representations. Besides, the supervised representation is limited to model the high reported visual variability intra Gleason grades. This work introduces a semi-Supervised Learning (SSL) approach that initially uses a reduced set of annotated visual patterns to built several GS deep representations. Then, the set of deep models automatically propagates annotations to unlabeled patches. The most confident predicted samples are used to retrain the ensemble deep representation. Over a patch-based framework with a total of 26259 samples, coded from 886 tissue microarrays, the proposed approach achieved remarkable results between grades three and four. Interestingly, the proposed SSL with only the 10% of samples achieves more general representation, achieving averages scores of ~75.93% and ∼ 71.88% concerning two expert pathologists
Multiscale Hierarchical Attention Fusion Network for Edge Detection

Edge detection is one of the basic challenges in the field of computer vision. The results of most existing methods now produce thick edges and background interference. The images generated by the network must be post-processed with non-maximum suppression. To tackle these problems, in this paper we proposes a novel edge detection model, which allows the network to focus on learning the contextual features of the image, thereby obtaining more accurate pixel edges. First, we use the multi-scale feature layering module in the model to obtain richer image multi-granularity features in the high-level features. Secondly, after the multi-scale feature layering module, we use the channel attention in the edge attention module and use the spatial attention for low-level features. and finally through a fusion layer to fuse the output of channel attention and spatial attention. Evaluate our method on two benchmark popular datasets, BSDS500[1] and NYUDv2[2], and achieved the best accuracy, 78.1%, 73% that has not been processed using non-maximum suppression (NMS). We apply out method to water ripple detection and get good performance.
Upcoming Meetings

Artificial OrgansVolume 45, Issue 11 p. 1429-1430 UPCOMING MEETING Upcoming Meetings First published: 14 October 2021 https://doi.org/10.1111/aor.14073Read 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 No abstract is available for this article. Volume45, Issue11November 2021Pages 1429-1430 RelatedInformation
Strategic path planning method for intervention AUVs in a complex environment

Autonomous Underwater Vehicles (AUVs) have come to play an essential role in marine applications. While AUVs have been used to some extent for monitoring, however, interventions have mainly remained unexploited. We develop an intervention method for low-cost AUVs, which are smaller and cheaper than conventional AUVs, but with lower hardware performance. The method can decide on actions strategically tailored to the characteristics of the mission. In this paper, we propose a new path planning method tailored to the characteristics of the mission in a complex environment for intervention AUVs. The performance of the method was verified by simulations using a 3D model of the real environment.
A Fault Diagnosis Technique of SMGFs in $k$-CNOT Based Reversible Circuits

This paper introduces a fault diagnosis technique to obtain the exact location of Single Missing Gate Faults (SMGFs) in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -CNOT based reversible logic circuits. The proposed fault diagnosis technique establishes that the generated single test vector can identify the exact location of SMGFs. For this purpose, we construct the augmented circuit that behaves as Circuit Under Test (CUT) and the testable augmented circuit is used for detecting the SMGF faults. The parity checking operations are included in the constructed augmented <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -CNOT circuit to obtain the exact location of SMGF. Finally, this paper presents the experimental results and analysis in order to show the effectiveness of determining the exact location of faults in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -CNOT circuit.
A Novel General (n, n)-Threshold Multiple Secret Images Sharing Scheme Based on Information Hiding in the Sharing Domain

(k,n)-threshold secret image sharing (SIS) protects an image by dividing it into n shadow images. The secret image will be recovered as we gather k or more shadow images. In complex networks, the security, robustness and efficiency of protecting images draws more and more attention. Thus, we realize multiple secret images sharing (MSIS) by information hiding in the sharing domain (IHSD) and propose a novel and general (n,n)-threshold IHSD-MSIS scheme (IHSD-MSISS), which can share and recover two secret images simultaneously. The proposed scheme spends less cost on managing and identifying shadow images, and improves the ability to prevent malicious tampering. Moreover, it is a novel approach to transmit important images with strong associations. The superiority of (n,n)-threshold IHSD-MSISS is in fusing the sharing phases of two secret images by controlling randomness of SIS. We present a general construction model and algorithms of the proposed scheme. Sufficient theoretical analyses, experiments and comparisons show the effectiveness of the proposed scheme.
Fractal Dimension Analysis Based Aging State Assessment of Insulating Paper with Surface Microscopic Images

The insulating paper of the transformer is affected by many factors during the operation, meanwhile, the surface texture of the paper is easy to change. To explore the relationship between the aging state and surface texture change of insulating paper, firstly, the thermal aging experiment of insulating paper is carried out, and the insulating paper samples with different aging times are obtained. After then, the images of the aged insulating paper samples are collected and pre-processed. The pre-processing effect is verified by constructing and calculating the gray surface of the sample. Secondly, the texture features of the insulating paper image are extracted by box dimension and multifractal spectrum. Based on that, the extreme learning machine (ELM) is taken as the classification tool with texture features and aging time as the input and output, to train the algorithm and construct the corresponding relationship between the texture feature and the aging time. After then, the insulating paper with unknown aging time is predicted with a trained ELM algorithm. The numerical test results show that the texture features extracted from the fractal dimension of the micro image can effectively characterize the aging state of insulating paper, the average accuracy can reach 91.6%. It proves that the fractal dimension theory can be utilized for assessing the aging state of insulating paper for onsite applications.
Gap between Prediction and Truth: A Case Study of False-Positives in Leakage Detection

Since leakage detection was introduced as a popular side-channel security assessment, it has been plagued by false-positives (a.k.a. type I errors). To fix this error, the previous solutions set detection thresholds based on an assumption-based prediction of false-positive rate (FPR). However, this study points out that such a prediction (of FPR) may be inaccurate. We notice that the prediction in EuroCrypt2016 is much smaller than (approximately <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>1</mn> <mo>/</mo> <mrow> <mn>779</mn> </mrow> </math> times) the true FPR. The gap between prediction and truth, called underpredicted false-positives (UFP), leads to severe false-positives in leakage detection. Then, we check the statistical distribution of test statistics to analyze the cause of UFP. Our analysis indicates that the overlap between cross-validation (CV) blocks gives rise to an assumption error in the distribution of the CV-based estimates of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>ρ</mi> </math> -statistics, which is the root cause of UFP. Therefore, we tackle the UFP by eliminating the overlap between blocks. Specifically, we propose a profiling-shared validation (PSV) and utilize this validation to improve the detection of any-variate any-order leakages. Our experiments show that the PSV solves the UFP and saves more than 75% of the test time costs. In summary, this article reports a potential flaw in leakage detection and provides a complete analysis of the flaw for the first time.