Reviewerly/exHarmony huggingface dataset:
A Novel Deformation Feature Flow Network for Industrial Fumes Video Segmentation

The emission of industrial fumes is one of the most important factors that lead to global warming, and it is important to achieve accurate monitoring of industrial fumes. Video segmentation of industrial fumes is the key to real-time monitoring the degree of fume pollution, however, available industrial fume segmentation techniques can only partition independently frame by frame, spatio-temporal consistency features and dynamic features cannot be obtained, the edge segmentation of fumes is not effective, and the accuracy of fume segmentation requires further improvement.Accordingly, this paper proposes a semantic video segmentation network based on a deep learning method that combines fume deformation information. In the static part, this network adopts LR-ASPP design to ensure real-time performance and no loss of detailed information; and in the dynamic part, spatio-temporal consistency is explicitly used as an additional constraint to review the fume deformation information from the previous frame to the current frame when segmenting the current frame,which is used to enhance the semantic information in the dynamic region and it achieves to enable the network to learn the non-rigid deformation dynamic features between the fume frames.Finally, it constructed a residual hybrid attention network for the motion region of fumes to achieve less loss of motion information in the forward propagation process.The result shows that the network presented in the study can be well used in industrial fumes video segmentation, with strong anti-interference capability in complex environments (more than 10% improvement in both IoU and F-score under the high-speed segmentation of 52 FPS is maintained), and can provide a new perspective for the accuratesegmentation of current industrial fumes videos.
Descan: Censorship-Resistant Indexing and Search for Web3

The popularity of blockchain technology has bootstrapped many "Web3" applications, e.g., Ethereum and IPFS, that apply distributed ledger technology to store transactions. The amount of transactions generated and stored in such Web3 applications is significant and, in its raw form, usually not searchable by users. Existing Web3 transaction indexing and search engines are predominantly centralized and, therefore, can manipulate search results or censor particular queries. With the proliferation of Web3 transactions and applications, a decentralized and censorship-resistant search primitive is becoming essential.We present DeScan, a decentralized and censorship-resistant indexing and search engine for Web3. Users index their local Web3 transactions using custom rules that output triples. Generated triples are bundled in a distributed transaction graph that is searchable by other users. To coordinate search and distribute the storage of the transaction graph over peers in the network, we build upon a Skip Graph (SG) data structure. Since the Skip Graph does not provide any resilience against adversarial peers that censor searches, we propose four modifications to improve its robustness. We implement DeScan and conduct experiments with up to 12'800 peers and 187'987 Ethereum transactions. Our experiments show that DeScan with our modifications enabled can tolerate 20% adversarial peers and 35% unresponsive peers without disruption. Moreover, we find that searches in DeScan are usually completed well within a second, even when the network grows. Finally, we show that storage and network costs are evenly distributed amongst peers as the network grows.
A Relaxation Approach to Layerwise Determination of Learning Rates in Deep Neural Networks

The determination of learning rate is critical in training neural networks. The optimal one can be determined by the line-search minimization. However, the line search involves matrix-vector multiplications that are expensive in the computational cost and its direct implementation is prohibitive.We introduce two relaxations of the line-search minimization. The relaxations are free from matrix-vector multiplications and can be directly solved. One of them, referred to as naive relaxation, is based on the global Lipschitz constant, a measure of the maximum ratio of the perturbation of gradient relative to a perturbation of weight in forward and backward passes. The other relaxation is based on a local Lipschitz constant, more efficient and adaptive than the former.Conventionally, the weights are concurrently updated with the same learning rate. The coincident updates make the propagations of perturbation collide and become too complicated to be analyzed. Instead, we take a layerwise approach : the weight of one layer is alternatively updated while the others are held fixed. The layerwise approach prevents the collisionand enables us to introduce rigorous convergence results. Empirical result furthermore shows that the optimal learning rates are quite different layerwisely and supports our layerwise approach.
Contrast Sensitivity Function in Deep Networks

The contrast sensitivity function (CSF) is a fundamental signature of the visual system that has been measured extensively in several species. It is defined by the visibility threshold for sinusoidal gratings at all spatial frequencies. Here, we investigated the CSF in deep neural networks using the same 2AFC contrast detection paradigm as in human psychophysics. We examined 240 networks pretrained on several tasks. To obtain their corresponding CSFs, we trained a linear classifier on top of the extracted features from frozen pretrained networks. The linear classifier is exclusively trained on a contrast discrimination task with natural images. It has to find which of the two input images has higher contrast. The network's CSF is measured by detecting which one of two images contains a sinusoidal grating of varying orientation and spatial frequency. Our results demonstrate characteristics of the human CSF are manifested in deep networks both in the luminance channel (a band-limited inverted U-shaped function) and in the chromatic channels (two low-pass functions of similar properties). The exact shape of the networks' CSF appears to be task-dependent. The human CSF is better captured by networks trained on low-level visual tasks such as image-denoising or autoencoding. However, human-like CSF also emerges in mid- and high-level tasks such as edge detection and object recognition. Our analysis shows that human-like CSF appears in all architectures but at different depths of processing, some at early layers, while others in intermediate and final layers. Overall, these results suggest that (i) deep networks model the human CSF faithfully, making them suitable candidates for applications of image quality and compression, (ii) efficient/purposeful processing of the natural world drives the CSF shape, and (iii) visual representation from all levels of visual hierarchy contribute to the tuning curve of the CSF, in turn implying a function which we intuitively think of as modulated by low-level visual features may arise as a consequence of pooling from a larger set of neurons at all levels of the visual system.
Credibility Estimation and Empirical Bayesian Analysis of Journal Impact Factor

The journal impact factor is an important standard to measure journal quality. However, the impact factor only uses the sample information to estimate the quality of journals, and ignores the prior information that affects the quality of journals. In this paper, the Bayesian model of journal quality parameter is established, and the estimation of journal quality parameter is limited to the linear combination of citation number. The optimal linear Bayesian estimation of journal quality parameter is obtained. The conclusion shows that the optimal estimation can be expressed as the weighted average of the weighted impact factor and the aggregation estimation, in which the weight satisfies the property of ”credibility”. The credibility estimation not only has a good statistical property, but also can correct the traditional journal impact factor, and more objectively measure the quality level of journals. Furthermore, the estimation of hyper-parameters are obtained with empirical Bayesian method, and the empirical Bayesian estimation of quality parameter is obtained. Finally, the convergence and asymptotic normality of the credibility estimation are verified by the numerical simulation. Compared with the traditional journal impact factor, the empirical Bayesian estimation in this paper not only uses the citation data of one journal, but also uses the prior information and the citation information of other journals, which more accurately reflects the true level of journal quality.
A New Paradigm for High-Capacity Reversible Data Hiding with Pixel Repetition and Adaptive Embedding

This paper introduces a Reversible data hiding (RDH) scheme for digital images with Pixel Repetition (PR). A generalized PR framework is first developed, extending the rate-distortion criterion for adaptive embedding. An input image is defined to be scaled up by replacing each original pixel (called seed) with a block of shape s×s, where s is the scaling factor. A new PR-based RDH scheme with two-phase embedding for s = 2 is thus proposed. An invertible mapping function is designed to embed 4-bit data in each 2 × 2 image block, preserving the seed. Additional bits in the preserved seeds are embedded in the second phase using an adaptive selection of their least significant bits. Compared to the prominent PR-based schemes, an impressive rate-distortion performance of the proposed scheme is recorded with a greater potential for applications that require a high and varying embedding rate with better image quality.
Low-Light Image Enhancement by Deep Learning Network for Improved Illumination Map

In the case of insufficient illumination conditions, the quality of the captured image is poor. At this time, increasing the brightness of the dark region of the whole image will inevitably aggravate noise pollution. Therefore, the ideal state of low-light image enhancement should be to make the dark areas brighter while not allowing the noise part to be enhanced as well. In this paper, we combine Retinex theory and convolutional neural networks to construct a simple and effective model, including three sub-modules: the decomposition module, illumination module, and reflection module, in which the decomposition module is used to decompose the input low-light image into illumination and reflection maps, the illumination module improves the feature extraction part of the simple convolutional layer used in the past and constitutes a new feature extraction structure by Depthwise separable convolution, which makes up for the shortcoming that some dark areas are not well brightened and better adjusts the brightness of the illumination map, and the reflection module adjusts the texture details more clearly because it adds the illumination map as a reference. In this way, the original space is decoupled into two smaller subspaces to the extent that better results can be achieved. We demonstrate through extensive experiments the effectiveness of the design and its superiority over state-of-the-art techniques, especially in terms of robustness to severe visual defects and flexibility in adjusting luminance.
Deep Learning-Based Blind Image Super-Resolution with Iterative Kernel Reconstruction and Noise Estimation

Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill–posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using naive deep learning approaches, where models are often trained on synthetically generated image pairs. Most of the effort so far has been focused on solving the inverse problem under some constraints, such as for a limited space of blur kernels and/or assuming noise-free input images. Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations. In this paper, we propose IKR-Net (Iterative Kernel Reconstruction Network) for blind SISR. In the proposed approach, kernel and noise estimation and high resolution image reconstruction are carried out iteratively using dedicated deep models. The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel even for noisy inputs. IKR-Net provides a generalized solution that can handle any type of blur and level of noise in the input low-resolution image. IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy images with motion blur.
Certain Investigation on Perpetualistic Fuzzy Outlier Data for Efficiency Evaluation of Centroid Stability with Cluster Boundary Fitness

This paper aims to investigate certain factors that hide outliers in two dimensions such as boundary partitioning and space angular parameters. In this proposed algorithm, boundary representation of clusters, the data points that lie on the cluster boundary is stored geometrically as coordinate values such as i_bound (inliers) and o_bound(outliers). Outliers that present in dataset are investigated by boundary fitness over centroid stability. In this paper we focus to examine whether the data point lie on the boundary is treated as inliers or outliers. Several iterations are manipulated to fix the outlier point deeply. Using fuzzy clustering, data points are clustered and boundary is fixed. If the space occupied by the cluster varies for every iteration, the distance from inlier to outlier between the boundaries is calculated. After calculation, if the data point is below the threshold value, it is treated as outlier. Our proposed method shows efficiency over evaluation metrics of outlier detection performance.
Information Security Challenges in the Absence of ICT in Protecting Personal and Organisational Data in Nairobi County, Kenya

There have been several cases of lost documents and misplaced or inaccurate information belonging to individuals or organisations reported in recent times at global, national and regional levels. Nairobi county is one of the regions on the spot as many people travelling or working in the city have reported lost identification documents such as certificates and licenses, whether through criminality or negligence. Furthermore, most businesses and organisations dealing with huge volumes of data need help to protect and provide accurate information. Many still need help with the pre-digital data management systems, thus risking the loss of both personal and organisational data. This paper, therefore, sought to examine the information security challenges due to the inadequate application of ICT in managing personal and corporate data and their implications on individuals and organisations in Nairobi County. The target population included employees from Kenya Revenue Authority and Kenya Data Networks in Nairobi headquarters offices, police officers deployed at NPS offices headquarters and members of the public. The selected target groups were known to deal with enormous data for both personnel and businesses, and the police officers were part of this study because of their mandate to help in tracking lost personal and business documents. Lastly, the researcher engaged members of the public to share their experiences of losing business and personal records. The study employed purposive and simple random sampling techniques to select the required sample of 110 participants drawn from the four groups. A structured questionnaire was administered to the sampled police officers, employees of Kenya Data Network and Kenya Revenue Authority and members of the public. The researchers interviewed key informants from the three organisations, NPS, KRA and KDA, to complement the data collected using the questionnaires. The findings established that challenges were associated with the need for more ICT in managing personal information and organisational data. The paper revealed that the need for an integrated system is a challenge to tracking missing identities or verifying the validity of provided information, thus compromising the productivity and security of businesses and institutions. Results from this study provide a solution to tracking any missing documents and correcting inaccurate personal data by adopting an integrated ICT system.
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation

Women are often perceived as junior to their male counterparts, even within the same job titles. While there has been significant progress in the evaluation of gender bias in natural language processing (NLP), existing studies seldom investigate how biases toward gender groups change when compounded with other societal biases. In this work, we investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models by introducing a novel framework for probing compound bias. We contribute a benchmark robustness-testing dataset spanning two domains, U.S. senatorship and professorship, created using a distant-supervision method. Our dataset includes human-written text with underlying ground truth and paired counterfactuals. We then examine GPT-2 perplexity and the frequency of gendered language in generated text. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
INSTALASI JARINGAN KOMPUTER SEBAGAI PENUNJANG LAYANAN ADMINISTRASI MASYARAKAT DESA KARANGDUREN KAB. MALANG

At the village level, administrative services are the main service and are directly related to the needs of the community. Administrative services can be in the form of issuing certificates, cover letters, letters of recommendation, legalization, and other letters. For administrative services in correspondence, the problems faced by the Karangduren village office regarding the installation of computer networks. Currently, computers that store data and applications for mailing repositories are not yet connected to other computers, resulting in queuing and slow document processing. With these problems, PPM (Pengabdian Pada Masyarakat) activities will be carried out in the form of "Implementation of Computer Network Installation to Support Mailing Repository Applications at the Karangduren Village Office, Kec. Pakisaji, Kab. Malang". Through PPM activities in Karangduren village, Kec. Pakisaji, Kab. Malang is done to help solve problems regarding the installation of computer networks. By installing a computer network at the village office, all computers in the village office can be connected. So with the installation of this computer network, it can improve the service and performance of the Karangduren Village Office in serving the community. The results of this PPM activity will produce a computer network installation design and a basic computer network manual.
BIMBINGAN TEKNIS TAMPILAN JADWAL SHOLAT DI MUSHOLLA AL-KHOIR DESA TEGAL WERU KABUPATEN MALANG

This activity aims to design an information system that contains prayer time schedules and other important information that is very useful for musholla Al-Khoir pilgrims. The purpose of the activity is to provide technical guidance to the mosque administrators. The result of this activity is that people in the Tegal Weru area become easier to find out the prayer schedule and other important information related to worship activities&#x0D;
LITERASI FITUR DATA-FORM MS. EXCEL UNTUK MENGELOLA DATABASE ANGGOTA PERKUMPULAN KEMATIAN “LAZARUS” MALANG

This community service aims to develop technical skills in managing databases. So far, the “Lazarus" commission processed all regular membership fees manually. The repetitive work and recording errors happened along the way. With a total of 428 family members out of 993, the above data processing was inefficient and ineffective.&#x0D; Ms. Excel, especially the Data-Form feature will make database management easier for someone to process data entry, edit, search, and delete in a database table. When a database has been formed, the preparation of the annual report will also be able to be compiled efficiently &amp; effectively, because the database is already stored in a soft file.&#x0D; Community service is carried out through training assistance. The activity begins with preparing teaching modules, followed by assisting to create a table model database in Ms. Excel. Subsequently, design a Data (Entry) Form model for recapitulation. &#x0D; The competencies have been achieved because in the end the database management has been simulated very well using real data &amp; problems. Therefore, the final evaluation results declared that the database management model uses the Data Form successfully to help the "Lazarus" commission maximize its service.&#x0D; &#x0D; &#x0D; &#x0D;
PELATIHAN AUGMENTED REALITY SEBAGAI MEDIA PEMBELAJARAN INTERAKTIF KEPADA GURU-GURU TK ISLAM AL-MA’ARIF SINGOSARI MALANG

The use of interactive learning media in the pandemic period is deemed necessary to be developed better, besides being able to increase the desire to learn from students who already feel bored just doing learning at home, interactive modules are considered more effective in delivering material. However, there are still many kindergarten teachers who are not optimal in applying interactive modules. This is because there is still rarely counseling or training in making interactive modules. So, it is necessary for counseling to be carried out by lecturers from Electronic Engineering who in the process will be assisted by students to introduce and teach the process of making to the application of augmented reality modules that can be used as interactive learning media for early childhood. It is hoped that after the counseling is held, learning media using augmented reality can be applied to subjects that require 3D visuals in delivering material.&#x0D;
The Effect of Learning Strategy and Critical Thinking Ability on English Essay Writing Skills

The Informatics Engineering Study Program at Mitra Karya University Bekasi has two English courses given in semesters 1 and 3. In semester I, students study English which is directed at understanding grammatical and vocabulary in the field of Informatics Engineering. Students must be able to write a minimum of three paragraphs about their field. Learning to write essays is also aimed at mastering thesis abstract writing. Writing is one aspect of productive language skills. For this reason, the principle of teaching writing is how to make students have sufficient competence in an effort to produce good writing skills in students. However, the process of learning to write English essays is not as easy as expected so far. Students still experience many difficulties in writing essays, this causes the quality of the content of their English essays to be not optimal. Even though it is known that students should be able to react critically when reading an essay text that is presented either on a website, newspaper, magazine, or book. This study uses an experimental method and a quantitative approach, while the data collection technique is carried out by means of observation, tests and documentation, so the results of this study explain that from the results of the description of descriptive statistical data it is known that the average value of students who learn to write English essays using metacognitive strategies had an average score of 81.5 higher than students who studied with cognitive strategies.