Reviewerly/exHarmony huggingface dataset:
Input Bias in Rectified Gradients and Modified Saliency Maps

Interpretation and improvement of deep neural networks relies on better understanding of their underlying mechanisms. In particular, gradients of classes or concepts with respect to the input features (e.g., pixels in images) are often used as importance scores or estimators, which are visualized in saliency maps. Thus, a family of saliency methods provide an intuitive way to identify input features with substantial influences on classifications or latent concepts. Several modifications to conventional saliency maps, such as Rectified Gradients and Layer-wise Relevance Propagation (LRP), have been introduced to allegedly denoise and improve interpretability. While visually coherent in certain cases, Rectified Gradients and other modified saliency maps introduce a strong input bias (e.g., brightness in the RGB space) because of inappropriate uses of the input features. We demonstrate that dark areas of an input image are not highlighted by a saliency map using Rectified Gradients, even if it is relevant for the class or concept. Even in the scaled images, the input bias exists around an artificial point in color spectrum. Our modification, which simply eliminates multiplication with input features, removes this bias. This showcases how a visual criteria may not align with true explainability of deep learning models.
A Review on Existing Learning Techniques Applied in Solving Optical Character Recognition Problem

Optical character recognition is treated as the classical problem in image processing that can be widely used for application areas related to computer vision. This process is highly dependent on the languages, as well as various formats of information like hand written/printed/sculptured documents as input. The chief operations that can be performed while handling this task are training the system and predicting the characters based on the trained knowledge. Conventionally, classification and regression mechanisms are used for this purpose. Due to the recent advents in technological progress, the same mechanism was served with the help of modern machine learning and deep learning concepts. The main agenda of this paper is to create awareness among the researchers by discussing existing mechanisms related to solve optical character recognition using various machine learning and deep learning concepts.
Physical Security Design of a Digital Forensic Lab

The physical security design of a digital forensic lab is one of the key components to ensure investigators are providing a secure environment to process and store evidence that has been entrusted ...
Removing Brightness Bias in Rectified Gradients.

Interpretation and improvement of deep neural networks relies on better understanding of their underlying mechanisms. In particular, gradients of classes or concepts with respect to the input features (e.g., pixels in images) are often used as importance scores, which are visualized in saliency maps. Thus, a family of saliency methods provide an intuitive way to identify input features with substantial influences on classifications or latent concepts. Rectified Gradients \cite{Kim2019} is a new method which introduce layer-wise thresholding in order to denoise the saliency maps. While visually coherent in certain cases, we identify a brightness bias in Rectified Gradients. We demonstrate that dark areas of an input image are not highlighted by a saliency map using Rectified Gradients, even if it is relevant for the class or concept. Even in the scaled images, the bias exists around an artificial point in color spectrum. Our simple modification removes this bias and recovers input features that were removed due to their colors. No Bias Rectified Gradient is available at \url{this https URL}
DenseNet-based attentive plot-aware recommendation

Since the ratings matrix is always sparse, auxiliary information has been proved very important in recommender systems. In this paper, we propose a DenseNet-based attentive plot-aware recommendation (DAPR) model, which combines attention mechanism and densely connected convolutional networks (i.e., DenseNet) to fully mine the semantic information in the movie plot text. This method effectively fuses rating information and text information for ratings prediction. Extensive experiments on three popular datasets demonstrate that our model performs better than other state-of-the-art approaches in common recommendation tasks.
Evaluation of cross-ontology association rules weighted by term specificity

The use of an ontology is a prevailing trend for management and analysis of biological big data. Consequently, we have encountered strong demands on developing algorithms for accurate analysis of ontology structures and annotated data. We can discover the association rules of cross-ontology terms, which provide the clues for predicting functions or phenotypes of a gene. However, because association rule mining algorithms are biased towards the rules of more general terms, it has been a challenge to discover the rules between more specific terms in concept. We propose a pairwise cross-ontology Weighted Rule Mining (WRM) approach which uses support and lift weighted by term specificity. For our experiments, Biological Process and Molecular Function sub-ontologies of Gene Ontology (GO), and Phenotypic Abnormality sub-ontology of Human Phenotype Ontology (HPO) were used. The results show IC-based WRM produced the rules of more specific terms in BP and PA than unweighted Association Rule Mining.
Adaptive Histogram Fusion-based Color Restoration and Enhancement for Underwater Images

Underwater image restoration is crucial for marine exploration, underwater archaeology and seafloor geological investigation. However, due to the complex underwater environment, the image obtained underwater often has problems with colour deviation and lack of details. To improve the quality and the visual effect of underwater images, we propose an adaptive histogram fusion-based colour restoration and enhancement method for underwater images. First, the proposed method restores the colour by pixel value adjusting in a channel and b channel according to colour distortion. Then the histogram is used in the H channel of the underwater source image to improve the colour hue. To make the enhanced result more in line with visual observation, the edge reserving-based method is used to enhance the details of colour restoration results. Experimental results demonstrate that our results are effective in subjective and objective evaluations, and superior to several existing methods.
Educational Technology to Improve Capacity – Integrating Adaptive Education Programmes in Public School in Kenya

Since the 1990s Kenya has been plagued by persistently low primary education outcomes. In the early 2000s, having identified the need to improve education services to increase global competitiveness, Kenyan politicians and policymakers began pursuing education system reform. Between 2003 and 2013, institutional and policy reforms led to an increase in enrolment of nearly 30%. In 2013, the government shifted its focus to enhancing education quality. However, traditional approaches to increasing quality take significant time to plan, implement and realise results. In an attempt to fast-track improvements, Kenya pursued two large-scale education technology (EdTech) programmes. The Digital Literacy Programme aimed to provide laptops to all primary students in Kenya. The Tusome programme used tablets to enhance teacher coaching and oversight. Lessons learned from these programmes reinforce that EdTech is not a silver bullet that can quickly solve complex education challenges. Amongst other lessons, the Kenyan experience highlights how equipping programme participants with the capacity to use EdTech, building trust in EdTech amongst stakeholders, and implementing accountability systems are critical to ensuring the success of EdTech programmes.
Combination of neural network model for enterprise accounting information quality assessment

In order to evaluate the quality of accounting information, this paper tries to find an effective evaluation method and construct a reasonable and scientific evaluation model, and puts forward an evaluation method of enterprise accounting information quality combined with neural network model. By evaluating the quantity and quality of enterprise accounting information, the dimension of evaluation matrix is determined, and the evaluation matrix is generated in real time. On this basis, a neural network model is constructed to improve the accuracy and adaptability of enterprise accounting information evaluation. Finally, the simulation results show that the applicability and superiority of the model can provide reference for the evaluation of enterprise accounting information quality.
Highly Accurate Registration under CT Guide Using Geometric Vectors

For a CT-guided robotic needle insertion, the registration to transfer coordinates between the robots and CT image is required for accurate insertion. Our previous work proposed a geometric marker allows for the direct registration between the CT and robot, and showed the proof-of-concept. This paper presents a registration algorithm for calculating the error of 6 DOF by one CT scan to be translated from the concept to a clinical application. To calculate the posture of the marker, we obtained the geometric shape of the marker from changes between a plurality of images and converted into coordinate axes of marker. From the result of evaluation, the error was 1.8 mm and 0.35 deg. The accuracy of proposed algorithm is satisfied for the lower abdominal insertion.
The Evolution Model For Big Data Storage Structure Of Online Learning Behavior Based On Parallel Algorithm

In order to overcome the heavy task of big data storage structure evolution computation, this paper proposes a parallel algorithm based network learning behaviour big data storage structure evolution model. This method introduces parallel algorithm, divides the whole dataset into several non overlapping data subsets randomly, mines the local frequent itemsets in the network learning behaviour big data in parallel and hierarchically, and connects the local frequent itemsets. Frequent itemsets can get all candidate sets. The actual support degree of different candidate sets is calculated by scanning datasets, and the evolution model of big data storage structure of network learning behaviour is established. The experimental results show that the operation efficiency of the proposed evolutionary model is as high as 99%, the cost is significantly lower than the other three evolutionary models, and the storage space consumption is the lowest.
ANALISIS KUALITAS SISTEM INFORMASI MANAJEMEN TERHADAP KINERJA KARYAWAN PDAM TIRTA WAMPU STABAT

This study to analyze, test and find out the effect of variable management information systems on the performance of employees in PDAM Tirta Wampu Stabat. This study uses a quantitative method with a sample of 50 employees. The research instruments used were questionnaires and documentation. Data analysis using Simple Linear Regression, get the following results: test results management information system significant effect on employee performance at PDAM Tirta Wampu Stabat. The results show the Adjusted R Square value of 0.798, this means that 79.8% of employee performance can be obtained and explained by management information systems. While the rest (100% - 79.8% = 20.2%) can be explained by other independent variables. Keywords: Management Information System, Employee Performance
≪명어림(明語林)≫의 ＜기선(企羨)＞편(篇) 역주(譯註)

Since Liu Yiqing(劉義慶) wrote Shishuoxinyu(≪世說新語≫) during Song Dynasty (420-479) in Southern Dynasties, the shishuoti(世說體) novels had been published which resembled its form. During Ming and Qing dynasties, the shishuoti novels were steadily published starting from He Liangjun(何良俊)’s Heshiyulin(≪何氏語林≫) during the mid-Ming dynasty to the period of Kangxi in Qing Dynasty. Mingyulin(≪明語林≫) is a shishuoti style novel published by Wu Sugong(吳肅公, 1626 ~ 1699) who was an adherent of Ming Dynasty in 1681 (20th year of Kangxi Emperor). Mingyulin is the first shishuoti styled work which was published in Qing dynasty. Its compiler, Wu Sugong, was a Confucian scholar, literary man and historian who chose to remain as an adherent of Ming Dynasty during the period of upheaval when the dynasty changed from Ming Dynasty to Qing Dynasty. Accordingly, Wu Sugong complied the book in order to keep the record of Ming dynasty, help the readers to reflect the history of dead dynasty, and pass down the history of indigenous Han people against the culture of other ethnic people. Mingyulin contains the description of 930 and more persons who lived from late Yuan Dynasty to early Qing Dynasty by classifying them into 38 items and listing 900 and more articles. As Mingyulin has a lot of episodes on the people of various classes, it can be described as encyclopedia of personal episodes for the people who lived in Ming dynasty. As the shishuoti style novel is made of the real life stories of people who lived in the past, thus having the nature of historical record. This study is the interpretation of Qixian Part out of 38 items recorded in Mingyulin. Qixian(＜企羨＞) means “to hope and want to be so”. In the Qixian Part, there are the episodes of the people who the complier Wu Sugong respected. Through the people introduced in the episodes, it is possible to take a look literature and life of Ming Dynasty’s literati. In addition, as the Mingyulin contains the description of the literati such as Yang Shiqi(楊士奇), Xue Xuan(薛瑄), Li Dongyang(李東陽), Chen Xianzhang(陳獻章), Tang Shunzhi (唐順之), Kang Hai(康海), Li Dongyang(李夢陽), Xu Wei(徐渭), Yuan Hongdao(袁宏道), this interpretation paper helps to widen our understanding of Ming Dynasty’s literati.
A low area VLSI implementation of extended tiny encryption algorithm using Lorenz chaotic system

The rapid growing impact of light weight applications (RFID tags, smartcards, sensor nodes and FGPAs) makes security a major concern in communication systems. Light weight cryptographic algorithm or ciphers can provide security and confidentiality of data or messages transmitted. In this paper, we proposed low area VLSI implementation of extended tiny encryption algorithm using Lorenz chaotic system (XTEA-LCS method) to improve the efficiency of encryption and decryption process. The key is the major concern of the encryption and decryption process. So in this research random numbers generated using LCS was used for key purpose, which efficiently reduced area and execution time of XTEA-LCS method and improved the system security. The XTEA-LCS method has been implemented in the Xilinx tool using Verilog code on different Virtex devices such as Virtex6, Low Power Virtex-6 (LP Virtex-6), and Virtex-7. In the field programmable gate array (FPGA) implementation, the number of look up tables (LUTs), slices and flip flops reduced and the frequency increased compared to the existing methods: QTL algorithm, DROM-CSLA-QTL and XTEA. The XTEA-LCS methodology improves the FPGA performances by reducing LUTs by 82.96% and slices by 74.28% than conventional XTEA method.
Practical Order Attack in Deep Ranking

Recent studies have unveiled the vulnerabilities of deep ranking models, where an imperceptible perturbation could trigger dramatic changes in the ranking result. However, previous attempts focus on manipulating absolute ranks of certain candidates, while the possibility of adjusting their relative order remains under-explored. The objective of this paper is to formalize and practically implement a new adversarial attack against deep ranking systems, i.e., the Order Attack, which covertly alters the relative order of a selected set of candidates according to a permutation vector predefined by the attacker, with only limited interference to other unrelated candidates. Although this Order Attack can be formulated as a triplet-style loss constraint imposing an inequality chain that reflects the attacker's desired permutation, direct optimization of such loss is inapplicable in a real-world black-box attack scenario due to the inaccessibility of gradients, limited query budget, truncated ranking results, and lack of similarity scores. To address these challenges, we propose a new Short-range Ranking Correlation metric as a surrogate objective function to approximate Kendall's ranking correlation while maintaining robustness to these practical limitations. The proposed white-box and black-box attacks are evaluated on the Fashion-MNIST and Stanford-Online-Products datasets. Moreover, the black-box attack is successfully implemented on a major e-commerce platform. Extensive quantitative and qualitative experimental evaluations demonstrate the effectiveness of our proposed methods, revealing deep ranking systems' vulnerability to the Order Attack.
Application an improved swarming optimisation in attribute reduction

In order to solve the attribute reduction algorithm's drawbacks of the extensive range of initial searching space and low convergence rate in the latter stage, we present an improved swarming optimisation base on ant colony optimisation in this paper. By strengthening the pheromone concentration of critical pipelines, the probability of the vital pipeline being selected in the path optimisation process is increased, thereby improving the development of the optimal solution by the ant colony algorithm. The improved algorithm puts forward attributes' of and adaptive choice model. The adaptive choice model enhances the possibility of choosing high-quality results. Simulation results show that the improved algorithm's success rate converges to the optimal results. Moreover, the improved algorithm has the capability of high accuracy and fast convergence.
THE FORMATION OF COMPETENCE OF FUTURE PRIMARY SCHOOL TEACHERS BY MEANS OF THE INTERNET RESOURCE

The article considers the formation of competence of future primary school teachers by means of Internet resources. The concept of competence is a concept that has been introduced in recent years in the field of pedagogy as a result of special attention to the subject experience of the individual
Data Mining Predicts The Graduation of Students of STMIK Atma Luhur Information System Using Neive Bayes Algorithm

In studying in higher education, the accuracy of graduation at the end of the semester is important, as is the case for the final semester students at STMIK Atma Luhur Pangkalpinang. STMIK Atma Luhur Pangkalpinang has three Study Programs, one of which is the Information Systems Study Program which receives less than 150 to 200 students each year and is expected to graduate on time. With the number of students who enter, it might not match those students who have completed their studies on time. In this study, the classification of the graduation punctuality was performed using the Naif Bayes algorithm. Neive Bayes predicts future opportunities based on past experience known as Bayes Theorem. The implementation in this study uses the RapidMiner 7.0.001 software. To help find accurate values, the attributes used in this study are NIM, Name, Level, Study Program, Province of Origin, Gender, SKS, GPA, and Year of Graduation. STMIK Atma Luhur.
Asymmetric convolution with densely connected networks

Convolutional neural networks are vital to some computer vision tasks, and the densely connected network is a creative architecture among them. In densely connected network, most convolution layer tends to have a much larger number of input channels than output channels, making itself to a funnel shape. We replace the 3 × 3 convolution in the densely connected network with two continuous asymmetric convolutions to make the DenseNet family more diverse. We also proposed a model in which two continuous asymmetric convolutions each outputs half of the output channels and concatenate them as the final output of these layers. Compared with the original densely connected network, our models achieve similar performance on CIFAR-10/100 dataset with fewer parameters and less computational cost.
Transfer Weight Based Conditional Adversarial Domain Adaptation

Considering the failure of the Conditional adversarial Domain AdaptatioN(CDAN) to fully utilize the sample transferability, which still struggle with some hard-to-transfer source samples disturbed the distribution of the target domain samples, a Transfer Weight based Conditional adversarial Domain AdaptatioN(TW-CDAN) is proposed. Firstly, the discriminant results in the domain discriminant model as the main factor are employed to measure the transfer performance. Then the weight is applied to class loss and minimum entropy loss. It is for eliminating the influence of hard-to-transfer samples of the model. Finally, experiments are carried out using the six domain adaptation tasks of the Office-31 dataset and the 12 domain adaptation tasks of the Office-Home dataset. The proposed method improves the 14 domain adaptation tasks and increases the average accuracy by 1.4% and 3.1% respectively.
True Belief and Mere Belief About a Proposition and the Classification of Epistemic-Doxastic Situations

Starting from standard logics of knowledge and belief with principles such as introspection of beliefs and 'knowledge implies belief', we study two non-normal modalities of belief: true belief about a proposition and what we call mere belief about a proposition. We show that these modalities suffice to define all possible epistemic-doxastic situations in a combinatorial manner. Furthermore, we show that two consecutive modalities that are indexed by the same agent can be reduced for two of the three logics of knowledge and belief that we consider.
Invariants in Interferometry: Geometric Insight into Closure Phases

Closure phase is the phase of a closed-loop product of correlations within a $\ge 3$-element interferometer array. Its invariance to element-based phase corruption makes it invaluable for interferometric applications that otherwise require high-accuracy phase calibration. However, its understanding has remained mainly mathematical and limited to the aperture plane (Fourier dual of image plane). Here, we lay the foundations for a geometrical insight. We develop a shape-orientation-size (SOS) conservation theorem for images made from a closed triad of elements, in which the relative location of the Null Phase Curves (NPCs) of the three interferometer responses (``fringes'') are preserved, even in the presence of large element-based phase errors, besides overall translation of the fringe pattern. We present two geometric methods to measure closure phase directly in the image plane (without an aperture-plane view) with a 3-element array: (i) the closure phase is measurable from the position offset of one fringe NPC from the intersection of the other two fringe NPCs, and (ii) the squared closure phase is proportional to the product of the areas enclosed by the triad of array elements and the three fringe NPCs in the aperture and image planes, respectively. We validate this geometric understanding across a wide range range of interferometric conditions using data from the Very Large Array and the Event Horizon Telescope. We generalize these geometric relationships to an N-element interferometer. This geometric insight can be potentially valuable to other interferometric applications including optical interferometry. Close parallels between interferometric closure phases, structure invariants in crystallography, interferometric synthetic aperture radar and seismic imaging, gravitational wave time-delay interferometry, and phases of Bargmann invariants in quantum mechanics are highlighted.