Reviewerly/exHarmony huggingface dataset:
Multi-Representation Ensemble in Few-Shot Learning

Deep neural networks (DNNs) compute representations in a layer by layer fashion, producing a final representation at the top layer of the pipeline, and classification or regression is made using the final representation. A number of DNNs (e.g., ResNet, DenseNet) have shown that representations from the earlier layers can be beneficial. They improved performance by aggregating representations from different layers. In this work, we asked the question, besides forming an aggregation, whether these representations can be utilized directly with the classification layer(s) to obtain better performance. We started our quest to the answer by investigating the classifiers based on the representations from different layers and observed that these classifiers were diverse and many of their decisions were complementary to each other, hence having the potential to generate a better overall decision when combined. Following this observation, we propose an ensemble method that creates an ensemble of classifiers, each taking a representation from a different depth of a base DNN as the input. We tested this ensemble method in the setting of few-shot learning. Experiments were conducted on the mini-ImageNet and tieredImageNet datasets which are commonly used in the evaluation of few-shot learning methods. Our ensemble achieves the new state-of-the-art results for both datasets, comparing to previous regular and ensemble approaches.
PENERAPAN MODEL PEMBELAJARAN MULTILITERASI DALAM MENINGKATKAN KECERDASAN INTERPERSONAL PADA MATA KULIAH MENULIS KARYA SASTRA BERBANTUAN YOUTUBE

The demands of learning in the 4.0 era require students not only to learn but to interpret learning as a whole, besides that during the pandemic period, learning is done online. The importance of literacy activities among students is not only limited to reading and writing skills but must include two other language skills such as listening skills and speaking skills. To hone these four skills requires continuous activities. Many students are skilled in writing skills but lacking in speaking skills. This can be overcome with a multiliterative learning model that optimizes the four language skills as well as other aspects such as the use of technology and the surrounding environment that can be used as learning materials. After the multiliteration learning stage is achieved, it is expected that students' interpersonal intelligence will change. How they understand other people's situations, how to empathize, work together in groups. This study aims to determine the optimization of the multiliteration learning model in improving interpersonal intelligence in writing literary subjects and to determine the results of multiliterative learning in making short stories. The method used in this research is pre-experimental method. Experimental research using one class with a research sample of 40 students. The results showed that this method was effectively used in improving interpersonal intelligence in writing literary works. The short story writing test results obtained by an average of 63.50 at the pretest while the posttest average was 83.13, so there was an increase of 19.63.
Efficient Implementation of Digital Standard Cells-Based True Random Number Generator for Securing FPGA Designs

Random numbers are fundamental resources in the field of computing and engineering. They have a wide scope of application including cryptography and simulation. True Random Number Generators (TRNGs) are considered to be the most secure based on the quality of its entropy sources. With the availability of several sources of entropy, ring oscillator architecture can easily be used as a quality source of entropy for a TRNG due to the inherent jitters and the simplicity of its design. Since there is still a possibility of a generator to generate random numbers that do not meet the required security metrics, hence, it is imperative for a TRNG to be able to quickly regenerate another set of random bit sequence. For these reasons, this research, proposes a high-speed array-sampling and a post-processing unit ring oscillator-based TRNG with improved statistical measures and throughput for securing FPGA devices. The core architecture consists of digital primitive cells – the ring oscillator, Q-Flip Flop, and the CubeHash algorithm. These are used as the building blocks for constructing the proposed TRNG architecture.  This proposed hardware architecture reveals an improvement in throughput and the statistical measure of the quality of generated bits. The architecture was modeled and simulated using Verilog HDL, Modelsim SE, and Xilinx’s ISim simulation tools. This architecture was designed using both the Xilinx ISE and Vivado tools. The proposed design was implemented on the Spartan 6 and Cyclone IV FPGA devices and occupies an area of 3287 LUTs and 1714 Slice registers and had a maximum throughput of 1422 Mbps. Sampled bitstreams’ statistical accuracies were ascertained using NIST’s statistical Test package program.
Poster: Centralized vs. Decentralized Contact Tracing: Do GDP and Democracy Index Influence Privacy Choices?

Contagious diseases such as COVID-19 spread rapidly, forcing governments and policymakers to employ corrective measures. Contact tracing is one of the critical tools to identify whether individuals came into contact with infected persons. Many countries, including Australia, Singapore, and India, have released contact tracing apps to reduce the community spread. Such apps follow either a centralized or decentralized architecture; the former lets government agencies store and manage the user's data without privacy support, while the latter allows the user more control over their information, providing privacy. We analyze how the GDP and the democracy index influence the adoption of contact tracing applications. Our study analyzes COVID-19 contact tracing projects announced between February 2020 and August 2020 from 63 countries. The data indicates that countries with high GDP and democracy index tend to opt for decentralized architectures, while autocratic and low GDP countries tend to adopt centralized architectures.
Research on Active Learning Method Based on Domain Adaptation and Collaborative Training

In recent years, more and more deep learning technologies have been widely applied in various fields, such as intelligent medical, intelligent manufacturing, to realize the intelligence of various systems. In order to solve the problems of traditional manual inspection of power equipment, we propose a Convolutional Neural Network (CNN) has brought a revolutionary change to computer vision, but the ability of CNN to study relies heavily on the amount of labeled data. Currently, most of the labeled datasets are made up of natural images, which makes it a difficult problem to acquire labeled data in specific fields such as biomedical because of the high cost of human labeling. Active learning is an important method to solve this problem. This thesis is oriented to the problem of image classification and studies the current challenges and solutions of active learning. How to select the samples to be labeled so that the best neural network model can be learned at the minimum labeling cost is the core of the active learning algorithm. This thesis focuses on the selection strategy of the samples to be labeled, analyzes, and summarizes the advantages and disadvantages of current active learning methods, and designs a learning model.
Document cluster summaries and labeling

Summarization is the notion of abstracting key content from information sources. The task of summarization has become vital in our everyday lives especially nowadays with the exponentially growing amounts of available information that is generated over the internet. In Natural Language processing, automatic text summarization is the problem of creating short, accurate and fluent text pieces from large textual data. In this thesis, I test state-of-the-art Deep Learning models for summarizing single documents and document clusters. Later, I compare the resulting cluster summaries with principal cluster labeling techniques to determine how accurate the Deep Learning models can be in labeling document clusters. For single-document summarization I chose a model consisted of multiple neural layers based on the encoder-decoder architecture with a novel Recurrent neural cell ( Rotational Unit of Memory-RUM ) in its core. I trained and evaluated the model on a new U.S. patent dataset. For document cluster summarization, I tested a framework that combines a fundamental extractive statistical natural language processing technique ( Maximal Marginal Relevance ) with the abstractive encoder-decoder model to summarize the document clusters. Finally, I evaluated the resulting summaries by comparing them with the top words of the clusters based on the Word Frequency and their TF*IDF scores. The RUM-based encoder-decoder model surpassed all the state-of-the-art models that were tested on the selected dataset and the MMR framework successfully summarized document clusters of up to 100 U.S. patent abstracts.
Time Series Decomposition for Anomalous E-commerce Transactions

Online trading is one of the pillars of the digital economy. The rapid increase of e-commerce transactions has increased the risk exposure of providers and made it virtually impossible to track consumer behaviour by relying on human experts alone. Here we show how time series decomposition can be used to automatically detect suspicious transactions and flag the mout for subsequent actions. The identified outliers have clear business meaning and can be interpreted as peaks in demand produced by idiosyncratic consumer behaviour or by malicious activity. Either way, they deserve sufficient attention and active management.
Authorship models for editorial board members: The ethical aspect

Various models of editorial board members’ publishing in parent journal are examined. Editorial membership makes an important stage of the academic career; hence, this requires careful bibliometric examination of editor researchers’ publication strategies. We examine the popular approaches of editors-in-chief toward publications by their editorial board members’ in the parent journal, in the range from encouragement to prohibition. Using a sample of Russian library and information science journals, we demonstrate that the publication models of editorial board members depend on the journal’s rank.
The history of the Central Scientific and Technical Library for Construction and Architecture of the Federal Center for Investment Programs Support

The history of the Central Scientific and Technical Library for Construction and Architecture and stages of its development are reviewed.
COMPARISON OF K-NEAREST NEIGHBOR AND NAÏVE BAYES FOR BREAST CANCER CLASSIFICATION USING PYTHON

Classification is widely used to determine decisions according to new knowledge gained from processing past data using algorithms. The number of attributes can affect the performance of an algorithm. Several data mining methods that are widely used for classification include the K-Nearest Neighbor and naïve Bayes algorithm. The best algorithm for one data type is not necessarily good for another data type. It is even possible that a good algorithm will be horrendous for other data types. To overcome this issue, this study will analyze the accuracy of the K-Nearest Neighbor and Naïve Bayes algorithms for the classification of breast cancer. So that patients with existing parameters can be predicted which are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that the cancer can be detected earlier and is expected to reduce the mortality rate from breast cancer.
Energy Saving Task Scheduling Based on Optimized Ant Colony Algorithm in Cloud Environment

In a cloud environment, the majority of computational power requirements are concentrated in the cloud, resulting in higher energy consumptionfor data centers. A method of reducing energy consumption while also reducing the time span of task scheduling has become an urgent problem tobe solved. In this paper, an optimal ant colony scheduling algorithm combined with a genetic algorithm is proposed, and an energy consumptionfactor is introduced into the algorithm. Experiments show that this algorithm can effectively improve the time efficiency of task scheduling and reduceenergy consumption.Keywords: Cloud computing; Task scheduling; Time span; consumption; Genetic algorithmCite AsH. Liu, H. Yi, Energy Saving Task Scheduling Based on Optimized Ant Colony Algorithm in Cloud EnvironmentEngineering Intelligent Systems, vol. 29 no. 1, pp. 27-32, 2021.
Banking system, Institutional quality, and Economic growth: Panel data analysis on a sample of countries in the MENA region

The nexus among financial development and economic growth has long remained a subject matter of considerable debate in the financial and economic literature. This article examines the relationships between financial development, institutions, and economic growth. This indicates that the marginal effect of financial development on economic growth depends on the quality of institutions. To do so, we employ a panel data of 14 MENA countries during the period of study from 2008 to 2019. For the econometric methodology, we use fixed and random effects models. To choose between fixed effects and random effects, we employ the Hausman test. Based on the empirical findings, we demonstrate that financial development has a positive effect on economic growth. Furthermore, we have observed that institutional quality seems to be a necessary complement to financial development. Consequently, it is important to implement policies leading to the deepening of financial systems, through a including a solid institutional framework. Thus, by promoting such development and better institutional quality, economic growth will thus be accelerated.
Application of image quality assessment for rockfall investigation

This paper presents an automatic image sensing method to detect rockfall events using time-lapse photographs and digital image processing. Based on the change detection concept, an image quality assessment procedure is proposed to detect rockfall events in this study. Sets of temporal images taken from a rockfall field test are selected to assess similarity between the images. Two representative image quality assessment algorithms have been employed to find the applicability of the algorithms for rockfall event detection. In this study, the error map which highlights rockfall movements by differences between two images is visualized using OpenCV and Python scripts. The results show that time-lapse photographs obtained from a fixed photographed position can be effectively used to detect rockfall event initiated from the captured area. Also, the well-known SSIM approach gives excellent results from its efficiency of similarity detection.
Keynote Talk #2: Multimodal Data Analysis with Applications in Imaging Genomics

Multi-modality matched datasets in healthcare capture information about the disease of the same patient from multiple views, often at different physical scales, thereby providing a more complete picture of complex diseases like cancer. We present a novel extension of the canonical correlation analysis (CCA) framework that takes into account underlying dependencies within individual modalities to better capture correlations between two modalities. We demonstrate the utility of the resulting embedding space as a fusion module in survival prediction for breast cancer patients using histology imaging and genomics data.
ShallowFake-Detection of Fake Videos Using Deep Learning

In recent years, we have come across a vast range of software tools like “Photoshop” and techniques like DeepFake that have made it easier to create unrealistic and believable face swaps in videos that end up leaving very few traces of manipulation. The realistic nature of DeepFake videos are exploited for carrying out unethical and false practices such as generation of pedopornographic materials, Fake News, Fake Surveillance footage, Fake Hoaxes, videos for blackmailing amongst many more. Many AI based tools have been developed to detect DeepFake based video manipulation by extracting tampered face features. Through our approach, we aim to provide a forensic tool for investigators to detect these DeepFake videos which analyses specific facial artifacts like Eye Blink and Pulse. Each artifact will give their own probabilistic output via there classifier model and then combine them to give probability of fakeness. Thus instead of allowing resource intensive algorithms to detect facial artifacts for DeepFake detection, we aim to analyze and interpret immutable facial artifacts for detecting a DeepFake video.
Broadening Selection Competitive Constraint Handling Algorithm for Faster Convergence

In this paper, a new algorithm incorporating broadening selection strategy in competitive constraint handling paradigm for finding the optimum solution in constrained problems has been proposed, referred as Broadening Selection Competitive Constraint Handling (BSCCH). Although, competitive constraint handling approaches have proved to be very efficient, but they lack faster convergence due to offspring generation from random individuals. By incorporating selection strategy such as broadening selection in the competitive approach, better results are obtained and convergence rate is improved significantly. Incorporating said strategy, the BSCCH algorithm has been proposed which is generic in nature and can be coupled with various evolutionary algorithms. In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. The proposed algorithm has been evaluated using 24 benchmark functions. The mean closure performance of the BSCCH algorithm is compared against seven selected state-of-the-art algorithms, namely Differential Evolution with Adaptive Trial Vector Generation Strategy and Cluster-replacement-based Feasibility Rule (CACDE), Improved Teaching Learning Based Optimization (ITLBO), Modified Global Best Artificial Bee Colony (MGABC), Stochastic Ranking Differential Evolution (SRDE), Novel Differential Evolution (NDE), Partical Swarm Optimization for solving engineering problems - a new constraint handling mechanism (CVI-PSO) and Ensemble of Constraint Handling Techniques (ECHT). The median convergence traces have been compared with two different algorithms based on differential evolution, i:e: Ensemble of Constraint Handling Techniques (ECHT) and Stochastic Ranking Differential Evolution (SRDE). ECHT is considered to be a flagship ensemble technique till date for constrained optimization problems, whereas SRDE employs a parent selection mechanism for constrained optimization. The proposed algorithm is found to provide better solutions and achieve significantly faster convergence in most of the problems.
COMPARATIVE STUDY OF MUTATION OPERATORS IN THE GENETIC ALGORITHMS FOR THE K-MEANS PROBLEM

The k-means problem and the algorithm of the same name are the most commonly used clustering model and algorithm. Being a local search optimization method, the k-means algorithm falls to a local minimum of the objective function (sum of squared errors) and depends on the initial solution which is given or selected randomly. This disadvantage of the algorithm can be avoided by combining this algorithm with more sophisticated methods such as the Variable Neighborhood Search, agglomerative or dissociative heuristic approaches, the genetic algorithms, etc. Aiming at the shortcomings of the k-means algorithm and combining the advantages of the k-means algorithm and rvolutionary approack, a genetic clustering algorithm with the cross-mutation operator was designed. The eﬃciency of the genetic algorithms with the tournament selection, one-point crossover and various mutation operators (without any mutation operator, with the uniform mutation, DBM mutation and new cross-mutation) are compared on the data sets up to 2 millions of data vectors. We used data from the UCI repository and special data set collected during the testing of the highly reliable semiconductor components. In this paper, we do not discuss the comparative eﬃciency of the genetic algorithms for the k-means problem in comparison with the other (non-genetic) algorithms as well as the comparative adequacy of the k-means clustering model. Here, we focus on the inﬂuence of various mutation operators on the eﬃciency of the genetic algorithms only.
SVGAN: Semi-supervised Generative Adversarial Network for Image Captioning

Image captioning is a task that enables computer to naturally describe the contents of an image like a human, moreover it involves two different major research fields of computer vision and natural language processing. In this paper, a new image captioning system is proposed, which can address the challenges of automatically describing images in the wild. Built on the state-of-the-art caption framework, we designed a deep visual detector to catch a broad range of visual concepts, a GAN(Generative Adversarial Network) with graph embedding is developed to generate accurate sentences for wild images.
MODIFIED CONVOLUTIONAL NEURAL NETWORK FOR ARIARY BANKNOTES AUTHENTICATION

This paper presents a new algorithm named modified convolutional neural network (MCNN) to authenticate current existing banknotes in Madagascar. At the end of processing, we are able to identify the money face value and fake notes according to the input. Convolutional neural network (CNN) is the promising technical to solve such problem although feature extraction layer takes time due of convolution and spooling operations. The solution that we propose here is to swap this layer with pulse coupled neural network (PCNN) which has a capability to collect easily input image characteristics before going through fully connected layer. So, we have three images as input because one banknote has recto, verso and watermarked faces which are converted to gray. Our pulse couple neural network extracts all features to form a single vector called image signature. This vector is presented to full connected neural network constituted by few hidden layers, one output layer using softmax function as neural activation function. It means that we have authentic' s probability.
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection.

This paper reports a new solution of leveraging temporal classification to support weakly supervised object detection (WSOD). Specifically, we introduce raster scan-order techniques to serialize 2D images into 1D sequence data, and then leverage a combined LSTM (Long, Short-Term Memory) and CTC (Connectionist Temporal Classification) network to achieve object localization based on a total count (of interested objects). We term our proposed network LSTM-CCTC (Count-based CTC). This learning from counting strategy differs from existing WSOD methods in that our approach automatically identifies critical points on or near a target object. This strategy significantly reduces the need of generating a large number of candidate proposals for object localization. Experiments show that our method yields state-of-the-art performance based on an evaluation on PASCAL VOC datasets.