Reviewerly/exHarmony huggingface dataset:
Multiserver System Configuration Scheme for Profit Maximization

As a promising and valuable IT commercial model, Cloud computing has been paid considerable attention by academia and industry [1], which makes the configuration of storage, computing, and communication resources a common commodity for pay-as-you-go management. Thanks to the rapid development of virtualization techniques, an increasing number of users can expediently receive cloud services, which spawns a group of excellent cloud service providers (e.g., Ali Cloud [2], Google [3], and Amazon [4]). The increasing number and variety of cloud service providers also promote the sustainable development of cloud computing. The pursuit of profit maximization is a natural goal for service providers. To realize this goal, cloud service providers are supposed to fully understand the relationship between the economics of cloud services and the system resource management of the cloud platform (also called the multiserver system), especially in a geographically distributed multiserver system, and serve the intermittent cloud service requests.
Signature Recognition using Siamese Neural Networks

The traditional role of a signature is to permanently affix a person’s uniquely identifiable, self-identification to a document as physical evidence of that person’s personal witness and certification of the content of the entire document, or a selected portion of it. Yet with the technology advancement and tools available now a day it is easy to catch the culprits with artificial intelligence and neural networks. The neural network we used is Siamese neural network which is an artificial neural network which takes in similar input vectors and produces comparable output vectors. We are trying to make a user friendly interface using modern web technologies and the most data science language python and a simple Flask backend using python, the model is trained as per the signature images uploaded to a form on a local server then the images are taken and fed to the Siamese neural network to predict if they are forged or not and the results are going to be displayed in a user friendly format in the homepage.
Rethinking Logits-Level Knowledge Distillation

Since Hinton et al. proposed knowledge distillation, extensive experiments have validated the poor performance of logits-level distillation. Therefore, a variety of feature distillation methods have emerged. In this paper, we found two reasons for the poor performance of logits-level knowledge distillation, one is the unreasonable hyperparameter settings, and the other is the logits distribution of the teacher. By rethinking the hyperparameter settings, we further simplify the original distillation loss function and propose a more effective distillation form. We also clarify what is effective logits distribution for the teacher model and how to correct an unreasonable logits distribution of the teacher simply. With the above two simple yet effective methods, we have rejuvenated the logits-level knowledge distillation.
Pengelolaan Media Pembelajaran di tingkat MI/SD

The management of learning media at the elementary school level is one of the supports for achieving the targeted learning outcomes. Media management is our way as educators/teachers to manage something that we will achieve in a program for providing learning media at the elementary school level. This research is a literature study that analyzes several sources, both books, journals, and other references to draw a conclusion as a result of the discussion. For this reason, it was found that there are four stages of learning media management, namely, media planning, media organization, media implementation, and media evaluation. Of the four, it is the management or management of media and learning resources at the MI and SD levels. With the management of learning media, the learning process will run smoothly and effectively.
Smart Stock Verification System Using Android Application

In recent years, the Android platform has gained popularity for its usability, availability, and low-cost handsets. Mobile technology is used to solve various problems in different fields. On an average any library of an institution will have at least a few thousand books. The books need to be brought to a scanner for verification which requires a lot of technical assistance. Stock verification is one such problem in a library. This process is tedious, time consuming and requires lot of manpower. There is hardly any problem in carrying a few books to the scanner, but this process becomes more difficult when a few thousand books need to be verified. Stock verification of books is a regular activity, which needs to be done every year. It is both cumbersome and error prone. Therefore, we propose an Android application. Barcodes can be scanned using the application and the corresponding status like available, mismatched, and traced will be tagged against the scanned book. This system will make the process of stock verification handy, unambiguous, and smooth.
Fast and Robust Online Traffic Classification Supporting Unseen Applications

Online traffic classification is a fundamental toolkit in network management, such as QoS and network security. The speed and generalization ability of online classification are two requirements that need to be satisfied simultaneously. However, existing methods may suffer from generalization degradation on the traffic with unseen applications which are constantly emerging in the network, due to the feature distribution drift (FDD) caused by their non-robust feature engineering approaches. Based on Deep Metric Learning which can restrict the distances between samples explicitly and clustering algorithm which can learn multiple clusters within each category, this paper presents Robot, a fast and robust online traffic classification system. At its core, Robot leverages two building blocks to classify high-speed traffic flows: 1) For fast classification, Fast model classifies traffic and detects FDD samples simultaneously based on only one packet. 2) For robust classification, once FDD samples are detected, a flow collector will be triggered to collect flows and then Robust model, a multi-center model, will further identify them based on the hybrid of packet-level and flow-level features. Our comprehensive experiments demonstrate that Robot can achieve comparative classification speed and better generalization ability on the mixed traffic datasets with seen and unseen applications (with the FDD detection accuracy of up to 85.8&#x0025; and with nearly 10&#x0025; improvement in classification accuracy), compared with the state-of-the-art methods.
Temporal Bottleneck Attention for Video Recognition

This work introduces temporal bottleneck (TBoT) attention for video-based human action recognition (VHAR). TBoT is a mechanism that aims to build short key-frame sequences from long videos and create more valuable representations for convolution-based models. These compact video clips demonstrate two benefits. First, video recognition models can benefit from compact inputs by learning and modeling the data distribution more quickly and accurately. Second, a network trained on short but informative clips can take advantage of longer sequences when predicting human actions which can improve recognition accuracy. We also propose a multi-head mechanism combining pooling and residual self-attentions that can integrate easily on top of a 2D CNN backbone (ResNet50) and build compelling contexts for human action recognition. We assess the performance of the TBoTNet on several ablation experiments on the Kinetics-400 dataset and compared our results with state-of-the-art architectures on RGB inputs.
Emerging Topics of Heterogeneous Graph Representation

Heterogeneous graph (HG) embedding, aiming to project HG into a low-dimensional space, has attracted considerable research attention. We have introduced some kinds of HG embedding methods, and there are also some other essential topics on HG embeddings. In this chapter, we will introduce three novel HG embedding methods. Specifically, to learn semantic-preserving and robust node representations, we study the problem of adversarial learning on HG. Also, we work with large-scale heterogeneous interaction graphs and focus on the problem of importance sampling on HG embedding. Moreover, we explore the intrinsic spaces of HG and propose a hyperbolic space based HG embedding method.
Single Image Based Depth Estimation for Maritime Surface Targets

When the intelligent water cannon strikes a surface target, it needs to know the distance to the strike target and automatically adjust the strike angle to complete the accurate strike mission. Based on this estimation, the control system of the water cannon would automatically achieve the strike mission. For a universal usage, a monocular image depth estimation method based on SC-SfMLearner is used, which first estimates the depth information of the image from one sample of the real-time video frames and then uses a polynomial fitting model to transfer a depth map into the physical distance in the real world. The experimental results show that the mean square deviation of the predicted distance results in the practical environment for shore-side water targets is between 0.02 and 0.03, and the accuracy rate is above 95 %, which is a good prediction and effectively addresses the accuracy of striking water targets in practical applications.
A Proof of Work Based on Key Recovery Problem of Cascade Block Ciphers with ASIC Resistance

Hashcash, which is a Proof of Work (PoW) of bitcoin, is based on a preimage problem of hash functions of SHA-2 and RIPEMD. As these hash functions employ the Merkle-Damgard (MD) construction, a preimage can be found with negligible memory. Since such calculations can be accelerated by dedicated ASICs, it has a potential risk of a so-called 51% attack. To address this issue, we propose a new PoW scheme based on the key recovery problem of cascade block ciphers. By choosing the appropriate parameters, e.g., block sizes and key sizes of underlying block ciphers, we can make this problem a memory-hard problem such that it requires a lot of memory to efficiently solve it. Besides, we can independently adjust the required time complexity and memory complexity, according to requirements by target applications and progress of computational power.
DSNet-MV: Fast summarization of Surveillance Video’s using Deep learning in Compressed Domain using Motion Vectors

Video summarization is an attractive choice to handle the large bulk of data that is produced by a home surveillance system. However, the video summarization algorithm should be faster than the video stream generation rate for effectiveness. Moreover, there are privacy concerns when a potentially sensitive video stream is processed. To alleviate these issues, Motion Vector(MV) information in the encoded stream can be used instead of decoded RGBffUV pixel values of the frames. However, most of the recent Deep Learning(DL)-based technologies for video summarization use pixel-level information. In this paper, we try to bridge this gap by modifying pixel domain-based DL techniques to use MV data. Specifically, we alter DSNet by introducing a Masked Multi-Headed Attention layer and an aggregation module. Moreover, we explain how training DSNet using Masked Multi-Headed Attention allows us to process live streams of data, as compared to stored clips. Finally, we show that even just by using MV information, impressive performance is achieved by our modified DSNet model on TVSum and SumMe dataset.
Deeply Feature Fused Video Super-resolution Network

The video super-resolution (VSR) task refers to the use of corresponding low-resolution (LR) frames and multiple neighboring frames to generate high-resolution (HR) frames. An important step in VSR is to fuse the features of the reference frame with the features of the supporting frame. The existing VSR method does not make full use of the information provided by the distant neighboring frame, and usually fuses in a one-stage manner. In this paper, we propose a deep fusion video super-resolution network based on temporal grouping. We divide the input sequence into groups according to different frame rates to provide more accurate supplementary information, and the method aggregates temporal and spatial information at different stages of fusion.
Deployment of Computer Vision Application on Edge Platform

In our work, we propose a low cost device which will aid visually impaired people to understand what is in their surroundings without the requirement of internet. Current technology makes use of Cloud Architecture and would require internet to achieve this purpose. But these systems will not work in areas with poor internet connectivity. Edge platform built on Raspberry Pi powered with Intel Neural Compute Stick is used by us for this purpose. Multi Label Image Classification Deep Learning Model is trained in the cloud. It is later optimised and deployed on Edge Device which is Raspberry Pi. Setup also consists of PiCamera which will record the video and give it as input to deployed model. Model will describe the items present in video, basically describing the surroundings. The output is in the form of audio which is played through speakers, thus enabling visually impaired people to understand their surroundings without the requirement of internet. Deployment of popular Machine Learning and Deep Learning Models is also examined in the edge device and a comprehensive performance evaluation is performed.
CIS Society Officers

Ethics, Diversity and Consciousness in AI E thics in Artificial Intelligence (AI) is discussed everywhere, in governmental circles as well as scientific forums.It is very often associated with the two widely used concepts of diversity and eXplainable Artificial Intelligence (XAI).The latter was promoted by DARPA 1 , and it is expected to propose methods preserving the rights of users to understand how the AI systems work and why decisions are made.Computational Intelligence methods have much to contribute to this effort to ensure that AI systems are ethical.In July 2020, the European Union has published an Assessment List for Trustworthy Artificial Intelligence (ALTAI) 2 covering a wide range of ethical issues.Among the requirements listed, we find transparency, that encompasses the traceability of the data and model used to make a decision, the explainability of decisions and the communication with the user.Transparency mainly corresponds to the general approach of XAI and Computational Intelligence has much to do to participate in the effort of transparency of XAI, by means of the various methods of automatic generation of explanations, the use of counterfactuals, the help of visualization or the construction of fuzzy systems providing interpretability and expressiveness, for example.Another ALTAI requirement sounds familiar to us, dealing with diversity, non-discrimination and avoidance of unfair bias.Such concerns are at the heart of the IEEE Computational Intelligence's actions, which is very committed to their respect in its scientific and associative life.It is therefore natural to also consider these properties as essential for all algorithms and products that are parts of AI systems, not only in the collection of data and choice of attributes leading to a decision, but also in the ability of all categories of users to interact with the AI system regardless of their characteristics.Privacy and data protection is another aspect of the respect of human users.Other requirements focus more on the cognitive components of the construction of AI systems, the effect they can have on human users, the possible governance of human agents in AI systems and the integration of AI systems in the human society.The technical robustness of the AI system, its reliability, its security and its capacity to identify and mitigate risks in a transparent way are not less important.Machine ethics and the notions of responsibility of AI systems or artificial moral agents 3 are also topics of research that deserve a lot of attention.Consciousness in AI has been debated for a long time 4 and it can take a form different from human consciousness 5 .Multidisciplinarity, and in particular the cooperation with cognitive
Face Generation using DCGAN for Low Computing Resources

Image generation is the task of generating brand new images from existing datasets. With the increasing development of the Generative Adversarial Network (GAN), it is now widely used in the field of image generation. Research based on its time cost and the convergence of its training process has been widely concerned. However, existing techniques for network convergence during the training process usually require high computing resources. This paper has used Deep Convolutional Generative Adversarial Network (DCGAN) on the CelebA dataset for experimental evaluation: The number of training epochs and the algorithm’s parameters is adjusted to achieve a balance between the quality of generation results and computing resources used. This is assumed to be convenient for future studies under the condition of limited computing resources. Besides, by adjusting the parameters of the optimization algorithm, the convergence of GAN under the conditions of a specific optimization algorithm is studied.
Research of Imbalanced Classification Based on Cascade Forest

With the rapid development of science, the quantity of data is increasing exponentially. And unprecedented opportunities are provided by machine learning and data mining. While data classification is commonly used as a primary data processing method, the diversity of data is also a great challenge. Among those, problems caused by class imbalance are attracting more attention, and there are also a number of strategies and improvement of original algorithms are proposed. Gcforest is a new integrated learning algorithm proposed by Professor Zhou Zhihua in 2017. It has the advantages of few super parameters, suitable for small-scale data sets and strong model expression ability. However, the algorithm does not optimize the unbalanced data classification. Inspired by the improvement of other ensemble learning algorithms for unbalanced data classification, this paper applies a variety of under sampling strategies to the cascaded forest of gcforest. Through experimental comparison, it has achieved better or similar performance than the current advanced learning algorithms for unbalanced data sets on a variety of typical unbalanced data sets.
Comparative analysis of various protocols for high-precision tomography of qudits

Quantum tomography is an important tool for obtaining information about the quantum state from experimental data. In this study, we conduct a comparative analysis of various quantum tomography protocols, including protocols based on highly symmetric sets of state vectors, on mutually unbiased bases (MUB), and two-level transformations. Using the universal distribution for the fidelity of quantum state tomography, we estimate the fidelity and efficiency of quantum measurements for qudits of various dimensions as applied to a set of random states uniformly distributed over the Haar measure.
Research on University Computer Teaching Based on Data Mining Learning Model

Abstract Under the background of the development of big data, the research of learning analysis technology has shifted from theoretical fields to practical application fields such as teaching big data analysis and learning behavior prediction. However, it is worth noting that the wide application of big data technology has led to a large increase in text data, and how to quickly obtain accurate information has become a decisive factor in improving students’ learning ability. Therefore, this article combines the current status of computer teaching in colleges and universities to construct a data mining learning model to evaluate the effects of current college computer teaching methods. On this basis, it helps computer teachers to choose the most reasonable teaching style and teaching methods, and guide students to better master computer skills in order to achieve the best computer teaching effect.
What users tell us: User Understanding and Modeling for Personalized Recommendation

Personalized recommender systems have been one of the major ways of information acquisition. Understanding the user's intent and behavior plays an important role and has been a trending topic in research and applications. In this talk, I will briefly introduce some observations and findings on our user understanding research in recent years, which are surprising or different from what people thought before. The topic in the talk involves user behavior analyses, dynamic intent modeling, and evaluation in multiple scenarios such as news streaming, e-commerce, job hunting, and music recommendation. Related researches have been published on ACM TOIS, WWW, SIGIR, WSDM, etc.
Iterative Outlier Removal Using Close-and-Compatible Neighbors for Robust Feature Correspondence

Establishing reliable feature correspondence for the image matching is a fundamental problem in computer vision and remote sensing. Currently, many state-of-the-art correspondence selection methods are based on the neighborhood consensus, while the following two problems limit their performance: 1. It is difficult to select the consensus neighbors while preserving spatial smoothness. 2. The location-based consensus constraint is loose and may lead to low precision. In this paper, we propose a simple iterative outlier removal to address these two issues. It searches close-and-compatible neighbors to measure the neighborhood consensus based on the local affine frame, and works by rejecting outliers sequentially and iteratively. The proposed approach is much more robust than state-of-the-art methods when dealing with outliers and large-scale geometric transformation. Numerous experimental results demonstrate the superiority of our method.
Empirical Research on Digital Resource Assessment of University Library : Taking the library of Jianghan University as an example

Secondary weight index system was used for making digital resource assessment and experiment on the library of Jianghan University. As a result, it is found that this system has many problems in practice such as too many indexes, fuzzy grading rules, unbalanced main grading structure, incomprehensive data and over-high time cost. Improvement methods are put forward accordingly.
A Solution for Finding Quasi-Shortest Path with Graph Coarsening

In recent years, different types of large-scale data with a graph structure, such as communication networks, transportation networks, and large-scale integrated circuits, are being commonly used, and it is expected that large-scale graphs will be used to represent such data. However, various graph algorithms for analyzing graphs cannot easily deal with large-scale graphs. To solve this problem, graph sparsification and graph coarsening, which are graph mining techniques, have been actively studied. We believe that a graph coarsening algorithm that reduces the number of dimensions of data while preserving the features of large-scale graphs can be applied not only to complex problems such as node clustering, graph partitioning, and graph visualization but also to the shortest path problem on graphs. In this study, we propose a quasi-shortest path algorithm with coarsening (QSPC) that uses a typical graph coarsening algorithm for finding the quasi-shortest path, that is, an approximate shortest path between two vertices in a large-scale graph. Furthermore, we investigate the effectiveness of QSPC through several experiments with different scales. Our findings indicate that QSPC can find the quasi-shortest path a constant time faster than Dijkstra’s algorithm, and the quasi-shortest path between two vertices in a tree graph or scale-free graph is equal to the shortest path.