Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching

This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation. First, we adopt the knowledge distilled from relevant and well solved tasks to generate high-quality event proposals. Then we incorporate contrastive loss and cycle-consistency loss typically applied to cross-modal retrieval tasks to build semantic matching between the proposals and sentences, which are eventually used to train the caption generation module. In addition, the parameters of matching module are initialized via pre-training based on annotated images to improve the matching performance. Extensive experiments on ActivityNet-Caption dataset reveal the significance of distillation-based event proposal generation and cross-modal retrieval-based semantic matching to weakly supervised DVC, and demonstrate the superiority of our method to existing state-of-the-art methods.
Scalable Federated Learning over Passive Optical Networks

Two-step aggregation is introduced to facilitate scalable federated learning (SFL) over passive optical networks (PONs). Results reveal that the SFL keeps the required PON upstream bandwidth constant regardless of the number of involved clients, while bringing ~10% learning accuracy improvement.
Dialogue Disentanglement in Software Engineering: How Far are We?

Despite the valuable information contained in software chat messages, disentangling them into distinct conversations is an essential prerequisite for any in-depth analyses that utilize this information. To provide a better understanding of the current state-of-the-art, we evaluate five popular dialog disentanglement approaches on software-related chat. We find that existing approaches do not perform well on disentangling software-related dialogs that discuss technical and complex topics. Further investigation on how well the existing disentanglement measures reflect human satisfaction shows that existing measures cannot correctly indicate human satisfaction on disentanglement results. Therefore, in this paper, we introduce and evaluate a novel measure, named DLD. Using results of human satisfaction, we further summarize four most frequently appeared bad disentanglement cases on software-related chat to insight future improvements. These cases include (i) Ignoring Interaction Patterns, (ii) Ignoring Contextual Information, (iii) Mixing up Topics, and (iv) Ignoring User Relationships. We believe that our findings provide valuable insights on the effectiveness of existing dialog disentanglement approaches and these findings would promote a better application of dialog disentanglement in software engineering.
Perturb, Predict &amp; Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning

Recent semi-supervised learning (SSL) methods are predominantly focused on multi-class classification tasks. Classification tasks allow for easy mixing of class labels during augmentation which does not trivially extend to structured outputs such as word sequences that appear in tasks like image captioning. Noisy Student Training is a recent SSL paradigm proposed for image classification that is an extension of self-training and teacher-student learning. In this work, we provide an in-depth analysis of the noisy student SSL framework for the task of image captioning and derive state-of-the-art results. The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image. We show that, even in the absence of raw image augmentation, the use of simple model and feature perturbations to the input images for the student model are beneficial to SSL training. We also show how a paraphrase generator could be effectively used for label augmentation to improve the quality of pseudo labels and significantly improve performance. Our final results in the limited labeled data setting (1% of the MS-COCO labeled data) outperform previous state-of-the-art approaches by 2.5 on BLEU4 and 11.5 on CIDEr scores.
An Advanced Distributed Backup Power Design with Lithium Iron Phosphate Battery for Data Center Energy Efficiency

Data centers have a huge impact on the world we live in. Today data centers account for 3% of the global electricity supply and consume more power than the total of some country consumption. Data centers also contribute 2% of the total global greenhouse gas emissions. Data center's energy efficiency is the eternal hot research and engineering hot topic. In this paper, an advanced distributed power backup battery power system (BBS) design is introduced, also engineering comparison between lead-acid battery (VRLA), lithium iron phosphate battery (LFP) and ternary lithium-ion battery (NCM) is detailed listed with key parameters. Moreover, this paper has proved the best engineering practice of using the cylindrical lithium iron phosphate (LiFePO4) battery for BBS design optimization considering the reliability, cost saving, and energy efficiency of data center deployment in a large scale. Engineering work summarized in this paper includes a serial of related tests of the electrical performance, life cycle and reliability items, and the corresponding test results reaching design targets of power supply capacity, energy efficiency improvement for data center which eventually contributed a total cost ownership (TCO) saving. The theoretical basis, proto product, engineering test result and deployment analysis of LiFePO4 battery backup power system is data-proved reference for ecosystem in their new data center construction.
A Graph Mining Approach for Ranking and Discovering the Interesting Frequent Subgraph Patterns

Abstract Graph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM framework A RA nked F requent pattern-growth F ramework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.
Design and exploration of neural network microsystem based on SiP

Abstract In recent years, microelectronics technology has entered the era of nanoelectronics/integrated microsystems. System in package (SiP) and system on chip (SoC) are two important technical approaches for the realization of microsystems. Deep learning technology based on neural networks is used in graphics and images. Computer vision and target recognition are widely used. The deep learning technology of convolutional neural network is an important research field in the miniaturization and miniaturization of embedded platforms. How to combine the lightweight neural network with the microsystem to achieve the optimal balance of performance, size, and power consumption is a difficult point. This article introduces a micro-system implementation scheme that combines SiP technology and FPGA-based convolutional neural network. It uses Zynq SoC and FLASH and DDR3 memory as the main components, and uses SiP high-density system packaging technology to integrate. PL end (FPGA) design Convolutional Neural Network, convolutional neural network accelerator, adopt the method of convolution multi-dimensional division and cyclic block to design the accelerator structure, design multiple multiplication and addition parallel computing units to provide the computing power of the system. Improving and accelerating perform on the YOLOv2_Tiny model. The test uses the COCO data set as the training and test samples. The microsystem can accurately identify the target. The volume is only 30 × 30 × 1.2 mm. The performance reaches 22.09GOPs and the power consumption is only 0.81 W under the working frequency of 150 MHz. Multi-objective balance (performance, size and power consumption) of lightweight neural network Microsystems has realized.
Leveraging CD Gain for Precise Barehand Video Timeline Browsing on Smart Displays

One common task when controlling smart displays is the manipulation of video timelines. Given current examples of smart displays that support distant bare hand control, in this paper we explore CD Gain functions to support both seeking and scrubbing tasks. Through a series of experiments, we demonstrate that a linear CD Gain function provides performance advantages when compared to either a constant function or generalised logistic function (GLF). In particular, linear gain is faster than a GLF and has lower error rate than Constant gain. Furthermore, Linear and GLF gains’ average temporal error when targeting a one second interval on a two hour timeline (±5 s) is less than one third the error of a Constant gain.
Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset Pages

Abstract Semantic markup, such as , allows providers on the Web to describe content using a shared controlled vocabulary. This markup is invaluable in enabling a broad range of applications, from vertical search engines, to rich snippets in search results, to actions on emails, to many others. In this paper, we focus on semantic markup for datasets, specifically in the context of developing a vertical search engine for datasets on the Web, Google’s Dataset Search. Dataset Search relies on to identify pages that describe datasets. While was the core enabling technology for this vertical search, we also discovered that we need to address the following problem: pages from 61% of internet hosts that provide markup do not actually describe datasets. We analyze the veracity of dataset markup for Dataset Search’s Web-scale corpus and categorize pages where this markup is not reliable. We then propose a way to drastically increase the quality of the dataset metadata corpus by developing a deep neural-network classifier that identifies whether or not a page with markup is a dataset page. Our classifier achieves 96.7% recall at the 95% precision point. This level of precision enables Dataset Search to circumvent the noise in semantic markup and to use the metadata to provide high quality results to users.
Study of Acceptance of Indian Political Cartoons in Facebook Landscape

Indian society changed after digitalization and economical reformation. Currently, the Facebook is the most popular social media in India. The political cartoonist took that platform as an opportunity to share their thoughts and raise socio-political issues through their cartoons. The aim of the paper is to study responses (likingness and affective) to political cartoons in respect to responsiveness towards cartoons, gender, and socio-economic status. The study was conducted among 875 Indians from different parts of India including males and females with various socio-economic backgrounds. The data captured in the form of sentiments (love, sadness, angry and happiness emotions) for each cartoon shared in the Facebook timeline. Then, relevant descriptive statistics were computed using IBM SPSS 20.0 software. Results showed that maximum Facebook users expressed their thoughts about political cartoons through emoticons in terms of like, sad, angry, and happiness. It is found that male user are more likely to be expressive to political cartoons than female user. Mostly user from low and middle socio-economic backgrounds relate themselves with the cartoon scenarios and shown interest than users from higher class of the society. Hence, political socio-political scenarios can be communicated effectively using political cartoons in Facebook as the people of India are relating themselves with various cartoons.
GraphScope

Due to diverse graph data and algorithms, programming and orchestration of complex computation pipelines have become the major challenges to making use of graph applications for Web-scale data analysis. GraphScope aims to provide a one-stop and efficient solution for a wide range of graph computations at scale. It extends previous systems by offering a unified and high-level programming interface and allowing the seamless integration of specialized graph engines in a general data-parallel computing environment. As we will show in this demo, GraphScope enables developers to write sequential graph programs in Python and provides automatic parallel execution on a cluster. This further allows GraphScope to seamlessly integrate with existing data processing systems in PyData ecosystem. To validate GraphScope's efficiency, we will compare a complex, multi-staged processing pipeline for a real-life fraud detection task with a manually assembled implementation comprising multiple systems. GraphScope achieves a 2.86× speedup on a trillion-scale graph in real production at Alibaba.
Oracle character detection based on improved Faster R-CNN

Oracle is the earliest known systematic writing in our country. It mostly originated from Shang Dynasty royal divination. The records cover a wide range of contents, including national politics, social atmosphere, and military wars. Oracle bone inscriptions are also the source of modem Chinese characters in my country, and they carry the history of Chinese civilization for thousands of years. At present, oracle bone characters still rely on manual identification and then handed over to experts to decipher, and the level of information is low. Therefore, in order to effectively detect oracle bones. In this paper, an improved object detection algorithm Faster R-CNN is proposed to detect oracle bone inscriptions. The algorithm uses ResNet101 as the backbone feature extraction network, and uses multi-scale feature fusion to improve text detection capabilities at different scales. Finally, the Soft-NMS algorithm is used to suppress redundant candidate regions. Experiments show that the average accuracy of the improved algorithm reaches 88.2%, which can effectively detect oracle bones.
Credit Card Fraud Detection Using Machine Learning Algorithms

Fraud: Fraud is any activity that causes financial loss to any other person. With the advancement of technology in the modern era, fraud related to credit card is continuously increasing. Credit card frauds are costing a huge amount of dollars to the customers. Nowadays, scammers are continuously finding new ways to do the scams. Also, to minimize the credit card fraud losses, different types of systems have been developed which detect frauds. We have to make a highly sensitive and powerful fraud detection system up to such an extent that it detects the fraud even before it takes place and in a sequential and exact manner. In this paper, we have mentioned three important algorithms, namely Isolation Forest, Support Vector Machine and Local Outlier Factor for credit card fraud detection. Also, we have discussed methodologies used in all three algorithms as mentioned above. We have also discussed the applications of the fraud detection system and how it will be implemented in the future.
A Systematic Analysis of the Human Activity Recognition Systems for Video Surveillance

In recent years, human activity recognition has become a prominent research area in numerous fields such as healthcare, smart home activity analysis, suspicious activity recognition, robotics, surveillance, and security. The focus of the current research work is the analysis of human activity recognition systems for video surveillance. The human activity recognition system involves the detection of normal as well as abnormal activities. The recognition of human activities is still considered a challenging issue despite the contributions of numerous researchers. The erratic human behavior and complexities of the video datasets create numerous challenges to precisely observe the human activities with significant performance. The analysis of the human activity detection systems for video surveillance is conducted on the basis of state-of-art contributions by different researchers in the field. The paper also describes the taxonomy of human activity detection. It ends with a discussion of the challenging issues in the field along with the concluding remarks.
University Web-Environment Readiness for Online Learning During COVID-19 Pandemic: Case of Financial University

The paper is devoted to the Financial University experience of transforming the learning process fully online during the COVID-19 pandemic. The architecture and principles of the web-environment were rethought significantly. It is shown that the competence in digital technologies formed in universities creates new challenges and tasks: continuity of digital footprints and technologies for learning process organization and implementation; openness and publicity of learning; professional development of teachers, and ethics in the digital space.
A Systematic Analysis of Regression Test Case Selection: A Multi-Criteria-Based Approach

In applied software engineering, the algorithms for selecting the appropriate test cases are used to perform regression testing. The key objective of this activity is to make sure that modification in the system under test (SUT) has no impact on the overall functioning of the updated software. It is concluded from the literature that the efficacy of the test case selection solely depends on the following metrics, namely, the execution cost of the test case, the lines of the code covered in unit time also known as the code coverage, the ability to capture the potential faults, and the code modifications. Furthermore, it is also observed that the approaches for the regression testing developed so far generated results by focusing on one or two parameters. In this paper, our key objectives are twofold: one is to explore the importance of the role of each metric in detail. The secondary objective is to study the combined effect of these metrics in test case selection task that is capable of achieving more than one objective. In this paper, a detailed and comprehensive review of the work related to regression testing is provided in a very distinct and principled way. This survey will be useful for the researchers contributing to the field of regression testing. It is noteworthy that our systematic literature review (SLR) included the noteworthy work published from 2007 to 2020. Our study observed that about 52 relevant studies focused on all of the four metrics to perform their respective tasks. The results also revealed that about 30% of the different categories of regression test case reported the results using metaheuristic regression test selection (RTS). Similarly, about 31% of the literature reported results using the generic regression test case selection techniques. Most of the researchers focus on the datasets, namely, Software-Artefact Infrastructure Repository (SIR), JodaTime, TreeDataStructure, and Apache Software Foundation. For validation purpose, following parameters were focused, namely, the inclusiveness, precision, recall, and retest-all.
Complete Practical Side-Channel-Assisted Reverse Engineering of AES-Like Ciphers

Public knowledge about the structure of a cryptographic system is a standard assumption in the literature and algorithms are expected to guarantee security in a setting where only the encryption key is kept secret. Nevertheless, undisclosed proprietary cryptographic algorithms still find widespread use in applications both in the civil and military domains. Even though side-channel-based reverse engineering attacks that recover the hidden components of custom cryptosystems have been demonstrated for a wide range of constructions, the complete and practical reverse engineering of AES-128-like ciphers remains unattempted. In this work, we close this gap and propose the first practical reverse engineering of AES-128-like custom ciphers, i.e., algorithms that deploy undisclosed SubBytes, ShiftRows and MixColumns functions. By performing a side-channel-assisted differential power analysis, we show that the amount of traces required to fully recover the undisclosed components are relatively small, hence the possibility of a side-channel attack remains as a practical threat. The results apply to both 8-bit and 32-bit architectures and were validated on two common microcontroller platforms.
Bangla Handwritten Digit Recognition

Due to the wide range of shapes, sizes, and writing styles, handwritten digit recognition has always been difficult. For all of the recognition algorithms built in this thesis, the rewarded method is used. NumtaDB is the largest dataset. It is a collection for Bengali handwritten digits. This is a massive dataset with over 85,000 images in it. This dataset, however, is incredibly difficult to work with due to its complexity. The aim of this paper is to preprocess images that can be used to train deep learning models with high accuracy. The reason for this is that, unlike the MNIST for English digits, there is no preprocessed data to deal with when it comes to Bengali digit recognition. This unbiased dataset, NumtaDB, is used in this paper for Bangla digit recognition. In this paper, various preprocessing techniques are developed for image processing, with a deep convolutional neural network (CNN) functioning as the classification algorithm. On the NumtaDB image dataset, the performance is systematically evaluated of this process. Finally, 93% accuracy is obtained for NumtaDB dataset, and 92% accuracy rate is obtained for our own dataset using same of the proposed method in experiments. In this paper, the accuracy for every training dataset in NumtaDB is separately calculated which has the maximum accuracy 98%.