Reviewerly/exHarmony huggingface dataset:
A Dynamic Attention Based Graph Neural Network for Anomaly Prediction in Multi-Variate Time-Series &amp; Its Application in Network Monitoring

Prediction of anomaly in time-varying datasets is at the core of many real-world applications. For example, prediction of anomalous events in different time-varying business metrics and proactively mitigating any underlying problem in the system is of utmost importance to any online business to evade any kind of disruption in smooth customer experience. A large body of literature exists which adopted different techniques for anomaly prediction in multi-variate time-series data. Recent advancements in deep-learning have resulted in multiple literature proposing different variants of graph neural network (GNN) for anomaly prediction in multi-variate time-series data. However, one major gap area that remained in this domain is the sensitivity of such networks to inherent noise in the data. This is where we make our contribution by proposing a graph neural network with dynamic attention which not only offers more robustness of the network to noise but also makes the network more trainable and accurate in prediction in the presence of noise. We carried out extensive experimentation against both publicly available datasets and real-world industrial datasets. The outcome of the experimentation is consistently in support of the superiority of our proposed network over the state-of-the-art GNN network for anomaly prediction.
LCM: A Surprisingly Effective Framework for Supervised Cross-modal Retrieval

Due to its increasing importance, cross-modal retrieval (CMR), where the query from one modality is used to retrieve objects from a different modality, has gained a lot of attention. A plethora of techniques have been proposed for this task, with deep learnt multi-modal models being the dominant paradigm. While these techniques have become increasingly sophisticated in terms of learning representations of multi-modal objects in a common space, relatively less attention is paid to the overall computational costs involved while training the model and during retrieval.
RE-RentFraud: A System to detect Frauds in rent payments for Real-Estate properties

Housing.com have launched Pay Rent feature which allows tenants to pay rents to their landlords using credit cards. Over time, with increase in number of transactions there is an increase in number of payment frauds occurring on the platform. Therefore, in this paper, we propose an end-to-end pipeline to detect frauds occurring on the platform and classify them into green, yellow and red channels based on the fraud probability. Concretely, the pipeline follows a 2-stage strategy where: i) at first stage, pre-payment fraud model detects fraud before user makes the payment; and ii) at second stage, post-payment fraud model detects fraud once the user made the payment. This allows us to block the fraud users at first stage and take necessary actions at second stage. As we are in highly imbalanced data setting where fraud class is under-represented as compared to normal transactions, we train the pre-payment and post-payment ML models using SMOTE oversampling technique followed by XGBoost classifier. We show the effectiveness of proposed approach on 3 real-world Housing.com datasets. Moreover, we perform the data drift analysis on the extracted dataset and show that there is high shift in distribution in case we use more than last 2 months of data for training during production. Finally, we have released a REST API running on a 2-core machine with 2GB RAM. The demo video is available here 1.
Monte Carlo Dropout Based BatchEnsemble For Improving Uncertainty Estimation

Modelling uncertainty in deep learning is important for several high-risk applications such as autonomous driving and healthcare. Existing techniques for uncertainty modelling in deep learning such as Monte Carlo (MC) Dropout [1] and BatchEnsemble [2] suffer from some drawbacks. MC dropout shares parameters across models resulting in highly correlated predictions while BatchEnsemble requires storing additional parameters for each model in the ensemble. In our work, we aim to bring the best of both worlds by combining MC-dropout in the process of ensemble creation in BatchEnsemble. The proposed approach, Monte-Carlo BatchEnsemble, helps in generating ensembles with less correlation in prediction with the addition of a few parameters. The experimental results show the effectiveness of the proposed technique for image classification.
BUDDI Table Factory: A toolbox for generating synthetic documents with annotated tables and cells

Tables are the most convenient way to represent structured information in a document. Understanding the table structure is critical to understanding its contents. Several deep learning-based approaches from the literature have shown promising results in understanding table structures, but they require large amounts of annotated data. However, the availability of annotated datasets to train these methods are expensive, laborious, and very limited. Moreover, human-annotated data suffers from inconsistencies in table and cell annotations. We propose BUDDI Table Factory (BTF) for synthetically generating annotated documents with a wide range of variations in table structures. We propose a heuristics-based method to generate a variety of table structures from which we generate synthetic documents using LaTeX. We propose a computer vision-based approach to localize table and cell regions and automatically generate annotations in PASCAL VOC challenge format. We empirically illustrate the advantage of adding synthetic BTF documents with limited original documents to the model training, which can significantly improve the TEDS and IoU performance of the table structure recognition tasks in public and real-world healthcare datasets.
Warning: It’s a scam!! Towards understanding the Employment Scams using Knowledge Graphs

Employment scams, such as scapegoat positions, clickbait and non-existing jobs, etc., are among the top five scams registered over online platforms.1 Generally, scam complaints contain heterogeneous information (money, location, employment type, organization, email, and phone number), which can provide critical insights for appropriate interventions to avoid scams. Despite substantial efforts to analyze employment scams, integrating relevant scam-related information in structured form remains unexplored. In this work, we extract this information and construct a large-scale Employment Scam Knowledge Graph consisting of 0.1M entities and 0.2M relationships. Our findings include discovering different modes of employment scams, entities, and relationships among entities to alert job seekers. We plan to extend this work by utilizing a knowledge graph to identify and avoid potential scams in the future.
Efficient Automatic Detection of Uterine Fibroids Based on the Scalable EfficientDet

Uterine fibroids refer to benign tumors formed by uterine smooth muscle tissue hyperplasia, high frequency in women between 30 and 50 years old. By the age of 50 years, 80% of women have one or more uterine fibroids, and about half of these patients are symptomatic and in need of treatment. It's ranking the third highest incidence of all gynecological diseases. Generally, it is a benign tumor, but it can also have certain effects on women's bodies, such as causing infertility. Early detection and treatment are essential measures to reduce morbidity. Ultrasound is the preferred imaging method, and with the continuous development of deep learning in the field of medical image analysis, many applications related to object detection have good performance. Computer-assisted diagnosis can further solve the subjective uncontrollability problem caused by different doctors' reading films. Because doctors' inexperience and fatigue can reduce the diagnostic accuracy of uterine fibroids, this paper proposes a scalable EfficientDet to detect the ultrasound images of uterine fibroids and uses the Convolutional Neural Network (CNN) to extract their features. The backbone network uses EfficientNet, and then it is used together with BiFPN to improve the accuracy of the model. This method can not only benefit non-professional ultrasonologists but also provide sufficient auxiliary diagnostic effects for high-quality ultrasonologists to provide a reliable basis for future treatment and surgical resection. Finally, the effectiveness of this method is experimentally compared with other existing methods. Our method has an average accuracy of 98.88% and an f1-score of 98%. We demonstrate that the methods of this study are superior to other neural networks. And it can bring sufficient benefits to ultrasonologists. We summarize and analyze various detection algorithms, and discuss their possible future research hotspots.
Intelligent Root Cause Detection for LTE Network Fault Based on Machine Learning

With the Long-Term Evolution (LTE) of mobile networks, the types of network fault have become more complex and diverse. In order to ensure reliable and safe run of the network, it is increasingly difficult for traditional manual administration and maintenance to cope with the complicated and heavy network faults. The super machine learning ability of artificial intelligence can well realize intelligent prediction and processing of network faults by sorting and analyzing a large amount of Key Performance Indicator (KPI) data on network operation and maintenance. In the face of complex network fault types, the number of labeled KPI samples used for training the model is very limited and it is difficult to obtain the samples. This paper proposes a multiclass classification algorithm based on semi-supervised transductive support vector machine for fault root cause detection in LTE networks, and compares its performance with supervised learning and unsupervised learning algorithms. Theoretical analysis and achieved results show that the algorithm proposed in this paper achieves a good learning effect on the mixed sample training set composed of a small number of labeled samples and a large number of unlabeled samples, and has a high classification accuracy for unlabeled samples.
Investigating the dynamics of women admission to Information Technology degrees in Russian Universities

Women remain strikingly under underrepresented in jobs related to engineering and technology; besides, in the US and Europe during several past years the shares of women in technology degrees have generally been showing either decline, or stagnation, or negligible growth. The reluctance of women to pursue engineering and technology degrees finds a variety of explanations, from the lack of interest to the unwillingness to study in a male-dominated culture. Some countries set specific measures to attain women to pursue degrees in engineering and technology and monitor women's admission to Information and Communication Technologies (ICT) degrees at the universities. Others, like Russia, focus mostly on the overall student population admitted to ICT degrees but do not monitor student population gender-wise. The aim of this research effort was to collect and analyze the data on women's admission in undergraduate ICT programs in Russia. The contribution of this study is that no data on women's enrollment to ICT degrees in Russia has been publicly available before. Nine universities in Russia provided these data on women's enrollment in ICT degree programs for several past years, mostly from 2011 to 2021. In 2021, cumulative intake in ICT degree programs of these universities was about 10k students, which constitutes about 1/3 of the total student body in such programs in Russia. Based on the analysis and comparison of the data for Russia with similar data for other countries in Europe, USA and Asia, the overall trend of women admission to ICT majors in Russia is currently aligned with the US and Europe trends. We have observed some fluctuations depending on the geographical location of the universities.
Safe Deep Reinforcement Learning Based on Sample Value Evaluation

In recent years, deep reinforcement learning has combined the advantages of reinforcement learning and deep learning, and has made great progress in decision-making tasks. However, the training of deep reinforcement learning requires frequent interactions between the agent and the environment and repeated experiments. Adversaries have chances to poison the sample data collected by the agent by attacking the experimental environment in the training process, thereby bringing security risks and serious consequences to the training process of reinforcement learning. This work is committed to addressing the security risks in the field of deep reinforcement learning. However, this work improves the algorithm from the perspective of sample data filtering, and improves the security performance of deep reinforcement learning algorithm. There are two contributions in this work: one is to defend against adversarial attacks against deep reinforcement learning through cluster analysis and sample value evaluation; the other is to propose a deep reinforcement learning algorithm based on sample value evaluation on the basis of deterministic strategy gradient algorithm. The algorithm uses the clustering method to classify the sample pool, and measures the contribution value and security risk of the sample to the model training through the sample value evaluation. The classic game experiments show that the proposed algorithm is safe and effective. It reduces the threat of the agent falling into the adversarial sample attack and improves the training performance of deep reinforcement learning.
Study of the correlation between server resources utilization and energy consumption

The amount of data to be treated in data centers is increasing more and more, which makes an obligation to optimize the use of servers in a way that reduces energy consumption and then minimizes the cost. The study of correlation between server resources and energy consumption is a crucial step and involves well-founded strategies to optimize both the use of resources servers and their energy consumption. There are various tools to measure energy consumption of servers. In our work we apply different workloads on servers and then we measure energy consumption using two methods. Internal sensors (performance counters) which allow extracting resources usage information by using listeners on the sensors or external tools, and energy meters and software to acquire data. Error rate of both methods varies according to the integrated sensors in the machine and the type and accuracy of the power meter used. We study the correlation between the power of CPU server and its utilization as well as the correlation between energy consumption of server and its utilization in term of CPU, Memory, and hard drive.
On Big Data Forensic and Forensic Cloud Environment

The number of electronic devices involved in cyber related crimes has increased significantly in the last few years. This is primarily due to advancements in technology and increased accessibility to devices. This poses significant challenges with respect to forensic investigations as investigators are faced with processing huge volumes of data from various sources within a limited time. Due to the urgency, frequency, and magnitude of some cases, investigators may miss deadlines, and crucial leads. In this paper, we consider these above stated challenges and propose a new definition for big data forensics, which recognises the time aspect and variety of sources when carrying our digital forensics. This definition will help digital forensic tool designers consider these critical aspects and design comprehensive tools to combat the limitations of the current state-of-the-art. We review an existing tool, the Forensic Cloud Environment (FCE), against the proposed definition. We show how its design leverages the power of Hadoop Distributed File System (HDFS), HBase, and MapReduce to provide a comprehensive solution for big data forensics. In conclusion, we provide an argument for FCE as a solution for resolving big data forensic challenges based on general Scientific Working Group on Digital Evidence (SWGDE) forensic principles.
A Higher Performance Accelerator for Resource-Limited FPGA to Deploy Deeper Object Detection Networks

Nowdays, CNN models become more and more popular in lots of fields due to its high performance. Unfortuna-tely, the complexity of the model increases along with accuracy, which limited its applications in some fields. Until now, a lot of researches are focused on some shallow networks such as Alexnet, VGG16. The state of art models in computer vision has over one hundred layers using ResNet structure. Besides, the power consumption of the model and latency of inference also leads to the difficulties to use AI models in reality. To solve the problem, we proposed an accelerator structure to apply yolov5 model to FPGA boards. Two types of parallelisms and pipeline structure are applied. Besides, to eliminate the time of loading and saving to off-chip buffer, ping-pong buffer are used. We improve the pipeline performance by rescheduling the mac operation. Eventually, we test the performance of accelerator on ZC702. So it can be easily implemented on some resource-limited boards. The acc-elerator can speed up the inference 6 times than CPU, 17.4 times than ARM CPU on ZC702. And the throughput of single DSP outperforms the previous works.
Real-time Video Data Traffic Management for Publish-Subscribe based Messaging System

There is a growing need to transmit video data over IoT infrastructures in the same processing pipeline with sensor edge devices. As the number of devices in the same pipeline increases, consumers experience more frequent message delays while getting data from multiple sources. In this study, we propose and analyze three priority queue management algorithms for publish-subscribe based Kafka framework to utilize realtime video streaming. The algorithms are implemented on a real test-bed by deploying multiple sensors with different Quality of Service (QoS) requirements. The results reveal that the proposed solutions enhance QoS provisioning when compared to the default Kafka queue model. Moreover, the Updated Weighted Fair Kafka Queue (UWFKQ) algorithm performs better than the Weighted Fair Kafka Queue (WFKQ) and the Priority Kafka Queue algorithms for relieving starvation as well as long wait times. Besides, the slider mechanism proposed for UWFKQ algorithm enables the system to utilize unused poll counts so that congestion between packet transmission flows can be controlled. We have also shown that several default Kafka parameters can successfully be tuned to optimize the throughput rate.
Classification of Lifts Using a Smart Insole

Smart Insoles are in-shoe measurement systems primarily designed to measure forces, exerted by the ground on a human body, as a result of gait. In this paper, we test the applicability of force measurements from a smart insole, in enforcing good lifting ergonomics for manual material handling. This was achieved by classifying a lift as either good or bad using DTW (Dynamic time warping), a popular algorithm for speech recognition. Our experiments have revealed that lift classifications using this technique is 98.8% accurate with 30 seconds of computation time.
Fine-Tuning Music Generation with Reinforcement Learning Based on Transformer

Deep supervised learning is the most common way of automatically music generation. However, this sort of model only learns probabilities from dataset, and such pattern does not leave much room for manully control, which could result in an out of expectation result. In this paper, we have proposed a novel approach of polyphonic music generation using Deep Reinforcement Learning base on Transformer skeleton. The principal novelty of our approach centres on having a well trained music Transformer network as basement, then using Reinforcement Learning to fine tune it to impose music theory into the model. The reward of RL consists of probability learned from training data and music theory proposed to follow. We analyzed quantitatively and qualitatively, and results show that the proposed model enhances the performance of deep supervised learning who only learns from data and the music generated comes out to be more creative. Eventually, we discussed the usefulness of our model.
Hybrid Attention Cascade Multi-View Stereo Network

The multi-view stereo reconstruction method based on deep learning is usually affected by the weak-textured area or occlusion in the real scene. Therefore we propose a multi-view stereo reconstruction network method with a hybrid attention mechanism. A hybrid attention module is added to the feature extractor to improve the performance in weak-textured regions. In order to reduce occlusion effects a module is used to adjust the view weights. We find adding depth-adaptive partitioning will improve the performance of our method. Our method is trained and tested on the DTU and Tanks and Temples datasets, the results show that our method has good results in terms of reconstruction accuracy and completeness.
Design of a Web-Based New Student Admission Information System Accompanied by a Midtrans Payment Gateway Payment Method at SMP PGRI 16 Sidoarjo

SMP PGRI 16 Sidoarjo is an educational unit with junior high school level with NPSN number 20501661 which has private status and is under the auspices of the Ministry of Education and Culture. SMP PGRI 16 Sidoarjo has its address at Jl. Raden Patah No. 91 E, Pucang Anom, Sidoarjo District, Sidoarjo Regency, East Java Province with postal code 61217.&#x0D; The problem that is owned by SMP PGRI 16 Sidoarjo is at the time of PPDB implementation, where implementation still takes a long time and requires extra energy for the committee in accepting new students. Starting with students having to come to school to queue for taking forms and filling out the forms provided. In addition, announcement information for prospective students is placed on the bulletin board, so that information may not be fully received by prospective students. For prospective students who have been declared graduated by the student body, prospective students will return to school and make payments offline. And for the process of accepting prospective students using conventional, namely using special notes and processing student data using the Microsoft Excel application, so that data processing of prospective students takes a long time and is not integrated with each other.&#x0D; Therefore, in this study, it is proposed to make a web-based application for Admission of New Students. It is hoped that it can help the registration process for new students, so that prospective participants do not need to come to school again. Starting from registration, payment of prospective students who have passed and filling out forms are all done online so that it can simplify and shorten the time for new students.
Result Publishing System Using Asymmetric Key Encryption

India is on the path of digitalisation, and many activities are already practised digitally, but there are still some fields, where digitisation hasn't taken place completely/properly. Like in many universities/institutes in India, student examination results are displayed on the bulletin board, which creates a lot of chaos for the students. And those who do provide results digitally, many of them didn't promise their confidentiality and authenticity will be maintained. This blockchain-based web app resolves the issue of bulletin board result declaration and also the confidentiality and authenticity of the result. The result can only be accessed by the respective registered student, no intruder can access and manipulate the grades. To ensure students do not misuse the result and temper their grades, we use blockchain.