Reviewerly/exHarmony huggingface dataset:
CrunchQA: A Synthetic Dataset for Question Answering over Crunchbase Knowledge Graph

The digital transformation in the finance and enterprise sector has been driven by the advances made in big data and artificial intelligence technologies. For instance, data integration enables businesses to make better decisions by consolidating and mining heterogeneous data repositories. In particular, knowledge graphs (KGs) are used to facilitate the integration of disparate data sources and can be utilized to answer complex queries. This work proposes a new dataset for question-answering on knowledge graphs (KGQA) to reflect the challenges we identified in real-world applications which are not covered by existing benchmarks, namely, multi-hop constraints, numeric and literal embeddings, ranking, reification, and hyper-relations. To build the dataset, we create a new Knowledge Graph from the Crunchbase database using a lightweight schema to support high-quality entity embeddings in large graphs. Next, we create a Question Answering dataset based on natural language question generation using predefined multiple-hop templates and paraphrasing. Finally, we conduct extensive experiments with state-of-the-art KGQA models and compare their performance on CrunchQA. The results show that the existing models do not perform well, for example, on multi-hop constrained queries. Hence, CrunchQA can be used as a challenging benchmark dataset for future KGQA reasoning models. The dataset and scripts are available on the project repository. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Intelligence Analysis of Crime of Helping Cybercrime Delinquency Based on Knowledge Ggraph

This paper studies the crime of helping cybercrime delinquency (CHCD) from two aspects: theoretical literature research and case empirical research. First of all, clarify the basic concept, development trend and case characteristics of the crime of facilitation, and make a comprehensive exposition of the crime of facilitation. Secondly, the basic situation analysis, co-word analysis, and keyword clustering of the theoretical research literature on the CHCD in the literature database in recent years are carried out, so as to obtain the research hotspots and evolution process of the CHCD, and analyze the research in the form of knowledge graph. Then, through the analysis and research of the crime cases, the relevant data and information are extracted from the judgment documents, and the regional differences of the crime, the characteristics of the criminal subject, the characteristics of the criminal behavior, and the judicial sentencing are comprehensively sorted out to provide information for the follow-up intelligence research and judgment support.
A Radar Emitter Fingerprint Feature Resistant to Receiver Distortion

In radar specific emitter identification, the non-cooperative receiver itself has fingerprint features similar to the emitter, and the fingerprint features extracted at the receiver are coupled of the two, which limited cross-receiver radar specific emitter identification. To solve this problem, a new fingerprint feature is constructed in this paper, which takes the absolute value of the forward quotient of the neighboring points of the pulse signal spectrum as the emitter feature, weakens the receiver distortion interference by improving the frequency resolution and can be quickly extracted by the Chirp-Z transform. Simulation experiments show that the proposed features can effectively overcome the fingerprint feature shift caused by receiver distortion, have good intra-class aggregation and inter-class differentiability, and achieve higher recognition accuracy than traditional features under the same conditions, proving that the proposed features has important reference value for the practical application of cross-receiver radar specific emitter identification.
Continuous Health Monitoring of Machinery using Online Clustering on Unlabeled Data Streams

Maintenance is an important support function to ensure the reliability, safety, and availability in the railway. Lately, machine learning has become a major player and allows practitioners to build intricate learning models for machinery maintenance. Commonly, a model is trained on static data and is retrained on new data that exhibit novelties unknown to the model. On the contrary, online machine learning is a learning paradigm that adapts the models to new data, thus enabling adaptive, lifelong learning. Our goal is to leverage online learning on unlabeled data streams to enhance railway machinery maintenance. We propose Continuous Health Monitoring using Online Clustering (CheMoc) as an unsupervised method that learns the health profiles of the systems incrementally, assesses their working condition continuously via an adaptive health score, and works efficiently on streaming data. We evaluate CheMoc on a real-world data set from a national railway company. The results show that CheMoc discovered relevant health clusters, as confirmed by a domain expert, and processed the data of an entire year under two hours using only 600 MB of memory.
Proximal Cost-sensitive Sparse Group Online Learning

Effective streaming feature selection in dynamic on-line environments is essential in numerous applications. However, most existing methods evaluate high-dimensional features individually and ignore the potentially pertainable group structures of features. Moreover, the class imbalance underlying streaming data may further decrease the discriminative efficacy of the selected features, resulting in deteriorated classification performance. Motivated by this observation, we propose a proximal cost-sensitive sparse group online learning (PCSGOL) framework to handle imbalanced and high-dimensional streaming data. Specifically, we formulate this issue as a new cost-sensitive online optimization problem by leveraging the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -norm, ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm, and group-wise sparsity constraints in the dual averaging regularization. The average weighted distance is also introduced in PCSGOL to achieve stable prediction results. We mathematically derive closed-form solutions to the optimization problems with four modified hinge loss functions, leading to four variants of PCSGOL. Extensive empirical studies on real-world streaming datasets demonstrate the effectiveness of our proposed method.
A Secure and Efficient Hybrid Encryption Scheme for Power Regulation and Control Business

The power communication network based on 5G network slicing is an important foundation to support smart grid, and the bearing of small granularity power regulation and control class services depends on the slicing soft isolation technology, and the data isolation between each soft isolation channel is crucial. In this paper, we propose a new symmetric cryptographic algorithm based on random coding, and establish a hybrid encryption method based on this symmetric algorithm, combined with SM2 and SM3 algorithms, which is suitable for encrypting the data of power regulation and control services. It is also verified through simulation that the proposed hybrid encryption method has high encryption efficiency while ensuring security.
Anomaly Detection of Argo Data using Variational Autoencoder and K-means Clustering

Aiming at the problem of abnormal data mining in marine Argo buoy monitoring data, due to the poor performance of traditional machine learning algorithms in anomaly detection, a two-stage anomaly detection method based on variational autoencoder and K-means clustering is proposed. The anomaly detection of Argo data is realized by combining deep learning and machine learning. The proposed method is divided into two parts, data preprocessing and anomaly detection. Specifically, standardization and normalization techniques are used to preprocess the input data. Then, the data are input into the training process of encoding and decoding in the variational autoencoder. Finally, the reconstruction error between the original data and the reconstructed data is calculated, and the upper and lower limits of the appropriate threshold are set. If the threshold exceeds the upper limit, it is directly determined as the abnormal data, and if the threshold is lower than the lower limit, it is determined as the normal data. It is called the boundary sample between the thresholds, and the extra clustering enhancement is used to determine the anomaly. The experimental verification is carried out on the simulation data set and the real data set, respectively. The experimental results show that compared with other methods, this method significantly improves the accuracy of anomaly detection.
Localization of IoT Devices based on Spatially Separated Multi-Antenna Radio Frequency Channel State Information

In this paper, we investigate a localization performance based on channel state information (CSI) of separated antenna. In multi-carrier transmission system, CSI has been considered as one of the promising signatures of radio frequency (RF) channel and employed to device localization. However, CSI can be correlated in typical indoor environment which may limit the unique representations of the signature for RF fingerprinting. To solve this, we propose a joint CSI-based device localization, exploiting the diversity of RF signatures. Even if the one CSI loses the reliability as a the fingerprint due to spatial correlation, the other CSI can support the device localization performance. For experimental evaluation, we construct the test-bed and confirmed the proposed scheme can support single CSI-based localization even if highly correlated CSI are reproduced occasionally at many locations. The test results in indoor environment show that the proposed scheme complements the single-CSI in terms of the reliability of authentication with localization of the devices.
Implementation and research of recommendation system based on trust mechanism

This paper analyzes and studies the traditional algorithm of recommendation system. At present, user based collaborative filtering algorithms and project-based collaborative filtering algorithms have been widely recognized, but they also have shortcomings. Such as the calculation error of user similarity is large, and there is a user preference problem in user rating. The collaborative filtering algorithm considers the relationship between project resources and user ratings, but does not consider the trust between users. To solve these problems, we changed the cosine similarity used in the traditional recommendation algorithm to the modified cosine similarity by comparing the similarity algorithm to avoid user preference problems. By using a hybrid collaborative filtering algorithm, combining user score similarity and user item attribute similarity, the nearest neighbor set is more accurate. .
Interactive Support System for Idea Divergence-Convergence Iteration

We propose a system to make user’s ideas more diverse as a breakthrough during creating ideas. Discussions such as brainstorming are held to stimulate user’s idea, though such composition requires much effort. For solving the problem, the proposed system presents cross-category articles related to the user’s idea as the divergence support. When the users choose one of them and the keywords shown in the articles, the sentence prototype are generated with the keywords. Afterward, the sentence prototype or its revision is input as a query of searching for the analogy-recalling articles. By iterating the processes, the user’s idea goes diversely. For obtaining the cross-category articles related to the user’s idea, the article texts and user’s idea text is input to GPT2 models to convert to vectors. The category-dependent components extracted with correlation of the category codes of the article is subtracted from the vectors to generate the category independent vectors. The cross-category but similar articles are searched based on the category independent vectors. And also, the experimental evaluation results are shown in this paper.
Application-aware multi-tenant scheduling mechanism model of power cloud platform

Power multi-tenant data centers widely use distributed parallel computing frameworks to process data, and applications from different tenants share and compete for bandwidth resources. The distributed parallel computing framework mostly uses Coflow as the traffic model, and optimizing the Coflow scheduling can improve the performance of the distributed parallel computing framework. Aiming at the problem of performance optimization and tenant isolation during Coflow scheduling, an application-aware multi-tenant Coflow scheduling mechanism, Aamt, is proposed. First, according to the tenant isolation problem, the progress is introduced as a measurement standard; according to the dynamic and elastic changes of tenant demand, the tenant arrival model is designed according to the NHPP modeling, and the long-term progress of tenant isolation guarantee is obtained to achieve application awareness; in order to effectively utilize the network bandwidth, Lagrange is designed Dual optimization, modeling from two aspects of bandwidth allocation and completion time, to obtain performance optimization progress; experimental results show that Aamt can not only ensure tenant isolation, but also increase bandwidth and improve link utilization.
Implementation of Empath X SLA predictive tool for a Government Agency in Singapore

Service Level Agreement (SLA) plays a significant role in the relationship between citizens and the government. It stipulates the quality levels required for the meaningful interaction between the two parties. Most SLA predictive models consider end-to-end duration and frequency of failed service requests as model inputs with little research on the analysis of textual details of the service request. This is an issue for government bodies as the latter do not just want to meet SLA, but also be proactive by knowing the citizens before assisting them. Inclusion of textual data potentially answer to this requirement of knowing the citizen before the officer tries to meet SLA. In this paper, we attempt to enrich SLA predictive process by analysing the textual data contained in the service requests. Based on a dataset of 800k case records from a customer service centre based in Singapore, we use text analytics to derive features from the dataset, which will be included with other commonly used variables in the prediction of SLA. We further explore the use of the Empath library to provide a categorical outcome that is more beneficial for the customer service officers to understand the citizen, than a numerical outcome. Based on our experiments, we observe that a predictive model built via logistic regression performs the best with an accuracy of 75%. This result remains valid when Empath categories are included as an input variable. This paper adds to the body of research work done in citizen service by proposing an SLA predictive model that incorporates lexical features from textual data to facilitate proactive citizen service delivery.
A Crowd Source System for YouTube Big Data Analytics: Unpacking Values from Data Sprawl

YouTube has emerged as the most popular video platform across the world. This paper proposes a system for stream based meta-data analytics, to gain insights and uncover hidden patterns, for YouTube videos. The system reports the number of videos uploaded for each category, the videos with the highest views and highest likes etc. It crowd-sources the calls to the YouTube’s search and data APIs, and feeds it to Kafka Stream to process using PySpark. Finally, the processed video meta-data in stored in Apache Cassandra for downstream consumption. By experimenting with different time windows from 5 to 30 minutes, it is observed that 15-minute window is optimum for getting adequate video data de-duplication. From the available 30 video categories, with the minimum video length of 30 minutes, our study observed that the top 10 video categories with the highest number of videos are Film & Animation, Autos & Vehicles, Music, Sports, Travel & Events, Entertainment, News & politics, Documentary, Science & Technology and Education. The highest videos are uploaded under Entertainment category, which clearly captures the content creator’s interest.
Location-based Alert System Using Searchable Encryption with Hilbert Curve Encoding

The location-based alert system plays a primary factor on determining who is at risk during an emergency, such as a war zone in Ukraine. While users are willing to reveal their locations in exchange for timely alert in those situations, there is no guarantee that their private information does not fall into the wrong hands. For example, a soldier may be killed if his movement pattern is known by the enemy. One resolution to this issue is to encrypt the location information by trusted authority public key before it is transmitted. This approach provides location privacy and allows decryption only when the recipient’s location satisfies a certain predicate. However, the encryption itself may still be compromised if the location encoding is leaked. In this paper, we propose a Hilbert Curve Encoding which encrypts the user’s message along with her locations for private processing with the trusted authority. We further propose a hybrid H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M−N</inf> Encoding which combines the Hilbert Curve Encoding and Gray Encoding. H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M−N</inf> has the proprieties of a Hilbert Curve Encoding in its identifier and a Hamming distance of 1 between neighboring cells in a subgrid. As a consequence, the proposed H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M−N</inf> is less likely to leak neighboring cell identifier than Gray Encoding under random guessing attacks. Extensive experiment results show that our encoding methods are better than Hierarchical Encoding and comparable to Gray Encoding in terms of user response time, token remaining percentage, and execution time.
Pseudo slicer on three dimensional brain tumor segmentation

A constructive way to assist surgeons before performing brain tumor surgery is by visualizing a three-dimensional (3D) MRI image to determine the brain tumor volume when a pre-operative examination. However, the availability of 3D MRI brain tumors is more limited than 2D, especially for meningioma type. Therefore, we attempt to propose a model to construct a pseudo-3D volume of a meningioma tumor from a few 2D MRI slices. In the proposed model, we first generate pseudo slices for each given 2D MRI slice until satisfying 3D MRI construction from interpolation techniques, called pseudoslicer. Next, we segment the pseudo-3D MRIs to capture the 3D meningioma tumor using 3D volume-to-volume generative adversarial networks (3D-V2V-GAN). That is, we call the proposed model 3D-slicer-V2V-GAN or 3DS-V2V-GAN. In this work, the proposed model was evaluated from 27 meningioma patients at a private university hospital in Indonesia. The proposed model outperformed the SOTA model in terms of the Jaccard Index and dice coefficient. Additionally, we have radiologists to affirm the segmentation results in good quality.
Secure Multi-party kNN Search in Large-scale Spatial Data Federation

kNN is a fundamental query in various location based services such as POI recommendation and ride planning. There is an increasing demand to scale such services by querying over a data federation, where the entire dataset is distributedly held by multiple data providers (a.k.a., silos), and each silo keeps its data partition private. However, it is challenging to provide secure kNN queries over a large-scale data federation. Prior secure kNN queries can be are highly inefficient if performed cross silos because they involve excessive secure distance operations, which can be two or three orders of magnitude slower than the corresponding plaintext operations. In this work, we propose a novel threshold based framework for efficient kNN queries over a spatial data federation. The key idea is to rewrite excessive secure distance computations as light-weight secure operations. We further propose an adaptive threshold algorithm to reduce the secure communication rounds and accelerate the query processing. Extensive evaluations on both synthetic and real-world datasets show that compared with the state-of-the-art secure kNN querying methods, our solutions reduce the time cost by up to 104.1 times and communication cost by three orders of magnitude.
A Hybrid Approach to Similar Stock Recommender with Taxonomy and Behavior Data

Stock recommender is one of the key utilities for user engagement and traffic recirculation at Yahoo Finance. It recommends a list of selected stocks to the users who visit the Quote Summary Page (QSP) of a given stock. In this paper, we introduce a Similar Stock (SS) recommender that is compared to the other existing popularity-based People Also Watch (PAW) recommender at Yahoo Finance. The proposed SS recommender is powered by both business similarity and shared popularity between two stocks. We demonstrate that this new algorithm is capable of helping retail investors discover additional relevant stocks. Statistically significant increase in user engagement was observed through online experimentation.
Blood Cells Counting and Localisation through Deep Learning Object Detection

Nowadays pathologists have to analyse blood cells manually with the aim to diagnose diseases. In order to perform this manual task blood samples must be collected from the patient and then placed on a microscope slide. This slide is studied with the aim to detect the abnormality presence. The manual method used to identify abnormality of blood cells is tedious, prone to human errors and time consuming. Hence there is a need for computer aided system which can analyze blood cells automatically at faster rate with accuracy. Such systems can be designed using image processing techniques. To automatise this process by helping pathologists, in this paper we propose the adoption of deep learning to automatically count and localise red blood cells, white blood cells and platelets by analysing blood microscopic images. We resort to the YOLO object detection model, able to look at the whole image so its predictions are informed by global context in the image. To show the effectiveness of the proposed method we evaluate our model on a dataset composed by 874 microscopic blood images, obtaining interesting results. Furthermore we show several examples related to how the proposed method can be helpful for the pathologists in their real-world work.
Research on Dynamic Access Control of Big Data Cloud Storage Based on Blockchain

Aiming at solving the inefficiency and flexibility issues of access control and preventing the security in access control contracts, a blockchain-based dynamic access control model of user roles and entity attributes based on big data cloud storage and BBAC-BDCS, is proposed. The access control strategy is stored in the blockchain through smart contracts, where agencies assign roles to relevant users. They obtain access rightsbased on credit and use metadata to query and parse data with public and private chains. The two-layer structure involves the access control calculation method of smart contracts to achieve access control of big data cloud storage resources. Experimental results show that this model makes a contribution by guaranteeing users secure, dynamic, and fine-grained access control, especiallywhen accessing data in a big data cloud storage environment.