Reviewerly/exHarmony huggingface dataset:
Review of Data Science and AI in Finance

According to Business Insider, computerized reasoning software would save banks and financial institutions $447 billion by 2023. According to Forbes, 70&#x0025; of financial firms are using AI to predict revenue events, adjust financial evaluations, and detect extortion. According to Forbes, 54 percent of financial aid organizations with 5,000 or more employees use artificial intelligence. The recent FinTech surge shows the years of important breakthroughs and potentials of AI in creating a finance and society system that makes sense. In AI, data technology, economics, finance, and other relevant research processes and commercial domain names, AI-empowered economy and finance has been a suggested and area that is becoming increasingly vital. The new-generation AI, Data Machine, and technology learning, which are fundamentally and seamlessly transforming the eyesight, Missions, Objectives, paradigms, theories, approaches, tools, and social areas of economics and driving and finance smart FinTech, are adding to this long history of finance. AI continues to authorize more complicated mechanisms, such as economic financial products, replicas, facilities, organizations, and applications that are more complex than customized and enhanced, safe and fresher normal and other mechanisms. This review highlights the long-term study of AI in finance and focuses on establishing a complete, multidimensional, and problem-driven economic-financial research linked with the roles and research instructions of both classic and modern AI in finance.
An Electromagnetic Covert Channel based on Neural Network Architecture

Outsourcing the design of deep neural networks may incur cybersecurity threats from the hostile designers. This paper studies a new covert channel attack that leaks the inference results over the air through a hostile design of the neural network architecture and the computing device's electromagnetic radiation when executing the neural network. Specifically, the hostile neural network consists of a series of binary models that correspond to all classes and are executed sequentially. The execution terminates once any binary model given the input is positive about its responsible class. We describe an approach to generate such binary models by pruning a benign neural network that is trained using the standard method to deal with all the classes. Compared with the benign neural network, the hostile one has similar memory usage and negligible classification accuracy drop, but distinct inference times for the samples of different classes. As a result, the hostile neural network's classification result can be eavesdropped by measuring the duration of the electromagnetic radiation emanated from the computing device. As neural networks are stored and transmitted as data files, this covert channel attack is more stealthy to the anti-malware than other code-based attacks. We implement the described attack on two edge computing devices that execute the hostile neural network on CPU or GPU. Evaluation shows 100% empirical accuracy in eavesdropping the inference results.
Construction and optimization of medical question answering system based on Knowledge Graph

Medical question answering system is one of the significant measures to effectively solve common sense queries related to diseases. Aiming at the loose structure, low efficiency, and weak linking of domain knowledge in the current traditional medical model field, this paper constructs a Medical question answering system based on knowledge graph(KG). We achieve the goal of improving classification performance through three aspects, including: (i) creation of a medical KG by building a dictionary and generating entities and relationships, (ii) design of the Bert+TextCnn model combined with the k-nrm text matching algorithm to calculate the question template that is closest to the user&#x0027;s question in the system, and (iii) construct a cypher sentence based on the medical entity and the question template. The experimental results demonstrate that the medical KG question answering system based on intent recognition and k-nrm text matching has a higher accuracy rate than the traditional question answering system, and has a extraordinary question answering ability.
Delica: Decentralized Lightweight Collective Attestation for Disruptive IoT Networks

The recent advance of the Internet of Things and autonomous systems brings massive security threats to the network of low-end embedded devices. Remote attestation is a hardware-assisted technique to verify the integrity and trustworthiness of software on remote devices. The recently proposed collective remote attestations have focused on attesting to the highly dynamic and disruptive device networks. However, they are generally inefficient due to the homogeneous node setting for the robustness of attestation reports aggregation. In this work, we propose Delica, an efficient and robust collective attestation framework for dynamic and disruptive networks. We differentiate the role of provers and aggregators to limit the redundant communications and attestation evidence aggregations for efficiency. Delica is capable of mitigating DoS attacks and detecting physical and black-hole attacks. The experimental results and analysis show that Delica can greatly reduce the per-node computational cost and reduce the network attestation cost by over 75% compared with the state-of-the-art approaches on disruptive networks.
GNAS: Generalized Neural Attentive Similarity Model for Recommendation

Item-based collaborative filtering captures user preferences by using the historical interactive items of users and generates a personalized recommendation list. Recently, the state-of-the-art collaborative filtering method is neural attentive item similarity model (NAIS). However, the exploration of ensemble models has obtained less investigation. In this work, we present a generalized neural attentive similarity (GNAS) model to perform recommendation based on implicit feedback. The key of our design is to integrate the generalized matrix factorization model and neural attentive item similarity model, which allows the model to have both the memorization ability from matrix factorization model and the generalization ability from neural model. To improve the user interest expression ability of the model, we add an ensemble kernel to the attention mechanism, learning the attention weight of historical items that users have interacted with before. Experimental results show that the recommended accuracy of our model is improved compared to the latest recommended models. In addition, GNAS also has the advantage of less time consumption and interpretability of recommendation.
Target Detection of Engineering Vehicles Based on Co-learning Labels

Vehicles detection from the transmission environment is one of the main challenges causing by various scenarios. Many techniques have been used to detect engineering vehicles. In this paper, we describe the co-learning mechanism to detect the engineering vehicles, which can effectively use for the external invasion risk early warning. This paper studies the problem of label distribution from the perspective of distillation technology, which we call label distribution distillation (Label Assignment Distillation). About Knowledge Distillation (Knowledge Distillation), a common approach is Teacher Model. For target detection, classification and positioning tasks are usually extracted independently, whether or not a priori value is used. On the contrary, we propose classification and positioning prediction and prior knowledge of middle school teachers are incorporated into the loss function. The final performance is validated in our collected dataset to prove the superiority.
Privacy Preservation Techniques for Social Networks Users

On the internet, Online Social Networks (OSNs) have become a popular service. It&#x0027;s completely changing the way people engage with one another. The latent information in social networks data presents interesting data mining and information extraction challenges. As a result, it is necessary to publish data from social media platforms, but this has resulted in the possibility of sensitive and confidential information about individuals being leaked. This necessitates the protection of privacy prior to the dissemination of data from such networks. In recent years, protecting the privacy of data from Online Social Networks has become a top priority. Several academic research have presented ways for protecting micro-data and social network data privacy. The strategies that secure data privacy are the focus of the literature review paper. Its objective is to help people realize the implications of maintaining their privacy. Then, as a protype model an experiment has been conducted by using ARX tool to preserve the privacy of users by considering the data of real-time social network CORA. It has been analyzed that K-anonymity is able to preserve the privacy of users in CORA dataset. The risk of compromising the privacy has been reduced by a factor of 1.6.
Visually Compatible Home Decor Recommendations Using Object Detection and Product Matching

Automatically recommending visually compatible products to Home Decor shoppers is a challenging task for ecommerce companies in the home improvement domain. However, few satisfactory solutions have been proposed to address this problem. In this paper, we propose a novel approach that uses a room scene image as the primary data source to generate visually compatible product recommendations. More specifically, we first detect products shown in the room scene image. Then, we use image retrieval techniques (e.g. color matching and triplet contrastive learning) to find the most similar products, if not the same, from the catalog. The system is designed to scale up to millions of products. To evaluate the performance of the proposed approach under various scenarios and use cases, we test it on several decor datasets, e.g. the catalog of a large home improvement retailer, a sizable public decor dataset, and customer-generated images posted on product reviews. We compare the approaches and determine that the triplet contrastive learning outperforms color matching for image retrieval. When tested live on the retailer&#x2019;s website, this new experience increases by 1.5% the user engagement and by 4.0% the Average Order Value (AOV) of transactions.
BSDP: A Novel Balanced Spark Data Partitioner

As a memory-based distributed big data computing framework, Spark has been widely used in big data processing systems. However, during the execution of Spark, due to the imbalance of input data distribution and the shortage of existing data partitioners in Spark, it is easy to cause partition skew problem and reduce the execution efficiency of Spark. Aiming at this problem, this paper proposes a balanced Spark data partitioner called BSDP (Balanced Spark Data Partitioner). By deeply analyzing the partitioning characteristics of Shuffle intermediate data, the Spark Shuffle intermediate data equalization partitioning model is established. The model aims to minimize the partition skew and find a Shuffle intermediate data equalization partitioning strategy. Based on the model, this paper designs and implements a data equalization partitioning algorithm of BSDP. This algorithm transforms the Shuffle intermediate data equalization partitioning problem into a classic List-Scheduling task scheduling problem, effectively realizes the balanced partitioning of Shuffle intermediate data. The experiment verifies that the BSDP can effectively realize the balanced partitioning of the Shuffle intermediate data and improve the execution efficiency of Spark.
Collaborative Random Forests Learning

Collaborative learning is an emerging field of machine learning. In this framework, multiple learning algorithms try to learn from a distributed database. The main idea is to improve the performance of each algorithm by exchanging information about the local structures of the data, without disclosing sensitive information. It is related to ensemble learning and federated learning. In this work, we presents a collaborative supervised learning approach. We used classification trees as a base algorithm, although the framework is generic and could be implemented with a wide class of algorithms. The process is divided into two steps: a local and a collaboration step. The local step uses a classical supervised learning algorithm, locally and independently on each dataset, which will result in obtaining multiple classifiers for each of those bases. During the collaboration step, each algorithm will collaborate with its remote counterparts. Our approach has been tested on several datasets and has shown promising results.
A Contextual and Content Features-Based Device Behavioral Fingerprinting Method in Smart Grid

Smart grid system faces various novel attacks that are caused by increasing connection to other networks. Adversaries can easily fabricate and modify packets between devices and the SCADA workstation. Thus, it is essential to monitor the behavior features of the devices. However, the conventional device behavioral fingerprint methods, such as Rule-Based, Statistical-Based, and Knowledge-Based, overly rely on feature selection, which limits its application range. This paper proposes a novel device behavioral fingerprinting method that automatically extracts high dimension features from data packets using a deep neural network. The fingerprint is a vector composed of certain contextual and content features. The content features are extracted from the grayscale images that are reshaped from the packet payload. Besides, the contextual features are extracted from the packet header sequence matrix that consists of several packets header in a sliding window w. Finally, an unencrypted public dataset collected from the Electric Power and Intelligent Control(EPIC) testbed is used to verify the classification performance. Our experimental results indicate that the optimal sliding window size and the image size are 15 and 10. Besides, compared with the method using only one kind of feature (contextual or content feature), the proposed method can identify devices more efficiently and effectively, and it can be a unique identifier to describe the network behavior of devices in the smart grid.
Load Balancing Optimization for Transformer in Distributed Environment

In recent years, the demand for artificial intelligence applications has increased dramatically. Complex models can promote machine learning to achieve excellent results, but computing efficiency has gradually reached a bottleneck. Therefore, more researchers are exploring the improvement of the efficiency of intelligent computing systems. Distributed machine learning can improve the efficiency of model training and inference, but problems such as communication delay and load imbalance between computing nodes still exist. In the multi-GPU distributed computing environment, this paper takes the vision field algorithm VIT (vision transformer) as the optimization object, which has the advantage of convenient parallel training, and proposes several related solutions. Firstly, the parameter server is used as the system logic architecture and in order to reduce the idleness of the computing devices during the training process, the device working status query mechanism is designed to realize load balancing. Secondly, combined with the pre-trained small VIT algorithm model, semi-asynchronous communication method is proposed to reduce the communication overhead of computing devices and accelerate global convergence. The results of this experiment carried out in the existing distributed environment has demonstrated that compared with the existing synchronization method, the computational efficiency has been improved well under the premise of slightly reducing the accuracy.
Clustering algorithm based on k-value adaptive neighborhood selection

In this paper, the k-value based adaptive neighborhood selection (KAN) algorithm is proposed, which can adaptively selects the number of K neighbors according to the data density. Then the algorithm calculates the high-density center, expands the clustering according to the knearest neighbor similarity, and finally obtains the clustering result. The algorithm effectively overcomes the shortage of setting the number of neighbors in advance based on KNN algorithm, and does not need prior knowledge to set the number of clusters, so as to avoid the wrong results caused by the wrong setting of important parameters. Compared with other algorithms, this algorithm shows obvious advantages.
Short Text Classification via Hypergraph Convolution Network

Effective classification of short texts is a key task in many areas, such as web search, literature recommendation and emotion analysis and so on. It can effectively help users find relevant information. With the increasing number of short texts, how to classify short texts with short content and sparse features has become a popular research theme in recent years. We propose a short text classification method via hypergraph convolution network, which can flexibly capture the complex high-order relationships of words in the short text. Specifically, firstly, the method models an individual text as hyperedges, so that all words are connected with each other. Different hyperedges are connected by shared words and form a hypergraph containing high-order correlation. Secondly, a hypergraph convolution network is performed to learn the feature representation of words and short text. Finally, the classification model is used to achieve short text classification. The results of the experiments suggest that this method can successfully classify short texts. Further experimental analysis verifies the importance of using hypergraph to model short text to alleviate the problems of sparse data and limited labeled data, and proves the rationality and effectiveness of this method.
Building Agile Workflow Microservice System for HPC Applications Based on Fast-start OSv

The advances of containers have significantly promoted the development of microservice architecture. This architecture splits a monolithic application into multiple independent components and the container orchestrator manages these components by the container in the cloud environment. The feasibility of deploying high performance computing(HPC) applications as microservices has been proven, but the existing container orchestrator incurs a large performance overhead as there is interference between different containers on the same physical host. In this paper, we design an agile workflow microservice system for HPC applications with fast-start OSv. We consider improving HPC workflow performance from two aspects: single OSv startup time optimization and workflow orchestration optimization. For single OSv startup time optimization, we design a fast-start OSv by analyzing the process of OSv startup and finding an optimization by modifying OSv source code. In this way, we get nearly 50% improvement of startup time. For workflow orchestration optimization, we propose four optimization techniques to speed up the execution of workflow by jointly considering OSv and workflow features, namely: node fusion, node merge, image preload, boot delay. Furthermore, we utilize our fast startup OSv to design an orchestration system for efficiently building an agile HPC workflow microservice by Kubevirt. Our experimental results optimization microservice system reduces the execution time by 30% compared with the original deployment with docker.
SRPeek: Super Resolution Enabled Screen Peeking via COTS Smartphone

The screens of our smartphones and laptops display our private information persistently. The term “shoulder surfing” refers to the behavior of unauthorized people peeking at our screens, easily causing severe privacy leakages. Many countermeasures have been used to prevent naked eye-based peeking by reducing the possible peeking distance. However, the risk from modern smartphones with powerful cameras is underestimated. In this paper, we propose SRPeek, a long-distance shoulder surfing attack method using smartphones. Our key observation is that although a single image captured by smartphone cameras is blurred, the attacker can leverage super-resolution (SR) techniques to recover the information from multiple blurry images. We design an end-to-end system deployed on commercial smartphones, including an innovative deep neural network (DNN) architecture, StARe, for efficient multi-image SR. We implement SRPeek in Android and conduct extensive experiments to evaluate its performance. The results demonstrate we can recognize 90% of characters at a distance of 6m with telephoto lenses and 1.8m with common lenses, calling for the vigilance of the Quietly growing shoulder surfing threat.
An Experimental Evaluation of a Function in Extremal Combinatorics

We investigate the validity of a candidate formula for an extremal function introduced by Ferrara et al. The function is defined to be the minimum degree sum such that every bigraphic pair with a given number of terms in each part and at least this degree sum is guaranteed to have a realization that contains a complete bipartite subgraph of a given size. We show that the formula is valid for some input ranges and invalid for other input ranges. We also show that the difference between the true function value and that given by the formula can be arbitrarily large and conjecture that it may be NP-hard to compute the function value.
A Two-Stage Deanonymization Attack Towards Bitcoin Hidden Service Nodes

With the increasing popularity of Bitcoin, considerable attention has been paid to its privacy and anonymity. To protect users&#x0027; privacy better from the P2P network perspective, Bitcoin encourages users to connect to the network through the anonymity network Tor. In this paper, we manage to prove that the combining of Bitcoin and Tor is easier to be exploited to deanonymize Bitcoin users. Specifically, we propose a two-stage deanonymization attack towards Bitcoin hidden service nodes, intending to reveal the IP addresses of the Bitcoin hidden services and identify their transactions. The experiments show that the node-level adversary and the gateway-level adversary both can correlate the IP address and the onion address of Bitcoin hidden services by shaping the traffic. On the premise of successful location deanonymization, the adversaries can identify the transactions from the target Bitcoin hidden service by delaying the packets. We also provide the theoretical analysis of the success probability and the countermeasures to mitigate this kind of attack.
IAP: Instant Auditing Protocol for Anonymous Payments

Blockchain(e.g., Bitcoin) has widespread use in digital currency, which is entirely public to all participants, revealing users' privacy and transaction details. Anonymous blockchains without auditing capability can offer strong privacy guarantees, they could be used by illegal activities. However, anonymous blockchains with auditing capability suffer from two limitations: (i) inefficient auditing capability; (ii) lower degree of decentralization. To address these problems, this paper presents IAP, an instant auditing protocol based on anonymous blockchain with strong anonymity guarantees, which uses audit node cluster to implement decentralized instant auditing. The experimental results show that IAP only needs 60 milliseconds to complete an audit on average with 16 audit nodes, which accounts for one thousand of a complete transaction time. IAP can still complete efficient auditing when there are more than half of the nodes are honest in the audit node cluster. Moreover, its performance is virtually unaffected by increased number of transactions.