In an open-bid auction, a bidder can know the budgets of other bidders. Thus, a sealed-bid auction that hides bidding prices is desirable. However, in previous sealed-bid auction protocols, it has been difficult to provide a “fund binding” property, which would guarantee that a bidder has funds more than or equal to the bidding price and that the funds are forcibly withdrawn when the bidder wins. Thus, such protocols are vulnerable to a false bidding. As a solution, many protocols employ a simple deposit method in which each bidder sends a deposit to a smart contract, which is greater than or equal to the bidding price, before the bidding phase. However, this deposit reveals the maximum bidding price, and it is preferable to hide this information. In this paper, we propose a sealed-bid auction protocol that provides a fund binding property. Our protocol not only hides the bidding price and a maximum bidding price, but also provides a fund binding property, simultaneously. For hiding the maximum bidding price, we pay attention to the fact that usual Ethereum transactions and transactions for sending funds to a one-time address have the same transaction structure, and it seems that they are indistinguishable. We discuss how much bidding transactions are hidden. We also employ DECO (Zhang et al., CCS 2020) that proves the validity of the data to a verifier in which the data are taken from a source without showing the data itself. Finally, we give our implementation which shows transaction fees required and compare it to a sealed-bid auction protocol employing the simple deposit method.

The use of reports in action has grown significantly in recent decades as data has become digitized. However, traditional statistical methods no longer work due to the uncontrollable expansion and complexity of raw data. Therefore, it is crucial to clean and analyze financial data using modern machine learning methods. In this study, the quarterly reports (i.e. 10Q filings) of publicly traded companies in the United States were analyzed by utilizing data mining methods. The study used 8905 quarterly reports of companies from 2019 to 2022. The proposed approach consists of two phases with a combination of three different machine learning methods. The first two methods were used to generate a dataset from the 10Q filings with extracting new features, and the last method was used for the classification problem. Doc2Vec method in Gensim framework was used to generate vectors from textual tags in 10Q filings. The generated vectors were clustered using the K-means algorithm to combine the tags according to their semantics. By this way, 94000 tags representing different financial items were reduced to 20000 clusters consisting of these tags, making the analysis more efficient and manageable. The dataset was created with the values corresponding to the tags in the clusters. In addition, PriceRank metric was added to the dataset as a class label indicating the price strength of the companies for the next financial quarter. Thus, it is aimed to determine the effect of a company's quarterly reports on the market price of the company for the next period. Finally, a Convolutional Neural Network model was utilized for the classification problem. To evaluate the results, all stages of the proposed hybrid method were compared with other machine learning techniques. This novel approach could assist investors in examining companies collectively and inferring new, significant insights. The proposed method was compared with different approaches for creating datasets by extracting new features and classification tasks, then eventually tested with different metrics. The proposed approach performed comparatively better than the other machine learning methods to predict future price strength based on past reports with an accuracy of 84% on the created 10Q filings dataset.

Recently several researchers have proposed various methods to build intelligent stock trading and portfolio management systems using rapid advancements in artificial intelligence including machine learning techniques. However, existing technical analysis-based stock price prediction studies primarily depend on price change or price-related moving average patterns, and information related to trading volume is only used as an auxiliary indicator. This study focuses on the effect of changes in trading volume on stock prices and proposes a novel method for short-term stock price predictions based on trading volume patterns. Two rapid volume decrease patterns are defined based on the combinations of multiple volume moving averages. The dataset filtered using these patterns is learned through the supervised learning of neural networks. Experimental results based on the data from Korea Composite Stock Price Index and Korean Securities Dealers Automated Quotation, show that the proposed prediction system can achieve a trading performance that significantly exceeds the market average.

This paper reports the results of a new test on what types of failure cause falls in the stock prices of telecommunication service providers. This analysis of stock price is complementary to our previous one on market share. A clear result of our new test is that the type of failure causing falls in stock price is different from the type causing decline in market share. Specifically, the previous study identified frequent failures as causes of decline in market share, while the current study indicates large failures affecting many users as causes of falls in stock price. Together, these analyses give important information for reliability designs of telecommunications networks.

In this letter, we investigate the price-based power allocation for non-orthogonal multiple access (NOMA) networks, where the base station (BS) can admit the users to transmit by pricing their power. Stackelberg game is utilized to model the pricing and power purchasing strategies between the BS and the users. Based on backward induction, the pricing problem of the BS is recast into the non-convex power allocation problem, which is equivalent to the rate allocation problem by variable replacement. Based on the equivalence problem, an optimal price-based power allocation algorithm is proposed to maximize the revenue of the BS. Simulation results show that the proposed algorithm is superior to the existing pricing algorithm in items of the revenue of BS and the number of admitted users.

Forecasting commodity futures price is a challenging task. We present an algorithm to predict the trend of commodity futures price based on a type of structuring data and back propagation neural network. The random volatility of futures can be filtered out in the structuring data. Moreover, it is not restricted by the type of futures contract. Experiments show the algorithm can achieve 80% accuracy in predicting price trends.

We discuss Nash equilibria in combinatorial auctions with item bidding. Specifically, we give a characterization for the existence of a Nash equilibrium in a combinatorial auction with item bidding when valuations by n bidders satisfy symmetric and subadditive properties. By this characterization, we can obtain an algorithm for deciding whether a Nash equilibrium exists in such a combinatorial auction.

In this study, we propose a deep neural generative model for predicting daily stock price movements given news articles. Approaches involving conventional technical analysis have been investigated to identify certain patterns in past price movements, which in turn helps to predict future price movements. However, the financial market is highly sensitive to specific events, including corporate buyouts, product releases, and the like. Therefore, recent research has focused on modeling relationships between these events that appear in the news articles and future price movements; however, a very large number of news articles are published daily, each article containing rich information, which results in overfitting to past price movements used for parameter adjustment. Given the above, we propose a model based on a generative model of news articles that includes price movement as a condition, thereby avoiding excessive overfitting thanks to the nature of the generative model. We evaluate our proposed model using historical price movements of Nikkei 225 and Standard & Poor's 500 Stock Index, confirming that our model predicts future price movements better than such conventional classifiers as support vector machines and multilayer perceptrons. Further, our proposed model extracts significant words from news articles that are directly related to future stock price movements.

This paper presents an efficient secure auction protocol for M+1st price auction. In our proposed protocol, a bidding price of a player is represented as a binary expression, while in the previous protocol it is represented as an integer. Thus, when the number of players is m and the bidding price is an integer up to p, compared to the complexity of the previous protocol which is a polynomial of m and p, the complexity of our protocol is a polynomial of m and log p. We apply the Boneh-Goh-Nissim encryption to the mix-and-match protocol to reduce the computation costs.

In this letter, the problem of how to set reserve prices so as to improve the primary user's revenue in the second price-sealed auction under the incomplete information of secondary users' private value functions is investigated. Dirichlet process is used to predict the next highest bid based on historical data of the highest bids. Before the beginning of the next auction round, the primary user can obtain a reserve price by maximizing the additional expected reward. Simulation results show that the proposed scheme can achieve an improvement of the primary user's averaged revenue compared with several counterparts.

This letter investigates price-based power control for cognitive radio networks (CRNs) with interference cancellation. The base station (BS) of the primary users (PUs) will admit secondary users (SUs) to access by pricing their interference power under the interference power constraint (IPC). We give the optimal price for BS to maximize its revenue and the optimal interference cancellation order to minimize the total transmit power of SUs. Simulation results show the effectiveness of the proposed pricing scheme.

This paper presents efficient secure auction protocols for first price auction and second price auction. Previous auction protocols are based on a generally secure multi-party protocol called mix-and-match protocol based on plaintext equality tests. However, the time complexity of the plaintext equality tests is large, although the mix-and-match protocol can securely calculate any logical circuits. The proposed protocols reduce the number of times the plaintext equality tests is used by replacing them with the Boneh-Goh-Nissim encryption, which enables calculation of 2-DNF of encrypted data.

The accurate and fast estimation of link price is the key component of network-based congestion control schemes. A fast estimation method A2DPM is presented. Multiple hashes on IP identifier of packet header are adopted to accelerate the side information transmission, so accurate estimation of maximum price on the flow forwarding path can be realized after the receipt of just a few probe packets, and the sender is capable of reacting to congestion more quickly, making it suitable to meet the demands of dynamic networks.

When a store sells items to customers, the store wishes to determine the prices of the items to maximize its profit. Intuitively, if the store sells the items with low (resp. high) prices, the customers buy more (resp. less) items, which provides less profit to the store. So it would be hard for the store to decide the prices of items. Assume that the store has a set V of n items and there is a set E of m customers who wish to buy those items, and also assume that each item i ∈ V has the production cost di and each customer ej ∈ E has the valuation vj on the bundle ej ⊆ V of items. When the store sells an item i ∈ V at the price ri, the profit for the item i is pi=ri-di. The goal of the store is to decide the price of each item to maximize its total profit. We refer to this maximization problem as the item pricing problem. In most of the previous works, the item pricing problem was considered under the assumption that pi ≥ 0 for each i ∈ V, however, Balcan, et al. [In Proc. of WINE, LNCS 4858, 2007] introduced the notion of "loss-leader," and showed that the seller can get more total profit in the case that pi < 0 is allowed than in the case that pi < 0 is not allowed. In this paper, we derive approximation preserving reductions among several item pricing problems and show that all of them have algorithms with good approximation ratio.

We propose a multiparty protocol for comparator networks which are used to compute various functions in statistical analysis, such as the maximum, minimum, median, and quartiles, for example, through sorting and searching. In the protocol, all values which are inputted to a comparator network and all intermediate outputs are kept secret assuming the presence of an honest majority. We also introduce an application of the protocol for a secure (M+1)-st price auction.

In wireless ad hoc networks, network services are provided through the cooperation of all nodes. Albeit that good teamwork could smoothly run a mobile network, selfish node behaviors would probably cause it to break down. Some examples of these selfish node behaviors would include, "listening only" for saving energy or "receiving the valuable" without forwarding the packets to others. To cope with this problem, we propose PDM, a price-demand function based pricing model, to restrains the selfish behaviors of mobile nodes. PDM is based on the packet sending requirements of the source nodes and the forwarding cost of relay nods. Using this pricing methodology, the packet forwarding activities will be profitable for the relay node and further stimulate cooperation in the network. In particular, the new model enjoys the merit of giving relay nodes no reason to dishonestly report their forwarding costs, because an honest cost claim has proven to be an optimal strategy for relay nodes. Furthermore, our new model uses a price-demand function to reflect the relationship between the service demand of the source nodes and the service supply of the relay nodes. As a consequence, our approach reduces the source nodes' payments to send packets, and at the same time guarantees that the packets sent by the source nodes are delivered to the destination.

Differentiated-services model has been prevailed as a scalable solution to provide quality of service over the Internet. Many researches have been focused on per hop behavior or a single domain behavior to enhance quality of service. Thus, there are still difficulties in providing the end-to-end guaranteed service when the path between sender and receiver includes multiple domains. Furthermore differentiated-services model mainly considers quality of service for traffic aggregates due to the scalability, and the quality of service state may be time varying according to the network conditions in the case of relative service model, which make the problem more challenging to guarantee the end-to-end quality-of-service. In this paper, we study class mapping mechanisms along the path to provide the end-to-end guaranteed quality of service with the minimum networking price over multiple differentiated-services domains. The proposed mechanism includes an effective implementation of relative differentiated-services model, quality of service advertising mechanism and class selecting mechanisms. Finally, the experimental results are provided to show the performance of the proposed algorithm.

Future IP networks will provide multi-class-services that have multiple levels of Quality of Services (QoS) at different prices. One of the issues for the network service provider (NSP) will be how to profit by providing them. This paper proposes a scheme that maximizes the profit of the NSP by controlling the service-list under the constraint of the available network resources. We introduce a model in which the users' selection from among the multiple classes is influenced not just by the price and QoS of one class, but the prices and QoS levels of all classes. In short, the user's selection involves a balance between the price and QoS levels of all classes. To model the users' class choice, we adopt discrete choice analysis; it can estimate the model parameters such that the model fits actual choice data. This paper proposes a functional framework that consists of User Choice Model Function, Original Demand Forecast Function, and Service-list Determination Function. The proposed model has the advantage of following actual changes adaptively. Effectiveness of the proposed scheme is evaluated by computer simulation for a multiple class service; even if the real parameters are changed, the proposal can follow the change and provide the optimal service-list that maximizes profit adaptively.

This paper provides a M+1-st price auction scheme using homomorphic encryption and the mix and match technique; it offers secrecy of bidding price and public verifiability. Our scheme has low round communication complexity: 1 round from each bidder to auctioneer in bidding and log p rounds from auctioneer to trusted authority in opening when prices are selected from p prefixed choices.

In this letter, we propose a grammatical structure of the finite state network (FSN) for the recognition of Korean price sentences. It is implemented by arranging the nodes and the arcs of the FSN. Two kinds of grammatical structure are presented. Both are designed according to the grammar constraints of Korean price sentences. The grammar constraints of Korean price sentences are similar to those of English price sentences; the unit is placed after the digit; several digits form a basic group; the basic group appears recursively followed by meta-units, etc. Speaker-independent recognition experiments were conducted, and the results of the FSN's with proposed grammatical structures were compared with those of the FSN without grammatical structure.