Small Number Of Transactions Research Articles

Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk

Evidence suggests that limiting access to firearms among individuals at high risk of suicide can be an effective means of suicide prevention, yet accurately identifying those at risk to intervene remains a key challenge. Firearm purchasing records may offer a large-scale and objective data source for the development of tools to predict firearm suicide risk. To test whether a statewide database of handgun transaction records, coupled with machine learning techniques, can be used to forecast firearm suicide risk. This prognostic study used the California database of 4 976 391 handgun transaction records from 1 951 006 individuals from January 1, 1996, to October 6, 2015. Transaction-level random forest classification was implemented to predict firearm suicide risk, and the relative predictive power of features in the algorithm was estimated via permutation importance. Analyses were performed from December 1, 2020, to May 19, 2022. The main outcome was firearm suicide within 1 year of a firearm transaction, derived from California death records (1996-2016). With the use of California's Dealer's Records of Sale (1996-2015), 41 handgun, transaction, purchaser, and community-level predictor variables were generated. There are a total of 4 976 391 transactions in the California's Dealer's Record of Sale database representing 1 951 006 individuals (1 525 754 men [78.2% of individuals]; mean [SD] age, 43.4 [13.9] years). Firearm suicide within 1 year occurred in 0.07% of handgun transactions (3278 transactions among 2614 individuals). A total of 38.6% of observed firearm suicides were among transactions classified in the highest-risk ventile (379 of 983 transactions), with 95% specificity. Among the small number of transactions with a random forest score above 0.95, more than two-thirds (24 of 35 [68.6%]) were associated with a purchaser who died by firearm suicide within 1 year. Important features included known risk factors, such as older age at first purchase, and previously unreported predictors, including distance to firearms dealer and month of purchase. This prognostic study presented the first large-scale machine learning analysis of individual-level handgun transaction records. The results suggested the potential utility of such records in identifying high-risk individuals to aid suicide prevention efforts. It also identified handgun, individual, and community characteristics that have strong predictive relationships with firearm suicide and may warrant further study.

ОБҐРУНТУВАННЯ РИНКОВОГО ПІДХОДУ ДО УПРАВЛІННЯ СЛАБОУРБАНІЗОВАНИМИ ТЕРИТОРІЯМИ В УМОВАХ ТРАНСФОРМАЦІЇ НАЦІОНАЛЬНОГО ГОСПОДАРСТВА

The article analyzes the factors that affect the management effectiveness of low-urbanized areas. A characteristic feature of the current state of socio-economic relations is the spread of competition in all spheres of human activity. Such megatrends as the processes of globalization and deregulation of markets, the integration of economies of individual countries only contribute to strengthening these trends. Its conditions are also integral in the case of various non-profit entities, public associations, political parties, as well as territorial entities of different levels: countries, regions, and territorial communities. Since, in the situation of transition from the "seller's market" to the "buyer's market", the answer to the challenges of competition from commercial entities was their focus on the concept of marketing, it is logical to extend it to all areas of human activity in which there is competition. In particular, today the market approach to management in the banking sector, energy, non-profit organizations, etc. is becoming increasingly important. In addition, the market approach is relevant in the management at the state level and at the level of individual territorial communities, which also compete with each other – for limited "resources" of investment and no less limited tourist "resources". Based on the preliminary analysis, the methods of territorial community management are singled out: budget-oriented management; cadence management; management based on addressing identified long-term needs; strategic approach. It is determined that now the market of low-urbanized territories and their products are at the initial stages of its development. This state of affairs is a characteristic feature of this market and is primarily due to the objective imperfection of information movement and the small number of transactions, their atypical nature, which is manifested in the gap between the costs incurred to promote and determine their effectiveness. These problems are even more obvious at home. It was the motivation to justify market approaches to the management of sparsely urbanized areas in Ukraine.

SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes, i.e., either based on the operation codes (opcodes) of the smart contract binaries or the transaction patterns of addresses. However, state-of-the-art approaches face several major limitations, including lacking interpretability and high false positive rates. Moreover, machine-learning based methods are susceptible to evasion techniques, and transaction-based techniques do not work on smart contracts that have a small number of transactions. These limitations render existing methods for detecting Ponzi schemes ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, by strictly following the definition of Ponzi schemes, we propose a heuristic-guided symbolic execution technique to first generate the semantic information for each feasible path in smart contracts and then identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.

Combination of individual and group patterns for time-sensitive purchase recommendation

Due to the availability of large amounts of data, recommender systems have quickly gained popularity in the banking sphere. However, time-sensitive recommender systems, which take into account the temporal behavior and the recurrent activities of users to predict the expected time and category of next purchase, are still an active field of research. Many researchers tend to use population-level features or their low-rank approximations because the client’s purchase history is very sparse with few observations for some time intervals and product categories. But such approaches inevitably lead to a loss of accuracy. In this paper, we present a generative model of client spending based on the temporal point processes framework. The model is built in the way, to bring more individuality for the clients’ purchase behavior which takes into account individual purchase histories of clients. We also tackle the problem of poor statistics for people with a low transactional activity using effective intensity function parameterizations, and several other techniques such as smoothing daily intensity levels and taking into account population-level purchase rates for clients with a small number of transactions. The model is highly interpretable, and its training time scales linearly to millions of transactions and cubically to hundreds of thousands of users. Different temporal-process models were tested, and our model with all the incorporated modifications has shown the best results in terms of both error of time prediction and the accuracy of category prediction.

Towards Increasing Residential Market Transparency: Mapping Local Housing Prices and Dynamics

This article attempts to use spatial maps as a way of presenting additional information about the phenomena occurring in the housing market. In our opinion, spatial maps may facilitate understanding and provide more detailed information, which undoubtedly should increase the transparency of the housing market. The study used 12,219 transactions of apartments in Poznań in the years 2013–2017. General principles of price visualization activity and housing market dynamics were established in this study. The map of prices may reflect the location values determined by the quality of the urban infrastructure, distance from specific locations, and environmental factors. Market activity maps reveal areas where the market is dynamically developing, while information on trends in the number of transactions and price changes may demonstrate the growing or declining attractiveness of areas. The research is based on a model of hedonic regression in the form of ordinary least squares (OLS), quantile regression (QR), and geographically weighted regression (GWR). The maps presented should increase the transparency of the residential market (e.g., by providing more detailed information). However, one should bear in mind the limitations in the use of these methods resulting from a small number of transactions in a thin market.

Improved Apriori Algorithm for Mining Association Rules

Apriori algorithm as a classic algorithm in data mining, it has a good performance in a small number of transactions in the database which has been widely used by people, but the algorithm has two inherent flaws, affect the efficiency of Apriori algorithm mining information in large database. Aiming at the Bottleneck Problem Restricting the Efficiency of Apriori Algorithm, in this paper, two inherent flaws of Apriori algorithm are improved, in order to improve Apriori algorithm in large database mining efficiency. The algorithm reduces the number of connections and the number of database scan to shorten the database scan time. Experimental results show, the optimized Apriori algorithm has some improvements in operation efficiency.

Changing Forms of Corruption in India

AbstractThis article has four important goals. First, I want to ask why liberalization and market-friendly reforms failed to curb corruption in India. Indeed, confounding the predictions of most proponents of reform, corruption seems to have increased after the neoliberal reforms of 1991. Second, I aim to develop a typology in which the importance of particular sectors to corrupt practices is highlighted and explained. Third, I point out that India has failed to make the ‘transition’ historically seen in low-income countries as they develop. Nation-states have in the past moved from a system of vertical corruption—marked by the extraction of small sums from a large number of transactions with citizens in everyday life—to a system of horizontal corruption, in which governmental elites extract large sums in a small number of transactions from corporate and commercial bodies. Finally, I argue that anti-corruption movements cannot be understood without paying attention to the affective and emotional ties that bind citizens to the state. We have to take account of contradictory feelings about the state: cynicism about the state and popular anger against corruption on one side, and an attachment to popular sovereignty and patronage on the other. These contradictory sentiments will better enable us to understand the conjunctures that lead to effective institutional change.

The Hedonic Housing Price Index For Poland Modelling on NBP BaRN Data

The literature abounds with arguments regarding the relevance of reliable measurement of home prices. The correct measurement of home price growth is very important due to decisions taken by various economic agents and the impact of these decisions on the current situation in the domestic economy and its development. The construction of a reliable home price index is a challenging task mainly due to the nature of the housing market and housing itself and availability of relevant data sources concerning this market. The most important issues which have to be addressed by a researcher involved in the measurement of home price trends, are mainly connected with the following two features of a good called housing and the housing market itself: heterogeneous nature of a housing good (there are no two identical housing goods) and rare turnover in housing stock (that is, relatively small number of transactions as compared to the housing stock volume). This paper presents solutions to the abovementioned problems. Especially, the paper shows the construction of the hedonic house price index for Poland and describes in detail ts modeling on NBP BaRN data.

CPM Algorithm for Mining Association Rules from Databases of Engineering Design Instances

In this paper, we propose an algorithm for mining associating rules based on transaction combination, attribute combination, pattern comparison and comparative pattern mapping (CPM), aiming at the databases with a large number of attributes but a small number of transactions which are common in engineering design. There are four main steps in the CPM algorithm. First, it scans and expands the database and converts it into a Boolean matrix. Second, it compresses the Boolean matrix to construct a transaction combination matrix (TCM) and an attribute combination matrix (ACM) for further calculation. Third, it generates comparative patterns by comparing every transaction with other transactions in the ACM and stores the comparative patterns in a CP-tree. Finally, it obtains all frequent closed itemsets by picking up the frequent nodes of each branch in the last layer of the CP-tree and eliminating false frequent closed nodes, and all frequent itemsets are found by disassembling the frequent closed itemsets. By comparing CPM with Apriori, FP-Growth, nonordfp and FPgrowth, it is indicated that CPM has a satisfactory performance in mining associating rules from databases with multiple attributes, especially for associating rules with low minimum support degree.

Closed Sequential Pattern Mining in High Dimensional Sequences

High dimensional sequences, such as biological sequences, are characterized by a small number of transactions, and a large number of items in each transaction. Mining sequential patterns in the sequences need to consider different forms of patterns, such as contiguous patterns, local patterns which appear more than one time in a special sequence, and so on. Mining closed patterns might lead to not only a more compact complete result set, but also better efficiency. In this paper, a novel algorithm based on BIDE (BI-Directional Extension) and multi-support is presented for high dimensional sequences specifically. It mainly mines three types of closed sequential patterns which are sequential patterns, local sequential patterns and total sequential patterns. Thorough experimental performances on biological sequences have demonstrated that the proposed algorithm could reduce memory consumption and generate more compact patterns.

Efficient colossal pattern mining in high dimensional datasets

‘Frequent pattern mining’ is considered as an important data mining problem which has been extensively studied over the last decade. There are a large number of algorithms which have been developed for frequent pattern mining on a traditional commercial dataset which usually contains a huge number of transactions besides a small number of items in each transaction. The advent of bioinformatics contributed to the development of new form of datasets – called high dimensional – which are characterized by small number of transactions and large number of items in each transaction. The running time of traditional algorithms increases exponentially with increasing average transaction length, thus these algorithms cannot be suitable for the high dimensional datasets. On the other hand, the mining algorithms on high dimensional datasets create a very large output set as result which includes small and mid-size frequent patterns which do not bear any useful information for scientists. Colossal pattern mining is described as a solution to reduce the amount of output set of mining patterns. Due to ignoring the mining of the small and mid-sized patterns, mining process speed is increased in colossal patterns mining algorithms. Therefore, only very large (colossal) patterns are extracted and mined in this approach. In this paper we represent an efficient vertical bottom up method to conduct mining of frequent colossal patterns in high dimensional datasets. In our algorithm, we use a bit matrix to compress the dataset and make it easy to use in mining process. Our experimental result shows that our algorithm attains very good mining efficiencies on various input datasets. Furthermore, our performance study shows that this algorithm outperforms substantially the best former algorithms.

Using the Structure of Prelarge Trees to Incrementally Mine Frequent Itemsets

The frequent pattern tree (FP-tree. is an efficient data structure for association-rule mining without generation of candidate itemsets. It was used to compress a database into a tree structure which stored only large items. It, however, needed to process all transactions in a batch way. In the past, we proposed a Fast Updated FP-tree (FUFP-tree. structure to efficiently handle new transactions and to make the tree update process become easier. In this paper, we propose the structure of prelarge trees to incrementally mine association rules based on the concept of pre-large itemsets. Due to the properties of pre-large concepts, the proposed approach does not need to rescan the original database until a number of new transactions have been inserted. The proposed approach can thus achieve a good execution time for tree construction especially when a small number of transactions are inserted each time. Experimental results also show that the proposed approach has a good performance for incrementally handling new transactions.

Exploring the Feasibility of Reputation Systems under Churn

This letter examines how high rates of churn - the continuous process of node arrival and departure - affect rating mechanisms for peer-to-peer (P2P) networks. In particular, short peer lifetimes mean reputations are often generated from a small number of transactions, and thus are few reliable. To understand this relationship, this letter introduces an analytical model which determines the optimal transaction rate and the expected time to produce a reliable reputation, under both exponential and Pareto lifetime distributions.

Tourism agents' attitudes on internet adoption: an analysis from Greece

PurposeThe purpose of this paper is to adopt a total view on the issue of usage and attitude of travel agents towards internet technology. Many aspects are being investigated, such as agents' attitudes towards internet applications, the current state of internet use, the relative maturity of the web sites, and internet marketing by Greek tourist agencies.Design/methodology/approachAn online survey was conducted. Greek Travel Pages (GTP), notified travel agents via e‐mail sent about the online questionnaire web site. Travel agents willing to participate visited a tailor made web site replied to the questionnaire and the data were recorded to a database. A total of 117 questionnaires were answered. The questionnaire used included five sections and questions were adopted from previous researches.FindingsFacilitation of global marketing and completion of online services are the most important reasons for using internet practices according to tourism agents. Travel agencies generally do not use internet practices effectively as a relatively small number of transactions are completed on line. The web pages are merely used as another means to exhibit their static information. Security difficulties and the need for social interaction are the main barriers to marketing over the internetResearch limitations/implicationsThis is an exploratory study addressed to agents on‐line. Further research and studies are needed by a broader range of travel agenciesPractical implicationsProvides suggestions for good practices to tourism agents along with an understanding of their attitudes on internet useOriginality/valueEnables travel agents to recognize the pros and cons of using internet and to make technology an essential part of the company.

Vertical Equity in the Taxation of Single-Family Homes

Vertical equity in ad valorem real property taxation is the concept that all properties within a taxing jurisdiction are assessed in equal proportion to their fair market value. This study examines the assessment of single-family homes in Bellingham, Washington, utilizing a database of 1,118 home sales in the southern half of Bellingham that sold during the time period January 1990 through June 1994. The results of several empirical tests suggest the presence of regressive vertical inequity. It appears that higher market value homes are assessed at a lower proportion of their value (sales price) than less expensive homes. These results suggest that property taxation at the local level magnifies the regressivity of Washington State's already highly regressive state tax system. Why does this apparent regressive vertical inequity exist? The authors offer several possibilities including the propensity of wealthy homeowners to challenge property tax assessments, the difficulty of valuing the amenities inside an upper-end home, the heterogeneity of the upper-end home market coupled with a small number of transactions, and the lack of ample staff and other resources at the County Assessor's office.

Shop rental value change in Nottingham

Summary In a previous paper (Land Development Studies, 5, 145–177), the problems of measuring property markets were reviewed. Some of the problems discussed included the quality and quantity of data caused by the small number of transactions as a proportion of total stock of property, secrecy in the property market, and the individual nature of each property. These result in a reliance on subjective analysis of transaction data (to form value estimates) rather than the use of actual transactions to construct rental value indices. This paper sets out the collection and analysis of data and the subsequent construction of shop rental value indices for the local property market of Nottingham which is situated in the East Midlands region of the UK. The attempt to construct a long‐term data set (from 1910–86) highlights the problems of data availability and quality of information. It also highlights the problems associated with national and regional location indices which use one rent point within a centre to r...

Dribble posting a master file

Many business applications employ sequential magnetic tape rather than random-access storage techniques to process a very small number of transactions against a voluminous master file. In such situations, it may prove economical to avoid creating a new master file during each updating run by producing instead a dribble ledger containing only those master file accounts which have experienced activity.