Threshold Rule Research Articles

Models of recognition algorithms based on linear threshold functions

The problem of constructing a model of recognition algorithms in the conditions of high dimensionality of feature space is considered. To solve this problem, a new approach suggests that it takes into account the interconnectedness of the given features. The main concern of this approach is to isolate representative traits and form preferred combinations of traits. A distinctive feature of the proposed model of algorithms is to determine a suitable set of elementary threshold rules within the framework of the preferred combinations of representative features and build a proximity function based on this set of feature combinations. Scientifically, the results of this work in the aggregate represent a new solution to a scientific problem related to improving the reliability of recognition algorithms based on radial functions. The practical significance of the results lies in the fact that the developed algorithms can be applied in medical and technical diagnostics, geological forecasting, biometric identification. To test the performance of the proposed model, experimental studies were performed.

A fuzzy identification method for persistent scatterers in PSInSAR technology

Persistent Scatterer SAR Interferometry (PSInSAR) is known as one of the most effective technique for monitoring and analyzing ground deformation. It is a key step that how to identify Persistent Scatterers (PS) effectively and automatically from time-series SAR images. In the past research, one pixel will be classified to "PS" set or "no-PS" set clearly by one or more threshold rules for PS features. However, it is easy to fall into the 'either this or that' logical paradox in some cases because the covered objects in study area usually possess ambiguous boundary for interested characteristics. In this paper, a fuzzy PS concept is present and a fuzzy identification method is designed based on fuzzy set theory by taking the fuzzy characteristics of the pixels into account. Two groups of real data tests indicate that the new method can not only recognize more effective and reliable PS, but also can obtain the better quality results of selected PS, which can be used to evaluate the result of deformation and further improve the PSInSAR technology.

Skin detection and lightweight encryption for privacy protection in real-time surveillance applications

An individual's privacy is a significant concern in surveillance videos. Existing research work into the location of individuals on the basis of detecting their skin is focused either on different techniques for detecting human skin on protecting individuals from the consequences of applying such techniques. This paper considers both lines of research and proposes a hybrid scheme for human skin detection and subsequent privacy protection by utilizing color information in dynamically varying illumination and environmental conditions. For those purposes, dynamic and explicit skin-detection approaches are implemented, simultaneously considering multiple color-spaces, i.e. RGB, perceptual (HSV) and orthogonal (YCbCr) color-spaces, and then detecting the human skin by the proposed Combined Threshold Rule (CTR)-based segmentation. Comparative qualitative and quantitative detection results with an average 93.73% accuracy, imply that the proposed scheme achieves considerable accuracy without incurring a training cost. Once skin detection has been performed, the detected skin pixels (including false positives) are encrypted, when standard AES-CFB encryption of skin pixels is shown to be preferable compared to selective encryption of a whole video frame. The scheme preserves the behavior of the subjects within the video. Hence, subsequent image processing and behavior analysis, if required, can be performed by an authorized user. The experimental results are encouraging, as they show that the average encryption time is 8.268 s and the Encryption Space Ratio (ESR) is an average 7.25% for a high definition video (1280 × 720 pixels/frame). A performance comparison in terms of Correct Detection Rate (CDR) showed an average 91.5% for CTB-based segmentation compared to using only one color space for segmentation, such as using RGB with 85.86%, HSV with 80.93% and YCbCr with an average 84.8%, which implies that the proposed method of combining color-space skin identifications has a higher ability to detect skin accurately. Security analysis confirmed that the proposed scheme could be a suitable choice for real-time surveillance applications operating on resource-constrained devices.

Production control of failure-prone manufacturing-remanufacturing systems using mixed dedicated and shared facilities

This paper addresses a control problem within hybrid manufacturing-remanufacturing systems (HMRS) evolving in a dynamic and stochastic environment. Unlike previous works, it contributes to the discourse by considering mixed dedicated and shared facilities (MDSF). It is motivated by the need to integrate the remanufacturing flow while increasing the capacity of the system to react to uncertainties (random failures and repairs of facilities, rates of returned products, etc.). The problem is to determine the production rates for the manufacturing and remanufacturing facilities as well as the setup decisions required to switch the remanufacturing facility from a mode supplied by returned products to a mode supplied by raw materials. The objective is to minimize the expected total cost. A dynamic stochastic model is thus proposed, and the developed optimality conditions are solved numerically. The obtained control policy combines hedging point policies and a stock-based setup strategy, under which switching is based on the finished products stock, according to threshold rules. Such control policy is then analyzed and compared to the literature by considering three different control policies. Two of those were developed in other contexts using only dedicated facilities or shared facilities in a dynamic and stochastic environment while the other is adapted to ours. For an effective comparison study, a simulation-based optimization approach is adopted and implemented for the problem under study. Extensive numerical results suggest that the use of MDSF managed by the proposed control policy gives the best performance in terms of costs.

Supporting Technology Analysis and Attitude Control Principle of MSJ5.8/1.6D Shaft Boring Machine

A double-layer fixed support structure, with four supporting boots on each platform is used in MSJ5.8/1.6D shaft boring machine to adapt to weak or fractured stratum. Under the stratigraphic conditions, 8 types of support forms are unbalanced. According to this condition, we analysed the specific construction conditions of MSJ5.8/1.6D shaft boring machine in this paper. Then under these construction conditions, we used the finite element simulation to obtain the stress distribution of the supporting parts of MSJ5.8/1.6D shaft boring machine for propulsion unit is optimized. The stress concentration phenomenon appears in the inside of the arc plate of the upper and lower parts of the frame structure. According to the construction technology of the MSJ5.8/1.6D shaft boring machine, the attitude measurement, the attitude adjustment and attitude adjustment threshold rules of the machine are determined.

Margin Trading and Comovement During Crises*

Abstract We exploit threshold rules governing margin trading eligibility in India to identify a causal link between margin trading and increased comovement during crises. Margin trading explains more than one-quarter of the increase return comovement that we observe during crises. To understand the mechanisms driving this result, we evaluate the relative importance of stock connections through common brokers (who provide margin financing) versus common margin traders. We find that common brokers are most important. Margin-eligible stocks that are more connected through common brokers experience larger crisis-period increases in pairwise return comovement, especially when those brokers’ clients have experienced recent portfolio losses, when their clients have outstanding margin loans in more volatile stocks, and when the brokers are large. These findings are consistent with Brunnermeier and Pedersen (2009), in which initial shocks propagate due to the tightening of margin constraints imposed by financial intermediaries.

Classification of rolling element bearing fault using singular value

PurposeThe purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig.Design/methodology/approachAn effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal.FindingsSingular values of the statistical features can be effectively used for REB classification.Practical implicationsREB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines.Originality/valueIt is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.

Wait-and-See or Step in? Dynamics of Interventions

We study the optimal intervention policy to stop projects in a relational contract between a principal and a policymaker. The policymaker is privately informed about his ability and privately chooses how much effort to exert. Before a project is completed, the principal receives a signal about its outcome and can intervene to stop it. Intervention may prevent a bad outcome, but no intervention leads to better learning about the policymaker's ability. In the benchmarks with observable effort or observable ability, optimal intervention follows a threshold rule. With unobservable effort and ability, the optimal policy switches between intervention and no intervention.

Information Diffusion on Complex Networks: A Novel Approach Based on Topic Modeling and Pretopology Theory

In this research, we exploit a novel approach for propagation processes on a network related to textual information by using topic modeling and pretopology theory. We first introduce the textual agent’s network in which each agent represents a node which contains specific properties, particularly the agent’s interest. Agent’s interest is illustrated through the topic’s probability distribution which is estimated based on textual information using topic modeling. Based on textual agent’s network, we proposed two information diffusion models. The first model, namely Textual-Homo-IC, is an expanded model of independent cascade model in which the probability of infection is formed on homophily that is measured based on agent’s interest similarity. In addition to expressing the Textual-Homo-IC model on the static network, we also reveal it on dynamic agent’s network where there is transformation of not only the structure but also the node’s properties during the spreading process. We conducted experiments on two collected datasets from NIPS and a social network platform, Twitter, and have attained satisfactory results. On the other hand, we continue to exploit the dissemination process on a multi-relational agent’s network by integrating the pseudo-closure function from pretopology theory to the cascade model. By using pseudo-closure or stochastic pseudo-closure functions to define the set of neighbors, we can capture more complex kind of neighbors of a set. In this study, we propose the second model, namely Textual-Homo-PCM, an expanded model of pretopological cascade model, a general model for information diffusion process that can take place in more complex networks such as multi-relational networks or stochastic graphs. In Textual-Homo-PCM, pretopology theory will be applied to determine the neighborhood set on multi-relational agent’s network through pseudo-closure functions. Besides, threshold rule based on homophily will be used for activation. Experiments are implemented for simulating Textual-Homo-PCM and we obtained expected results. The work in this paper is an extended version of our paper [T. K. T. Ho, Q. V. Bui and M. Bui, Homophily independent cascade diffusion model based on textual information, in Computational Collective Intelligence, eds. N. T. Nguyen, E. Pimenidis, Z. Khan and B. Trawiski, Lecture Notes in Computer Science, Vol. 11055 (Springer International Publishing, 2018), pp. 134–145] presented in ICCCI 2018 conference.

Strengthening the Multi-Party Presidential Government in Indonesia

The Multi-Party Presidential Government in Indonesia has reached a critical point. The 2.5 percent parliamentary threshold rule in the 2009 Election was incapable to address the issue. The parliamentary threshold was raised to 3.5 percent in 2014, in the hope to reduce the number of party joining the election, but it failed to do so. There were 9 national parties participating in the 2009 election, and it will be increased to 16 in the 2019 election. Theoretically, the combination of multi-party parliament in a Presidential Government is rather strange. It is not surprising that the "conflict" between the president and parliament often occurs. There suppose to be a coalition supporting the government in parliament, but the coalition is not a firm one. The coalition did not have a significant influence in strengthening the presidential government. Therefore, this study intends to provide a complete picture of multi-party system practices while trying to provide solutions for strengthening the presidential government in Indonesia. To achieve this goal, this study uses the literature study method in collecting relevant information, using a qualitative approach. This approach is considered appropriate because multi-party phenomena and presidential systems are multidimensional. In contrast to previous research which was limited to the description and problems of multiparty systems, this research besides describing the system of government also provided moderate solutions that were considered to be in accordance with the Indonesian context. This study assumes that strengthening presidential systems can be done if the political parties are more modest. In addition, parliamentary support for the president must be optimized. The results of the study concluded that the strengthening of presidential systems must be carried out through the purification of the government system contained in the constitution, forming and strengthening the ranks of government coalitions in parliament, and carrying out a number of institutional engineering through various forms. These three things must be wrapped in a constitutional frame (amendments to the 1945 Constitution) and regulations (revisions to laws and government regulations). The amendments and revisions can be done through three corridors, namely the intra-parliamentary movement, the extra-parliamentary movement, and the referendum.

Linear Programming Based Online Policies for Real-Time Assortment of Reusable Resources

Motivated by applications of rental services in e-commerce, we consider real-time assortment of reusable products. In our model, arriving consumers with heterogeneous types choose rental products from the offered assortment, pay the rental fees, and return the product to the platform after a rental time. Consumers' types specify their choice models, rental fees and rental time distributions for various products. Our goal is to design competitive online policies against an appropriate benchmark for both the prior-free setting, in which types are arbitrary (or adversarial), and the Bayesian setting, in which types are drawn independently from known distributions. Our contribution is threefold. We first introduce offline linear programming benchmarks in both settings, that use time-varying inventory constraints to capture feasibility of a policy under rentals, and are required to satisfy these constraints only in expectation. Second, in the prior-free setting, we develop a randomized primal-dual framework based on our introduced LP to settle that inventory balancing policies of Golrezaei et al. (2014) obtain same (asymptotically optimal) competitive ratios as with non-reusable resources when product rental times are fixed over time. As a corollary, we obtain the optimal competitive ratio of (1 − 1/e) when inventories are large. We also show this family of policies are constant competitive under i.i.d. (over time) stochastic rental times. Third, we change gear to the Bayesian setting by introducing simulation-based policies that use the expected LP solution as guidance. By using primal-dual analysis, we obtain a (1/2)-competitive simple and static simulation-based policy against the expected LP for general type-varying rental time distributions and rental fees. We further show optimal (1 − 1/ (cmin+3)0.5)-competitive adaptive policies against the same benchmark when rental times are infinite, where cmin is the smallest product inventory. Our analysis extends tools in the literature on prophet inequalities to design discarding threshold rules that maintain feasibility of a simulation-based policy in the Bayesian real-time assortment. We further justify the revenue performance of our proposed policies using numerical simulations.

Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes.

Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propose a modified SpikeProp learning algorithm, which ensures better learning stability for SNNs and provides more diverse network structures and coding schemes. Specifically, we designed a spike gradient threshold rule to solve the well-known gradient exploding problem in SNN training. In addition, regulation rules on firing rates and connection weights are proposed to control the network activity during training. Based on these rules, biologically realistic features such as lateral connections, complex synaptic dynamics, and sparse activities are included in the network to facilitate neural computation. We demonstrate the versatility of this framework by implementing three well-known temporal codes for different types of cognitive tasks, namely, handwritten digit recognition, spatial coordinate transformation, and motor sequence generation. Several important features observed in experimental studies, such as selective activity, excitatory-inhibitory balance, and weak pairwise correlation, emerged in the trained model. This agreement between experimental and computational results further confirmed the importance of these features in neural function. This work provides a new framework, in which various neural behaviors can be modeled and the underlying computational mechanisms can be studied.

Outlier detection for multinomial data with a large number of categories

This paper develops an outlier detection procedure for multinomial data when the number of categories tends to infinity. Most of the outlier detection methods are based on the assumption that the observations follow multivariate normal distribution, while in many modern applications, the observations either are measured on a discrete scale or naturally have some categorical structures. For such multinomial observations, there are rather limited approaches for outlier detection. To overcome the main obstacle, the least trimmed distances estimator for multinomial data and a fast algorithm to identify the clean subset are introduced in this work. Also, a threshold rule is considered through the asymptotic distribution of measure distance to identify outliers. Furthermore, a one-step reweighting scheme is proposed to improve the efficiency of the procedure. Finally, the finite sample performance of our method is evaluated through simulations and is compared with that of available outlier detection methods.

Global Solar Radiation Forecasting Using Square Root Regularization-Based Ensemble

In recent decades, the integration of solar energy sources has gradually become the main challenge for global energy consumption. Therefore, it is essential to predict global solar radiation in an accurate and efficient way when estimating outputs of the solar system. Inaccurate predictions either cause load overestimation that results in increased cost or failure to gather adequate supplies. However, accurate forecasting is a challenging task because solar resources are intermittent and uncontrollable. To tackle this difficulty, several machine learning models have been established; however, the forecasting outcomes of these models are not sufficiently accurate. Therefore, in this study, we investigate ensemble learning with square root regularization and intelligent optimization to forecast hourly global solar radiation. The main structure of the proposed method is constructed based on ensemble learning with a random subspace (RS) method that divides the original data into several covariate subspaces. A novel covariate-selection method called square root smoothly clipped absolute deviation (SRSCAD) is proposed and is applied to each subspace with efficient extraction of relevant covariates. To combine the forecasts obtained using RS and SRSCAD, a firefly algorithm (FA) is used to estimate the weights assigned to individual forecasts. To handle the complexity of the proposed ensemble system, a simple and efficient algorithm is derived based on a thresholding rule and accelerated gradient method. To illustrate the validity and effectiveness of the proposed method, global solar radiation datasets of eight locations of Xinjiang province in China are considered. The experimental results show that the proposed RS-SRSCAD-FA achieves the best performances with a mean absolute percentage error, root-mean-square error, Theil inequality coefficient, and correlation coefficient of 0.066, 20.21 W/m2, 0.016, 3.40 s, and 0.98 in site 1, respectively. For the other seven datasets, RS-SRSCAD-FA still outperforms other approaches. Finally, a nonparametric Friedman test is applied to perform statistical comparisons of results over eight datasets.

Secure image compression approach based on fusion of 3D chaotic maps and arithmetic coding

Advances in digital image processing and communications have created a great demand for real-time secure image transmission over the networks. However, the development of effective, fast and secure dependent image compression-encryption systems are still a research problem as the intrinsic features of images. A new approach is suggested in this study for partial image encryption-compression that adopts chaotic three-dimensional (3D) cat map to de-correlate relations among pixels in conjunction with an adaptive thresholding technique that is utilised as a lossy compression technique instead of using complex quantisation techniques and also as a substitution technique to increase the security of the cipher image. The proposed scheme is based on employing both of lossless compression with encryption on the most significant part of the image after contourlet transform. However, the least significant parts are lossy compressed by employing a simple thresholding rule and arithmetic coding to render the image totally unrecognisable. Due to the weakness of 3D cat map to chosen plain text attack, the suggested scheme incorporates a mechanism to generate random key depending on the contents of the image (context key). Several experiments were done on benchmark images to insure the validity of the proposed technique.

Action Keyframe Connection Network for Temporal Action Proposal Generation

Temporal action detection is an important research topic in computer vision, of which Temporal Action Proposal (TAP) generation is a key step for finding candidate action segments. Our paper provides an action proposal generation network for temporally untrimmed videos in which a new effective and efficient deep architecture named action keyframe connection network for temporal action proposal Generation. Firstly, a two-stream network is adopted to extract frame-level features which inclued appearance feature and optical flow feature. The temporal information helps the subsequent network to determine whether a frame is the beginning or the ending of the action. Secondly, a position discrimination network is designed to infer the probability of each frame being starting frame or ending frame. The network outputs a starting probability sequence and an ending probability sequence which indicates the start of the action and the end of the action respectively. Finally, our network generates a proposal by a specific threshold rule combining the points in the starting probability sequence and the ending probability sequence. We carry out experiments on ActivityNet dataset to compare our proposed method with the state-of-the-art methods. Experiment results show that our method achieves superior performance over other methods.

Five Core Problems in the Attribution of Profits to Permanent Establishments

The rules regulating the attribution of profit to permanent establishments (PEs) are a fundamental feature of the existing international tax system yet are beset by a multitude of problems. This article identifies five “core” problems with these rules: (1) the absence of a single standard for PE profit attribution; (2) conceptual and practical problems arising in the application of the Authorised OECD Approach; (3) new pressures arising as a result of the changes made to the PE threshold rules by the BEPS Project; (4) the failure to deal with the uncertainties between the transfer pricing rules and the PE attribution rules in the aftermath of BEPS; and (5) a raft of new challenges arising from the work on the digitalization of the economy. To a large degree, these issues are symptomatic of the problems faced by the international tax system as a whole. This article analyses the source, nature and impact of these core problems, before concluding with some brief thoughts on potential solutions.

ENHANCED DYNAMIC THRESHOLD ALGORITHM OF QRS COMPLEX DETECTION

The dynamic threshold algorithm (DTA) presented by Pan Tompkins is a popular QRS detection method, and it has high sensitivity and specificity. However, the accuracy of this algorithm would be compromised if its sensitivity is increased. In this study, an enhanced dynamic threshold algorithm (EDTA) based on dynamic threshold rules is proposed, which add a compensation scheme to reduce the rate of misdetection and missed detection of R wave in low signal-to-noise ratio condition, sensitivity and detection error rate are calculated on simulated and clinical data to compare the performance between EDTA and DTA, and EDTA yields a competitive results. For the clinical data, the average accuracy rate of EDTA is 99.24%, which is higher than that of DTA at 95.98%. Further compared experiments among EDTA and the two other popular algorithms are conducted and the results of their validation over a public database are given and discussed, which prove our superiority.

When Enough is Enough: The Use of Stopping Rules in Auditor Determinations of Evidence Sufficiency

Advances in auditing technology and PCAOB reports documenting deficiencies by public accounting firms have increased recent dialog about what constitutes sufficient audit evidence, particularly given that the determination of sufficiency depends largely on professional judgment. We analyze interview responses of 15 financial statement auditors through a framework depicting four information gathering stopping rules that may be descriptive of how auditors make evidence sufficiency judgments (Browne and Pitts 2004). The analysis shows all four information stopping rules to be present in auditor’s determination of evidence sufficiency, with a preference for the mental list and magnitude threshold rules in easier sufficiency judgments and a preference for the difference threshold and representational stability rules in more difficult judgments. Our findings suggest that we have more to learn about audit evidence sufficiency determinations, particularly in the performance of difficult audit tasks and how audit procedures directing evidence collection become institutionalized in audit programs.

A modified image selective encryption-compression technique based on 3D chaotic maps and arithmetic coding

The advances in digital image processing and communications have created a great demand for real–time secure image transmission over the networks. However, the development of effective, fast and secure dependent image compression encryption systems are still a research problem as the intrinsic features of images such as bulk data capacity and high correlation among pixels hinds the use of the traditional joint encryption compression methods. A new approach is suggested in this paper for partial image encryption compression that adopts chaotic 3D cat map to de-correlate relations among pixels in conjunction with an adaptive thresholding technique that is utilized as a lossy compression technique instead of using complex quantization techniques and also as a substitution technique to increase the security of the cipher image. The proposed scheme is based on employing both of lossless compression with encryption on the most significant part of the image after contourlet transform. However the least significant parts are lossy compressed by employing a simple thresholding rule and arithmetic coding to render the image totally unrecognizable. Due to the weakness of 3D cat map to chosen plain text attack, the suggested scheme incorporates a mechanism to generate random key depending on the contents of the image (context key). Several experiments were done on benchmark images to insure the validity of the proposed technique. The compression analysis and security outcomes indicate that the suggested technique is an efficacious and safe for real time image’s applications.