Timeliness of Annual Financial Reporting: Evidence from the Tehran Stock Exchange

Timeliness is one of the effective factors on transparency of reporting and increases the ability of shareholders in understanding the capacity of the business unit in the production of income, cash flows and financial conditions. This paper examines factors which are related to the timeliness of annual reporting of financial statements in Tehran stock exchange companies. The good news, age, size and opinion of the independent auditor, industry, consolidate the financial reporting and the quality of the costing system during the years 2008 to 2011 have been studied. A regression test is employed in order to test hypotheses. The results show that the effect of independent auditor size and opinion, industry, consolidated financial reporting and costing system confirmed by an independent auditor has been meaningful about financial reporting timeliness. Statistical coefficients indicated that despite unqualified opinion and appropriate costing system, the reporting timeliness has improved. Nevertheless, auditing by a large auditing institution, the consolidated reporting and machinery industry has reduced. However, a significant meaningful relationship between the reporting timeliness and the good and bad news is not observed

Analog Multi-Party Computing: Locally Differential Private Protocols for Collaborative Computations

We consider a fully decentralized scenario in which no central trusted entity exists and all clients are honest-but-curious. The state-of-the-art approaches to this problem often rely on cryptographic protocols, such as multiparty computation (MPC), that require mapping real-valued data to a discrete alphabet, specifically a finite field. These approaches, however, can result in substantial accuracy losses due to computation overflows. To address this issue, we propose A-MPC, a private analog MPC protocol that performs all computations in the analog domain. We characterize the privacy of individual datasets in terms of $(\epsilon, \delta)$-local differential privacy, where the privacy of a single record in each client's dataset is guaranteed against other participants. In particular, we characterize the required noise variance in the Gaussian mechanism in terms of the required $(\epsilon,\delta)$-local differential privacy parameters by solving an optimization problem. Furthermore, compared with existing decentralized protocols, A-MPC keeps the privacy of individual datasets against the collusion of all other participants, thereby, in a notably significant improvement, increasing the maximum number of colluding clients tolerated in the protocol by a factor of three compared with the state-of-the-art collaborative learning protocols. Our experiments illustrate that the accuracy of the proposed $(\epsilon,\delta)$-locally differential private logistic regression and linear regression models trained in a fully-decentralized fashion using A-MPC closely follows that of a centralized one performed by a single trusted entity

A New Technique in saving Fingerprint with low volume by using Chaos Game and Fractal Theory

Fingerprint is one of the simplest and most reliable biometric features of human for identification. In this study by using fractal theory and by the assistance of Chaos Game a new fractal is made from fingerprint. While making the new fractal by using Chaos Game mechanism some parameters, which can be used in identification process, can be deciphered. For this purpose, a fractal is made for each fingerprint, we save 10 parameters for every fingerprint, which have necessary information for identity, as said before. So we save 10 decimal parameters with 0.02 accuracy instead of saving the picture of a fingerprint or some parts of it. Now we improve the great volume of fingerprint pictures by using this model which employs fractal for knowing the personality

Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms

We consider the low rank matrix completion problem over finite fields. This problem has been extensively studied in the domain of real/complex numbers, however, to the best of authors' knowledge, there exists merely one efficient algorithm to tackle the problem in the binary field, due to Saunderson et al. [1]. In this paper, we improve upon the theoretical guarantees for the algorithm provided in [1]. Furthermore, we formulate a new graphical model for the matrix completion problem over the finite field of size $q$, $\Bbb{F}_q$, and present a message passing (MP) based approach to solve this problem. The proposed algorithm is the first one for the considered matrix completion problem over finite fields of arbitrary size. Our proposed method has a significantly lower computational complexity, reducing it from $O(n^{2r+3})$ in [1] down to $O(n^2)$ (where, the underlying matrix has dimension $n \times n$ and $r$ denotes its rank), while also improving the performance

Structural Identifiability of Impedance Spectroscopy Fractional-Order Equivalent Circuit Models With Two Constant Phase Elements

Structural identifiability analysis of fractional-order equivalent circuit models (FO-ECMs), obtained through electrochemical impedance spectroscopy (EIS) is still a challenging problem. No peer-reviewed analytical or numerical proof does exist showing that whether impedance spectroscopy FO-ECMs are structurally identifiable or not, regardless of practical issues such as measurement noises and the selection of excitation signals. By using the coefficient mapping technique, this paper proposes novel computationally-efficient algebraic equations for the numerical structural identifiability analysis of a widely used FO-ECM with Gr\"{u}nwald-Letnikov fractional derivative approximation and two constant phase elements (CPEs) including the Warburg term. The proposed numerical structural identifiability analysis method is applied to an example from batteries, and the results are discussed. Matlab codes are available on github