An Improved Scheduling Algorithm for Traveling Tournament Problem with Maximum Trip Length Two

The Traveling Tournament Problem(TTP) is a combinatorial optimization problem where we have to give a scheduling algorithm which minimizes the total distance traveled by all the participating teams of a double round-robin tournament maintaining given constraints. Most of the instances of this problem with more than ten teams are still unsolved. By definition of the problem the number of teams participating has to be even. There are different variants of this problem depending on the constraints. In this problem, we consider the case where number of teams is a multiple of four and a team can not play more than two consecutive home or away matches. Our scheduling algorithm gives better result than the existing best result for number of teams less or equal to 32

Classification of Encryption Algorithms using Fisher’s Discriminant Analysis

Fisher’s Discriminant Analysis (FDA) is a method used in statistics and machine learning which can often lead to good classification between several populations by maximizing the separation between the populations. We will present some applications of FDA that discriminate between cipher texts in terms of a finite set of encryption algorithms. Specifically, we use ten algorithms, five each of stream and block cipher types. Our results display good classification with some of the features. In the present case we have little in terms of an existing standard; however, our limited study clearly shows that further exploration of this issue could be worthwhile

A secure end-to-end verifiable e-voting system using zero knowledge based blockchain

In this paper, we present a cryptographic technique for an authenticated, end-to-end verifiable and secret ballot election. Voters should receive assurance that their vote is cast as intended, recorded as cast and tallied as recorded. The election system as a whole should ensure that voter coercion is unlikely, even when voters are willing to be influenced. Currently, almost all verifiable e-voting systems require trusted authorities to perform the tallying process. An exception is the DRE-i and DRE-ip system. The DRE-ip system removes the requirement of tallying authorities by encrypting ballot in such a way that the election tally can be publicly verified without decrypting cast ballots. However, the DRE-ip system necessitates a secure bulletin board (BB) for storing the encrypted ballot as without it the integrity of the system may be lost and the result can be compromised without detection during the audit phase. In this paper, we have modified the DRE-ip system so that if any recorded ballot is tampered by an adversary before the tallying phase, it will be detected during the tallying phase. In addition, we have described a method using zero knowledge based public blockchain to store these ballots so that it remains tamper proof. To the best of our knowledge, it is the first end-to-end verifiable Direct-recording electronic (DRE) based e-voting system using blockchain. In our case, we assume that the bulletin board is insecure and an adversary has read and write access to the bulletin board. We have also added a secure biometric with government provided identity card based authentication mechanism for voter authentication. The proposed system is able to encrypt ballot in such a way that the election tally can be publicly verified without decrypting cast ballots maintaining end-to-end verifiability and without requiring the secure bulletin board

IoT-Applicable Generalized Frameproof Combinatorial Designs

Secret sharing schemes are widely used to protect data by breaking the secret into pieces and sharing them amongst various members of a party. In this paper, our objective is to produce a repairable ramp scheme that allows for the retrieval of a share through a collection of members in the event of its loss. Repairable Threshold Schemes (RTSs) can be used in cloud storage and General Data Protection Regulation (GDPR) protocols. Secure and energy-efficient data transfer in sensor-based IoTs is built using ramp-type schemes. Protecting personal privacy and reinforcing the security of electronic identification (eID) cards can be achieved using similar schemes. Desmedt et al. introduced the concept of frameproofness in 2021, which motivated us to further improve our construction with respect to this framework. We introduce a graph theoretic approach to the design for a well-rounded and easy presentation of the idea and clarity of our results. We also highlight the importance of secret sharing schemes for IoT applications, as they distribute the secret amongst several devices. Secret sharing schemes offer superior security in lightweight IoT compared to symmetric key encryption or AE schemes because they do not disclose the entire secret to a single device, but rather distribute it among several devices

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Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data

Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world