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Document classification is an open problem in library, information, and computer sciences towards assigning documents to one or more classes. The interest of linguistic researchers in this domain has increased day by day due to interesting applications like language identification, readability assessment, sentiment analysis, spam filtering, etc. However, researchers focussing on natural language processing of resource-scaring languages have faced many hurdles due to the absence of benchmark datasets. Bengali is among the most-spoken resource-scaring or low-resource language. Although Bengali NLP researchers have endeavoured towards creating their own datasets, they are only useful for performance evaluation of their proposed document classification techniques only. Therefore, there is a gap in the literature on the availability of benchmark datasets. To overcome this barrier, this paper presents a benchmark dataset for Bengali document classification, which is publicly accessible and freely available. This dataset consists of a two-tier architecture, the first-tier for hard classification and the second-tier for soft classification techniques. Hard classification techniques follow supervised learning based models for the classification of documents, while on the other hand, soft classification techniques follow unsupervised learning based models for the clustering of documents. The proposed dataset consists of thirteenunique characteristics. This paper also introduces fournew feature sets to evaluate the performance of the proposed dataset, namely: location revealing factor, part of speech tagging factor, relative frequency, and prominence factor.

A digital microfluidic biochip (DMFB) with cyber-physical adaptation implements complex bio-protocols with high precision and high throughput dealing with safety-critical applications including point-of-care diagnosis, personalized medicine, and drug development. Having integrated sensors with network connectivity, a cyber-physical DMFB is undeniably susceptible to attacks. A number of leading research works are carried out to assess various attacks and their impacts. Several defense mechanisms are developed by arranging on-chip monitoring systems through deployment of checkpoints. As checkpoints are external resources imposing a cost-overhead to the system, a cost-effective detection mechanism is of utmost importance. Moreover, after detecting an attack, an efficient recovery process is imperative to execute the associated bioassay in a vulnerable environment. Here, an attack-tolerant synthesisis proposed with two-way securitythrough integrating attack-detection and attack-recovery from various Denial of Service attacks. Moreover, a selective re-synthesis approach has been introduced to allow multiple recovery steps to be executed simultaneously on the biochip. The recovery strategy is closely coupled with the detection process which makes the system adaptive towards attack-tolerance. Experimental results on several benchmarks demonstrate the efficacy of the proposed two-way attack-tolerancestrategy.

Access control is an essential security service for computing devices, applications, and information. Among the different entry-point user access controls, keystroke dynamics (KDs) has gained popularity owing to its several merits, such as low cost, ease of usage, etc. In this study, we proposed a unique distance-based anomaly detector together with an appropriate template construction method leading to more realistic and accurate results. We validated our approach with ten standard datasets and compared the performance with 50 state-of-the-art anomaly detectors. In our consideration, recent anomaly detectors have been re-evaluated in the same experimental setting for sound comparison. An analysis of variance (ANOVA) was conducted to compare the performance of our approach to those detectors in both desktops and recent smartphones. This study provides an in-depth understanding of each detector's performance which will aid in the design of efficient KD-based access control in the next generation of smart devices and applications.

The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H. sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer.

With increasing effectiveness of flow-based microfluidic biochips in the field of biochemical experiments and point-of-care diagnosis, design automation demands enormous attention to integrate the flow-layer design and control-layer design instead of considering them in isolation. Some severe design issues like valve reliability, control-channel delay, pressure skew, etc. are obvious and they must be tactfully dealt along with minimization of several cost-driving issues like schedule length, pin count, and control-channel length while ensuring planarity of the flow-layer components as well as control-layer channel routes. Hence, this paper aims to propose a unified flow-control co-design methodology considering various flow-control cost-driving issues while reducing the design cycles to obtain a low-cost and efficient platform. The performance is evaluated over several benchmark bioassays that show the efficacy of the proposed co-design method which reduces the number of required valves by 30%, control-channel length by 48.5%, and number of flow-channel crossings by 72.2% than the existing works, on an average.

The computation of natural language enables a suitable transmission through the universe by retrieving the correct sense of each word. A word may be monosemous or polysemous. The use of polysemous words in an appropriate context plays a critical role in communication. Over the last 2 decades, a significant amount of research has been done for automatically solving the correct sense of a polysemous word in the context of word sense disambiguation. A word sense disambiguation algorithm identifies the proper sense of a polysemous word by analysing the contextual data. Nevertheless, there is a gap in the contemporary literature regarding the availability of datasets in Asian languages, especially Bengali. Therefore, in this work, we have presented a dataset comprising hundred Bengali polysemous words. Each word in this dataset consists of three or four disjoint senses, and each sense comprises ten paragraphs. Each paragraph describes the sense of a particular polysemous word. We have performed statistical analysis on the basis of seven relevant and important characteristics. A general framework has also been presented for training and testing with possible guidelines for performance analysis. A baseline strategy has been introduced based on four feature sets. Finally, a set of experiments have been performed to analyse the system performance.

ABSTRACTThe involvement of Digital Microfluidic Biochips (DMFBs) in the field of disease detection, automated drug discovery, on-chip DNA (Deoxyribonucleic acid) analysis has become well-accepted during last decades. Though trustworthiness of Digital Microfluidic Biochip system plays a vital role in point-of-care diagnosis, defective electrodes are the main reasons for various misleading assay performances. It also affects overall assay completion time; even it leads to discard a chip in the worst scenario. Hence, fault detection is a crucial need. In this paper, an efficient fault detection mechanism is formulated to identify multiple numbers of defective/faulty electrodes on an m × nbiochip array, where mand ncan be of any positive number. Test droplets are strategically routed throughout an arbitrary sized electrode array and capacitive sensing circuit is deployed to take decision about existence of faults. The basic idea of the proposed technique is to identify actual positions of faulty electrodes through boundary test, row test, and diagonal test. In some complicated cases, instead of finding exact faulty location(s), through an extended testing our proposed technique identifies faulty zone(s) (it is a bounded region where one or more defective electrodes reside) to ensure safe assay performance on a chip. A detailed study evaluates the proposed approach considering different faulty environments.

In this study, we have explained a constrained travelling salesman problem (TSP) where total travelling cost must maintain a maximum level. The objective of this proposed TSP is to minimise the total travel time. In this proposed TSP we have considered a time interval dependent constraint as well. There is a time interval in which a traveller must visit a predetermined set of cities (city-set). Here, a city-set consists of a set of cities and the time interval is a time slot in his/her total time to complete the tour. The problem is solved in fuzzy random environment. The travel cost, time, and total travelling cost limit are considered as fuzzy random in nature. The proposed TSP is solved by an ant colony optimisation (ACO)-based approach. The basic ACO algorithm is improved by adopting a filtering operation. Finally, experimental results are given to illustrate the proposed approach; the computed results obtained are also highly encouraging.

Programmable Microfluidic Devices (PMDs) are revolutionizing the traditional biochemical experiments due to their flexibility of performing various functionalities on a platform without any amendment in the underlying hardware. To enhance the inherent tractability of a PMD, microchannels are frequently shared among the operations; however, this leads to cross-contamination problem due to the residues trapped on the channel. For producing safe outcomes, a flow-level synthesis minimizing contamination as well as an efficient washing strategy become immediate requisites. Moreover, each unit of wash fluid possesses a finite capacity for washing and therefore, cannot clean the entire contaminated area on a chip. Hence, capacity-aware washing scheme is the urgent requirement to fulfil the practical constraints of a flow-layer design. In this paper, a design synthesis minimizing the amount of contamination is proposed which is followed by a model for wash optimization targeting to reduce wash time and total loss of capacity, while removing all the contaminations. The efficacy of the proposed synthesis and the washing scheme has been assessed considering various baseline approaches and the existing works on the same.

Generation of all possible spanning trees of a graph is a major area of research in graph theory as the number of spanning trees of a graph increases exponentially with graph size. Several algorithms of varying efficiency have been developed since early 1960s by researchers around the globe. This article is an exhaustive literature survey on these algorithms, assuming the input to be a simple undirected connected graph of finite order, and contains detailed analysis and comparisons in both theoretical and experimental behavior of these algorithms.

Accurate reconstruction of gene regulatory networks from time-series gene expression data is a significant challenge for computer scientists. In this paper, we have proposed a genetic algorithmand flower pollination algorithmhybrid for the reverse engineering of gene regulatory network based on decoupled S-systems. Here, genetic algorithmhas been used to select the best combination of genes, which act as regulators in the network. Flower pollination algorithmhas been used to calculate the best possible S-system parameters for which the training error is minimal for those regulators. The proposed method has been tested on small-scale and medium-scale; synthetic benchmark networks and in-slico benchmark networks extracted from the GeneNetWeaver database, as well as the real-world experimental datasets of the yeastIMRA and DNA SOS repair network of Escherichia coli. The experiments reveal that the proposed hybrid methodology is capable of inferring gene regulatory networks more accurately with lesser training data and in lesser computational time compared to other existing methods.

This paper presents an advanced ant colony optimisation approach to solve sub-tour constant travelling salesman problem (SCTSP) with time limit in fuzzy environment. In SCTSP, there is a set of sub-tours (two cities for each sub-tour). A traveller must complete his/her tour with predetermined sequence of distinct sub-tours. In this proposed model, the total time of travel must not exceed a predetermined time limit, i.e., the salesman should maintain a maximum time limit to complete his/her tour. In this paper, we present a modified form of the basic ACO algorithm, obtained by introducing a new operation known as restoring operation for solving the problem. In restoring operation, the low-quality path/solution given by any ant is replaced by a nearest better solution. The proposed problem is solved by considering fuzzy travel costs and time. Fuzzy credibility measure and graded mean integration method are used to obtain optimal decision. Computational results with different datasets are presented for illustration.

In this paper, we have presented a solid travelling salesman problem (STSP) with introduction of time-window and constraints. In STSP, a traveller can avail different conveyance for travelling. Also as constraint, a risk factor is there joining a path between two cities. During a tour, the traveller must ensure that the entire risk of the tour is within a prearranged risk level. Costs, time, and risk factor of travel using different conveyances are dissimilar and interval in nature. Finding a complete tour with minimum cost is the goal of the problem, where risk and time-window constraints are also to be satisfied. The standard genetic algorithm (GA) is modified by adding three features, namely refinement, immigration, and refreshing population, and thus a modified GA is devised to solve the intended problem. The efficiency of the algorithm is tested by using some model datasets from TSPLIB and some existing benchmark test functions.

This paper presents a continuous ACO approach to solve 0-1 knapsack problem. In this method, groups of candidate values of the components are constructed, and an amount of pheromone is initialised randomly for each candidate value (a real random number between 0.1 and 0.9) in each candidate group. To solve binary knapsack problem for each object a candidate group is constructed where candidate value is either 0 or 1. Each ant selects a value from each group to construct a path or a solution. After certain number of generation, store the best solution in a temporary population. When temporary population size is equal to the number of ants, then temporary population will be considered as initial population by re-initialising fresh set of pheromone. This procedure will continue until the maximum generation (defined) is reached. In experimental section, we compare the results of standard test functions and 0-1 knapsack problem with existing literature.

An all-optical adder/subtractor (A/S) unit with the help of terahertz optical asymmetric demultiplexer (TOAD) is proposed. The all-optical A/S unit with a set of all-optical full-adders and optical exclusive-ORs (XORs), can be used to perform a fast central processor unit using optical hardware components. We try to exploit the advantages of TOAD-based optical switch to design an integrated all-optical circuit which can perform binary addition and subtraction. With computer simulation results confirming the described methods, conclusions are given.

We propose and describe an all-optical prefix tree adder with the help of a terahertz optical asymmetric demultiplexer (TOAD) using a set of optical switches. The prefix tree adder is useful in compound adder implementation. It is preferred over the ripple carry adder and the carry lookahead adder. We also describe the principle and possibilities of the all-optical prefix tree adder. The theoretical model is presented and verified through numerical simulation. The new method promises higher processing speed and accuracy. The model can be extended for studying more complex all-optical circuits of enhanced functionality in which the prefix tree adder is the basic building block.