LFSR Next Bit Prediction through Deep Learning

Pseudorandom bit sequences are generated using deterministic algorithms to simulate truly random sequences. Many cryptographic algorithms use pseudorandom sequences, and the randomness of these sequences greatly impacts the robustness of these algo-rithms. Important crypto primitive Linear Feedback Shift Register (LFSR) and its combina-tions have long been used in stream ciphers for the generation of pseudorandom bit sequences. The sequences generated by LFSR can be predicted using the traditional Ber-lekamp Massey Algorithm, which solves LFSR in 2×n number of bits, where n is the de-gree of LFSR. Many different techniques based on ML classifiers have been successful at predicting the next bit of the sequences generated by LFSR. However, the main limitation in the existing approaches is that they require a large number (as compared to the de-gree of LFSR) of bits to solve the LFSR. In this paper, we have proposed a novel Pattern Duplication technique that exponentially reduces the input bits requirement for training the ML Model. This Pattern Duplication technique generates new samples from the available data using two properties of the XOR function used in LFSRs. We have used the Deep Neural Networks (DNN) as the next bit predictor of the sequences generated by LFSR along with the Pattern Duplication technique. Due to the Pattern Duplication tech-nique, we need a very small number of input patterns for DNN. Moreover, in some cases, the DNN model managed to predict LFSRs in less than 2n bits as compared to the Ber-lekamp Massey Algorithm. However, this technique was not successful in cases where LFSRs have primitive polynomials with a higher number of tap points

Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported hallucinations within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.Comment: Preprint submitted to IoTCPS Elsevier (2023

Transformative effects of ChatGPT on modern education: emerging era of AI chatbots

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported “hallucinations” within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial

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Not AvailableThe experiment was conducted in on Main Experimental Station of A.N.D. University of Agriculture & Technology, Narendra Nagar (Kumarganj), Ayodhya during kharif season 2019 to estimate genetic variability in rice four cross combination including six generations viz., parents (P1, P2), the F1s, F2s, and back crosses with both the parents (B1 and B2) of crosses Swarna Sub-1 x CSR-10, Sambha Sub-1 x CSR-10, Pusa Sugandha -5 x CSR-10, Pusa Sugandha -5 x NDR-2064 with respect to yield and quality traits. Observation was recorded on twenty characters. The estimates of high genotypic and phenotypic variances in cross I for the characters like days to 50% flowering, chlorophyll a, chlorophyll b, carotene, total chlorophyll, plant height (cm), number of effective tillers/plant, flag leaf area (cm2 ), number of spikelet’s/panicle, protein content (%), in cross I, while in cross II, high GCV and PCV was recorded for days to 50% flowering, chlorophyll a, chlorophyll b, carotene, total chlorophyll, plant height (cm), number of effective tillers/plant, flag leaf area (cm2 ), number of spikelet’s/panicle, protein content (%) and grain yield/plant. Cross III shows high GCV and PCV for chlorophyll a, chlorophyll b, carotene, total chlorophyll, plant height (cm), number of effective tillers/plant, grains/panicle, flag leaf area (cm2 ), number of spikelet’s/panicle, grain size (l: b ratio), protein content (%). In cross IV high GCV was recorded for chlorophyll a, chlorophyll b, carotene, total chlorophyll, , flag leaf area (cm2 ), number of spikelet’s/panicle, grains/panicle, grain size (l: b ratio), grain yield/plant (g) and PCV was recorded for trait days to 50% flowering, chlorophyll a, chlorophyll b, carotene, total chlorophyll, , flag leaf area (cm2 ), biological yield/plant (g), number of spikelet’s/panicle, grains/panicle, spikelet fertility (%), grain size (l: b ratio) and grain yield/plant (g). Therefore, all the cross (F1, F2, B1 and B2) combinations can be used further for selecting the novel recombinants for improvement under sodic soil for sustainability.Not Availabl

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality