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Nowadays, natural language processing (NLP) is one of the most popular areas of, broadly understood, artificial intelligence. Therefore, every day, new research contributions are posted, for instance, to the arXiv repository. Hence, it is rather difficult to capture the current "state of the field" and thus, to enter it. This brought the idea of applying state-of-the-art NLP techniques to analyse the NLP-focused literature. As a result, (1) meta-level knowledge, concerning the current state of NLP has been captured, and (2) a guide to use of basic NLP tools is provided. It should be noted that all the tools and the dataset described in this contribution are publicly available. Furthermore, the originality of this review lies in its full automation. This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.

In this article, we focus on the concept of locally-APN-ness (``APN" is the abbreviation of the well-known notion of Almost Perfect Nonlinear) introduced by Blondeau, Canteaut, and Charpin, which makes the corpus of S-boxes somehow larger regarding their differential uniformity and, therefore, possibly, more suitable candidates against the differential attack (or their variants). Specifically, given two coprime positive integers $m$ and $k$ such that $\gcd(2^m+1,2^k+1)=1$, we investigate the locally-APN-ness property of an infinite family of Niho type power functions in the form $F(x)=x^{s(2^m-1)+1}$ over the finite field ${\mathbb F}_{2^{2m}}$ for $s=(2^k+1)^{-1}$, where $(2^k+1)^{-1}$ denotes the multiplicative inverse modulo $2^m+1$. By employing finer studies of the number of solutions of certain equations over finite fields (with even characteristic) as well as some subtle manipulations of solving some equations, we prove that $F(x)$ is locally APN and determine its differential spectrum. It is worth noting that computer experiments show that this class of locally-APN power functions covers all Niho type locally-APN power functions for $2\leq m\leq10$. In addition, we also determine the boomerang spectrum of $F(x)$ by using its differential spectrum, which particularly generalizes a recent result by Yan, Zhang, and Li.

The diversity-multiplexing tradeoff (DMT) provides a fundamental performance metric for different multiple-input multiple-output (MIMO) schemes in wireless communications. In this paper, we explore the block fading optical wireless communication (OWC) channels and characterize the DMT in the presence of both optical peak- and average-power constraints. Three different fading distributions are considered, which reflect different channel conditions. In each channel condition, we obtain the optimal DMT when the block length is sufficiently large, and we also derive the lower and upper bounds of the DMT curve when the block length is small. These results are dramatically different from the existing DMT results in radio-frequency (RF) channels. These differences may be due to the fact that the optical input signal is real and bounded, while its RF counterpart is usually complex and unbounded.

We study the classical expander codes, introduced by Sipser and Spielman [M. Sipser and D. A. Spielman, 1996]. Given any constants 0 < ��, �� < 1/2, and an arbitrary bipartite graph with N vertices on the left, M < N vertices on the right, and left degree D such that any left subset S of size at most �� N has at least (1-��)|S|D neighbors, we show that the corresponding linear code given by parity checks on the right has distance at least roughly {�� N}/{2 ��}. This is strictly better than the best known previous result of 2(1-��) �� N [Madhu Sudan, 2000; Viderman, 2013] whenever �� < 1/2, and improves the previous result significantly when �� is small. Furthermore, we show that this distance is tight in general, thus providing a complete characterization of the distance of general expander codes. Next, we provide several efficient decoding algorithms, which vastly improve previous results in terms of the fraction of errors corrected, whenever �� < 1/4. Finally, we also give a bound on the list-decoding radius of general expander codes, which beats the classical Johnson bound in certain situations (e.g., when the graph is almost regular and the code has a high rate). Our techniques exploit novel combinatorial properties of bipartite expander graphs. In particular, we establish a new size-expansion tradeoff, which may be of independent interests., LIPIcs, Vol. 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022), pages 43:1-43:3

Many of today’s software systems are created by leveraging ecosystems consisting of heterogeneous “complementors” and “hub” firms. In fact, the reliance on ecosystems is prevalent in the enterprise resource planning (ERP) domain, where larger ERP vendors form collaborative relationships with smaller industry-specific vendors to co-create value for themselves and their customers. However, value creation and destruction processes are often intertwined. A key motivation for this study is to shed light on the behavioral contingencies and underlying mechanisms that might lead to value destruction over time instead of the initially intended value co-creation. Furthermore, although value co-creation in collaborative relationships associated with ecosystems is often highlighted, research has been scarce on offering an in-depth analysis of the challenges in these relationships that can destroy value. This study attempts to address this issue by uncovering the underlying mechanisms that lead a hub firm and its complementors toward value destruction. Our mixed-methods approach involves the use of a combination of interpretive case study and analytical modeling to highlight nuances and develop conceptual propositions about the conditions that can potentially lead to value destruction. Our context is a globally reputed information technology (IT) firm known for providing business solutions (SOFTCo, a pseudonym) and numerous relatively small, less powerful customer-facing service firms (PartnerCos, a pseudonym). Our findings show that opportunism, unjust appropriation of rents, shirking, exploitation of asymmetric power, and undue dependence can initiate a value destruction process. Furthermore, our study revealed an unexpected emergence of a “pack of wolves,” where resentful PartnerCos formed a collective to tackle the opportunistic behaviors of SOFTCo by starting to align with its competitor, further destroying value for SOFTCo’s ecosystem. Overall, this study contributes to the literature on value co-creation/destruction in IT ecosystems. It also offers an illustration of a mixed-methods study where seemingly incommensurable approaches are harnessed to develop a theoretical understanding.

A common goal in many research areas is to reconstruct an unknown signal x from noisy linear measurements. Approximate message passing (AMP) is a class of low-complexity algorithms that can be used for efficiently solving such high-dimensional regression tasks. Often, it is the case that side information (SI) is available during reconstruction. For this reason, a novel algorithmic framework that incorporates SI into AMP, referred to as approximate message passing with side information (AMP-SI), has been recently introduced. In this work, we provide rigorous performance guarantees for AMP-SI when there are statistical dependencies between the signal and SI pairs and the entries of the measurement matrix are independent and identically distributed Gaussian. The AMP-SI performance is shown to be provably tracked by a scalar iteration referred to as state evolution. Moreover, we provide numerical examples that demonstrate empirically that the SE can predict the AMP-SI mean square error accurately., arXiv admin note: text overlap with arXiv:1902.00150

We study the following learning problem with dependent data: Observing a trajectory of length $n$ from a stationary Markov chain with $k$ states, the goal is to predict the next state. For $3 \leq k \leq O(\sqrt{n})$, using techniques from universal compression, the optimal prediction risk in Kullback-Leibler divergence is shown to be $\Theta(\frac{k^2}{n}\log \frac{n}{k^2})$, in contrast to the optimal rate of $\Theta(\frac{\log \log n}{n})$ for $k=2$ previously shown in Falahatgar et al. (2016). These rates, slower than the parametric rate of $O(\frac{k^2}{n})$, can be attributed to the memory in the data, as the spectral gap of the Markov chain can be arbitrarily small. To quantify the memory effect, we study irreducible reversible chains with a prescribed spectral gap. In addition to characterizing the optimal prediction risk for two states, we show that, as long as the spectral gap is not excessively small, the prediction risk in the Markov model is $O(\frac{k^2}{n})$, which coincides with that of an iid model with the same number of parameters. Extensions to higher-order Markov chains are also obtained., Comment: New results for higher-order Markov chains, 66 pages

As of late, the use of ‘digital’ as a qualifier to established research concepts has been increasing. However, we have yet to clearly establish what makes a ‘digital x’ concept distinct from an ‘IT x’ concept, where ‘x’ stands for well-established concepts such as strategy, infrastructure, innovation, or transformation, among others. In this paper, we review the need for, and merit of, labelling focal concepts in our field as ‘digital x’ in contrast to using the dominant, incumbent label of ‘IT x’. We position the shift as a call to attend to new salient features that characterize contemporary settings of information technology use and its effects. Recognizing this need, we develop guidelines for future research by arguing what novel phenomena the label of ‘digital x’ foregrounds, and how insights gained through such foregrounding contribute to scholarship in ways that the term ‘IT x’ does not. By doing so, this paper promotes clarity for the use of the ‘digital x’ concepts and introduces explicit guidelines to delineate between the nascent stream of ‘digital x’ research and established modes of ‘IT x’ research. We hope that the essay helps IS scholars and scholars in neighboring disciplines attending to digital phenomena identify novel research opportunities grounded on sound conceptual foundations that will foster cumulative generation of knowledge around ‘digital x’.

We consider distributed optimization methods for problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We leverage randomized sketches for reducing the problem dimensions as well as preserving privacy and improving straggler resilience in asynchronous distributed systems. We derive novel approximation guarantees for classical sketching methods and establish tight concentration results that serve as both upper and lower bounds on the error. We then extend our analysis to the accuracy of parameter averaging for distributed sketches. Furthermore, we develop unbiased parameter averaging methods for randomized second order optimization for regularized problems that employ sketching of the Hessian. Existing works do not take the bias of the estimators into consideration, which limits their application to massively parallel computation. We provide closed-form formulas for regularization parameters and step sizes that provably minimize the bias for sketched Newton directions. Additionally, we demonstrate the implications of our theoretical findings via large scale experiments on a serverless cloud computing platform., arXiv admin note: text overlap with arXiv:2002.06540

Grassmannian $\mathcal{G}_q(n,k)$ is the set of all $k$-dimensional subspaces of the vector space $\mathbb{F}_q^n.$ Recently, Etzion and Zhang introduced a new notion called covering Grassmannian code which can be used in network coding solutions for generalized combination networks. An $\alpha$-$(n,k,\delta)_q^c$ covering Grassmannian code $\mathcal{C}$ is a subset of $\mathcal{G}_q(n,k)$ such that every set of $\alpha$ codewords of $\mathcal{C}$ spans a subspace of dimension at least $\delta +k$ in $\mathbb{F}_q^n.$ In this paper, we derive new upper and lower bounds on the size of covering Grassmannian codes. These bounds improve and extend the parameter range of known bounds., Comment: 17 pages

We consider here the identification of change-points on large-scale data streams. The objective is to find the most efficient way of combining information across data stream so that detection is possible under the smallest detectable change magnitude. The challenge comes from the sparsity of change-points when only a small fraction of data streams undergo change at any point in time. The most successful approach to the sparsity issue so far has been the application of hard thresholding such that only local scores from data streams exhibiting significant changes are considered and added. However the identification of an optimal threshold is a difficult one. In particular it is unlikely that the same threshold is optimal for different levels of sparsity. We propose here a sparse likelihood score for identifying a sparse signal. The score is a likelihood ratio for testing between the null hypothesis of no change against an alternative hypothesis in which the change-points or signals are barely detectable. By the Neyman-Pearson Lemma this score has maximum detection power at the given alternative. The outcome is that we have a scoring of data streams that is successful in detecting at the boundary of the detectable region of signals and change-points. The likelihood score can be seen as a soft thresholding approach to sparse signal and change-point detection in which local scores that indicate small changes are down-weighted much more than local scores indicating large changes. We are able to show second-order optimality of the sparsity likelihood score in the sense of achieving successful detection at the minimum detectable order of change magnitude as well as at the minimum detection asymptotic constant with respect this order of change., Comment: This manuscript includes "Multi-sequence segmentation via score and higher-criticism tests" by Chan and Chen in arXiv:1706.07586

The current aimed to formulate herbal shampoo and to evaluate the various property of herbal shampoos. The herbal shampoo was formulated by adding the extracts of Spinacia Oleracea leaves, Ipomoea batatas roots, Acacia concinna, Sapindus Indica, Cymbopogon citrus in different proportions to a 100 ml. Small amount of gelatin and citric acid were also added for viscosity and pH respectively. Several tests such as visual inspection, pH, dirt dispersion, rheological evaluation and skin irritation test etc, were performed to determine the physicochemical properties of prepared shampoos. The formulated herbal shampoo was clear and appealing. It showed good cleansing and detergency and good foam stability. The results indicated the formulated shampoo is having excellent conditioning performance, similar to commercially available shampoo. However, further research and development is required to improve its quality and safety.

YouTube is a popular social networking platform that allows users to upload, watch and comment on videos. However, YouTube also attracts spammers who post unwanted or abusive comments on videos, which can affect the quality and credibility of the content and platform. In this paper, we propose a novel approach to detect and delete YouTube spam comments using natural language processing (NLP) algorithms such as Rapid Fuzz and Levenshtein distance. Rapid Fuzz is a Python library that provides fast and accurate fuzzy string matching, while Levenshtein distance is a metric that measures the minimum number of edits required to transform one string into another. We use these algorithms to compare the similarity of comments and identify duplicates, which are a common form of spam. We also use the YouTube API to access and delete a channel’s spam comments. We evaluate our approach on comments of YouTube Video and show that it can effectively detect and delete spam comments with high accuracy and efficiency

A brain stroke, is a saviour disease in which a blood cot or bleeding occur in the brain during a stoke, which can result in long-term harm. This can affects person’s move, think, see, or communicate. The another medical name for the Brain Stroke is cerebrovascular or CVA is when The body part that the blood-starved brain cells regulate ceases to function. Brain Stroke is very critical situation as it directly leads to death or permanent disability. The stroke is classified into two areas ischemic stroke and Haemorrhagic. When blood clots re created in the brain and goes through the patient’s streamed lodge in the brain then there is the high chances of ischemic stroke occur. Homographic stroke is second type of stoke which occurs when there is leak of blood or ruptures in the artery in brain. In early stage if the treatment get started we can treat to the ischemic stroke efficiently. If a stroke is feared, or bystanders should call emergency medical services right away. Symptoms disappear as their own in a Ischemic stroke reminded by Transient Ischemic Attack (TIA). As per the research of World Health Organization the 2 nd leading cause of the death worldwide is Brain Stroke which is also responsible for the approximately 11% deaths. but can prevent up to 80% of stokes if they can be identified or predicted early stage stroke. Our ML model uses dataset to predict whether the person has any chances of getting stroke the parameters that are considered to predict stroke are gender, age, disease, smoking status, Cystatin-c , MMP10, Tau Our dataset focuses on major factors which has causes of brain stroke.