SYNTHESIS METHODS OF SELF-CHECKING COMBINATIONAL DEVICES OF AUTOMATIC CONTROLS BASED ON LOGICAL COMPLEMENT TO THE EQUILIBRIUM 1-OUT-OF 4 CODE

The article is devoted to the development of the logical complement method for the synthesis of self-checking digital devices and systems. The article highlights the features of using the equilibrium 1-out-of 4 code for the synthesis of integrated control circuits of combinational logic devices by the method of logical complement. The basic structures of the organization of built-in control schemes by the method of logical complement to the equilibrium 1-out-of 4 code are described and several ways of organizing control schemes are considered. The fi rst one implies the control of devices with the allocation of outputs fours and the use of conversion of three outputs out of four in the basic structure. There is another way of organizing the control circuit by the method of logical complement to the equilibrium code and it involves the search for outputs triplets and their addition to the four-digit code word of the 1-out-of 4 code by directly connecting the output of the supervisor logic block to the tester input without any signal conversion. The third method is associated with the use of a basic structure with the circuit for the preliminary signal compression from the unit under test and the reduced number of outputs control. The article presents the results of experimental studies for each of the methods of organizing the built-in control circuit and estimates the structures technical implementation complexity as well as the probability of detecting errors at the outputs of the unit under test when single-stuck faults are introduced. In many cases the logical complement method allows to synthesize simpler self-checking devices compared to the duplication method with high rates of error detection at the unit under test outputs.

Linguistic Patterns for Code Word Resilient Hate Speech Identification

The permanent transition to online activity has brought with it a surge in hate speech discourse. This has prompted increased calls for automatic detection methods, most of which currently rely on a dictionary of hate speech words, and supervised classification. This approach often falls short when dealing with newer words and phrases produced by online extremist communities. These code words are used with the aim of evading automatic detection by systems. Code words are frequently used and have benign meanings in regular discourse, for instance, “skypes, googles, bing, yahoos” are all examples of words that have a hidden hate speech meaning. Such overlap presents a challenge to the traditional keyword approach of collecting data that is specific to hate speech. In this work, we first introduced a word embedding model that learns the hidden hate speech meaning of words. With this insight on code words, we developed a classifier that leverages linguistic patterns to reduce the impact of individual words. The proposed method was evaluated across three different datasets to test its generalizability. The empirical results show that the linguistic patterns approach outperforms the baselines and enables further analysis on hate speech expressions.

Splitting Sequences for Coding and Hybrid Incremental ARQ with Fragment Retransmission

This paper proposes a code defined on a finite ring ℤpM, where pM = 2m−1 is a Mersenne prime, and m is a binary size of ring elements. The code is based on a splitting sequence (splitting set) S, defined for the given multiplier set ℰ={±20, ±21,…, ±2m−1}. The elements of ℰ correspond to the weights of binary error patterns that can be corrected, with the bidirectional single-bit error being the representative that occurs the most. The splitting set splits the code-word into sub-words, which inspired the name splitting code. Each sub-word, provided with auxiliary control symbols that are a byproduct of the coding procedure, corrects a single symbol error. The code can be defined, with some constraints, for general Mersenne numbers as well, while the multiplier set can be adjusted for adjacent binary errors correction. The application proposed for this code is a hybrid three-stage incremental ARQ procedure that transmits the code-word in the first stage, auxiliary control symbols in the second stage, and retransmits the sub-words detected as incorrect in the third stage. At each stage, error correction can be turned on or off, keeping both the retransmission rate and residual error rate at a low level.

Discriminative Codebook Hashing for Supervised Video Retrieval

In recent years, hashing learning has received increasing attention in supervised video retrieval. However, most existing supervised video hashing approaches design hash functions based on pairwise similarity or triple relationships and focus on local information, which results in low retrieval accuracy. In this work, we propose a novel supervised framework called discriminative codebook hashing (DCH) for large-scale video retrieval. The proposed DCH encourages samples within the same category to converge to the same code word and maximizes the mutual distances among different categories. Specifically, we first propose the discriminative codebook via a predefined distance among intercode words and Bernoulli distributions to handle each hash bit. Then, we use the composite Kullback–Leibler (KL) divergence to align the neighborhood structures between the high-dimensional space and the Hamming space. The proposed DCH is optimized via the gradient descent algorithm. Experimental results on three widely used video datasets verify that our proposed DCH performs better than several state-of-the-art methods.

A Brief Review: Antibacterial Activity of Quinone Derivatives

Quinones and their derivatives play an important role in pharmaceutical, medicinal, and environmental applications. The biological activities of quinone derivatives against various gram-positive and negative bacteria and pathogens show strategies for discovering new drug efficiency and various physio-chemical properties. This review discusses the antibacterial activities of various notable quinones and their derivatives against various bacteria and pathogens. A wide literature survey is done on the database PubMed, Scopus, and Google scholars by using code word antibacterial activity of quinone derivatives and the role of various quinone derivatives to discover new potent pharmaceutical applications. Herein, we wish to discuss various quinone derivatives used as potent drug molecules for antibacterial activity against various gram-positive and negative bacteria and pathogens. Proper health care systems are essential for newly develop methodologies to fight against unexpected bacteria and pathogens. The antibacterial activities of various quinone derivatives deliver notable supervision for additional design and synthesis of quinone derivatives and examine the pharmaceutical, industrial and medicinal applications.

Disclosure of Silent Branding During COVID-19 Pandemic: A Study of Sarsiwa Village in Chhattisgarh State of India

The study tried to explore the adopted branding strategy in trading essential goods in lockdown during the COVID-19 pandemic. The lead researcher observed the buying behaviour of 600 rural customers as a participant–observer in grocery shops. Additionally, he interviewed five grocery shopkeepers in that village to cross-check the customers’ observations. The study categorised these observations into similar themes using content analysis. The sorting of the study data resulted in six main categories. Out of six categories, two are the most important, which reveal that customers don’t want to compromise with their choice of products/brands even in this adverse situation and they ask their preferred products/brands in code word very often to their nearby shopkeepers. Actually, these code words are the impact of silent branding of a product in their mind. The current study reveals that silent branding works long-lasting for customers and could be beneficial for firms. The study imperatively adds the concept of silent branding in the existing literature on branding.

Novel Use of Text-Bot Automation for Residency Recruitment

Purpose. To describe the employment of an automated text messaging text-bot during the 2019 American Society of Health-System Pharmacists (ASHP) Midyear Clinical Meeting Residency Showcase and its impact on the number of applications received for the postgraduate year 1 (PGY1) and postgraduate year 2 (PGY2) pharmacy residency programs at a medium-sized community hospital. Methods. Visitors at the residency showcase booth were asked to text a code word to a program number. The text-bot collected the visitor’s contact information and program of interest (PGY1 or PGY2). A series of automated messages were sent to all visitors following the showcase and up until the residency application deadline. Results. 71% (20/28) of visitors to the program’s showcase booth registered with the text-bot and of these, 65% (13/20) submitted applications to the residency program in phase I of the match. Both the PGY1 and PGY2 programs saw an increase in the amount of applications received compared to previous year. Conclusion. A text messaging text-bot may be a useful residency recruitment tool.

Human Authentication using Face, Voice and Fingerprint Biometrics

Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.

ALGORITHMIC PROVISION OF ERROR-CORRECTING MULTI-COLORED BARCODE PATTERNS BASED ON GF(p)

Background. In recent years, there has been a steady trend towards the using of multi-colored barcodes. This increases the information density of the data compared to black and white barcodes. However, this complicates the processes of recognition and decoding of bar code images. Therefore, in order to reliably read multi-colored barcodes from an object, it is necessary to ensure noise immunity of bar code patterns – the minimum structural units of the bar code image. Objective. The purpose of the paper is development of a method for the synthesis of symbolics of multi-colored bar codes, which have the property of noise immunity at the level of bar code patterns. Methods. This goal is achieved through the using of multivalued incomplete Hemming codes as the basis for constructing bar code patterns. The numerical equivalent of the bar code pattern is a code word of the multi-valued Hemming code, in which the encoding-decoding operations are performed according to the rules of a finite field GF(p). Results. A number of error-correcting multi-colored barcodes have been proposed, in which one-time distorted element (error) is corrected at the level of barcode pattern and a significant number of multiple distortions are detected. The error-correcting capability of multi-colored barcode patterns has been evaluated. Conclusions. The proposed method of the synthesis of symbolics of multi-colored barcodes allows creating barcodes with improved error-correcting characteristics, which provides the appropriate level of reliability of the process of reading multi-colored barcodes in automatic identification systems.