Reviewerly/exHarmony huggingface dataset:
An IOT Method for Reducing Classification Error In Face Recognition With The Commuted Concept Of Conventional Algorithm

The initial work has discussed the conventional approach of algorithms along with their drawbacks and features. Apart from that types of face recognition methodologies have been discussed with application of IOT trends. We specifically depicts a descriptive idea about working and applications of all conventional algorithms which have been commuted concept wise in proposed methodologies section of our work Our work consists of literature survey so we can provide a reason for the previous work and get basic ground for performing and implementing proposed work. One of a common procedures of face detection has been discussed that’s has been worked out in past with accuracy .The observation in this work leads to propose the method by commuting the conventional algorithm, Basically the work done with conventional approach has been discussed in this section with a strong focus over the role of Iot in face recognition and what is importance of Iot in this domain and what changes Iot concept has bring about as far as face recognition with different approach has been concerned . Not only PCA concept has been commuted but along with Pca, Svm, naïve bayes classifier, DCT, Gabor, neural network efficiency and their combined effect has been performed and analyzed later. Our work has been focusing around commuted concept of conventional algorithms so this particular chapter is very much important to discuss the conventional methodologies perform by classical mathematically implemented techniques for classifications. With the help of the analysis we will discuss the problem formulation and comparison of proposed work with existing work .So our work is basically about the problem existing with conventional algorithm for classifications and what lead us to propose the commuted concept further to deal or minimize the effect of that particular problem ,Our work is not primarily based on face recognition but to calculate the classification error through conventional algorithm and then compare it with our proposed commuted concept and combined effect of conventional algorithms as well, like PCA+SVM PCA+ Kernel SVM, Commuted Concept of PCA +Naïve bayes Classifier .We have gone through with different cases to ensure the minimization of classification error through proposed method .The goal of the work is to associate the application of IOT and proposed algorithm can proved to be efficient in getting better accuracy in the results. As we have compared the results on different methodologies discussed earlier and our proposed work, Even the Iot concept has efficient collaboration with proposed method as far as minimization of classification error is concerned.
Marketing Approach in Educational Institutions Management: Transformation and Development Management

The relevance of this article is to study the market of educational services for preschool children, since education is the fundamental beginning, the Foundation of human socialization and the most important means of cultural communication between generations. In relation to the modern period of Russian education development, it is a close attention and control to all areas of educational activity, as well as a duty to the continuous development of each educational institution, including the modern specifics in the field of manage0ment, using a marketing approach. The purpose of the study is to investigate the practice of educational services consumption, using a marketing approach. Research methods: an online survey was used as a research method, which allows us most effectively to investigate the quality and specifics of educational services provided. Research results: the article examines the market of educational services for preschoolers, describes the quality of services offered in the view of consumers. Practices of educational services consumption are investigated. The novelty and originality of the research lies in the fact that the market of educational services was studied for the first time using a marketing approach. It shows that producers of additional educational services primarily focus on the cost of services in their advertising messages, and most educational centers use information about highly qualified teaching staff. It determines that the future of promoting educational services on the Internet belongs to social networks. The main semantic units of competitive advantages of educational institutions stated in the advertising message are identified: training program; cost; qualified personnel; individual approach; unique additional offer. The article shows that the choice of additional classes is extremely limited and does not meet the needs of modern parents. It is determined that choosing additional classes, following things are important for parents: strengthening the child's health, the appearance of new acquaintances, which directly affects the socialization of the child in society. It is shown that fewer students attend foreign language classes and music classes. There are quite interesting and rare wishes of parents for narrow specializations: gymnastics, theater Studio, classes with a speech therapist, social sections. It is revealed that the highest level of satisfaction the following characteristics of educational services showed: the location of the institution; the schedule; the result of a child's development after attending; the attitude of teachers to children. Practical significance: the data Obtained in this work can be used in marketing, social psychology, pedagogy, and age psychology.
Modeling Soft Analytical Side-Channel Attacks from a Coding Theory Viewpoint

One important open question in side-channel analysis is to find out whether all the leakage samples in an implementation can be exploited by an adversary, as suggested by masking security proofs. For attacks exploiting a divide-and-conquer strategy, the answer is negative: only the leakages corresponding to the first/last rounds of a block cipher can be exploited. Soft Analytical Side-Channel Attacks (SASCA) have been introduced as a powerful solution to mitigate this limitation. They represent the target implementation and its leakages as a code (similar to a Low Density Parity Check code) that is decoded thanks to belief propagation. Previous works have shown the low data complexities that SASCA can reach in practice. In this paper, we revisit these attacks by modeling them with a variation of the Random Probing Model used in masking security proofs, that we denote as the Local Random Probing Model (LRPM). Our study establishes interesting connections between this model and the erasure channel used in coding theory, leading to the following benefits. First, the LRPM allows bounding the security of concrete implementations against SASCA in a fast and intuitive manner. We use it in order to confirm that the leakage of any operation in a block cipher can be exploited, although the leakages of external operations dominate in known-plaintext/ciphertext attack scenarios. Second, we show that the LRPM is a tool of choice for the (nearly worst-case) analysis of masked implementations in the noisy leakage model, taking advantage of all the operations performed, and leading to new tradeoffs between their amount of randomness and physical noise level. Third, we show that it can considerably speed up the evaluation of other countermeasures such as shuffling.
A Recurrent Neural Network for the Prediction of Vital Sign Evolution and Sepsis in ICU

Sepsis is a life-threatening reaction to an infection, responsible for 6 million deaths globally each year.Moreover, this condition is one of the major cost to healthcare.Our aim is to develop a new technique for the early detection of the sepsis onset.Such an early detection would allow for the improvement of sepsis outcome.Our technique is based on the assumption that accurate and early prediction of sepsis requires to be able to predict the evolution of the vital signs.This idea was translated in the use of a recurrent neural network, a Long Shortterm memory (LSTM) network, which was trained to accomplish two tasks: the prediction of (i) sepsis and (ii) the vital signs at time t+6.We assume that the use of this auxiliary task allows for a better training of the network given the low prevalence of sepsis.The network consists in three modules: (i) an embedding module aiming at providing a compact representation of the inputs, (ii) a recurrent module with three LSTMs layers with highway connection between each layer (iii) the prediction modules consisting in linear layers for the prediction of two tasks.The network achieved a final utility score of 0.309 on the full hidden test set (0.387 on the test set A, 0.365 on the set B , and -0.148 on the set C).The team name was "IADI".Further improvements are required before transferring such an approach into clinical practice.
IoT Security Challenges and Counters Measures

Internet of Things (IoT) is an alliance of material object linked via internet. Material objects embedded with RFID, WSNs and many more through which objects remain connected with each other. Each material objects which is the part of communication are having a unique identifier. IoT is extremely heterogeneous, so security is a big challenge in IoT. In this article, many security challenge and counters measures and research objectives are studied.
Alumnichain: Blockchain Based Records Verification Service

The proposed system is a ledger of student qualifications that aims at making recruitment processes simpler for the universities, recruiters as well as the students. Instead of continuing with the traditional way, of background checks that take even days worth of time and cost a lot of money, we have proposed an instant student background verification service that allows verification of data by recruiters, that has been signed by the university and uploaded to an immutable, secure ledger called the blockchain or as we call it, the Alumnichain. The students simply have to include a code provided to them, to their CVs which can be scanned by the recruiters, to have all student data instantly verified. The data stored on the network is not in plaintext and is encrypted to ensure security against potential data thefts. All this is achieved through a blockchain implementation of the above described use case, which has proved to be a solution to so many problems since its inception, starting with Bitcoin in 2008.
A Research on Cloud Kitchen Prerequisites and Branding Strategies

The Emerging trend for smart food delivery is cloud kitchen concept, this is a modern phenomenon in a contemporary scenario around the world, and this concept is developing at the most noteworthy stage with a Compound Annual Growth Rate (CAGR) when contrasted with other slice of food and beverage outlets like Restaurants and fast food outlets. The aim of this study is to analyze demand and requirements of cloud kitchen. Secondly it focuses on the marketing strategy need to be adopted by cloud kitchen. As the fundamental method is to adopt a right branding strategy for cloud kitchen the results proveswith Pearson’s correlation about the requirements as Online APP by (r = .690**, p = .000) is significant with 0.01 level in association and also with online application then web ads with a value(b = .245, p = .000) from linear regression for the reference of online reviews before ordering food.
Evichain: Evaluating and Scrutinizing Crime using Block Chain

Evidence and witness play an important role to investigate crime and lawful jurisdiction in any case. But often, the victims do not get justice due to the middlemen and the altered evidences in the centralized network. Block chain is an ideal solution to ensure transparency till the highest hierarchy of jurisdiction using a decentralized peer to peer network to store data which is immutable. It is like a distributed ledger working on the proof of work algorithm that validates each amendment and modification made in a particular chain. Evichain inculcates the transparency and security of the same decentralized network in the crime investigation process. It is an application on which the entire data regarding any particular investigation is stored in a block chain with limited people having access rights. It also includes cases filed by the victims themselves. Every amendment made in the block chain is validated. Also the data access is restricted to the users with that specific private key.
Deep Neural Networks for Recommender Systems

The data available online, helps users to get information about anything of his/her interest. But since the data is huge and complex it is difficult to get useful information from it. Recommender Systems are effective software techniques to overcome this problem. Based on the user’s and item’s information available, these techniques provide recommendations to users in their area of interest. Recommender systems have wide applications like providing suggestive list of items to customers for online shopping, recommending articles or books for online reading, movie or music recommendations, news recommendations etc. In this paper we provide a study of Deep Neural Networks (DNN) approaches that can be used for recommender systems. They have been used widely in last decade in many fields like image processing, video streaming, Natural Language Processing etc. including recommendations to overcome the drawbacks of traditional systems. The paper also provides performance of Denoising AutoEncoders (DAE) which are feed forward neural networks and its comparison with traditional systems. Denoising Autoencoders are a type of autoencoders wherein some part of input is corrupted, i.e., noise is added to the input. While learning to remove noise from input, the DAE also learns to predict unknown values. This property of Denoising Autoencoders can help in recommendation systems to predict unknown values before recommending new items. Experimentation has shown improvement in the performance of recommendation systems with denoising autoencoders. The evaluation is performed on MovieLens-1M dataset with and without additional features of users (age and gender) and items (movie genres) provided in the dataset.
Multi-target pig tracking algorithm based on joint probability data association and particle filter

In order to evaluate the health status of pigs in time, monitor accurately the disease dynamics of live pigs, and reduce the morbidity and mortality of pigs in the existing large-scale farming model, pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs. However, it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets. In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig, this study proposed a method that used color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter. Experimental results show the proposed algorithm can quickly and accurately track pigs in the video, and it is able to cope with partial occlusions and recover the tracks after temporary loss. Keywords: joint probability data association, pig tracking, particle filter, centroid DOI: 10.25165/j.ijabe.20211404.6105 Citation: Sun L Q, Li Y Y. Multi-target pig tracking algorithm based on joint probability data association and particle filter. Int J Agric & Biol Eng, 2021; 14(4): 199–207.
Evolution of Chatbots for Smart Assistance

Every year, innovation is coming in the area of Artificial intelligence. Currently, the bots are used for matching to give desired or predefined acknowledge. Coming era is the era of robots and software bots using supervised, adaptive, and intelligent algorithms. In the paper, our focus is on the smart software which takes input and performs pre-processing, processing on raw data. The result should be a processed output after applying learning processes and intelligent approaches. This paper is introducing the prototype explaining were to use the chatbots and which procedure is appropriate for creating Chatbots.
Image Steganography using LSB and encrypted message with AES, RSA, DES, 3DES, and Blowfish

In today’s world, confidential information is growing due to various areas of works. Internet is the main area of transmission of digital data, so security must be more considered. Two common ways of providing security is cryptography and steganography. Employing a hybrid of cryptography and steganography enhances the security of data. This paper employs LSB (Least significant Bit) as the steganography algorithm and AES, RSA, DES, 3DES, and Blowfish algorithms as cryptographic algorithms to encrypt a message that should be hidden in a cover image. The results are represented in the form of execution time, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and the histogram of main and covered image. The experimental results reveal that all the algorithms achieve appropriate quality of stego image. They can be used as cryptographic algorithms to encrypt a message before applying steganography algorithms.
Blockchain: Application and Utilization in Higher Education

This research examines Blockchain and how it can be utilized in higher education. Before exploring how blockchain can be used in higher education, one must understand just how blockchain is formed, how it accumulates information and its attributes both good and bad. To appreciate how blockchain functions, its use in other industries and applications are explored. Based upon blockchain utilization, a discussion of how it can be applied to higher education is explored and discussed.
Usage of Augmented Reality Glasses in Automotive Industry: Age-Related Effects on Cognitive Load

With the development of technology, augmented reality (AR) devices were introduced in many areas. One of these devices, AR glasses, is more convenient and easy to fulfil and still continuing to evolve. The AR glasses, which are frequently used in sectors such as education, entertainment, construction and automotive, bring many research areas. In this article, the cognitive load that AR glasses bring to the users who is working on an assembly line has been investigated. Cognitive load refers to the resources used by working memory in the brain. This study was carried out with healthy participants in the assembly line of an automotive manufacturing factory and 60 tests were performed for measurement. Effect of the AR glasses for participants under 35 and over 35 years of age was measured by electroencephalography (EEG). EEG is a common objective technique used in cognitive load measurements. EEG data collected were examined and no significant difference was observed between participants of under and over 35 years of age. When compared with the data obtained in the experiments without glasses, it was seen that the use of the AR glasses in assembly lines of automotive manufacturing factories did not create an additional cognitive load. It can be seen from the article that AR glasses did not have an age-related effect and can be use in the automotive manufacturing industry.
Personalization of Moodle with the integration of most used web technologies in higher education

The purpose of the research was to integrate Moodle with the tools of the web 2.0 most used in the teaching learning processes jointly. Anki, Cmap, Etherpad, Facebook, Google Scholar, Kahoot, Skype, Simple Solver, Twitter, Wolfram Alpha, Wordsift and YouTube were selected for their educational approach, interdisciplinary and ease of use. It was analyzed the kinds of extensions that can be developed for Moodle and its functionalities and it was determined that the block extension is the appropriate one to integrate Moodle with several technologies at the same time. The block was implemented in the PHP programming language. The plugin “Web Technologies” creates a technological environment focused on the student and guided by the teacher. It incorporates all the tools, contents and people involved in the digital part of the educational process. It also converts Moodle into a Personal learning environment.
Related-Tweak Statistical Saturation Cryptanalysis and Its Application on QARMA

Statistical saturation attack takes advantage of a set of plaintext with some bits fixed while the others vary randomly, and then track the evolution of a non-uniform plaintext distribution through the cipher. Previous statistical saturation attacks are all implemented under single-key setting, and there is no public attack models under related-key/tweak setting. In this paper, we propose a new cryptanalytic method which can be seen as related-key/tweak statistical saturation attack by revealing the link between the related-key/tweak statistical saturation distinguishers and KDIB (Key Difference Invariant Bias) / TDIB (Tweak Difference Invariant Bias) ones. KDIB cryptanalysis was proposed by Bogdanov et al. at ASIACRYPT’13 and utilizes the property that there can exist linear trails such that their biases are deterministically invariant under key difference. And this method can be easily extended to TDIB distinguishers if the tweak is also alternated. The link between them provides a new and more efficient way to find related-key/tweak statistical saturation distinguishers in ciphers. Thereafter, an automatic searching algorithm for KDIB/TDIB distinguishers is also given in this paper, which can be implemented to find word-level KDIB distinguishers for S-box based key-alternating ciphers. We apply this algorithm to QARMA-64 and give related-tweak statistical saturation attack for 10-round QARMA-64 with outer whitening key. Besides, an 11-round attack on QARMA-128 is also given based on the TDIB technique. Compared with previous public attacks on QARMA including outer whitening key, all attacks presented in this paper are the best ones in terms of the number of rounds.
When differential privacy meets NLP: The devil is in the detail

Differential privacy provides a formal approach to privacy of individuals.Applications of differential privacy in various scenarios, such as protecting users' original utterances, must satisfy certain mathematical properties.Our contribution is a formal analysis of ADePT, a differentially private autoencoder for text rewriting (Krishna et al., 2021).ADePT achieves promising results on downstream tasks while providing tight privacy guarantees.Our proof reveals that ADePT is not differentially private, thus rendering the experimental results unsubstantiated.We also quantify the impact of the error in its private mechanism, showing that the true sensitivity is higher by at least factor 6 in an optimistic case of a very small encoder's dimension and that the amount of utterances that are not privatized could easily reach 100% of the entire dataset.Our intention is neither to criticize the authors, nor the peer-reviewing process, but rather point out that if differential privacy applications in NLP rely on formal guarantees, these should be outlined in full and put under detailed scrutiny. Theoretical backgroundFrom a high-level perspective, DP works with the notion of individuals whose information is contained in a database (dataset).Each individual's datapoint (or record), which could be a single bit, a number, a vector, a structured record, a text document, or any arbitrary object, is considered private