Reviewerly/exHarmony huggingface dataset:
The Forms of Categorical Proposition

An exhaustive survey of categorical propositions is proposed in the present paper, both with respect to their nature and the logical problems raised by them.Through a comparative analysis of Term Logic and First-Order Logic, it is shown that the famous problem of existential import may be solved in two ways: with a model-adaptive strategy, in which the square of opposition is validated by restricting the models; with a language-adaptive strategy, in which the logical form of categorical propositions is extended in order to validate the square in every model.The latter strategy is advocated in the name of logic, which means truth in every model.Finally, the present paper needs some automatic process in order to determine the nature of logical relations between any pair of the available 256 categorical propositions.This requires the implementation of a programming machine in the style of Prolog.1 What is a categorical proposition?It is taken for granted that the roots of modern logic stem from Aristotle's syllogistics, as a general inferential activity.However, the content of such inferences was initially restricted to a specific kind of formulas: categorical propositions.A general definition of the latter is the following: by a categorical proposition, it is meant any proposition in which something, say A, is affirmed or denied of something, say B (A and B may be two names of the same thing). 1Every thought may be expressed in that way, provided that the grammatical form of the proposition includes a first term, the subject, and a second term, the predicate.The aim of the following is not to deal with syllogistics as it stands; rather, we want to deal with the various ways in which categorical propositions may be analyzed, especially in the light of their logical form.A number of questions may arise from this perspective: 'Every A is B' is a typical categorical proposition, whereas 'Everything is A and B' doesn't seem so.Isn't it?Does 'Every A is B' entail that 'Some A is B'?What is the difference between 'A is not-B' with respect to 'A is not B'?Here are some issues that are tackled in the following work, in order to give a comprehensive survey of what is meant by the so-called 'categorical propositions'.A basic proposition is of the form 'A is B' and constitutes the core expression of the so-called traditional Term logic (henceforth: TL)(see [7]).According to TL, every proposition is a pair of terms bound together by a (logical) copula to form a single unified expression.Modern logic results from a choice: for a number of reasons, Frege defeated Aristotle and the contemporary First-Order Logic (henceforth: FOL) is the mainstream formal language that is mainly used in mathematics and the other formal sciences.Assuming that the value of a language lies in its expressive power, FOL is expected to be able to express as much of our thoughts as Term Logic.This is not so easy in any case, however.Let us consider
Review on IoT based Healthcare systems

The world is moving to Internet of Things (IoT) remote monitoring technology and quick control of objects as well more precisely. IoT can be beneficial in medicine as well health care facilities as it allows for long-term research of chronic diseases, vital symptom monitoring, emergency perception, diagnosis and prediction of patient level or disease. Internet of Things becomes transparent and is useful in the context of health care and venerable care, i.e., in most cases, they are activities that require the full presence of a caretaker or medical personal. The purpose of this survey is to enlighten the significance and categories of IoT-include adult health care programs. This paper consolidates a summary of research that report on the development and use of IoT-include health care adult programs. The paper covers with various available IoT based techniques used for health care applications.
kTRACKER: Passively Tracking KRACK using ML Model

Recently, a number of attacks have been demonstrated (like key reinstallation attack, called KRACK) on WPA2 protocol suite in Wi-Fi WLAN. In this paper, we design and implement a system, called kTRACKER, to passively detect anomalies in the handshake of Wi-Fi security protocols, in particular WPA2, between a client and an access point using COTS radios. A state machine model is implemented to detect KRACK attack by passively monitoring multiple wireless channels. In particular, we perform deep packet inspection and develop a grouping algorithm to group Wi-Fi handshake packets to identify the symptoms of the KRACK in specific stages of a handshake session. Our implementation of kTRACKER does not require any modification to the firmware of the supplicant i.e., client or the authenticator i.e., access point or the COTS devices, our system just needs to be in the accessible range from clients and access points. We use a publicly available dataset for performance analysis of kTRACKER. We employ gradient boosting-based supervised machine learning models, and show that an accuracy around 93.39% and a false positive rate of 5.08% can be achieved using kTRACKER.
Building Image Feature Extraction Using Data Mining Technology

At present, data mining technology is continuously researched in science and application. With the rapid development of remote sensing satellite industry, especially the launch of remote sensing satellites with high-resolution sensors, the amount of information obtained from remote sensing images has increased dramatically, which has largely promoted the application of remote sensing data in various industries. This technique mines useable information from less complete and accurate data while ensuring low program complexity. In order to determine the impact of data mining techniques on feature extraction of graphic images, this paper explores the relevant steps in the image recognition process, especially the image preenhancement and image extraction processes. This paper develops a preliminary set of relevant data and investigates two different extraction methods based on the availability or absence of nursing information. Aiming at the advantages and disadvantages of the two house extraction methods, this work discusses how to effectively integrate remote sensing data. It uses different data sources to describe different characteristics of buildings, analyzes and extracts effective information, and finally derives building information. The research results show that, using the SVM algorithm in data mining for image feature extraction, in the verified filtering window, the accuracy can be effectively improved by about 20%. Buildings are important objects in high-resolution remote sensing images, and their feature extraction and recognition technology is of great significance in many fields such as digital city construction, urban planning, and military reconnaissance.
Omni-Directional Robot Based on Swerve Drive

Robots are machines being extensively used in various domains, in some cases replacing human effort. One of the challenges that are prevalent when it comes to the structural build of the robot is the mobility, that is defined by the ease with which the robot can navigate in rough terrains, industrial applications and dynamic environments. This problem can be solved by employing the concept of holonomic robots, wherein the controllable degrees of freedom is equivalent to the total degrees of freedom. This paper proposes one such structural design of an Omni-Directional robot, whose wheels can freely move in any direction. They can move like general wheels or can move sideways along its circumference. The design makes use of Swerve Drive which allows the robot to move in all the directions by just pointing the wheels in that particular way. Rotation is attained by tilting the wheels to 45° from the line of axis. Also, this design aims to develop the Omni-Directional robot as an autonomous robot. The Omni-Directional drive mechanism proves to be very helpful in challenging situations as it offers very good mobility.
Web Application to Track Student Attentiveness during Online Class using CNN and Eye Aspect Ratio

During this COVID-19 pandemic online class platforms are the only solution to transfer the knowledge in the field of education. Even though the physical classes are being practiced slowly in some countries, still academicians are in the need of online classes. In addition to content delivery, teachers are in the need to concern about throughout the class time whether the students are listening and be active in online classes. Due to more bandwidth consumption of the audio and video streaming, students can't be compelled to unmute the audio and video when the teacher delivers the content. So, there is no option for the teachers to observe the student&#x2019;s activity. With the advancement of technology and enhanced image analysis capacity of deep learning techniques, a system is proposed to compute the student&#x2019;s activity and can report it to the teachers during the class time itself. Drowsiness detection is tested using CNN based segmentation on our own set of 5000 images collected from 1000 students. The observed result shows 90% accuracy in predicting the drowsiness of the student by observing the face pattern of the student without streaming the video to the teacher&#x2019;s device.
Sarcasm Detection in News Headlines

Sarcasm is an important part of communication. Sarcasm is expressed in words, facial expressions and can be noticed in the intonation of voice. So in this digital age, sarcastic comments are passed everyday via tweets, comment sections of different social media outlets , and even news headlines. Newspapers often seem to use sarcasm in their headlines to grab the reader&#x2019;s attention. Some of these headlines can be misunderstood and taken to mean something different than the original intentions. However, more often than, not the readers find it difficult to detect the irony within the headlines thus, getting the incorrect idea about the actual news and further passing on their understanding to their friends and colleagues. This leads to a need to detect sarcasm especially in the news and on social media. The sarcasm detection of text has its challenges because text lacks intonations and deflection of voices that occur when a sarcastic statement is formed vocally by a person&#x2019;s. This task centers around the impacts of different encoding strategies utilized recorded as a hard copy to erase machine- produced objects. Use inside and out research strategies and think about outcomes. Present data innovation in movements of every kind, for example, tokenization, preparing, estimation preparing, just as analyst criticism and relaxation exercises, like record examination, prior to eliminating highlights. This strategy for pre-handling is generally utilized, broadly acknowledged and utilized in this venture. Different ways of unloading things like Count Vectorizer and enter the words utilized in this venture. Standard memory, XGBoost classifier and Logistic relapse were the preparation machines utilized in this task.
Server Lateral Backend Processing of Data Information Tags using Blockchain and Key Encryption Concerning Extremity Data

Server rendering of secured clusters requires a high amount of authorization header parameters as bearer encrypted keys with a hash generation as a session environment. However, information tags processing and authentication needs to be securely designed using authentication tokens so that the leaf of clusters should be pre-processed and has a track of its neighboring leaf data positions. The activity record of all the retrieval processes of data stored in clusters should be noted and needs to be decentralized as well as supply chain overheads quickly. The digital IDs generated for a node can be hashed with its upcoming node such that the block overheads together make immutable data and role control and can keep track of every activity. The hashed keys remain environmental encrypted variables which will be expired on synchronous data block offloading timeout. Our proposed system uses a blockchain for its decentralized way of storage and payload encryption for real-time environment keys with expiry timer with regeneration. Block memory optimization can be done by offloading the block overheads that are hashed as keys and storing them via serverless storage. The verification tag key can be embedded in an agile returnable code, and the authentication of the genuineness of its inheritance can be articulated using hash key-based server authentication to a request over the blockchain network. Thus, the proposed solution can overcome the duplication of information tags over tangible variation of articles.
Image Classification for Advanced Driving Assistant System in Electric Wheelchair

Demands for welfare equipment such as electric wheelchairs are increasing in Japan, because of traffic accidents by the elderly. In the field of automobiles, the safety driving supports have been researched in many divergences. However, in the field of electric wheelchairs, not advanced too much. In this study, we propose a method of machine learning by Self-Supervised Learning (SSL) and a method of image classification according to the results, using actual sidewalk images as a machine learning dataset, to Advanced Driving Assistant System (ADAS) of electric wheelchairs.
Proximal Instance Aggregator networks for explainable security vulnerability detection

Security vulnerabilities in software are the root cause of cyberattacks. Considering that these defects have huge associated costs, they should be proactively detected and resolved before shipping the software. Data-driven approaches like Artificial Intelligence (AI) are vastly explored for automatic vulnerability detection, given their potential to leverage large-scale vulnerability data feeds and learn from these scenarios. This work introduces a novel Proximal Instance Aggregator (PIA) neural network for accurately capturing insecure C code patterns from Abstract Syntax Tree (AST). It is built upon the concept of Multiple Instance Learning (MIL), which treats the AST representation of the code as a ‘ bag’ of tree path ‘ instances’ . The security vulnerability can manifest in one or multiple such AST path instances. The PIA model dynamically learns a set of abstract concepts to describe the patterns associated with the AST paths. Specifically, the vulnerable nature of an AST path is characterized by its proximity to these concepts. The model also employs the attention mechanism to generate deep representations. By drawing cross-correlation of features between the path instances, the self-attention robustly weighs the relevance of each AST path towards vulnerability classification. The MIL utilizes these deep feature sets to construct the concept space. Thus, even without explicit supervision for localizing the line of defect, the AI automatically learns AST instance classification in a weakly supervised manner. Since AST-level prediction is formed as an aggregation of instance classifications, the AI is inherently explainable. The model outperforms state-of-the-art methods by a fair margin. It achieves 95.63% detection accuracy and 95.65% F1-score on the benchmarked NIST SARD, NVD datasets for a range of vulnerabilities. • Vulnerabilities in software are the key points for cyber exploitation. • AI can learn to distinguish vulnerable patterns from code Abstract Syntax Tree. • Multi-instance learning implicitly creates concepts to characterize bug traces. • AI design follows explainability; achieves 95.65% F1-score on SARD, NVD data.
Energy Efficient Load Balancing Algorithm through MetaHuristics Approaches for Cloud-Computing-Environment

This research work is related to load balancing for implementing the performance of resource utilization. The issues for maximum load balancing requirement in cloud computing. And are using more resources to balance the load in research solutions. So that reduces the parameters such as response time, request time, and makespan. So this work focuses on load balancing because when do we compare with the other parameters in load balancing and cloud services provided by models such as services model and deployment models such as IaaS, PaaS, and SaaS and deployment model public, private, and community in cloud computing. And hybrid is a combination of two models like public and private. This paper is about of goal of load balancing coming from multiple users. Perfectly balancing in a cloud or datacenter. Because it will be necessary to develop an effective load balancing strategy to appropriately allocate Virtual Machine to physical resources. The simulated result will be using the cloudsim tool to succeed in load balancing balanced based on random optimizations.
Facial Recognition using OpenCV and Python on Raspberry Pi

Face acknowledgment is a typical piece of a security framework, and its value is to recognize the face is human or not. Picture process is the foundation of any security framework like unique mark lock framework, face lock framework, eye recognition. Face discovery utilizing raspberry pi 4 is a quicker picture process and its legitimate ID of a face. The raspberry pi 4 is the most recent minimal expense Mastercard size PC. In this paper face location framework for security, intention depends on open-source PC vision (OpenCV) with python programming. By utilizing an alternate information base it’s not difficult to prepare the mentor and identifier. This idea is higher degree on security and robotization frameworks.
A novel visual classification framework on panoramic attention mechanism network

Fine-grained classification is a challenging task due to the difficulty of finding discriminative features and the localization of feature regions. To handle these challenges, a novel visual classification framework on panoramic attention mechanism that combines multiple attention networks to locate and identify features with more semantic interest is proposed. Firstly, based on the classical convolutional neural network, the global information of the image feature is expressed by linear fusion. Secondly, the foreground attention branch is used to further extract the distinguishing details of the salient features. Then, more features are mined from the complementary object area through the background attention branch to learn more perfect fine-grained feature expression. Finally, three network branches are trained together to enhance the network's ability to express representative features of fine-grained images. Our model can be viewed as a multi-branch network, which benefits each other and optimizes the network together. Experiments were conducted on CUB-200-2011, Stanford Dogs and FGVC-Aircraft datasets, and the accuracy was used as the quantitative measurement. Experimental results show that the proposed method has the highest accuracy; the average accuracy is 89.8%. It is effective and superior to the current advanced methods.
Soft Support: Specially Abled Communication

Mobile applications are now a part of our life in many ways like ordering food, watching news, booking tickets etc. The same mobile applications can be used for betterment of the disabilities in human too. Our approach deals with a mobile application that will help disabled people to communicate with others without any problem. The inefficiency eventualities blanked in this project help humans having speech impairment, vision impairment, listening impairment to deal with real lifestyle troubles easily. Application is created using mobile sensors. In this research paper a vital assessment of present technologies and lots of issues of disabled people has been presented and pointed out for further development. Furthermore, the proposed technique should gain better performance and efficiency.
Driver Hand Activity Recognition using NIR Camera and Deep Neural Network

Distraction in driving is a significant factor in accidents. Hands off the wheel is one of the main sources of driver distraction. In some countries, such as Japan, using two hands to grasp the steering wheel in driving is suggested in the traffic regulation. This paper proposes a driver hand activity recognition system, which can be used for assisting drivers and increasing safety in driving. The proposed system adopts Near Infrared (NIR) camera to obtain the stable observation for the driver's hands in different lighting conditions. A Deep Neural Network (DNN) with a real-time performance is employed to recognize the hand activities. The proposed system can distinguish right and left hands, also can classify each hand into three categories: controlling steering wheel, idling, and performing other tasks. The experiment results demonstrate the proposed system outperforms the baseline method.
Automated Gate Control System using Face Mask Detection

This research paper gives a brief idea of controlling entrance gates of different areas like metro stations, railway stations, airports, corporate offices, restaurants, hotels and home with the face mask detection technology. In this, the camera will capture the real time video of a person using Artificial Intelligence[15],whosoever is entering the gate, processes the video and detects if the concerned person is wearing the mask properly or not. If the person is wearing a mask then the gate will open, if not then the gate will remain closed until the mask has been worn properly. The main motivation for this project comes from the current situation in the world where Covid-19 is spreading at a pace which is being difficult to control. This upcoming technology prototype can fuel in new ideas into different projects which are already ongoing to battle the pandemic. Also, the scope of this technology is not just limited to the face mask detection and has a wider and a more complex use-case in the real world.
Poisoning Attacks against Feature-Based Image Classification

Adversarial machine learning and the robustness of machine learning is gaining attention, especially in image classification. Attacks based on data poisoning, with the aim to lower the integrity or availability of a model, showed high success rates, while barely reducing the classifiers accuracy - particularly against Deep Learning approaches such as Convolutional Neural Networks (CNNs). While Deep Learning has become the most prominent technique for many pattern recognition tasks, feature-extraction based systems still have their applications - and there is surprisingly little research dedicated to the vulnerability of those approaches. We address this gap and show preliminary results in evaluating poisoning attacks against feature-extraction based systems, and compare them to CNNs, on a traffic sign classification dataset. Our findings show that feature-extraction based ML systems require higher poisoning percentages to achieve similar backdoor success, and also need a consistent (static) backdoor position to work.