Reviewerly/exHarmony huggingface dataset:
Combining neural and symbolic approaches to solve the Picasso problem: A first step

In this work we create a bridge between Convolutional Neural Networks and Answer Set Programming in order to tackle the known Picasso Problem in the automated detection of images. The basic idea is to first exploit the main features of the neural network approach for image recognition, and then to address the problem of identifying well-formed (not meshed up) images by means of explicit knowledge expressed by logical rules. Preliminary experiments suggest that the proposed approach is promising and can be considered as a first step in the direction of solving the Picasso Problem, as well as a witness of the benefits that can be obtained by the combination of a neural approach with a pure symbolic one.
Monitoring Of Industrial Electrical Equipments

There is a considerable chance of fire breakouts in industries such as petroleum, chemical, oils, & gas, resulting in massive devastation, destruction of livelihood, and the great majority of all, the loss of life. When an event happens, it's essential to have a mechanism in place that can alert authorized personnel and ensure that the premises are safe. In order to detect fires (via smoke as well as temperature sensors) and LPG leaks, an IOT-based industrial problem detection project was created. Data is sent to a remote location through the Internet of Things (IoT). 'Things' can communicate with sensors, circuits, programs, and accessibility through the Internet of Things (IOT). Human interaction is unnecessary for these technologies.(\p)(p)Monitoring voltage and current consumption by industry is also a feature of this system. Use this tool to locate the hottest area, which may be assessed by determining the most hazardous area for people or production. A current sensor measures the overall amount of current consumed by the industrial sector. ' If the power exceeds the threshold and the load is tripped, the IOT cloud app will notify the appropriate person.
Authentication and Facial Expression Analysis System using Deep Transfer Learning Approach

Nowadays, deep and transfer learning technologies and its algorithms have become more popular in research area of computer vision field for image recognition and classification tasks. There are various kinds of neural networks broadly used in research area of image recognition and classification problems such as CNN, DNN, etc. In this research work, the researcher proposed and developed two customized modules. The first module is used for authentication of students in the online examination system. The second module is about to find the behavior analysis and facial expression recognition of students during online examination. The proposed authentication and facial expression analysis module is constructed by combining features of CNN and transfer learning approach with pre-trained architectures. To develop proposed system the models used are trained and validated by using various datasets. It also calculates the performance of students during the online examination period. The accuracy of these deep learning models highly based on data quantity used for training and testing model. Training and testing datasets depend on types of problems considered for which models are to be built.
Exploration of Geological Informatization Based on The Internet of Things and Cloud Computing in The Era of Big Data

With the rapid development of computer science and technology, cloud computing and Internet of Things technology have been widely used in the research of geological information technology. As the current big data era has more extensive data requirements, and the Internet of Things and cloud computing technologies are widely used in daily life and work. Especially in the fields of geological information surveying and mapping, management, query, storage, analysis, etc., applications are gradually increasing. This article mentions that the geoinformatization research algorithm based on the Internet of Things and cloud computing in the era of big data has improved the accuracy of geological information by 5% and 7.5%, respectively. The results show that the Internet of Things and cloud computing models play an important role in geological informatization.
Iteratively sampling scheme for stochastic optimization with variable number sample path

Optimal search methods are proposed for solving optimization problems with analytically unobtainable objectives. This paper proposes a method by incorporating sampling schemes into the directional direct search with variable number sample path and investigates its effectiveness in solving stochastic optimization problems. We also explore the conditions on sample sizes at each iteration under which the convergence in probability can be guaranteed. Finally, a set of benchmark problems are numerically tested to show the effectiveness in different sampling schemes.
Decision-Based Fusion for Vehicle Matching

In this work, a framework is proposed for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. Our approach then examines the extent of reliance between signatures generated from vehicle images to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. To that end, a dataset was collected that contains hundreds of thousands of side-view vehicle images under different illumination conditions and elevation angles. Experiments show that our approach could achieve better matching accuracy by taking into account the decisions made by a whole-vehicle or wheels-only matching network.
IHAR—A fog‐driven interpretable human activity recognition system

Abstract Human activity recognition (HAR) is an active research area that is currently being applied to various healthcare applications such as fall detection, assisted living, etc. These applications make use of the Internet of Things which is widespread across today's world. One of the major challenges in these applications is the need for quick intelligent decisions. The deployment of hierarchical edge‐fog‐cloud computing architecture is the potential solution to address the latency issue. In this article, we proposed an interpretable human activity recognition (IHAR) framework that supports HAR, leveraging the advantages of the fog layer. The proposed framework used a deep learning (DL) model at the cloud infrastructure to classify the activities of humans. The trained DL model is made to run on local fog nodes. However, the DL model being black‐box does not provide any explanations for the output to make them acceptable by the users and physicians. Hence, this article also incorporated an explainable artificial intelligence model at the fog layer, to gain insights into the classified output of the DL model. The effective model‐level insights emphasized the need for explainable HAR. The article also included an analysis of the results of the existing explainable AI models such as LIME and SHAP to understand which model leads to better performance in the domain of HAR. Results show that the SHAP model has a 33% higher success rate in generating explanations when compared to the LIME model.
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings

One intriguing property of adversarial attacks is their “transferability” – an adversarial example crafted with respect to one deep neural network (DNN) model is often found effective against other DNNs as well. Intensive research has been conducted on this phenomenon under simplistic controlled conditions. Yet, thus far there is still a lack of comprehensive understanding about transferability-based attacks (“transfer attacks”) in real-world environments.To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account. The study leads to a number of interesting findings which are inconsistent to the existing ones, including: (i) Simple surrogates do not necessarily improve real transfer attacks. (ii) No dominant surrogate architecture is found in real transfer attacks. (iii) It is the gap between posterior (output of the softmax layer) rather than the gap between logit (so-called κ value) that increases transferability. Moreover, by comparing with prior works, we demonstrate that transfer attacks possess many previously unknown properties in real-world environments, such as (i) Model similarity is not a well-defined concept. (ii) L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm of perturbation can generate high transferability without usage of gradient and is a more powerful source than L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> norm. We believe this work sheds light on the vulnerabilities of popular MLaaS platforms and points to a few promising research directions. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Performance evaluation of Spam and Non-Spam E-mail detection using Machine Learning algorithms

All of us are familiar with the importance of social media in facilitating communication. e-mail is one of the safest social media platforms for online communications and information transfer over the internet. As of now, many people rely on email or communications provided by strangers. Because everyone may send emails or a message, spammers have a great opportunity to compose spam messages about our many hobbies and passions, interests, and concerns. Our internet speeds are severely slowed down by spam, which also collects personal information like our phone numbers from our contact list. There is a lot of work involved in identifying these fraudsters and also identifying spam content. Email spam refers to the practice of sending large numbers of messages via email. The recipient bears the bulk of the cost of spam, therefore it's practically free advertising. Spam email is a form of commercial advertising for hackers that is financially viable due of the low cost of sending email. Anti-spam filters have become increasingly important as the volume of unwanted bulk e-mail (also spamming) grows. We can define a message, if it is a spam or not using this proposed model. Machine learning algorithms can be discussed in detail, and our data sets will be used to test them all, with the goal of identifying the one that is most accurate and precise in its identification of email spam. Society of machine learning techniques for detecting unsolicited mass email and spam.
Small Object Detection using Prediction head and Attention

UAV(Unmanned aerial vehicle)-captured image contains a number of small object. Object captured in UAV's low altitude flight are expressed as low resolution in the image and have ambiguous boundaries. The detection problem of small objects expressed in limited pixels in UAV-captured images is difficult. In this paper, we used an additional prediction head to improve the detection performance o small objects, and modified the channel attention module of CBAM and added it to the PANet. The experiment showed that the proposed method showed a 4.1% improvement in mAP performance compared to the existing method in VisDrone 2020-DET dataset (Abstract)
FinRED: A Dataset for Relation Extraction in Financial Domain

Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets. In the current literature, there is no extensive open-source relation extraction dataset specific to the finance domain. In this paper, we release FinRED, a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. FinRED has been created by mapping Wikidata triplets using distance supervision method. We manually annotate the test data to ensure proper evaluation. We also experiment with various state-of-the-art relation extraction models on this dataset to create the benchmark. We see a significant drop in their performance on FinRED compared to the general relation extraction datasets which tells that we need better models for financial relation extraction.
Physical Education Teaching Strategy under Internet of Things Data Computing Intelligence Analysis

Racket sports such as tennis are amongst the most popular recreational sports activities. Optimizing tennis teaching methods and improving teaching modes can effectively improve the teaching quality of tennis. In this study, a video and image action recognition system based on image processing techniques and Internet of things is developed to overcome the shortcomings of the traditional tennis teaching methods. To validate its performance, the students of tennis courses are divided into experimental group and control group, respectively. The control group is taught by using the traditional tennis teaching method whereas the experimental group is taught by using the IoT video and image recognition teaching system. Three factors of students including service throwing height, arm elbow angle, and knee bending angles of both groups are measured and compared with those of world elite tennis players. The results show that the students' serving abilities in the experimental group are significantly improved using the video and image recognition system based on IoT, and they are better than those of the students in the control group. The proposed video and image processing technique can be applied in students' physical education and can be employed to provide the basis for the innovation of tennis teaching strategies in physical education.
An adaptive estimation method with exploration and exploitation modes for non-stationary environments

Dynamic systems are highly complex and hard to deal with due to their subject- and time-varying nature. The fact that most of the real world systems/events are of dynamic character makes modeling and analysis of such systems inevitable and charmingly useful. One promising estimation method that is capable of unlearning past information to deal with non-stationarity is Stochastic Learning Weak Estimator (SLWE) by Oommen and Rueda (2006). However, due to using a constant learning rate, it faces a trade-off between plasticity and stability. In this paper, we model SLWE as a random walk and provide rigorous theoretical analysis of asymptotic behavior of estimates to obtain a statistical model. Utilizing this model, we detect changes in stationarity to switch between exploratory and exploitative learning modes. Experimental evaluations on both synthetic and real world data show that the proposed method outperforms related algorithms in different types of drifts.
Instance Segmentation using Mask RCNN for Surveillance

Instance Segmentation combines object detection, where the goal is to classify and locate every object according to its bounding box, and semantic segmentation, which defines each pixel according to its object category. Deep learning uses Mask R-CNN architecture to perform instance segmentation on data. This architecture is an enhancement of a well-known object detection algorithm called Faster R-CNN. With the addition of a mask branch to the Faster R-CNN model, the Mask R-CNN model has been evolved. This paper contains the idea of how Mask R-CNN can perform instance segmentation by using some examples of surveillance detection and segmentation. In this algorithm, masks are applied to the objects present in the image in order to detect and classify them. Mask RCNN algorithm is more accurate and has a lower time complexity than existing Faster RCNN algorithm for video surveillance.
3D joints estimation of human body using part segmentation

In this paper, we propose a novel method for 3D human joint estimation using part segmentation, and introduce an application for size measurement based on the obtained joints. A human segmentation dataset is first prepared as training set for the advanced neural network architecture. Different human parts yielded from the neural network are utilized to extract human joints. In the proposed method, the joints are categorized into the active joints and inert joints. In the extraction process of the active joints, the mathematical analysis method is adopted to calculate the joint positions. The geometric features of different human segments are further used to extract the inert joints. Moreover, we test on the dataset to compare its performance with our previous method based on geometrical features. The results show the average error of the joints is less than 4.2 cm, which is significantly improved from 5.8 cm demonstrated in our previous research. We also investigate the human size measurement. The distance between the joints is used to calculate the length, and the ellipse fitting method based on multi-frame point cloud is adapted to calculate the human girths. Compared with the manual measurement data, the size error is less than 4.1 cm.
Perceived individual fairness with a molecular representation for medicine recommendations

Medicine recommendations, as important research fields of artificial intelligence, could provide patients with treatments. However, existing medicine recommendation algorithms aim to maximize the overall accuracy while ignoring individual fairness. It is of great importance to ensure that medicine recommendation algorithms fairly treat every patient. To ensure individual fairness, we propose individual fairness-based medicine recommendation systems (IFMR). In fairness scenario, similar patients can get similar performance of recommendation. Specifically, we first adopt dual-pipeline gate recurrent units to capture the health condition with electrical health records. IFMR then utilizes the molecular graph encoder and molecular substructure encoder to capture medicine features at the molecular level. Finally, IFMR maintains the spatial distance of similar patients by utilizing multi-similarity individual fairness loss. Extensive experiments on benchmark datasets demonstrate that IFMR is more effective and fairer than all strong baselines.
Performance analysis of coexistence of traditional communication system and emerging semantic communication system

As a new paradigm, semantic communication (SC) is attracting attention as the next-generation communication. In order to develop semantic communication systems, the research trend is to adopt deep learning networks with the best performance in each field of deep learning. Deep-learning based SC systems are task-dependent because the deep networks employed are specialized to perform specific tasks. Therefore, SC systems cannot be used for users using applications that do not work with the deep networks exploited by the SC systems. Such users still should be served by a traditional communication (TC) system. Hence, in the near future, communication networks will face the coexistence of both SC system and TC system, and thus a base station (BS) has to simultaneously transmit symbol streams encoded by an SC system and a TC system via orthogonal frequency-division multiple access. Although the current works verify that SC systems outperform a TC system in a point-to-point communication, it is necessary to analyze whether the introduction of SC systems can also improve the network performance provided by a BS. In this paper, we investigate the impact of introducing the new emerging semantic communication on network performance by modeling signal-to-noise ratio of SC users served by SC systems and analyzing sum-rate maximization with a minimum required SNR constraint for SC users. Via extensive numerical results, changes in network performance with and without an SC system are discussed with varying the distances from a BS, the number of SC users served by an SC system, and the degree of SC performance.
The Synergies Between Understanding Belief Formation and Artificial Intelligence

Understanding artificial intelligence (AI) and belief formation have interesting bidirectional synergies. From explaining the logical derivation of beliefs and their internal consistency, to giving a quantitative account of mightiness, AI still has plenty of unexploited metaphors that can illuminate belief formation. In addition, acknowledging that AI should integrate itself with our belief processes (mainly, the capacity to reflect, rationalize, and communicate that is allowed by semantic coding) makes it possible to focus on more promising lines such as Interpretable Machine Learning.
When one speaker’s <i>broccoli</i> is another speaker’s <i>cauliflower</i>: the real-time processing of multiple speaker vocabularies

Once interlocutors settle on a specific label in conversation, they tend to maintain the linguistic precedent and reuse the same label (i.e. they become lexically entrained). This helps to facilitate comprehension, with listeners identifying referents more quickly when repeated labels are used compared to new labels. In the current study, we looked at whether listeners are additionally sensitive to repeated infelicitous labels (Experiment 1), as when non-native speakers, for example, overgeneralise a term (e.g. identifying a chair as the chair with tires). In addition, we investigated the extent to which listeners’ expectations of incorrect labels are influenced by knowledge of community speaking patterns, testing whether listeners could disregard recently encountered lexical errors from a non-native speaker as possible labels when processing a native speaker, who should not be expected to produce such errors (Experiment 2). Our results provide no evidence that listeners were able to take into account speaker information.
Discussion on the Construction of Interactive Chinese Teaching Mode of Mobile App Application under the Internet Background

Under the new situation of Chinese internationalization promotion and educational informatization, online learning of Chinese as a foreign language is the key to learning Chinese as a foreign language in the future. Today, people learning Chinese as a second language must learn Chinese quickly online. However, it is not the best thing to open a website for learning Chinese as a foreign language at present. Learning Chinese online may not meet the needs of students, and it is not uncommon for students to give up in the process of Chinese learning. Therefore, the article discusses the teaching methods of Chinese as a foreign language and the use of new media and analyzes the shortcomings of Chinese teaching. This paper demonstrates the possibility and scientificity of using mobile app to conduct online interactive teaching of Chinese as a foreign language from both theoretical and practical aspects, aiming to promote the development of online teaching of Chinese as a foreign language, and then promote the promotion of foreign language Chinese as a foreign language. The results of the survey show that 75%, 96.1%, and 96.2% of the students who have studied Chinese for three months, six months, and one year, respectively, have downloaded the app Chinese language-assisted teaching software to learn Chinese.