Reviewerly/exHarmony huggingface dataset:
AI and Machine Learning

In the most recent decade, an enormous number of learning strategies have been presented in the field of the AI. Supervised learning has emerged as a major area of research in machine learning. Large numbers of the supervised learning methods have discovered application in their preparing and investigating assortment of information. One of the principle attributes is that the managed learning has the capacity of commenting on preparing information. The supposed marks are class names in the order cycle. There is an assortment of calculations that are utilized in the managed learning strategies. This chapter sums up the crucial parts of a couple of regulated techniques. The principle objective and commitment of this chapter is to introduce the outline of AI and give AI procedures.
Analysis of Weights and Feature Patterns in Popular 2D Deep Neural Networks Models for MRI Image Classification

A deep neural network (DNN) includes variables whose values keep on changing with the training process until it reaches the final point of convergence.These variables are the co-efficient of a polynomial expression to relate to the feature extraction process.In general, DNNs work in multiple 'dimensions' depending upon the number of channels and batches accounted for training.However, after the execution of feature extraction and before entering the SoftMax or other classifier, there is a conversion of features from multiple N-dimensions to a single vector form, where 'N' represents the number of activation channels.This usually happens in a Fully connected layer (FCL) or a dense layer.This reduced 2D feature is the subject of study for our analysis.For this, we have used the FCL, so the trained weights of this FCL will be used for the weight-class correlation analysis.The popular DNN models selected for our study are ResNet-101, VGG-19, and GoogleNet.These models' weights are directly used for fine-tuning (with all trained weights initially transferred) and scratch trained (with no weights transferred).Then the comparison is done by plotting the graph of feature distribution and the final FCL weights.
3.105 A 10-Year Data Analysis of Sexual and Domestic Violence Against Children and Adolescents in Korea

This study aimed to examine the current state of sexual and domestic violence against children and adolescents in Korea.
Ore mineral formation stages of the Sredniy site (Mnogovershinnoye deposit)

The Mnogovershinnoye gold deposit (Khabarovsk Territory, Russia) today is a significant industrial facility, which features extension additions as a results of additional in-mine exploration of deep horizons and identification of new objects within the ore field. The purpose of this work is to study the formation stages of ore minerals of one of the deposit sites as well as to identify the mineralogical and structural-textural features of ores. The methods of mineralogical and petrographic study of the material composition of ores are widely used in geological practice and allow to determine the material features of the studied objects as well as describe the ore formation process. The study of ore types was carried out using an Olympus BX51 microscope. Field observations performed by the authors served the base for the mineralogical and petrographic study of ore samples and host rocks of the Sredniy site of the Mnogovershinnoye deposit, which led to a slightly different understanding of formation stages of ore mineralization of this section of the deposit. The study revealed at least three formation stages of the material complex of this site of the deposit. The first stage is a magmatic one. It characterizes with the formation of pyrite of the first generation. The second stage is an ore hydrothermal one, as a result of which the gold-sulfide mineralization was formed including pyrite of the second generation, chalcopyrite, magnetite, kustelite. Gangue quartz was formed at the end of the second stage together with such ore minerals as pyrite, chalcopyrite, magnetite, and an insignificant amount of kustelite and native copper. The third stage is an exogenous one, during which substitution and oxidation structures were formed with iron hydroxide evolution. The studies of the ores of the Mnogovershinnoye deposit have shown once again the presence of polysulfide multistage mineralization, which had been developing in close connection with the magmatic process. Knowledge about the deposit formation stages is an integral part of the creation of geological and structural models of the deposit, which are necessary to increase the efficiency of geological exploration within the studied objects.
EgoTaskQA: Understanding Human Tasks in Egocentric Videos

Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism from multi-tasking and partial observations in multi-agent collaboration. Most prior works leverage action localization or future prediction as an indirect metric for evaluating such task understanding from videos. To make a direct evaluation, we introduce the EgoTaskQA benchmark that provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos. We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others. These questions are divided into four types, including descriptive (what status?), predictive (what will?), explanatory (what caused?), and counterfactual (what if?) to provide diagnostic analyses on spatial, temporal, and causal understandings of goal-oriented tasks. We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos. We hope this effort will drive the vision community to move onward with goal-oriented video understanding and reasoning.
Robustness of Unsupervised Representation Learning without Labels

Unsupervised representation learning leverages large unlabeled datasets and is competitive with supervised learning. But non-robust encoders may affect downstream task robustness. Recently, robust representation encoders have become of interest. Still, all prior work evaluates robustness using a downstream classification task. Instead, we propose a family of unsupervised robustness measures, which are model- and task-agnostic and label-free. We benchmark state-of-the-art representation encoders and show that none dominates the rest. We offer unsupervised extensions to the FGSM and PGD attacks. When used in adversarial training, they improve most unsupervised robustness measures, including certified robustness. We validate our results against a linear probe and show that, for MOCOv2, adversarial training results in 3 times higher certified accuracy, a 2-fold decrease in impersonation attack success rate and considerable improvements in certified robustness.
Research and design of medical data sharing and insurance automatic claim system based on blockchain technology

In recent years, blockchain technology has been applied to various business scenarios with its characteristics of decentralization, openness, self-consistency and information immutable. In the medical insurance industry, disputes often occur between insurance institutions and policyholders due to inaccurate personal information and different confirmation of contract terms. With the digitalization of medical information and the rapid development of science and technology, insurance claim settlement schemes based on blockchain and homomorphic encryption can enable the hospital, the insurance institution and policyholders to establish a trusted mechanism, which can ensure the authenticity and security of the policyholder's personal information and the validity of the contract. The scheme combines Paillier homomorphic encryption and blockchain smart contract while designing access control list to control patients' data. In the scenario of insurance claim settlement, it can not only protect the privacy of patients' medical data, but also ensure the correct settlement result of insurance claim.
Referee: Towards reference-free cross-speaker style transfer with low-quality data for expressive speech synthesis

Cross-speaker style transfer (CSST) in text-to-speech (TTS) synthesis aims at transferring a speaking style to the synthesised speech in a target speaker's voice. Most previous CSST approaches rely on expensive high-quality data carrying desired speaking style during training and require a reference utterance to obtain speaking style descriptors as conditioning on the generation of a new sentence. This work presents Referee, a robust reference-free CSST approach for expressive TTS, which fully leverages low-quality data to learn speaking styles from text. Referee is built by cascading a text-to-style (T2S) model with a style-to-wave (S2W) model. Phonetic PosteriorGram (PPG), phoneme-level pitch and energy contours are adopted as fine-grained speaking style descriptors, which are predicted from text using the T2S model. A novel pretrain-refinement method is adopted to learn a robust T2S model by only using readily accessible low-quality data. The S2W model is trained with high-quality target data, which is adopted to effectively aggregate style descriptors and generate high-fidelity speech in the target speaker's voice. Experimental results are presented, showing that Referee outperforms a global-style-token (GST)-based baseline approach in CSST.
Detecting Insider Threat through Psychometric Scores and Work Environment

Nowadays the cyber-threat is looming large from disgruntled employees rather than external attackers. With the advent of modern threat models which help in development of adversary attack scenarios, the security designers can get an idea of attack mitigation strategies. The analysis of threat model helps in knowing the unforeseen circumstances which can lead to a cyber-security risk. If the weakness in the network or in any other element of a cyber-ecosystem is identified during the design of security models, then eventually it will lead to stronger security applications. This paper formulates a framework of identification of insider threat which may be a result of various factors in an organization. The proposed technique considers psychometric condition of an employee along with other factors which may give rise as a threat to the organization. The dataset has been used to train the model and the experimental results have found an effective way to detect the insider threat in specific cases.
Implementation of stereo matching algorithm based on Xavier edge computing platform

In view of the existing high-precision stereo matching based on deep learning which network structure is complex, and it is difficult to deploy and run in real time on edge platform. An improved stereo matching algorithm based on RTStereoNet is proposed. Firstly, the channel attention mechanism is introduced in the matching cost aggregation stage of RTStereoNet, so that the network can adaptively enhance the extraction of effective information and reduce the ambiguity of matching. Secondly, in the disparity refinement stage of RTStereoNet, the color image is introduced to compensate for the loss of details caused by the large-scale downsampling of the network, and a lightweight disparity refinement module is constructed to expand the receptive field of the network. In addition, based on Jetson Xavier NX edge computing module, a special edge computing platform is constructed, with the help of TensorRT inference framework, the calculation support problem of special operators is solved through CUDA programming, and achieved deployment acceleration on the platform for both models before and after the improvement. The results show that after the accelerated deployment, the inference speed of the improved model can reach 30 fps on the KITTI2015 test set, and the improved model has higher accuracy than the original model.
Hybrid Approach to Project Management: Still Have a Long Way to Go

Project success has been inevitably influenced by project management approaches. Researchers and project managers have studied and adopted three different approaches, namely traditional, agile and hybrid, to explore the most suitable approach for the development and success of projects. In this study, the literature research method has been applied. Comparison of the performance, which were provided by previous studies, among three approaches in the fields of technology-based products and services, education and a wide range of international cross-industry projects have been made in the article to prob into the most effective project management approach. Even if the case studies from the literature review were mainly based on some certain countries and regions, which may affect the correctness of the universality of the conclusion, it is noticeable that although more reliable data is needed, currently hybrid approach has been regarded as the most favorable one with both advantages from traditional and agile approaches.
Intelligent learning assistants help oversea students ask questions

With the development of the economy and the advancement of technology, as well as the positive effects of economic globalization, more and more students are choosing to study abroad. However, for overseas students who are not very fluent in language communication, it will be troublesome, not only for the understanding of the lesson, but also in the communication with the instructors and professors there are great obstacles, in order to stimulate the economy of study abroad, in order to make the students have less difficulties with the teachers in academic research, to achieve the real sense of barrier-free communication, and to improve the academic performance, we use automatic speech recognition (ASR) technology as a template and basic framework. In order to stimulate the economy of study abroad and to make it less difficult for students to communicate with teachers in academic research and to improve their academic performance, we designed an application for automatic speech recognition and language conversion language simplification by using ASR as a template and a basic framework, respectively.
Design and Simulation of Modified Type-2 Fuzzy Logic Controller for Power System

This Article exhibits the structure of a Modified Type-2 (MT2) Fuzzy Logic (FL) Controller (MT2FLC), direction line programming of development, also performance optimization for different power systems. The implementation of the MT2FLC for control of a power system. New participation capacities were considered in adjusting a domain for an Interval Type-2 (IT2) Fuzzy Logic (FL) System (IT2FLS). Another structure in graphic user interface (GUI) mimicked four controllers: an optimal PID controller, FLC, a Type-1(TIFLC), an Interval Type-2 ((IT2FLC), and the MIT2FLC. Their yields were analysed, different periods of the structure procedure for the fuzzy framework, from beginning depiction to conclusive execution, can be gotten from the altered tool compartment (whose capacity to create complex frameworks and adaptability in broadening the accessible usefulness into working with adjusted type2 fuzzy administrators, phonetic factors, IT2 participation capacities, and defuzzification strategies, just as in assessing the MIT2FLC are its best characteristics). Case study for this work, all the optimization controllers implemented for a Brushless DC (BLDC) Motor with MATLAB Ver.2012a was utilized in the recreation and plan of the entirety of the procedure GUIs. Satisfactory results are obtaining which improve the implementation of the using of MIT2FLC controller as practical solution of power system.
GENIE: Higher-Order Denoising Diffusion Solvers

Denoising diffusion models (DDMs) have emerged as a powerful class of generative models. A forward diffusion process slowly perturbs the data, while a deep model learns to gradually denoise. Synthesis amounts to solving a differential equation (DE) defined by the learnt model. Solving the DE requires slow iterative solvers for high-quality generation. In this work, we propose Higher-Order Denoising Diffusion Solvers (GENIE): Based on truncated Taylor methods, we derive a novel higher-order solver that significantly accelerates synthesis. Our solver relies on higher-order gradients of the perturbed data distribution, that is, higher-order score functions. In practice, only Jacobian-vector products (JVPs) are required and we propose to extract them from the first-order score network via automatic differentiation. We then distill the JVPs into a separate neural network that allows us to efficiently compute the necessary higher-order terms for our novel sampler during synthesis. We only need to train a small additional head on top of the first-order score network. We validate GENIE on multiple image generation benchmarks and demonstrate that GENIE outperforms all previous solvers. Unlike recent methods that fundamentally alter the generation process in DDMs, our GENIE solves the true generative DE and still enables applications such as encoding and guided sampling. Project page and code: https://nv-tlabs.github.io/GENIE.
SINS/DVL integrated based on interacting multiple model approach by integrating feedback particle filter for ship navigation

To improve the performance of strapdown inertial navigation system (SINS)/ Doppler velocity logs (DVL) integrated navigation system, a new Interacting Multiple Model Feedback Particle Filter (IMMFPF) approach is proposed under the condition of complex and changeable underwater environment. In this scheme, the Interacting Multiple Model uses to deal with the model switch, while FPF approach takes each particle as a controllable stochastic system. By finding the solution about the Euler-Lagrange boundary value, the optimal feedback control law of Kullback-Leibler distance is obtained, and then the state of the particle itself is corrected and updated. Both simulations and field test illustrate that proposed IMMFPF approach can achieve a more accurate solution and make a more correct and effective response for SINS/DVL integrated navigation system.
Unveiling Real-Life Effects of Online Photo Sharing

Social networks give free access to their services in exchange for the right to exploit their users' data. Data sharing is done in an initial context which is chosen by the users. However, data are used by social networks and third parties in different contexts which are often not transparent. In order to unveil such usages, we propose an approach which focuses on the effects of data sharing in impactful real-life situations. Focus is put on visual content because of its strong influence in shaping online user profiles. The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation. These components are combined in LERVUP, a method which learns to rate visual user profiles in each situation. LERVUP exploits a new image descriptor which aggregates object ratings and object detections at user level and an attention mechanism which boosts highly-rated objects to prevent them from being overwhelmed by low-rated ones. Performance is evaluated per situation by measuring the correlation between the automatic ranking of profile ratings and a manual ground truth. Results indicate that LERVUP is effective since a strong correlation of the two rankings is obtained. A practical implementation of the approach in a mobile app which raises user awareness about shared data usage is also discussed.
Extracting Multi-Language Text from Video into Editable Form

For semantic video retrieval (from archives), alert production (live streams), and large levels applications like assessment mining and content reiterating, textual material in videos is an appealing index. The discovery and recognition of textual content are essential factors of similar systems, and this is the focus of our research. This case study talks about all types of the framework for text recognition and segmentation in video frames. We focus on cursive scripts, in particular, using English text as an example. Fine-tuning deep neural network-based item finders for the particular instance of text identification is used to recognize textual patches in video frames. Convolutional neural networks (CNNs) are used to identify the script of the discovered textual material, while an EngNet, a hybrid of CNNs, and long short-term memory (LSTM) (Rajesh Kanna and Santhi in Expert Syst Appl 194, 2022 [1]) systems is proposed for identification. Also being generated is a benchmark dataset with cursive writing and over 13,000 video frames. An F-proportion of 88.3% for identification and an F-proportion of 87% for acknowledgment was later a total arrangement of preliminaries.
Identification of industrial control devices based on CNN-PCA hybrid feature extraction

With the development of information technology and intelligent manufacturing technology, more and more industrial control system (ICS) devices with IP address can be accessed through the Internet. In order to quickly search and monitor the networked industrial control device or system, and master the type, scale and regional distribution of the device, it is urgent to use relevant technologies to identify the networked industrial control system or device. In this paper, we first analyze the industrial control protocols widely used in industrial control systems and the special fields in the protocols. Then, we design a detection scheme to collect the data packets of ICS device, and select the specific word segment of industrial control protocol according to the diversity and consistency of field values. Finally, we propose an automatic detection system for industrial control device, which uses convolutional neural network (CNN) and principal component analysis (PCA) to extract feature field and construct feature fingerprint, and uses limit learning machine (ELM) to realize fine-grained ICS device recognition.
BoxMask: Revisiting Bounding Box Supervision for Video Object Detection

We present a new, simple yet effective approach to uplift video object detection. We observe that prior works operate on instance-level feature aggregation that imminently neglects the refined pixel-level representation, resulting in confusion among objects sharing similar appearance or motion characteristics. To address this limitation, we propose BoxMask, which effectively learns discriminative representations by incorporating class-aware pixel-level information. We simply consider bounding box-level annotations as a coarse mask for each object to supervise our method. The proposed module can be effortlessly integrated into any region-based detector to boost detection. Extensive experiments on ImageNet VID and EPIC KITCHENS datasets demonstrate consistent and significant improvement when we plug our BoxMask module into numerous recent state-of-the-art methods.