Reviewerly/exHarmony huggingface dataset:
Embedding Principle: A Hierarchical Structure of Loss Landscape of Deep Neural Networks

We prove a general Embedding Principle of loss landscape of deep neural networks (NNs) that unravels a hierarchical structure of the loss landscape of NNs, i.e., loss landscape of an NN contains all critical points of all the narrower NNs.This result is obtained by constructing a class of critical embeddings which map any critical point of a narrower NN to a critical point of the target NN with the same output function.By discovering a wide class of general compatible critical embeddings, we provide a gross estimate of the dimension of critical submanifolds embedded from critical points of narrower NNs.We further prove an irreversibility property of any critical embedding that the number of negative/zero/positive eigenvalues of the Hessian matrix of a critical point may increase but never decrease as an NN becomes wider through the embedding.Using a special realization of general compatible critical embedding, we prove a stringent necessary condition for being a "truly-bad" critical point that never becomes a strict-saddle point through any critical embedding.This result implies the commonplace of strict-saddle points in wide NNs, which may be an important reason underlying the easy optimization of wide NNs widely observed in practice.
Positive aspects of the communicative psychological effect of social networks on the individual

In today's age of the Internet, computers and mobile phones for people of all ages are available in almost every home. This, in turn, draws the individual into social media, bringing him or her into the communicative world of social media, creating a comfort zone for himself or herself. This, in turn, has a psychological effect on the individual, both positively and negatively on our society and national values. In this article, I have tried to highlight the reasons for engaging a person in communicative communication on social media and the current aspects of the positive psychological impact on the individual. In the article we will focus on the role of the virtual world in human life, the need not to depend on it, to use it for useful purposes.
Distributed Artificial Intelligent Model Training and Evaluation

Machine Learning (ML) and in particular Neural Networks (NN) are currently being used for different image/video processing, speech recognition and other tasks. The goal of supervised NN is to classify raw input data according to the patterns learned from an input training set. Training and validation of NN is very computationally intensive. In this paper we present an NN infrastructure to accelerate model training, specifically tuning of hyper-parameters, and model inference or prediction using distributed systems techniques. By accelerating model training, we give ability to researchers to obtain a large set of potential models to use and compare in a shorter amount of time. Automating this process not only reduces development time but will provide an easy means for comparing results for different classifiers and/or different hyper-parameters. With a single set of training data, our application will run different classifiers on different servers each running models with tweaked hyper-parameters. To give more control over the automation process the degree by which these hyper-parameters will be tweaked can be set by the user prior to running. The prediction step in most ML algorithms can also be very slow, especially on video prediction where current systems calculate their inference predictions on an entire input video, and then evaluate accuracy based on human annotations of objects of interest within the video. To reduce this bottleneck, we also accelerate and distribute this important part of the ML algorithm development. This process involves sending to each server the data; the model weights; and human annotations within the video segmentation. Our efficient distribution of input frames among each node greatly reduced the amount taken for testing and to generate accuracy metrics. To make our implementation robust to common distributed system failures (servers going down, lost of communication among some nodes, and others) we use heartbeat/gossip style protocol for failure detection and recovery. We tested our infrastructure for fast testing and inference of ML on video with data generated by a group of marine biologists researching the behavior of different marine species on the deep sea. Results show that by using our infrastructure times improved by a factor of 15.
Mobile app based pictorial identification key for Indian anophelines.

Malaria control strategies for a particular area largely depends on the correct identification of the mosquito species prevalent in that area. This study aimed to develop an android mobile app named 'Pictorial Indian anophelines key', which can be used to identify the 58 species of Indian female adult anophelines based on morphological characteristics.The pre-requisite to use this app requires a minimum basic knowledge of mosquito morphological characteristics and features. The user needs to view the collected mosquito under a dissecting binocular microscope and has to select the observations on the app presented in the form of labelled picture format. The app uses a divide and conquer algorithm which narrows the selection from genus level to final identification of species.The app is user-friendly, quick and it accurately identifies the mosquito species in a maximum of six consecutive clicks. The result displays a complete image of identified mosquito along with its quick identification features. It can be used for both subgenus Cellia and Anopheles.This pictorial key-based mobile app could be helpful for entomologists, malariologists, researchers, public health workers as well as students in the quick and correct identification of anopheline mosquitoes prevalent in India which in turn could be helpful in devising appropriate approaches towards malaria control and prevention. The app can be modified as per future requirements.
Using Contrastive Learning to Improve the Performance of Steganalysis Schemes

To improve the detection accuracy and generalization of steganalysis, this paper proposes the Steganalysis Contrastive Framework (SCF) based on contrastive learning. The SCF improves the feature representation of steganalysis by maximizing the distance between features of samples of different categories and minimizing the distance between features of samples of the same category. To decrease the computing complexity of the contrastive loss in supervised learning, we design a novel Steganalysis Contrastive Loss (StegCL) based on the equivalence and transitivity of similarity. The StegCL eliminates the redundant computing in the existing contrastive loss. The experimental results show that the SCF improves the generalization and detection accuracy of existing steganalysis DNNs, and the maximum promotion is 2% and 3% respectively. Without decreasing the detection accuracy, the training time of using the StegCL is 10% of that of using the contrastive loss in supervised learning.
Color Image Enhancement Algorithm on the Basis of Wavelet Transform and Retinex Theory

For enhance the contrast of low-illuminance images, reduce image noise, and maintain image edge detail information, a color image enhancement algorithm on the basis wavelet transform and Retinex theory is proposed in this paper. First, convert the low-illuminance color image to be enhanced from RGB space to HSV color space, perform wavelet transformation on the brightness component V, and separate several high-frequency and low-frequency children; then use improved single-scale Retinex algorithm to enhance the low-frequency offspring, reduce the influence of external light factors on the image and retain the edge texture of the image; the high-frequency children are subjected to blur enhancement processing to achieve image denoising and enhancement. The saturation component S is enhanced by segmented exponential transformation to make the image color more suitable for human visual habits; finally, the processed image is changed back to a color RGB image. The experimental results show that the edge texture of the image processed by the algorithm are maintained well, the contrast has been significantly improved, color distortion is avoided, and it has a good visual effect.
The Design and Implementation of an Efficient Quaternary Network Flow Watermark Technology

With the increasing demand for network security, because passive traffic analysis technology has the characteristics of low efficiency, high overhead, and susceptibility to interferences, network flow watermark technology has emerged. As an active traffic analysis method, network flow watermark technology can effectively track malicious anonymous communication users and real attackers behind the stepping-stone chain, which has the advantages of high accuracy and low overhead. However, the existing flow watermark codec is embedded in the software, which is only suitable for low-rate or small-sample network traffic. In the face of modern network high-rate data stream (such as 100gbps per port), it has exceeded the software and traditional switch processing power. In order to improve the coding efficiency and processing capacity of network flow watermark, this paper combines flow watermark technology with smart NIC (Network Interface Card), and proposes an efficient quaternary network flow watermark technology, deployed in the switch to form an efficient dynamic watermark mechanism. Theoretical analysis and experimental results show that the network flow watermark technology can efficiently process high-rate data stream, with higher coding efficiency and good robustness to disturbances.
Performance Evaluation of Hospital Economic Management with the Clustering Algorithm Oriented towards Electronic Health Management

In order to study the clustering algorithm based on density grid, the performance evaluation index system of hospital economic management under the application of electronic health management system is constructed. Firstly, this work designs the basic architecture of electronic health management system, classifies and screens the process of index system of electronic health management system, compares the clustering algorithm based on density grid with the simple clustering algorithm based on density or grid, and then applies it to the performance evaluation index system of hospital economic management. According to the principle of Mitchell scoring method, the expert questionnaire of hospital economic management performance evaluation index system was designed, and Delphi method was used to evaluate the candidate indexes from the three dimensions of right, legitimacy, and urgency. The results show that, compared with simple network clustering algorithm and density clustering algorithm, the clustering algorithm based on density network produces higher purity (94% VS 73% VS 67%) and lower entropy (0.9 VS 1.4 VS 1.54), which effectively saves memory consumption, and the difference is statistically significant (P < 0.05). The core indicators with scores above 4.5 in both dimensions include budget revenue implementation rate, budget expenditure implementation rate, implementation rate of special financial appropriation, asset-liability ratio, hospitalization income cost rate, medical insurance settlement rate, average cost of discharged patients, and drug proportion. The coefficient of variation of the first grade index is between 0.05 and 0.14 and that of the second grade index is between 0.05 and 0.15. Clustering algorithm based on density network has higher purity and lower entropy, which can effectively save memory consumption. The performance evaluation index system of hospital economic management finally determines 6 first-level indexes: budget management, financial fund management, cost management, medical expense management, medical efficiency, medical quality, and 25 second-level indexes.
Defender Policy Evaluation and Resource Allocation With MITRE ATT&amp;CK Evaluations Data

Protecting against multi-step attacks of uncertain start times and duration forces the defenders into indefinite, always ongoing, resource-intensive response. To allocate resources effectively, the defender must analyze and respond to an uncertain stream of potentially undetected multiple multi-step attacks and take measures of attack and response intensity over time into account. Such response requires estimation of overall attack success metrics and evaluating effect of defender strategies and actions associated with specific attack steps on overall attack metrics. We present a novel game-theoretic approach GPLADD to attack metrics estimation and demonstrate it on attack data derived from MITRE’s ATT&CK Framework and other sources. In GPLADD, the time to complete attack steps is explicit; the attack dynamics emerges from attack graph and attacker-defender capabilities and strategies and therefore reflects “physics” of attacks. The time the attacker takes to complete an attack step is drawn from a probability distribution determined by attacker and defender strategies and capabilities. This makes time a physical constraint on attack success parameters and enables comparing different defender resource allocation strategies across different attacks. We solve for attack success metrics by approximating attacker-defender games as discrete-time Markov chains and show evaluation of return on detection investments associated with different attack steps. We apply GPLADD to MITRE’s APT3 data from ATT&CK Framework and show that there are substantial and un-intuitive differences in estimated real-world vendor performance against a simplified APT3 attack. We focus on metrics that reflect attack difficulty versus attacker ability to remain hidden in the system after gaining control. This enables practical defender optimization and resource allocation against multi-step attacks.
Face mask detection using semantic region based convolutional neural networks

Due to Covid-19, most of the health care organizations and governments have ordered their citizens to wear face mask to protect themself. The proposed novel research presents a tactical methodology for rapid detection of whether a person is wearing a face mask or not. It is entirely different from the existing system, aims at training the deep learning model with a minimum number of image samples and to operate face mask instance segmentation along with object box detection. The system proposes a novel and semantic pixel-to-pixel region based deep learning network, which can detect number of face mask instances in different categories pixel wise to organize the segment bounding box and the confidence of various categories for each pixel. The system experimentally demonstrates that it can effectively and precisely detect the face mask with multi-feature combination. It is also reported that the proposed application performance outperforms the existing system.
A Digital Currency Architecture for Privacy and Owner-Custodianship

Objective : to present the new approach to perform monetary transactions with digital currency. Methods : abstract-logical, analytical methods. Results : in recent years, electronic retail payment mechanisms, especially e-commerce and card payments at the point of sale, have increasingly replaced cash in many developed countries. As a result, societies are losing a critical public retail payment option, and retail consumers are losing important rights associated with using cash. To address this concern, we propose an approach to digital currency that would allow people without banking relationships to transact electronically and privately, including both e-commerce purchases and point-of-sale purchases that are required to be cashless. The article shows the advantages of cash payments compared to non-cash ones and defines the possibility to transform these advantages into the central bank digital currencies. The disputable issues of commercial banks development under the spread of digital currencies are discussed. The architecture of digital currencies is described, including distributed ledgers technology. It was shown that, for the digital currency to function effectively, it is necessary to include the privacy of end-users into its architecture; measures to achieve that are determined. Scientific novelty : the approached proposed in the article should be used to develop the digital currencies infrastructure. It should be government-backed, privately-operated and ensure that every transaction is registered by a bank or money services business, relying upon non-custodial wallets backed by privacy-enhancing technology, such as blind signatures or zero-knowledge proofs, to ensure that transaction counterparties are not revealed. This approach can also facilitate more efficient and transparent clearing, settlement, and management of systemic risk. We argue that our system can restore and preserve the salient features of cash, including privacy, owner-custodianship, fungibility, and accessibility, while also preserving fractional reserve banking and the existing two-tiered banking system. Practical significance : the proposed approach can be applied in the practical organization of perform monetary transactions using digital currencies. The article was first published in English language by Future Internet. For more information please contact the editorial office. For original publication: Goodell G., Al-Nakib H. D., Tasca P. A Digital Currency Architecture for Privacy and Owner-Custodianship, Future Internet, 2021, 13, 130. https://doi.org/10.3390/fi13050130 Publication URL: https://www.mdpi.com/1999-5903/13/5/130
A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text

Medical information extraction consists of a group of natural language processing (NLP) tasks, which collaboratively convert clinical text to pre-defined structured formats. This is a critical step to exploit electronic medical records (EMRs). Given the recent thriving NLP technologies, model implementation and performance seem no longer an obstacle, whereas the bottleneck locates on a high-quality annotated corpus and the whole engineering workflow. This study presents an engineering framework consisting of three tasks, i.e., medical entity recognition, relation extraction and attribute extraction. Within this framework, the whole workflow is demonstrated from EMR data collection through model performance evaluation. Our annotation scheme is comprehensive and compatible between tasks; the resulting corpus, which was manually annotated by experienced physicians, is of large scale and high quality. Built upon this corpus, the medical information extraction system show performance that approaches human annotation. The annotation scheme, (a subset of) the annotated corpus, and the code are all publicly released, to facilitate further research.
Comparative Analysis of Lexicon and Machine Learning Approach for Sentiment Analysis

Opinion mining or analysis of text are other terms for sentiment analysis.The fundamental objective is to extract meaningful information and data from unstructured text using natural language processing, statistical, and linguistics methodologies.This further is used for deriving qualitative and quantitative results on the scale of 'positive', 'neutral', or 'negative to get the overall sentiment analysis.In this research, we worked with both approaches, machine learning, and an unsupervised lexicon-based algorithm for sentiment calculation and model performance.Stochastic gradient descent (SGD) is utilized in this work for optimization for support vector machine (SVM) and logistic regression.AFINN and Vader lexicon are used for the lexicon model.Both the feature TF-IDF and bag of a word are used for classification.This dataset includes "Trip advisor hotel reviews".There are around 20k reviews in the dataset.Cleaned and preprocessed data were used in our work.We conducted some training and assessment.A classifier's accuracy is measured using evaluation metrics.In TF-IDF, the Support Vector Machine is the more accurate of the two classifiers used to assess machine learning accuracy.The classification rate in Bag of Words was 95.2 percent and the accuracy in TF-IDF was 96.3 percent on the support vector machine algorithm.VADER outperforms the Lexicon model with an accuracy of 88.7%, whereas AFINN Lexicon has an accuracy of 86.0%.When comparing the Supervised and unsupervised lexicon approaches, support vector machine model outperforms with a TFIDF accuracy of 96.3 percent and a VADER lexicon accuracy of 88.7%.
Study on Geometry Based Collision Avoidance for Formation Flight by Redefinition of Safety Radius

In this paper, collision avoidance algorithm in a formation flight of multiple UAVs is investigated. The guidance algorithm of the leader-follower concept and the CPA (Closest Point of Approach)-based collision avoidance algorithm for the formation is employed. When the UAVs encounters obstacles during formation flight, if the leader uses the CPA-based collision avoidance algorithm applicable to a single UAV, the safety of the UAVs composing the formation cannot be guaranteed. It is necessary to redefine the safety radius for the entire UAVs in the formation to safely perform collision avoidance. To verify the collision avoidance algorithm by redefining the proposed safety radius, a flight experiments were performed using a PX4-based multicopter.
Pemanfaatan Sistem Informasi Bencana Banjir di Kabupaten Bandung Untuk Mewujudkan Masyarakat Tangguh Bencana

Bandung Regency often experiences flooding and the number of Early Warning System (EWS) equipment is still not sufficient. One of the targets of the Sendai Framework is to increase the coverage and access to the EWS until 2030. To prevent natural disasters and determine the efforts that must be made during a disaster to post-disaster, a disaster information system is needed. The purpose of the study was to determine the implementation of a flood disaster information system as an effort to reduce disaster risk and to determine the use of a flood disaster information system to create a disaster-resilient society. This research was a qualitative research with an exploratory research design. The research was conducted in May 2021 – January 2022 in Bandung Regency. Data collection techniques used were interviews, documentation, and observation, with analytical techniques using the Miles and Huberman model. There was no specific flood disaster information system however the information collected and processed by the Pusdalops was used as the basis for the preparation of a disaster risk reduction plan in Bandung Regency. Information on the potential for flooding was conveyed by the Pusdalops to the community so that people were alert and evacuate if it is not possible to stay at home. Community resilience, especially in three flood-prone villages, was in the primary category. A disaster information system is needed to support disaster risk reduction efforts and create a disaster-resilient society.