Reviewerly/exHarmony huggingface dataset:
The online detection system of concentric ring size based on machine vision

Abstract In today’s rapid industrial development, to improve production efficiency and product quality size detection process must be completed in real-time. Aiming at the problems of low production efficiency and large error rate caused by manual detection in the past, this paper proposes an online detection system of concentric ring size based on machine vision and develops a GUI software operation page. This system first processes the image gray level and uses the median filter for image denoising, and further uses Otsu threshold segmentation for image segmentation. Secondly, the Canny edge detection algorithm is used to extract the edge of the ring image. Finally, the radius and coordinates of the ring are measured by the improved Hough transform loop detection. Experiments show that the size measurement error of the measurement system is less than 0.015 mm. This system can meet the requirements of small and medium-sized enterprises for real-time detection of circular products, and improve the accuracy of quality inspection of circular products.
Response surface methodology based parametric analysis of the impact of installation factors on bifacial gain

The performance estimation of a bifacial PV array is significantly more sophisticated than that of a normal monofacial installation because of its sensitivity to several additional parameters. The optimal operation of bifacial PV is mainly governed by the module's orientation, i.e., its tilt angle (β), albedo of the site (ρg), elevation above ground (E), and azimuth angle (γ). A sensitivity study on the influence different bifacial PV installation factors have on the bifacial gain (BG) is presented. RADIANCE based ray tracing model is utilized to simulate the effective irradiance on a bifacial photovoltaic module. The sensitivity study is carried out through response surface methodology (RSM) to determine the impact of different installation parameters on effective irradiance and BG of a bifacial module. The analysis have high significance (p<0.05) for all installation parameters as well as significant interaction among the factors. Furthermore, the simulations results show that module height and ground albedo have the maximum impact in governing the BG of a bifacial module.
SCRATCH as Social Network: Topic Modeling and Sentiment Analysis in SCRATCH Projects

Societal matters like the Black Lives Matter (BLM) movement influence software engineering, as the recent debate on replacing certain discriminatory terms such as whitelist/blacklist has shown. Identifying relevant and trending societal matters is important, and often done for traditional social media channels such as Twitter. In this paper we explore whether this type of analysis can also be used for introspection of the software world, by looking at the thriving scene of Scratch programmers. The educational programming language Scratch is not only used for teaching programming concepts, but also offers a platform for young programmers to express and share their creativity on any topics of relevance. By automatically analyzing titles and project comments in a dataset of 106.032 Scratch projects, we explore which topics are common in the Scratch community, whether socially relevant events are reflected, and how the sentiment in the comments discussing these topics is. It turns out that the diversity of topics within the Scratch projects makes the analysis process challenging. Our results nevertheless show that topics from pop and net culture are present, and even recent societal events such as the Covid-19 pandemic or BLM are to some extent reflected in Scratch. The tone in the comments is mostly positive with catchy youth language. Hence, despite the challenges, Scratch projects can be studied in the same way as social networks, which opens up new possibilities to improve our understanding of the behavior and motivation of novice programmers. Analyzing the content and emotions within a social network provides insights on how software engineers communicate and what they talk about. The Scratch programming environment is extremely popular with young, learning programmers. In this paper we investigate whether social network analysis in terms of automatically identifying topics and sentiments can also be ap-plied to Scratch. We analyze the topics of the projects that young programmers create and share, and determine the tone of their conversations. Although the process turns out to be technically challenging, we encounter pop and net culture references but also societal matters, and the overall tone of communication is positive.
The Role of Hermeneutics in the Mechanism of Social Recognition

The article attempts to consider hermeneutics in a special capacity – as a mechanism that allows for the exercise and receipt of social recognition. The presence of recognition as part of interpretation points to a certain order of interaction between these processes, namely, cyclical, according to the principle of the hermeneutic circle: recognition begets hermeneutics, and hermeneutics begets recognition. The impulse to hermeneutics arises in the interpreter because of the assumption that there are ideas in the text that are hidden from him and that need to be extracted with the help of special technologies. When the meaning of the text is discovered, it needs to be evaluated, which is projected onto the text itself and thus turns into recognition. The interpreter has such resources as the source text and the context as benchmarks of truth. The article considers the main stages at which the algorithm of hermeneutics cannot fully function without the mechanism of recognition. Hypothesis verification implies the recognition of the fact that in the text itself or in the context there are traces of true meaning, which can be checked, or reliable facts, which can be relied upon. In the stages of synthesizing and creating a hermeneutic product, the interpreter must share the value of recognition, for if such interpretation is made at all, it is in order to present it to another. Thus, the interpreter is interested in potential recognition.
Guiding Random Walks by Effective Resistance for Effective Node Embedding

One of the most effective and successful approaches to construct effective Euclidean representation of graphs, i.e., embedding, are based on random walks. This solution allows to learn a latent representation by capturing nodes similarities from a series of walk contexts while optimizing the likelihood of preserving their neighborhood in the Euclidean space. In this paper, we address the question of enhancing the existing random walks based methods by making the walk generation process aware of the global graph structure. To this end, we propose a node embedding method based on random walks guided by weights calculated considering a macro observation of the graph structure rather than a micro one. Experimental results on both synthetic and real-world networks show that our computed embedding allows to reach better accuracy rates on two tasks: node classification and link prediction.
Demo Abstract: Technology-agnostic Approach to RF based Human Activity Recognition

Human activity recognition (HAR), as an essential component of many emerging smart applications, has attracted an increasing interest in recent years. Various radio-frequency (RF) sensing technologies, such as Radio-Frequency Identification (RFID), WiFi, and RF radar, have been utilized for developing non-invasive HAR systems. However, most of the RF based HAR solutions are closely designed for the specific, chosen RF technology, which incurs a significant barrier for the wide deployment of such systems. In this demo, we present at technology- a gnostic approach for RF -based HAR, termed TARF, which aims to overcome such constraints and perform HAR with various RF sensing technologies.
Balancing Sampling Frequencies for Multi-modality IoT Systems: Smart Shoe as an Example

With the rapid development of Internet of Things, more and more sensing modalities are being integrated into single small devices. The data throughput, however, is always limited due to factors like transmission bandwidth and storage. How to balance the sampling frequencies among different sensing modalities, is little studied. Given a concrete system and its application, the question becomes: 1) how to qualitatively evaluate the loss of information as the sample frequency drops? 2) how to find the optimal sampling frequency ratio and set the multi-modality system according to it? In this paper, we will provide a general workflow to answer these questions. The workflow is evaluated on a self-developed smart-shoe, equipped with one pressure sensing matrix and two Inertial Measurement Units. This workflow could be interesting to both multi-modality IoT system developers and users, support them in finding the best frequency setting and obtaining datasets of higher qualities.
Mission-Oriented Trajectory Optimization for Search-and-Rescue Multirotor UAVs in Cluttered and GPS-Denied Environments

Planning Search-and-rescue (SAR) missions for UAVs in cluttered and GPS-impaired environments remains a challenging topic in both robotics and aerospace related research areas. Small multirotors, especially quadrotors, are deployed by many in indoor or confined spaces due to their structural simplicity, ability to hover and take off and land vertically, and good maneuverability. However, most of small multirotors suffer from less-than-30-min flight time on one charge imposing constraints on missions including searching for survivors and payload delivery. In this paper, we introduce a mission-oriented trajectory generation approach in order to improve the quantitative performance of multirotors by reducing single-flight energy and time consumption. In our methodology, each flight mission is initially planned using computationally cheap path planning algorithms to generate a set of waypoints as an initial guess of the trajectory. Next, a multi-phase optimal control problem is formulated with the mission-specific objective function. Apart from constraints of initial and final conditions, this multi-phase optimal control problem is also subject to dynamics constraints from both quadrotor equations of motion and rotor aerodynamics models as well as path constraints. The optimal control problem is then solved in the frame of multi-disciplinary design optimization (MDO) by setting the energy consumption, times, control inputs, and states as design variables, and it has the potential to be converted into a simultaneous design and control process when including quadrotor design parameters as new design variables. After that, simulations are performed to validate the effectiveness of proposed methodology and to compare its results with those from peer methods. Three mission scenarios are set up to validate the proposed methodology. The simulation results indicate that the proposed method can decrease the flight time by up to 6.69% or mechanical energy consumption by 3.00%, and it outperforms two other trajectory generation methods for specific mission performance.
BabelFish: A Seamless Solution to Communicate with Multi-Lingual Individuals

The goal of our work is to build a tool that allows any number of people to communicate comfortably in their native language with anyone around the globe in real time. As globalization and the mixing of economies grows throughout the world people from different locales will need to constantly be communicating with each other to collaborate and get work done. This collaboration will oftentimes include crucial conversations where language barriers might be an obstacle that prevents efficient work from getting done. We want to demonstrate that a server-less implementation of the MQTT protocol and by leveraging google’s translation engine, that seamless native communication can happen between multiple individuals who speak different languages. To accomplish this goal we will develop a rudimentary chat application which will allow one user to type a message in their native language and have that language translate in transit to the other users preferred language. By offering this feature users from around the globe will be able to feel confident that they can communicate complex thoughts and ideas to anyone anywhere and will be a huge boon to industry as our global economy continues to get more tightly coupled.
DU-Net Based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images

In this paper, we introduce an unsupervised cancer segmentation framework for histology images. The framework involves an effective contrastive learning scheme for extracting distinctive visual representations for segmentation. The encoder is a Deep U-Net (DU-Net) structure which contains an extra fully convolution layer compared to the normal U-Net. A contrastive learning scheme is developed to solve the problem of lacking training sets with high-quality annotations on tumour boundaries. A specific set of data augmentation techniques are employed to improve the discriminability of the learned colour features from contrastive learning. Smoothing and noise elimination are conducted using convolutional Conditional Random Fields. The experiments demonstrate competitive performance in segmentation even better than some popular supervised networks.
A Data-Centric Machine Learning Approach for Controlling Exploration in Estimation of Distribution Algorithms

Exploration plays a key role in the performance of metaheuristics. An algorithm should perform more exploration when reaching the "ideal search scale"; this happens when solutions are regularly sampled from different attraction basins. The moment this search scale is reached depends on the topological features of the objective function and the inherent randomness of the heuristic optimization process. Previous work on adjusting exploration have mostly used fixed rules based on fitness improvement, in this paper, we model it as a supervised machine learning problem. We apply a data-centric approach to understand whether variations in the data are more relevant than variations in the classification models. For our study we use the Estimation Multivariate Normal Algorithm with Thresheld Convergence, which provides an ideal framework as it allows us to directly control exploration through the γ parameter. Optimization results show that the machine learning hybrid significantly outperforms the baseline algorithm.
STUN: Secret-Free Trust-Establishment For Underground Wireless Networks

Emerging agricultural internet-of-things (Ag-IoT) is increasing the efficiency of farming. The data collected by the wireless-enabled Ag-IoT infrastructure is highly sensitive as corrupting the data can cause significant damages to farm production and the livelihood of growers. The trust of the data can be established by initial secure bootstrapping of the wireless underground end nodes. This paper tackles the problem of scalable and secret-free trust-establishment for commercial off-the-shelf (COTS) underground nodes with an aboveground gateway applicable to heterogeneous end nodes. Secure boot-strapping requires authentication and secret establishment, which are achieved in-band, aided by a trusted underground node by exploiting the unique and hard-to-forge underground wireless signal propagation laws. The secret-free trust-establishment for underground wireless networks (STUN) protocol is resistant to active signal injection attacks and is scalable with an increasing number of underground nodes. Further, it is theoretically proven that STUN has security equivalent to the unbalanced oil and vinegar scheme in public cryptography. STUN is validated based on experimental data from an underground wireless testbed.
PRESSGenDB: PRivacy-prEserving Substring Search on Encrypted Genomic DataBase

Efficient sequencing methods produce a large amount of genetic data, and make it accessible to researchers. This leads genomics to be considered a legitimate big data field. Hence, outsourcing data to the cloud is necessary as the genomic dataset is large. Data owners encrypt sensitive data before outsourcing to maintain data confidentiality and outsourcing aids data owners in resolving the issue of local storage management. Because genomic data is so enormous, safely and effectively performing researchers’ queries is challenging. In this paper, we propose a method, PRESSGenDB, for securely performing string and substring searches on the encrypted genomic sequences dataset. We leverage searchable symmetric encryption (SSE) and design a new method to handle these queries. In comparison to the state-of-the-art methods, PRESSGenDB supports various types of queries over genomic sequences such as string search and substring searches with and without a given requested start position. Moreover, it supports strings of alphabets as sequences rather than just a binary sequence of 0,1s. It can search for substrings (patterns) over a whole dataset of genomic sequences rather than just one sequence. Furthermore, by comparing PRESSGenDB’s search complexity analytically with the state-of-the-art, we show that it outperforms the recent efficient works.
Impact of Subjectivity in Deep Reinforcement Learning based Defense of Cloud Storage

Cloud storage is a target of advanced persistent threats (APTs), where a sophisticated adversary attempts to steal sensitive data in a continuous manner. Human monitoring and intervention is an integral part of the reinforcement learning (RL) approaches to defend against APTs. In this paper, prospect theory (PT) is used to model the subjective behavior of the cloud storage defender in assigning computing resources (processing units) to scan and monitor the cloud storage system against an APT attacker bot, which attempts to steal information from the cloud. Under a constraint on the total number of processing units and a lack of knowledge of the opponent’s resource allocation strategy, we study the defense performance of a federated maximum-likelihood deep Q-network (FMLDQ) RL algorithm against a sophisticated branching dueling deep Q-network (BDQ) RL attack algorithm. Specifically, the RL strategy for the defender is affected by subjective decisions in estimating the processing units of the attacker. Simulation results show that when the defender has more resources than the attacker, an EUT-based defense strategy (without human intervention) yields better data protection. On the other hand, when the defender has fewer resources, a PT-based defense strategy (with human intervention) is better.
Automatic Selection Attacks Framework for Hard Label Black-Box Models

The current adversarial attacks against machine learning models can be divided into white-box attacks and black-box attacks. Further the black-box can be subdivided into soft label and hard label black-box, but the latter has the deficiency of only returning the class with the highest prediction probability, which leads to the difficulty in gradient estimation. However, due to its wide application, it is of great research significance and application value to explore hard label blackbox attacks. This paper proposes an Automatic Selection Attacks Framework (ASAF) for hard label black-box models, which can be explained in two aspects based on the existing attack methods. Firstly, ASAF applies model equivalence to select substitute models automatically so as to generate adversarial examples and then completes black-box attacks based on their transferability. Secondly, specified feature selection and parallel attack method are proposed to shorten the attack time and improve the attack success rate. The experimental results show that ASAF can achieve more than 90% success rate of nontargeted attack on the common models of traditional dataset ResNet-101 (CIFAR10) and InceptionV4 (ImageNet). Meanwhile, compared with FGSM and other attack algorithms, the attack time is reduced by at least 89.7% and 87.8% respectively in two traditional datasets. Besides, it can achieve 90% success rate of attack on the online model, BaiduAI digital recognition. In conclusion, ASAF is the first automatic selection attacks framework for hard label blackbox models, in which specified feature selection and parallel attack methods speed up automatic attacks.
Examples of Machine Learning Models from Classic to Modern

This paper provides some intuitions of how deep learning works. Two examples are presented. They are region identification and object recognition. These intuitions are helpful in understanding deep learning in general and are helpful in classroom teaching.
Deep CAPTCHA Recognition Using Encapsulated Preprocessing and Heterogeneous Datasets

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an important security technique designed to deter bots from abusing software systems, which has broader applications in cyberspace. CAPTCHAs come in a variety of forms, including the deciphering of obfuscated text, transcribing of audio messages, and tracking mouse movement, among others. This paper focuses on using deep learning techniques to recognize text-based CAPTCHAs. In particular, our work focuses on generating training datasets using different CAPTCHA schemes, along with a pre-processing technique allowing for character-based recognition. We have encapsulated the CRABI (CAPTCHA Recognition with Attached Binary Images) framework to give an image multiple labels for improvement in feature extraction. Using real-world datasets, performance evaluations are conducted to validate the efficacy of our proposed approach on several neural network architectures (e.g., custom CNN architecture, VGG16, ResNet50, and MobileNet). The experimental results confirm that over 90% accuracy can be achieved on most models.
Protection Metric Model of White-Box Algorithms

Systems based on WB protection have a limited lifetime, measured in months and sometimes days. Unfortunately, to understand for how long the application will be uncompromised, if possible, only empirically. However, it is possible to make a preliminary assessment of the security of a particular implementation, depending on the methods and their number used in the implementation, it will allow reallocating resources to more effective means of protection.
Intelligent Feature Selection on Multivariate Dataset using Advanced Data Profiling

The differential diagnosis of diseases which share similar clinical features is a real and difficult problem in medicine. This paper demonstrates the use of data mining (DM) techniques to augment standard data profiling methods and establishes an efficient approach for an intelligent feature selection method for disease that share similar features. The results from experiments returned show that by using DM techniques to select features as an additional layer on top of data profiling, there is considerable improvement in the performance of the prediction model built to predict a disease such as "Psoriasis". A brief comparison between features selected by existing mining tools such as Weka and the proposed approach with respect to predictive accuracy is recorded in this paper. The proposed algorithm works as a promising tool for assisting diagnosis of disease like erythemato-squamous diseases, where the symptoms are overlapping. By combining data cleansing and knowledge discovery techniques, the algorithm aims to be "agnostic" and can be used on a wide variety of data standards with variable data quality. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
An extension of imagechain concept that allows multiple images per block

This paper extends the concept of imagechain by a new feature. Now it is possible to store multiple images per block. If necessary, the block is split into parts and each of them is embedded in an individual image. All images from the previous block take part in hash computation. Described construction uses secret splitting for creating shares and steganographic algorithms for embedding and recovering data.