Reviewerly/exHarmony huggingface dataset:
Common Component in Black-Boxes Is Prone to Attacks

Neural network models are getting increasingly complex. Large models are often modular, consisting of multiple separate sharable components. The development of such components may require specific domain knowledge, intensive computation power, and large datasets. Therefore, there is a high incentive for companies to keep these components proprietary. However, when a common component is included in multiple black-box models, it could potentially provide another attack vector and weaken security. In this paper, we present a method that “extracts” the common component from black-box models, using only limited resources. With a small number of data samples, an attacker can (1) obtain accurate information about the shared component, stealing propriety information of the intellectual property, and (2) utilize this component to train new tasks or execute subsequent attacks such as model cloning, class inversion, and adversarial attacks more effectively. Comprehensive experiments demonstrate that our proposed method successfully extracts the common component through hard-label and black-box access only. Moreover, the consequent attacks are also effective against straightforward defenses that introduce noise and dummy classifiers.
Modeling and Representation of Power Electronic Converter Parameters

The paper concerns modeling of significant parameters of an electric power converter. All they are presented in two- and three-dimensional graphs in order dependences to be viewed in a proper way. An original approach and its software implementation, both based on index matrixes and developed by the authors, are used for generation of voltage and amperage sequences.
Efficient Feasibility Checking on Continuous Coverage Motion for Constrained Manipulation

Many industrial robotic applications require a manipulator to move the end-effector in a constrained motion to cover a surface region, including painting, spray coating, abrasive blasting, polishing, shotcreting. etc. The manipulator has to satisfy both task constraints imposed on the end-effector (such as maintaining certain distance and angle with respect to the target surface while traversing it) and manipulator joint constraints. Given a robot manipulator and a target surface patch, an important question is whether there exists a feasible path for the manipulator to move continuously along the surface patch to cover it entirely while satisfying both manipulator and task constraints. This question is largely open as it has not been addressed systematically, even though there is substantial literature on path planning of constrained manipulation motion. In this paper, we introduce a general and efficient method to provide answers to this question.
Multiple approximate instances in neural processing units for energy-efficient circuit synthesis

We present an architectural approach toward energy-efficient synthesis of circuits used in neural processing units. Neural network applications are shown to tolerate varying operand precisions between different inputs, accuracy targets, their phases, and learning methods, without significantly impacting the classification accuracy. Using multiple instances of systolic arrays at different precisions, we show that significant energy gains are possible beyond the conventional approach, using the same circuit for all precisions.
Noise Robust Video Super-Resolution Without Training on Noisy Data

Previous CNN-based video super-resolution (VSR) approaches can not be directly applied to noisy images, otherwise the noise will be enhanced after super-resolution (SR) reconstruction models. Some methods are robust to noise but all of them need to be trained on specific noisy training datasets. In this paper, we propose a noise-robust VSR network which only needs to be trained on the clean images. That is, in our deep network for VSR, the model can appropriately super-resolve noisy images without any training on noisy data. We put forward a non-local spatio-temporal module, which not only achieves motion estimation and compensation, but also improves the robustness of our VSR model to noise. A inter-frame fusion module is further presented to fuse the complementary information from different frames. The experiments conducted on both additive noise and multiplicative noise demonstrate that the proposed method can generate visually and quantitatively high-quality results, superior to state-of-the-art methods.
Dlts: A Deep Learning Approach For Twitter Spam Detection

In today's society, online social networking is rapidly expanding. Nonetheless, attacks against it are becoming more frequent; one such attack is the Twitter attack. This spammer disseminated various fraudulent tweets that may take the shape of links or hashtags on the website and online services, damaging legitimate users. However, existing methods do not properly and completely disclose whether or not Twitter is spamming. Spam tweets are detected and blocked by Google Safe Browsing and Twitter's Bot Maker tools, which help to curb spammers. These programs are capable of blocking harmful links. As a result, businesses and academics have taken several methods to create spam-free social network (SN) systems. Some are solely based on user-based features, while others are solely based on tweet-based features. We presented the DLTS technique for Twitter spam detection (TSD) to address this problem. The suggested approach integrates deep learning methods such as LR (Logistic Regression), Naive Bayes, Random Forest, CNN, DT, and ETC for effective spam identification. This method detects spammers by analysing both tweet content and user meta-data (e.g., account age, number of followers/followers, and so on). To assess the proposed method's performance on two distinct real-world datasets using two ML-based and two DL-based techniques, showing a performance improvement when using our methodology. The study's performance assesses detection accuracy, true positive/false positive, and the F measure; stability analyses algorithm using training samples of varying sizes. The goal of scalability is to understand better the effect of decreasing training time in learning algorithms.
Evaluating the Impact of Oversampling on Arabic L1 and L2 Readability Prediction Performances

Most Arabic educational corpora, which are used to elaborate readability prediction models, suffer from an unbalanced distribution of texts among difficulty levels. We argue that readability prediction using machine learning (ML) methods should be addressed through a balanced learning corpus. In this work, we address, in a first experiment, the problem of imbalanced data by clustering classes, an approach adopted by several state-of-the art studies. We then present the results of a second experiment in which we adopted an oversampling technique on a unbalanced corpus in order to train the models on balanced data. This experiment was carried out on four corpora, including three dedicated to Arabic as a foreign language (L2) and one for Arabic as a first language (L1). The results show that balanced data give a significant improvement on readability prediction model performances.
SMBFT: A Modified Fuzzy c -Means Algorithm for Superpixel Generation

Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classified. In order to solve this problem, this paper proposes a Superpixel Method Based on Fuzzy Theory (SMBFT), which uses fuzzy theory as a guide and traditional fuzzy c-means clustering algorithm as a baseline. This method can make full use of the advantage of the fuzzy clustering algorithm in dealing with the images with the fuzzy characteristics. Boundary pixels which have higher uncertainties can be correctly classified with maximum probability. The superpixel has homogeneous pixels. Meanwhile, the paper also uses the surrounding neighborhood pixels to constrain the spatial information, which effectively alleviates the negative effects of noise. The paper tests on the images from Berkeley database and brain MR images from the Brain web. In addition, this paper proposes a comprehensive criterion to measure the weights of two kinds of criterions in choosing superpixel methods for color images. An evaluation criterion for medical image data sets employs the internal entropy of superpixels which is inspired by the concept of entropy in the information theory. The experimental results show that this method has superiorities than traditional methods both on natural images and medical images.
Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN

Abstract Knowledge graphs have played a significant role in various applications and knowledge reasoning is one of the key tasks. However, the task gets more challenging when each fact is associated with a time annotation on temporal knowledge graph. Most of the existing temporal knowledge graph representation learning methods exploit structural information to learn the entity and relation representations. By these methods, those entities with similar structural information cannot be easily distinguished. Incorporating other information is an effective way to solve such problems. To address this problem, we propose a temporal knowledge graph representation learning method d-HyTE that incorporates entity descriptions. We learn structure-based representations of entities and relations and explore a deep convolutional neural network with attention to encode description-based representations of entities. The joint representation of two different representations of an entity is regarded as the final representation. We evaluate this method on link prediction and temporal scope prediction. Experimental results showed that our method d-HyTE outperformed the other baselines on many metrics.
Topic Classification using Active Learning for Sinhala Language Documents

In the field of Classifying Text data, Text Classification and Topic Modeling plays the higher role. When compared between the two techniques, Text Classification provides outputs with higher accuracy level. Due to this Data analysts tend to move towards this technique. Text classification is also referred to as text categorization/tagging, and it is a task of categorizing text according to its specified class. Text classifiers can automatically examine a set of text and classify it under a pre-defined category according to the content of the set of text with the help of Natural Language Processing (NLP) [1]. As this is a Supervised learning, it requires a vast range of classified dataset to make the classification efficient. But when it comes to languages with scarcity of classified dataset such as Sinhala, it becomes a problem to train the model due to the insufficiency of the dataset. Thus, the author proposes a solution for performing Text classification using Active learning. This solution utilizes the available classified dataset, learns from this supervised model, and produces outcomes (Classified Text Data) with a high accuracy level.
Research on Evolutionary Construction Method of Software Service Architecture Based on ACP

Abstract In the open-source computing environment, the software service individuals that constitute the system are highly independent. The construction of software system is changed into combination and iteration, which puts forward higher requirements for the system architecture. Based on algebra of communication process, this paper abstracts the individual software service into service items, and uses the operation rules in communication algebra to realize the construction and modification of software architecture, thus ensuring the preciseness of the construction process and the stability of the architecture. Finally, a system with 12 sub services is used as a case to demonstrate the evolution process of service adding, deleting and replacing operation, which also verified the effectiveness and feasibility of the proposed method.
Distribution of Various Types of Educational Information in the School Physics Course

The modern physics course appeared as a result of a long-term evolution of the physics teaching methods. The model of the school course is textbooks; they reflect the fundamental laws of cognition of the surrounding world and methodological ideas, therefore the problem of studying the distribution of various types of training material in the physics course is relevant. The article uses previously obtained estimates of the differential complexity of various topics of the school physics course. This takes into account: 1) the differential complexity of the text, depending on the information amount in the volume unit, on which the difficulty of understanding it by the student depends; 2) the integral complexity (general informativeness), which characterizes the total amount of information in the text; the amount of time and effort required to study training material depends on it; 3) the share of educational information related to mechanics, molecular physics and thermodynamics, electrodynamics, optics and quantum physics. It also took into account the fact that physical and mathematical complexity are independent characteristics of the physics textbooks. The carried out content analysis of standard school textbooks made it possible to determine the integral complexity of each topic and build a graph with an accumulation that shows the dynamics of changes in the contribution of each physics section to the total information amount over time. In particular, it follows from the graph that: 1) the speed of transmission of educational information in the 10-11th grades is 5 times higher than in the 7-9th grades; 2) the amount of information on physics reported to a student by the end of the 11th grade is 4,4 times more than the amount of information reported in the 7th, 8th and 9th grades.
ON THE QUESTION OF QUALITY MANAGEMENT OF SOFTWARE WEB SYSTEMS BY DEVELOPMENT TOOLS

This article presents the results of research on existing tools and the definition of theoretical aspects of the use of modern frameworks for software development, as well as justification of the feasibility and impact of variability of choice on the quality of software applications. The main results of the study are the rationale for the choice of frameworks for software development. It should be noted that despite the large number of frameworks, the structured analysis is carried out by a rather small number of researchers, which determines the scientific novelty of this issue. Choosing the right framework is one of the main principles that determine the quality of the future site. Each of the technologies presented in this article can be considered exhaustive in all respects because they are used both in the practice of converting to a graphical interface (frontend) and to develop a variant of software architecture (backend). Therefore, you should choose a platform according to the convenience of programming languages. So Agile is best suited for open-ended projects, such as launching Internet services, developing computer games, and operating systems. However, flexibility can lead to loss of focus and loss of predictability. It is very important to separate the errors of the flexible approach from the shortcomings of the methodology itself. It will take some time to adapt to the realities of specific tasks before all the benefits of the approach are realized. The practical significance lies in the possibility of using certain frameworks in accordance with the needs of the customer and the developer to achieve certain results in software development.
Skin Detection Based on Convolutional Neural Network

Skin detection is an essential step in many human–machine interaction systems such as e-learning, security, communication… etc., it consists of extracting regions containing the skin in a digital image. This problem has become the subject of considerable research in the scientific community where a variety of approaches has been proposed in the literature; however, few recent reviews exist. Our principal goal in this paper is to extract skin regions using a Convolutional neural network called LeNet5. Our framework is divided into three main parts: At first, a deep learning is performed to Lenet5 network using 3354 positive examples and 5590 negative examples from SFA dataset, then and after a preprocessing of each arbitrary image the trained network will classify image pixels into skin/non-skin. Lastly, a thresholding and prost-processing of classified regions is carried out. The tests were carried out on images of variable complexity: indoor, outdoor, variable lighting, simple and complex background. The results obtained are very encouraging, we show the qualitative and quantitative results obtained on SFA and BAO datasets.
Fuzzy Control System for Predicting The Quality of Thermally Processed Raw Milk

Milk, which has a very important place in human nutrition, can be harmful in terms of human health when it is not produced, stored, processed, and necessary controls are performed under hygienic conditions. Even if raw milk contains a small number of bacteria, it is degraded in a very short time after the milking by the effects of microorganisms transmitted from the environment in various ways and constitutes the potential source of many pathogens that cause diseases in humans.Milk proteins contain essential amino acids that cannot be synthesized by the human body and must be taken from the outside. The microorganism load of milk is one of the most important indicators in determining milk quality and determining the hygienic properties of milk in the process from raw milk production to consumption. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. In this study, it was aimed to develop a fuzzy logic-based decision support system aimed at predicting the deterioration of heat-treated raw milk. As the subject of this project, it was developed to determine whether the milk produced in troops, farms and cooperatives is spoiled before the milk goes from production households to businesses.
Anomaly Detection-Oriented High-Frequency Signal Storage for Diesel Engines

Marine diesel engine has been the main power of civil ships, small and medium-sized ships and conventional submarines. Anomaly detection on diesel engines is very important for the safety of marine systems. Diesel engine anomaly detection requires access to a large number of high-frequency signals. How to store these signals to support anomaly detection applications is currently rarely studied. This article discusses the design of high-frequency signal storage systems that provide data support for intelligent anomaly detection systems. It mainly focuses the design of database system and file system for high-frequency signal storage requirements of diesel engines. The actual operation shows that the system designed in this paper can support the storage of high-frequency signals and the deployment of anomaly detection applications.
Reversible Data Hiding in Color Image with Grayscale Invariance using Sorting

Most reversible data hiding (RDH) algorithms for color images directly embed secret information in the luminance channel. As a result, the grayscale version of the color image is usually distorted. Since many color image processing algorithms work on the grayscale version, their performance on marked color images may get seriously impacted. Therefore, RD H with grayscale invariance (RDH-GI) is advanced to eliminate such shortcomings and has received great attention thereafter. However, their performance is still far from satisfactory. This paper presents a novel RDH-GI method by exploiting the pixel clustering technique to segment host pixels into different complexity levels. Data embedding tends to take place in less complex regions, and thus image distortion is reduced while maintaining grayscale invariance. Experiments prove the effectiveness of the proposed work.
Innovation Mode of Intelligent Tracking and Positioning Service for Logistics Enterprises under the background of Data Tagging

With the development of transportation, the time of modern logistics has been greatly shortened. Due to the immaturity of refrigeration and transportation management technology in China, the quality of fresh agricultural products can not be guaranteed. Therefore, it is an urgent need to improve the fresh-keeping technology, especially the real-time monitoring of the transportation process, timely find and track the source of problems. Considering that the sample data has the characteristics of noise, redundancy, heterogeneity and privacy, this paper designs a series of effective data marking techniques based on incentive mechanism.
A Self-Attention Based Method for Facial Expression Recognition

We present a self-attention-based method termed as Vision Transformer (ViT) to efficiently classify the human facial expressions. Our work can be divided into two contributions. First, the facial expression image is divided to N*N patches, each of which corresponds to word, and the whole image data is used as a paragraph that composed of n words. Second, we design a learnable module, the ViT, with sequence length of L, latent dimension, and 12 attention layers which are integrated together as a unified framework. We also train the proposed model on the normalized and augmented version of FER2013plusdataset. We show empirically that ViT has superior performance compared to alternative approaches.
Poster: Approximate Caching for Mobile Image Recognition

Many emerging mobile applications rely heavily upon image recognition of both static images and live video streams. Image recognition is commonly achieved using deep neural networks (DNNs) which can achieve high accuracy but also incur significant computation latency and energy consumption on resource-constrained smartphones. We introduce an in-memory caching paradigm that supports infrastructure-less collaborative computation reuse in smartphone image recognition. We propose using the inertial movement of smartphones, the locality inherent in video streams, as well as information from nearby, peer-to-peer devices to maximize the computation reuse opportunities in mobile image recognition. Experimental results show that our system lowers the average latency of standard mobile neural network image recognition applications by up to 94% with minimal loss of recognition accuracy.
Explainable event recognition

The literature shows outstanding capabilities for Convolutional Neural Networks (CNNs) in event recognition in images. However, fewer attempts are made to analyze the potential causes behind the decisions of the models and explore whether the predictions are based on event-salient objects/regions? To explore this important aspect of event recognition, in this work, we propose an explainable event recognition framework relying on Grad-CAM and an Xception architecture-based CNN model. Experiments are conducted on four large-scale datasets covering a diversified set of natural disasters, social, and sports events. Overall, the model showed outstanding generalization capabilities obtaining overall F1 scores of 0.91, 0.94, and 0.97 on natural disasters, social, and sports events, respectively. Moreover, for subjective analysis of activation maps generated through Grad-CAM for the predicted samples of the model, a crowd-sourcing study is conducted to analyze whether the model’s predictions are based on event-related objects/regions or not? The results of the study indicate that 78%, 84%, and 78% of the model decisions on natural disasters, sports, and social events datasets, respectively, are based on event-related objects/regions.