Reviewerly/exHarmony huggingface dataset:
An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture

Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset.
Towards Privacy-Preserving Classification-as-a-Service for DGA Detection

Domain generation algorithm (DGA) classifiers can be used to detect and block the establishment of a connection between bots and their command-and-control server. Classification-as-a-service (CaaS) can separate the classification of domain names from the need for real-world training data, which are difficult to obtain but mandatory for well performing classifiers. However, domain names as well as trained models may contain privacy-critical information which should not be leaked to either the model provider or the data provider. Several generic frameworks for privacy-preserving machine learning (ML) have been proposed in the past that can preserve data and model privacy. Thus, it seems high time to combine state-of-the-art DGA classifiers and privacy-preservation frameworks to enable privacy-preserving CaaS, preserving both, data and model privacy for the DGA detection use case. In this work, we examine the real-world applicability of four generic frameworks for privacy-preserving ML using different state-of-the-art DGA detection models. Our results show that out-of-the-box DGA detection models are computationally infeasible for privacy-preserving inference in a real-world setting. We propose model simplifications that achieve a reduction in inference latency of up to 95%, and up to 97% in communication complexity while causing an accuracy penalty of less than 0.17%. Despite this significant improvement, real-time classification is still not feasible in a traditional two-party setting. Thus, more efficient secure multi-party computation (SMPC) or homomorphic encryption (HE) schemes are required to enable real-world feasibility of privacy-preserving CaaS for DGA detection.
Semantic Descriptors into Representation for Robust Indoor Visual Place Recognition

Visual place localization (VPL) is a problem finding the closest database image from a query image. Since the outdoor images can be recognized from GPS sensors, VPL in an indoor scene is a difficult problem. Also, Image changes indoors are more severe than outdoors. It is because the position of objects can be easily changed indoors. To tackle this problem, we propose a novel localization dataset with 3D objects considering their physical locations in a scene and encode semantic information using neural networks. Experimental results show that our proposed method outperforms other baseline methods on our localization dataset.
Age Detection from Facial Images Using Python

Age detection is a process of automatically discerning the age of a person solely forms a photo of their face. This involves two stages, face detection and then age recognition by giving it as a feed to age detector algorithm. The main aim of the project is to build a robust age detection model by using model weights as pre trained model and use it to predict the age of the person in an image. This research work will use webcam feed as input to get live results.
Assistive technology: A multimodal interface to control PC mouse pointer and click

Although the use of technology, e.g., personal computers and smartphones, is essential in nowadays world, many users do not have enough motor skills to use them fluently. The commercially available assistive technology is expensive, compared to the conventional one, and many of these devices have limited configurability also limiting the number of users who can benefit from them. This paper addresses the design and prototype implementation of a plug &#x0026; play mouse that can be used in four configurable modes in order to meet different users&#x0027; requirements. The proposed mouse characteristics and prototype price is compared to various commercial devices proving the potential of the proposed solution.
Operation assessment of the Kabertane’s photovoltaic power plant

In order to operate effectively renewable energies in Adrar isolated power system, the present article is dedicated to assessing the operation of a solar photovoltaic (PV) plant implemented under the first phase of Algeria’s national renewable energy program. A statistical approach study of the PV plant installed in Kabertene site is investigated. For performance assessment, we study different meteorological variables of two cloudy and two clear days. In order to illustrate the impact of the wind speed in PV operation on Kabertene site, a period of week was investigated. Our study shows how the measured of different meteorological variables should be preferred for the future development and operation of solar PV power projects. Further, the presented statistical study is presumed as approach to evaluate the performance of any solar PV plant.
Matrix Neo-Fuzzy-System and its Online Learning in Image Recognition Task

The paper proposes a 2D-hybrid system of computational intelligence, which is based on the generalized neo-fuzzy neuron. The system is characterised by high approximate abilities, simple computational implementation, and high learning speed. The characteristic property of the proposed system is that on its input the signal is fed not in the traditional vector form, but in the image-matrix form. Such an approach allows getting rid of additional convolution-pooling layers that are used in deep neural networks as an encoder. The main elements of the proposed system are a fuzzified multidimensional bilinear model, additional softmax layer, and multidimensional generalized neo-fuzzy neuron tuning with cross-entropy criterion. Compared to deep neural systems, the proposed matrix neo-fuzzy system contains gradually fewer tuning parameters – synaptic weights. The usage of the time-optimal algorithm for tuning synaptic weights allows implementing learning in an online mode.
Communicative Performance of Interactive Space in Social Relationship Through Actor-Network Theory

The paper defines performance of interactive space in social relationships using Actor-network theory as a methodological tool. It examines interactive space as one of the actors in a diverse network consisting of human and non-human components with their own agency. The paper investigates ways of spatial communication by means of non-verbal signals (light, sound and motion). Using Actor-network theory allows to understand the rules of interaction between the built environment and its users, and, evaluate their responsiveness to the system signals and define participants' stages of involvement in this interaction. A full-scale original interactive prototype was designed and built as a proof of concept to analyze how interactive space impacts the visitors' behaviour and space perception, examining this interaction in real-time and analyzing the impact it has on human actions.
Decision List-Based Representation of Bithreshold Functions

The paper deals with the issues concerning the computation of Boolean bithreshold functions by decision lists. We give and justify conditions ensuring the possibility of the realization of a decision list by a bithreshold neural unit. It is shown that a decision list comprising monomials and special 2-term-2-DNF formulas represents a bithreshold Boolean function. The simple and quick learning algorithm is proposed, which yields the weights and the thresholds of the sought bithreshold neuron. We also give the time complexity of this algorithm and estimate the upper bound on the size of the weight vector and thresholds.
Stereo Perception in the Dark using Uncalibrated Line Laser

Perception using stereo requires external light. In the absence of natural light, active, structured light provides light where it is needed. In this work, we demonstrate how a free moving line striping laser can be used to perceive and model terrains. In this formulation, we do not need to know the position of the laser with respect to the stereo pair which precludes the need for calibrating the laser position. This also saves an actuator as the laser can be passively articulated using a spring. In this paper, we present the algorithms to efficiently extract the laser pixels from the stereo image pair. We also present techniques to use the structure of the light and overcome degenerate cases to build cleaner maps. This perception method benefits micro-rovers specifically those trying to operate in the extreme lighting conditions.
Depth Completion Network Guided by RGB Attention

The goal of the depth completion task is to generate a dense depth map from a sparse depth map. The RGB image is usually introduced as a guide to help the network obtain the missing depth information. Due to the rich semantic features of RGB images, the method based on RGB guidance could obtain better performance. However, RGB guidance will bring wrong information in feature fusion, because the segmentation boundary of RGB features does not match the real depth distribution. A depth completion network based on the attention mechanism is proposed to correct the wrong fusion information. Bidirectional attention is introduced to correct wrong segmentation boundaries in RGB images. Compared with the SOTA methods, the proposed method still maintains excellent performance under different sparse input conditions. And the proposed method has shorter inference time and smaller model size, while obtaining competitive results.
Implementation and Evaluation of Block Cipher Algorithm with Content Addressable Memory-Based Massive-Parallel SIMD Matrix Core

In recent years, many people have been handing various information using consumer mobile devices. Therefore, it is important to efficiently process several multimedia data with a single device. In this paper, we propose an processing architecture of content addressable memory-based massive-parallel SIMD matrix processor that can realize iterative arithmetic processing in parallel and can realize match search processing at a high speed. As performance verification, we implement the AES, which is standard encryption, and the PRESENT, which is lightweight encryption, on the proposed processor. The results confirmed that AES and PRESENT processing are about 2.5 times and about 90 times faster than the Raspberry Pi 4, respectively.
A revisit to Bai–Saranadasa's two-sample test

Bai–Saranadasa's two-sample test for high-dimensional data, namely BS-test, has been widely cited in the literature. However, it may not control the size well when the required conditions are not satisfied. In this paper, a revisit to the BS-test is conducted. It is shown that under some regularity conditions and the null hypothesis, the BS-test statistic and a chi-square-type mixture have the same limiting distribution. It is then natural to approximate the null distribution of the BS-test using that of the chi-square-type mixture, which is actually obtained from the BS-test statistic when the two high-dimensional samples are normally distributed. The resulting test is then referred to as a normal-reference test. Two simulation studies and a real data example demonstrate that in terms of size control, the proposed normal-reference test performs very well and it performs substantially better than the BS-test and three other existing competitors proposed in the literature.
WeCare i-Board: A Tangible User Interface for Communication

The idea of this work is to create an interaction between people to keep them connected emotionally during the Covid crisis. The goal is to provide two interactive boards(i-Boards) and to let people communicate using it wireless. The device embodies digital information in physical space and aims to go beyond the limits of graphical user interfaces. It also expands the affordances of simple physical objects (e.g cubes) and surfaces (board). It provides a tangible interface and also has its own elements of fun to keep the interaction as intuitive and as fun as possible. The user interface design choices and the limitations were also investigated.
Flipping Bits to Share Crossbars in ReRAM-Based DNN Accelerator

Future deep neural networks (DNNs) tend to grow deeper and contain more trainable weights. Although methods such as pruning and quantization are widely adopted to reduce DNN’s model size and computation, they are less applicable in the area of ReRAM-based DNN accelerators. On the one hand, because the cells in crossbars are accessed uniformly, it is difficult to explore fine-grained pruning in ReRAM-based DNN accelerators. On the other hand, aggressive quantization results in poor accuracy coupled with the low precision of ReRAM cells to represent weight values.In this paper, we propose BFlip – a novel model size and computation reduction technique – to share crossbars among multiple bit matrices. BFlip clusters similar bit matrices together, and finds a combination of row and column flips for each bit matrix to minimize its distance to the centroid of the cluster. Therefore, only the centroid bit matrix is stored in the crossbar, which is shared by all other bit matrices in that cluster. We also propose a calibration method to improve the accuracy as well as a ReRAM-based DNN accelerator to fully reap the storage and computation benefits of BFlip. Our experiments show that BFlip effectively reduces model size and computation with negligible accuracy impact. The proposed accelerator achieves 2.45 × speedup and 85% energy reduction over the ISAAC baseline.
Emotion Analysis System Based on SKEP Model

In recent years, with the rise and development of e-commerce and social networking, appeared on the Internet a lot of subjective texts, and emotional analysis task is defined with the text analysis, the subjectivity of the emotional color processing, summing up. Reasoning process, so the sentiment analysis technology is designed to deal with subjective text. This paper proposes an emotional analysis system based on the SKEP model based on network neologies, ambiguous sentences, irony, or implicit emotional expressions in subjective texts. In addition, traditional sentiment analysis methods often have disadvantages such as low accuracy, weak semantic representation, incomplete feature extraction, and low contextual correlation. Through text pre-training of large-scale unsupervised e-commerce, fine-tuning is carried out on small-scale supervised data. Based on the same data set, compared with THE SEKP model, the F1 value of the opinion extraction task and the accuracy of the sentence-level sentiment analysis task were significantly improved. Still, the accuracy of the evaluation object-level sentiment analysis did not show good results.
Using Orthogonal Signals to Hide Information in Images

Spread-Spectrum Steganography (SSS) technology is used to hide information messages in image containers. For this purpose, propagating discrete signals are used, in specially formed pseudo-random sequences (PRS) with special correlation properties. This technology is similar to the direct-sequence spread spectrum (DSSS) used in telecommunications systems to reduce the effects of random noise in information channels. The properties of expansion PRS directly affect the quality of communication in DSSS and, accordingly, the efficiency of SSS. This article investigates various methods of PRS formation and conducts experiments to assess the intensity of bit errors and the quality of the image container. We show that it is most appropriate to use orthogonal discrete Walsh-Hadamard signals that provide the best performance.
Research on the Elastic Development of Spatial Layout of Hospitals Based on GIS Platform under COVID-19 Background: A Case Study of Wuhan

COVID-19 has become a major public health emergency in the world. At the beginning of the outbreak of the COVID-19 epidemic, a series of medical squeezes appeared in Wuhan, which exposed the problems existing in the implementation of the hierarchical diagnosis and treatment system. Based on GIS platform, this paper analyzes the spatial layout of medical facilities at all levels in Wuhan and compares and analyzes the usage of daily and epidemic periods. It is found that the interlevel gap in the development of daily medical resources leads to spatial differences in the reserved development flexibility, showing the characteristics of weak elastic coping ability and low reserved development flexibility in the surrounding urban areas, strong elastic coping ability and high-reserved development flexibility in the central old urban area. Therefore, to improve the resilience of medical institutions at all levels, we should promote the balanced layout of primary medical institutions and the “decentralization” of high-level medical institutions in spatial layout, coordinate the number and organizational structure of medical resources at different levels, and implement differentiated allocation strategies.
DEVELOPMENT OF COMPETENCIES AND COMPETENCIES OF TEACHERS IN MODERN CONDITIONS

The article discusses the concepts of “competence” and “competence” of a teacher, their distinctive characteristics, reveals effective ways of forming pedagogical competencies and competence in the process of using collective training sessions.
Design of Digital Image Encryption Scheme With Cryptographic Analysis

In this paper a new cryptographic algorithm, de-signed for digital image encryption is proposed. The encryption scheme is based on two chaotic maps, combined for constructing a pseudorandom generator. Using the chaotic behavior of the pseudorandom generator, permutation and substitution operations are performed for completing the encryption process. For proving the security of the cryptographic scheme, an extensive cryptographic analysis is performed including statistical tests for randomness, key-space analysis, key sensitivity, visual analysis, histogram analysis, correlation analysis, entropy analysis, number of pixels change rate and encryption/decryption key sensitivity analysis. The results from empirical experiments are presented in this paper.
Computer-Assisted Cohort Identification in Practice

The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef , in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.
Visual Question Answering Based on Position Alignment

The alignment of information from images and questions is of great significance in the visual question answering task. Whether an object in image is related to the question or not is the basic judgement relied on the feature alignment. Many previous works have proposed different alignment methods to build better cross modality interaction. The attention mechanism is the most used method in making alignment. The classical bottom up and top down model builds a top down attention distribution by concatenating question features to each image features and calculates the attention weights between question and image. However, the bottom up and top down model didn't consider the positional information in image and question. In this paper, we revisit the attention distribution from a position perspective which aligns question to object's positional information. We first embed the positional information of each object in image and calculate a position attention distribution to indicate the relevance between objects' positions in context of the current question. Through the attention distribution model can select the related position in image to answer the given question. The position attention distribution is concatenated to the feature attention distribution to get the final distribution. We evaluate our method on visual question answering (VQA2.0) dataset, and show that our method is effective in multimodal alignment.
Machine Control via Real Time Eye Detector

As the population ages, the number of people dependent on others who are paralyzed or losing their self-movement is increasing. This paper is focusing on the development of a smart robot based on wireless vision control which is designed for physically challenged individuals. The research employs a human pupil movement hands-free control machine for the moderate or severe physically disabled individuals by applying Convolution Neural Networks (CNNs) method to pre-train the model. The dataset contains the annotation information. By using the coordinates of the eye to identify the Iris’ location. This feature is to ensure the alignment of the eyes to identify the dominant eyes for Strabismus users. The result of this paper works as per expected with a preferable accuracy which fulfilled the objectives of doing this project. The delay time of the transmission data is negligible. Therefore, the synchronizing between the prototype and the eyeball movement of the user’s intention is nearly perfect in which the eyeball moves upward, the robot will go forward, and so on. In short, this project is to have a contribution to society in a small way by presenting an idea for a system that can truly improve the lives of physically disabled people around the world.
Malicious Conspiracy on Permissioned Blockchain to Mount Denial-of-endorsement Attacks

With the application of blockchains in cryptocurrencies, Internet-of-things and smart contracts etc., many attacks on blockchains and related vulnerabilities have been found. In this paper, we propose a conspiracy scheme on permissioned blockchain, Hyperledger Fabric, through the subliminal channel in the digital signature of Gossip message. For the first time, we propose an attack, called denial-of- endorsement (DoE) attack, on the channel where malicious peer nodes deliberately refuse to endorse the transaction proposals from client applications, trying to break down the normal business workflow. Based on our proposed conspiracy scheme, we design a DoE attack conspiracy protocol, which enables malicious peer nodes to secretly conspire to mount a DoE attack on the channel. We also make some mitigation analysis, aiming at helping Hyperledger Fabric users avoid such DoE attacks and eliminate conspiracy.