Reviewerly/exHarmony huggingface dataset:
Person Re-Identification using Deep Learning Networks: A Systematic Review

Person re-identification has received a lot of attention from the research community in recent times. Due to its vital role in security based applications, person re-identification lies at the heart of research relevant to tracking robberies, preventing terrorist attacks and other security critical events. While the last decade has seen tremendous growth in re-id approaches, very little review literature exists to comprehend and summarize this progress. This review deals with the latest state-of-the-art deep learning based approaches for person re-identification. While the few existing re-id review works have analysed re-id techniques from a singular aspect, this review evaluates numerous re-id techniques from multiple deep learning aspects such as deep architecture types, common Re-Id challenges (variation in pose, lightning, view, scale, partial or complete occlusion, background clutter), multi-modal Re-Id, cross-domain Re-Id challenges, metric learning approaches and video Re-Id contributions. This review also includes several re-id benchmarks collected over the years, describing their characteristics, specifications and top re-id results obtained on them. The inclusion of the latest deep re-id works makes this a significant contribution to the re-id literature. Lastly, the conclusion and future directions are included.
Modeling and Counteracting Exposure Bias in Recommender Systems

What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions. Similarly, the predictive accuracy of learning machines heavily depends on the feedback data that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown biases which can be exacerbated after several iterations of machine learning predictions and user feedback. Machine-caused biases risk leading to undesirable social effects ranging from polarization to unfairness and filter bubbles. In this paper, we study the bias inherent in widely used recommendation strategies such as matrix factorization. Then we model the exposure that is borne from the interaction between the user and the recommender system and propose new debiasing strategies for these systems. Finally, we try to mitigate the recommendation system bias by engineering solutions for several state of the art recommender system models. Our results show that recommender systems are biased and depend on the prior exposure of the user. We also show that the studied bias iteratively decreases diversity in the output recommendations. Our debiasing method demonstrates the need for alternative recommendation strategies that take into account the exposure process in order to reduce bias. Our research findings show the importance of understanding the nature of and dealing with bias in machine learning models such as recommender systems that interact directly with humans, and are thus causing an increasing influence on human discovery and decision making
CNN based facial aesthetics analysis through dynamic robust losses and ensemble regression

Abstract In recent years, estimating beauty of faces has attracted growing interest in the fields of computer vision and machine learning. This is due to the emergence of face beauty datasets (such as SCUT-FBP, SCUT-FBP5500 and KDEF-PT) and the prevalence of deep learning methods in many tasks. The goal of this work is to leverage the advances in Deep Learning architectures to provide stable and accurate face beauty estimation from static face images. To this end, our proposed approach has three main contributions. To deal with the complicated high-level features associated with the FBP problem by using more than one pre-trained Convolutional Neural Network (CNN) model, we propose an architecture with two backbones (2B-IncRex). In addition to 2B-IncRex, we introduce a parabolic dynamic law to control the behavior of the robust loss parameters during training. These robust losses are ParamSmoothL1, Huber, and Tukey. As a third contribution, we propose an ensemble regression based on five regressors, namely Resnext-50, Inception-v3 and three regressors based on our proposed 2B-IncRex architecture. These models are trained with the following dynamic loss functions: Dynamic ParamSmoothL1, Dynamic Tukey, Dynamic ParamSmoothL1, Dynamic Huber, and Dynamic Tukey, respectively. To evaluate the performance of our approach, we used two datasets: SCUT-FBP5500 and KDEF-PT. The dataset SCUT-FBP5500 contains two evaluation scenarios provided by the database developers: 60-40% split and five-fold cross-validation. Our approach outperforms state-of-the-art methods on several metrics in both evaluation scenarios of SCUT-FBP5500. Moreover, experiments on the KDEF-PT dataset demonstrate the efficiency of our approach for estimating facial beauty using transfer learning, despite the presence of facial expressions and limited data. These comparisons highlight the effectiveness of the proposed solutions for FBP. They also show that the proposed Dynamic robust losses lead to more flexible and accurate estimators.
Digital Training in Building Chatbot-based Online Learning Media: Action Research for Teachers in Semarang City through the "Train The Teachers" Training

The lack of student learning motivation and teacher difficulties in monitoring student learning progress is considered to be the main problems in distance learning, especially during the COVID-19 pandemic. This research employed action research methods by involving 20 teachers in several elementary schools in Semarang City spread across several sub-districts. The research results obtained: First, six steps to determine the increase in teacher creativity and understanding, namely the introduction of chatbot and chatbot templates as digital learning media in schools; the implementation of the first training session; the assignment of creating and designing chatbots from templates; the implementation of the second training session and finale chatbot feedback. Second, during digital chatbot training, Acita template was used to see an increase in teacher creativity and understanding of chatbots. The result revealed that, in the aspect of teacher creativity, the point of chatbot design creativity had increased by 41%, creativity in material integration had increased by 46%, and interactive creativity in chatbots had increased by 33%. In addition, in the aspect of teacher understanding, the points of understanding of chatbot structure and understanding of chatbot development had increased by 47%.
Evaluation of CRYSTALS-Kyber and Saber on the ARMv8 architecture

This paper presents preliminary experimental results for the implementation of the third-round NIST finalists CRYSTALS-Kyber and Saber on the ARMv8 architecture. Our implementation uses NEON instructions to speed up key generation, encapsulation, and decapsulation of the reference codes. The benchmarks are performed on three devices: an Orange Pi WinPlus (Cortex-A53), a Raspberry Pi 4 (Cortex-A72), and a MacBook Air based on an Apple M1 chip. The experimental results show substantial improvements for Kyber and Saber, with speed-ups in the ranges 1.16-1.38x and 1.21-1.96x, respectively. We focused on the most time-consuming operations of each cryptosystem; however, similar works suggest that more expressive speed-ups can be obtained by extending the use of NEON instructions to other primary sub-routines.
Adaptive Two-Index Fusion Attribute-Weighted Naive Bayes

Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to its high efficiency and easy implementation, the filter-attribute-weighted NB methods have received great attentions. However, there still exist several challenges, such as the poor representation ability for a single index and the fusion problem of two indexes. To overcome the above challenges, we propose a general framework of an adaptive two-index fusion attribute-weighted NB (ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, the intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a regulatory factor β to fuse two indexes, which can adaptively adjust the optimal ratio of any two indexes on various datasets. Furthermore, a range query method is proposed to infer the optimal interval of regulatory factor β. Finally, the weight of each attribute is calculated using the optimal value β and is integrated into an NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter-weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive regulatory factor β. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive regulatory factor β.
Private key encryption and recovery in blockchain

The disruptive technology of blockchain can deliver secure solutions without the need for a central authority. In blockchain protocols, assets that belong to a participant are controlled through the private key of an asymmetric key pair that is owned by the participant. Although, this lets blockchain network participants to have sovereignty on their assets, it comes with the responsibility of managing their own keys. Currently, there exists two major bottlenecks in managing keys; $a)$ users don't have an efficient and secure way to store their keys, $b)$ no efficient recovery mechanism exists in case the keys are lost. In this study, we propose secure methods to efficiently store and recover keys. For the first, we introduce an efficient encryption mechanism to securely encrypt and decrypt the private key using the owner's biometric signature. For the later, we introduce an efficient recovery mechanism using biometrics and secret sharing scheme. By applying the proposed key encryption and recovery mechanism, asset owners are able to securely store their keys on their devices and recover the keys in case they are lost.
Sentimental Analysis Applied on Movie Reviews

Nowadays, Natural Language Processing has received the widespread attention from the natural sciences, and sentimental analysis is one of the most widely used NLP applications. In the age of big data, how to find the required information accurately and quickly has become the hotspot of current research. Based on the movie reviews of two movies from the same series, this paper studies the sentimental trend of movies reviews, in order to help the audience obtain a reference for movie choices. Term frequency-Inverse Document Frequency (TF-IDF) algorithm is applied to evaluate the importance of words in the reviews, and TextBlob sentiment analysis library of Python software is used to grade the sentiment scores of the two films. Finally, the sentiment score graph is drawn, which provides a strong support for the further identification of the movie characteristics of two films from the same series. What’s more, Support vector machines (SVM) model is utilized to do the classification of the movie reviews and achieved 85.2% accuracy.
Research on laser center positioning under CV model segmentation

Spot positioning accuracy is an important index of laser processing system and ranging system. When the laser spot is noisy or the gray level is not uniform, the positioning accuracy is easily affected. Aiming at the above problems, this paper proposes a laser spot segmentation method based on the Chan-Vese model, which can improve the accuracy of spot center localization in combination with the gray centroid method. Firstly, the laser spot image is decomposed by two-dimensional wavelet, and the high-frequency component is suppressed by soft threshold function to eliminate the noise in the laser spot image. Secondly, the level set algorithm based on Chan-Vese model is used to segment the laser spot image with adaptive improvement of the initial coordinates of the evolution curve. Finally, the center coordinates are calculated inside the segmentation curve using the gray centroid method. Experimental results show that the method is more accurate and robust.
Human action recognition from inertial sensors with Transformer

Human action recognition is an attractive research topic because it opens many practical applications such as healthcare, entertainment or robot interaction. Hand gestures in particular are becoming one of the most convenient means of communication between humans and machines. In this study, transformer model - a deep learning neural network developed primarily for the natural language processing and vision tasks, is investigated for analysis of time-series signals. The self-attention mechanism inherent in the transformer expresses individual dependencies between signal values within time series. As a result, it can boost the performance of state-of-the-art convolutional neural networks in terms of memory requirement and computational times. We evaluate the proposed method on three published sensor datasets (CMDFALL, C-MHAD and DaLiAc) and showed that the proposed method achieves better performance than conventional ones, specifically on the S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> group in the CMDFall data set, the F1 Score is 19.04 % higher than that of the conventional method. On C-MHAD dataset, the accuracy is up to 99.56 %. The results confirms the role of transformer models for human activity recognition.
N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

The extractive summarization approach involves selecting the source document’s salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of Transformer-based Bidirectional Encoder Representations (BERT) pretrained linguistic model and graph attention network (GAT) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model N-GPETS by combining heterogeneous graph attention network with BERT model along with statistical approach using TF-IDF values for extractive summarization task. Apart from sentence nodes, N-GPETS also works with different semantic word nodes of varying granularity levels that serve as a link between sentences, improving intersentence interaction. Furthermore, proposed N-GPETS becomes more improved and feature-rich by integrating graph layer with BERT encoder at graph initialization step rather than employing other neural network encoders such as CNN or LSTM. To the best of our knowledge, this work is the first attempt to combine the BERT encoder and TF-IDF values of the entire document with a heterogeneous attention graph structure for the extractive summarization task. The empirical outcomes on benchmark news data sets CNN/DM show that the proposed model N-GPETS gets favorable results in comparison with other heterogeneous graph structures employing the BERT model and graph structures without the BERT model.
SeDR: Segment Representation Learning for Long Documents Dense Retrieval

Recently, Dense Retrieval (DR) has become a promising solution to document retrieval, where document representations are used to perform effective and efficient semantic search. However, DR remains challenging on long documents, due to the quadratic complexity of its Transformer-based encoder and the finite capacity of a low-dimension embedding. Current DR models use suboptimal strategies such as truncating or splitting-and-pooling to long documents leading to poor utilization of whole document information. In this work, to tackle this problem, we propose Segment representation learning for long documents Dense Retrieval (SeDR). In SeDR, Segment-Interaction Transformer is proposed to encode long documents into document-aware and segment-sensitive representations, while it holds the complexity of splitting-and-pooling and outperforms other segment-interaction patterns on DR. Since GPU memory requirements for long document encoding causes insufficient negatives for DR training, Late-Cache Negative is further proposed to provide additional cache negatives for optimizing representation learning. Experiments on MS MARCO and TREC-DL datasets show that SeDR achieves superior performance among DR models, and confirm the effectiveness of SeDR on long document retrieval.
Block Switching: A Stochastic Approach for Deep Learning Security

Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models. That is, subtly crafted perturbations of the input can make a trained network with high accuracy and produce arbitrary incorrect predictions, while maintaining imperceptible to human vision system. In this paper, we introduce Block Switching (BS), a defense strategy against adversarial attacks based on stochasticity. BS replaces a block of model layers with multiple parallel channels, and the active channel is randomly assigned in the run time, hence unpredictable to the adversary. We show empirically that BS leads to a more dispersed input gradient distribution and superior defense effectiveness compared with other stochastic defenses such as stochastic activation pruning. Compared to other defenses, BS is also characterized by the following features: (i) BS causes less test accuracy drop; (ii) BS is attack-independent; and (iii) BS is compatible with other defenses and can be used jointly with others.
On the Evolution of Code Readability

Code reading is an activity frequently performed by developers. Before modifying code, developers have to read it, especially if it was authored by others. Several studies have been carried out to find insights related to code readability. However, they were mostly conducted on single (static) code snippets, i.e., they did not consider the ever-changing nature of software. In a preliminary study, we observed that refactoring operations performed by developers to improve several nonfunctional aspects of open-source software systems often result in improved code readability. Motivated by such results, in this research, we studied code readability in the context of software evolution. Especially, we aimed at understanding: (i) to what extent software developers are interested in code readability; (ii) how to code readability evolves in complex software systems;(iii) the influence of developers’ personal characteristics on the evolution of code readability. The results achieved indicate that most of the developers we surveyed reported that they consider readability as an important aspect of the source code. Then, by mining several software repositories we observed that readability rarely changes in software evolution. Therefore, files created unreadable rarely become readable, and vice versa. Finally, we noticed that some personal characteristics of developers have an impact on code readability. In particular, developers’ orienting network (related to their attention) positively correlates with code readability.
Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition

Human-Computer Interaction (HCI) is a sub-area within computer science focused on the study of the communication between people (users) and computers and the evaluation, implementation, and design of user interfaces for computer systems.HCI has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive science.Usability is an aspect of HCI dedicated to guaranteeing that human-computer communication is, amongst other things, efficient, effective, and sustaining for the user.Simultaneously, Human activity recognition (HAR) aim is to identify actions from a sequence of observations on the activities of subjects and the environmental conditions.The vision-based HAR study is the basis of several applications involving health care, HCI, and video surveillance.This article develops a Fire Hawk Optimizer with Deep Learning Enabled Activity Recognition (FHODL-AR) on HCI driven usability.In the presented FHODL-AR technique, the input images are investigated for the identification of different human activities.For feature extraction, a modified SqueezeNet model is introduced by the inclusion of few bypass connections to the SqueezeNet among Fire modules.Besides, the FHO algorithm is utilized as a hyperparameter optimization algorithm, which in turn boosts the classification performance.To detect and categorize different kinds of activities, probabilistic neural network (PNN) classifier is applied.The experimental validation of the FHODL-AR technique is tested using benchmark datasets, and the outcomes reported the improvements of the FHODL-AR technique over other recent approaches.
Extracting Training Data from Diffusion Models

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images. In this work, we show that diffusion models memorize individual images from their training data and emit them at generation time. With a generate-and-filter pipeline, we extract over a thousand training examples from state-of-the-art models, ranging from photographs of individual people to trademarked company logos. We also train hundreds of diffusion models in various settings to analyze how different modeling and data decisions affect privacy. Overall, our results show that diffusion models are much less private than prior generative models such as GANs, and that mitigating these vulnerabilities may require new advances in privacy-preserving training.
Verification of role of data scanning direction in image compression using fuzzy composition operations

A digital image is a numerical representation of visual perception that can be manipulated according to specifications. In order to reduce the cost of storage and transmission, digital images are compressed. Depending upon the quality of reconstruction, compression methods are categorized as Lossy and Lossless compression. The lossless image compression techniques, where the exact recovery of data is possible, is the most challenging task considering the tradeoff between the compression ratio achieved and the quality of reconstruction. The inherent data redundancies like interpixel redundancy and coding redundancy in the image are exploited for this purpose. The interpixel redundancy is treated by decorrelation using Run-length Encoding, Predictive Coding, and other Transformation Coding techniques. While entropy-based coding can be achieved using Huffman codes, arithmetic codes, and the LZW algorithm, which eliminates the coding redundancy. During the implementation of these sequential coding algorithms, the direction used for data scanning plays an important role. A study of various image compression techniques using sequential coding schemes is presented in this paper. The experimentation on 100 gray-level images comprising 10 different classes is carried out to understand the effect of the direction of scanning of data on its compressibility. Depending upon this study the interrelation between the maximum length of the Run and compression achieved similarly the resultant number of Tuples and compression achieved is reported. Considering the fuzzy nature of these relations, fuzzy composition operations like max-min, min-max, and max-mean compositions are used for decision-making. In this way, a rational comment on which data scanning direction is suitable for a particular class of images is made in the conclusion.