Reviewerly/exHarmony huggingface dataset:
Design of MobileNetV1 SSD Target Detection Accelerator Based on FPGA

Object detection based on convolutional neural network has become one of the important algorithms in the field of computer target detection. However, due to the speed and power limitation brought by convolution computation, the algorithm of the convolutional network used for target recognition generally needs to be accelerated by the convolution accelerator before it can be effectively deployed to edge computing devices. In this paper, a new architecture of the convolution accelerator is proposed. On this basis, the convolution accelerator is used to build and complete the convolutional acceleration system, and the MobileNetV1 SSD detection algorithm network is realized. The convolution accelerator has a special operation channel, which can normalize convolution kernels of different sizes. Besides, it innovatively optimizes the addition operation mode in the convolution kernels, realizes pipeline convolution calculation, shortens the image recognition time and increases the versatility of the convolution accelerator.
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness

Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating AT into SSL, multiple prior works have accomplished a highly significant yet challenging task: learning robust representation without labels. A widely used framework is adversarial contrastive learning which couples AT and SSL, and thus constitutes a very complex optimization problem. Inspired by the divide-and-conquer philosophy, we conjecture that it might be simplified as well as improved by solving two sub-problems: non-robust SSL and pseudo-supervised AT. This motivation shifts the focus of the task from seeking an optimal integrating strategy for a coupled problem to finding sub-solutions for sub-problems. With this said, this work discards prior practices of directly introducing AT to SSL frameworks and proposed a two-stage framework termed Decoupled Adversarial Contrastive Learning (DeACL). Extensive experimental results demonstrate that our DeACL achieves SOTA self-supervised adversarial robustness while significantly reducing the training time, which validates its effectiveness and efficiency. Moreover, our DeACL constitutes a more explainable solution, and its success also bridges the gap with semi-supervised AT for exploiting unlabeled samples for robust representation learning. The code is publicly accessible at https://github.com/pantheon5100/DeACL .
The Depression of Noises in Acoustic Signals of Transformer Based on Adversarial Auto-encoder

Acoustic signals produced by an operated transformer are the important parameters for the assessment of noise level of substation and condition monitoring of transformer. When acoustic signals of transformer transmit in the air, some interference components are inevitably contained in the measured acoustic signals by microphone sensors with the features of diversity and a certain degree of uncertainty. To obtain the real acoustic signals resulting from the transformer, the adversarial convolutional de-noising auto-coder (CDAE) network is proposed and trained to carefully depress the interference components of acoustic signals in an end-to-end manner. The similarity of power spectrum (SPS) is defined to evaluate the de-noising effects of the proposed method. The acoustic signals for an operated transformer in a substation are measured to build the data set. The calculated results have shown that the adversarial CDAE network is capable of eliminating the interference components contained in acoustic signals of transformer with better generalization ability and high accuracy.
Modeling of a Cognitive Hybrid Architecture for the Hygrothermal Regulation of a Room Manipulated by an Agent

This paper applies components of cognitive architectures to hygrothermal (humidity and temperature) control in a simulated room; an agent can affect the room hiygrothermal state. Hygrothermal control and its optimization is an important field of research due to constraints on fuel acquisition and the transition to renewable energies. The cognitive theory looks to model a mind's behavior as the joint work of multiple components. This first work implements two features of cognition related to hygrothermal regulation: procedural memory and attention. An agent uses these two modules to control the hygrothermal state of a room under environmental pressure.
Communication and self-management skills as metaprofessional competences of would-be engineers

The article presents the results of a research of the maturity of transprofessional competences in would-be engineers. The communication and self-management skills, including time management, may enhance the performance efficiency of technical specialists and increase their value in the labor market. The importance of universal cross-cutting competences is emphasized by their inclusion into federal state educational standards and, accordingly, into the curricula of disciplines. Socio-psychological techniques were used to assess the work of Russian technical universities in the sphere of developing transprofessional competences. An author-designed questionnaire, a modified chart for evaluating and self-evaluating preparedness for self-education by G. M. Kodzhaspirova, a technique for diagnosing communicative social competence, and a “Team roles” test by R. Belbin allowed revealing the problem zones and weaknesses in this sphere. Thus, an absolute majority of the respondents admit the importance of self-education as an element of professionalization, but preparedness for self-education is rather declarative. 40.6% of the respondents are not aware of time management; the same amount could not name the specific time management tools. Although the respondents are prepared to take on several team roles, their distribution among the would-be engineers is not sufficiently balanced.
A Novel Approach for Constructing Intangible Cultural Heritage Knowledge Graphs

The existing intangible cultural heritage knowledge exists in different data formats such as structured, semi-structured and unstructured. It is stored in a variety of ways and cannot be used effectively. How to reasonably store intangible cultural heritage knowledge, improve the singleness of knowledge form, better retrieve knowledge and provide relevant services have become the main problems. In order to alleviate these problems, this paper constructs the intangible cultural heritage knowledge graph (KG), which provides a set of KG construction paradigm for the intangible cultural heritage field. The intangible cultural heritage knowledge is crawled by the crawler, stored in the graph database, and the intelligent question answering (IQA) system is realized by using the matching algorithm. The intangible cultural heritage KG spectrum is successfully constructed. The results show that the method proposed in this paper is effective and has certain scalability and popularization.
Aplikasi Prakiraan Perkembangan Covid-19 Di Indonesia Menggunakan Metode Single Exponential Smoothing Berbasis Web

ARSCoV2 (Severe Acute Respiratory Syndrome Coronavirus 2) is a virus cause of COVID-19 that has spread to many other countries. A variety of efforts is carried out mostly countries in the world to face this disease. The result of increased case of COVID-19 the economy is inspiring and providing pressure on the future economic growth including Indonesia’s economic growth. UN-defined COVID-19 pandemi, impact on sector transportation, tourism, trading, health and others. The purpose of this research is to design and create COVID-19 case development forecast in Indonesia by implementing REST API services and using the single exponential smoothing method as a mathematical method to calculate the development forecast COVID-19 cases in Indonesia are confirmed with the date to help minimize losses materials and immaterials, for the readiness of the Indonesian government in conducting rapid precautions and appropriately to help the general public to obtain COVID-19 and forecast information the development. Research results on COVID-19 data in Indonesia in March 2020 until April 2022 obtaining the value of the MAPE error model of 7.97% where 7.97% ≤ 10% so included in the level category high accuracy with forecast results in the next month, May 2022 is 5,327,773 cases.
Implementation of the trimmed k-means clustering method in mapping the distribution of Covid-19 in Indonesia

The Coronavirus that appeared in (COVID-19), caused by SARSCoV-2, started at Wuhan in the Hubei province of China and has spread with great speed around the world; it has caused a severe health crisis all around the world, including Indonesia. This study aims to use a clustering technique to assess the risk of the COVID-19 pandemic in Indonesia, based on data obtained between March 2020 and July 2021 in that country (http://www.covid19.go.id ). Provinces in Indonesia were grouped based on COVID-19 infection rates and mortality data. Since the data con-tained some outliers, i.e. provinces with a very high number of cases, we used a robust clustering method; this method is sensitive to outliers. The analysis was performed using the Trimmed k-means clustering method. Based on the results of this study, with four provinces detected as outliers in the data, there were three optimal clusters with the maximum separation index. Cluster 1 consisted of 14 provinces, and clusters 2 and 3 consisted of 10 and 6 provinces, respectively. The four outliers, i.e. Jakarta, West Java, Central Java and East Java, formed a separate cluster.
Representation research based on sketch images

In modern computer science, tasks such as optical character recognition are of great importance. Yet, for artificial neural networks it’s never easy to distinguish what a scrawled image indicates.In December 2016, Google post a drawing game which would randomly pick up a word and ask the player to draw an outline within 20 seconds. An AI was used to judge if your work looks similar to what you were asked to draw. With more and more people playing this game, Google has collected over 1 billion drawings.Freehand sketching is a dynamic process where points are sequentially sampled and grouped as strokes for sketch acquisition on electronic devices. While most existing models simply convert the sketch vectors into binary images and pay little attention to the time information, we organize training data and adapt traditional image recognition networks in a novel way. In this report, we will introduce our solution to this doodle recognition problem, including data pre-processing and image recognition. We would also explain experiment process and the main challenges we met in detail.
PBLF: Prompt Based Learning Framework for Cross-Modal Recipe Retrieval

Nowadays, widespread attention is drawn to cross-modal recipe retrieval due to the various food-relevant applications and the increasing concern on health. This task is addressable through a combination of multi-modal data (e.g., images and texts), which have far-reaching meaning on the merging of vision and language. Early researchers focus on learning joint representation by projecting food images and recipe texts (e.g., ingredients and instructions) to the same embedding space and proposing different cross-modal fusion structure. Recently, most methods adopt a pre-train model and fine-tune strategy to help capture the alignment between modalities. While offering appreciable retrieval performance, two limitations still exist in these methods: 1) with the increasing complexity of the pre-trained model, the data requirements and the cost of calculating in fine-tune stage are also rising. And 2) the down-stream fine-tune tasks they designed for cross-modal recipe retrieval have a gap with the pre-trained model. To this end, we propose a novel fusion framework named Prompt Based Learning Framework (PBLF) to adopt transferable visual model CLIP (Contrastive Language-Image Pre-training) into the recipe retrieval task for the first time, and design an appropriate prompt to train the model efficiently, which bridge the gap between the pre-trained model and the downstream task and transfer the knowledge of the CLIP model to specific recipe retrieval task. The extensive experiments on the large-scale cross-modal recipe dataset Recipe1M demonstrate the superiority of our proposed PBLF model compared to the state-of-the-art approaches.
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control

Uncertainty quantification is one of the central challenges for machine learning in real-world applications. In reinforcement learning, an agent confronts two kinds of uncertainty, called epistemic uncertainty and aleatoric uncertainty. Disentangling and evaluating these uncertainties simultaneously stands a chance of improving the agent's final performance, accelerating training, and facilitating quality assurance after deployment. In this work, we propose an uncertainty-aware reinforcement learning algorithm for continuous control tasks that extends the Deep Deterministic Policy Gradient algorithm (DDPG). It exploits epistemic uncertainty to accelerate exploration and aleatoric uncertainty to learn a risk-sensitive policy. We conduct numerical experiments showing that our variant of DDPG outperforms vanilla DDPG without uncertainty estimation in benchmark tasks on robotic control and power-grid optimization.
Trends and Findings in Measuring Software Quality Metrics in the Industry

The importance of software quality and the role of metrics is well recognized. However, they haven’t been standardized into the software life cycle. The main goal of this article is to identify the existence of quality metrics in terms of quality characteristics which has been applied within the industry. Trough a literature review we were able to extract the quality characteristics and the set of metrics from the relevant studies, from which we were able to build a catalogue of ready-to-apply metrics. This catalogue contains 177 metrics group by the quality characteristics: Functional Suitability, Performance Efficiency, Usability, Reliability, Fault Tolerance, Safety, Security, Maintainability, Testability, and Evolvability. A set of findings have been delineated to understand the trends within the analyzed studies.
SAR Image Segmentation Based on Super-Pixel and Kernel-Improved CV Model

The paper focuses on the effect of pixel intensity random variation in the homogeneous region of Synthetic Aperture Radar (SAR) images. Considering the shortage of CV (Chan-Vese) model to describe the energy variation inside and outside the curve, a SAR image segmentation algorithm based on super-pixel and kernel-improved CV model is proposed. The Simple Linear Iterative Clustering 0 (SLICO) algorithm is employed for pixel reconstruction. Simultaneously, our improved model combines a Laplacian kernel function with 12 fitting energy to construct the novel fitting energy, which can describe the energy inside and outside the curve clearly. Specifically, a distance regularized term is embedded into our proposed model to avoid the reinitialization of level set and accelerate the convergence of the improved model. Finally, experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrates our method achieves competitive performances compared with other related algorithms.
A Raft Algorithm with Byzantine Fault-Tolerant Performance

In view of the disadvantage that Raft algorithm does not have byzantine fault tolerance, this paper improves Raft algorithm and proposes a Raft algorithm with Byzantine fault tolerance – NRaft algorithm. First, NRaft algorithm uses the Shamir threshold secret sharing algorithm to solve the log tampering and information quantity fraud in Byzantine nodes; second, RSA encryption algorithm is used to further ensure the integrity of logs. Finally, the experimental data show that NRaft algorithm outperforms PBFT algorithm as the number of nodes increases. Therefore, the NRaft algorithm is not only suitable for environmentally secure private blockchain, but also for consortium blockchain where Byzantine nodes exist.
A Mathematical Deduction of Variational Minimum Distance in Gaussian Space and Its Possible Application to Artificial Intelligence

Artificial intelligence is an area of knowledge that in many of its applications involves statistical inferences to achieve the desired results: the Gaussian space has allowed modeling and adapting different algorithms in machine learning, digital image processing, evolutionary algorithms, and natural language processing, among others. One form of the procedure followed by such algorithms is to sample in space and infer distances or Gaussian behavior empirically, thus generating uncertainty or convergence deficiencies. We propose a variational derivation of the minimum distance in Gaussian space in terms of the parameters µ and σ, which allows us to infer behavioral changes in this space using the minimum distance. As a case study, we analyze an estimation distribution algorithm with our minimum distance inference, which allows controlling changes in the search space using reduced populations due to its analytical nature. Finally, we demonstrate the mathematical results of comparison between other estimation distribution algorithms using the proposed minimum distance.
On the Robustness of Cross-lingual Speaker Recognition using Transformer-based Approaches

Most speaker recognition systems presume that the language for enrollment and testing is the same. Cross-lingual speaker recognition is rarely investigated. This study collected trilingual (including Mandarin, English, and Taiwanese) cross-language recordings named MET-40. A total of 40 participants (20 male, 20 female) contribute to the dataset which contains 740 minutes of audio. Spoken texts are mainly taken from elementary school textbooks, and some English texts use TIMIT.We employ ResNet, vision transformer (ViT), and convo-lutional vision transformer (CvT) in combination with three acoustic features, namely, spectrogram, Mel spectrogram, and Mel frequency cepstral coefficient for single, mixed and cross-language speaker recognition tasks. In the mixed-language setting, the language to be tested is included in the training set, while in the cross-language scenario the language to be tested is not used for training. Experimental results show that the highest accuracy is 97.16% for single language models. Mixture of two languages improves the performance to 99.17%. In cross-language situations, the accuracy drops significantly to 79.64%, as the spoken language is not present in the training data. When two languages are employed for training, the accuracy rose to 90.92%. In general, CvT-based models demonstrate the best stability in all cases.The robustness of the model is critical to security in practical applications. Therefore, we analyze how adversarial attacks impact different speaker identification models. The results show that although CvT-based model exhibits excellent performance, it is easily affected by the perturbation caused by the adversarial attack. The effect is less pronounced when more languages are used for training, with an average increase of 5.11% in accuracy. Finally, extra caution needs to be taken when MFCC is chosen to be the acoustic feature, as attacks can still take place without training data, and the recognition rate is reduced by 31.57% using FGSM cross-language attack.
A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference

This paper considers statistical inference in contexts where only incomplete prior information is available. We develop a practical construction of a suitably valid inferential model (IM) that (a) takes the form of a possibility measure, and (b) depends mainly on the likelihood and partial prior. We also propose a general computational algorithm through which the proposed IM can be evaluated in applications.
Detox Loss: Fairness Constraints for Learning With Imbalanced Data

Recent studies have highlighted a major caveat in several popular deep learning based face recognition algorithms. Despite their high accuracy, they have shown to exhibit biased behavior by achieving sub-par performance on images belonging to different sub-groups. Such behavior can often lead to unfair predictions, thus presenting a need to develop fairer models with unprejudiced behavior. This research proposes Detox loss, a novel bias invariant feature learning loss function for learning unbiased models. The proposed loss can be used to: (i) learn fairer deep learning classifiers, and (ii) mitigate bias from existing pre-trained networks, especially in the challenging constraint of imbalanced training data with respect to a protected attribute. Conceptually, the Detox loss enforces that the learned features are distinguished based on the task label only, while eliminating any distinction based on the biasing-attribute. This is achieved by incorporating three fairness constraints while training with the traditional classification loss: (i) proposed Exclusion loss, (ii) proposed Inclusion loss, and (iii) proposed Feature-distillation loss. The efficacy of the Detox loss is demonstrated on two facial analysis tasks: (i) age-group prediction and (ii) gender prediction, under the protected attribute of ethnicity for learning fairer models and de-biasing existing models, with varying imbalanced training data distributions. Across two different protocols, three setups, and two tasks, the Detox loss obtains state of the art performance without the need of multi-labeled training data. For example, on the challenging Pilot Parliaments Benchmark (PPB) dataset, the Detox loss obtains a balanced accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score of 96.1% and 96.6%, as compared to the state-of-the-art performance of 94.1% and 93.6%, respectively.
Integrity Assurance System for Document Security Using Keccak and Quick Algorithm Response Code

Still at risk from threats, public documents must have several security models, namely confidentiality, integrity, and availability. because many violations of public documents are carried out for various reasons that are utilized from one of these security models. art is needed to ensure such security, the method used to ensure that a document can be trusted can be confirmed who its owner is, its veracity, and ease of access. Maintain document security with the Hash Message Authentication Code method and use the Keccak algorithm which is sensitive if there are differences and changes in data. The sensitive nature of these methods and algorithms can guarantee the integrity of the documents. from all the processes above, this research aims to create an “Integrity Guarantee System” for documents that are distributed publicly.