Reviewerly/exHarmony huggingface dataset:
PPEFL: An Edge Federated Learning Architecture with Privacy-Preserving Mechanism

The emergence of federal learning makes up for some shortcomings of machine learning, and its distributed machine learning paradigm can effectively solve the problem of data islands, allowing users to collaboratively model without sharing data. Clients only need to train locally and upload model parameters. However, the computational power and resources of local users are frequently restricted, and ML consumes a large amount of computer resources and generates enormous communication consumption. Edge computing is characterized by low latency and low bandwidth, which makes it possible to offload complicated computing tasks from mobile devices and to execute them by the edge server. This paper is dedicated to reducing the communication cost of federation learning, improving the communication efficiency, and providing some privacy protection for it. An edge federation learning architecture with a privacy protection mechanism is proposed, which is named PPEFL. Through the cooperation of the cloud server, the edge server, and the edge device, there are two stages: the edge device and the edge server cooperate to complete the training and update of the local model, perform several lightweight local aggregations at the edge server, and upload to the cloud server and the cloud server aggregates the uploaded parameters and updates the global model until the model converges. The experimental results show that the architecture has good performance in terms of model accuracy and communication consumption and can well protect the privacy of edge federated learning.
Learning to Classify Logical Formulas Based on Their Semantic Similarity

An important task in logic, given a formula and a knowledge base which represents what an agent knows of the current state of the world, is to be able to guess the truth value of the formula. Logic reasoners are designed to perform inferences, that is, to decide whether a formula is a logical consequence of the knowledge base, which is stronger than that and can be intractable in some cases. In addition, under the open-world assumption, it may turn out impossible to infer a formula or its negation. In many practical situations, however, when an agent has to make a decision, it is acceptable to resort to heuristic methods to determine the probable veracity or falsehood of a formula, even in the absence of a guarantee of correctness, to avoid blocking the decision-making process and move forward. This is why we propose a method to train a classification model based on available knowledge in order to be able of accurately guessing whether an arbitrary, unseen formula is true or false. Our method exploits a kernel representation of logical formulas based on a model-theoretic measure of semantic similarity. The results of experiments show that the proposed method is highly effective and accurate.
Disparity Estimation Networks for Aerial and High-Resolution Satellite Images: A Review

This study reviews the PSM and HSM deep learning architectures for disparity estimation from an input stereo pair and assesses their applicability for satellite stereo reconstruction. All methods are tested on urban landscapes unseen at training time, using pre-trained weights learned from a stereo matching benchmark for aerial imagery. The quality of the disparity maps output by each method is assessed based on the subsequent surface models, which are evaluated using a lidar reference model. The conducted experiments give insight into the robustness of each architecture (e.g. robustness to different input resolutions, color spaces or acquisition dates), as well as their generalizability across different cities. Lastly, the results obtained with the different networks are compared with those of a state-of-the-art variant of the semi-global matching algorithm, which is a well-known classic methodology for satellite dense stereo matching. **This is an MLBriefs article, the source code has not been reviewed!**<br> **The original source codes are available [[here|https://github.com/JiaRenChang/PSMNet]] and [[here|https://github.com/gengshan-y/high-res-stereo]] (last checked 2022/11/14).**<br> <span style="color:red">**BEST STUDENT PAPER MLBRIEFS 2022**</span>.
Boson sampling for generalized bosons

We introduce the notion of ``generalized bosons'' whose exchange statistics resemble those of bosons, but the local bosonic commutator $[a,{a}^{\ifmmode\dagger\else\textdagger\fi{}}]=1$ is replaced by an arbitrary single-mode operator that is diagonal in the generalized Fock basis. Examples of generalized bosons include boson pairs and spins. We consider the analog of the boson sampling task for these particles and observe that its output probabilities are still given by permanents, so that the results regarding hardness of sampling directly carry over. Finally, we propose implementations of generalized boson sampling in circuit-QED and ion-trap platforms.
Improved Matching Algorithm with Anchor Argument for Rotate Target Detection

Convolutional neural networks (CNNs) have been widely used in the task of object detection in remote sensing. Remote sensing targets can have arbitrary angles, and many anchor-base methods use a lot of anchors with different angles which cause efficiency and precision problems. To solve the problem caused by too many anchors, this paper presents a novel matching algorithm in the matching stage of the rotating anchor and object, which determines a more accurate rotating region of interests (RRoIs) for target regression using the copies set for each oriented anchor. It makes use of the high recall rate brought by a large number of anchor boxes with different angles and avoids the computation brought by a large number of anchor boxes. We use the remote sensing datasets DOTA and HRSC2016 with rotation bounding boxes to evaluate our improved algorithm on Rotation RetinaNet and compare it with it. For the targets of high aspect ratios, such as large vehicles and ships, our method is superior to Rotation RetinaNet and achieves a better performance.
Nighttime Image Dehazing Based on Multi-Scale Gated Fusion Network

In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input, which can be adapted for nighttime image dehazing. The proposed algorithm hinges on a trainable neural network realized in an encoder–decoder architecture. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result using the learned representations attributed to the encoder. The constructed network adopts a novel fusion-based strategy which derives three inputs from an original input by applying white balance (WB), contrast enhancing (CE), and gamma correction (GC). We compute pixel-wise confidence maps based on the appearance differences between these different inputs to blend the information of the derived inputs and preserve the regions with pleasant visibility. The final clear image is generated by gating the important features of the derived inputs. To train the network, we introduce a multi-scale approach to avoid the halo artifacts. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art dehazing for nighttime images.
Spatial co-location pattern mining over extended objects based on cell-relation operations

Spatial co-location pattern mining (SCPM) is intended to discover subsets of spatial features whose instances are frequently located together in geographic areas. Traditional SCPM methods are designed for point spatial instances. However, in reality, instances are mostly in the form of extended objects, e.g., lines, polygons. In addition, current SCPM methods with extended objects are less well researched and have two disadvantages: (1) Existing researches cannot effectively capture neighborhood relationships between extended objects and their mining results cannot properly reflect the distribution dependence of features; (2) These methods are not efficient enough with large datasets. This paper proposes a novel framework called cell-relation operations framework to overcome these issues. To eliminate the first shortcoming, the framework uses the area overlapping of buffers between objects to gain the neighbor relationships between extended objects and introduces participation index under buffer size k to identify prevalent co-location patterns. To address the second problem, our framework employs cell-relation operations rather than instance relation computing as the basic computing unit for co-location mining, which substantially speeds up the computation. The framework obtains spatial co-locations by counting the feature transactions of the cells and calculates the feature overlap ratio of the cells to generate co-locations. We implement experiments with real datasets to demonstrate that our framework's mining results are more reasonable and the proposed framework's runtime outperforms the baselines by 2 to 4 orders of magnitude.
Feature Selection: Binary Harris Hawk Optimizer Based Biomedical Datasets

Feature selection (FS) is an essential preprocessing step in utmost solutions for the high-dimensional problem to reduce the number of features by deleting irrelevant and redundant data that preserve a suitable grade of classification accuracy. Feature selection can be treated as an optimization problem. Heuristic optimization algorithms are hopeful approaches to solve feature selection problems because of their difficulty, especially in high-dimensional data. Binary Harris hawk optimization (BHHO) is one of the lately suggested metaheuristic algorithms that has been demonstrated to be used more efficiently in facing some optimization problems. Support vector machines (SVMs) are a vital technique that are employed competently to resolve classification issues. We modified the BHHO algorithm with SVM classifier to solve the feature selection issue. This study suggests BHHO-FS to fix the feature selection problem in biomedical datasets. We ran the proposed approach BHHO-FS on real biomedical datasets with 17 types of cancer for Iraqi patients in 2010-2012. The experimental results demonstrate the supremacy of the proposed BHHO-FS in terms of three performance metrics: feature selection accuracy, runtime and number of selected features compared to four other state-of-art algorithms: Fire Fly (FF) algorithm, Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA) and Particle Swarm Algorithm (PSO). Comparative experiments designate the importance of the proposed approach in comparison with the other four mentioned algorithms. The implementation of the proposed BHHO-FS approach on 17 datasets for different types of cancers reveals 99.967% average accuracy.
Finding Skill Neurons in Pre-trained Transformer-based Language Models

Transformer-based pre-trained language models have demonstrated superior performance on various natural language processing tasks. However, it remains unclear how the skills required to handle these tasks distribute among model parameters. In this paper, we find that after prompt tuning for specific tasks, the activations of some neurons within pre-trained Transformers are highly predictive of the task labels. We dub these neurons skill neurons and confirm they encode task-specific skills by finding that: (1) Skill neurons are crucial for handling tasks. Performances of pre-trained Transformers on a task significantly drop when corresponding skill neurons are perturbed. (2) Skill neurons are task-specific. Similar tasks tend to have similar distributions of skill neurons. Furthermore, we demonstrate the skill neurons are most likely generated in pre-training rather than fine-tuning by showing that the skill neurons found with prompt tuning are also crucial for other fine-tuning methods freezing neuron weights, such as the adapter-based tuning and BitFit. We also explore the applications of skill neurons, including accelerating Transformers with network pruning and building better transferability indicators. These findings may promote further research on understanding Transformers. The source code can be obtained from https://github.com/THU-KEG/Skill-Neuron.
Structured abstract summarization of scientific articles: Summarization using full‐text section information

Abstract The automatic summarization of scientific articles differs from other text genres because of the structured format and longer text length. Previous approaches have focused on tackling the lengthy nature of scientific articles, aiming to improve the computational efficiency of summarizing long text using a flat, unstructured abstract. However, the structured format of scientific articles and characteristics of each section have not been fully explored, despite their importance. The lack of a sufficient investigation and discussion of various characteristics for each section and their influence on summarization results has hindered the practical use of automatic summarization for scientific articles. To provide a balanced abstract proportionally emphasizing each section of a scientific article, the community introduced the structured abstract, an abstract with distinct, labeled sections. Using this information, in this study, we aim to understand tasks ranging from data preparation to model evaluation from diverse viewpoints. Specifically, we provide a preprocessed large‐scale dataset and propose a summarization method applying the introduction, methods, results, and discussion (IMRaD) format reflecting the characteristics of each section. We also discuss the objective benchmarks and perspectives of state‐of‐the‐art algorithms and present the challenges and research directions in this area.
Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure

Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated content such as online reviews. In this work, we introduce GEN-SCL-NAT, which consists of two techniques for improved structured generation for ACOS quadruple extraction. First, we propose GEN-SCL, a supervised contrastive learning objective that aids quadruple prediction by encouraging the model to produce input representations that are discriminable across key input attributes, such as sentiment polarity and the existence of implicit opinions and aspects. Second, we introduce GEN-NAT, a new structured generation format that better adapts autoregressive encoder-decoder models to extract quadruples in a generative fashion. Experimental results show that GEN-SCL-NAT achieves top performance across three ACOS datasets, averaging 1.48% F1 improvement, with a maximum 1.73% increase on the LAPTOP-L1 dataset. Additionally, we see significant gains on implicit aspect and opinion splits that have been shown as challenging for existing ACOS approaches.
Waste Management with the Use of Heuristic Algorithms and Internet of Things Technology

Studies have been performed to improve the process of waste management. They were fulfilled by changing the base of waste logistics management using a combination of intelligent algorithms and the IMPACT IoT platform instead of a human factor. The research was carried out on the example of real data with respect to waste management in a given area. The proposed solution includes a program that simulates the filling of specific waste containers located in various areas. The determined aspects are inconveniences on the routes, affecting the time of moving between the receiving points and the distances between the containers. The variability of the speed and intensity of the containers filling up over time is an additional factor taken into account. The proposed methods yielded the performance of the control of the containers' filling status in real time, which apparently results in the possibility of a reaction to the current demand just in time. The proposed solution enables the improvement of the waste logistics management process, including avoiding the too-frequent emptying of containers or overfilling them. The combination of the device prototype, the simulation program, and the developed algorithms opens the possibility for further research in the smart city and optimization areas.
A tradition of open, academy-owned, and non-profit research infrastructure in Latin America

In this paper, we offer an overview of Latin America’s current scientific information infrastructure, highlighting its key role in the adoption of Open Access and Open Science in the region. Some of the most relevant open research information infrastructures are presented, and we discuss the importance of institutional, national, and regional open-access repositories, and journals’ portals on the development of a pioneering collaborative and academy-owned regional infrastructure. Although very successful and well-established, these infrastructures need to be strengthened and supported by national governmental agencies, and regional collaboration.
Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory)

Humans in carrying out communication activities can express their feelings either verbally or non-verbally. Verbal communication can be in the form of oral or written communication. A person's feelings or emotions can usually be seen by their behavior, tone of voice, and expression. Not everyone can see emotion only through writing, whether in the form of words, sentences, or paragraphs. Therefore, a classification system is needed to help someone determine the emotions contained in a piece of writing. The novelty of this study is a development of previous research using a similar method, namely LSTM but improved on the word weighting process using the TF-IDF method as a further process of LSTM classification. The method proposed in this research is called Natural Language Processing (NLP). The purpose of this study was to compare the classification method with the LSTM (Long Short-Term Memory) model by adding the word weighting TF-IDF (Term Frequency–Inverse Document Frequency) and the LinearSVC model, as well to increase accuracy in determining an emotion (sadness, anger, fear, love, joy, and surprise) contained in the text. The dataset used is 18000, which is divided into 16000 training data and 2000 test data with 6 classifications of emotion classes, namely sadness, anger, fear, love, joy, and surprise. The results of the classification accuracy of emotions using the LSTM method yielded a 97.50% accuracy while using the LinearSVC method resulted in an accuracy value of 89%.
Measurement-evaluation applications of context-based activities in hybrid learning environments

Students may be at a disadvantage when learning if they cannot follow lessons face to face due to such reasons as epidemics, disasters, transportation, or family. The main purpose of this study is to perform alternative measurement and evaluation practices in hybrid learning environments in a way that will make students in online physics lessons active participants in the process. The research uses the developmental, emancipatory, and critical action research models within the scope of the qualitative research method. The research was carried out over three weeks under the guidance of the researcher with 32 10th-graders at the school where the researcher taught physics for 12 years. Semi-structured interview forms, rubric forms, and documents were used as data collection tools. The interviews and documents were evaluated using content analysis, while the rubrics were evaluated using descriptive analysis. The students’ active and decisive roles during the assessment and evaluation activities within the context-based learning activities regarding physics subjects as well as at the end of learning encouraged the students attending the lesson online and those attending in person to learn under the same conditions. In this context, activities in which students are a part of the learning and measurement-evaluation processes should be encouraged in online and hybrid-learning environments. Developing context-based activities with regard to experiments, analogy, and theoretical applications and developing qualified practices in which students will be active throughout the process under the guidance of action researchers will be beneficial for ensuring this.
A Blockchain-Based Model for Efficient, Privacy-Preserving Online Medical Diagnoses

Patient personal data is growing exponentially due to the digitization of medical systems and the increasing number of patients and diseases which can be collected during online diagnostics for commercial use in the manufacture of medicines for example or for scientific research without their authorization as well as to invade the patient's privacy, knowing his medical record, which can affect his psyche. Scaling, maintaining, and managing a centralized security model can be very difficult and expensive. A centralized security infrastructure introduces a single point of failure, making it an easy target for DDoS attacks (Denial-of-service attacks) and Centralized infrastructure is difficult to implement in industrial configurations where peripheral nodes are geographically dis-persed. During this study, we propose a blockchain based medical data retention system, we demonstrate the ability of this technology to maintain the confidentiality and transparency of data stored online, thanks to encrypted information that is difficult to access or use, knowing and verifying all transactions. Our result through this study was largely satisfactory since we demonstrated the power of this technology at two levels of security, namely data security and privacy.
Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm

Object detection is basic research in the field of computer vision to detect objects in an image or video. the TensorFlow framework is a widely adopted framework to create object detection programs and models. In this study, an object detection program and model are designed to detect the shape of a swallow's nest which consists of three classes, namely oval, angular, and bowl. The purpose model creation is to find out the likeliness of the swallow's nest to the three classes for the swallow's nest sorting machine. The adopted architecture in the modeling is the MobileNet V2 FPNLite SSD since the model obtained from this architecture results in a good speed in detecting objects. Based on the evaluation results that has been carried out, the model can detect the shape of the swallow's nest which is divided into 3 classes, but in some cases swallow's nest are detected into two classes. This issues can still be handled by adjustmenting several parameterss to the object detection program. Results shows that the obtained mAP value of 61.91%, indicating the model can detect the shape of a swallow's nest moderately.