Reviewerly/exHarmony huggingface dataset:
Spatial-Coordinate Attention and Multi-Path Residual Block Based Oriented Object Detection in Remote Sensing Images

Remote sensing images have been widely used in various fields due to the great success of deep learning. However, as the basis of other tasks, remote sensing image object detection is still challenging. In this paper, we propose a Spatial-Coordinate Attention (SCA) and Multi-Path Residual Block (MPRB) guided oriented object detection algorithm based on an extended ResNet-18 backbone network. For specially, we first modify the coordinate attention module and spatial attention module through a multi-branch manner, and then combine them to generate the new Spatial-Coordinate Attention module. In this module, the channel information, location information and spatial information are integrated to enhance the representative capability of the object. After this, in order to further improve the feature representative power of a shallow CNN, such as ResNet-18. We increase the residual connection in the Residual Block and designed a Multi-Path Residual Block to make it more powerful. Moreover, we also introduce a new skip-connection architecture into the upsampling process to ensure that the predicted feature map can integrate high-level and low-level semantic information. We conduct experiments on the optical remote sensing image dataset UCAS-AOD, HRSC2016 and the SAR aerial dataset SSDD. Experiments show the effectiveness and feasibility of the algorithm in this paper.
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation

We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs). In this setting, the agent works in two phases. In the exploration phase, the agent interacts with the environment and collects samples without the reward. In the planning phase, the agent is given a specific reward function and uses samples collected from the exploration phase to learn a good policy. We propose a new provably efficient algorithm, called UCRL-RFE under the Linear Mixture MDP assumption, where the transition probability kernel of the MDP can be parameterized by a linear function over certain feature mappings defined on the triplet of state, action, and next state. We show that to obtain an $\epsilon$-optimal policy for arbitrary reward function, UCRL-RFE needs to sample at most $\tilde{\mathcal{O}}(H^5d^2\epsilon^{-2})$ episodes during the exploration phase. Here, $H$ is the length of the episode, $d$ is the dimension of the feature mapping. We also propose a variant of UCRL-RFE using Bernstein-type bonus and show that it needs to sample at most $\tilde{\mathcal{O}}(H^4d(H + d)\epsilon^{-2})$ to achieve an $\epsilon$-optimal policy. By constructing a special class of linear Mixture MDPs, we also prove that for any reward-free algorithm, it needs to sample at least $\tilde \Omega(H^2d\epsilon^{-2})$ episodes to obtain an $\epsilon$-optimal policy. Our upper bound matches the lower bound in terms of the dependence on $\epsilon$ and the dependence on $d$ if $H \ge d$.
Application of artificial intelligence in computer network security

Abstract At present, artificial intelligence technology is more and more common, and has a very broad development prospect. Nowadays, in people’s daily work and life cannot do without artificial intelligence devices, such as intelligent home appliances, intelligent society greatly improves the quality of life and work efficiency of people. At the same time, with the continuous improvement of the development level of information technology, the application of artificial intelligence technology has gradually expanded to all walks of life in the society, and plays a positive role in promoting and value, improving people’s lives, changing society, and gradually changing the world. In the computer network technology, the full use of artificial intelligence technology, can promote the computer network function more rich and powerful, improve the efficiency of the use of computer network.
Detection of Malaria Parasites using Thresholding in RGB, YCbCr and Lab Color Spaces

In Indonesia, malaria remains an endemic disease and a public health burden. Thus, the Indonesian Government has set a national priority program to eliminate malaria. The goal is to increase the percentage of laboratory-confirmed malaria suspects. In this study, a computer-aided diagnosis is presented to detect and count the Plasmodium parasites in a thick blood smear. The malaria parasite can be detected using the color features. In the thick blood smear, the malaria parasite has purplish or dark red color. Therefore, this study utilizes a color-based segmentation to detect the parasite in three color spaces, i.e. RGB, YCbCr, and Lab. Furthermore, image-based object counting is introduced to count the number of parasites. The best results are achieved by color-based segmentation in RGB color space. In total, there are 27,558 images consisting of infected (13,779) and uninfected (13,779) blood smears. The threshold values are set to 105–240, 10–80, and 75–175 for R, G, and B channels. Of all experiments, the accuracy, sensitivity, and specificity achieved using this scheme are 94.75%, 96.02%, and 93.47%.
Time-Domain Image Filtering for DIC Vibration Measurements

A time-domain filter is presented in this paper for the noise reduction of the camera images, then elaborated for the full-field 3D-DIC vibration analysis of mechanical components. The basic idea behind this filtering is to initially decompose the light signal of each pixel of the raw images, both for the left and the right cameras, with the Fast Fourier Transform (FFT). The signal is then reconstructed with the Inverse FFT by keeping the zero-order harmonic and the first harmonic and just discarding all the others, which only introduce noise contributions. The down-sampling approach is also used in combination with this filtering to reach high loading frequencies, even without (expensive) high-speed cameras, provided that a quite short exposure time is available. A cantilever aluminum plate was tested at 591 Hz, and a similar experiment was repeated on a bladed disk (or blisk) excited at high frequency, 6458 Hz, obtaining clear and smooth displacement maps even in the range of 10 microns. In the blisk application, a single blade with a significantly larger displacement amplitude was clearly observed, which could be interpreted as mistuning evidence.
Associational Memory Networks.

We introduce associational memory networks(AMNs) that memorize and remember any data. This neural network has two memories. One consists of a queue-structured short-term memory to solve the class imbalance problem and long-term memory to store the distribution of objects, introducing the contents of storing and generating various datasets.
Realtime Trajectory Smoothing with Neural Nets

In order to safely and efficiently collaborate with humans, industrial robots need the ability to alter their motions quickly to react to sudden changes in the environment, such as an obstacle appearing across a planned trajectory.In Realtime Motion Planning, obstacles are detected in real time through a vision system, and new trajectories are planned with respect to the current positions of the obstacles, and immediately executed on the robot.Existing realtime motion planners, however, lack the smoothing post-processing step -which are crucial in sampling-based motion planning -resulting in the planned trajectories being jerky, and therefore inefficient and less human-friendly.Here we propose a Realtime Trajectory Smoother based on the shortcutting technique to address this issue.Leveraging fast clearance inference by a novel neural network, the proposed method is able to consistently smooth the trajectories of a 6-DOF industrial robot arm within 200 ms on a commercial GPU.We integrate the proposed smoother into a full Vision-Motion Planning-Execution loop and demonstrate a realtime, smooth, performance of an industrial robot subject to dynamic obstacles.
Identification of barriers and challenges to teaching English at an early age in Indonesia: an international publication analysis study

This paper will identify some of the barriers and challenges in teaching English early in Indonesia. We believe that teaching English as a foreign language has many obstacles and challenges that must be raised and published so that all parties with interest in teaching foreign languages ??in Indonesia, especially in early childhood, will receive enlightenment. So, we have first received much input from various data sources that we access electronically. All of our data is related to the purpose of this study, and we analyze it with a descriptive qualitative design under the control of a phenomenological approach so that the results will be valid and reliable findings. Based on the discussion of the findings, we can conclude that many teachers are often hampered by a lack of enthusiasm, limited study schedules, teaching resources, inadequate materials, and excess students in class. On the other hand, students have shallow learning motivation and very few skills related to learning English as a foreigner. Therefore, this is an insight for many parties who work in succeeding in English teaching in Indonesia.
Gesture Recognition using PYTHON

Hand gesture recognition is a domain of technology in robotics and human-computer interaction. It has applications in daily life activities. In this paper is presented a preliminary study of a system which could help a disabilities people to write a word on the computer using an intelligent glove. The mimic-gestural language of the deaf-mute can be translated into words using the computer and a glove equipped with accelerometric and resistive sensors. These sensors send information to a computer using an Arduino board. Data is processed using Python, an open-source development environment. The neural network is used for signs recognition using glove equipped with sensors.
Instance-Conditional Knowledge Distillation for Object Detection

Despite the success of Knowledge Distillation (KD) on image classification, it is still challenging to apply KD on object detection due to the difficulty in locating knowledge. In this paper, we propose an instance-conditional distillation framework to find desired knowledge. To locate knowledge of each instance, we use observed instances as condition information and formulate the retrieval process as an instance-conditional decoding process. Specifically, information of each instance that specifies a condition is encoded as query, and teacher's information is presented as key, we use the attention between query and key to measure the correlation, formulated by the transformer decoder. To guide this module, we further introduce an auxiliary task that directs to instance localization and identification, which are fundamental for detection. Extensive experiments demonstrate the efficacy of our method: we observe impressive improvements under various settings. Notably, we boost RetinaNet with ResNet-50 backbone from 37.4 to 40.7 mAP (+3.3) under 1x schedule, that even surpasses the teacher (40.4 mAP) with ResNet-101 backbone under 3x schedule. Code will be released soon.
AAD-KWS: a sub-µW keyword spotting chip with a zero-cost, acoustic activity detector from a 170nW MFCC feature extractor in 28nm CMOS

As a widely used speech-triggered interface, deep-learning based keyword spotting (KWS) chips require both ultra-low power and high detection accuracy. We propose an always-on keyword spotting chip with an acoustic activity detection (AAD) to achieve the above two requirements. Extracted from feature extractor, this AAD has zero overhead and zero miss rate. It is used to clock gate the neural network and post processing unit to achieve ultra-low power at silent scenarios. We also propose a tunable detection window to fit keywords with different widths to get better accuracy. Besides, a non-overlapping-frame Mel frequency cepstrum coefficient (MFCC) is used in the KWS system to reduce memory footprint and processing cycles. Implemented in a 28nm CMOS technology, its power consumption is only 0.36µW for AAD at quiet scenarios and 0.8µW for KWS, operating at 0.4V supply voltage with 8kHz for MFCC and 200kHz for other parts. And MFCC circuit has only 170nW power consumption. The accuracy can reach 97.8% for two keywords in the Google speech command data set (GSCD).
Exploring Classification For Sentiment Analysis From Halal Based Tweets

Globally, social media is gaining popularity and redefining how people interact with one another online. Malaysian individuals, for example, are increasingly reliant on social media platforms such as Facebook and Twitter as well as LinkedIn, Pinterest, Instagram, and other similar sites. Consider sentiment analysis to be a sub-category of social listening. A social media sentiment analysis has uncovered the public's current feelings on a particular topic or brand. Sentiment analysis is a technique for characterizing and capturing emotional states from unstructured text. The most important part of sentiment analysis is to evaluate a body of text to comprehend the opinion expressed by it. It usually assigns a polarity of “positive”, “negative” or “neutral”. It uses an algorithmic technique to capture people's thoughts, sentiments, and emotions by incorporating Natural Language Processing and Machine Learning technology. Sentiment analysis in Malaysia's social media is challenging to perform since posts are frequently written in a mixed language, usage of English and Malay with embedded jargon and various district dialect. The classification was performed based on Malaysia halal certification scheme for each tweet to acquire the class label's frequency value based on the sentiment analysis process's polarity results. It will demonstrate social media users' proclivity for posting and can act as a reference point for users when making decisions. An analysis of amounted 500 tweets with the hashtag #sijilhalal elicited information regarding people's feelings, preconceptions, and attitudes toward various issues related to halal certification in Malaysia. The discovery of a person's emotions concerning halal topics is visualized. Muslims' views are of importance to #sijilhalal awareness.
Creating accessible evidence bases: Opportunities through the integration of interactive tools into literature review synthesis

The COVID-19 pandemic has shown that an immediate access to relevant information is key for timely interventions and forming of public opinion and discourse. In this regard, dashboards present themselves as invaluable tools for the democratization of data and for the creation of accessible evidence bases. Building on this momentum, it is proposed to integrate interactive means such as dashboards into academic literature review synthesis, in order to support the summarization, narration, and dissemination of findings, and furthermore, to increase transparency and support the transferability and comparability of findings. Exemplified for a systematic literature review on urban forests as nature-based solutions,•Key functionalities, requirements and design considerations for the development of dashboards for use in academic literature reviews synthesis are identified.•An application architecture that embeds dashboard development into an R workflow is presented, with emphasis on the steps needed to transform the data collected during the review process into a structured form.•Technical and methodological means for the actual dashboard implementation are highlighted, considering the identified key functionalities and requirements.
[Spatial Distribution, Risk, and Influencing Factors of River Water-Sediment Heavy Metals in the Lower Reaches of the Qianhe River].

The spatial distribution of heavy metals in rivers is affected by human activities and the natural environment, posing a risk to human health related to heavy metal pollution. In order to study the characteristics, health risk levels, and influencing factors of heavy metal distribution and pollution in the lower reaches of the Qianhe River, 19 surface sediments and 20 water samples were collected, and the contents of As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn were measured by inductively coupled plasma mass spectrometry(ICP-MS). Using the DEM, air temperature, precipitation and other 11 factors as independent variables, the spatial differentiation of heavy metal pollution in sediments were explored based on geo-detector and geo-weighted regression model. The results showed that the eight heavy metal contents in the lower reaches of the Qianhe River did not exceed the "Surface Water Environmental Quality Standards" Ⅱ for water-like bodies, in which the variation coefficient of Pb element was 3.11, and the high content areas were mainly concentrated around Dongling Smelting Company and Fengxiang Railway Station. The average Rc value of adult carcinogens in water bodies was 7.72 E-06, showing a low risk level, and the children's carcinogens average Rc value was 1.17 E-04, showing a strong risk degree. The non-carcinogen risks for adults and children were both tolerable. The total high R value for children was mainly concentrated around Fengxiang Railway Station, posing a high risk. Sedimentation of heavy metals, except As and Mn, exceeded the soil background value in Shaanxi Province. The average content of Cd element was 1.12 mg·kg-1, which was 12 times the soil background value in Shaanxi Province. The pollution of Cd, Zn, and Pb was high, and distributed mainly in Changqing Village, Nanwan Village and Niujiatan Village, Gaozhuang, and Dongling Smelting Company. PLIzone of heavy metals in the sediments in the study area was 1.71, which was light pollution. DEM, temperature and precipitation were the main natural factors influencing the spatial pattern of heavy metal pollution load index(PLI) in sediments, and their nonlinear interactions were enhanced, which may play a role in the spread of heavy metals in sediments. This research can provide a scientific basis for urban planning and human health risks prevention in the Qianhe River.
UAV trajectory optimisation with limited onboard energy budget

Purpose The purpose of this paper is to generate optimised trajectories for an unmanned aerial vehicle (UAV) during a forest fire detection mission. It is assumed that the UAV flies 3D curvature-constrained Dubins manoeuvres and has a limited amount of battery energy that prevents it from covering the entire search area in a single trip. Design/methodology/approach In this paper, the search area is discretised into a grid of multiple targets, and each target assigned with a score that is proportional to the time elapsed since the last UAV visit. This problem, known as Dubins Airplane Orienteering Problem, consists of finding the number and order of targets to visit and the UAV heading at each target that maximises the total trip score without exceeding the UAV battery energy. The solution is found using the Randomised Variable Neighbourhood Search metaheuristic. All target scores are updated after each trajectory generation according to the elapsed time since the last UAV visit. Findings The proposed approach produced feasible results when generating optimised trajectories for a fire detection mission context where energy battery constraints are important. Practical implications The authors carry out the planning of UAV missions with limited amounts of onboard energy such as a real fire detection mission using a single electric propulsion and fixed-wing UAV. Originality/value This paper introduces an energy-based approach to the Dubins Airplane Orienteering Problem, which takes into account the UAV performance and energy budget when generating optimised trajectories.
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity

Object detection has achieved promising success, but requires large-scale fully-annotated data, which is time-consuming and labor-extensive. Therefore, we consider object detection with mixed supervision, which learns novel object categories using weak annotations with the help of full annotations of existing base object categories. Previous works using mixed supervision mainly learn the class-agnostic objectness from fully-annotated categories, which can be transferred to upgrade the weak annotations to pseudo full annotations for novel categories. In this paper, we further transfer mask prior and semantic similarity to bridge the gap between novel categories and base categories. Specifically, the ability of using mask prior to help detect objects is learned from base categories and transferred to novel categories. Moreover, the semantic similarity between objects learned from base categories is transferred to denoise the pseudo full annotations for novel categories. Experimental results on three benchmark datasets demonstrate the effectiveness of our method over existing methods. Codes are available at https://github.com/bcmi/TraMaS-Weak-Shot-Object-Detection.
Application of Neural Graph Collaborative Filtering in Movie Recommendation System

With the gradual development of movie recommendation system, in order to improve the recommendation effect of movie recommendation system, we must learn how to get a more effective embedding. The main purpose of this paper is to combine the neural graph collaborative filtering with the movie recommendation system. It utilizes the user-item graph data by propagating embeddings on it. Owing to the method, the expressive modeling of high-order connectivity in user-item graph could injecting the collaborative signal into the embedding process in an clear way. We make a large number of experiments on MovieLens dataset, and verified the effectiveness and correctness of the algorithms we used.
Improving the Efficiency of AES Protocols in Multi-Party Computation

The AES is a standardized symmetric block cipher, whose efficiency has been studied widely. This has resulted in very efficient software and hardware implementations of AES, which allow for the encryption of millions of blocks per second. However, AES was not designed with Multi-Party Computation in mind. Though there are many real-world applications of MPC requiring block ciphers, standard ciphers such as AES are far from being efficient for real-world applications of MPC. In this paper, we study how to improve the efficiency of AES modes of operation in the actively secure MPC setting with dishonest majority with precomputation as put forward by SPDZ and its variants. We propose two new protocols. The first one is aimed at improving the efficiency of the Sbox computation, the only non-linear layer in the AES. In particular, we use an (equally secure) inverse Sbox computation instead of the standard forward Sbox. The second protocol improves on the overall AES computation by optimizing the off-line phase and computing special (Beaver)-tuples specifically designed to improve the performance of the Sbox AES computation. Our proposals, result in an overall improvement of 3.33. The on-line phase of the protocols is fully implemented using the MP-SPDZ framework.
Hate Speech Detection in English and Non-English Languages: A Review of Techniques and Challenges

The exponential growth of social media has spurred an increase in the propagation of hate nowadays. Recent evidence shows that hate speech on social media is detrimental to the mental and physical health of individuals. Thus, there is an emerging need for automated hate speech detection. Automated hate speech detection rests on the intersection between Natural Language Processing (NLP) techniques and machine learning models. An introduction of NLP and its utilities, as well as commonly employed features and classification methods in hate speech detection, are discussed. Hate speech detection in non-English languages is needed to tackle this emergent issue in countries where multiple languages are used. Hence, an overview of the current literature on hate speech detection in non-English languages are covered too. Challenges in the field of hate speech detection are explored and the importance of standardized methodologies for building corpora and data sets are emphasized.