Reviewerly/exHarmony huggingface dataset:
Spatial Pattern Analysis of Malaria Cases in Muara Enim Regency using Moran Index and Local Indicator Spatial Autocorrelation

BACKGROUND: Malaria is a disease which still becomes a global health issue, including in Indonesia, because of its potential vector which can infect and spread causing a wide impact. Until currently, malaria still becomes a serious threat to people living in tropical and subtropical areas. Muara Enim Regency is a malaria-endemic regency with the second highest positive case in South Sumatra Province, with an API value in 2019 was 0.18/1000 population. AIM: The current research was performed to identify and change the spatial pattern of malaria cases, environmental variability (rainfall), population density using Moran index and local indicator spatial autocorrelation (LISA), and habitat in Muara Enim Regency. METHODS: This research employed a quantitative research design with an analytical survey research method and a case–control approach. This research method was designed using a geographic information systems approach. RESULTS: The results of the study showed that malaria cases in Muara Enim Regency in 2017 occurred in groups with a Moran index of 0.263, indicating a positive autocorrelation. Meanwhile, based on the LISA index, it was found that there were three districts categorized as high-high (HH) (quadrant 1), those are Lawang Kidul District, Muara Enim District, and Gunung Megang District, while in the low-high (LH) category (quadrant 2), there was Benakat District. In 2018, it also occurred in groups where the Moran index was 0.129, indicating a positive autocorrelation, while the LISA index found that there was one district categorized as HH (quadrant 1) which is Lawang Kidul District, and district categorized as LH (quadrant 2) was Gunung Megang District. In 2019, it happened randomly or spread with a Moran index of −0.022 indicating a negative autocorrelation, while based on the LISA index, it was found that there was one subdistrict categorized as HH category (quadrant 1) which is Lawang Kidul District and two districts categorized as LH (quadrant 2) which are Semende Darat Laut and Rambang Niru. CONCLUSION: There was a change in the results of Moran index from a positive autocorrelation in 2017, 2018, to a negative autocorrelation in 2019 with the results of the LISA index for malaria cases in 2017–2019 in one subdistrict, namely, Lawang Kidul District categorized as a HH category (quadrant 1).
Few-Shot Text Classification with External Knowledge Expansion

The performance of most current models for text classification drops dramatically when annotated data is scarce. In such challenging scenarios, the existing models for few-shot text classification are not accurate or robust enough due to limited capture of semantic knowledge. In this paper, we propose a method of few-shot text classification based on external knowledge expansion and two strategies of expansion to supervise richer information during training and prediction, by leveraging WordNet and pre-trained model BERT. We split texts into sentences, develop techniques to select terms to semantically expand sentences based on knowledge and measure the text instance representation after knowledge expansion. In this way, we find the method is capable of improving the performance on the task of few-shot text classification. We evaluate our method on two English text classification datasets - IMDB and ASRS across a range of training set sizes. Experiment results show that by knowledge expansion, our method is robust and yields better or comparable performance to the state-of-the-art methods on both datasets, which achieves 2.7% relative improvement compared with previous method on the ASRS test set with the training set size of 380.
Exploring Sequential and Collaborative Contexts for Next Point-of-Interest Recommendation

Sequential methods based on recurrent neural networks, as well as session-based long- and short-term approaches have become the state-of-the-art methods for next Point-of-Interest (POI) recommendation recently. However, most of them use spatial and temporal correlations among check-ins while they fail to model category-based sequences explicitly. Moreover, most of the session-based methods only consider users’ own sessions, while they neglect the information from collaborative sessions. Besides, most of the sequential methods only consider the information in users’ own sequences, while they neglect inherent similarities among POIs from a global perspective. To this end, we propose a method to explore sequential and collaborative contexts (SCC) for next POI recommendation. We simultaneously model temporal, spatial and categorical correlations among check-ins to capture sequential contexts. We generate collaborative sessions for current sessions, then leverage the collaborative information to better predict users’ recent visit intents. Besides, a similarity graph is proposed to leverage collaborative information from POI side on a global scale. Finally, we combine sequential and collaborative contexts to capture preferences of users. Extensive experimental results demonstrate our model outperforms other state-of-the-art baselines consistently.
Fault diagnosis of substation secondary system based on association analysis

At present, the observability and controllability of the secondary system in intelligent substation are greatly improved, so it can be analyzed with the help of the operation data of the secondary system. In this paper, the fault data modeling of the secondary system in substation is realized based on FP growth algorithm. Finally, taking the secondary system fault data in a period of time in a city as an example, the fault causes are analyzed, at the same time, auxiliary suggestions are given, and the unbalance ratio is set to improve the accuracy of association analysis. Compared to traditional fault diagnosis methods, the FP-growth algorithm-based diagnosis method is labour-independent and simple.
Adaptive Chosen Plaintext Side-Channel Attacks for Higher-Order Masking Schemes

With the wide use of wireless sensor network (WSN), it is very important to ensure the security of wireless devices, especially for the prevention of side channel attacks. Higher-order masking schemes have been proved in theory to be the secure countermeasures against side-channel attacks. When d th-order masking is involved, the complexity of performing a higher-order SCA grows exponentially with the order d, which can be regarded as the main difficulty for the higher-order analysis. Since the plaintext is random in the traditional analysis process, the attack can be successfully carried out but the efficiency is not high. To reduce the number of traces for higher-order analysis, we combine the measurement setup with selecting plaintexts. We first describe an efficient chosen plaintext strategy for the unprotected design, called Adaptive Chosen Plaintext Power Analysis (ACPPA). Moreover, we apply the adaptive chosen plaintext strategy for different combining functions which is more efficient than the existing higher-order attacks. Finally, we finish the experiments to verify the efficiency of our methods in unmasked and masked contexts by success rate, guessing entropy and the number of traces to recover the secret key.
Improved Methodology for Typical Meteorological Year Month Selection Matching Annual Irradiance

Long-term solar resource and weather data, and typical meteorological year (TMY) time series derived from long term observations, are key inputs for solar plant modeling and financing. In particular, PV plant analyses using single-year TMY data representing the 50 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile (average) and 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile are typically demanded by banks and other stakeholders. The standard methodology used to construct TMY series does not ensure that the selected months accurately match the annual resource values. This paper describes an improved month selection methodology to incorporate match to annual resource values as a criterion, resulting in improved overall fidelity of the TMY series in representing the underlying long-term data. This improved methodology has been applied to a satellite model dataset to produce 50 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile data series throughout Saudi Arabia on a 0.1°x0.1° resolution grid to support solar industry stakeholders in the Kingdom.
Study of the public perspectives on the problems of Batang Kuranji River Basin management in Padang City, West Sumatra, Indonesia

Abstract Batang Kuranji is one of the main rivers that divide the city of Padang, West Sumatera, Indonesia from the Bukit Barisan mountains in the east to the Indian Ocean in the west. Batang Kuranji River Basin has a 202.70 km 2 watershed with the main river length of 32.41 km. Over the last three decades, there has been an increase in the frequency of problems caused by the declining quality of watershed management, such as upstream landslides, and flooding downstream in urban areas. This study aim to explore and identify the watershed management issues, from upstream to downstream, based on community perspectives along the river basins. Qualitative and quantitative data collection methods were applied through surveys with respondents, semi structured interviews with key informant and Focus Group Discussion with stakeholders. In general, each community group along the river basin has a explain more details of the more frequent flooding problems and the higher of flood. However, the factors that cause the problems for each community are different with each group blaming one another. This study explores these differing views between groups and provides recommendations for policy makers towards integrated management.
Adaptive rate decision algorithm for dash based on deep reinforcement learning

Aiming at the problems of the existing HTTP-based Dynamic Adaptive Streaming over HTTP (DASH) bit rate adaptive algorithm in mobile scenes, wireless network environment, frequent bit rate switching and low average bit rate, a problem is proposed. DASH rate adaptive decision algorithm based on deep reinforcement learning. Using a combination of deterministic strategy gradient algorithm and deep neural network in reinforcement learning, experiments on real network trajectory data sets prove that this algorithm can obtain higher user experience quality than existing algorithms.
Mobile Augmented Reality Technology in Museum Cultural and Creative Products Design

To enrich the design of cultural and creative products derived from museums, this paper discusses the key technologies of mobile enhancement technology in cultural and creative products. During the design process, we adopt visual technology to extract the features of product modeling. Then the virtual 3D model or animation is loaded under the unity3d platform to complete the construction of virtual background. Finally, the interactive application of augmented reality is completed by combining the augmented reality plug-in Vuforia SDK. The experiment shows that our scheme can realize the superposition and mixed rendering of virtual environment and real scene, which can provide users with rich visual experience.
Quantitative Model of Enterprise Financial Risk Management Based on Nonlinear Differential Equation

In order to accurately assess the financial risks of enterprises, a quantitative model of enterprise financial risk management based on nonlinear differential equations is designed. According to the hierarchical running track of enterprises, 30 evaluation indexes are selected, the index weights are determined, and the financial risk evaluation index system is constructed. Constructing allocation judgment matrix based on binary allocation scale value. Copula nonlinear differential equation is selected to comprehensively quantify the risk management of enterprise financial portfolio. Realize the quantitative model of enterprise financial risk management. The accuracy of the design model is verified by experiments. The results show that the upper quantile of the design model analysis is closer to the standard value, which is consistent with the actual situation and is effective
On the Construction of a Monitoring System for Third Language Teaching Process Under the Full Credit System

In the past two decades, third language education has become increasingly important in various universities in China. In the curriculum of many universities, it is no longer just a compulsory second foreign language course for foreign language majors, but also gradually becomes a university-wide elective course for other non-foreign language students. The question of how to guarantee the overall quality of third language teaching has become a question of reflection for every foreign language department and an important issue for the sustainable development of foreign language teaching in universities. This paper analyzes the difficulties of teaching process monitoring in the context of a full credit system, applies the PDCA model of a quality management system to the management of third language teaching, and adopts measures such as reasonable use of information platform, test and evaluation means, and focus on feedback from multiple subjects in the two key monitoring stages of instruction and implementation (D) and inspection and evaluation (C), to provide a kind of reference standard for monitoring the process of third language teaching in colleges and universities. The aim is to provide a reference standard for monitoring the process of third language teaching in universities.
Object Detection on Embedded Systems for Traffic in Asian Countries

In this paper, we present a novel embedded deep learning solution for traffic object detection. Considering the memory, computing speed, and environmental requirements in the MediaTek Dimensity 1000 Series embedded device, it is worth mentioning that we are the first one to re-implement and propose an efficient object detection algorithm based on You Only Look Once version 5 (YOLOv5) to address this issue in TensorFlow framework. The backbone of our network is mainly constructed by Cross Stage Partial (CSP) modules, which significantly boost the accuracy of our model and keep the model lightweight. Besides, to enhance the prediction effectiveness, we propose to combine the official training dataset and several external open datasets as our comprehensive training data. We also adopt multiple data augmentation techniques in the training phase, making the model learn a stronger feature extraction ability for various object categories. According to the results of extensive experiments and the final competition scores, our solution can get a not bad performance under the condition of low parameters and complexity. Our team is the third-place winner in Embedded Deep Learning Object Detection Model Compression Competition in ACM International Conference on Multimedia Retrieval 2021.
Rewriting Fictional Texts Using Pivot Paraphrase Generation and Character Modification

Gender bias in natural language is pervasive, but easily overlooked. Current research mostly focuses on using statistical methods to uncover patterns of gender bias in textual corpora. In order to study gender bias in a more controlled manner, we propose to build a parallel corpus in which gender and other characteristics of the characters in the same story switch between their opposite alternatives. In this paper, we present a two-step fiction rewriting model to automatically construct such a parallel corpus at scale. In the first step, we paraphrase the original text, i.e., the same storyline is expressed differently, in order to ensure linguistic diversity in the corpus. In the second step, we replace the gender of the characters with their opposites and modify their characteristics by either using synonyms or antonyms. We evaluate our fiction rewriting model by checking the readability of the rewritten texts and measuring readers’ acceptance in a user study. Results show that rewriting with antonyms and synonyms barely changes the original readability level; and human readers perceive synonymously rewritten texts mostly reasonable. Antonymously rewritten texts were perceived less reasonable in the user study and a post-hoc evaluation indicates that this might be mostly due to grammar and spelling issues introduced by the rewriting. Hence, our proposed approach allows the automated generation of a synonymous parallel corpus to study bias in a controlled way, but needs improvement for antonymous rewritten texts.
The Impact of Feature Selection Techniques on Software Defect Identification Models

Defect identification is an important task for ensuring the quality of software. Recently, researchers have begun to utilize artificial intelligence techniques to improve the usability of static analysis tools by automatically identifying true defects from the reported SA alarms. Existing methods mainly focus on using the static code features to represent the defective code. However, a challenge that threatens the performance of these machine learning methods is the irrelevant and redundant features. Feature selection techniques can be applied to alleviate this problem. Since many feature selection methods have been proposed, this paper conducts a rigorous experimental evaluation on the impact of feature selection techniques for defect identification and explores whether there is a smallest ratio when using the feature selection techniques for building defect identification models with acceptable performance. Additionally, this paper proposes an effective feature selection approach based on the idea of majority voting, combing the output results of different feature selection techniques. The experimental results for five open-source projects show that there is a best ratio (20%) for feature selection which achieves satisfied performance with far fewer features used for defect identification. This finding can serve as a practical guideline for software defect identification.
Einstein Field Equations

General relativity field equations are a set of differential equations that can be solved for the metric tensor which defines curved space. They are derived from two physical assumptions that the speed of light is constant in any frame of reference that may be moving relative to one another and space is curved by the mass of physical bodies. Some other assumptions related to mathematics such as symmetry and continuity are also needed. First, the metric tensor is defined as a transformation from Euclidean space to curved space. The equation of geodesics describes the equations of motion of a particle within a gravitational field. The Riemann tensor and Ricci tensor are obtained by differentiation of the metric tensor with respect to an assumed coordinate system. The Einstein tensor scales the Riemann tensor proportional to the mass present. The mathematics use Einstein summation notation and are well defined in many sources. Since navigators use matrix notation, some of the key derivations are repeated in matrix notation. Also, a computer solution for the metric tensor and equations of motion is obtained by processing simulated data in an orbit determination filter thus bypassing the analytic solution obtained from the Einstein tensor.
Waste Segregator: An Optimized Neural Learning Approach Towards Real-Time Object Classification

In the present era, natural components such as air, water and land tend to act as pollutant collectors. On the road to proper waste management, human beings have developed multiple methods, and most of these require the categorical separation of waste at the earliest possible stage. This paper on a smaller scale focuses on identifying and classifying the waste into two broad categories, recyclable and organic. We used a dataset that contains nearly 22,000 images including all the classes. The model used for the work was convolutional neural network (CNN) and can be optimized more by finding optimal hyperparameters. The CNN consists of two convolutional layers followed by max pooling, fully connected layer, and an efficient GPU to make the training faster. An accuracy of 92% and above was observed with the given dataset using CNN. The experiments conducted prove that CNN is more efficient than support vector machines (SVM); however, SVM tends to give better results on smaller datasets, depending on how the data is partitioned for training.
A New Algorithm for Color-Image Encryption Using 3D-Lorenz Chaotic Map and Random Modulus Decomposition in Transform Domain

Joshi, Anand B. Kumar, Dhanesh Singh, Sonali Srivastava, KeertiThis paper proposes a new algorithm for encryption and decryption of digital color-image using 3D-Lorenz chaotic map (LCM) and random modulus decomposition (RMD) in 2D-fractional discrete cosine transform domain (FrDCT) domain. The color-image to be encrypted is converted to its indexed formats by extracting its color map. A 3D-LCM is used for generating a keystream, and this keystream is used to generate public key as well as secret keys. The indexed image is combined with the public key as a complex matrix. Now, this complex matrix is encrypted using 2D-FrDCT with fractional order a and b. Subsequently, the RMD is implemented to obtain the trapdoor one-way function. Finally, a scrambling scheme is used to randomize the encrypted image and private key. In the decryption, encrypted image and private are calculated by the inverse pixels scrambling process. The retrieval data is transformed by 2D-inverse fractional discrete cosine transform (IFrDCT). The experimental results and the security analysis are given to validate the feasibility and robustness of the proposed method.
Expanding One-Handed Input Vocabulary for Smartphone Using In-Air Gesture of Index Finger Captured by Rear Camera

This paper presents an in-air gestural input method using the index finger of the hand gripping a smartphone. Our method allows the user to interact with the smartphone through a bending gesture and swiping gesture performed around the back of the device. Therefore, the user can interact with the smartphone without occluding the screen during one-handed use. Moreover, since our method uses the built-in rear camera, it can be easily introduced to off-the-shelf smartphones without additional sensors. To demonstrate our method, we built a prototype system using an off-the-shelf smartphone. In a pilot study to measure the gesture detection accuracy of our system, we obtained the total detection accuracy of 66.17%.
KDST: K-Anonymous Node Discovery Using Separation of Trust

The cybersecurity community has always sought low-latency high-throughput anonymous systems. A common problem between all approaches to the problem of anonymity is the discovery process of an anonymous path between the sender and receiver. Currently, the most robust model for anonymous path discovery is TOR directory servers. Despite being distributed, TOR directory servers force clients to expose themselves to a potentially malicious directory. In this paper, we present KDST (K-Anonymous Node Discovery Using Separation of Trust), a model that provides a methodology to have true anonymous path discovery. The model is based on the separation of trust, and thus exposing the client would require full compromise of the system. Even with a fully compromised system, our approach incorporates K-anonymity to frustrate such de-anonymization attacks. We implement our model and assess its performance and security. Our analysis shows that KDST is very effective in solving the problem of anonymous node discovery.