Reviewerly/exHarmony huggingface dataset:
Livelihood Diversification and Farmers' Well-Being: Lessons From South-West, Nigeria

Livelihood diversification is an adaptation and a coping mechanism used by individuals to mitigate tragic episodes, and to build buffer against social, environmental, health and economic shocks, as well as other extreme events which impact disproportionately on people's lives, in a bid to ensuring sustained livelihood and improved living conditions. The effect of livelihood diversification on farmers' well-being in South-West Nigeria was therefore investigated using the dataset elicited from 359 farmers randomly drawn from the study area. The data were analyzed with composite score technique, simpson index of diversity, fractional probit, and fractional heteroskedastic probit models. The results revealed that livelihood diversification pattern among the farmers was at a moderate level, while farmers' categories of well-being status were partly associated with the levels of livelihood diversification in other sectors considered remunerative with high returns. Findings also indicated that age of the farmer, primary occupation, farm size, neighborhood effect, incidence of shocks and farmer's food insecurity status had significant relationships with the rate of livelihood diversification in the study area. Meanwhile, age of the farmer, years of formal education, livelihood diversification, access to credit, neighborhood effect, primary occupation of the respondents, and farming experience significantly influenced farmers' well-being status. In essence, this study has provided converging lines of evidences and demonstrated that livelihood diversification and other farmers' and farm based characteristics were meaningful predictors of the differences in the categories of farmers' well-being status in the study area. Arising from the findings, the study recommended human capital development through free and/or affordable educational system in the rural areas, as well as diversification of income sources among the farmers, while informal social protection can also be scaled up through social networks and neighborhood effect hinged on collective norms of solidarity, reciprocity, altruism and obligations towards achieving an effective cultural and social driven development.
XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages

Multiple critical scenarios need automated generation of descriptive text in low-resource (LR) languages given English fact triples. For example, Wikipedia text generation given English Infoboxes, automated generation of non-English product descriptions using English product attributes, etc. Previous work on fact-to-text (F2T) generation has focused on English only. Building an effective cross-lingual F2T (XF2T) system requires alignment between English structured facts and LR sentences. Either we need to manually obtain such alignment data at a large scale, which is expensive, or build automated models for cross-lingual alignment. To the best of our knowledge, there has been no previous attempt on automated cross-lingual alignment or generation for LR languages. We propose two unsupervised methods for cross-lingual alignment. We contribute XAlign, an XF2T dataset with 0.45M pairs across 8 languages, of which 5402 pairs have been manually annotated. We also train strong baseline XF2T generation models on XAlign. We make our code and dataset publicly available1, and hope that this will help advance further research in this critical area.
An Auto-Encoder Based TinyML Approach for Real-Time Anomaly Detection

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Condition monitoring plays a crucial role in the automotive space because they reduce the downtime and maintenance costs by preventing sudden catastrophic breakdown of vehicles. Condition monitoring solutions primarily monitor onboard sensor data to detect anomalies. However, with vehicles becoming more complex each day, the number of sensors to be monitored is also growing. This increases the data volume to be processed to make decisions. It is not feasible to send all the embedded sensor data captured to the cloud for processing. The network bandwidth, latency for transferring the data and finally the cost associated with data transfer are the factors which impede us from taking this approach. Much of the previous work done to address these issues has proposed Deep Learning driven onboard anomaly detection approaches. The deployment of these implementations requires power hungry and costly hardware with high computational resources. The recent advances in the field of Tiny Machine Learning have made it possible to design and deploy Deep Neural Networks on resource constrained low-cost embedded hardware. In this paper, we propose an Auto-Encoder based approach for anomaly detection in time-series vibration sensor data. The designed Auto-Encoder model has a 7.5 KB footprint and was finally deployed on a highly resource constrained ARM Cortex-M4 microcontroller with 256KB of SRAM and 1MB Flash. The model has been validated on the previous machine data. It has achieved an accuracy and precision close to 80%. Currently, by using post training quantization we are trading-off model accuracy for a reduction in model size. In future, we plan to use Quantization Aware Training which will help us in achieving even higher model accuracy.&lt;/div&gt;&lt;/div&gt;
Caption Generation for Traffic Signs Using a Deep Neural Scheme

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Deriving a meaningful caption for a Traffic Sign image is a challenging topic which is never addressed in prior literature. It is gaining a lot of traction with the introduction of tools and technologies in the fields of Natural Language Processing and Computer Vision. We discuss several concepts in this article, ranging from utilizing y, u, v color-space images to construct an artificial neural network to a single-mode neural architecture that functionally integrates image feature information with textual knowledge. We believe we are the first in the field to apply image captioning to traffic signs. Our model has been tested on GTSRB dataset and obtained a BLEU-1 score of 0.91. While many image captioning methods achieve good results by stacking one feature model on top of the sequential model, our traffic sign captioning work achieved better results by defining two concurrent models, one for feature extraction and one for sequence processing, and fusing them into a single architecture. Our architecture is quicker and capable of learning lengthy dependencies because of the usage of pre-trained models and an LSTM-based network.&lt;/div&gt;&lt;/div&gt;
Rei Miyata: controlled document authoring in a machine translation age

Machine translation (MT) has already been widely applied in various fields, such as businesses, health, communication, and international relations. However, its performance is not satisfactory in specific domains. Researchers have devised different solutions, such as controlled languages (CLs). Nonetheless, as the previous CLs have placed too much emphasis on words or sentences, the author believes it is important to consider contextual factors. Therefore, the author created a controlled document authoring system that integrates document formalization, CLs, MT, and terminology management. Evaluation work was then conducted to assess whether the system was effective and satisfactory. This research provides fascinating insights into CLs, MT, technical writing, terminology management, translation technology, and natural language processing applications.
A STUDY ON GENERALIZING BUILDING EXTRACTION MODELS TO UNSEEN DATASETS USING SOURCE DOMAIN TRANSFER

Building footprint detection from remote sensing imagery remains an activate field of research. In particular, training models that generalise well remains challenging. A common approach to building generic models is to leverage domain adaptation approaches based on style transfer to adapt labelled datasets to an unlabelled domain. These assume the availability of labelled and unlabelled data during training. In applications such as emergency mapping however, the target domain is not always known in advance, and there is therefore a need to build models that generalise to domains unseen at training time. Using SpaceNet data various domain adaptation approaches are evaluated to improve model gen-eralisation. This article demonstrates that domain adaptation improves model generalisation, and the choice of source and target domain can be more significant than the choice of style transfer algorithm.
A Novel End-to-End Visual Odometry Framework Based on Deep Neural Network

Most of the current visual odometry (VO) methods are designed based on a set of standard procedures, including camera calibration, feature extraction, feature matching (or tracking), motion estimation, local optimization, etc. Though some of these methods excel in accuracy and robustness, they usually require careful design and specific fine-tuning to ensure performance in different scenarios. Also, recovering the absolute scale of the monocular VO usually requires some prior knowledge. In this paper, we propose an end-to-end framework based on U-Net and deep recurrent neural networks (RNNs) for training and deployment, which directly estimates poses from a sequence of raw RGB images without using any modules of the conventional VO system and thus without fine-tuning the parameters of the VO system. Also, no prior knowledge of the scene is necessary in our method. To solve the problem that current deep learning methods only predict poses between frames, we first use U-Net to automatically learn effective feature representations for VO, and then utilize RNN to implicitly model sequence dynamics and relationships. This method makes full use of the context information of sequence frames to achieve accurate and robust VO localization. Experimental results show the superiority of our method compared with several widely used VO methods.
From Question Generation to Problem Mining and Classification

One of the typical problems in question generation is the low cognitive level of the generated questions. The problem generation field seeks to generate higher-level learning problems using either constraint-based random generation or template-based generation. Both strategies have drawbacks and often require human participation. However, learning problems on high cognitive levels often have real-world equivalents that makes problem mining a viable approach in various fields of learning. The main challenge to overcome in problem mining is problem classification: automatic evaluation of the parameters of the problem’s pedagogical usage. Automatically generated and classified problems can be used in adaptive tutoring systems without further work or with minimal human supervision. The paper discusses examples of possible fields for problem mining, the advantages and challenges of this approach.
A semiconductor laser stand-alone neural network

We experimentally demonstrate a high-performance, fully autonomous, and scalable neural network of 350 nodes based on a semiconductor laser. And we study the impact of several key physical parameters on its performance in a 3-bit header recognition task.
Secret Message Hiding inside image using Steganography

This paper proposes an algorithm for hiding text inside an image. This Technique is called Steganography and the method used is LSB steganography where some changes in the LSB can be made and the message is hidden inside the image. With the help of this Technique, a system is developed, which will help its user to hide text inside an image an even reveal the hidden text inside the image.
MethApp4Mob: A Methodological Approach for Mobile Applications Development

The aspects of mobile technology have introduced challenges for developers, raising the need to improve the software development methodologies traditionally used. As a result of the analysis of the state of the art, an agile methodological approach for its development is presented, consisting of 8 phases: negotiation, planning, design, implementation and alpha testing, beta testing and feedback analysis, application publication, release and maintenance, promotion, and a series of steps, good practices, principles and tools, to offer mechanisms to accompany and streamline the development process. Some validations were carried out including a exploratory study, seeking to propose a robust methodology but at the same time easy to apply in a company.
A Cloud Based Intelligent Model for Predicting the Pre-Testing Corona Virus Infection Outbreak

Corona virus disease (COVID) is a transmittable disease caused by a newly discovered corona virus. For this a system is require which trace the location and predict the health of the people. In the present study, a cloud based a model is proposed. The proposed model will be connect with a cloud computing system that will predict the corona virus infected patients using na&#x00EF;ve bayes classifier and provides geographic based danger areas to prevent the spreading of corona virus. This way will provide the great help to the local administration and health care agencies to control the spreading of covid.
Multi-source Transfer Learning for Signal Detection over a Fading Channel with Co-channel Interference

For signal detection tasks in wireless communications, most of the existing algorithms either ignore the co-channel interference or treat it as Gaussian noise, which may result in unsatisfactory accuracy when the interference is non-negligible and complex-distributed. When neural networks are motivated in the design of the detectors, difficulty arises in the training due to the fact that there are few accessible pilots in each packet. In this paper, we consider a data-driven detector based on multi-source transfer learning (MSTL) for signal detection in a fading channel with interference. The MSTL detector transfers channel knowledge of previous packets into the latest detection. In particular, we consider a linear combination of the pilots and historical symbols in the distribution space, and design the optimal combination coefficients based on the number of those symbols as well as distributions similarity. Numerical simulations on a Gauss-Markov flat Rayleigh fading channel with co-channel interference validate the advantages of our algorithms, compared with several existing training schemes including directly applying the fully connected deep neural network (FCDNN) and conventional linear minimum mean square error (LMMSE) detector.
A Conceptual Model for Data-Driven Threat Analysis for Enhancing Cyber Security

Technology has become increasingly adopted by businesses for achieving overall objectives. Systems within these technologies generate a huge amount of data. It is necessary to identify the data and undertake appropriate controls to protect the data from any potential threats. Data, in general, is different types, such as operational and business which have different costs and impact on the overall business continuity. Threat analysis needs to consider various data types and associated weaknesses related to an organisational context’s systems and applications. There are numerous threat models available, but there is a lack of focus on analysing and prioritizing threats relating to the data. This paper presents a data-driven approach for threat analysis and a conceptual model. The model includes several concepts, i.e., actor, infrastructure, data and weakness, to analyse the data and threats from three phases management, control and business. Finally, a running example is used to demonstrate the applicability of the work.
OEmoBERTa: Only Using Emotion for Emotion Recognition in Conversation

With the universal application of human-computer interaction systems in people's life, users' requirements for the performance of these systems are gradually improving. For example, when a user uses a smart speaker, they want it to recommend music according to their moods. When treating a patient, if the medical machine can read the current mood of patients and give comfort, it can help doctors complete treatment more efficiently. When users chat with the chatbot, they wish that the robot can feel their emotions and make personalized responses. In order to meet the needs of users and provide them with good services, researchers put a lot of energy on the development of human-computer interaction system. This also makes the number of available public datasets for emotion recognition gradually increase, and more and more scholars put forward their own optimization ideas and developed new models according to the developed models. Thus, in order to make the chatbot respond flexibly according to user's emotion, this research, through improving on the basis of EmoBERTa model, aims to develop a suitable model for this field. Experiments show that the performance of the model is better than other models, the method of reducing input segment makes the prediction time faster than the original model, and the addition of emotion increases the accuracy. The experimental results also confirmed how much the speaker's own emotions can affect the outcome of emotion prediction.
Study of facial generation methods after orthodontic treatment

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on Encoder-Decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.
RepChaBug: Automatically Repairing Incorrect Change Bugs in Software Evolution

Software systems become inevitable to have bugs due to the change complexity in a large portion of code. Program repair is a critical maintenance task in software evolution. To reduce maintenance effort and time, the technique of copying and pasting code snippets (code clone) is generally practiced by both the novice and large parts of the developer community. Debugging errors on such replication changes is an effort-prone and time-consuming task. To address this problem, we present an automated program repair approach, called REPCHABUG that helps developers find and repair code change anomalies when they mistakenly apply contradictory updates (contradiction bugs) or miss required edits (exclusion bugs) on code cloning activity. Given the code change portion, REPCHABUG generalizes the code modification into a dependency-aware change template by computing data and control dependency relationships. In our evaluation, a case study with open source applications and a user study with computer science students showed that REPCHABUG should improve developer productivity in finding and fixing replication change bugs.
SpringNet: A Novel Deep Neural Network Architecture for Histopathological Image Analysis

Reliable and capable tools are needed for segmentation and classification of nuclei in histopathology images to improve the workflow of histopathologists and lessen the burden of diagnosis. Analyzing histopathology images can be challenging for deep learning models due to the various sources of noise. We propose a pipeline for segmenting and classifying nuclei, trained on a combination of datasets to ensure robustness. This makes our pipeline capable of generalizing on image tiles with varying Hematoxylin & Eosin concentrations. For nuclei classification, we present a novel convolutional neural network classifier that demonstrate state-of-the-art classification performance on the Lizard dataset.
Centralized Multi-Sensor Poisson Multi-Bernoulli Mixture Tracker for Autonomous Driving

With recent advances in Advanced Driver Assistance Systems (ADAS), autonomous driving has increased the need for reliable perception techniques. To achieve reliability, automotive sensors are being applied to autonomous driving vehicles, such as cameras, LiDAR, and radars. Various methods for fusing sensors have been studied to increase performance. In this study, we propose a centralized multi-sensor tracker, which is a first attempt to take advantage of fusing heterogeneous onboard sensors while accounting for data uncertainties. The proposed approach uses a Random Finite Set based Poisson Multi-Bernoulli Mixture filter. Experimental results from an actual vehicle dataset show that the proposed method tracks accurately even when objects are occluded or overlapped. It demonstrates the capability of tracking objects for autonomous driving in an urban environment.
How to Reduce the Time Necessary for Evaluation of Tree-Based Models

The paper focuses on a medical diagnostic procedure supported by decision models generated by suitable tree-based machine learning algorithms like C4.5. The typical result in this situation is represented by a set of trees that should be evaluated by the medical expert. This step is often lengthy because the models may be too detailed and extensive, or the expert is not always 100% available, several experts differ in their opinion. Based on our experience with this type of tasks like diagnostics of Metabolic Syndrome, Mild Cognitive Impairment, or cardiovascular diseases, we have designed and implemented a prototype of a Clinical Decision Support System to improve the tree-based model with selected interpretability methods like LIME, SHAP, and SunBurst interactive visualization. Next, we designed a mechanism containing selected methods from Mul-tiple-Criteria Decision Making (MCDM) and evaluation metrics like functional correctness, usability, stability, and others. We primarily focused on metrics used to evaluate the quality of software products like functional suitability, performance efficiency, usability, etc. Presented proof of concept is further developed into a functional prototype which will be experimentally verified in the form of a pilot study.
What Kind of Restroom Sign Image is Preferred?

Since 2016, the restroom-sign database (RSDB) has collected approximately 1,700 public restroom signs. Each record in the database has one or more photographs of the restroom signs, the actual location of the restroom, and a brief explanation of the item. A previous analysis of the collection revealed several tendencies and categories of restroom signs; the current study seeks to determine the degree of popularity of the various signs. Generally, public restroom signs indicate the location and gender segregation. However, the uniqueness of symbols affects their popularity in the context of social culture. Therefore, the study attempted to identify the most popular signs by creating a web application that compares signs randomly sampled from the database. The application was gamified to incentivize participation. This paper provides an overview of the application and tentative results of the analysis.