Reviewerly/exHarmony huggingface dataset:
Afterword

In Benin, top businesspeople not only capture the economy but also the executive, and possibly legislative power. This book develops a comprehensive analysis aimed at identifying and reducing the institutional constraints that impede Benins rapid, sustainable, and inclusive development. The research reveals a chain of causality between four main categories of institutional weaknesses in Benin, namely corruption, inefficiencies in governance, opacity in public decision-making, and excessive informality in the Beninese economy. These institutional weaknesses are traced back to proximate and ultimate causes. The immediate causes include political instability, elite capture of key state functions, weakness of the state, and the possibility of easy but illegal rents. In turn, these causes are linked to deep-rooted underlying factors such as the nature of the political game, essentially neo-patrimonialism with multiple economic and/or political Big Men, but also geographical or ethnic factors. We elaborate on policy reforms aiming at overcoming or circumventing these institutional problems.
A Patrimonial Regime with Multiple Oligarchs

This chapter devises a synthetic ordering of the factors identified as impeding long-run development in Benin, namely corruption, weak public management, opacity of public decision-making and policy implementation, and excessive informality. These institutional weaknesses are traced back to proximate and ultimate causes, while in the other direction the economic consequences of these weaknesses are unfolded. We identify the immediate causes as political instability, elite capture of key state functions, weakness of the state, and the possibility of easy but illegal rents. In turn, these causes are linked to five deep factors: (1) neo-patrimonialism with multiple oligarchs; (2) a multiple ethnic groups; (3) a geographical/neighbourhood landscape in which Benin appears as a small country that possesses a long border with its big neighbour (Nigeria); (4) a legacy of centralised management of key economic sectors, dating back to the French colonial period; and (5) the heavy presence of aid agencies. Policy reforms aiming at overcoming or circumventing these institutional problems are then discussed.
Institutional Diagnostic Project: Preamble

In Benin, top businesspeople not only capture the economy but also the executive, and possibly legislative power. This book develops a comprehensive analysis aimed at identifying and reducing the institutional constraints that impede Benins rapid, sustainable, and inclusive development. The research reveals a chain of causality between four main categories of institutional weaknesses in Benin, namely corruption, inefficiencies in governance, opacity in public decision-making, and excessive informality in the Beninese economy. These institutional weaknesses are traced back to proximate and ultimate causes. The immediate causes include political instability, elite capture of key state functions, weakness of the state, and the possibility of easy but illegal rents. In turn, these causes are linked to deep-rooted underlying factors such as the nature of the political game, essentially neo-patrimonialism with multiple economic and/or political Big Men, but also geographical or ethnic factors. We elaborate on policy reforms aiming at overcoming or circumventing these institutional problems.
The Institutional Diagnostic Project Presentation

In Benin, top businesspeople not only capture the economy but also the executive, and possibly legislative power. This book develops a comprehensive analysis aimed at identifying and reducing the institutional constraints that impede Benins rapid, sustainable, and inclusive development. The research reveals a chain of causality between four main categories of institutional weaknesses in Benin, namely corruption, inefficiencies in governance, opacity in public decision-making, and excessive informality in the Beninese economy. These institutional weaknesses are traced back to proximate and ultimate causes. The immediate causes include political instability, elite capture of key state functions, weakness of the state, and the possibility of easy but illegal rents. In turn, these causes are linked to deep-rooted underlying factors such as the nature of the political game, essentially neo-patrimonialism with multiple economic and/or political Big Men, but also geographical or ethnic factors. We elaborate on policy reforms aiming at overcoming or circumventing these institutional problems.
An Idealist’s Approach for Smart Contract Correctness

In this work, we experiment an idealistic approach for smart contract correctness verification and enforcement, based on the assumption that developers are either desired or required to provide a correctness specification due to the importance of smart contracts and the fact that they are immutable after deployment. We design a static verification system with a specification language which supports fully compositional verification (with the help of function specifications, contract invariants, loop invariants and call invariants). Our approach has been implemented in a tool named iContract which automatically proves the correctness of a smart contract statically or checks the unverified part of the specification during runtime. Using iContract, we have verified 10 high-profile smart contracts against manually developed detailed specifications, many of which are beyond the capacity of existing verifiers. Specially, we have uncovered two ERC20 violations in the BNB and QNT contracts.
Monotone Fuzzy Rule Interpolation for TSK-FIS-Like n-Ary Aggregation Functions

Fuzzy Rule Interpolation (FRI) is important for fuzzy inference systems modeling pertaining to a sparse fuzzy rule base system. The focus of this paper is on a specific class of FRI, i.e., monotone FRI (MFRI), for modeling monotone Takagi-Sugeno-Kang Fuzzy Inference System (TSK-FIS) in the presence of a monotone sparse fuzzy rule base. On the other hand, a function is denoted as an n-ary aggregation function for a given n-dimensional input space and an output space when both the monotone and boundary properties are satisfied. In this paper, a set of sufficient conditions derived from the principles of Ordered Weighted Averaging (OWA) and the concept of orness for TSK-FIS to obey the monotone property is firstly formulated. We show that it is necessary to have a dense fuzzy rule base, which can be obtained by interpolation of fuzzy rules in a sparse fuzzy rule base, for constructing a monotone TSK-FIS. We then devise a two-stage MFRI for establishing monotone TSK-FIS. The first stage comprises a sufficient condition, inspired from the orness concept, to generate intermediate fuzzy membership functions (FMFs). The second stage deduces the monotone consequent of each intermediate rule from the available sparse fuzzy rules. We further extend our MFRI formulation to form TSK-FIS-like n-ary aggregation functions.
The tutor team construction in the "honeycomb model" of professional degrees training for postgraduate

This paper analyzes the current situation of professional degree graduate education in China, summarizes the main problems existing in the construction of tutor team at the present stage, and constructs the "honeycomb model" of civil engineering master's degree graduate training. The "honeycomb model" replaces the double mentors with the mentor team, and takes the cultivation of practical innovation ability as the goal. Demand-oriented, it establishes substantive connections between the multiple agents represented by the mentor team members. In addition, the "honeycomb model" promotes the mutual promotion and integration of multiple entities through cost-effectiveness, and realizes the sustainable development of multiple entities through win-win cooperation. Finally, the model relies on the project matrix operation mode to promote the construction of tutor team and realize the unification of academic and professional graduate training.
Model Compression Algorithm via Reinforcement Learning and Knowledge Distillation

Traditional model compression techniques are dependent on handcrafted features and require domain experts, with a tradeoff between model size, speed, and accuracy. This study proposes a new approach toward resolving model compression problems. Our approach combines reinforcement-learning-based automated pruning and knowledge distillation to improve the pruning of unimportant network layers and the efficiency of the compression process. We introduce a new state quantity that controls the size of the reward and an attention mechanism that reinforces useful features and attenuates useless features to enhance the effects of other features. The experimental results show that the proposed model is superior to other advanced pruning methods in terms of the computation time and accuracy on CIFAR-100 and ImageNet dataset, where the accuracy is approximately 3% higher than that of similar methods with shorter computation times.
MSDS: A Novel Framework for Multi-Source Data Selection Based Cross-Network Node Classification

In this paper, we study the problem of multi-source cross-network node classification, which aims to classify unlabeled nodes in a target network by leveraging the knowledge learned from the rich labeled nodes in multiple source networks. The existing multi-source transfer learning approaches generally fail to model the structural information of networks, and the current cross-network node classification models mainly neglect that not all source networks can boost the task performance in the target network. Thus, none can be directly applied to the multi-source cross-network node classification task. To this end, in this paper, we propose a novel multi-source data selection (MSDS) based framework for cross-network node classification, which integrates multi-source transfer learning with network embedding to learn label-discriminative and network-invariant node representations. In MSDS, we first propose the multi-source network data selection, which applies three distances to jointly select the transferable source networks to well alleviate the problem of suboptimal solution or even negative transfer. In addition, we devise a new feature information alignment technique to make node vector representations network-invariant. Moreover, we incorporate aggregated structural information and feature information to make node representations label-discriminative. Extensive experiments on real-world datasets demonstrate that the proposed approaches outperform the state-of-the-art non-transfer and single-source transfer approaches in terms of classification accuracy.
Coarse-to-Fine Response Generation for Document Grounded Conversations

Generating fluent and informative responses is crucial for dialogue systems. Most existing studies introduce documents as additional document to improve dialogue generation performance. However, understanding complex and multi-topic documents as well as extracting useful information related to the dialogue context from them is challenging. To solve this problem, we propose a Coarse-to-Fine Response Generation (CFRG) model with an encoder-decoder for document-grounded conversations,where the encoder is used to aware the themes of the dialogue context, and the decoder parse the semantics of replies. Specifically, the CFRG extracts coarse-grained features guided by the response from the context and documents and then uses them as input of decoder. It further interacts with the current sequence in the cross-attention layer to generate fine-grained features, on which a non-linear transformation function is trained to map to a word space. The experimental results on datasets CMU_DoG and Wizard of WikiPedia show that our model demonstrates superior performance compared to the majority of other models and achieves the best or comparable results.
Balanced Fuzzy Petri Nets

The paper presents a new Petri Net model: BFPN. Balanced Fuzzy Petri Nets make it possible to model processes that consider gain and loss calculations at the same time. Many business processes need to consider the ability to model a scarcity or loss situation. Balanced fuzzy sets and relations allow a complete visualization and interpretation of the processes.
Liver Tumor Segmentation Based on Multi-Scale and Self-Attention Mechanism

Liver cancer has the second highest incidence rate among all types of malignant tumors, and currently, its diagnosis heavily depends on doctors’ manual labeling of CT scan images, a process that is time-consuming and susceptible to subjective errors. To address the aforementioned issues, we propose an automatic segmentation model for liver and tumors called Res2Swin Unet, which is based on the Unet architecture. The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation, respectively. Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections, while Swin Transformer captures long-range dependencies and models the input globally. And the model uses deep supervision and a hybrid loss function for faster convergence. On the LiTS2017 dataset, it achieves better segmentation performance than other models, with an average Dice coefficient of 97.0% for liver segmentation and 81.2% for tumor segmentation.
The Implementation of Bantu Warga Apps and Metabase for Spatial Data Processing In Surabaya

Civil registration and vital statistics (CRVS) is known as an essential supporting system for effective policy-making and service delivery. Its primary purpose is establishing an individual's legal identity and vital events to ensure all citizens get their rights well. Furthermore, an advanced level of CRVS is equipped with a forecasting system to serve more useful data. This paper discusses the potential benefits and challenges of using Metabase and Bantu Warga as civil ministry support systems. The spatial data visualization system using open-source Metabase ran well and resulted in many visualization geospatial maps like birth, marriage and divorce, education, and employment. The forecasting autoregression model configuration and cumulative sum dataset reach the best error result in 1056618. The other aspects, like the self-isolation management system, civil documents classification system, and OCR system, have a good result.
Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection

Deep neural networks (DNNs) are increasingly being used in malware detection and their robustness has been widely discussed. Conventionally, the development of an adversarial example generation scheme for DNNs involves either detailed knowledge concerning the model ( i.e ., gradient-based methods) or a substantial quantity of data for training a surrogate model. However, under many real-world circumstances, neither of these resources is necessarily available. Our work introduces the concept of the instance-based attack, which is both interpretable and suitable for deployment in a black-box environment. In our approach, a specific binary instance and a malware classifier are utilized as input. By incorporating data augmentation strategies, sufficient data are generated to train a relatively simple and interpretable model. Our methodology involves providing explanations for the detection model, which entails displaying the weights assigned to different components of the specific binary. Through the analysis of these explanations, we discover that the data subsections have a significant impact on the identification of malware. In this study, a novel function preserving transformation algorithm designed specifically for data subsections is introduced. Our approach involves leveraging binary diversification techniques to neutralize the effects of the most heavily-weighted section, thus generating effective adversarial examples. Our algorithm can fool the DNNs in certain cases with a success rate of almost 100%. Instance attack exhibits superior performance compared to the state-of-the-art approach. Notably, our technique can be implemented in a black-box environment and the results can be verified utilizing domain knowledge. The model can help to improve the robustness of malware detectors.
Value-gradient iteration with quadratic approximate value functions

We propose a method for designing policies for convex stochastic control problems characterized by random linear dynamics and convex stage cost. We consider policies that employ quadratic approximate value functions as a substitute for the true value function. Evaluating the associated control policy involves solving a convex problem, typically a quadratic program, which can be carried out reliably in real-time. Such policies often perform well even when the approximate value function is not a particularly good approximation of the true value function. We propose value-gradient iteration, which fits the gradient of value function, with regularization that can include constraints reflecting known bounds on the true value function. Our value-gradient iteration method can yield a good approximate value function with few samples, and little hyperparameter tuning. We find that the method can find a good policy with computational effort comparable to that required to just evaluate a control policy via simulation.
Standardized Transfer Learning Models Enhance Classification of Breast Ultrasound Data

Ultrasound imaging is an often employed technique in diagnosing of breast cancer, although the prediction reliability depends on the specialist’s experience. Computer Aided Diagnosis (CAD) systems have been introduced for the enhancement the quality and time invested in classifying breast ultrasound(BUS) images. Deep Convolutional Neural Networks based algorithms is considered one of the most successful strategy in breast ultrasound image analysis. Data limitation is one of the prioritizing issues at the current moment. This problem is referred by introducing transfer-learning-based models and stratification as a data augmentation technique for achieving a better accuracy of the classification. The paper has demonstrated that the deep feature extraction and feature selection can categorized the breast ultrasound images using the pre-training methods. A dataset containing 1578 breast ultrasound images was used for model training and testing, and the optimal level of achievement was reached by InceptionResNetV2 and DenseNet121 with an accuracy of 83% and an ”one over the rest” AUC score of 0.933 for DenseNet121, respectively 0.923 for InceptionResNetV2.
Development and design of an intelligent home care system APP based on technology

&lt;p&gt;The purpose of this article’s research is to the background of “Internet +”, and whether “Internet + elderly care” can become an important development trend to improve the quality of elderly care services to support family-friendly elderly care services. On this basis, the design background of the smart home elderly care system application or application (APP) is analyzed, and the system architecture and equipment requirements are analyzed based on the characteristics of modern high-tech, which provides prerequisites for the establishment of a new social service system. The problem here is that in the context of population aging, improving the quality of elderly care services has become an important development trend. As for the methodology for the study, reviews of several reading materials from the existing literature become essential. The findings indicate that in the elderly care service, we should actively use big data, the Internet of Things and cloud systems, and other high-tech, combined with the needs of the elderly to scientifically develop and design intelligent home care system apps, to meet the comprehensive construction of a moderately prosperous society and the new normal economic development of society.&lt;/p&gt;
Antecedents of Intention to Use Social Media for Virtual Events

Imposing the movement control order and social distancing measures during the outbreak of the COVID-19 pandemic increased the adoption of information and communication technology. The practices of remote work, virtual classes, and even entertainment were forced to be conducted remotely from home. Despite the current relaxation of restricted measurements on the COVID-19 pandemic in different nations, people still prefer online meetings via social media because of the potential threats of new recurrence of the COVID-19 pandemic and the significant saving of time, money, and effort. Therefore, the objectives of this research intend to investigate the direct and indirect relationships between attitude, perceived direct benefit, trust, and intention to use social media for virtual events. Moreover, this research will also assess the role of top management support in moderating the relationship between attitude and intention to use social media for virtual events. To accomplish the research objectives, 400 samples were collected through an online self-administered questionnaire survey. The data were analysed by using Statistical Package for Social Sciences (SPSS) and Partial Least Square–Structural Equation Modelling (PLS–SEM). Based on the generated statistical outcomes, all the direct relationships between attitude, perceived direct benefit, trust, and intention to use social media for virtual events are significantly supported. For the indirect relationships, all the mediation relationships are significantly supported. However, the findings indicated that the moderation variable of top management support does not have any moderating effect on the relationship between the attitude and intention to use in this research study.
Peachy Parallel Assignments (EduHPC 2023)

Peachy Parallel Assignments are model assignments for teaching parallel computing concepts. They are competitively selected for being adoptable by other instructors and “cool and inspirational” for students. Thus, they allow instructors to easily add high-quality assignments that will engage students to their classes.
Pelatihan Pemanfaatan Aplikasi Mentimeter dalam Mengembangkan Kompetensi Mengajar Guru di Yayasan Attaqwa 08 Karang Tengah, Bekasi

The development of learning media, especially learning evaluation media, during the Covid-19 pandemic brought new directions in the learning process. Based on the results of the needs analysis, most teachers at the Attaqwa 08 Karang Tengah Foundation are proficient in using zoom and google forms to organize the learning process. However, problems arised when 73.3% of teachers still use printed books, 42% use PowerPoint, 10% take videos from YouTube, and only 10% make videos to explain learning materials. As many as 63% of teachers want application-based, smartphone-based, or online-based learning development training, especially learning evaluation applications. The Mentimeter platform is a solution that can be offered to these teachers. Mentimeter offers convenience in making learning evaluations with a variety of attractive displays with very diverse animations. The training program was carried out for 2 days with workshop and mentoring methods and was attended by 50 participants. The results of the activity showed that participants gained new insights that were useful in enhancing creative and innovative learning.&#x0D;