Reviewerly/exHarmony huggingface dataset:
Role of institutions in promoting finance in emerging markets: A panel data analysis

Differences in institutions have garnered much attention in recent years as one of the primary reasons for long-term economic performance, and a vast corpus of theoretical and empirical research demonstrates that financial development can have a substantial impact on economic and financial performance. In fast-growing emerging markets, the demand for financial services has considerably increased, which amplified the need for an efficient regulatory framework to deal with the sensitive nature of financial activities. Surprisingly, the existing literature on the impact of institutions and finance is focused on developed economies, while emerging markets are rarely focused on. Since emerging markets are becoming increasingly important as a result of the swift evolution of institutional and financial transformation, it is crucial to examine how the growth of financial markets reacts to institutional quality. This study investigates the role of institutions in promoting finance in emerging economies using the balanced panel dataset of 21 emerging market. Given the endogenous relationship between institutions and finance, the paper applies a two-stage least squares estimator to test the settler mortality hypothesis in this context. Aligned with theoretical intuition, the empirical result shows that settler mortality is a relevant instrument to institutions. Furthermore, the study performs robustness, using framework that deals with heterogeneity, and cross-section dependence. This empirical effort validates La Porta’s Law and Finance Hypothesis in the context of emerging markets. The results of the study are found robust across the variety of alternative measures of institutions and finance, and standard error correction specifications. Therefore, the study offers useful insight into policy implications.
The Imitation Game: Leveraging CopyCats for Robust Native Gate Selection in NISQ Programs

Quantum programs are written in high-level languages, whereas quantum hardware can only execute low-level native gates. To run programs on quantum systems, each high- level instruction must be decomposed into native gates. This process is called gate nativization and is performed by the compiler. Recent quantum computers support a richer native gate set to reduce crosstalk by tackling frequency crowding and enable compilers to generate quantum executables with fewer native gates. On these systems, any two-qubit CNOT instruction can be decomposed using more than a single two-qubit native gate. For example, a CNOT can be decomposed using either XY, CPHASE, or CZ native gates on Rigetti machines. Unfortunately, two-qubit native gates have high-error rates and exhibit temporal and spatial variations, which limits the success-rate of quantum programs. Therefore, identifying the native gate that maximizes the success-rate of each CNOT operation in a program is crucial.Our experiments on Rigetti machines show that noise-adaptive gate nativization to select the native gate with the highest fidelity for each CNOT operation is often sub-optimal at the application level. This is because the performance of such nativization heavily depends on the correctness of the device calibration data which only provides the average gate fidelities and may not accurately capture the error trends specific to the qubit state space of a program. Moreover, the calibration data may go stale due to device drifts going undetected. To overcome these limitations, we propose Application-specific Native Gate Selection (ANGEL). ANGEL designs a CopyCat that imitates a given program but has a known solution. Then, ANGEL employs the CopyCat to test different combinations of native gates and learn the optimal combination, which is then used to nativize the given program. To avoid an exponential search, ANGEL uses a divide-and-conquer- based localized search, the complexity of which scales linear with the number of device links used by the program. Our evaluations on Rigetti Aspen-11 show that ANGEL improves the success-rate of programs by 1.40x on average and by up-to 2x.
Mix-GEMM: An efficient HW-SW Architecture for Mixed-Precision Quantized Deep Neural Networks Inference on Edge Devices

Deep Neural Network (DNN) inference based on quantized narrow-precision integer data represents a promising research direction toward efficient deep learning computations on edge and mobile devices. On one side, recent progress of Quantization-Aware Training (QAT) frameworks aimed at improving the accuracy of extremely quantized DNNs allows achieving results close to Floating-Point 32 (FP32), and provides high flexibility concerning the data sizes selection. Unfortunately, current Central Processing Unit (CPU) architectures and Instruction Set Architectures (ISAs) targeting resource-constrained devices present limitations on the range of data sizes supported to compute DNN kernels.This paper presents Mix-GEMM, a hardware-software co-designed architecture capable of efficiently computing quantized DNN convolutional kernels based on byte and sub-byte data sizes. Mix-GEMM accelerates General Matrix Multiplication (GEMM), representing the core kernel of DNNs, supporting all data size combinations from 8- to 2-bit, including mixed-precision computations, and featuring performance that scale with the decreasing of the computational data sizes. Our experimental evaluation, performed on representative quantized Convolutional Neural Networks (CNNs), shows that a RISC-V based edge System-on-Chip (SoC) integrating Mix-GEMM achieves up to 1.3 TOPS/W in energy efficiency, and up to 13.6 GOPS in throughput, gaining from 5.3× to 15.1× in performance over the OpenBLAS GEMM frameworks running on a commercial RISC-V based edge processor. By performing synthesis and Place and Route (PnR) of the enhanced SoC in Global Foundries 22nm FDX technology, we show that Mix-GEMM only accounts for 1% of the overall area consumption.
Efficient Distributed Secure Memory with Migratable Merkle Tree

Hardware-assisted enclaves with memory encryption have been widely adopted in the prevailing architectures, e.g., Intel SGX/TDX, AMD SEV, ARM CCA, etc. However, existing enclave designs fall short in supporting efficient cooperation among cross-node enclaves (i.e., multi-machines) because the range of hardware memory protection is within a single node. A naive approach is to leverage cryptography at the application level and transfer data between nodes through secure channels (e.g., SSL). However, it incurs orders of magnitude costs due to expensive encryption/decryption, especially for distributed applications with large data transfer, e.g., MapReduce and graph computing. A secure and efficient mechanism of distributed secure memory is necessary but still missing.This paper proposes Migratable Merkle Tree (MMT), a design enabling efficient distributed secure memory to support distributed confidential computing. MMT sets up an integrity forest for distributed memory on multiple nodes. It allows an enclave to securely delegate an MMT closure, which contains both data and metadata of a subtree, to a remote enclave. By reusing the memory encryption mechanisms of existing enclaves, our design achieves secure data transfer without software re-encryption. We have implemented a prototype of MMT and a trusted firmware for management, and further applied MMT to real-world distributed applications. The evaluation results show that compared with existing systems using the AES-NI instruction, MMT can achieve up to 13x speedup on data transferring, and gain 12%~58% improvement on the end-to-end performance of MapReduce and PageRank.
FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

Federated Learning (FL) is an innovative area of machine learning that enables different clients to collaboratively generate a shared model while preserving their data privacy. In a typical FL setting, a central model is updated by aggregating the clients’ parameters of the respective artificial neural network. The aggregated parameters are then sent back to the clients. However, two main challenges are associated with the central aggregation approach. Firstly, most state-of-the-art strategies are not optimised to operate in the presence of certain types of non-iid (not independent and identically distributed) applications and datasets. Secondly, federated learning is vulnerable to various privacy and security concerns related to model inversion attacks, which can be used to access sensitive information from the training data. To address these issues, we propose a novel federated learning strategy FedNets based on ensemble learning. Instead of sharing the parameters of the clients over the network to update a single global model, our approach allows clients to have ensembles of diverse-lightweight models and collaborate by sharing ensemble members. FedNets utilises graph embedding theory to reduce the complexity of running Deep Neural Networks (DNNs) on resource-limited devices. Each Deep Neural Network (DNN) is treated as a graph, from which respective graph embeddings are generated and clustered to determine which part of the DNN should be shared with other clients. Our approach outperforms state-of-the-art FL algorithms such as Federated Averaging (Fed-Avg) and Adaptive Federated Optimisation (Fed-Yogi) in terms of accuracy; on the Federated CIFAR100 dataset (non-iid), FedNets demonstrates a remarkable 63% and 92% improvement in accuracy, respectively. Furthermore, FedNets does not compromise the client’s privacy, as it is safeguarded by the design of the method.
Improved Object Re-Identification via More Efficient Embeddings

Object reidentification (ReID) in cluttered rigid scenes is a challenging problem especially when same-looking objects coexist in the scene.ReID is accepted to be one of the most powerful tools for matching the correct identities to each individual object when issues such as occlusion, missed detections, multiple same-looking objects coexisting in the same scene, and disappearance of objects from the view and/or revisiting the same region arise.We propose a novel framework towards more efficient object ReID, improved object reidentification (IO-ReID), to perform object ReID in challenging scenes with real-time processing in mind.The proposed approach achieves distinctive and efficient object embedding via training with the triplet loss, with input from both the foreground/background split by bounding box, and the full input image.With extensive experiments on two datasets serving for Object ReID, we demonstrate that the proposed method, IO-ReID, obtains a higher ReID accuracy and runs faster compared to the state-of-the-art methods on object ReID.
Braille block detection at shortest distance by mobile devices

This research proposes a method based on image recognition technology to guide the visually impaired to the shortest Braille block in situations where they cannot find the existence of Braille blocks. In this paper, we make four proposals. First, we propose a method for detecting Braille blocks using SSD, a deep learning network with our own image dataset. Various kinds of Braille blocks and out-of-specification Braille blocks look different by weathering indoors and outdoors. Second, we propose a detection method that can handle differences in camera height. By constructing a training dataset with images of Braille blocks taken at a person's height or a general robot, we can achieve detection with cameras of different heights. Third, we propose a standalone method, real-time recognition of Braille blocks using the cameras of mobile devices. We incorporate MobileNet, a lightweight deep-learning network, into the SSD network. As a result, we achieve standalone and real-time Braille block recognition by optimizing the network for mobile devices. Finally, we propose a method of estimating the orientation and shortest distance of Braille blocks. It considers the continuous and linear arrangement pattern of guiding blocks that indicate the direction of travel.
Development of E-modul Flipbook Buffer Solution Based On Discovery Learning Model

This research and development aim to produce a valid, practical, and effective e-module buffer solution flipbook. The development of this e-module refers to the ADDIE development model which includes the analysis stage, the design stage, the development stage, the implementation stage, and the evaluation stage. There are four subjects in this research, namely media experts and material experts as validity test subjects, chemistry teachers as the practicality test subject,ct, and 35 students in class XI MIPA 3 as the practicality and effectiveness tests subjects. The instruments used for validity are validation sheets by media experts and material experts, practicality, namely observation sheets on the implementation of e-module, teacher and student response questionnaires, and effectiveness, namely learning outcomes tests. The results of this study are: (1) the validity of the digital module from the material expert is 3,6 and the media expert is 3,5 which is in the very valid category with a consistency test between validators are 100% which is in very high criteria, (2) the average percentage of e-module practicality is 85,66%, where the percentage of practicality based on the questionnaire responses of students and teachers the to the digital issue is 85,33% and 86% in the very practical category, 3) e- module meet the effective criteria (≥85%) based on the achievement of class completeness with percentage 88,57%. Based on these data, it is concluded that the e-module flipbook buffer solution developed with the ADDIE model is valid, practical, and effective for use in learning.
Specification-Driven Code Generation for Inter-parameter Dependencies in Web APIs

The generation of code templates from web API specifications is a common practice in the software industry. However, existing tools neglect the dependencies among input parameters (so called inter-parameter dependencies), extremely common in practice and usually described in natural language. As a result, developers are responsible for implementing the corresponding validation logic manually, a tedious and error-prone process. In this paper, we present an approach for the automated generation of code for inter-parameter dependencies in web APIs. Specifically, we exploit the IDL4OAS extension for specifying inter-parameter dependencies as a part of OpenAPI Specification (OAS) files. To make our approach applicable in practice, we present an extension of the popular OpenAPI Generator tool ecosystem, automating the generation of Java and Python code for the management of inter-parameter dependencies in both servers and clients. Evaluation results show the effectiveness of the approach in accelerating the development of APIs, generating up to 9.4 times more code than current generators, while making APIs potentially more reliable.
An Ensemble Approach for Cyber Bullying: Text Messages and Images

Text mining (TM) is a domain used to find valuable patterns from various text documents.Cyberbullying is the term used to abuse a person online or offline platform.Nowadays, cyberbullying has become more dangerous to people who are using social networking sites (SNS).Cyberbullying is of many types, such as text messaging, morphed images, videos, Etc.It is a challenging task to prevent this type of abuse of the person in online SNS.Finding accurate text mining patterns gives better results in detecting cyberbullying on any platform.Cyberbullying is developed with the online SNS to send defamatory statements or orally bully other persons, or by using the online forum to abuse in front of SNS users.Deep Learning (DL) is one of the significant domains used to extract and learn the quality features dynamically from the low-level text inclusions.In this scenario, Convolution neural network (CNN) are DL models used to train text data, images, and videos.CNN is a compelling approach to preparing these data types and achieving better text classification.This paper describes the Ensemble model with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos.Feature extraction technique extracts the features of cyber-bullying patterns from the text and images.A limited number of datasets are used to classify the data.The proposed approach also focused on reducing the training time and memory usage, which helps the classification improvement.
DACS: A Double-layer Application Classification Scheme for Hybrid Zero-day Traffic

As a fundamental countermeasure for network management and security, traffic classification has attracted public attention for a long time. All the time, the presence of zero-day traffic, network traffic of unknown applications in a classification system, leads to a significant reduction in the practicability and effectiveness of conventional traffic classification methods. This paper innovatively proposes a traffic classification scheme named DACS, which achieves accurate zero-day traffic detection, application traffic classification and high-performance incremental model updating, fitting for open-world and online traffic classification with plenty of zero-day traffic. DACS uses training samples to cross-simulate zero-day flows and combines the idea of distributed training. With a two-layer structure and a special voting mechanism, DACS is able to perform comprehensive traffic classification tasks in hybrid unknown traffic. In addition, DACS provides great convenience for updating the system knowledge and supports efficient incremental updates. The evaluations with real-world traffic verify the core advantages of the proposed scheme. DACS maintains over 95% classification accuracy on a public dataset (NUDT_MobileTraffic), which is better than the compared methods, and it only needs 1/K retraining computational cost to achieve model updates for new applications, where K is the number of sub-classifiers.
Advances in Artificial Intelligence for Perception Augmentation and Reasoning

AI has seen great progress in recent decades, with a rapidly increasing computing capacity and the exponentially growing amount and types of processed data [...]
Yapay Zeka Film Afişlerinin Göstergebilimsel Analizi: Artificial Intelligence, Frank &amp; Robot, Ex_Machina Örneği

One of the areas where semiotics is used extensively is movie posters.In this research, the artificial intelligence image created through movie posters is discussed.The study was analyzed with the semiotics method.The films that are the subject of the study; Artificial Intelligence in 2001, Frank & Robot in 2012 and Ex_Machina in 2014.Imdb scores were effective in theselection of the movies.According to the findings obtained as a result of the analysis, artificial intelligence has been touted as both a friendand an enemy to humans.The Colors Used in the poster also vary according to the subject of the film.The poster, in which art artificial intelligence is described as a friend, uses intensely brant colors.On the other hand, palet ones were prefer red in the posters that reflecting a hostile attitude.When the process of these movie posters, which have a promotional role, between 2001 and 2014 is examined, it is seent hatthese element shave started to be come simpler.Accordingly, it has been found that artificial intelligence movie posters containless data about the movie content in theprocess.In addition, it was concluded that the film stories were processed from different perspectives.In this context, when artificial intelligence movie posters are examined, it is seen that these posters have become more simplified over the years and introduced the movie with less elements.
Sentiment Analysis of Electricity Company Service Quality Using Naïve Bayes

In facing the era of technological disruption, a large company providing electricity in Indonesia, namely PT PLN is transforming to digitize all business processes and improve the quality of customer service. PLN Mobile application was developed in December 2020, and 18 million users have downloaded it. PLN Mobile application provides various electrical services for users. There are a lot of online opinions today. Organizations need to know the public perception of their product or service, sales projections, and customer happiness. Our research will identify public opinion (positive and negative) about PLN Mobile Application using sentiment analysis by taking review data from Google Play Store. Sentiment analysis is classified using Naïve Bayes and analyzed based on the dimensions of the quality of electricity services: empathy, responsiveness, and reliability. The results of this study indicate that Naïve Bayes is quite well used for binomial labels (positive and negative) with an accuracy of 73%. Still, for service quality dimensions, the accuracy is 45%. Indonesian language datasets are quite difficult to process due to non-standard language, foreign words, mixed language variations, and abbreviations. Determination of ground truth or manual labeling requires consistency and skilled personnel to determine the context of the text data to obtain a model with optimal performance. This study informs the classification of each dimension of the quality of electricity services in Indonesia based on positive and negative sentiment data for PLN Mobile Application users. Reliability received the most negative sentiments. This can be used for PT PLN to improve the quality-of-service reliability to customers.&#x0D;
An Assessment Model of Digital Literacy for the Students in Vocational Education Based on Principal Component Analysis in Machine Learning

The deep integration and development of advanced manufacturing and modern service industries, the new power, vitality and potential stimulated by the formation of new models, new business models and new scenes that keep emerging, all put forward higher requirements for the overall improvement of the digital literacy of the technical talents of enterprises, especially the high-level technical skills of enterprises. In this paper, 34 basic elements of digital literacy of the students in vocational education are initially condensed by using literature research method, and then 20 constitutive elements are further determined through expert consultation; through the main factor analysis method, 4 main common components of digital literacy of the students in vocational education based on principal component analysis in machine learning were calculated, and an assessment model of digital literacy of vocational education students is established.
Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques

Social relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. {Violent interactions in this sphere are very frequent, and have serious effects on the victims}. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments.