Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and Its application Research in Supply Chain Financial Risk Control

China's economy is in a critical period of transformation and upgrading, and the financial industry has also entered a deep reform zone. It is necessary to improve financial services to better assist the development of SMEs. Difficult and expensive financing has always been a problem faced by SMEs in the development process. Supply chain finance provides important financial support for financing difficulties of SMEs. In the process of continuous development of industrial upgrading, the development model of supply chain finance has also become more diversified. It is necessary to improve the current operation mode of supply chain finance. Blockchain technology has been developing rapidly. Relying on its characteristics of decentralization, non-tampering, smart contracts, etc., the research of various "blockchain +" scenarios continues to bring forth new ideas, which can effectively solve the practical application of supply chain finance Existing risk issues. This paper studies the technical principles and characteristics of blockchain, analyzes the current situation of supply chain financial risk management and control, and discusses the use of blockchain technology in supply chain financial risk management and control.
Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia

Measuring the status of achievement of the SDGs is the task and concern of many countries in the world, including Indonesia. Indicators for achieving the SDGs enclose three main pillars, namely environmental, economic, and social. The updated land use/land cover information is needed for environmental pillars. One imperative land cover information is built-up land, which acts as a detector for expanding urban areas and measuring SDGs' target indicators. Indonesia's cultural diversity affects the distribution pattern of built-up land, especially settlements. This is a challenge in the up-to-date and rapid mapping of built-up land. This research aims to analyze the ability and transferability of the Random Forest model for built-up areas and settlements using Google Earth Engine (GEE) in Banyumas, Cilacap, and Tegal. Around 19 predictors from multi-sources satellites are integrated to identify four land cover classes. Discussion on predictor composition to improve model accuracy also carried on. The results showed that the algorithm separated four land cover classes, with the highest accuracy for separating water bodies and other classes (vegetation and open land), OA above 90%. Machine confusion regarding the separation between housing classes and other buildings was still found (F1 score 0.67 - 0.69). Applying the model to the other two areas resulted in a similar statistical trend to the trained model. However, the classification method developed in this paper can assist in the rapid description of land cover if up-to-date data from official sources are not available.
Learn to Fuse Input Features for Large-Deformation Registration with Differentiable Convex-Discrete Optimisation

Hybrid methods that combine learning-based features with conventional optimisation have become popular for medical image registration. The ConvexAdam algorithm that ranked first in the comprehensive Learn2Reg registration challenges completely decouples semantic and/or hand-crafted feature extraction from the estimation of the transformation due to the difficulty of differentiating the discrete optimisation step. In this work, we propose a simple extension that enables backpropagation through discrete optimisation and learns to fuse the semantic and hand-crafted features in a supervised setting. We demonstrate state-of-the-art performance on abdominal CT registration.
Training Text-to-Text Transformers with Privacy Guarantees

Recent advances in NLP often stem from large transformer-based pre-trained models, which rapidly grow in size and use more and more training data.Such models are often released to the public so that end users can fine-tune them on a task dataset.While it is common to treat pre-training data as public, it may still contain personally identifiable information (PII), such as names, phone numbers, and copyrighted material.Recent findings show that the capacity of these models allows them to memorize parts of the training data, and suggest differentially private (DP) training as a potential mitigation.While there is recent work on DP fine-tuning of NLP models, the effects of DP pre-training are less well understood: it is not clear how downstream performance is affected by DP pre-training, and whether DP pre-training mitigates some of the memorization concerns.We focus on T5 and show that by using recent advances in JAX and XLA we can train models with DP that do not suffer a large drop in pre-training utility, nor in training speed, and can still be fine-tuned to high accuracy on downstream tasks (e.g.GLUE).Moreover, we show that T5's span corruption is a good defense against data memorization.
A White-Box Speck Implementation Using Self-equivalence Encodings

In 2002, Chow et al. initiated the formal study of white-box cryptography and introduced the CEJO framework. Since then, various white-box designs based on their framework have been proposed, all of them broken. Ranea and Preneel proposed a different method in 2020, called self-equivalence encodings and analyzed its security for AES. In this paper, we apply this method to generate the first academic white-box speck implementations using self-equivalence encodings. Although we focus on speck in this work, our design could easily be adapted to protect other add-rotate-xor (ARX) ciphers. Then, we analyze the security of our implementation against key-recovery attacks. We propose an algebraic attack to fully recover the master key and external encodings from a white-box speck implementation, with limited effort required. While this result shows that the linear and affine self-equivalences of speck are insecure, we hope that this negative result will spur additional research in higher-degree self-equivalence encodings for white-box cryptography. Finally, we created an open-source Python project implementing our design, publicly available at https://github.com/jvdsn/white-box-speck . We give an overview of five strategies to generate output code, which can be used to improve the performance of the white-box implementation. We compare these strategies and determine how to generate the most performant white-box speck code. Furthermore, this project could be employed to test and compare the efficiency of attacks on white-box implementations using self-equivalence encodings.
Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA

We propose the Video Language Co-Attention Network (VLCN) -a novel memory-enhanced model for Video Question Answering (VideoQA).Our model combines two original contributions: A multimodal fast-learning feature fusion (FLF) block and a mechanism that uses selfattended language features to separately guide neural attention on both static and dynamic visual features extracted from individual video frames and short video clips.When trained from scratch, VLCN achieves competitive results with the state of the art on both MSVD-QA and MSRVTT-QA with 38.06% and 36.01%test accuracies, respectively.Through an ablation study, we further show that FLF improves generalization across different VideoQA datasets and performance for question types that are notoriously challenging in current datasets, such as long questions that require deeper reasoning as well as questions with rare answers 1 .
A Deep Learning Approach for Plagiarism Detection System Using BERT

The processing of natural language processing is changed after the evident of deep learning algorithms. The machine learning algorithms use numerical data for processing; therefore, categorical data are converted into equivalent vectors for processing by the machines. Word embeddings are the real vectored representation of words which store semantic information. These embeddings are the significant tool of natural language being used in various tasks like name-entity-recognition and parsing, etc. Authorship attribution is a major problem in natural language processing. A framework for identification of authorship attribution has two layers of processing—one is attribution (feature selection), and another is verification (classification). Solution for the problem is to obtain a similarity score with the content. Similarity between the contents is identified by plagiarism detection system by finding PDS score of the given documents/contents. The paper proposed a plagiarism detection algorithm using explicit semantic detection algorithm. The system obtains contextualized word embeddings using BERT pre-trained model. STS-benchmark dataset is used for fine-tuning of BERT model. The proposed algorithm compares the word embeddings of the suspicious content with the reference collection using sentence similarity function. The experiments have been performed using python and deep learning Keras framework. The research has shown that results obtained through experimentation have improved the efficiency of the proposed system compared to existing systems.
Towards Responsible Natural Language Annotation for the Varieties of Arabic

When building NLP models, there is a tendency to aim for broader coverage, often overlooking cultural and (socio)linguistic nuance.In this position paper, we make the case for care and attention to such nuances, particularly in dataset annotation, as well as the inclusion of cultural and linguistic expertise in the process.We present a playbook for responsible dataset creation for polyglossic, multidialectal languages.This work is informed by a study on Arabic annotation of social media content.
Measuring and Mitigating Name Biases in Neural Machine Translation

Neural Machine Translation (NMT) systems exhibit problematic biases, such as stereotypical gender bias in the translation of occupation terms into languages with grammatical gender.In this paper we describe a new source of bias prevalent in NMT systems, relating to translations of sentences containing person names.To correctly translate such sentences, a NMT system needs to estimate the gender of names.We show that leading systems are particularly poor at this task, especially for female given names.This bias is deeper than given name gender: we show that the translation of terms with ambiguous sentiment can also be affected by person names, and the same holds true for proper nouns denoting race.To mitigate these biases we propose a simple but effective data augmentation method based on randomly switching entities during translation, which effectively eliminates the problem without any effect on translation quality.
Deep Tri-Training for Semi-Supervised Image Segmentation

Semantic segmentation is of great value to autonomous driving and many robotic applications, while it highly depends on costly and time-consuming pixel-level annotation. To make full use of unlabeled data, this work proposes a deep tri-training framework (dubbed DTT) to utilize labeled along with unlabeled data for training in a semi-supervised manner. Concretely, in the DTT framework, three networks are initialized with the same structure but different parameters. The networks are optimized circularly, where one network is trained in each optimization step with the guidance of the other two networks. A simple yet effective voting mechanism is adopted to construct reliable training sets from unlabeled data for the training stage and fusing multi-experts prediction in the testing stage. Exhaustive experiments on Cityscapes and PASCAL VOC 2012 demonstrate that the proposed DTT realizes state-of-the-art performance in the semi-supervised segmentation task. The source code is made publicly available.
Visualizing the breakdown of quantum multimodality in coherently driven light-matter interaction

We show that the saturation of a multiphoton transition is accompanied by a gradual collapse of quantum multimodality in the strong-coupling limit of the weakly driven Jaynes-Cummings (JC) model. By means of a perturbative method, we illustrate the prominent role of quantum fluctuations by focusing on the two- and three-photon resonance operation in a regime where the steady-state average photon number is below or marginally above unity. We also reveal two coexistent quantum beats in the intensity correlation function of the forwards scattered photons. These beats, originating from the states mediating the cascaded decay, arise as a direct consequence of the distinct JC spectrum. Their interference coordinates with the alternation of positive and negative values of the Wigner function around the phase-space origin in the transient conditioned on a photodetection.
HybridRepair: towards annotation-efficient repair for deep learning models

A well-trained deep learning (DL) model often cannot achieve expected performance after deployment due to the mismatch between the distributions of the training data and the field data in the operational environment. Therefore, repairing DL models is critical, especially when deployed on increasingly larger tasks with shifted distributions.
Balancing teams with quality-diversity for heterogeneous multiagent coordination

Evolutionary optimization is difficult in domains that require heterogeneous agents to coordinate on diverse tasks as agents often converge to a limited set of "acceptable" behaviors. Quality-Diversity methods alleviate this problem by shifting the focus from optimizing to finding a diverse repertoire of behaviors. However, in multiagent environments with diverse and tightly-coupled tasks, exploring the entire space of behaviors is often intractable. Agents must focus on searching regions of the behavior space that yield behaviors for good team performance. We extend Behavior Exploration for Heterogeneous Teams (BEHT)[4], a multi-level training framework that allows systematic exploration of the agents' behavior space required to complete diverse tasks as a coordinated team in a dynamic environment. We show that BEHT allows agents to learn diverse synergies that are demonstrated by the diversity of acquired agent behavior in response to the changing environment and agent behaviors.
Reinforcement learning reward function in unmanned aerial vehicle control tasks

This paper presents a new reward function that can be used for deep reinforcement learning in unmanned aerial vehicle (UAV) control and navigation problems. The reward function is based on the construction and estimation of the time of simplified trajectories to the target, which are third-order Bezier curves. This reward function can be applied unchanged to solve problems in both two-dimensional and three-dimensional virtual environments. The effectiveness of the reward function was tested in a newly developed virtual environment, namely, a simplified two-dimensional environment describing the dynamics of UAV control and flight, taking into account the forces of thrust, inertia, gravity, and aerodynamic drag. In this formulation, three tasks of UAV control and navigation were successfully solved: UAV flight to a given point in space, avoidance of interception by another UAV, and organization of interception of one UAV by another. The three most relevant modern deep reinforcement learning algorithms, Soft actor-critic, Deep Deterministic Policy Gradient, and Twin Delayed Deep Deterministic Policy Gradient were used. All three algorithms performed well, indicating the effectiveness of the selected reward function.
649 CNN-based histopathology image analysis for early-stage melanoma recurrence

Emerging data suggests that the majority of melanoma mortality occurs in patients with recurrence of disease that was initially early-stage (Stage I or II). Thus, there is a need for prognostic tools to identify patients at high risk of recurrence. This study examines the capability of weakly-supervised Convolutional Neural Networks (CNNs) for detecting prognostic signals of early-stage melanoma recurrence using whole-slide histopathology images (WSIs). We collected 224 WSIs from 58 patients with recurrence and 203 WSIs from 58 patients without recurrence and a minimum of 5-year follow-up. Each gigapixel WSI was processed into 512 x 512 pixel patches. We annotated regions of tumor for 50 patients and performed a two-step analysis. First, a model was trained to classify melanoma and non-melanoma patches. Second, a model was trained to differentiate between patches at high- and low-risk of recurrence. We compared performance of two CNN architectures, VGG16 and ResNet50, using 5-fold cross-validation. The median study follow-up for non-recurrent tumors was 6.5 years. Both architectures successfully identified melanoma patches: VGG16 (ACC 94.5%, AUC 98.9%) and ResNet50 (ACC 95%, AUC 99%). For recurrence prediction, VGG16 achieved slightly better patient-level performance (ACC 85.3%, AUC 93.5%) compared to ResNet50 (ACC 85.3%, AUC 91.3%). We demonstrate that a state-of-the-art CNN pipeline can accurately detect prognostic signals for early-stage melanoma recurrence using only WSIs. Our patch-based approach achieved these results without requiring detailed region of interest annotation. This approach can be used to automatically identify tumor areas with high-risk features, enabling subsequent molecular analyses of these regions to understand specific mechanisms for recurrence.
The Impact of Technical Competency, Experience and Communication Skills on the Level of ICT Innovation in Tanzania

This study determined the impact of the technical competency, communication skills, and the experience of experts on the level of ICT innovation in an organisation. The study is relevant to Tanzania because the country depends on imported technological business solutions, which could be developed in the country. The study used the survey strategy, with 116 respondents representing different organisations within Tanzania. The study used the structured questionnaire as a tool for data collection. Additionally, the study conducted an analysis using inferential statistics to test hypotheses. The findings of the study concluded that the technical competency of experts, the level of communication skills and the experience of experts in dealing with complex projects significantly impacted the level of ICT innovations within Tanzania. This study recommends the improvement of the experts’ technical skills, ability to deal with complex projects, and ability to communicate their innovation needs. This improvement will contribute to the increase of innovative products among local organisations.
Mining Software Information Sites to Recommend Cross-Language Analogical Libraries

Software development is largely dependent on libraries to reuse existing functionalities instead of reinventing the wheel. Software developers often need to find analogical libraries (libraries similar to ones they are already familiar with) as an analogical library may offer improved or additional features. Developers also need to search for analogical libraries across programming languages when developing applications in different languages or for different platforms. However, manually searching for analogical libraries is a time-consuming and difficult task. This paper presents a technique, called XLibRec, that recommends analogical libraries across different programming languages. XLibRec collects Stack Overflow question titles containing library names, library usage information from Stack Overflow posts, and library descriptions from a third party website, Libraries.io. We generate word-vectors for each information and calculate a weight-based cosine similarity score from them to recommend analogical libraries. We performed an extensive evaluation using a large number of analogical libraries across four different programming languages. Results from our evaluation show that the proposed technique can recommend cross-language analogical libraries with great accuracy. The precision for the Top-3 recommendations ranges from 62-81&#x0025; and has achieved 8-45&#x0025; higher precision than the state-of-the-art technique.
PR-SZZ: How pull requests can support the tracing of defects in software repositories

The SZZ algorithm represents a standard way to identify bug fixing commits as well as inducing counterparts. It forms the basis for data sets used in numerous empirical studies. Since its creation, multiple extensions have been proposed to enhance its performance. For historical reasons, related work relies on commit messages to map bug tickets to possibly related code with no additional data used to trace inducing commits from these fixes. Therefore, we present an updated version of SZZ utilizing pull requests, which are widely adopted today. We evaluate our approach in comparison to existing SZZ variants by conducting experiments and analyzing the usage of pull requests, inner commits, and merge strategies. We base our results on 6 open-source projects with more than 50k commits and 35k pull requests. With respect to bug fixing commits, on average 18% of bug tickets can be additionally mapped to a fixing commit, resulting in an overall F-score of 0.75, an improvement of 40 percentage points. By selecting an inducing commit, we manage to reduce the false-positives and increase precision by on average 16 percentage points in comparison to existing approaches.
Entanglement protection in higher-dimensional systems

The inevitable dissipative interaction of an entangled quantum system with its environment causes degradation in quantum correlations present in the system. This can lead to a finite-time disappearance of entanglement, which is known as entanglement sudden death (ESD). Here, we consider an initially entangled qubit-qutrit system and a dissipative noise which leads to ESD, and propose a set of local unitary operations, which when applied on the qubit, qutrit, or both subsystems during the decoherence process, cause ESD to be hastened, delayed, or avoided altogether, depending on its time of application. The physical implementation of these local unitaries is discussed in the context of an atomic system. The simulation results of such ESD manipulations are presented for two different classes of initially entangled qubit-qutrit systems. A prescription for generalization of this scheme to a qutrit-qutrit system is given. This technique for entanglement protection in the noisy environment is compared with other related techniques such as weak measurement reversal and dynamic decoupling.
A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems

Abstract Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2 sets and process interval membership grades. The key role in the design of type-2 interval fuzzy logic systems is played by the type-2 inference defuzzification method. In type-2 systems this generally takes place in two steps: type-reduction first, then standard defuzzification. The only precise type-reduction method is the iterative method known as Karnik-Mendel (KM) algorithm with its enhancement modifications. The known non-iterative methods deliver only an approximation of the boundaries of a type-reduced set and, in special cases, they diminish the profits that result from the use of type-2 fuzzy logic systems. In this paper, we propose a novel type-reduction method based on a smooth approximation of maximum/minimum, and we call this method a smooth type-reduction. Replacing the iterative KM algorithm by the smooth type-reduction, we obtain a structure of an adaptive interval type-2 fuzzy logic which is non-iterative and as close to an approximation of the KM algorithm as we like.