Reviewerly/exHarmony huggingface dataset:
DC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor

Traditional camera sensors rely on human eyes for observation. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. Object recognition technology is an important technology used to judge the object’s category on a camera sensor. In order to solve this problem, a small-size object detection algorithm for special scenarios was proposed in this paper. The advantage of this algorithm is that it not only has higher precision for small-size object detection but also can ensure that the detection accuracy for each size is not lower than that of the existing algorithm. There are three main innovations in this paper, as follows: (1) A new downsampling method which could better preserve the context feature information is proposed. (2) The feature fusion network is improved to effectively combine shallow information and deep information. (3) A new network structure is proposed to effectively improve the detection accuracy of the model. From the point of view of detection accuracy, it is better than YOLOX, YOLOR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny, and YOLOv8. Three authoritative public datasets are used in these experiments: (a) In the Visdron dataset (small-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 2.5%, 1.9%, and 2.1% higher than those of YOLOv8s, respectively. (b) On the Tinyperson dataset (minimal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 1%, 0.2%, and 1.2% higher than those of YOLOv8s, respectively. (c) On the PASCAL VOC2007 dataset (normal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 0.5%, 0.3%, and 0.4% higher than those of YOLOv8s, respectively.
Trendminer: An Architecture for Real Time Analysis of Social Media Text

The emergence of online social networks (OSNs) and the accompanying availability of large amounts of data, pose a number of new natural language processing (NLP) and computational challenges. Data from OSNs is different to data from traditional sources (e.g. newswire). The texts are short, noisy and conversational. Another important issue is that data occurs in a real-time streams, needing immediate analysis that is grounded in time and context. In this paper we describe a new open-source framework for efficient text processing of streaming OSN data (available at www.trendminer-project.eu). Whilst researchers have made progress in adapting or creating text analysis tools for OSN data, a system to unify these tasks has yet to be built. Our system is focused on a real world scenario where fast processing and accuracy is paramount. We use the MapReduce framework for distributed computing and present running times for our system in order to show that scaling to online scenarios is feasible.We describe the components of the system and evaluate their accuracy. Our system supports easy integration of future modules in order to extend its functionality.
Learning Compact Appearance Representation for Video-Based Person Re-Identification

This paper presents a novel approach for video-based person re-identification using multiple convolutional neural networks (CNNs). Unlike the previous work, we intend to extract a compact yet discriminative appearance representation from several frames rather than the whole sequence. Specifically, given a video, the representative frames are selected based on the walking profile of consecutive frames. A multiple CNN architecture incorporated with feature pooling is proposed to learn and compile the features of the selected representative frames into a compact description about the pedestrian for identification. Experiments are conducted on benchmark data sets to demonstrate the superiority of the proposed method over existing person re-identification approaches.
Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection

Although the incidence of credit card fraud is limited to a small percentage of transactions, the related financial losses may be huge. This demands the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with high precision and deal with the heterogeneous nature of the fraudster behavior. Indeed, the nature of the fraud behavior may strongly differ according to the payment system (e.g. e-commerce or shop terminal), the country and the population segment. Given the high cost of designing data-driven FDSs, it is more and more important for transactional companies to reuse existing pipelines and adapt them to different domains and contexts: this boils down to a well-known problem of transfer learning. This paper deals with deep transfer learning approaches for credit card fraud detection and focuses on transferring classification models learned on a specific category of transactions (e-commerce) to another (face-to-face). In particular we present and discuss two domain adaptation techniques in a deep neural network setting: the first one is an original domain adaptation strategy relying on the creation of additional features to transfer properties of the source domain to the target one and the second is an extension of a recent work of Ganin et al. The two methods are assessed, together with three state-of-the-art benchmarks, on a five-months dataset (more than 80 million e-commerce and face-to face transactions) provided by a major card issuer.
Seeing Isn’t Believing: QoE Evaluation for Privacy-Aware Users

More and more network media users concern about their privacy issues since they know that their network behaviors are being observed, and thus the observable users' data are not reliable and sufficient in this case. How to evaluate the true quality of experience (QoE) of the privacy-aware users has become a significant technical challenge because of the most majority of existing data-driven QoE evaluation schemes based on the premise of the true and adequate users' observations. To get over this dilemma, this paper proposes a systematic and robust QoE evaluation scheme with unreliable and insufficient observation data. Specifically, we first translate the subjective privacy-aware QoE evaluation problem into an objective rational user analysis procedure. Then, a semantics-based similarity measurement for multidimensional correlation analysis is constructed to classify the observable data. Subsequently, the highlight of this paper lies in proposing a class-level joint user classification and data cleaning strategy by frequently updating the training processes. Through elaborately designing an iterative framework, it can effectively resolve the data sparsity and inconsistency problems due to the user privacy-aware preferences. Importantly, we also introduce an efficient QoE model construction method for online implementation, and numerical results validate its efficiency for different kinds of privacy-aware users.
Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading

Existing computational approaches have not yet resulted in effective and efficient computer-aided tools that are used in pathologists' daily practice. Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing, with an F1-score of 94.35%. This result is higher than the results using classical image processing techniques and also higher than the approaches combining CCNs with handcrafted features. The second approach is an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6, higher than the existing results using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last sections; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the proposed technology.
New margin-based subsampling iterative technique in modified random forests for classification

Diversity within base classifiers has been recognized as an important characteristic of an ensemble classifier. Data and feature sampling are two popular methods of increasing such diversity. This is exemplified by Random Forests (RFs), known as a very effective classifier. However real-world data remain challenging due to several issues, such as multi-class imbalance, data redundancy, and class noise. Ensemble margin theory is a proven effective way to improve the performance of classification models. It can be used to detect the most important instances and thus help ensemble classifiers to avoid the negative effects of the class noise and class imbalance. To obtain accurate classification results, this paper proposes the Ensemble-Margin Based Random Forests (EMRFs) method, which combines RFs and a new subsampling iterative technique making use of computed ensemble margin values. As for comparative analysis, the learning techniques considered are: SVM, AdaBoost, RFs and the Subsample based Random Forests (SubRFs). The SubRFs uses Out-Of-Bag (OOB) estimation to optimize the training size. The effectiveness of EMRFs is demonstrated on both balanced and imbalanced datasets.
High-speed low-crosstalk detection of a 171Yb+ qubit using superconducting nanowire single photon detectors

Abstract Qubits used in quantum computing suffer from errors, either from the qubit interacting with the environment, or from imperfect quantum logic gates. Effective quantum error correcting codes require a high fidelity readout of ancilla qubits from which the error syndrome can be determined without affecting data qubits. Here, we present a detection scheme for 171 Yb + qubits, where we use superconducting nanowire single photon detectors and utilize photon time-of-arrival statistics to improve the fidelity and speed. Qubit shuttling allows for creating a separate detection region where an ancilla qubit can be measured without disrupting a data qubit. We achieve an average qubit state detection time of 11 μs with a fidelity of 99.931(6). The detection crosstalk error, defined as the probability that the data qubit coherence is lost due to the process of detecting an ancilla qubit, is reduced to ~2 × 10 −5 by creating a separation of 370 μm between them.
APE at Scale and Its Implications on MT Evaluation Biases

In this work, we train an Automatic Post-Editing (APE) model and use it to reveal biases in standard Machine Translation (MT) evaluation procedures.The goal of our APE model is to correct typical errors introduced by the translation process, and convert the "translationese" output into natural text.Our APE model is trained entirely on monolingual data that has been round-trip translated through English, to mimic errors that are similar to the ones introduced by NMT.We apply our model to the output of existing NMT systems, and demonstrate that, while the human-judged quality improves in all cases, BLEU scores drop with forward-translated test sets.We verify these results for the WMT18 English→German, WMT15 English→French, and WMT16 English→Romanian tasks.Furthermore, we selectively apply our APE model on the output of the top submissions of the most recent WMT evaluation campaigns.We see quality improvements on all tasks of up to 2.5 BLEU points.
Data Integrity Auditing without Private Key Storage for Secure Cloud Storage

Using cloud storage services, users can store their data in the cloud to avoid the expenditure of local data storage and maintenance. To ensure the integrity of the data stored in the cloud, many data integrity auditing schemes have been proposed. In most, if not all, of the existing schemes, a user needs to employ his private key to generate the data authenticators for realizing the data integrity auditing. Thus, the user has to possess a hardware token (e.g., USB token, smart card) to store his private key and memorize a password to activate this private key. If this hardware token is lost or this password is forgotten, most of the current data integrity auditing schemes would be unable to work. In order to overcome this problem, we propose a new paradigm called data integrity auditing without private key storage and design such a scheme. In this scheme, we use biometric data (e.g., iris scan, fingerprint) as the user’s fuzzy private key to avoid using the hardware token. Meanwhile, the scheme can still effectively complete the data integrity auditing. We utilize a linear sketch with coding and error correction processes to confirm the identity of the user. In addition, we design a new signature scheme which not only supports blockless verifiability, but also is compatible with the linear sketch. The security proof and the performance analysis show that our proposed scheme achieves desirable security and efficiency.
Combined application of blockchain technology in fractional calculus model of supply chain financial system

This paper mainly studies the application of fractional calculus system in the financial system of blockchain supply chain. Firstly, based on the blockchain supply chain game model, the paper introduces the theory of fractional calculus, establishes a three-dimensional supply chain fractional calculus game model which is closer to the actual situation, and analyzes its dynamics by using the theory of nonlinear dynamics. Learning characteristics, controlling the behavior of chaos. Finally, the dissertation, distributed storage, traceability, tamper-proof and intelligent contract features of blockchain technology are applied to the bank credit system to verify the feasibility of this system.
MIRAS: Model-based Reinforcement Learning for Microservice Resource Allocation over Scientific Workflows

Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows.
Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain

The internal structure of the human hippocampus is challenging to map using histology or neuroimaging due to its complex archicortical folding. Here, we aimed to overcome this challenge using a unique combination of three methods. First, we leveraged a histological dataset with unprecedented 3D coverage, BigBrain. Second, we imposed a computational unfolding framework that respects the topological continuity of hippocampal subfields, which are traditionally defined by laminar composition. Third, we adapted neocortical parcellation techniques to map the hippocampus with respect to not only laminar but also morphological features. Unsupervised clustering of these features revealed subdivisions that closely resemble gold standard manual subfield segmentations. Critically, we also show that morphological features alone are sufficient to derive most hippocampal subfield boundaries. Moreover, some features showed differences within subfields along the hippocampal longitudinal axis. Our findings highlight new characteristics of internal hippocampal structure, and offer new avenues for its characterization with in-vivo neuroimaging.
Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation

Background Based Conversation (BBCs) have been introduced to help conversational systems avoid generating overly generic responses. In a BBC, the conversation is grounded in a knowledge source. A key challenge in BBCs is Knowledge Selection (KS): given a conversational context, try to find the appropriate background knowledge (a text fragment containing related facts or comments, etc.) based on which to generate the next response. Previous work addresses KS by employing attention and/or pointer mechanisms. These mechanisms use a local perspective, i.e., they select a token at a time based solely on the current decoding state. We argue for the adoption of a global perspective, i.e., pre-selecting some text fragments from the background knowledge that could help determine the topic of the next response. We enhance KS in BBCs by introducing a Global-to-Local Knowledge Selection (GLKS) mechanism. Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp. In order to effectively learn the topic transition vector, we propose a distantly supervised learning schema. Experimental results show that the GLKS model significantly outperforms state-of-the-art methods in terms of both automatic and human evaluation. More importantly, GLKS achieves this without requiring any extra annotations, which demonstrates its high degree of scalability.
Augmented Virtual Teleportation for High-Fidelity Telecollaboration

Telecollaboration involves the teleportation of a remote collaborator to another real-world environment where their partner is located. The fidelity of the environment plays an important role for allowing corresponding spatial references in remote collaboration. We present a novel asymmetric platform, Augmented Virtual Teleportation (AVT), which provides high-fidelity telepresence of a remote VR user (VR-Traveler) into a real-world collaboration space to interact with a local AR user (AR-Host). AVT uses a 360° video camera (360-camera) that captures and live-streams the omni-directional scenes over a network. The remote VR-Traveler watching the video in a VR headset experiences live presence and co-presence in the real-world collaboration space. The VR-Traveler's movements are captured and transmitted to a 3D avatar overlaid onto the 360-camera which can be seen in the AR-Host's display. The visual and audio cues for each collaborator are synchronized in the Mixed Reality Collaboration space (MRC-space), where they can interactively edit virtual objects and collaborate in the real environment using the real objects as a reference. High fidelity, real-time rendering of virtual objects and seamless blending into the real scene allows for unique mixed reality use-case scenarios. Our working prototype has been tested with a user study to evaluate spatial presence, co-presence, and user satisfaction during telecollaboration. Possible applications of AVT are identified and proposed to guide future usage.
A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM

The practical significances and complexities of financial time series analysis induce highly demand more reliable hybrid model that denoised the data efficiently, handled with both linear and nonlinear patterns in the data, to achieve more accurate results.This paper suggests a new forecasting hybrid model for financial time series data combined Empirical Wavelet Transform (EWT) technique with improved Artificial Bee Colony (ABC) algorithm, Extreme Learning Machine (ELM) neural network, and Auto-Regressive Integrated Moving Average (ARIMA) linear analysis algorithm.The EWT is used to decompose and denoise the data to reconstruct the data more suitable for forecast.The improvement of the ABC algorithm is according to the Good Point Sets (GPS) theory and adaptive Elite-based Opposition (EO) strategy (GPS-EO-ABC) to overcome the drawbacks of the original algorithm and enhance the optimization performance.The optimized ELM with GPS-EO-ABC, as well as the ARIMA, are utilized independently to generated different forecasting results and combined by the weight-based procession.We testify the performance of the proposed improved ABC algorithm by ten benchmark functions, simulating the proposed forecasting models by three financial time-series datasets.The results indicate that: (1) The proposed algorithm shows outstanding capacities in parameter optimization.The optimized ELM generated more stable and precise results compared with original ELM, ABC-ELM, single LSTM, and ANN; (2) The proposed hybrid model has not only effectiveness but also efficiencies in denoising data, correcting outliers, coordinating both linear and nonlinear patterns, its performance in financial time series forecasting is more excellent than existing hybrid models. INDEX TERMS Financial time-series forecasting,
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding

Distance-based knowledge graph embeddings have shown substantial improvement on the knowledge graph link prediction task, from TransE to the latest state-of-the-art RotatE. However, complex relations such as N-to-1, 1-to-N and N-to-N still remain challenging to predict. In this work, we propose a novel distance-based approach for knowledge graph link prediction. First, we extend the RotatE from 2D complex domain to high dimensional space with orthogonal transforms to model relations. The orthogonal transform embedding for relations keeps the capability for modeling symmetric/anti-symmetric, inverse and compositional relations while achieves better modeling capacity. Second, the graph context is integrated into distance scoring functions directly. Specifically, graph context is explicitly modeled via two directed context representations. Each node embedding in knowledge graph is augmented with two context representations, which are computed from the neighboring outgoing and incoming nodes/edges respectively. The proposed approach improves prediction accuracy on the difficult N-to-1, 1-to-N and N-to-N cases. Our experimental results show that it achieves state-of-the-art results on two common benchmarks FB15k-237 and WNRR-18, especially on FB15k-237 which has many high in-degree nodes.