Reviewerly/exHarmony huggingface dataset:
Study on Visual Detection Algorithm of Sea Surface Targets Based on Improved YOLOv3

Countries around the world have paid increasing attention to the issue of marine security, and sea target detection is a key task to ensure marine safety. Therefore, it is of great significance to propose an efficient and accurate sea-surface target detection algorithm. The anchor-setting method of the traditional YOLO v3 only uses the degree of overlap between the anchor and the ground-truth box as the standard. As a result, the information of some feature maps cannot be used, and the required accuracy of target detection is hard to achieve in a complex sea environment. Therefore, two new anchor-setting methods for the visual detection of sea targets were proposed in this paper: the average method and the select-all method. In addition, cross PANet, a feature fusion structure for cross-feature maps was developed and was used to obtain a better baseline cross YOLO v3, where different anchor-setting methods were combined with a focal loss for experimental comparison in the datasets of sea buoys and existing sea ships, SeaBuoys and SeaShips, respectively. The results showed that the method proposed in this paper could significantly improve the accuracy of YOLO v3 in detecting sea-surface targets, and the highest value of mAP in the two datasets is 98.37% and 90.58%, respectively.
EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification

Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of the majority class to balance the class distributions. Evolutionary-based approaches are prominent, treating undersampling as a binary optimisation problem that determines which examples are removed. However, their utilisation is limited to small datasets due to fitness evaluation costs. This work proposes a two-stage clustering-based surrogate model that enables evolutionary undersampling to compute fitness values faster. The main novelty lies in the development of a surrogate model for binary optimisation which is based on the meaning (phenotype) rather than their binary representation (genotype). We conduct an evaluation on 44 imbalanced datasets, showing that in comparison with the original evolutionary undersampling, we can save up to 83% of the runtime without significantly deteriorating the classification performance.
Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures

Distributed stream processing engines are designed with a focus on scalability to process big data volumes in a continuous manner. We present the Theodolite method for benchmarking the scalability of distributed stream processing engines. Core of this method is the definition of use cases that microservices implementing stream processing have to fulfill. For each use case, our method identifies relevant workload dimensions that might affect the scalability of a use case. We propose to design one benchmark per use case and relevant workload dimension. We present a general benchmarking framework, which can be applied to execute the individual benchmarks for a given use case and workload dimension. Our framework executes an implementation of the use case's dataflow architecture for different workloads of the given dimension and various numbers of processing instances. This way, it identifies how resources demand evolves with increasing workloads. Within the scope of this paper, we present 4 identified use cases, derived from processing Industrial Internet of Things data, and 7 corresponding workload dimensions. We provide implementations of 4 benchmarks with Kafka Streams and Apache Flink as well as an implementation of our benchmarking framework to execute scalability benchmarks in cloud environments. We use both for evaluating the Theodolite method and for benchmarking Kafka Streams' and Flink's scalability for different deployment options.
Informative Manual Evaluation of Machine Translation Output

This work proposes a new method for manual evaluation of Machine Translation (MT) output based on marking actual issues in the translated text.The novelty is that the evaluators are not assigning any scores, nor classifying errors, but marking all problematic parts (words, phrases, sentences) of the translation.The main advantage of this method is that the resulting annotations do not only provide overall scores by counting words with assigned tags, but can be further used for analysis of errors and challenging linguistic phenomena, as well as inter-annotator disagreements.Detailed analysis and understanding of actual problems are not enabled by typical manual evaluations where the annotators are asked to assign overall scores or to rank two or more translations.The proposed method is very general: it can be applied on any genre/domain and language pair, and it can be guided by various types of quality criteria.Also, it is not restricted to MT output, but can be used for other types of generated text.
Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm

In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online.
Reducing Overfitting and Improving Generalization in Training Convolutional Neural Network (CNN) under Limited Sample Sizes in Image Recognition

In deep learning, application of Convolutional Neural Network (CNN) is prolific in image recognition. CNN assumes that large amount of samples are available in the dataset in order to implement an effective CNN model. However, this assumption may not be practical or possible in some real world applications. It is commonly known that training a CNN model under limited samples available often leads to overfitting and inability to generalize. Data augmentation, batch normalization and dropout techniques have been suggested to mitigate such problems. This work studies the effect of overfitting and generalization in image recognition of intentionally contracted CIF AR-10 dataset. Application of these techniques and their combination are considered as well as injection of data augmentation at different epochs. The result of this work reveals that utilizing injection at 30 epoch in the application of width and height shift data augmentation together with dropout yields the best performance and can overcome the overfitting effect best.
Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session)

The number and importance of AI-based systems in all domains is growing. With the pervasive use and the dependence on AI-based systems, the quality of these systems becomes essential for their practical usage. However, quality assurance for AI-based systems is an emerging area that has not been well explored and requires collaboration between the SE and AI research communities. This paper discusses terminology and challenges on quality assurance for AI-based systems to set a baseline for that purpose. Therefore, we define basic concepts and characterize AI-based systems along the three dimensions of artifact type, process, and quality characteristics. Furthermore, we elaborate on the key challenges of (1) understandability and interpretability of AI models, (2) lack of specifications and defined requirements, (3) need for validation data and test input generation, (4) defining expected outcomes as test oracles, (5) accuracy and correctness measures, (6) non-functional properties of AI-based systems, (7) self-adaptive and self-learning characteristics, and (8) dynamic and frequently changing environments.
A multi-stage hierarchical clustering algorithm based on centroid of tree and cut edge constraint

The minimum spanning tree clustering algorithm is known to be capable of detecting clusters with irregular boundaries. The paper presents a novel hierarchical clustering algorithm based on minimum spanning tree (MST), which tends to reduce the complexity of the merging process with guaranteed clustering performance. There are two core ideas in the proposed method: (1) The inter-cluster distance is calculated with the centroid of MST instead of the center of cluster. (2) The length of cut edge at the intersection of two adjacent clusters is taken as a merge condition. Based on this idea, we propose a three-stage MST-based hierarchical clustering algorithm (CTCEHC). In Stage 1, a preliminary partition is performed with the degrees of vertices in MST. In Stage 2, small subclusters are merged via the geodesic distance between the centroids of MST in two clusters and the cut edge constraint I. In Stage 3, the adjacent cluster pairs satisfying the cut edge constraint II are merged. The experimental results on the synthetic data sets and real data sets demonstrate a good performance of the proposed clustering method.
This Time is Different: Facebook s Libra Can Improve Both Financial Inclusion and Global Financial Stability as a Viable Alternative Currency to the U.S. Dollar

Purpose:The aim of this study is to determine the relationship between the propagation of high-magnitude crises since the late 1990s and emergence of cryptocurrencies in the aftermath of the global financial crisis of 2008. Design and Methodology:The study was based on a literature review of the interaction between financial crises and evolution of money in the digital age.A high-level technical overview of Libra and blockchain is provided.The broad analysis of Libra coin looks at various models and categories of implementation approaches.The study discusses the components of blockchain technology and provides illustrative visuals when possible.We also compare consensus models used in the Libra and Bitcoin blockchain networks.The analysis also touches on the use of blockchain technology in applications such as smart contracts. Findings:The study shows that cryptocurrencies are not only a natural but an inevitable transformation in the evolution of money.As with any new technology, Facebook's Libra is going to cause a great deal of disruption in the existing ecosystem of cryptocurrencies that has taken a decade to form.On the other hand, Libra's financial inclusion and global stability as a public good promises to revolutionize the cryptocurrency world.Practical Implications: If Facebook's Libra doesn't sputter out, it will spur central banks to introduce their own cryptocurrency projects.Libra's vast scale will make access to intermediation by banks easier, faster, and cheaper.Unlike Bitcoin, Libra will be backed by a basket of stable currencies as well as low-risk government bonds and central bank reserve assets.Originality/Value: This study presents a clear picture of both advantages and potential risks of Libra which is considered to be a new invention eventhough Bitcoin has been around more than a decade.The study warns regulators and law makers along with central banks who are running headlong into backlash to Libra can harm consumers more than protect them.Punishing Facebook with a troubled past for violation of privacy and exploitation of users' data could adversely affect innovation and discourage developments of cryptocurrencies.
Estimating Indoor Occupancy Through Low-Cost BLE Devices

Detecting the presence of persons and estimating their quantity in an indoor environment has grown in importance recently. For example, the information if a room is unoccupied can be used for automatically switching off the light, air conditioning, and ventilation, thereby saving significant amounts of energy in public buildings. Most existing solutions rely on dedicated hardware installations, which involve presence sensors, video cameras, and carbon dioxide sensors. Unfortunately, such approaches are costly, subject to privacy concerns, have high computational requirements, and lack ubiquitousness. The work presented in this article addresses these limitations by proposing a low-cost occupancy detection system. Our approach builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans. The effectiveness of this approach is evaluated by performing comprehensive tests on five different datasets. We apply several pattern recognition models and compare our methodology with systems building upon IEEE 802.11 (WiFi). On average, in multifarious environments, we can correctly classify the occupancy with an accuracy of 97.97%. When estimating the number of people in a room, on average, the estimated number of subjects differs from the actual one by 0.32 persons. We conclude that our system's performance is comparable to that of existing ones based on WiFi, while significantly reducing cost and installation effort. Hence, our approach makes occupancy detection practical for real-world deployments.
Geometrical Formalism for Dynamically Corrected Gates in Multiqubit Systems

The ability to perform gates in multiqubit systems that are robust to noise is of crucial importance for the advancement of quantum information technologies. However, finding control pulses that cancel noise while performing a gate is made difficult by the intractability of the time-dependent Schrödinger equation, especially in multiqubit systems. Here, we show that this issue can be sidestepped by using a formalism in which the cumulative error during a gate is represented geometrically as a curve in a multidimensional Euclidean space. Cancelation of noise errors to leading order corresponds to closure of the curve, a condition that can be satisfied without solving the Schrödinger equation. We develop and uncover general properties of this geometric formalism, and derive a recursion relation that maps control fields to curvatures for Hamiltonians of arbitrary dimension. We demonstrate the utility of the formalism by employing it to design pulses that simultaneously correct against both noise errors and crosstalk for two qubits coupled by an Ising interaction. We give examples both of a single-qubit rotation and a two-qubit maximally entangling gate. The results obtained in this example are relevant to both superconducting transmon qubits and semiconductor quantum-dot spin qubits. We propose this geometric formalism as a general technique for pulse-induced error suppression in quantum computing gate operations.Received 17 September 2020Revised 22 January 2021Accepted 5 February 2021DOI:https://doi.org/10.1103/PRXQuantum.2.010341Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum gatesQuantum Information
A systematic literature review and taxonomy of modern code review

Modern Code Review (MCR) is a widely known practice of software quality assurance. However, the existing body of knowledge of MCR is currently not understood as a whole. Our goal is to identify the state of the art on MCR, providing a structured overview and an in-depth analysis of the research done in this field. We performed a systematic literature review, selecting publications from four digital libraries. A total of 139 papers were selected and analyzed in three main categories. Foundational studies are those that analyze existing or collected data from the adoption of MCR. Proposals consist of techniques and tools to support MCR, while evaluations are studies to assess an approach or compare a set of them. The most represented category is foundational studies, mainly aiming to understand the motivations for adopting MCR, its challenges and benefits, and which influence factors lead to which MCR outcomes. The most common types of proposals are code reviewer recommender and support to code checking. Evaluations of MCR-supporting approaches have been done mostly offline, without involving human subjects. Five main research gaps have been identified, which point out directions for future work in the area.
Decentralized Finance : The Possibilities of a Blockchain “Money Lego” System

With the adoption of blockchain technology, initiatives to provide financial, investment and insurance services to a wide range of users in a decentralized manner have emerged.But can decentralized finance be the alternative to the traditional financial system, or has it only created another "technology playground" for users who are biasedly enthusiastic about crypto-assets?The study examines the key definitions of decentralized finance and then synthesizes them to formulate a new, more complete definition.This is followed by a presentation of the different layers of decentralized finance and their prevalence, as well as an analysis of its benefits and risks.In the conclusions, the author finds that decentralized finance has the potential to provide financial services with an open, transparent and robust infrastructure, and has the possibility of reaching a broad range of users with its basic financial services.However, this requires further development of the sector and effective management of emerging risks.
S-Box Design Based on Discrete Chaotic Map and Improved Artificial Bee Colony Algorithm

To further enhance the nonlinear and cryptographic properties of chaotic S-boxes, a novel S-box design is proposed based on the discrete chaotic map and improved artificial bee colony algorithm.Firstly, the S-box generated by chaotic sequence is initialized by the opposition-based optimization.Then, the dual transposition artificial bee colony algorithm is used to optimize the population, where the Gaussian swap is engaged to strengthen the optimum efficiency, and make the population acquire a better quality with each other.With the Gaussian mutation operator, the opposition-based optimization performs self-learning to obtain better population.Comparing with some recent S-boxes based on chaos.The S-boxes generated by the proposed scheme can achieve better performance in bijection, nonlinearity, strict avalanche criterion(SAC), bit independence criterion(BIC), I / O XOR distribution and linear approximation probability(LP).It provides a new and effective method for designing S-boxes with good cryptographic properties.
EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.
On Hallucination and Predictive Uncertainty in Conditional Language Generation

Despite improvements in performances on different natural language generation tasks, deep neural models are prone to hallucinating facts that are incorrect or nonexistent.Different hypotheses are proposed and examined separately for different tasks, but no systematic explanations are available across these tasks.In this study, we draw connections between hallucinations and predictive uncertainty in conditional language generation.We investigate their relationship in both image captioning and data-to-text generation and propose a simple extension to beam search to reduce hallucination.Our analysis shows that higher predictive uncertainty corresponds to a higher chance of hallucination.Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties.It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
Combining hard and soft decoders for hypergraph product codes

Hypergraph product codes are a class of constant-rate quantum low-density parity-check (LDPC) codes equipped with a linear-time decoder called small-set-flip (SSF). This decoder displays sub-optimal performance in practice and requires very large error correcting codes to be effective. In this work, we present new hybrid decoders that combine the belief propagation (BP) algorithm with the SSF decoder. We present the results of numerical simulations when codes are subject to independent bit-flip and phase-flip errors. We provide evidence that the threshold of these codes is roughly 7.5% assuming an ideal syndrome extraction, and remains close to 3% in the presence of syndrome noise. This result subsumes and significantly improves upon an earlier work by Grospellier and Krishna (arXiv:1810.03681). The low-complexity high-performance of these heuristic decoders suggests that decoding should not be a substantial difficulty when moving from zero-rate surface codes to constant-rate LDPC codes and gives a further hint that such codes are well-worth investigating in the context of building large universal quantum computers.