Reviewerly/exHarmony huggingface dataset:
BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

Label ranking tasks are concerned with the problem of ranking a finite set of labels for each instance according to their relevance.Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms.While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked.Herein, we present a novel boosting algorithm, BoostLR, that was specifically designed for label ranking tasks.Similarly to other boosting algorithms, BoostLR, proceeds in rounds, where in each round, a single weak model is trained over a sampled set of instances.Instances that were identified as harder to predict in the current round, receive a higher (boosted) weight, and therefore also a higher probability to be included in the sample of the forthcoming round.Extensive evaluation of our proposed algorithm on 24 semi-synthetic and real-world label ranking datasets concludes that our algorithm significantly outperforms the current state-of-the-art label ranking methods.
A Secure IoT-Based Authentication System in Cloud Computing Environment

The Internet of Things (IoT) is currently the most popular field in communication and information techniques. However, designing a secure and reliable authentication scheme for IoT-based architectures is still a challenge. In 2019, Zhou et al. showed that schemes pro-posed by Amin et al. and Maitra et al. are vulnerable to off-line guessing attacks, user tracking attacks, etc. On this basis, a lightweight authentication scheme based on IoT is proposed, and an authentication scheme based on IoT is proposed, which can resist various types of attacks and realize key security features such as user audit, mutual authentication, and session security. However, we found weaknesses in the scheme upon evaluation. Hence, we proposed an enhanced scheme based on their mechanism, thus achieving the security requirements and resisting well-known attacks.
Symmetry-breaking patterns, tricriticalities, and quadruple points in the quantum Rabi model with bias and nonlinear interaction

Quantum Rabi model (QRM) is fascinating not only because of its broad relevance but also due to its few-body quantum phase transition. In practice, both the bias and the nonlinear coupling in QRM are important controlling parameters in experimental setups. We study the interplay of the bias and the nonlinear interaction with the linear coupling in the ground state which exhibits various patterns of symmetry breaking and different orders of transitions. Several situations of tricriticalities are unveiled in the low-frequency limit and at finite frequencies. We find that the full quantum-mechanical effect leads to transitions, tricriticalities, quadruple points, and a fine structure of spontaneous symmetry breaking, which are much beyond the semiclassical picture. We clarify the underlying mechanisms by analyzing the energy competitions and the essential changeovers of the quantum states, which enables us to extract most analytic phase boundaries.
Generalized isotopic shift construction for APN functions

Abstract In this work we give several generalizations of the isotopic shift construction, introduced recently by Budaghyan et al. (IEEE Trans Inform Theory 66:5299–5309, 2020), when the initial function is a Gold function. In particular, we derive a general construction of APN functions which covers several unclassified APN functions for $$n=8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:math> and produces fifteen new APN functions for $$n=9$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>9</mml:mn> </mml:mrow> </mml:math> .
BANSHEE–A MATLAB toolbox for Non-Parametric Bayesian Networks

Bayesian Networks (BNs) are probabilistic, graphical models for representing complex dependency structures. They have many applications in science and engineering. Their particularly powerful variant – Non-Parametric BNs – are for the first time implemented as an open-access scriptable code, in the form of a MATLAB toolbox “BANSHEE”.1 The software allows for quantifying the BN, validating the underlying assumptions of the model, visualizing the network and its corresponding rank correlation matrix, and finally making inference with a BN based on existing or new evidence. We also include in the toolbox, and discuss in the paper, some applied BN models published in most recent scientific literature.
A Word Embedding Based Approach for Focused Web Crawling Using the Recurrent Neural Network

Learning-based focused crawlers download relevant uniform resource locators (URLs) from the web for a specific topic.Several studies have used the term frequency-inverse document frequency (TF-IDF) weighted cosine vector as an input feature vector for learning algorithms.TF-IDF-based crawlers calculate the relevance of a web page only if a topic word co-occurs on the said page, failing which it is considered irrelevant.Similarity is not considered even if a synonym of a term co-occurs on a web page.To resolve this challenge, this paper proposes a new methodology that integrates the Adagrad-optimized Skip Gram Negative Sampling (A-SGNS)based word embedding and the Recurrent Neural Network (RNN).The cosine similarity is calculated from the word embedding matrix to form a feature vector that is given as an input to the RNN to predict the relevance of the website.The performance of the proposed method is evaluated using the harvest rate (hr) and irrelevance ratio (ir).The proposed methodology outperforms existing methodologies with an average harvest rate of 0.42 and irrelevance ratio of 0.58.
CNN Acceleration With Hardware-Efficient Dataflow for Super-Resolution

The convolutional neural network (CNN)-based super-resolution (SR) has shown outstanding performance in the field of computer vision. The implementation of inference hardware for CNN-based SR has suffered from the intensive computation with severely unbalanced computation load among layers. Various light-weighted SR networks have been researched with little performance degradation. However, the hardware-efficient dataflow is also required to efficiently accelerate inference hardware within limited resources. In this article, we propose the hardware-efficient dataflow of CNN-based SR that reduces computation load by increasing data reuse and increases process element (PE) utilization by balancing the computation load among layers for high throughput. In the proposed dataflow, row-wise pixels in the receptive field are computed by circularly shifting memory addresses to maximize data reuse. The partial convolution is exploited in a layer-based pipeline architecture to relieve intensive computation in a single pipeline stage. The delay-balancing with adjusting parallelism is employed for balancing computations precisely in the overall layers. Furthermore, the inference hardware of CNN-based SR is implemented for 4K ultrahigh definition at 60 fps on a field-programmable gate array (FPGA). For hardware-friendly computation, the quantization of activation and weight is adopted. The proposed hardware shows an average peak signal-to-noise ratio of 36.42 dB in the Set-5 dataset with a memory usage of 53 KB and an average PE utilization of 76.7% in the overall layers. Thus, it achieves the lowest memory usage and highest PE utilization compared with other inference hardware for CNN-based SR.
Smart Home Voice Assistants: A Literature Survey of User Privacy and Security Vulnerabilities

Intelligent voice assistants are internet-connected devices, which listen to their environment and react to spoken user commands in order to retrieve information from the internet, control appliances in the household, or notify the user of incoming messages, reminders, and the like. With their increasing ubiquity in smart homes, their application seems only limited by the imagination of developers, who connect these off-the-shelf devices to existing apps, online services, or appliances. However, since their inherent nature is to observe the user in their home, their ubiquity also raises concern of security and user privacy. To justify the trust placed into the devices, the devices must be secure from unauthorized access and the back-end infrastructure tasked with speech-to-text analysis, command interpretation, and connection to other services and appliances must maintain confidentiality of data. To investigate existing possible vulnerabilities, approaches to mitigate them, as well as general considerations in this emerging field, we supplement the findings of a recent study with results from a systematic literature review. We were able to compile a list of six main types of user privacy vulnerabilities, partially confirming previous findings, but also finding additional issues. We discuss these vulnerabilities, their associated attack vectors, and possible mitigations users can take to protect themselves.
Automatic ultrasound image report generation with adaptive multimodal attention mechanism

Text report writing for medical images is a fundamental task for diagnosis and treatment in clinical medicine. However, this work is tedious and time-consuming because of the special report features (e.g., boundary conditions and fixed templates). The existing works mainly adopt image captioning methods for medical report generation but the special report features are not fully considered in these models. This paper proposes an Adaptive Multimodal Attention network (AMAnet) to generate high-quality medical image reports. First, a Multi-Label Classification network is designed to predict the essential local properties. And then the word embedding vectors of these properties can serve as the semantic features to aid report generation. Second, we develop a semantic attention mechanism to imitate the spatial attention. Third, we introduce an adaptive attention mechanism with a sentinel gate to control the attention level at current visual features and language model memories when generating the next word. Experimental results demonstrate AMAnet outperforms the state-of-the-art image captioning methods with over 1 CIDEr score improvement.
Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall’s impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.
The Field Wheat Count Based on the Efficientdet Algorithm

It is of great significance to realize the low cost and fast statistics of wheat ears to predict wheat yield. At present, a variety of methods have been applied to the measurement of wheat planting density, although these methods can count ears of wheat, they are expensive and difficult to be put into actual production. In order to further improve the accuracy of wheat ear identification and detection counting under field environment, based on the image processing and deep learning technology, EfficientDet algorithm is proposed to detect the wheat ear image. The idea is to frame out the wheat in the wheat ear image and then count the detected target number to realize the automatic counting of wheat ears. EfficientDet-D3 model is adopted in this paper, and adamw algorithm is used to train the model, with a learning rate of 1e5. The experimental results show that the model can quickly and accurately recognize wheat ear images with different densities under various lighting conditions. The final accuracy rate reaches 92.92%, and the test time of the single sheet is 0.2s. We carry out ear counting test on 20 wheat ear images with different densities, and the average accuracy rate reaches 95.30%. This method can detect wheat ear image in a very short time and the detection effect is good. To some extent, the accuracy of wheat ear counting has been increased, and has the potential to be put into actual production.
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art

Despite the significant progress on entity coreference resolution observed in recent years, there is a general lack of understanding of what has been improved.We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.
Stochastic modified equations for the asynchronous stochastic gradient descent

Abstract We propose stochastic modified equations (SMEs) for modelling the asynchronous stochastic gradient descent (ASGD) algorithms. The resulting SME of Langevin type extracts more information about the ASGD dynamics and elucidates the relationship between different types of stochastic gradient algorithms. We show the convergence of ASGD to the SME in the continuous time limit, as well as the SME’s precise prediction to the trajectories of ASGD with various forcing terms. As an application, we propose an optimal mini-batching strategy for ASGD via solving the optimal control problem of the associated SME.
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data

Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data.However, collecting large-scale dialogue data is usually time-consuming and labor-intensive.To address this data dilemma, we propose a novel data augmentation method for training opendomain dialogue models by utilizing unpaired data.Specifically, a data-level distillation process is first proposed to construct augmented dialogues where both post and response are retrieved from the unpaired data.A ranking module is employed to filter out low-quality dialogues.Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data.Automatic and manual evaluation indicates that our method can produce high-quality dialogue pairs with diverse contents, and the proposed data-level and model-level dialogue distillation can improve the performance of competitive baselines.
A hybrid quantum-classical paradigm to mitigate embedding costs in quantum annealing

Despite rapid recent progress towards the development of quantum computers capable of providing computational advantages over classical computers, it seems likely that such computers will, initially at least, be required to run in a hybrid quantum-classical regime. This realization has led to interest in hybrid quantum-classical algorithms allowing, for example, quantum computers to solve large problems despite having very limited numbers of qubits. Here we propose a hybrid paradigm for quantum annealers with the goal of mitigating a different limitation of such devices: the need to embed problem instances within the (often highly restricted) connectivity graph of the annealer. This embedding process can be costly to perform and may destroy any computational speedup. In order to solve many practical problems, it is moreover necessary to perform many, often related, such embeddings. We will show how, for such problems, a raw speedup that is negated by the embedding time can nonetheless be exploited to give a real speedup. As a proof-of-concept example we present an in-depth case study of a simple problem based on the maximum-weight independent set problem. Although we do not observe a quantum speedup experimentally, the advantage of the hybrid approach is robustly verified, showing how a potential quantum speedup may be exploited and encouraging further efforts to apply the approach to problems of more practical interest.
Maximum<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>-body correlations do not in general imply genuine multipartite entanglement

The existence of correlations between the parts of a quantum system on the one hand, and entanglement between them on the other, are different properties. Yet, one intuitively would identify strong $N$-party correlations with $N$-party entanglement in an $N$-partite quantum state. If the local systems are qubits, this intuition is confirmed: The state with the strongest $N$-party correlations is the Greenberger-Horne-Zeilinger (GHZ) state, which does have genuine multipartite entanglement. However, for high-dimensional local systems the state with strongest $N$-party correlations may be a tensor product of Bell states, that is, partially separable. We show this by introducing several novel tools for handling the Bloch representation.
A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems

Virtual worlds have become global platforms connecting millions of people and containing various technologies. For example, No Man’s Sky (nomanssky.com), a cross-platform virtual world, can dynamically and automatically generate content with the progress of user adventure. AltspaceVR (altvr.com) is a social virtual reality platform supporting motion capture through Microsoft’s Kinect, eye tracking, and mixed reality extension. The changes in industrial investment, market revenue, user population, and consumption drive the evolution of virtual-world-related technologies (e.g., computing infrastructure and interaction devices), which turns into new design requirements and thus results in the requirement satisfaction problem in virtual world system architecture design. In this paper, we first study the new or evolving features of virtual worlds and emerging requirements of system development through market/industry trend analysis, including infrastructure mobility, content diversity, function interconnectivity, immersive environment, and intelligent agents. Based on the trend analysis, we propose a new design requirement space. We, then, discuss the requirement satisfaction of existing system architectures and highlight their limitations through a literature review. The feature-based requirement satisfaction comparison of existing system architectures sheds some light on the future virtual world system development to match the changing trends of the user market. At the end of this study, a new architecture from an ongoing research, called Virtual Net, is discussed, which can provide higher resource sufficiency, computing reliability, content persistency, and service credibility.
High-order Semantic Role Labeling

Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships.Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has focused on the relationships between predicates and arguments and the correlations between arguments at most, while the correlations between predicates have been neglected for a long time.High-order features and structure learning were very common in modeling such correlations before the neural network era.In this paper, we introduce a highorder graph structure for the neural semantic role labeling model, which enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs.Experimental results on 7 languages of the CoNLL-2009 benchmark show that the highorder structural learning techniques are beneficial to the strong performing SRL models and further boost our baseline to achieve new stateof-the-art results.
Scalable repeater architectures for multi-party states

Abstract The vision to develop quantum networks entails multi-user applications, which require the generation of long-distance multi-party entangled states. The current rapid experimental progress in building prototype-networks calls for new design concepts to guide future developments. Here we describe an experimentally feasible scheme implementing a two-dimensional repeater network for robust distribution of three-party entangled states of GHZ type in the presence of excitation losses and detector dark counts — the main sources of errors in real-world hardware. Our approach is based on atomic or solid state ensembles and employs built-in error filtering mechanisms peculiar to intrinsically two-dimensional networks. This allows us to overcome the performance limitation of conventional one-dimensional ensemble-based networks distributing multi-party entangled states and provides an efficient design for future experiments with a clear perspective in terms of scalability.
Practical integer-to-binary mapping for quantum annealers

Recent advancements in quantum annealing hardware and numerous studies in this area suggest that quantum annealers have the potential to be effective in solving unconstrained binary quadratic programming problems. Naturally, one may desire to expand the application domain of these machines to problems with general discrete variables. In this paper, we explore the possibility of employing quantum annealers to solve unconstrained quadratic programming problems over a bounded integer domain. We present an approach for encoding integer variables into binary ones, thereby representing unconstrained integer quadratic programming problems as unconstrained binary quadratic programming problems. To respect some of the limitations of the currently developed quantum annealers, we propose an integer encoding, named bounded-coefficient encoding, in which we limit the size of the coefficients that appear in the encoding. Furthermore, we propose an algorithm for finding the upper bound on the coefficients of the encoding using the precision of the machine and the coefficients of the original integer problem. We experimentally show that this approach is far more resilient to the noise of the quantum annealers compared to traditional approaches for the encoding of integers in base two. In addition, we perform time-to-solution analysis of various integer encoding strategies with respect to the size of integer programming problems and observe favorable performance from the bounded-coefficient encoding relative to that of the unary and binary encodings.