Reviewerly/exHarmony huggingface dataset:
Fast greedy $$\mathcal {C}$$-bound minimization with guarantees

Abstract The $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> -bound is a tight bound on the true risk of a majority vote classifier that relies on the individual quality and pairwise disagreement of the voters and provides PAC-Bayesian generalization guarantees. Based on this bound, MinCq is a classification algorithm that returns a dense distribution on a finite set of voters by minimizing it. Introduced later and inspired by boosting, CqBoost uses a column generation approach to build a sparse $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> -bound optimal distribution on a possibly infinite set of voters. However, both approaches have a high computational learning time because they minimize the $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> -bound by solving a quadratic program. Yet, one advantage of CqBoost is its experimental ability to provide sparse solutions. In this work, we address the problem of accelerating the $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> -bound minimization process while keeping the sparsity of the solution and without losing accuracy. We present CB-Boost, a computationally efficient classification algorithm relying on a greedy–boosting-based– $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> -bound optimization. An in-depth analysis proves the optimality of the greedy minimization process and quantifies the decrease of the $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> -bound operated by the algorithm. Generalization guarantees are then drawn based on already existing PAC-Bayesian theorems. In addition, we experimentally evaluate the relevance of CB-Boost in terms of the three main properties we expect about it: accuracy, sparsity, and computational efficiency compared to MinCq, CqBoost, Adaboost and other ensemble methods. As observed in these experiments, CB-Boost not only achieves results comparable to the state of the art, but also provides $$\mathcal {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> -bound sub-optimal weights with very few computational demand while keeping the sparsity property of CqBoost.
Virtual Reality as Cost Effective Tool for Distance Healthcare

VR can provide a rich, interactive environment in distance assisted actions for serious games in healthcare. The location-based decisions can be transferred for distance-based assisted decisions, when the specialists cannot be present in time at the probe/case location. The specialists can consult at distance, a probe in the real lab, by using images that are taken with high resolution cameras and that are sent over the internet. In advanced healthcare the virtual environment is conducted by a surgeon and decision is transmitted to a robotic instrument that mimics the actions. In this case the cost effective solution refers to regular healthcare activity such as visual assisted diagnosis in real time, training of the specialists, assessing a situation and give an expert opinion on it, future actions and treatment. The advancement in technology allowed VR systems to run on personal computers thus decreasing the implementation cost for a sophisticated system. In the present implementation, a cost-effective solution is presented. The solution is easy to use by the medical specialists and is helpful to compensate the time-delay for the doctor mobility at the probe/case location. A user-friendly interface was developed in order to facilitate the communication between a specialist in the lab and the expert at distance, using the immersive technology of VR, in order to replicates an environment via computer-simulated reality.
Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding

Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely replacing the functional blocks of communication systems with neural networks, a hybrid manner of BCJRNet symbol detection is proposed to combine the advantages of the BCJR algorithm and neural networks. However, its separate block design not only degrades the system performance but also results in additional hardware complexity. In this work, we propose a BCJR receiver for joint symbol detection and channel decoding. It can simultaneously utilize the trellis diagram and channel state information for a more accurate calculation of branch probability and thus achieve global optimum with 2.3 dB gain over separate block design. Furthermore, a dedicated neural network model is proposed to replace the channel-model-based computation of the BCJR receiver, which can avoid the requirements of perfect CSI and is more robust under CSI uncertainty with 1.0 dB gain.
SLA-Driven modeling and verifying cloud systems: A Bigraphical reactive systems-based approach

We propose a formal approach based on Bigraphical Reactive Systems (BRSs) and model checking techniques for modeling and verifying the interaction behaviours of SLA-based cloud computing systems. In the first phase of this approach, we address the modeling of the static structure and the dynamic behavior of cloud systems using BRSs. We show how bigraphs enable the description of the different cloud entities, including cloud actors, cloud services, Service Level Agreements (SLAs), the diversity of their properties, and the complex interactions and dependencies among them. Furthermore, we propose a four-stages SLA lifecycle, and define a set of bigraphical reaction rules to abstract these stages and model the dynamic nature of the cloud. The second phase of this approach verifies that the behavior of services and cloud actors will cope with the agreed SLA terms during the lifecycle of the SLA. We map the proposed bigraphical models into SMV descriptions. Then, we express the interaction behaviors as a set of liveness and safety properties using Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) formulas, and we use the NuSMV model checker to verify them. Finally, we define a case study on which we illustrate the application of our proposed approach.
Application of Ontology Matching Algorithm in Linguistic Features

To discuss the role of Ontology Matching Algorithm in linguistic features, the application fields of Ontology Matching Algorithm are introduced firstly. Then, the major Ontology Systems and typical Ontology Matching Frameworks are discussed so that the concept of Ontology Matching Algorithm is Clear. Thirdly, to further explore the importance of Ontology Matching Algorithm in linguistic, the Ontology Matching Algorithm based on semantic (Omabos) is Analysed. At last, the Ontology Matching Algorithm based on linguistic features (Omablf) is designed and verified. The results show that the presented Algorithm has good effects. However, any kind of Matching Algorithm always has its drawbacks and is difficult to do well in each aspect of matching. Therefore, in practical applications, Matching Systems are usually synthesized by a variety of different types of Matching Algorithms (element level or structure level), to achieve a good matching effect.
Student Session: Turning Over Detecting Using Super-Resolution Thermal Image

A low resolution infrared camera can use to collect the data of our daily activity, and protect privacy. However, pose detection on an extremely low resolution image will be a big challenge to deal with. Many researches show that Super-resolution may improve the accuracy of object detection on low resolution images. In this work, we detect the turning over while sleeping with low-resolution thermal sensors. To improve the accuracy of detection, we fuse the data from multiple sensors and use SRCNN to enhance the resolution. We also propose a method to reduce the noise caused by the residual body temperature on bed after turn over while detect the pose and turning over. The accuracy of pose detection is 65%, and the turning over detection has 50% recall rate and 83% precision.
Understanding the Relationship Between the Dark Triad of Personality Traits and Neutralization Techniques Toward Cybersecurity Behaviour

This study considers the framework of neutralization techniques, which responds to the attitude/rationalization leg of the fraud triangle with respect to the dark triad of personality traits' predictability toward compliant cybersecurity behaviour. Logically, organizations focus on reducing the insider's motivation and opportunities for malfeasance. However, it may be prudent to consider the rationalization for insider crime, which is arguably the most obscure element. This study is a unique contribution as it considers both the roles of neutralization techniques and the dark triad of personality traits with respect to insider threats and compliance intention. Furthermore, the specific categories of neutralization techniques will be considered as antecedents towards insider maleficence.
Measurable Counterfactual Local Explanations for Any Classifier

We propose a novel method for explaining the predictions of any classifier. In our approach, local explanations are expected to explain both the outcome of a prediction and how that prediction would change if athings had been different'. Furthermore, we argue that satisfactory explanations cannot be dissociated from a notion and measure of fidelity, as advocated in the early days of neural networks' knowledge extraction. We introduce a definition of fidelity to the underlying classifier for local explanation models which is based on distances to a target decision boundary. A system called CLEAR: Counterfactual Local Explanations via Regression, is introduced and evaluated. CLEAR generates b-counterfactual explanations that state minimum changes necessary to flip a prediction's classification. CLEAR then builds local regression models, using the b-counterfactuals to measure and improve the fidelity of its regressions. By contrast, the popular LIME method [17], which also uses regression to generate local explanations, neither measures its own fidelity nor generates counterfactuals. CLEAR's regressions are found to have significantly higher fidelity than LIME's, averaging over 40% higher in this paper's five case studies.
A Preliminary Design for Authenticity of IoT Big Data in Cloud Computing

The cloud computing, as a more distributed and more efficient paradigm with better performance, has played an important role in many fields. The cloud environment under the IoT has also assumed many roles such as the storage, the processor, the service provider and so on. However, the complex deployment and usage environment of the IoT brings new security risks to cloud computing. In response to this situation, this poster preliminarily designed a security scheme for the cloud environment of IoT. Based on the identity verification algorithms and blockchain technology, the credibility of data stored in the cloud can be ensured, while the security of data transmission from the cloud to data consumers is achieved.
Local Geometry Analysis For Image Tampering Detection

In this paper, we propose a compliant scheme of image hashing that is based on gradient measurement. The proposed approach relies on a local geometry variation analysis in multi-channel images. Derivative filters are used to estimate the image gradient and a suitable representation of geometric color features is introduced. A gradient norm is provided as an intermediate hash, so we compute subsequently the magnitude of the Fourier spectrum in order to provide a final hash. The aim is to preserve accurately the structural information along with smoothing operations. We conduct experiments on the CASIA-V2 database for the tampering detection task, and the UCID database to demonstrate the hash robustness. We use the metrics of true positive rate (TPR) and false positive rate (FPR) to investigate the performance of the proposed method. A comparison is carried out using two corresponding schemes; the first operates on the quaternion discrete Fourier transform (QDFT) to take into account the image color planes, and the second exploits this transform into the log-polar domain. According to the TPR results, our method is quite robust against different content-preserving operations applied on the UCID database with regard to a predefined threshold. The FPR results over CASIA-V2 further demonstrate a superior capability of the proposed approach in detecting image forgeries.
Generating Solar Irradiance Data Series with 1-minute Time Resolution Based on Hourly Observational Data

The large-scale integration of intermittent renewable energy sources into electricity distribution is one of the significant challenges in meeting energy demand in the near future. The solar energy resource has a fluctuating spatial and temporal character intrinsic to the radiative processes with the atmosphere and the Earth’s surface. Efficient use of solar energy requires technologies that consider such variability and its impacts on the electrical system. In this context, a reliable computational model to estimate the surface solar irradiance is beneficial for operational issues. This work presents a methodology based on Markov chains for the generation of a global irradiance time series with one-minute temporal resolution using observational database acquired by a basic weather station. The statistical model was developed using irradiance data acquired in Sao Martinho da Serra/RS and Petrolina/PE to verify their performance in two different climates. The transition probability matrices were determined for the following cloud conditions: cloudy, partly cloudy or clear sky using ten years of observational data. Next, Markov’s walking technique was used to generate a data series with one-minute temporal resolution taking into account the cloudiness classification. Model validation was performed using solar irradiance data not considered to calculate the transition probability matrix (MPT). The mean squared deviation of the relative distribution frequencies indicated a deviation of only 1.5%, and the Kolmogorov-Smirnov test indicated that the synthetic and observational series presented similar cumulative frequency distributions. Thus, the proposed methodology showed high reliability in reproducing the temporal variability due to the stochastic nature of the incoming solar energy in both sites.
Attention-based Deep Learning Model for Text Readability Evaluation

Text readability is useful to measure the comprehensibility of a piece of writing and plays an important role in the field of education. The classic evaluation methods are linear functions with two or three parameters. However, it needs laborious human tests to find the appropriate parameters. This paper presents a recurrent neural network to quickly build a text readability evaluation model. The model consists of bi-directional gated recurrent unit (bi-GRU) and attention layer. It can predict the readability level of the sentences or paragraphs. The weight of the attention layer can finely locate the distribution of difficulty in the comprehension of the sentences. We train the model on rough leveled reading materials without tremendous reading comprehension tests. Our experiments are performed on various text materials. Compared with the popular readability formulas, the results show good performance on readability measurement and visualization.
Understanding a Deep Neural Network Based on Neural-Path Coding

Recently, the deep neural network has achieved considerable success in the field of machine learning. However, for most tasks, the current neural network models are still considered as the “black-box” structure, essentially because the knowledge a neural network learns from data is often unpredictable and unexplainable. Similar to neural coding in the brain, one neuron may be simultaneously involved in encoding one or even several tasks. Information may be transmitted between neurons along a particular neural-path (neural circuit) to end neurons that encode a specific decision. Inspired by this, we aim to explore the reason for the performance of a neural model based on a neural-path. First, for a trained neural model, we quantify the neural-path in which the role of a neuron was assumed to control the amount of information that can be passed through; Second, we define a Euclidean distance (ED) for every two neural-paths, and by analyzing the EDs between two classes of a neural classifier, we explain the ease of prediction for some classes, whereas others are not. We performed extensive experiments for architectures of ResNet and DenseNet models on several benchmark datasets, and determined that the shorter the distance between the neural-paths of two classes, the easier it is to make mistakes. Finally, we proposed a method for controlling the formation of a “neural-path” to build a partially understandable neural model. For the new ResNet model, each feature map in redirect layers was assigned to participate in encoding only one class. The feasibility of the method is also verified through experiments.
PALMS Image Partitioning - A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model

We present a method for computing approximate solutions of the piecewise affine-linear Mumford-Shah model -PALMS Image Partitioning.The piecewise affine-linear Mumford-Shah model is a variational approach to image partitioning.The underlying algorithm is based on a splitting approach using ADMM.The emerging subproblems are solved exactly and efficiently.We detail the solver for these subproblems which is based on dynamic programming and incorporates an acceleration strategy.The subproblems are solved in parallel in our implementation to provide an efficient overall algorithm.We conduct extended studies on the effects of the algorithmic parameters.Thereby, the implemented algorithm is further optimized w.r.t.runtime and efficiency.Finally, we underpin the efficiency of the algorithm by a comparison with the state-of-the-art which shows that the presented algorithm has lower computation times and yields lower mean functional values.
A comparison of human brainwaves-based biometric authentication systems

Several decades ago, attention was directed to biometrics as an alternative to passwords that can be discovered or 'shoulder surfing' by others. Various authentication methods have been provided that rely on the user's biometrics, such as a fingerprint of a face, iris, voice, and others. Unfortunately, ways were found to imitate these visible fingerprints for using them in penetrations. Therefore, many researchers were interested in studying the possibility of using brainwaves for authentication purposes, as relying on hidden vital features increases the difficulty of breaking and imitation. This paper presents an analytical study of the proposed brainwave-based biometric authentication systems. It provides a comparison of signal acquisition methods for the brainwave-based authentication system. Also, the paper classifies brainwaves using its relevant features. It also presents the phases of the brainwave-based authentication system. Finally, it provides a detailed discussion of several factors that affect the accuracy of the brainwave-based authentication system results, and evaluate the compatibility level of the brainwave with the biometric requirements.
Automatic Information Extraction from Scanned Documents

This paper deals with the task of information extraction from a structured document scanned by an ordinary office scanner device. It explores the processing pipeline from scanned paper documents to the extraction of searched information such as names, addresses, dates, and other numerical values.We propose system design decomposed into four consecutive modules: preprocessing, optical character recognition, information extraction with a database, and information extraction without a database. In the preprocessing module, two essential techniques are presented – image quality improvement and image deskewing. Optical Character Recognition solutions and approaches to information extraction are compared using the whole system performance. The best performance of information extraction with the database was obtained by the Locality-sensitive Hashing algorithm.
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus

We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. Each graph node is a scene interpretation layer, while each edge is a deep net that transforms one layer at one node into another from a different node. During the supervised phase edge networks are trained independently. During the next unsupervised stage edge nets are trained on the pseudo-ground truth provided by consensus among multiple paths that reach the nets' start and end nodes. These paths act as ensemble teachers for any given edge and strong consensus is used for high-confidence supervisory signal. The unsupervised learning process is repeated over several generations, in which each edge becomes a "student" and also part of different ensemble "teachers" for training other students. By optimizing such consensus between different paths, the graph reaches consistency and robustness over multiple interpretations and generations, in the face of unknown labels. We give theoretical justifications of the proposed idea and validate it on a large dataset. We show how prediction of different representations such as depth, semantic segmentation, surface normals and pose from RGB input could be effectively learned through self-supervised consensus in our graph. We also compare to state-of-the-art methods for multi-task and semi-supervised learning and show superior performance.
A unified framework for the application and evaluation of different methods for neural parameter optimization

Automated parameter search has become a standard method in the modeling of neural systems. These studies could potentially take advantage of recent developments in nonlinear optimization, and the availability of software packages containing high-quality implementations of algorithms that proved useful in other domains. However, a systematic comparison of the available algorithms for problems that are typical in neuroscience has not been performed. We developed a software tool for fitting the parameters of neural models, which provides intuitive, uniform access to a variety of state-of-the-art optimization algorithms implemented by four different Python packages. We also established a set of benchmark problems of different complexity that involve a variety of widely used neuronal models. We then used our optimization tool to systematically evaluate the performance of the algorithms on our set of benchmark problems. We found that several evolutionary and related algorithms consistently provided good solutions for all of our benchmarks. However, the relative performance of the different methods, both in terms of the quality of the final result and in terms of convergence speed, depended substantially on the nature of the problem. We hope that our software tool and benchmarking results will facilitate the choice and application of the best parameter-fitting methods in neuroscientific research.