Reviewerly/exHarmony huggingface dataset:
PRNU-based Detection of Finger Vein Presentation Attacks

In this work, we evaluated the effectiveness of the Photo Response Non-Uniformity (PRNU) to detect presentation/ spoofing attacks for finger vein imagery. The performance is evaluated on two publicly-available finger vein presentation/ spoofing attack datasets (IDIAP and SCUT-FVD). Maximum likelihood estimation (MLE) is used to estimate the sensor's PRNU. To decide whether a query image is real or spoofed, we compare its residual to the estimated sensor PRNU using PCE and NCC as similarity measures. We observe that the classification performance is heavily dependent on the set of images used for PRNU estimation. We assume different degrees of variability in image content caused by distinct light scattering properties in real tissue and artifacts to be one of the main reasons for the differences in classification performance.
Hierarchical Variational Network for User-Diversified &amp; Query-Focused Video Summarization

This paper focuses on the query-focused video summarization, which is an extended task of video summarization and aims to automatically generate user-oriented summary by highlighting frames/shots relevant to the query. This task is different from traditional video summarization in paying attention to users' subjectivity through queries. Diversity is a recognized important property in video summarization. However, existing methods only consider diversity as the dissimilarity between frames/shots which is far from user-oriented summarization. Users' different understandings of video should be an important source of diversity, reflected in the process of eliminating query-unrelated redundancy. To this end, this paper explores user-diversified & query-focused video summarization via a well-devised hierarchical variational network called HVN. HVN has three distinctive characteristics: (i) it has a hierarchical structure to model query-related long-range temporal dependency; (ii) it employs diverse attention mechanisms to encode query-related and context-important information and makes them balanced; (iii) it employs a multilevel self-attention module and a variational autoencoder module to add user-oriented diversity and stochastic factors. Experimental results demonstrate that HVN not only outperforms the state-of-the-arts but also improves the user-oriented diversity to some extent.
A Fetching Tale: Covert Communication with the Hardware Prefetcher

Modern processors have employed various methods to increase performance, such as speculative execution, branch prediction, and prefetching. While these enhancements provide excellent performance benefits, many of them also leak confidential information via side channels or can be utilized to communicate surreptitiously via a covert channel. This paper presents a new covert channel within the modern Intel processor, found in the oft-overlooked hardware prefetcher. The discovered covert channel allows two processes scheduled on the same core to communicate without any need to access data that should be mapped to the same cache set. Experimental results on Intel Core i7-6700 show that the channel is able to achieve a 41.6 KBps transmission speed with low error rates. It is also shown that the state-of-the-art side channel and covert channel detection schemes have little impact on this prefetcher-based covert channel.
One Shot Segmentation: Unifying Rigid Detection and Non-Rigid Segmentation Using Elastic Regularization

This paper proposes a novel approach for the non-rigid segmentation of deformable objects in image sequences, which is based on one-shot segmentation that unifies rigid detection and non-rigid segmentation using elastic regularization. The domain of application is the segmentation of a visual object that temporally undergoes a rigid transformation (e.g., affine transformation) and a non-rigid transformation (i.e., contour deformation). The majority of segmentation approaches to solve this problem are generally based on two steps that run in sequence: a rigid detection, followed by a non-rigid segmentation. In this paper, we propose a new approach, where both the rigid and non-rigid segmentation are performed in a single shot using a sparse low-dimensional manifold that represents the visual object deformations. Given the multi-modality of these deformations, the manifold partitions the training data into several patches, where each patch provides a segmentation proposal during the inference process. These multiple segmentation proposals are merged using the classification results produced by deep belief networks (DBN) that compute the confidence on each segmentation proposal. Thus, an ensemble of DBN classifiers is used for estimating the final segmentation. Compared to current methods proposed in the field, our proposed approach is advantageous in four aspects: (i) it is a unified framework to produce rigid and non-rigid segmentations; (ii) it uses an ensemble classification process, which can help the segmentation robustness; (iii) it provides a significant reduction in terms of the number of dimensions of the rigid and non-rigid segmentations search spaces, compared to current approaches that divide these two problems; and (iv) this lower dimensionality of the search space can also reduce the need for large annotated training sets to be used for estimating the DBN models. Experiments on the problem of left ventricle endocardial segmentation from ultrasound images, and lip segmentation from frontal facial images using the extended Cohn-Kanade (CK+) database, demonstrate the potential of the methodology through qualitative and quantitative evaluations, and the ability to reduce the search and training complexities without a significant impact on the segmentation accuracy.
Polyphony

This paper introduces a new Graphical User Interface (GUI) and Interaction framework based on the Entity-Component-System model (ECS). In this model, interactive elements (Entities) are characterized only by their data (Components). Behaviors are managed by continuously running processes (Systems) which select entities by the Components they possess. This model facilitates the handling of behaviors and promotes their reuse. It provides developers with a simple yet powerful composition pattern to build new interactive elements with Components. It materializes interaction devices as Entities and interaction techniques as a sequence of Systems operating on them. We present Polyphony, an experimental toolkit implementing this approach, and discuss our interpretation of the ECS model in the context of GUIs programming.
ALSR: An adaptive label screening and relearning approach for interval-oriented anomaly detection

Anomaly detection using KPIs (Key Performance Indicators) is a key part of AIOps (Artificial Intelligence for IT Operations). Recent anomaly detection approaches have adopted Machine Learning to detect anomalies on the perspective of individual time points more than events. These approaches do not effectively utilize the labels of continuous anomaly intervals, nor do they pay attention to the differences among anomaly points. The detection performances are therefore not precise enough to be applied in practice, and the differences in length of anomaly intervals also cause loss of performance. In this paper, we propose an anomaly detection approach named ALSR, which uses a label screening model and a relearning model to analyze and utilize the continuous anomaly intervals of KPIs in finer granularity. The label screening model takes advantage of the continuity of anomaly intervals to remove some unnecessary data from the training set, making it more suitable for interval-oriented anomaly detection. The relearning model based on random forest reclassifies the true/false positive points within domain of detected anomalies, thus effectively reduces the number of false positive points. ALSR uses several features extracted by sliding windows, and the feature set is proved to better describe the characteristics of KPI time series. Finally, we conduct comprehensive experiments on 25 KPIs. The total F-score of ALSR is 0.965, which outperforms state-of-the-art anomaly detection approaches.
Predictive lithologic mapping of South Korea from geochemical data using decision trees

Two machine learning algorithms, C4.5 and random forest, collectively known as decision trees were utilized to directly establish the relationship between geochemical maps of South Korea and its geology. Using a large database containing geochemical and lithologic properties, inconsistencies in the a priori lithologic information were fixed using confusion matrix analysis and F-measure comparison via iterative C4.5 implementation. This corrective method resulted in eighteen rock classes but the succeeding C4.5 and random forest application only focused on classifying the 10 most common rock units. Geologic age was included as an attribute at such stage. Results were assessed using accuracy, precision, recall, kappa statistics, and F-measure. Average concentration of major oxides using records of correctly classified rock units were evaluated through Z-score normalization. C4.5 classification successfully predicted the spatial distribution of key lithologic units at 87% whereas random forest classification was at 96%. For both decision tree models, average standardized concentration of major oxides in each lithology adhere to perceived geologic knowledge, thereby proving the validity of the results. Rock age is determined as the most important predictor whereas major elements Al2O3, Na2O, and MgO together with trace elements Cr, Ni, and Cu are the strongest numeric predictors. Misinterpreted data points are mainly due to interpolation errors at or near map polygon boundaries, especially where map polygons are less than 50 km2, and/or natural and anthropogenic contamination. Despite the misclassifications, decision trees are proven to be effective techniques in classifying lithologic units, and thus can reproduce a reliable geologic map from geochemical data of South Korea.
A survey on resource scheduling for data transfers in inter-datacenter WANs

Today, datacenter providers spend a lot of money on inter-datacenter WANs to transmit traffic flows between geographically distributed datacenter sites. It is very important for datacenter providers to design resource scheduling schemes to schedule inter-datacenter networking resources to satisfy data transfer requests and achieve maximum performance or economic benefits. In this article, we conduct a review study on research efforts being conducted on the inter-datacenter networking resource scheduling problem. Our survey study is presented in three parts. The first part focuses on formulating the problem theoretically, including the definition and features of a data transfer, network models of inter-DC WANs, scheduling objectives, and scheduling dimensions. We also summarize a conceptual implementation architecture of scheduling systems. Second, we classify existing schemes according to their objectives and scheduling dimensions and then explain how they formulate and solve their own problems. In the third part, we examine practical challenges in developing scheduling systems and also point out some future directions. Since we do not see any survey focusing on the scheduling problem of inter-DC WANs, we believe this article can provide some help for research developments in this important field by organizing logically the recent research efforts from industry and academia.
An Efficient CGM-Based Parallel Algorithm for Solving the Optimal Binary Search Tree Problem Through One-to-All Shortest Paths in a Dynamic Graph

The coarse-grained multicomputer parallel model (CGM for short) has been used for solving several classes of dynamic programming problems. In this paper, we propose a parallel algorithm on the CGM model, with p processors, for solving the optimal binary search tree problem (OBST problem), which is a polyadic non-serial dynamic programming problem. Firstly, we propose a dynamic graph model for solving the OBST problem and show that each instance of this problem corresponds to a one-to-all shortest path problem in this graph. Secondly, we propose a CGM parallel algorithm based on our dynamic graph to solve the OBST problem through one-to-all shortest paths in this graph. It uses our new technique of irregular partitioning of the dynamic graph to try to bring a solution to the well-known contradictory objectives of the minimization of the communication time and the load balancing of the processors in this type of graph. Our solution is based on Knuth’s sequential solution and required $${\mathcal {O}}\left( \dfrac{n^2}{p} \right)$$ time steps per processor and $$\lceil \sqrt{2p} \rceil + k \times \left( {\left\lceil \dfrac{\lceil \sqrt{2p} \rceil }{2} \right\rceil } + 1 \right)$$ communication rounds. Integer k is a parameter used in the partitioning technique of our algorithm. This new CGM algorithm performs better than the previously most efficient solution, which uses regular partitioning of the tasks graph.
Stacked sparse autoencoder and case-based postprocessing method for nucleus detection

Accurate nucleus detection is of great importance in pathological image analyses and diagnoses, which is a critical prerequisite for tasks such as automated grading hepatocellular carcinoma (HCC) nuclei. This paper proposes an automated nucleus detection framework based on a stacked sparse autoencoder (SSAE) and a case-based postprocessing method (CPM) in a coarse-to-fine manner. SSAE, an unsupervised learning model, is first trained using image patches of breast cancer. Then, the transfer learning and sliding window techniques are applied to other cancers’ pathological images (HCC and colon cancer) to extract the high-level features of image patches via the trained SSAE. Subsequently, these high-level features are fed to a logistic regression classifier (LRC) to classify whether each image patch contains a complete nucleus in a coarse detection process. Finally, CPM is developed for refining the coarse detection results which removes false positive nuclei and locates adhesive or overlapped nuclei effectively. SSAE-CPM achieves an average nucleus detection accuracy of 0.8748 on HCC pathological images, which can accurately locate almost all nuclei on the pathological images with serious differentiation. In addition, our proposed detection framework is also evaluated on a public dataset of colon cancer, with a mean F1 score of 0.8355. Experimental results demonstrate the performance advantages of our proposed SSAE-CPM detection framework as compared with related work. While our detection framework is trained on the pathological images of breast cancer, it can be easily and effectively applied to nucleus detection tasks on other cancers without re-training.
A covariance matrix adaptation evolution strategy in reproducing kernel Hilbert space

The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well-defined parameter space in which feature functions are often defined manually. Therefore, the performance of those techniques strongly depends on the quality of the chosen features or the underlying parametric function space. Hence, enabling CMA-ES to optimize on a more complex and general function class has long been desired. In this paper, we consider modeling the input spaces in black-box optimization non-parametrically in reproducing kernel Hilbert spaces (RKHS). This modeling leads to a functional optimisation problem whose domain is a RKHS function space that enables optimisation in a very rich function class. We propose CMA-ES-RKHS, a generalized CMA-ES framework that is able to carry out black-box functional optimisation in RKHS. A search distribution on non-parametric function spaces, represented as a Gaussian process, is adapted by updating both its mean function and covariance operator. Adaptive and sparse representation of the mean function and the covariance operator can be retained for efficient computation in the updates and evaluations of CMA-ES-RKHS by resorting to sparsification. We will also show how to apply our new black-box framework to search for an optimum policy in reinforcement learning in which policies are represented as functions in a RKHS. CMA-ES-RKHS is evaluated on two functional optimization problems and two bench-marking reinforcement learning domains.
Result diversification in image retrieval based on semantic distance

User requirements for result diversification in image retrieval have been increasing with the explosion of image resources. Result diversification requires that image retrieval systems are made capable of handling semantic gaps between image visual features and semantic concepts, and providing both relevant and diversified image results. Context information, such as captions, descriptions, and tags, provides opportunities for image retrieval systems to improve their result diversification. This study explores a mechanism for improving result diversification using the semantic distance of image social tags. We design and compare nine strategies that combine three different semantic distance algorithms (WordNet, Google Distance, and Explicit Semantic Analysis) with three re-ranking algorithms (MMR, xQuAD, and Score Difference) for result diversification. In order to better prove the effectiveness of our strategy of applying semantic information, we also make use of visual features of images for result diversification experiment and make comparison. Our data for experimentation were extracted from 269,648 images selected from the NUS-WIDE datasets with manually annotated subtopics. Experimental results affirm the effectiveness of applying semantic information for improving result diversification in image retrieval. In particular, WordNet-based semantic distance combined with the Score Difference (WordNet-DivScore) outperformed other strategies in diversifying image retrieval results.
A Comparative Study on Question-Worthy Sentence Selection Strategies for Educational Question Generation

Automatic question generation, which aims at converting sentences in an article to high-quality questions, is an important task for educational practices. Recent work mainly focuses on designing effective generation architectures based on deep neural networks. However, the first and possibly the foremost step of automatic question generation has largely been ignored, i.e., identifying sentences carrying important information or knowledge that is worth asking questions about. In this work, we (i) propose a total of 9 strategies, which are grounded on heuristic question-asking assumptions, to determine sentences that are question-worthy, and (ii) compare their performance on 4 datasets by using the identified sentences as input for a well-trained question generator. Through extensive experiments, we show that (i) LexRank, a stochastic graph-based method for selecting important sentences from articles, gives robust performance across all datasets, (ii) questions collected in educational settings feature a more diverse set of source sentences than those obtained in non-educational settings, and (iii) more research efforts are needed to further improve the design of educational question generation architectures.
Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes

The transfer of a neural network (CNN) trained to recognize objects to the task of scene classification is considered. A Bag-of-Semantics (BoS) representation is first induced, by feeding scene image patches to the object CNN, and representing the scene image by the ensuing bag of posterior class probability vectors (semantic posteriors). The encoding of the BoS with a Fisher vector (FV) is then studied. A link is established between the FVof any probabilistic model and the 0-function of the expectation-maximization (EM) algorithm used to estimate its parameters by maximum likelihood. This enables 1) immediate derivation of FVs for any model for which an EM algorithm exists, and 2) leveraging efficient implementations from the EM literature for the computation of FVs. It is then shown that standard FVs, such as those derived from Gaussian or even Dirichlet mixtures, are unsuccessful for the transfer of semantic posteriors, due to the highly non-linear nature of the probability simplex. The analysis of these FVs shows that significant benefits can ensue by 1) designing FVs in the natural parameter space of the multinomial distribution, and 2) adopting sophisticated probabilistic models of semantic feature covariance. The combination of these two insights leads to the encoding of the BoS in the natural parameter space of the multinomial, using a vector of Fisher scores derived from a mixture of factor analyzers (MFA). A network implementation of the MFA Fisher Score (MFA-FS), denoted as the MFAFSNet, is finally proposed to enable end-to-end training. Experiments with various object CNNs and datasets show that the approach has state-of-the-art transfer performance. Somewhat surprisingly, the scene classification results are superior to those of a CNN explicitly trained for scene classification, using a large scene dataset (Places). This suggests that holistic analysis is insufficient for scene classification. The modeling of local object semantics appears to be at least equally important. The two approaches are also shown to be strongly complementary, leading to very large scene classification gains when combined, and outperforming all previous scene classification approaches by a sizable margin.
English teaching model and cultivation of students’ speculative ability based on internet of things and typical case analysis

The development of English teaching mode and students’ speculative ability training mode is slow. In order to improve the quality of English teaching and improve students’ speculative ability, the English teaching mode and the cultivation of students’ speculative ability based on Internet of Things (IoT) are proposed and typical cases are analyzed. The computer data simulation model is used to construct an English teaching innovation model. The model proposes an optimization scheme from the algorithm flow, and uses the data transformation technology to improve the real-time teaching and strengthen the efficiency of teaching management. In order to further improve the students’ speculative ability in English teaching, the particle swarm optimization algorithm is added to the model to realize the sequence optimization of the data, and the authenticity and efficiency of the particle swarm optimization algorithm in the English teaching model are verified. The test results show that the particle swarm algorithm can self-improve and repair functions, continuously improve the accuracy of the English teaching model, and optimize the teaching method. The research shows that the particle swarm optimization algorithm can improve the quality of English teaching and optimize the architecture, which can provide reference for the future integration of English teaching and computer technology.
Towards a Design Framework for Trust in Digital Civics

There is growing interest in Digital Civics regarding how trust can inform the design of technology in the civic space. Previous work has identified several key elements of trust in digital civics to consider in design such as the connection between trust and distance, trust as sociotechnical, trust as process and trust design affordances. However, these elements have not yet been applied to design. We address this gap by engaging these elements in ongoing design research within Atlanta's Department of Immigrant Affairs. Our research inquiry with the department centered on developing a design intervention to improve the department's community engagement work. We developed a design intervention with the department through co-design which was then assessed and advanced using the elements of trust. By reflecting on this design process informed through the elements, we unify the elements of trust in digital civics towards a design framework.