Reviewerly/exHarmony huggingface dataset:
Face expression recognition using LDN and Dominant Gradient Local Ternary Pattern descriptors

Facial Expression Recognition (FER) is an attractive and demanding problem in human computer interaction. Facial expressions are one of the most dominant and usual means for a human to communicate their feelings and intentions. This paper proposes the Enhanced Modified Decision Based Unsymmetric Trimmed Median Filter (EMDBUTMF) method which removes the noisy pixels from the face image. Also, this paper proposes the Local Directional Number (LDN) pattern, Dominant Gradient Local Ternary Pattern (DGLTP) descriptor for feature extraction and Support Vector Machine (SVM) classifier for classification. The histogram features are extracted from face images using the LDN and DGLTP descriptors. The experimental result shows that the proposed method gives an improved performance of 88% accuracy on two publicly available facial expression datasets JAFFE and CK.
A Super Descriptor Tensor Decomposition for Dynamic Scene Recognition

This paper presents a new approach for dynamic scene recognition based on a super descriptor tensor decomposition. Recently, local feature extraction based on dense trajectories has been used for modeling motion. However, dense trajectories usually include a large number of unnecessary trajectories, which increase noise, add complexity, and limit the recognition accuracy. Another problem is that the traditional bag-of-words techniques encode and concatenate the local features extracted from multiple descriptors to form a single large vector for classification. This concatenation not only destroys the spatio-temporal structure among the features but also yields high dimensionality. To address these problems, first, we propose to refine the dense trajectories by selecting only salient trajectories in a region of interest containing motion. Visual descriptors consisting of oriented gradient and motion boundary histograms are then computed along the refined dense trajectories. In case of camera motion, a short-window video stabilization is integrated to compensate for global motion. Second, the extracted features from multiple descriptors are encoded using a super descriptor tensor model. To this end, the TUCKER-3 tensor decomposition is employed to obtain a compact set of salient features, followed by feature selection via Fisher ranking. Experiments are conducted using two benchmark dynamic scene recognition datasets: Maryland “in-the-wild” and YUPPEN dynamic scenes. Experimental results show that the proposed approach outperforms several existing methods in terms of recognition accuracy and achieves a performance comparable with the state-of-the-art deep learning methods. The proposed approach achieves classification rates of 89.2% for Maryland and 98.1% for YUPPEN datasets.
Encoding Shaky Videos by Integrating Efficient Video Stabilization

This paper presents a novel video coding method by integrating video stabilization for shaky videos. By reusing the stabilized motion of feature points and geometric transformations, a better predictor can be established to replace the original motion vectors in the motion estimation stage of video coding. Then, these motion vectors are optimized based on statistics of the residuals between the stabilization predictor and the standard one to improve the efficiency of motion search. As a result, our method brings much less computational cost to motion estimation than encoding the stabilized frames separately (e.g., 24% for the enhanced predictive zonal search algorithm, 17% for the unsymmetrical-cross multi-hexagon-grid search algorithm), while the Bjontegaard distortion (BD) bit rate and BDpeak signal-to-noise ratio still have the comparable performance with multiple quantization parameters. Specially, the implementation of our integrative system based on ×264 is very fast and of low latency, by which full HD videos can be simultaneously stabilized and encoded with more than 30 frames per second in the fastest mode, even on the mobile platform. The experiments on a variety of shaky videos demonstrate the potential of our method in terms of effectiveness and efficiency.
A unified latent variable model for contrastive opinion mining

There are large and growing textual corpora in which people express contrastive opinions about the same topic. This has led to an increasing number of studies about contrastive opinion mining. However, there are several notable issues with the existing studies. They mostly focus on mining contrastive opinions from multiple data collections, which need to be separated into their respective collections beforehand. In addition, existing models are opaque in terms of the relationship between topics that are extracted and the sentences in the corpus which express the topics; this opacity does not help us understand the opinions expressed in the corpus. Finally, contrastive opinion is mostly analysed qualitatively rather than quantitatively. This paper addresses these matters and proposes a novel unified latent variable model (contraLDA), which: mines contrastive opinions from both single and multiple data collections, extracts the sentences that project the contrastive opinion, and measures the strength of opinion contrastiveness towards the extracted topics. Experimental results show the effectiveness of our model in mining contrasted opinions, which outperformed our baselines in extracting coherent and informative sentiment-bearing topics. We further show the accuracy of our model in classifying topics and sentiments of textual data, and we compared our results to five strong baselines.
Cost Optimal Data Center Servers: A Voltage Scaling Approach

Data centers have experienced dramatic growth in recent years in order to meet the ever-increasing demand for computing. As a result, minimizing the electrical cost to operate data centers has become a crucial issue. In this paper, we observe that electricity prices change over time, and that we can take advantage of periods with low prices by scaling up processor speeds to perform more work, while scaling down speeds during high price periods to reduce cost. We apply this observation to several settings. First, we consider an offline setting which assumes future electricity prices are given, and propose an efficient algorithm for optimally scaling a processor's speed in order to minimize the total electrical cost for completing a task by a deadline. We then consider a more realistic stochastic setting in which future prices are not known, but vary according to a Markov model. We present another efficient algorithm for minimizing the expected cost to meet a deadline. We performed a number of experiments using real electricity price traces to test the performance of our algorithms. We show that our stochastic algorithm is light-weight and relies only on easily obtainable price data, but that it achieves excellent performance, with only a 1 percent cost difference on average from the optimal offline algorithm. In addition, the stochastic algorithm significantly reduced costs compared to several candidate algorithms.
Quantum computing with classical bits

A bit-quantum map relates probabilistic information for Ising spins or classical bits to quantum spins or qubits. Quantum systems are subsystems of classical statistical systems. The Ising spins can represent macroscopic two-level observables, and the quantum subsystem employs suitable expectation values and correlations. We discuss static memory materials based on Ising spins for which boundary information can be transported through the bulk in a generalized equilibrium state. They can realize quantum operations as the Hadamard or CNOT-gate for the quantum subsystem. Classical probabilistic systems can account for the entanglement of quantum spins. An arbitrary unitary evolution for an arbitrary number of quantum spins can be described by static memory materials for an infinite number of Ising spins which may, in turn, correspond to continuous variables or fields. We discuss discrete subsets of unitary operations realized by a finite number of Ising spins. They may be useful for new computational structures. We suggest that features of quantum computation or more general probabilistic computation may be realized by neural networks, neuromorphic computing or perhaps even the brain. This does neither require small isolated entities nor very low temperature. In these systems the processing of probabilistic information can be more general than for static memory materials. We propose a general formalism for probabilistic computing for which deterministic computing and quantum computing are special limiting cases.
Genetic programming with semantic equivalence classes

In this paper, we introduce the concept of semantics-based equivalence classes for symbolic regression problems in genetic programming. The idea is implemented by means of two different genetic programming systems, in which two different definitions of equivalence are used. In both systems, whenever a solution in an equivalence class is found, it is possible to generate any other solution in that equivalence class analytically. As such, these two systems allow us to shift the objective of genetic programming: instead of finding a globally optimal solution, the objective is now to find any solution that belongs to the same equivalence class as a global optimum. Further, we propose improvements to these genetic programming systems in which, once a solution that belongs to a particular equivalence class is generated, no other solution in that class is accepted in the population during the evolution anymore. We call these improved versions filtered systems. Experimental results obtained via seven complex real-life test problems show that using equivalence classes is a promising idea and that filters are generally helpful for improving the systems' performance. Furthermore, the proposed methods produce individuals with a much smaller size with respect to geometric semantic genetic programming. Finally, we show that filters are also useful to improve the performance of a state-of-the-art method, not explicitly based on semantic equivalence classes, like linear scaling.
Local-global nested graph kernels using nested complexity traces

In this paper, we propose two novel local-global nested graph kernels, namely the nested aligned kernel and the nested reproducing kernel, drawing on depth-based complexity traces. Both of the nested kernels gauge the nested depth complexity trace through a family of K-layer expansion subgraphs rooted at the centroid vertex, i.e., the vertex with minimum shortest path length variance to the remaining vertices. Specifically, for a pair of graphs, we commence by computing the centroid depth-based complexity traces rooted at the centroid vertices. The first nested kernel is defined by measuring the global alignment kernel, which is based on the dynamic time warping framework, between the complexity traces. Since the required global alignment kernel incorporates the whole spectrum of alignment costs between the complexity traces, this nested kernel can provide rich statistic measures. The second nested kernel, on the other hand, is defined by measuring the basic reproducing kernel between the complexity traces. Since the associated reproducing kernel only requires time complexity O(1), this nested kernel has very low computational complexity. We theoretically show that both of the proposed nested kernels can simultaneously reflect the local and global graph characteristics in terms of the nested complexity traces. Experiments on standard graph datasets abstracted from bioinformatics and computer vision databases demonstrate the effectiveness and efficiency of the proposed graph kernels.
Strategy Generation for Multiunit Real-Time Games via Voting

Real-time strategy (RTS) games are a challenging application for artificial intelligence (AI) methods. This is because they involve simultaneous play and adversarial reasoning that is conducted in real time in large state spaces. Many AI methods for playing RTS games rely on hard-coded strategies designed by human experts. The drawback of using such strategies is that they are often unable to adapt to new scenarios during gameplay. The contribution of this paper is a new approach, called strategy creation via voting (SCV), that uses a voting method to generate a large set of novel strategies from existing expert-based ones. Then, SCV uses an opponent modeling scheme during the game to choose which strategy from the generated pool of possibilities to use. By repeatedly choosing which strategy to use, SCV is able to adapt to different scenarios that might arise during the game. We implemented SCV as a bot for μRTS, a recognized RTS testbed. The results of a detailed empirical study show that SCV outperforms all approaches tested in matches played on large maps and is competitive in matches played on smaller maps.
Cultural distance for service composition in cyber–physical–social systems

Cyber–physical–social systems can be organized as workflows that interconnect the resources in physical, cyber, and social worlds in real time. This integration of these worlds with various resource as services requires service composition approaches that can integrate essential components and share relevant information in these worlds. Although numerous service composition approaches have been proposed, there still exist many challenges in the representation of user preference of service composition in cyber–physical–social systems. In this paper, a service composition approach based on the cultural distance is proposed to improve the reliability and satisfaction. This approach employs the cultural distance to measure the user preference quantitatively in a simple mathematical form. The user preference degree and the user preference vector are defined to select the service from a global view and an accurate point separately. Then, a 0-1 mixed-integer programming algorithm is used to identify the most suitable services for composition. The experimental results based on two real-world datasets show that the proposed approach for representing the user preference quantitatively is more simple and effective than other approaches. In a word, the proposed approach yields satisfactory performance of service composition in CPSS with regard to the user preference and efficiency.
Application of multivariate statistical analyses to Itrax core scanner data for the identification of deep-marine sedimentary facies: A case study in the Galician Continental Margin

The validity and usefulness of multivariate statistical tools for the facies characterization in deep-marine environments have been applied on the geochemical, sedimentological and magnetic data from a piston core extracted from the Transitional Zone in the Galician Continental Margin. The combination of geochemical profiles of Fe, Mn, Ti, Ba and Ca and magnetic susceptibility (MS) obtained using the ItraxTM Core Scanner at the University of Vigo, together with the grain-size, grey level and R (red) G (green) B (blue) colour analyses have allowed characterizing and classifying the sediments of the core PC13-3 using SPSS package v. 23. Cluster Analysis (CA) displays, in the first level of the hierarchy, two major groups that correspond with clay-silt and sand facies. In a second level, it is possible to observe six subfacies that match de visu preliminary classification and allowed us to complete and improve the characterization and the facies limits in the whole core. Discriminant Analysis (DA) confirmed the validity of the cluster analyses and enhanced the results of the classification. The Principal Component Analysis (PCA) shows four principal components: coarse lithogenic fraction (PC1), fine lithogenic fraction (PC2), high density fraction (PC3) and biogenic fraction (PC4). These results are in concordance with the Pearson correlation coefficient and the SEM observations. In general terms, the results confirm the utility of the multivariate statistical methods applied on high resolution geochemical and magnetic data acquired with ItraxTM corer scanner, as a quick and complementary tool in sedimentary facies analysis and description in deep marine environments.
Decal-Lenses: Interactive Lenses on Surfaces for Multivariate Visualization

We present decal-lenses, a new interaction technique that extends the concept of magic lenses to augment and manage multivariate visualizations on arbitrary surfaces. Our object-space lenses follow the surface geometry and allow the user to change the point of view during data exploration while maintaining a spatial reference to positions where one or more lenses were placed. Each lens delimits specific regions of the surface where one or more attributes can be selected or combined. Similar to 2D lenses, the user interacts with our lenses in real-time, switching between different attributes within the lens context. The user can also visualize the surface data representations from the point of view of each lens by using local cameras. To place lenses on surfaces of intricate geometry, such as the human brain, we introduce the concept of support surfaces for designing interaction techniques. Support surfaces provide a way to place and interact with the lenses while avoiding holes and occluded regions during data exploration. We further extend decal-lenses to arbitrary regions using brushing and lassoing operations. We discuss the applicability of our technique and present several examples where our lenses can be useful to create a customized exploration of multivariate data on surfaces.
Movie fill in the blank by joint learning from video and text with adaptive temporal attention

Video understanding is a challenging problem and it attracts a lot of research attention. Lately, a new task called movie fill in the blank (MovieFIB) is proposed. In this task, given a movie clip and a description which has one blank, we need to predict the word in the blank accurately. Previous studies make many contributions to tackling this problem. However, some of them do not utilize the relationship between words and video frames, and some others treat visual information as essential elements for blank word prediction, which fail to distinguish the effects of texts before and after the blank. To overcome the limitations, in this paper we propose to use adaptive temporal attention and fuse text information with attention. We first extract video and word features. Then, adaptive temporal attention is used to update original description. For the updated description, we extract its text information. Attention mechanism is applied to fuse text information. Finally, we use adaptive temporal attention to predict the blank word. Extensive experiments demonstrate that our model achieves satisfactory performance.
Real-Time Solution of Time-Varying Yau Filtering Problems via Direct Method and Gaussian Approximation

Direct method for Yau filtering system has been studied since 1990s and all these work are limited in time-invariant systems. In this work, we extend the direct method so that it is applicable to time-varying cases. We need less assumptions compared with our previous work. The novelty of this work is that we propose several transformations on the forward Kolmogorov equation so that it can be solved by means of solving some ordinary differential equations if the initial distribution is Gaussian. The corresponding results for any non-Gaussian initial distributions can be obtained via Gaussian approximation. It can be seen that our new scheme direct method can treat nearly most general Yau filtering problems under natural assumptions. Our algorithm has been compared with the extended Kalman filter, multilevel particle filter, and ensemble Kalman filter by numerical examples and the simulation results show the efficiency of our method.
Hardware Optimizations and Analysis for the WG-16 Cipher with Tower Field Arithmetic

This paper explores tower field constructions and hardware optimizations for the WG-16 stream cipher. The constructions <inline-formula><tex-math notation="LaTeX">${\mathbb {F}}_{(((2^2)^2)^2)^2}$</tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\mathbb {F}}_{(2^{4})^4}$</tex-math></inline-formula> were chosen because their small subfields enable high speed arithmetic implementations and their regularity provides flexibility in pipeline granularity. A design methodology is presented where the tower field constructions guide how to proceed systematically from algebraic optimizations, through initial hardware implementation, selection of submodules, pipelining, and finally detailed hardware optimizations to increase clock speed. The highest frequency WG(16, 32) keystream generator, obtained for the 65 nm ASIC library, reached a clock speed of 2.44 GHz at 26.3 kGE, and the smallest area keystream generator achieved a clock speed of 0.33 GHz at 9.9 kGE. The highest frequency FPGA implementation on a Xilinx Spartan 6 reached a clock speed of 256 MHz using 631 slices. In addition, the paper demonstrates that LFSR feedback polynomials can be optimized to increase security without hurting performance, and retiming optimizations can be used to increase clock speed without increasing area.
SARVE-2: Exploiting Social Venue Recommendation in the Context of Smart Conferences

Globally, the superfluity of scholarly research conferences in varying disciplines has introduced the issue of scholarly big data and information overload related to both research papers and conference proceedings/sessions. This evident scholarly expansion in different disciplines has increased the collaborative importance of conferences. Consequently, the problem regarding attendees selecting the right conference session(s) to attend in academic conferences requires further and urgent attention. Using a smart conference scenario, this paper aims to address the problem above by proposing an improved venue recommender algorithm called Socially-Aware Recommendation of Venues and Environments-2 (SARVE-2). Using a closeness centrality approach, SARVE-2 initially employs Breadth First Search (BFS) and Depth First Search (DFS) strategies to search for relevant presenters for a target attendee. Then, the tie strength of the (searched) presenter and target attendee is computed to generate reliable social (conference session) recommendations for the target attendee. Through the utilization of a relevant (real-world) dataset, our benchmark experiments reveal that, in comparison with other contemporary methods, SARVE-2 exhibits better performance in terms of effective social recommendation search, as well as social recommendation quality, coverage and accuracy.