Reviewerly/exHarmony huggingface dataset:
Retrieving color patterns on surface meshes using edgeLBP descriptors

Texture image retrieval and classification tasks are conducted widely in computer vision but few studies have considered the same problem for decorations and textures applied to surfaces. Thus, in order to address this problem, we propose an algorithm for retrieving and classifying color patterns over surface meshes. In this study, we explored the capability of edgeLBP descriptors for retrieving and classifying color patterns over surface tessellations. Our experimental results demonstrated that these descriptors performed well at pattern retrieval, including for patterns on degraded and corrupted archeological fragments. We compared the performance of the edgeLBP descriptors with a recent benchmark and conducted an extended analysis of the robustness of this technique to mesh remeshing and surface decimation.
Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers

In this paper, we apply a Markov decision process to find the optimal asynchronous dynamic policy of an energy-efficient data center with two server groups. Servers in Group 1 always work, while servers in Group 2 may either work or sleep, and a fast setup process occurs when the server’s states are changed from sleep to work. The servers in Group 1 are faster and cheaper than those of Group 2 so that Group 1 has a higher service priority. Putting each server in Group 2 to sleep can reduce system costs and energy consumption, but it must bear setup costs and transfer costs. For such a data center, an asynchronous dynamic policy is designed as two sub-policies: The setup policy and the sleep policy, both of which determine the switch rule between the work and sleep states for each server in Group 2. To find the optimal asynchronous dynamic policy, we apply the sensitivity-based optimization to establish a block-structured policy-based Markov process and use a block-structured policy-based Poisson equation to compute the unique solution of the performance potential by means of the RG-factorization. Based on this, we can characterize the monotonicity and optimality of the long-run average profit of the data center with respect to the asynchronous dynamic policy under different service prices. Furthermore, we prove that a bang–bang control is always optimal for this optimization problem. We hope that the methodology and results developed in this paper can shed light on the study of more general energy-efficient data centers.
Optimization of Cloud Datacenter Using Heuristic Strategic Approach

Task scheduling is extremely challenging as it is very difficult to utilize resources in the best possible manner with low response time and high throughput. Task scheduling can be designed on the basis of different criteria under several rules and regulations. This is simply nothing but an agreement between cloud users and cloud providers. Task scheduling has attracted a lot of attention. It is very challenging due to the heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. The scheduling algorithm has been emphasized not only on appropriate resource utilization but also on efficient resource utilization. The proposed algorithm performance is estimated based on load balancing of tasks over the nodes and makespan time. Scheduling algorithm is used to enhance the performance of the system by maximizing the CPU utilization, reducing the turnaround time, and maximizing throughput. Tasks are statically scheduled based on which different available resources are allocated at compile time or dynamically. The prime objective for scheduling of task approach in the cloud is to minimize the task completion time, task waiting time and makespan. And also to optimize the utilization of resources.
Tiler: Software for Human-Guided Data Exploration

Understanding relations in datasets is important for the successful application of data mining and machine learning methods. This paper describes tiler, a software tool for interactive visual explorative data analysis realising the interactive Human-Guided Data Exploration framework. tiler allows a user to formulate different hypotheses concerning the relations in a dataset. Data samples corresponding to these hypotheses are then compared visually, allowing the user to gain insight into relations in the dataset. The exploration process is iterative and the user gradually builds up his or her understanding of the data. Code related to this paper is available at: https://github.com/aheneliu/tiler .
Food-availability SMS system for U.S. homeless communities

We present the design and preliminary evaluation of a mobile text-based system and that provides homeless people in the United States with information about the availability of free meals. The system, piloted in Ann Arbor, Michigan, provides information about when and where free meals are offered based on zipcode as entered by users. It has features to address issues around information access, incompleteness, and irrelevance. We found that SMS text messages are preferred by our homeless users even when they are non-literate and own smartphones, and that formal systems are useful even in contexts where word-of-mouth predominates.
VQ/GMM-Based Speaker Identification with Emphasis on Language Dependency

The biometric recognition of human through the speech signal is known as automatic speaker recognition (ASR) or voice biometric recognition. Plenty of acoustic features have been used in ASR so far, but among them Mel-frequency cepstral coefficients (MFCCs) and Gammatone frequency cepstral coefficients (GFCCs) are popularly used. To make ASR language and channel independent (if training and testing microphones and languages are not same), i-Vector feature and unwanted variability compensation techniques like linear discriminative analysis (LDA) or probabilistic LDA (PLDA), within-class covariance normalization (WCCN) are extensively used. At the very present days, the techniques for modeling/classification that are used are Gaussian mixture models (GMMs), vector quantization (VQ), hidden Markov model (HMM), deep neural network (DNN), and artificial neural network (ANN). Sometimes, model-domain normalization techniques are used to compensate unwanted variability due to language and channel mismatch in training and testing data. In the present paper, we have used maximum log-likelihood (MLL) to evaluate the performance of ASR on the databases(DBs), namely ELSDSR, Hyke-2011, and IITG-MV SR Phase-I & II, based on MFCCs and VQ/GMM where the scoring technique MLL is used for the recognition of speakers. The experiment is carried out to examine the language dependency and environmental mismatch between training and testing data.
Creative tagline generation framework for product advertisement

The ubiquitous nature of the Internet and its applicability as an inexpensive advertising media has resulted in Web and mobile platforms to be on target for product advertising. Existing advertising approaches on different social media platforms include sponsored search, e-mail advertising, banner ads, etc., to attract customers. The current systems for advertising on these platforms require the advertisers to manually come up with a catchy tagline, which is time-consuming and a challenging task. To overcome this problem, we propose a novel framework to automatically generate novel and catchy taglines tailored for advertisers' products. The framework consists of a domain-specific knowledge graph, a novel modified long short-term memory (LSTM) (we call it CopyLSTM), and a neural paraphrase model. We also curate and build a database of fashion products with 100K input product features and 300K sentences (three sentences per product data). This dataset is used to populate the knowledge graph and learn the modified LSTM to generate taglines. The proposed framework is evaluated on different metrics for sentence quality and compared with the state-of-art methods for tagline generation. Subjective evaluation was performed to understand the novelty and creativity of the generated tagline.
I (Don't) See What You Typed There! Shoulder-surfing Resistant Password Entry on Gamepads

Using gamepad-driven devices like games consoles is an activity frequently shared with others. Thus, shoulder-surfing is a serious threat. To address this threat, we present the first investigation of shoulder-surfing resistant text password entry on gamepads by (1) identifying the requirements of this context; (2) assessing whether shoulder-surfing resistant authentication schemes proposed in non-gamepad contexts can be viably adapted to meet these requirements; (3) proposing "Colorwheels", a novel shoulder-surfing resistant authentication scheme specifically geared towards this context; (4) using two different methodologies proposed in the literature for evaluating shoulder-surfing resistance to compare "Colorwheels", on-screen keyboards (the de facto standard in this context), and an existing shoulder-surfing resistant scheme which we identified during our assessment and adapted for the gamepad context; (5) evaluating all three schemes regarding their usability. Having applied different methodologies to measure shoulder-surfing resistance, we discuss their strengths and pitfalls and derive recommendations for future research.
Spatial distribution of sulfur isotope ratios associated with the Eagle Ni-Cu sulfide deposit in Upper Peninsula, Michigan: Implications on country-rock contamination in a dynamic magma conduit

The Eagle Ni-Cu magmatic sulfide deposit in the Upper Peninsula of Michigan is hosted in mafic-ultramafic rocks associated with the 1.1 Ga Midcontinent Rift event. The deposit consists of massive, semi-massive and disseminated sulfide textural zones located in two closely spaced funnel-shaped intrusive bodies, called the Eagle and Eagle East intrusions. The intrusions are surrounded by metamorphosed supracrustal Paleoproterozoic rocks of the Michigamme Formation and at depth by a Neoarchean migmatitic banded gneiss. δ34S values were obtained from sulfide minerals separated by micro-drilling from different parts of the intrusions and the surrounding rocks. The δ34S values from the intrusions cluster within a small range between 1 and 3‰ relative to V-CDT regardless of the mode of occurrence, textural type, mineral type and location of the sample. However, disseminated sulfide minerals in the metasedimentary country rocks of the Michigamme Formation show a substantially greater range in δ34S values between 4 and 35‰ with a median of 7.7‰ while disseminated sulfides in the basement granite-gneiss show values ranging from −9 to 13‰ with a median of 2.5‰. The narrow range of δ34S values within the intrusions despite wide variabilities in the surrounding country rocks can be explained by the incorporation of crustal sulfur by a mantle-derived magma, followed by a homogeneous mixing of the immiscible sulfide liquid, with a silicate liquid to sulfide liquid ratio (R-factor) of 200 or more. The assimilation of crustal rocks and the mixing of the resultant immiscible sulfide liquid must have been facilitated by the rapid movement of magma along a narrow conduit system.
Accelerating white blood cells image segmentation using GPUs

Summary White Blood Cell (WBC) segmentation is one of the important topics in the medical image processing field. Many researchers proposed several clustering approaches to segment WBC from blood smear microscopic images. However, a fast and robust segmentation of WBCs is still a challenging task. In this work, we propose parallel algorithms that utilize the parallelism capabilities of the Graphics Processing Units (GPUs) to accelerate the segmentation of WBC from microscopic images. In this research, we implement the main image segmentation clustering algorithms using one thread that we run on a single CPU (sequential implementation) and using multiple threads that we run on both the CPU and the GPU (hybrid CPU‐GPU). We focus our work on the most common four segmentation algorithms: Standard K‐means (SKM), Adaptive K‐means (AKM), Fuzzy C‐means (FCM), and Fuzzy Possibilistic C‐means (FPCM). We implement these algorithms and the pre‐processing steps for WBC image segmentation in CUDA programming to take the advantages of the large number of cores in GPUs. In this work, our hybrid implementation accelerated the four studied sequential algorithms by 4X, 3.8X, 3.4X, and 3.4X, respectively, without affecting WBC segmentation quality.
Metaheuristics for Frequent and High-Utility Itemset Mining

Metaheuristics are often used to solve combinatorial problems. They can be viewed as general purpose problem-solving approaches based on stochastic methods, which explore very large search spaces to find near-optimal solutions in a reasonable time. Some metaheuristics are inspired by biological and physical phenomenons. During the last two decades, two population-based methods named evolutionary algorithms and swarm intelligence have shown high efficiency compared to many other metaheuristics. Frequent Itemset Mining (FIM) and High Utility Itemset Mining (HUIM) are the process of extracting useful frequent and high utility itemsets from a given transactional database. Solving FIM and HUIM problems can be very time consuming, especially when dealing with large-scale data. To deal with this issue, different metaheuristic-based methods were developed. In this chapter, we study the application of metaheuristics to FIM and HUIM. Several metaheuristics have been presented, based on evolutionary or swarm intelligence algorithms, such as genetic algorithms, particle swarm optimization, ant colony optimization and bee swarm optimization.
A Novel Differential Fault Analysis on the Key Schedule of SIMON Family

As a family of lightweight block ciphers, SIMON has attracted lots of research attention since its publication in 2013. Recent works show that SIMON is vulnerable to differential fault analysis (DFA) and existing DFAs on SIMON assume the location of induced faults are on the cipher states. In this paper, a novel DFA on SIMON is proposed where the key schedule is selected as the location of induced faults. Firstly, we assume a random one-bit fault is induced in the fourth round key KT−4 to the last. Then, by utilizing the key schedule propagation properties of SIMON, we determine the exact position of induced fault and demonstrate that the proposed DFA can retrieve 4 bits of the last round key KT−1 on average using one-bit fault. Till now this is the largest number of bits that can be cracked as compared to DFAs based on random bit fault model. Furthermore, by reusing the induced fault, we prove that 2 bits of the penultimate round key KT−2 could be retrieved. To the best of our knowledge, the proposed attack is the first one which extracts a key from SIMON based upon DFA on the key schedule. Finally, correctness and validity of our proposed attack is verified through detailed simulation and analysis.
Joint Gaze Correction and Face Beautification for Conference Video Using Dual Sparsity Prior

Gaze mismatch is a common problem in video conferencing, where the viewpoint captured by a camera (usually located above or below a display monitor) is not aligned with the gaze direction of the human subject, who typically looks at his counterpart in the center of the screen. This means that the two parties cannot converse eye-to-eye, hampering the visual communication experience. A recent popular approach to the gaze mismatch problem is to synthesize a gaze-corrected face image as viewed from the screen center via depth-image-based rendering (DIBR), assuming texture and depth maps are both available at the camera-captured viewpoint. Due to self-occlusion, however, there will be missing pixels in the DIBR-synthesized view image that require satisfactory filling. In this paper, we propose to jointly solve the hole-filling problem and the face beautification problem (subtle modifications of facial components and contour to enhance attractiveness of the rendered face) using dual sparsity prior. Specifically, prior to the start of a video conference session, we first train two dictionaries separately offline using two large data sets: one with general face images, and the other with “beautiful” human faces, which means faces with high beauty scores. During the actual conference session, we solve the hole-filling and facial components beautification problems simultaneously by seeking two code vectors-one is sparse in the first dictionary and explains the available DIBR-synthesized pixels, the other is sparse in the second dictionary and matches well with the first vector in terms of feature space distance. This ensures an acceptable level of recognizability of the conference subject, while increasing proximity to “beautiful” facial features to improve attractiveness. Experimental results show naturally rendered human faces with noticeably improved attractiveness.
A Cost-Aware Fair Allocation Mechanism for Multi-Resource Servers

We propose a cost-aware measure to establish fair-sharing of multi-resource/heterogeneous servers among multiple users. Each server attributes a virtual cost to the total allocated resources to each user, and strives to allocate resources to users attaining the minimum virtual cost. With the new mechanism, called per-server virtual cost fairness (PS-VCF), users do not envy the cost-allotments of each other. We develop a distributed implementation for the proposed mechanism which is also adapted to apply resource-throttling on a per-server basis. We employ numerical experiments using real-world traces to show the effectiveness of PS-VCF in reducing servers' operational costs.
Decoherence of quantum systems sequentially interacting with a common environment

A composite quantum channel is derived for two independent quantum systems that interact sequentially with a common environment. One of the two quantum systems first interacts with the environment for a finite time, and after that the other one interacts with the same environment. In the process, the environment does not simultaneously interact with the two quantum systems. It is important to note that the second quantum system interacts with the environment that has been disturbed by the first one. As a result, the correlation between the two quantum systems, not directly interacting with each other, is created through the environment. An approximation to the composite quantum channel is also provided, which is applicable if a correlation time of the environment is not so long. When two independent qubits interact sequentially with a common bosonic environment via a dephasing coupling, it is explicitly shown that the time evolution of the second qubit can be non-Markovian due to the disturbance effect caused by the first qubit, even if the time evolution of the first qubit is Markovian. Entanglement, total, classical, and quantum correlations are calculated to find how the disturbance affects bipartite correlations. Furthermore, in state transmission through the composite quantum channel, it is found that the fidelity of quantum states can be enhanced by the disturbance effect.
AlloX

GPUs are considered as the accelerators for CPUs. We call these applications GPU applications. Some machine learning frameworks like Tensorflow support their machine learning (ML) jobs running either on CPUs or GPUs. Nvidia claims that Titan GPU K80 12GB can speed up 5-10x on average. Although GPUs offer the advantages on performance, they are very expensive. For example, a GPU K80 roughly costs $4000 while an Intel Xeon E5 Quad cores costs $350. The coexist of traditional CPU and GPU applications urges cloud computing operators to build hybrid CPU/GPU clusters. While the traditional applications are executed on CPUs, the GPU applications can run on either CPUs or GPUs. In the CPU/GPU clusters, how to provision the hybrid CPU/GPU clusters for CPU and GPU applications and how to allocate the resources across CPUs and GPUs? Interchangeable resources like CPUs and GPUs are not rare in large clusters. Some network I/O cards like wireless, ethernet, infinityband with different bandwidths can also be interchangeable. In this paper, we focus on CPU/GPU systems. We develop a tool that estimates the performance and resource for an ML job in an online manner (§2). We implement AlloX system that supports resource allocation and places applications on right resources (CPU or GPU) to maximize the use of computational resource (§3). The proposed AlloX policy achieves up to 35% progress improvement compared to default DRF [2]. We build a model that minimizes the total cost of ownership for CPU/GPU data centers (§4).
Research Issues in Outlier Detection

This chapter provides an overview of the outlier detection problem and brings out various research issues connected with this problem. It presents a detailed survey of the available literature on this problem with respect to the research issues identified. Basically, this chapter sets the direction for the material presented in the rest of the chapters of this book.
Computation and comparison of estimators under different linear random-effects models

Estimations of unknown parameters in linear regression models have been main objects of study in statistical data analysis and inference. This work concerns the comparison problem of different estimations of unknown parameters in two linear random-effects models (LREMs) with different structures. We shall derive analytical formulas for calculating best linear unbiased predictors/best linear unbiased estimators (BLUPs/BLUEs) of all unknown parameter spaces in the two competing LREMs. We then discuss relationships among BLUPs/BLUEs under the two LREMs by using some matrix analysis tools, including various consequences and conclusions on some special cases of LREMs.
The application and design of neural computation in visual perception

Visual perception is an important way for organisms to perceive the external world. Simulation of visual cognitive process can enhance the cognitive ability of machine vision. Therefore, how to simulate visual perception system and make computer have a high world understanding ability is a hotspot of current neurocomputing. Based on the information processing mechanism of visual perception system, this paper establishes a neural computing model based on visual perception mechanism. The simulation results of standard face database and natural landscape images show that the proposed method can recognize face samples better when other noise samples are added to the face image samples. In landscape contour fitting simulation, the results show that although this method has little advantage for large contour image recognition, but for small contour recognition, this method is obviously superior to other methods.
Introduction to Metaheuristics Methods

This chapter presents an overview of optimization techniques, describing their main characteristics. The goal of this chapter is to motivate the consideration of metaheuristic schemes for solving optimization problems. The study is conducted in such a way that it is clear the necessity of using metaheuristic approaches for the solution of power system problems.