Reviewerly/exHarmony huggingface dataset:
Multimedia retrieval by deep hashing with multilevel similarity learning

Deep multimodal hashing has received increasing research attention in recent years due to its superior performance for large-scale multimedia retrieval. However, limited e orts have been made to explore the complex multilevel semantic structure for deep multimodal hashing. In this paper, we propose a novel deep multimodal hashing method, termed as Deep Hashing with Multilevel Similarity Learning (DHMSL), for learning compact and discriminative hash codes, which explores multilevel semantic similarity correlations of multimedia data. In DHMSL, multilevel similarity correlation is explored to learn the unified binary hash codes by exploiting the local structure and semantic label information simultaneously. Meanwhile, the bit balance and quantization constraints are taken into account to further make the unified hash codes compact. With the unified binary codes learned, two deep neural networks are jointly trained to simultaneously learn feature representations and two sets of nonlinear hash functions. Specifically, the well-designed loss functions are introduced to minimize the prediction errors of the feature representations as well as the errors between the unified binary codes and outputs of the networks. Extensive experiments on two widely-used multimodal datasets demonstrate that the proposed method can achieve the state-of-the-art performance for both image-query-text and text-query-image tasks.
A Hybrid Discriminative Mixture Model for Cumulative Citation Recommendation

This paper explores Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to deal with unseen entities without annotation. A compromised solution is to build a global entity-unspecific model for all entities without respect to the relationship information among entities, which cannot guarantee achieving a satisfactory result for each entity. Moreover, most previous methods can not adequately exploit prior knowledge embedded in entities or documents due to considering all kinds of features indifferently. In this paper, we propose a novel entity and document class-dependent discriminative mixture model by introducing one intermediate layer to model the correlation between entity-document pairs and hybrid latent entity-document classes. The model can better adjust to different types of entities and documents, and achieve better performance when dealing with a broad range of entity and document classes. An extensive set of experiments has been conducted on two offical datasets, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.
Multi-Modal Data Semantic Localization With Relationship Dependencies for Efficient Signal Processing in EH CRNs

Due to spectrum scarcity and energy consumption caused by processing and transmitting multimodal data signals in cognitive radio networks (CRNs), locating key information in the signal for further energy management in EH CRNs is necessary. Therefore, to adaptively capture semantic associations of multimedia signals, we present a novel visual-semantic reasoning framework for phrases simultaneously localization. To address the preferences limitations of current algorithms caused by the independent localizing of phrases and the ignorance of inter-phrase dependencies, our framework models the phrases simultaneously followed by inter-phrase dependencies-based jointly localization. Specifically, the framework consists of two core modules, including spatial-semantic perception tensor factorization and visual-semantic relationship reasoning network which can be denoted as SSPTF and VSRN, respectively. That is, SSPTF integrates regions and phrases into a tensor so that tensor factorization can be used to capture a shared potential association for all phrases. Furthermore, based on the predefined phrases-semantic dependencies graph, VSRN explicitly exploits the conjunctions between phrases to refine the phrase-region matching scores from SSPTF to achieve jointly localization. By constructing it as an end-to-end training architecture, the strong performance of the framework over Flicker-Entities30K on accuracy and the state-of-the-art results on some categories demonstrate the effectiveness of the proposed unified framework.
Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images

Accurate and efficient detection of cell nuclei is an important step towards the development of a pathology-based Computer Aided Diagnosis. Generally, high-resolution histopathology images are very large, in the order of billion pixels, therefore nuclei detection is a highly compute intensive task, and software implementation requires a significant amount of processing time. To assist the doctors in real time, special hardware accelerators, which can reduce the processing time, are required. In this paper, we propose a Field Programmable Gate Array (FPGA) implementation of automated nuclei detection algorithm using generalized Laplacian of Gaussian filters. The experimental results show that the implemented architecture has the potential to provide a significant improvement in processing time without losing detection accuracy.
Cryptanalysis of Reduced sLiSCP Permutation in Sponge-Hash and Duplex-AE Modes

This paper studies security of a family of lightweight permutations sLiSCP that was proposed by AlTawy et al. at SAC 2017. sLiSCP also specifies an authenticated encryption (AE) mode and a hashing mode based on the sponge framework, however the designers’ analysis focuses on the indistinguishability of the permutation, and there is no analysis for those modes. This paper presents the first analysis of reduced-step sLiSCP in the AE and hashing modes fully respecting the recommended parameters and usage by the designers. Forgery and collision attacks are presented against 6 (out of 18) steps of the AE and hashing modes. Moreover, rebound distinguishers are presented against 15 steps of the permutation. We believe that those results especially about the AE and hashing modes provide a better understanding of sLiSCP, and bring more confidence about the lightweight version sLiSCP-light.
Trajectory optimization using quantum computing

We present a framework wherein the trajectory optimization problem (or a problem involving calculus of variations) is formulated as a search problem in a discrete space. A distinctive feature of our work is the treatment of discretization of the optimization problem wherein we discretize not only independent variables (such as time) but also dependent variables. Our discretization scheme enables a reduction in computational cost through selection of coarse-grained states. It further facilitates the solution of the trajectory optimization problem via classical discrete search algorithms including deterministic and stochastic methods for obtaining a global optimum. This framework also allows us to efficiently use quantum computational algorithms for global trajectory optimization. We demonstrate that the discrete search problem can be solved by a variety of techniques including a deterministic exhaustive search in the physical space or the coefficient space, a randomized search algorithm, a quantum search algorithm or by employing a combination of randomized and quantum search algorithms depending on the nature of the problem. We illustrate our methods by solving some canonical problems in trajectory optimization. We also present a comparative study of the performances of different methods in solving our example problems. Finally, we make a case for using quantum search algorithms as they offer a quadratic speed-up in comparison to the traditional non-quantum algorithms.
An improved method of optical flow using human body-following wheeled robot

The theory and technology of human–machine coordination and natural interaction have a wide range of application prospect in future smart factories. This paper elaborates on the design and implementation of a body-following wheeled robot system based on Kinect, as well as the use of gesture recognition function to enhance the interactive performance. An improved optical flow method is put forward to obtain the direction and speed of the target movement. The smoothing parameters in traditional optical flow are replaced by variables. The new smoothing parameter is related to the local gradient value. Compared with the traditional optical flow method, it can reflect the status of moving objects more clearly, reduce noise and ensure real-time performance, solving the problem of tracking state oscillation caused by the skeleton node drifts when the target is occluded. The experiment on the wheeled robot confirms that the system can accomplish the tracking task in a preferable way.
Use of KRACK Attack to Obtain Sensitive Information

This project presents an implementation of the recent KRACK attack for hacking WPA2 networks and obtaining sensitive information such as passwords, credit card numbers, messages, pages visited by the victim, etc., this attack works against any current modem, and depending on the operating system, and configuration of the network, you can inject or modify information. This project will show its operation for a Linux operating system laptop that connects to a Wi-Fi network encrypted with WPA2 protocol, and you will obtain sensitive information through KRACK attacks. Additionally, it will explain how to identify if you are being victim of this attack.
Does governance quality promote innovation in sub-Saharan Africa? An empirical study across 37 countries

This inquiry examines the effect of quality of governance institutions on innovation in 37 sub-Saharan African countries for the period 1996–2016. The empirical analysis followed the instrumental variables regression technique. The motivation for using this estimation technique is to provide special focus to the issue of endogeneity by estimating IV general methods of moment's model. The following general findings are presented. First, governance quality does, in fact, appear to promote innovativeness. Second, for all governance indicators, the effect of the quality of governance institutions follows two channels: directly and indirectly through its positive impact on human capital development. The empirical findings suggest that countries with better quality of governance infrastructure are able to promote innovation in better ways. That is, the results do support theories that argue in favour of the development of governance quality and the improvement of human capital infrastructure to foster the national innovation system. These results are found to be robust across alternative empirical specifications tested.
Analysis of Coordination Patterns between Gaze and Control in Human Spatial Search

Human spatial search combines visual search and motion control problems. Both have been investigated separately over decades, however, the coordination between visual search and motion control has not been investigated. Analyzing coordination of sensory-motor behavior through teleoperation could help improve understanding of human search strategies as well as autonomous search algorithms. This research proposes a novel approach to analyze the coordination between visual attention via gaze patterns and motion control. The approach is based on estimation of human operators’ 3D gaze using Gaussian mixture model (GMM), hidden Markov model (HMM) and sparse inverse covariance estimation (SICE). The analysis of the human experimental data demonstrates that fixation is used primarily to look at the target, smooth pursuit is coupled to robot rotation and used to search for new explored area, and saccade is coupled with forward motion and used to search for new explored area. These insights are used to build a functional model of human teleoperation search.
High-fidelity reversible data hiding using dynamic prediction and expansion

With the aim of reducing the distortion of the marked image to realize high-fidelity reversible data hiding (RDH), we propose an RDH scheme based on dynamic prediction and expansion (DPE). By introducing a dynamic pixels-value-ordering (D-PVO) method into DPE, a full reversibility can be achieved. The pixels of the cover image are predicted by sorting their cross-over neighbors and using the two end pixels or the median pixels to conduct the prediction. The covert data are embedded by using prediction-error-expansion. We have theoretically proved higher prediction accuracy for the proposed method compared with the two existing classical prediction methods. In our experiments, we first verify the prediction accuracy of D-PVO by applying it to Gaussian distributed sampled data. Next, we perform experiments on the standard pictures from USC-SIPI and Kodak image datasets. The experimental results demonstrate that the proposed scheme can ensure a full recovery of the original image and a higher fidelity of the marked cover images, and the peak signal-to-noise ratio can exceed 60.00 dB when embedding capacity reaches 10,000 bits.
Evolution Traceability Roadmap for Business Processes

For traceable evolution of software systems, tracking of continuous changes in their constituent artifacts is essential. The paper outlines a roadmap for evolution traceability of software artifacts with a focus on evolution traceability of descriptions of business processes. Several traceability approaches are overviewed, and a categorization of evolution traceability is presented based on the type of evolving software artifacts that need to be traced through their evolution. Existing approaches for tracing evolution of business processes are discussed, and it is noted that there is a need for representational and analytical techniques in this area. Challenges in research on automation of evolution traceability of business processes enriching the present practices are also discussed.
Analyzing and Predicting Player Performance in a Quantum Cryptography Serious Game

An adaptive 3D serious game, QuaSim for imparting to learners the fundamental concepts of quantum cryptography and their applications in designing computer security protocols is described. QuaSim emulates an often used instructional model of practice exercises followed by timed-tests (practice-timed-test) in a serious game setting by automatically designing timed-tests guided by models learned from data about the performance of players in practice exercises. QuaSim also automatically selects next practice exercises based on player performance in previous exercises. The game was played by 150 students and the results are highly encouraging. They show that the model learned by the game is able to select next practice exercises to improve player performance in the timed tests and is able to generate meaningful timed-tests.
A new benchmark for evaluating pattern mining methods based on the automatic generation of testbeds

Mining patterns is one of the most attractive topics in the field of software design. Knowledge about the number, type, and location of pattern instances is crucial to understand the original design decisions. Several techniques and tools have been presented in the literature for mining patterns in a software system. However, evaluating the quality of the detection results is usually done manually or subjectively. This can significantly affect the evaluation results. Therefore, a fair comparison of the quality of the various mining methods is not possible. This paper describes a new benchmark to evaluate pattern mining methods in source code or design. Our work aims at overcoming the challenges faced in benchmarking in pattern detection. The proposed benchmark is comprehensive, fair, and objective, with a repeatable evaluation process. Our proposed benchmark is based on automatic generation of testbeds using graph theory. The generated testbeds are Java source codes and their corresponding class diagrams in which various types of patterns and their variants are inserted in different locations. The generated testbeds differ in their levels of complexity and full information is available on the utilized patterns. The results show that our proposed benchmark is able to evaluate the pattern mining methods quantitatively and objectively. Also, it can be used to compare pattern mining methods in a fair and repeatable manner. Based on our findings, it can be argued that benchmarking in the pattern mining field is significantly less mature than topics such as presenting a new detection method. Therefore, special attention is needed in the pattern evaluation topic. Our proposed benchmark is a step towards achieving a comparative understanding of the effectiveness of detection methods and demonstrating their strengths and weaknesses.
Predicting lab values for gastrointestinal bleeding patients in the intensive care unit: A comparative study on the impact of comorbidities and medications

Since a significant number of frequent laboratory blood tests are unnecessary and these tests may have complications, developing a system that could identify unnecessary tests is essential. In this paper, a value prediction approach is presented to predict the values of Calcium and Hematocrit laboratory blood tests for upper gastrointestinal bleeding patients and patients with unspecified hemorrhage in their gastrointestinal tract. The data have been extracted from the MIMIC-II database. By considering the issues of MIMIC-II in the process of data extraction and using expert knowledge, comprehensive preprocessing has been performed to validate the data. The first prediction system is developed using zero order Takagi-Sugeno fuzzy modeling and the sequential forward selection method. The results of this prediction system for target laboratory tests are promising. In the second proposed prediction system, patients are clustered using their comorbidity information before the final prediction phase. For each cluster, a medication feature is constructed and added to the data for the final feature selection. Although it was expected that clustering patients based on comorbidity data could improve the results of value prediction, the results were not improved in average. The reason for this could be the small number of abnormal laboratory test samples and their dispersion in clusters. These abnormal values would be more dispersed in the model with clustering phase, when they are scattered over different clusters.
Heterogeneous Acceleration of HAR Applications

Human action recognition (HAR) is an important field of research that intercepts with areas such as image processing, computer vision, and the design of fast algorithms, among others. HAR has several important applications including healthcare monitoring, security and surveillance, assisted living, smart homes, and video search and indexing. Despite recent developments in the field, major challenges remain. For instance, HAR is computationally expensive. Tasks such as video preprocessing, feature extraction, feature quantization, and feature classification require the execution of millions of arithmetic operations for a video sequence lasting a few seconds. To address these problems, we propose a heterogeneous approach that is based on an extensive algorithmic and experimental analysis of the histogram of gradients application. We divide the application into four stages and evaluate each on the CPU, GPU, and FPGA platforms. Our heterogeneous design combines the strengths of both the FPGA and GPU platforms, and achieves a 1.3X speedup compared with a state-of-the-art GPU while being 1.5X more energy efficient than other homogeneous solutions, including FPGA-based designs. Moreover, our heterogeneous HAR design using fixed-point arithmetic has comparable accuracy to those of HAR algorithms using single precision floating point arithmetic.
Accurate Classification of Cancer in Mammogram Images

In the last decade, machine learning plays a vital role in the detection of breast cancer. Mammography is a proficient tool for early stage detection of breast cancer. In this work, a simple technique for breast cancer image classification in l mammogram images is proposed. Highly discriminant local binary patterns are extracted from the wavelet normalized mammogram images. K-nearest neighbor classifier is used to categorize the abnormal cancer cell images. A mammogram database is created to evaluate the efficacy of our algorithm. From the experimental results, the performance of our algorithms is comparatively good with very less computational time.
Provisioning and Performance Evaluation of Parallel Systems with Output Synchronization

Parallel server frameworks are widely deployed in modern large-data processing applications. Intuitively, splitting and parallel processing of the workload provides accelerated application response times and scaling flexibility. Examples of such frameworks include MapReduce, Hadoop, and Spark. For many applications, the dynamics of such systems are naturally captured by a Fork-Join (FJ) queuing model, where incoming jobs are split into tasks each of which is mapped to exactly one server. When all the tasks that belong to one job are executed, the job is reassembled and leaves the system. We consider this behavior at the output as a synchronization constraint. In this article, we study the performance of such parallel systems for different server properties, e.g., work-conservingness, phase-type behavior, and as suggested by recent evidence, for bursty input job arrivals. We establish a Large Deviations Principle for the steady-state job waiting times in an FJ system based on Markov-additive processes. Building on that, we present a performance analysis framework for FJ systems and provide computable bounds on the tail probabilities of the steady-state waiting times. We validate our bounds using estimates obtained through simulations. In addition, we define and analyze provisioning, a flexible division of jobs into tasks, in FJ systems. Finally, we use this framework together with real-world traces to show the benefits of an adaptive provisioning system that adjusts the service within an FJ system based on the arrival intensity.
Accelerating distributed stochastic gradient descent with adaptive periodic parameter averaging

Communication overhead is a well-known performance bottleneck in distributed Stochastic Gradient Descent (SGD), which is a popular algorithm to perform optimization in large-scale machine learning tasks. In this work, we propose a practical and effective technique, named Adaptive Periodic Parameter Averaging, to reduce the communication overhead of distributed SGD, without impairing its convergence property.