Reviewerly/exHarmony huggingface dataset:
JobPacker

In spite of many advantages of hybrid electrical/optical datacenter networks (Hybrid-DCN), current job schedulers for data-parallel frameworks are not suitable for Hybrid-DCN, since the schedulers do not aggregate data traffic to facilitate using optical circuit switch (OCS). In this paper, we propose JobPacker, a job scheduler for data-parallel frameworks in Hybrid-DCN that aims to take full advantage of OCS to improve job performance. JobPacker aggregates the data transfers of a job in order to use OCS to improve data transfer efficiency. It first explores the tradeoff between parallelism and traffic aggregation for each shuffle-heavy recurring job, and then generates an offline schedule including which racks to run each job and the sequence to run the recurring jobs in each rack that yields the best performance. It has a new sorting method to prioritize recurring jobs in offline-scheduling to prevent high resource contention while fully utilizing cluster resources. In real-time scheduler, JobPacker uses the offline schedule to guide the data placement and schedule recurring jobs, and schedules non-recurring jobs to the idle resources not assigned to recurring jobs. Trace-driven simulation and GENI-based emulation show that JobPacker reduces the makespan up to 49% and the median completion time up to 43%, compared to the state-of-the-art schedulers in Hybrid-DCN.
Pseudo Topic Analysis for Boosting Pseudo Relevance Feedback

Traditional Pseudo Relevance Feedback (PRF) approaches fail to mode real-world intricate user activities. They naively assume that the first-pass top-ranked search results, i.e. the pseudo relevant set, have potentially relevant aspects for the user query. It is make the major challenge in PRF lies in how to get the reliability relevant feedback contents for the user real information need. Actually, there are two problems should not be ignored: (1) the assumed relevant documents are intertwined with the relevant and the non-relevant content, which influence the reliability of the expansion resource and can not concentrate in the real relevant portion; (2) even if the assumed relevant documents are real relevant to the user query, but they are always semantic redundance with various forms because the peculiarity of natural language expression. Furthermore, it will aggravate the 'query drift' problem. To alleviate these problems, in this paper, we propose a novel PRF approach by diversifying feedback source, which main aim is to converge the relatively single semantic as well as diversity relevant information from the pseudo relevant set. The key idea behind our PRF approach is to construct an abstract pseudo content obtained from topical networks modeling over the set of top-ranked documents to represent the feedback documents, so as to cover as diverse aspects of the feedback set as possible in a small semantic granularity. Experimental results conducted in real datasets indicate that the proposed strategies show great promise for searching more reliable feedback source by helping to achieve query and search result diversity without giving up precision.
User Group Management and Smart Society: Readiness Assessment of UU Adminduk (National Administration)

Abstract The conceptual model of smart societies has been aligned with smart offices, building and cities concurrently, which should concern various aspects of the individual such as attitude, behaviour and cognitive. In short, it should start from the fundamental and basic characteristic of human or bottom up approach not the other way around. In these concept, the new generation engages with the combination of smart energies, smart devices and smart transportations as the inheritance part of the advancement of smart system in the technologies utilization. For example, ubiquitous apps that provide rapid information about the direction to specific destination, the available spot to park, the reminder of respected area to avoid traffic congestion regardless the location of users. However, somehow, the utilization process often meet with failed in term of matching the user expectation with the system needs or, the most important thing, the readiness of current regulation to accommodate the trend changes and implementation of technology system. Thus, this study assess the interpretation of legal expert towards UU Adminduk (National Administration) by using content analysis through 21 selected coders from diverse background to gain more understanding of the complexity and reliability of the regulation. This act extremely necessary to govern the recording, changes, management and publication process of the personal data the purpose of national identity.
On the appropriate pattern frequentness measure and pattern generation mode

The classic case pattern mining is a fundamental subject in data mining and big data science. The goal of the mining is to find correctly from a given dataset the patterns and their respective intrinsic frequentness. This paper examines two important yet misused instruments, the pattern frequentness measure "support" and the full enumeration pattern generation mode, which cause serious Overfitting thus deviate from the mining goal. A theoretic combined solution for the two critical issues is then proposed. This solution plus the equilibrium condition introduced in this paper forms a set of three fundamental rationality check criteria that every mining approach should observe. As such, the rationality of the mining theory and the reliability of the mining results would be substantially improved from the previous work. These together promise a significant change towards more effective pattern mining.
Who Is the Abnormal User: Anomaly Detection Framework based on the Graph Convolutional Networks

Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern in a dataset. It is applicable in a variety of domains, such as intrusion detection, fault detection, medical and public health anomaly monitoring. Existing model usually detects the anomaly according to the data’s feature. However, two drawbacks exist if the model only detects anomaly by using the feature. On the one hand, model could not make use of the relationship between users, which contains a large amount of potential information that can strengthen the prediction ability of the model. On the other hand, existing model could not adjust their learning ability automatically with the increasing of the data. To address the issues referred above, we focus on proposing an anomaly detection system based on the Graph Convolutional Networks (GCN). The framework consists of four mechanisms. It can detect the anomalies by using the user features as well as the relationship between users. Experiment result shows that our framework has outstanding performance compared with other state-of-the-art detection models.
An Enhanced Genetic Virtual Machine Load Balancing Algorithm for Data Center

Data centers with cloud computing platform host several resources using numerous virtual machines. Such situations may cause the degradation of performance and violations of service level agreement. These challenges are addressed by providing an efficient load balancing mechanism for data centers, and also the workload must be distributed dynamically between the nodes. In this paper, hybrid meta-heuristic genetic algorithm based load balancing technique using active virtual has been proposed and simulated by Cloud Analyst. Simulation results of this hybrid meta heuristic approach found to be encouraging. Obtained results of the proposed algorithm are compared and analyzed with existing traditional strategy and it outperformed which makes it suitable for the deployment over the data centers.
Non-dominated Sorting Genetic Algorithm

Evolutionary Algorithms mimic natural evolutionary process in nature. One of the most well-regarded evolutionary algorithms is Genetic Algorithm (GA) [1]. This algorithm has been inspired from the Drawin’s theory of evolutionary. This theory states that natural organisms develop using the natural selection. Natural selection refers to the process of selecting and propagating the best genes from one generation to the next that helps the new generation to better survive, compete, and reproduce.
Audio Watermarking over the Air with Modulated Self-correlation

We propose a novel audio watermarking system that is robust to the distortion due to the indoor acoustic propagation channel between the loudspeaker and the receiving microphone. The system utilizes a set of new algorithms that effectively mitigate the impact of room reverberation and interfering sound sources without using derever- beration procedures. The decoder has low-latency and it operates asynchronously, which alleviates the need for explicit synchroniza- tion with the encoder. It is also robust to standard audio processing operations in legacy watermarking systems, e.g., compression and volume change. The effectiveness of the system is established with a real-time system under general room conditions.
BackgroundNet: Small Dataset-Based Object Detection in Stationary Scenes

Deep learning algorithms have made remarkable progress on the object detection based on huge amount of images. However, it is really difficult to train a model with well generalization for the small-scale images with the limited computation resources. To address the problem, BackgroundNet is proposed to guide the learning process of deep learning-based object detection by the extracted information from the background images. The corresponding network learns not only the features of objects, but also the difference between the object and the non-object area, with the purpose of improve the classification performances for small-scale datasets. Based on YOLO, the background images are employed as the extra input data, and then the input layer of BackgroundNet is not traditionally three RGB but six channels. The experimental results done for coal mine dataset and six public datasets show that the proposed method has better performance when dealing with the object detection with small-scale images and their AP-values are averagely larger than YOLO about 27.8 $$\%$$ for six public datasets.
A Generic Framework for Engineering Graph Canonization Algorithms

The state-of-the-art tools for practical graph canonization are all based on the individualization-refinement paradigm, and their difference is primarily in the choice of heuristics they include and in the actual tool implementation. It is thus not possible to make a direct comparison of how individual algorithmic ideas affect the performance on different graph classes. We present an algorithmic software framework that facilitates implementation of heuristics as independent extensions to a common core algorithm. It therefore becomes easy to perform a detailed comparison of the performance and behavior of different algorithmic ideas. Implementations are provided of a range of algorithms for tree traversal, target cell selection, and node invariant, including choices from the literature and new variations. The framework readily supports extraction and visualization of detailed data from separate algorithm executions for subsequent analysis and development of new heuristics. Using collections of different graph classes, we investigate the effect of varying the selections of heuristics, often revealing exactly which individual algorithmic choice is responsible for particularly good or bad performance. On several benchmark collections, including a newly proposed class of difficult instances, we additionally find that our implementation performs better than the current state-of-the-art tools.
On the Covering Radius of Small Codes Versus Dual Distance

Tietäväinen's upper and lower bounds assert that for block-length-n linear codes with dual distance d, the covering √ radius R is at most (n/2) - ((1/2) - o(1)) dn and typically at least (n/2) - θ((dn log (n/d)) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> ). The gap between those bounds on R - (n/2) is a Θ((log (n/d))1/2) factor related to the gap between the worst covering radius given d and the sphere-covering bound. Our focus in this paper is on the case when d = o(n), i.e., when the code size is subexponential and the gap is w(1). We show that up to a constant, the gap can be eliminated by relaxing the covering requirement to allow for missing o(1) fraction of points. Namely, if the dual distance d = o(n), then for sufficiently large d, almost all points can be covered with radius R ≤ (n/2)-Θ((dn log (n/d)) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> ). Compared with random linear codes, our bound on R - (n/2) is asymptotically tight up to a factor less than 3. We give applications to dual-BCH codes. The proof builds on the author's previous work on the weight distribution of cosets of linear codes, which we simplify in this paper and extend from codes to probability distributions on {0, 1}n, thus enabling the extension of the earlier result to (d - 1)-wise independent distributions.
Certified ethical hacker v.10 online course

CEH v.10 Self-study Course is an online course preparing learners for one of the most prestige cyber security certifications in the world - the Certified Ethical Hacker (CEH) v.10 Certification. Due to a pay wall and the practical rather than theoretical nature, most researchers have limited exposure to this course. For the first time, this paper will analyze the course's instructional design based on the highest US national standards, giving fair evaluations, and identifying five general directions for improvements. The main contribution is a 30-point check list that can be used to evaluate any other online courses. Methodology involves mapping QualityMatters and iNacol evaluation items into 30 items within five categories of "Meaningful", "Engaging", "Measurable", "Accessible", and "Scalable". This instructional design evaluation strategy is more intuitive to business leaders, and allows the combination of any other standards that are relevant to an organization's evaluation needs.
Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues

Prosodic cues in conversational speech aid listeners in discerning a message.We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn.Individuals who are Deaf and Hard of Hearing often rely on real-time captions in live meetings.Word error rate, a traditional metric for evaluating automatic speech recognition (ASR), fails to capture that some words are more important for a system to transcribe correctly than others.We present and evaluate neural architectures that use acoustic features for 3-class word importance prediction.Our model performs competitively against state-ofthe-art text-based word-importance prediction models, and it demonstrates particular benefits when operating on imperfect ASR output.
Marrying tracking with ELM: A Metric constraint guided multiple features fusion method

Object tracking is one important problem in computer vision and surveillance system. The existing models mainly exploit the single-view feature (i.e. color, texture, shape) to solve the problem, failing to describe the objects comprehensively. In this paper, we solve the problem from multi-view perspective by leveraging multi-view complementary and latent information, so as to be robust to the partial occlusion and background clutter especially when the objects are similar to the target, meanwhile addressing tracking drift. However, one big problem is that multi-view fusion strategy can inevitably result tracking into non-efficiency. To this end, we propose to marry ELM (Extreme learning machine) to multi-view fusion to train the global hidden output weight, to effectively exploit the local information from each view. Following this principle, we propose a novel method to obtain the optimal sample as the target object, which avoids tracking drift resulting from noisy samples. Our method is evaluated over 12 challenge image sequences with different attributes including illumination, occlusion, deformation, etc., which demonstrates better performance than several state-of-the-art methods in terms of effectiveness and robustness.
Optical neural network for cancer morphodynamics sensing

We employ living three-dimensional tumour brain models to demonstrate a bio-inspired optical neural network trained via image transmission to detect cancer mor-phodynamics inaccessible by optical imaging.
New Forms of Literacy and Transformation of Reading Practices in Bulgaria

The study presents the results of a survey on the reading practices in Bulgaria based on up-to-date library statistics. The statistical data cover the dynamics of using the traditional library collections and the databases in the Bulgarian National Library and the Library of the Bulgarian Academy of Sciences. The study found that reading habits have been transformed due to changes in the information environment. Terms like “digital” and “mobile literacy” have emerged underlying the differences in user experiences and technical skills that are required when using new technologies to locate, evaluate, and use information. Reading in the library is regarded in the study as specialized reading with its institutional characteristics. Common tendencies and features in the change of reading practices have been established by using quantitative methods based on comparative analysis. These quantitative methods help in determining the changes in reading practices as a result of the development of technology, thus leading to new forms of literacy. There is a productive interaction between reading practices and the introduction of new technologies. The results of the study can help in defining the role of the library in this process, and predict future tendencies.
Paper evolution graph: multi-view structural retrieval for academic literature

Academic literature retrieval concerns about the selection of papers that are most likely to match a user’s information needs. Most of the retrieval systems are limited to list-output models, in which the retrieval results are isolated from each other. In this paper, we aim to uncover the relationships between the retrieval results and propose a method to build structural retrieval results for academic literature, which we call a paper evolution graph (PEG). The PEG describes the evolution of diverse aspects of input queries through several evolution chains of papers. By using the author, citation, and content information, PEGs can uncover various underlying relationships among the papers and present the evolution of articles from multiple viewpoints. Our system supports three types of input queries: keyword query, single-paper query, and two-paper query. The construction of a PEG consists mainly of three steps. First, the papers are soft-clustered into communities via metagraph factorization, during which the topic distribution of each paper is obtained. Second, topically cohesive evolution chains are extracted from the communities that are relevant to the query. Each chain focuses on one aspect of the query. Finally, the extracted chains are combined to generate a PEG, which fully covers all the topics of the query. Experimental results on a real-world dataset demonstrate that the proposed method can construct meaningful PEGs.
Mixing and moments properties of a non-stationary copula-based Markov process

We provide conditions under which a non-stationary copula-based Markov process is geometric β-mixing and geometric ρ-mixing. Our results generalize some results of Beare who considers the stationary case. As a particular case we introduce a stochastic process, that we call convolution-based Markov process, whose construction is obtained by using the C-convolution operator which allows the increments to be dependent. Within this subclass of processes we characterize a modified version of the standard random walk where copulas and marginal distributions involved are in the same elliptical family. We study mixing and moments properties to identify the differences compared to the standard case.
DeepDial: Passage Completion on Dialogs

Many neural models have been built to carry out reading comprehension tasks. However, these models mainly focus on formal passages like news and book stories. Although human dialog is the most important part of daily life, machine reading comprehension on dialogs (i.e., passage completion on dialogs) has not been sufficiently explored. Existing models show some weaknesses when comprehending dialogs and they are unable to capture global information over a distance and local detailed information at the same time. This paper introduces a neural network model DeepDial that aims at addressing the problems mentioned above. The model explores both word-level and utterance-level information in a dialog, and achieves the state-of-the-art performance on the benchmark dataset constructed from a TV series Friends.
Sensor-Based Activity Recognition Inside Smart Building Energy and Comfort Management Systems

The challenge of Smart Building Energy and Comfort Management (BECM) systems is to schedule home appliances according to users' comfort requirements, while contributing to an efficient and sustainable use of the available energy sources, supplied by either the electricity grid or Renewable Energy Sources (RES). To this aim, BECM systems have to monitor users' habits and learn their preferences, so that their actions can be predicted and appliances can be scheduled accordingly.This paper stems from the observation that actions are usually performed in sequences that repeat according to a pattern. Therefore, activities can be recognized and predicted as soon as a pattern of actions is detected. The framework proposed in this paper aims to predict activities by analyzing the sequences of actions detected by sensors deployed in a Smart Building. Furthermore, a correlation between subsequent activities is found so that sequences of activities can be predicted. Simulation results show that activities can be predicted with an accuracy of 74.78%.
The Effects of Digital Literacy Support Tools on First Grade Students’ Comprehension of Informational e-Books

This mixed-methods study examined the patterns between 14 first graders’ use of digital literacy support tools (annotating connections, annotating I wonders, and looking back in the text) and comprehension scores when reading informational e-books. Quantitative data sources included e-book comprehension quiz scores and literacy support tools’ tally system. Two types of qualitative data were collected: teacher interviews and researcher’s anecdotal notes. A chi-square analysis indicated significant patterns between the e-book comprehension scores and the usage of the literacy support tools, X2(6, n = 211) = 25.79, p = 0.001. The qualitative data highlights the students’ digital literacy support tool preference, their ability to use the digital literacy support tools, and understanding the relevant application of the digital literacy support tools. This study is an initial attempt to shift teachers’ and researchers’ views of independent informational reading to include digital text, such as e-books to supplement the classroom library.