Reviewerly/exHarmony huggingface dataset:
Modeling Terms by Graphs with Structure Constraints (Two Illustrations)

In the talk at the workshop my aim was to demonstrate the usefulness of graph techniques for tackling problems that have been studied predominantly as problems on the term level: increasing sharing in functional programs, and addressing questions about Milner's process semantics for regular expressions. For both situations an approach that is based on modeling terms by graphs with structure constraints has turned out to be fruitful. In this extended abstract I describe the underlying problems, give references, provide examples, indicate the chosen approaches, and compare the initial situations as well as the results that have been obtained, and some results that are being developed at present.
Scheduling Algorithms for High-Performance Computing: An Application Perspective of Fog Computing

High-performance computing (HPC) demands many computers to perform multiple tasks concurrently and efficiently. For efficient resource utilization and for better response time, different scheduling algorithms have been proposed which aim to increase throughput, scalability, and performance of HPC applications. In this paper, our contribution is twofold. Firstly, the classification of scheduling algorithms on the basis of multiple factors like throughput, waiting time, fairness, overhead, etc. is presented. This paper investigates the recent research that has been carried out from 2009–2017. With this categorization, we aim to provide an easy and concise view of the HPC algorithms. Secondly, the forecasting has been done on HPC applications to predict the growth rate for 2020 and beyond.
SuperDense Coding Step by Step

Scholars of quantum computing all become familiar with Alice and Bob when learning about superdense coding and entanglement. However, in every research book and video that we found, the assumption is made that the student will automatically understand how those two classical bits at the end come to their values when they started as two qubits. This vagueness was unavoidable when quantum computers were purely theoretical. After exhaustive search of every quantum superdense coding Bob and Alice example in the research literature since late 2017, we found not one that presented evidence from a real quantum computer. However, moving from theory to practice is necessary. Today, using results from a real IBM Q Experience quantum computer, we illustrate each step of the Bob and Alice qubit journey and make it all crystal clear.
Relevance prediction in similarity-search systems using extreme value theory

Among the challenges present in the design of retrieval systems, how to accurately assess their performance is perhaps one of the most important. Many applications such as rank aggregation or relevance feedback can be significantly improved with online effectiveness estimation of queries. Thus, developing methodologies that can estimate performance with minimal supervision and at query time is of utmost importance for improving the results of existing retrieval systems. In this work, we explore score-based, post-retrieval approaches for relevance prediction of search systems. We first introduce two statistical methods based on the Extreme Value Theory to estimate which of the top-k objects retrieved for a query are relevant. Our prediction approach uses this estimation as a method to infer the overall performance of a query. The two relevance prediction methods were evaluated in image datasets covering several modalities and scoring approaches. We conducted experiments comparing the ground-truth relevances of several ranks with predictions generated by our proposed approach, measuring their effectiveness by way of normalized accuracy and Matthews Correlation Coefficient. Furthermore, we also evaluate the precision deducted from our approaches with the system’s expected performance. Those experiments show that the proposed approaches succeed in most relevance prediction scenarios of the top-ranked objects of a query, obtaining high accuracy.
Transformer

Form-based data insertion or querying is often one of the most time-consuming steps in data-driven workflows. The small screen and lack of physical keyboard in devices such as smartphones and smartwatches introduce imprecision during user input. This can lead to data quality issues such as incomplete responses and errors, increasing user input time. We present Transformer, a system that leverages the contents of the database to automatically optimize forms for constrained input settings. Our cost function models the user input effort based on the schema and data distribution. This is used by Transformer to find the user interface (UI) widget and layout with ideal input cost for each form field. We demonstrate through user studies that Transformer provides a significantly improved user experience, with up to 50% and 57% reduction in form completion time for smartphones and smartwatches respectively.
An Algorithmic Scheme for Statistical Thesaurus Construction in a Morphologically Rich Language

Corpus-based automatic thesaurus construction uses linguistic methods, such as Part-of-Speech taggers and parsers, which often perform poorly on MRLs. Therefore, in this paper, we focused on the complex task of adapting corpus-based thesaurus construction methods for MRLs. We investigated two statistical approaches for thesaurus construction; a) a first-order co-occurrence-based approach and b) a second-order distributional-based approach. We explored alternative levels of morphological term representations complemented by grouping the morphological variants. We then introduced and adopted a generic algorithmic scheme for thesaurus construction in MRLs for both first-order and second-order approaches. Our scheme investigated alternative representation levels and offered alternative configurations. We demonstrated the empirical benefits of our methodology for a diachronic Hebrew thesaurus construction. We used morphological analysis tools, defined and applied a new annotation scheme, and demonstrated its optimal configuration, which outperforms the baseline for both first and second order corpus-based thesaurus construction approaches.
WT-MO Algorithm

Visual examination of blood smears is an essential tool for analysis, prevention, and remediation of several types of maladies. The interest of computer-aided decision has been acknowledged in many medicinal instances (e.g., automatic ways and means are being explored to spot, classify, and measure visual items in hematological cytology [HC]). This chapter proposes an entirely automated blood smear diagnosis system for hemograms, which can lessen the time spent to scrutinize a slide. The present framework relies on morphological operations (MOs) and soft segmentation by means of the watershed transform (WT). Experiments demonstrate the method efficacy to count white blood cells (WBCs) and red blood cells (RBCs). Some considerations about implementations, design advice and possible variants, as well as improvements are discussed. The future of automated medical analysis is contemplated.
Impact of diminished-1 encoding on residue number systems arithmetic units and converters

Diminished-1 (D1) encoding, recodes each modulo-(2n + 1) residue r ∈ [1, 2n] with (n + 1)-bit normal encoding (NE) to n-bit codes r′ = r − 1 ∈ [0, 2n − 1], where an exclusive zero-indicator bit represents r = 0. Performance of modulo-(2n + 1) arithmetic is generally enhanced via D1 encoding. However, the cases of zero inputs/output of D1 arithmetic circuits entail especial handling, with some overhead, let alone the forward and reverse residue number system (RNS) conversions. Nevertheless, to highlight the advantages of D1 representation, and in order to encourage its utilization in RNS applications, we were motivated to study the impact of D1 representation on modulo-(2n + 1) addition, subtraction, unified add/sub unit, and multiplication, as well as the corresponding RNS conversions. We present new D1 conversion units and multiplier, and compare them with previous relevant D1 and NE works. In addition, the prominent D1 impact on typical RNS applications is investigated via synthesis of finite impulse response filters and discrete cosine transform circuits.
A new look at conditional probability with belief functions

We discuss repeatable experiments about which various agents may have different information. This information can vary from a full probabilistic description of the experiment in the sense that the probabilities of all outcomes are known to the agent, to having no information whatsoever, except the collection of possible outcomes. We argue that belief functions are very suitable for modeling the type of information we have in mind. We redevelop and rederive various notions of conditional belief functions, using a viewpoint of relative frequencies. We call the two main forms of conditioning contingent and necessary conditioning, respectively. The former is used when the conditioning event may also have not occurred, whereas the latter is used when it turns out that the event on which we condition occurs necessarily. Our approach unifies various notions in the literature into one conceptual framework, namely, the updated belief functions of Fagin and Halpern, the unnormalized conditional belief function of Smets, and the notions of updating and focusing as used by Dubois and Prade. We show that the original Dempster–Shafer definition of conditional belief functions cannot be interpreted directly in our framework. We give a number of examples illustrating our interpretation, as well as the differences between the various notions of conditioning.
A Hybrid and Parameter-Free Clustering Algorithm for Large Data Sets

As an important unsupervised learning method, clustering can find the hidden structures in data effectively. With the amount of data grows larger, clustering of large data sets is a challenging task. Many clustering algorithms have been developed to deal with small data sets, but they are often inefficient when the data sets are large. Meanwhile, most clustering algorithms require some extra parameters as input, which may not be easy to obtain in practical applications. This paper proposed a new clustering algorithm called hybrid and parameter-free clustering method (HPFCM). HPFCM is able to rapidly perform clustering on large data sets without knowing the number of clusters in advance. HPFCM is based on sampling on large data sets (MMRS* sampling), assessing the clustering tendency on samples (eVAT), determining the number of clusters (EPB), forming different partitions (MST tree cutting), and extending the results to the rest of the data sets. We compare HPFCM with the other three methods, which are popular in clustering large data sets. Several numerical and real-world experiments have been conducted to verify our algorithm. The results show the great potential and effectiveness of HPFCM for clustering large data sets.
Correlation filter-based visual tracking via holistic and reliable local parts

Visual tracking is a challenging task in computer vision due to various appearance changes of the target. Although correlation filter-based trackers have achieved competitive results, they may easily lead to tracking failure because of the high sensitivity of correlation filter to occlusion. Part-based correlation filter trackers can deal with partial occlusion to some extent, but they may easily drift to the background in the case of fast motion or heavy occlusion. To better solve the above-mentioned problems, a kernelized correlation filter-based tracker that processes both holistic and reliable local parts is proposed. For local parts, reliable parts are identified by peak-to-sidelobe ratio. When all parts are unreliable, we propose to apply a sliding window on each part to generate patches, among which a reliable patch is identified, and the part is replaced by the reliable patch. In holistic level, holistic tracking is performed with the rough position voted by reliable local parts, and then the holistic tracking result is used to provide feedback for parts to update its scale and filter. Moreover, we propose to reset unreliable parts when the holistic tracking result is reliable. The experimental results illustrate that the proposed tracker outperforms those of several state-of-the-art trackers.
Perceived Benefits and Challenges of Learning Startup Methodologies for Software Engineering Students

The need of skills other than technical from software developers is becoming evident. The DevOps movement is an example of that applied to operational tasks. Startup development methodologies focus on business activities in innovative organizations. Several universities offer courses based on these methodologies to software engineering students, mainly to improve their creativity, problem solving, and business skills. This paper investigates how software engineering students learned startup development methodologies and discusses what are the challenges and benefits in their learning process. We conducted a multi-method study in three different universities. The data was collected in two phases and analyzed using thematic analysis. Our study reveals that students realized the importance of collaboration with other courses and the importance of user involvement in development. However, students tend to over-simplify concepts, trying to adapt them to what they are familiar with. The results indicate the necessity of business education for technical students and directions for improvements.
Enhancing the DISSFCM Algorithm for Data Stream Classification

Analyzing data streams has become a new challenge to meet the demands of real time analytics. Conventional mining techniques are proving inefficient to cope with challenges associated with data streams, including resources constraints like memory and running time along with single scan of the data. Most existing data stream classification methods require labeled samples that are more difficult and expensive to obtain than unlabeled ones. Semi-supervised learning algorithms can solve this problem by using unlabeled samples together with a few labeled ones to build classification models. Recently we proposed DISSFCM, an algorithm for data stream classification based on incremental semi-supervised fuzzy clustering. To cope with the evolution of data, DISSFCM adapts dynamically the number of clusters by splitting large-scale clusters. While splitting is effective in improving the quality of clusters, a repeated application without counter-balance may induce many small-scale clusters. To solve this problem, in this paper we enhance DISSFCM by introducing a procedure that merges small-scale clusters. Preliminary experimental results on a real-world benchmark dataset show the effectiveness of the method.
Incremental Theory Closure Reasoning for Large Scale Knowledge Graphs

Knowledge graph(KG)s have become more and more important for the field of cybersecurity. However, they usually contain much implicit semantic information, which needs to be further mined through semantic inference to be more useful for both analysts and automated systems. In this paper, we propose an incremental reasoning algorithm KGRL-Incre for the scenario that the instances of a KG is expanded with only a small set of triples, which can perform an incremental update to the previous reasoning result effectively to avoid a full re-reasoning over the expanded KG. The main contributions of our approach are the irrelevant triple filtering algorithms which reduce the scale of data that need to be processed and a delay reasoning strategy which limits the number of time-consuming iterations while still preserves relative completeness of the final result. The extensive experiments and comprehensive evaluations are conducted and the experimental results show that the KGRL-Incre can significantly reduce time consumption compared with the expanding and reasoning approach in the target scenario.
Let's Google: Uncertainty and bilingual search

This study applies Kuhlthau's Information Search Process stage (ISP) model to understand bilingual users' Internet search experience. We conduct a quasi‐field experiment with 30 bilingual searchers and the results suggested that the ISP model was applicable in studying searchers' information retrieval behavior in search tasks. The ISP model was applicable in studying searchers' information retrieval behavior in simple tasks. However, searchers' emotional responses differed from those of the ISP model for a complex task. By testing searchers using different search strategies, the results suggested that search engines with multilanguage search functions provide an advantage for bilingual searchers in the Internet's multilingual environment. The findings showed that when searchers used a search engine as a tool for problem solving, they might experience different feelings in each ISP stage than in searching for information for a term paper using a library. The results echo other research findings that indicate that information seeking is a multifaceted phenomenon.
Common Object Discovery as Local Search for Maximum Weight Cliques in a Global Object Similarity Graph

In this paper, we consider the task of discovering the common objects in images. Initially, object candidates are generated in each image and an undirected weighted graph is constructed over all the candidates. Each candidate serves as a node in the graph while the weight of the edge describes the similarity between the corresponding pair of candidates. The problem is then expressed as a search for the Maximum Weight Clique (MWC) in this graph. The MWC corresponds to a set of object candidates sharing maximal mutual similarity, and each node in the MWC represents a discovered common object across the images. Since the problem of finding the MWC is NP-hard, most research of the MWC problem focuses on developing various heuristics for finding good cliques within a reasonable time limit. We utilize a recently very popular class of heuristics called local search methods. They search for the MWC directly in the discrete domain of the solution space. The proposed approach is evaluated on the PASCAL VOC image dataset and the YouTube-Objects video dataset, and it demonstrates superior performance over recent state-of-the-art approaches.
Developing a Deep Learning Model to Implement Rosenblatt’s Experiential Memory Brain Model

This paper describes initial work to develop a Deep Learning model for long-term sequential memory storage to implement Rosenblatt's experiential memory Perceptron architecture brain model. In recent years, deep learning techniques have solved many problems in the area of computer vision, language modeling, speech recognition, and audio/video processing. Further, CNN based models are considered state-of-the-art algorithms to solve perceptron related problems. However, can Deep Learning models store the learned knowledge representation to make better use of classifying and recognizing images and other patterns? The Deep Learning models explored here include CNNs pre-trained models (ResNet50, VGG16, InceptionV3, and MobileNet) on ImageNet datasets and trained model on MNIST datasets.
Collection Creation as Collection Management: Libraries as Publishers and Implications for Collection Development

Digital collections, institutional repositories, open educational resources, open access publishing, and new forms of digital scholarship are all ways in which academic libraries are growing their collections from the inside out as well as making this content widely available to other libraries and users. This article will explore national and global trends in library-enabled publishing and content creation, focusing on the kinds of collections being published by libraries, the ways that metadata can bring these collections together for improved access, and the role of consortia in the process.
A Soft Proposal Segmentation Network (SPS-Net) for Hand Segmentation on Depth Videos

Hand segmentation is an important prerequisite for acquiring accurate 3D hand poses on depth images, as it can significantly reduce the complicity of hand pose estimation. However, it has been ignored or treated as a trivial problem for a long time, since most of the hand pose estimation works suppose the hand part is given or can be easily segmented by a depth threshold, which is not practical in many realistic scenarios (e.g., side/egocentric view). In order to perform the robust hand segmentation in various scenarios, we propose a Soft Proposal Segmentation Network (SPS-Net). A key difference from existing hand segmentation methods is that the SPS-Net can utilize the temporal information on depth videos. As a benefit, significant performance gains over the existing methods can be obtained, as demonstrated on two popular public datasets with diverse camera viewpoints and background. Moreover, integrating SPS-Net with a simple 3D hand pose estimator, we achieved the leading result on the 3D Hand Pose Tracking Task of the Hand2017 Challenge - the only world-wide open challenge of its kind.
Understanding the latency distribution of cloud object storage systems

As a fundamental cloud service, the cloud object storage system stores and retrieves millions or even billions of read-heavy data objects. Serving for a massive amount of requests each day makes the response latency be a vital component of user experiences. Timeout is also a key issue as it has a great impact on the response latency. Due to the lack of suitable understanding on the distribution of the response latency and the occurrence of timeouts, current practice is to use overprovision resources to meet a Service Level Agreement (SLA) on response latency. Hence, firstly, we build a performance model for the cloud object storage system, which assumes no timeout occurring. Our model predicts the percentage of requests meeting an SLA, in the context of complicated disk operations, event-driven programming model and requests waiting for being accept()-ed. Secondly, we propose a method that determines whether or not our model is applicable by predicting the occurrence of timeouts. We evaluate our model with a production system using a real-world trace. In a variety of scenarios, our model reduces the prediction errors by up to 90% compared with baseline models, and its overall average error is 2.63%. Moreover, we could also accurately predict the applicability of our model.
Insecurity of an IBEET Scheme and an ABEET Scheme

Cloud computing is a novel pattern, which can allow users to outsource their data to the cloud servers in order to save the resource of clients. For the sake of protecting the clients' privacy, the clients' data are stored or computed in the encrypted data. This leads to a new issue: how to quickly search these encrypted data. One of the methods to solve the problem is to construct public-key encryption schemes with the equality test. In this paper, we point out that an identity-based encryption scheme with the equality test and a ciphertext-policy attribute-based encryption scheme with equality test is not secure on their security models, respectively.