Reviewerly/exHarmony huggingface dataset:
Machine Learning of SPARQL Templates for Question Answering over LinkedSpending.

We present a Question Answering system aimed to answer natural language questions over the open RDF spending data provided by LinkedSpeding. We propose an original machine-learning approach to learn generalized SPARQL templates from an existing training set of (NL question, SPARQL query) pairs. In our approach, the generalized SPARQL templates are fed to an instance-based classifier that associates a given user-provided question to an existing pair that is used to answer the user question. We employ an external tagger, delegating the Named-Entity Recognition (NER) task to a service developed for the domain we want to query. The problem is particularly challenging due to the small training set size available, counting only 100 questions/SPARQL queries. We illustrate the results of our new approach using data provided by the Question Answering over Linked Data challenge (QALD-6) task 3, showing that we can provide a correct answer to 14 of the 50 questions of the test set. These results are then compared to existing systems, including our previous system, QA3, where templates were provided by an expert rather than being generated automatically from a training set.
LibGuides: MLA Micro-course: Practice

Using hands-on practice, learn how to create in-text and Works Cited citations, as well as craft paraphrases and summaries of source material.
80/20 vis-à-vis Core Journals: Use of e-ShodhSindhu Resources at the University of Delhi

The study aims to identify the core collection of the University of Delhi from electronic resources provided by e-ShodhSindhu, consortia for higher educational institutes of India on the basis of Pareto 80/20 rule. The study is based on empirical data downloaded from the e-ShodhSindhu portal for the period of 2010–2017. The usage has been statistically analyzed by different parameters such as average download per year, % of journals that cover 80% of downloads, number of titles contributed by each resource into the 80% downloads, identifying the core titles across the resources covering 80% of the downloads, identification of lesser/unused titles with the help of tables and graphs. The results suggest that the Pareto’s theory needs to be relooked in the bundled subscription. In the case of the University of Delhi, 90% demands can be met by subscribing less than 20% of the titles or 10% titles can satisfy 80% of the information needs.
Object Detection on Video for Shipborne Environmental Perception System

We present an application based on convolutional neural networks (CNN) as the first step of environmental perception, to identify objects nearby unmanned vessels. A Single Shot MultiBox Detector (SSD) architecture was built, with a MobileNet network as feature extractor. To test the feasibility, three datasets were generated and applied to our CNN model as training data. The trained model was used to identify ships in videos. Videos were cut into frames, and applied to the trained model. After objects in each frame were recognized, new videos with bounding boxes were generated as outputs. With the first two datasets, ships are successfully detected. Using the third dataset, a big cargo ship is well identified, while small ships are ignored for lack of training data. Results indicate that with a sufficient training, our implementation has good object detection performance, and verified to be suitable for an environmental perception system.
The Construction of Undergraduate Pathway Curriculum System in Higher Vocational Education in Mechanical Engineering

At the National Vocational Education Work Conference, General Secretary Xi emphasized that “it is necessary to train hundreds of millions of high-quality laborers and skilled personnel”. General Secretary Xi’s speech further clarified that the focus of vocational education talent training is “to make the best use of talent”. This requires the ability to train in the middle and high vocational undergraduate connection, and constantly explore the talent training model, theoretical and practical curriculum system and teaching methods and means suitable for the linkage of middle and high vocational schools. To this end, based on the investigation of the status quo of the undergraduate connection in middle and high vocational colleges in Jilin Province, this paper takes the 3 + 4 transfer course of the undergraduate course in our college as an example to expound the construction of the curriculum system based on the professional ability of students. Provide reference on the issue of undergraduate convergence in middle and high vocational schools.
Cyber-threats and cybersecurity challenges: A cross-cultural perspective

As is the case of any economic activity, cultural factors are tightly linked to cybercrimes, cyberattacks and cybersecurity. Just like any other activities, some forms of cybercrime may be more acceptable in some cultures than in others. For some categories of cyberoffenses, cultural factors appear to play more important roles than other environmental factors. For instance, cybercrimes are more justifiable in some cultures. Quoting a Russian hacker-turned-teacher, Blau (2004) describes how he and his friends hacked programs and distributed them for free during their childhood: “It was like our donation to society, it was a form of honor; [we were] like Robin Hood bringing programs to people.” Likewise, it is argued that culture and ethical attitudes may be a more crucial factor in driving software piracy as well as a number of other cybercrimes than the levels of economic development (Donaldson, 1996; Kshetri, 2009b, 2013a, b, c, d; Kwong et al., 2003).
Industrial Network Topology Analysis with Episode Mining

Industrial network communication is highly deterministic as result of availability requirement of control systems in automated industrial production systems. This deterministic character helps with initial step of self-learning anomaly detection systems to detect periodic production cycle in industrial network communication. The methods for frequent episode mining in event sequences fits well to solve thechallenge of production cycle detection for self-learning system. We encode the network communication events to serial and parallel episodes. Methods for discovery of frequent episodes in event sequences are briefly explained. These methods would be further adapted in future to our encoded network communication traffic to extract production cycle comprised of serial and parallel episodes.
Information Transmission and Nonspecificity in Feature Selection.

In this paper we propose a novel feature selection method which is based on fuzzy measures. More specifically, we apply a similarity measure to form similarity matrices from the data and apply nonspecificity on similarity degrees in order to conduct feature selection. To measure how relevant a particular feature is, we apply an information transmission measure. We exemplify our method on a simple artificial case to demonstrate its ability to select informative features. Moreover, we test our method on two real world data sets, the chronic kidney disease and the diabetic retinopathy Debrecen dataset. The nonspecificity-based feature selection method leads for both datasets to improvements in the mean classification performance. In comparison with the popular ReliefF algorithm and the Fisher Score, the new method reaches competitive results and also accomplishes the highest mean accuracy for both datasets.
Contagious Diseases Prediction in Healthcare Over Big Data

As Data trends go higher and higher, the data creation process tends to increase at an unprecedented rate. They are not only increasing, they also change, and incarnate from one form to the another. Recently, Big data revolution has introduced an incarnation of how the data is captured, accessed, aggregated, converted and stored. Healthcare communities is one of the big data procreations, where data gets generated rapidly. The appropriate analysis of healthcare data helps to detect the diseases at an early stage to save the patients' life. Although, analysis is required to make a proper decision, this analysis is declined due to the incomplete data. This paper focuses on the prediction towards the emerging contagious diseases to provide enhanced accuracy in the process of disease detection. To solve the incomplete e.g. missing data, the Improved Expectation Maximization (IEM) has been employed. Here, the experiment is carried out on Ebola disease. This research work proposed a new Ensemble Neural Network algorithm using structured and unstructured data. A proposed model was trained on prediction from dataset and evaluate each model. In this work, the Ensemble Neural Network shows an average performance, but leverages a very firm and more consistent performance analysis accuracy. This method will deliver an enhanced potential for the decision makers in healthcare communities.
Usability Evaluation of Digital Libraries in Tehran Public Universities

For a sustainable development and greater success, digital libraries need to be evaluated at different time points. Usability study is a type of digital library evaluation.The aim of this study is identifying the usability of digital libraries in Tehran's public universities i.e. Sharif, Amirkabir and Tehran universities. Evaluative Survey methodology was used for the survey applying measures of search, navigation, forms layout, contrast and scan ability, optimization, help, usage of windows and speed and errors through a questionnaire containing 36 questions. The research population included 124,800 undergraduate, Master and PhD students, from three governmental universities of Sharif, Amirkabir and Tehran. Stratified random sampling method was applied to select 378 students as a sample which are 130 people in Tehran, Amirkabir 125 people and Sharif 123 people. For data analysis, SPSS software was used along with descriptive techniques as well as independent test and ANOVA where needed.The findings indicated that the criteria relating to the usability of digital libraries in the three Tehran governmental universities evaluated to be in relatively good condition and there is no significant difference between the digital libraries for the usability criteria. The results of this study suggest that according to the usability of digital library design creates more willingly resort to this type of user to visit the library and the realization of the objectives of the parent organization provides.
Real-time path planning for high speed UGVs

The application of a modified A-star (A*) global search algorithm and trajectory planner based on the tentacles algorithm approach are investigated for real-time path and trajectory planning on an unmanned ground vehicle operating at a speed of 40 km/h. The fundamental assumption made is that for high speed applications, the requirement for an optimal path is secondary to the requirement for short processing times, provided that a solution, if it exists, is found. The proposed solution is benchmarked against the original A* algorithm and shows a reduction in search space of up to 84% and a reduction in processing time of up to 97%. Results for the trajectory planner are also presented, though no direct comparative evaluation against the original tentacles algorithm was executed. The combined path and trajectory processing time of the proposed solution translates to less than 2 mm of travel distance before a reaction to a change in the environment can be processed.
Deep Strategies in Computer-Assisted Diagnosis and Classification of Abnormalities in Medical Images

Digital diagnosis based on computer-assisted technologies is a procedure that provides another objective consultation to radiologist beyond their knowledge for the assistance of medical image in terms of proper diagnosis and their interpretation. The digital diagnosis became a handful implementation in various imaging forms, such as magnetic resonance imaging, mammography, computed tomography scan, histopathology imaging, and ultrasound diagnostic. Digital diagnosis has found full dependency on how well the proper related informative features were extracted, which were later used for machine learning followed by automatic prediction and analysis of medical images. Deep neural network architecture is a hierarchical computation of multiple layers of neuron stacks. The trends of the associated work on medical images present in literature have focused on some of the benign and malignant images in terms of segmentation, classification, and prediction of malignancy using World Health Organization scale.
Violation of space–time Bell-CHSH inequality beyond the Tsirelson bound and quantum cryptography

Here we show that if we insert context-dependent local unitary evolutions into the spatial (i.e. normal) Bell–Clauser–Horne–Shimony–Holt (Bell-CHSH) test, then it is possible to violate the space–time Bell-CHSH inequality maximally (i.e. up to 4). The correct context dependency can be achieved via post-selection. However, this does not contradict the Tsirelson quantum bound ( $$2\sqrt{2}$$ ), because the latter has been derived without taking into consideration the context-dependent unitary evolutions and / or post-selection. As an important application, this leads to a more efficient (in terms of resource (singlets) and classical communication) and more sensitive (to eavesdropping) quantum key distribution (QKD) protocol, compared to Ekert’s and Wigner’s QKD protocols.
Consumers’ Brand Experiences When Using Voice Interfaces

Voice interfaces are conquering consumer lifeworlds. They offer new ways to consumers to interact with brands and challenge existing brand management theories. By drawing on an explorative approach, this paper identifies four different voice experiences that emerge when consumers access brands through voice interfaces (for instance through personal voice assistants such as Amazon Alexa): (1) brand camouflage, (2) category salience, (3) brand evocation and (4) brand amplification.
LibGuides: Interlibrary Loan: Interlibrary Loan

Interlibrary Loan (ILL) is the process through which Link Library requests items from other libraries on behalf of the CUNE community. This guide provides ILL forms and explains the process and procedures of Interlibrary Loaning materials.
Data-Driven Intelligent Scheduling For Long Running Workloads In Large-Scale Datacenters

Cloud computing is becoming a fundamental facility of society today. Large-scale public or private cloud datacenters spreading millions of servers, as a warehouse-scale computer, are supporting most business of Fortune-500 companies and serving billions of users around the world. Unfortunately, modern industry-wide average datacenter utilization is as low as 6% to 12%. Low utilization not only negatively impacts operational and capital components of cost efficiency, but also becomes the scaling bottleneck due to the limits of electricity delivered by nearby utility. It is critical and challenge to improve multi-resource efficiency for global datacenters. Additionally, with the great commercial success of diverse big data analytics services, enterprise datacenters are evolving to host heterogeneous computation workloads including online web services, batch processing, machine learning, streaming computing, interactive query and graph computation on shared clusters. Most of them are long-running workloads that leverage long-lived containers to execute tasks. We concluded datacenter resource scheduling works over last 15 years. Most previous works are designed to maximize the cluster efficiency for short-lived tasks in batch processing system like Hadoop. They are not suitable for modern long-running workloads of Microservices, Spark, Flink, Pregel, Storm or Tensorflow like systems. It is urgent to develop new effective scheduling and resource allocation approaches to improve efficiency in large-scale enterprise datacenters. In the dissertation, we are the first of works to define and identify the problems, challenges and scenarios of scheduling and resource management for diverse long-running workloads in modern datacenter. They rely on predictive scheduling techniques to perform reservation, auto-scaling, migration or rescheduling. It forces us to pursue and explore more intelligent scheduling techniques by adequate predictive knowledges. We innovatively specify what is intelligent scheduling, what abilities are necessary towards intelligent scheduling, how to leverage intelligent scheduling to transfer NP-hard online scheduling problems to resolvable offline scheduling issues. We designed and implemented an intelligent cloud datacenter scheduler, which automatically performs resource-to-performance modeling, predictive optimal reservation estimation, QoS (interference)-aware predictive scheduling to maximize resource efficiency of multi-dimensions (CPU, Memory, Network, Disk I/O), and strictly guarantee service level agreements (SLA) for long-running workloads. Finally, we introduced a large-scale co-location techniques of executing long-running and other workloads on the shared global datacenter infrastructure of Alibaba Group. It effectively improves cluster utilization from 10% to averagely 50%. It is far more complicated beyond scheduling that involves technique evolutions of IDC, network, physical datacenter topology, storage,…
A Method of Automatically Calculating Vaginal Cleanliness Degree from Leukorrhoea's Microscope Images

Vaginal cleanliness degree is one of the most important diagnosis criteria in regular examination of leucorrhea. It can be used to judge whether inflammation occurs. The most common clinical way to obtain vaginal cleanliness degree is manual microscopic examination. In current research, some groups have already proposed some methods to automatically analyse images from microscopic examination of leucorrhea secretion. But none of these methods can recognize four important targets at the same time, so they can’t obtain the vaginal cleanliness degree. This paper reports about a method that can automatically calculate the vaginal cleanliness degree of a microscopic examination image. This method includes a convolutional network similar to fully convolutional networks, and some morphological operations to extract contour of each cell and count their number. Experiments prove that our algorithm is simple, fast, efficient and accurate. It has good clinical potential.
Session Layer Security Enhancement Using Customized Protocol and Strong Cryptographic Mechanism

Cloud computing has been considered as a mechanism to deliver information technology services. Data security is significant aspect of Internet for enterprises of each size and type. Security of Data is must for protective digital privacy measures. However there have been several existing researches that propose security but they have certain limitations. The proposed work introduces security at multiple layers and user defined port to define more secure data transmission protocol in order to enhance the session layer [1] security of network. More over the early session timeout has been also controlled by reducing packet size. Proposed work would be capable to increase the efficiency of data communication along with security. Moreover unauthentic access of data has been restricted using IP validation process.
Adding Social Aspects to the Automatic Generation of Narratives

Initially, the computation models to study automatic storytelling were based on artificial intelligence techniques such as problem solving or planning. Nowadays, these systems incorporate cognitive and psychological models of creativity. However, most of them lack of accurate representations for social norms, mechanisms to extract, use or detect their rupture. Our research presents a model with these characteristics, and it can be incorporated into a narrative generator to enrich their creations. Additionally, we describe the implementation of the model within an automatic narrative generator. Finally, we show results of evaluating the perception of the people on the use of social norms when using our model in the process of creating narratives.
Spinning the AI Pinwheel

Advances in supervised machine learning have frequently been fueled by access to large-scale labeled data. In business, however, natural labels may not exist. In these cases, a common industry playbook involves using manual, human annotation to label enough data points in order to train a model. This paper describes a general structure for accelerating the annotation process using artificial intelligence and combining it with model quality assurance (QA). In this talk we walk through this process in detail. We start with rich manual annotation of a small number of unlabeled data points. These can then be used to train a series of coarse predictive models that are used to prepopulate some default selections in the annotation tool and speed up annotator performance. With more data points, models can be retrained on a regular cadence and less human intervention is required. Finally, models can provide defaults for all fields, and re-training continues until the annotator override rate reaches a production-grade level. Tradeoffs of this type of approach include balancing the in- creased annotation efficiency with engineering costs associated with building annotation and quality assurance tools. We will walk through these tradeoffs, which depend on the problem class and complexity of the model. Finally, we will include a detailed industry case study based on the use of artificial intelligence in the annotation process at KeepTruckin, where we use annotation to label vehicle location history data.
Augmentation of Virtual Agents in Real Crowd Videos

Augmentation of virtual agents in real crowd videos is an important task for different applications from design simulations of social environments to modeling abnormalities in crowd behavior. We propose a framework for this task, namely for augmenting virtual agents in real crowd videos. Our framework utilizes homography-based video stabilization, Dalal-Triggs detector [1] for pedestrian detection and state-based tracking algorithms to automatically locate the pedestrians in video frames and project them into our 3D simulated environment, where the navigable area of the simulated environment is available as a manually designed and located navigation mesh. We represent the real pedestrians in the video as simple three-dimensional (3D) models in our simulation environment. 3D models representing real, projected agents and the augmented virtual agents are simulated using local path planning coupled with a collision detection and avoidance algorithm, called Reciprocal Velocity Obstacles (RVO) [2]. The virtual agents augmented into the video move plausibly without colliding with static and dynamic obstacles, including other virtual agents and real pedestrians. We provide an extensive graphical user interface for controlling the virtual agents in the scene, including collision avoidance parameters, adjusting the camera in the scene and some standard video player options.