Reviewerly/exHarmony huggingface dataset:
Automating Metadata Logging Through Artificial Intelligence

In 2007, NASCAR designed and implemented a media asset management solution for their sport. Now, in 2018, the library has grown to more than 500,000 hr of content containing video, audio, and still images dating back to 1933—one of the most vast sports libraries in the world. Since the inception of the library, NASCAR has employed a staff to manually apply metadata to each video frame, amassing 10 million entries. While the logging by our staff has been impressive, the incoming data avalanche cannot be addressed by using the current system, let alone the glacier of data hiding in the archive. At present, we have calculated that it would take our existing staff nearly 150 years to log all of the historical content as it stands today. Over the past ten years, we have extensively analyzed open-source and proprietary tools aimed at dealing with the data logging gap and have determined that machine learning is the ideal solution to address metadata logging on large-scale media libraries. As machine learning has become more accessible through the scalability of cloud computing, training data, and implementing convolutional neural networks, it is now within the reach of media production companies and asset stakeholders. NASCAR is on the verge of revolutionizing how all data asset management systems can be restructured in the future to integrate machine learning to harness efficiencies in metadata logging.
A Scheduling Algorithm for Urine Cell Dyeing Machine

In the early diagnosis of diabetes mellitus, staining analysis of urine cells in urinary sediment is a very effective method. The traditional method of cell staining is artificial staining, which is inefficient, inefficient and inconsistent. However, the full-automatic cytostain apparatus which are homemade has many disadvantages. For example, the dyeing efficiency is low and the performance is unstable. In this paper, a new dyeing scheduling algorithm is proposed, which can solve the problems of cell dyeing machines. This algorithm called accuracy-first algorithm is a parallel dyeing algorithm, which takes time registration as the core and is implemented by backtracking method. It can guarantee dyeing accuracy and improve dyeing efficiency. Experiments show that the proposed accuracy-first dyeing scheduling algorithm has higher dyeing accuracy and higher dyeing efficiency.
Monitoring Fluid Level in Fluid Bags of Patient through Wireless Sensors in Hospital

Recent advances in Internet of medical Things (IoMT) have been applied in various healthcare organizations as it is cost-effective and profitable. Apart from this, it provides high customer satisfaction and meets all their needs. Several of limitations in the medical field involve negligence of medical staff, inability to monitor patients, loss of lives due to incapability to reach patients at the right time. However with the introduction of IoMT, it is possible to remove these limitations and facilitate humans with such facilities, which will not only lead to a new revolution for mankind, but also change the face of existing facilities. This chapter proposed an innovative approach to check for fluid flow rate of drops in patient’s body and also find the accurate critical level drop for a saline bag and send immediate alarm to the nurse with the help of IoMT. The proposed method is cost-effective and needs less human effort in hospitals which will lead to a new revolution in the field of medical science.
Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks system (Erratum)

The <i>Journal of Medical Imaging</i> allows for the peer-reviewed communication and archiving of fundamental and translational research, as well as applications, focused on medical imaging, a field that continues to benefit from technological improvements and yield biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal conditions.
Learning with Vertically-Partitioned Data, Binary Feedback, and Random Parameter Update

Machine learning models can deal with data samples scattered among distributed agents, each of which holds a nonoverlapping set of sample features. In this paper, we propose a training algorithm that does not require communication between these agents. A coordinator can access ground-truth labels and produces binary feedback to guide the optimization process towards optimal model parameters. We mimic the gradient descent technique with information observed locally at each agent. We experimented with the logistic regression model on multiple benchmark datasets and achieves promising results in terms of convergence rate and communication load.
Semantic-Guided Hashing for Cross-Modal Retrieval

In the Big Data era, information retrieval across heterogeneous data or multimodal data is a very significant issue. Cross-modal hashing has recently attracted increasing attention for multimodal retrieval with benefits of fast retrieval efficiency and low storage cost. Many supervised cross-modal hashing approaches have been explored to achieve better performance according to label information. However, most of these existing methods take the form of 0/1 binary labels or pairwise relationships as supervised information, resulting in the neglect of valuable semantic correction among different classes. To address this problem, we propose a novel two-step supervised cross-modal hashing approach, termed Semantic-Guided Hashing (SeGH), to obtain the discriminative binary codes. Particularly, in Step 1, our method takes the encoder-decoder paradigm based on label semantics obtained by the word vector of class names to learn the discriminative projection from original feature space to common semantic space. In Step 2, semantic representations of different modalities in the common space are projected into a Hamming space while preserving intra-modality and inter-modality similarity. Extensive experiments compared against several state-of-the-art baselines on two datasets highlight the superiority of the proposed SeGH for cross-modal retrieval, and also demonstrate its effectiveness for zero-shot cross-modal retrieval.
On the Matrices of Pairwise Frequencies of Categorical Attributes for Objects Classification

This paper proposes two new algorithms for classifying objects with categorical attributes.These algorithms are derived from the assumption that the attributes of different object classes have different probability distributions.One algorithm classifies objects based on the distribution of the attribute frequencies, and the other classifies objects based on the distribution of the pairwise attribute frequencies described using a matrix of pairwise frequencies.Both algorithms are based on the method of invariants, which offers the simplest dependencies for estimating the probabilities of objects in each class by an average frequency of their attributes.The estimated object class corresponds to the maximum probability.This method reflects the sensory process models of animals and is aimed at recognizing an object class by searching for a prototype in information accumulated in the brain.Because these matrices may be sparse, the solution cannot be determined for some objects.For these objects, an analog of the k-nearest neighbors method is provided in which for each attribute value, the class to which the majority of the k-nearest objects in the training sample belong is determined, and the most likely class value is calculated.The efficiencies of these two algorithms were confirmed on five databases.
The Design and Development of Virtual Teaching System based on Action-oriented

For the problem that exists in actual theory-practice integration instructional practice in some vocational schools, which included unbalanced development of students' key vocational skills, the lack of teaching equipment, absence of integrated multimedia instructional resources and instructional design schemes, a suitable instructional system scheme is designed based on action-oriented teaching concept and Job work process. The Unity3D engine is used to build a virtual teaching software that assisted teaching and training in the theory-practice integration and then conducts integrated teaching that based on action-oriented which can make up for the lack of hardware and software resources in actual teaching. By creating a teaching situation that can mobilize students' conscious activities, and better play the advantages of teaching mode in the integration of theory and practice, which can cultivate high-quality talents. Taking an engine as an example, the construction process of virtual teaching system that based on automobile engine of theory-practice integration was elaborated.
Basics and Drivers of Artificial Intelligence

In this chapter we discuss the basics and drivers of Artificial Intelligence. The key drivers are Moore’s Law, digitalization and dematerialization of products, services and processes, the connection of these within global network, big data and new technologies. We also analyse the investments in Artificial Intelligence.
Estimation of Model Capacity for Image Classification

Convolutional neural network (CNN) has shown phenomenal results on several image classification applications. The performance of CNN is improved with deeper architectures. However, increasing depth comes with certain drawbacks like overfitting and need for more computational resources. Thus, it is necessary to choose optimal depth for a network. In this work, the empirical relation between model depth, capacity and complexity of data is estimated using proposed extended VGGNET (EVGGNET). The EVGGNET consists of feature extraction and classification network. The feature extraction network is divided into pre-trained and extended sections which extract the significant features. The classification network uses these features to classify input image into one of the categories. The basic idea behind EVGGNET is to extend the pre-trained feature extraction network by adding convolutional layers which help to establish the model capacity relationship. VGGNET is used as a pre-trained feature extraction network. The experiments are performed on benchmark datasets Caltech 101 and Caltech 256. The results show that the accuracy of EVGGNET is almost exponentially proportional to the complexity of data.
Impact of Deception Information on Negotiation Dialog Management: A Case Study on Doctor-Patient Conversations

Almost all of existing negotiation systems assumeThe Tung, Nguyen thatYoshino, Koichiro theirSakti, Sakriani interlocutors (the user) are telling the truth. However, in negotiations, participants canNakamura, Satoshi tell lies to earn a profit. In this research, we proposed a negotiation dialog management system that detects user’s lies and designed a dialog behavior on how should the system react with. As a typical case, we built a dialog model of doctor-patient conversation on living habits domain. We showed that we can use partially observable Markov decision process (POMDP) to model this conversation and use reinforcement learning to train the system’s policy.
Cost-Aware Learning for Improved Identifiability with Multiple Experiments

We analyze the sample complexity of learning from multiple experiments where the experimenter has a total budget for obtaining samples. In this problem, the learner should choose a hypothesis that performs well with respect to multiple experiments, and their related data distributions. Each collected sample is associated with a cost which depends on the particular experiments. In our setup, a learner performs m experiments, while incurring a total cost C. We first show that learning from multiple experiments allows to improve identifiability. Additionally, by using a Rademacher complexity approach, we show that the gap between the training and generalization error is O(C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/2</sup> ). We also provide some examples for linear prediction, two-layer neural networks and kernel methods.
Integrated Learning of Features and Ranking Function in Information Retrieval

Recent deep learning models for information retrieval typically aim to learn features either about the contents of the document and the query, or about the interactions between them. However, the existing literature shows that document ranking depends simultaneously on many factors, including both content and interaction features. The integration of both types of neural features has not been extensively studied. In addition, many studies have also shown that the deep neural features cannot replace completely the traditional features, but are complementary. It is thus reasonable to combine deep neural features with traditional features. In this paper, we propose an integrated end-to-end learning framework based on learning-to-rank (L2R) to learn both neural features and the L2R ranking function simultaneously. The framework also has the flexibility to integrate arbitrary traditional features. Our experiments on public datasets confirm that such an integrated learning strategy is better than separate learning of features and ranking function, and integrating traditional features can further improve the results.
Research on Cross-media Semantic Retrieval Methods of Information Resources Based on Deep Learning

This paper is to study cross-media semantic retrieval method of information resources based on deep learning. By analyzing the concept of deep learning, the depth structure and the prerequisites for deep learning, this paper studies the relationship between deep learning and information resources cross-media semantic retrieval, and points out the cross-media correlation learning technology of information resources on the basis of depth structure, buildings a distinct framework for information retrieval. As a new information retrieval model, the combination of deep learning and cross-media semantic retrieval can solve the problems of retrieving semantic information across the media and processing complex dimensional data, greatly improving the efficiency of data retrieval and integration. This model will replace the existing information retrieval tools, to become a sword to enhance the level of knowledge service in the era of big data.
Towards an Open-Doman Dialog System

Developing an intelligent dialogue system that not only emulates human conversation, but also can answer questions of topics ranging from latest news of a movie star to Einstein's theory of relativity, and fulfill complex tasks such as travel planning, has been one of the longest running goals in AI. In this talk, we use Microsoft XiaoIce [1] as a case study to discuss the state-of-the-art conversational AI [2], focusing on three types of dialogues: (1) question answering bots that can provide concise direct answers to user queries; (2) task-oriented bots that can help users accomplish tasks ranging from meeting scheduling to vacation planning; and (3) social bots which can converse seamlessly and appropriately with humans, and often plays roles of a chat companion and a recommender.
A Distributed Approach to 3D Reconstruction in Marker Motion Capture Systems

Optical motion capture systems have attracted much interest over the past years, due to their advantages with respect to non-optical counterparts. Moreover, with the technological advances on camera devices, computer graphics and computational methodologies, it becomes technically and economically feasible to consider motion capture systems made of large networks of cameras with embedded communication and processing units on board (i.e., smart cameras). In this case, the approaches relying on the classical 3D reconstruction methods would become inefficient, since their nature is intrinsically centralized. For this reason, we propose a distributed 3D reconstruction algorithm, which relies on a specific organization of cameras to remarkably speed up the scene reconstruction task. Indeed, numerical and experimental results show that the proposed computational scheme overcomes classical centralized solutions, in terms of reconstruction speed. Furthermore, the high processing speed does not compromise the estimation accuracy, since the algorithm is designed to be robust to occlusions and noise.
Randomness Generation Based Quantum Key Agreement for Secured Routing in Mobile Ad Hoc Network

Randomness Generation based Quantum Key Agreement (RGQKA) technique have the route discovery phase and data packet transmission. The route discovery process finds the shortest route path from the source node to the destination node through the control message distribution. After finding the route path, RGQKA technique is carried out to transmit the data packets in a secured manner. In quantum key agreement phase, the quantum keys are randomly generated for the source node and the destination node. Then, a data packet gets encrypted in the form of binary bits. Then, binary bits are converted into qubits through randomly selected bases and sent to the destination node. Receives qubits, it decrypts the qubits to acquire the original data packet information using the same quantum key. Simulation of RGQKA technique and existing methods have parameters such as packet delivery ratio, routing overhead, packet loss, and throughput with respect to a number of data packets.
Modeling Human Intelligence in Customer-Agent Conversation Using Fine-Grained Dialogue Acts

Smart service chatbot, aiming to provide efficient, reliable and natural customer service, has grown rapidly in recent years. The understanding of human-agent conversation, especially modeling the conversational behavior, is essential to enhance the machine intelligence during the customer-chatbot interaction. However, there is a gap between qualitative behavior description and the corresponding technical application. In this paper, we developed a novel fine-grained dialogue act framework specific to smartphone customer service to tackle this problem. First of all, following a data-driven process, we defined a two-level classification to capture the most common conversational behavior during smartphone customer service such as affirm, deny, gratitude etc., and verified it by tagging chatlog generated by human agent. Then, using this framework, we designed a series of technically feasible dialogue policies to output human-like response. As an example, we realized a smart service chatbot for a smartphone customer using the dialogue-act-based policy. Finally, a user study was conducted to verify its efficiency and naturalness. Since the dialogue acts are meaningful abstraction of conversational behavior, the dialogue-act-based chatbot could be more explainable and flexible than the end-to-end solution.
The strategic use of argumentation from example in re-evaluating a people

Abstract With the help of the extended pragma-dialectical theory, this paper aims to analyze how Al Aswany, an Egyptian political columnist who argued in favor of the feasibility of democratization before the Arab Spring, maneuvered strategically by argumentation from example in two of his columns in supporting the standpoint that the Egyptian people had become no longer politically inactive. The analysis is conducted in view of the institutional preconditions of political columns and the specific rhetorical exigency a columnist may face in this specific argumentative situation.
Can Deep Learning Save Us and Itself From the Avalanche of Threats in Cyberspace?

I present a computationally efficient and accurate feedforward neural network for sentiment prediction capable of maintaining high transfer accuracy when coupled with an effective semantics model of the text. Experimental results show the advantages of the new approach. Applications to security validation programs are discussed.
Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas

This paper deals with the trick-taking game of Klaverjas, in which two teams of two players aim to gather as many high valued cards for their team as possible. We propose an efficient encoding to enumerate possible configurations of the game, such that subsequently $$\alpha \beta $$ -search can be employed to effectively determine whether a given hand of cards is winning. To avoid having to apply the exact approach to all possible game configurations, we introduce a partitioning of hands into $$981,\!541$$ equivalence classes. In addition, we devise a machine learning approach that, based on a combination of simple features is able to predict with high accuracy whether a hand is winning. This approach essentially mimics humans, who typically decide whether or not to play a dealt hand based on various simple counts of high ranking cards in their hand. By comparing the results of the exact algorithm and the machine learning approach we are able to characterize precisely which instances are difficult to solve for an algorithm, but easy to decide for a human. Results on almost one million game instances show that the exact approach typically solves a game within minutes, whereas a relatively small number of instances require up to several days, traversing a space of several billion game states. Interestingly, it is precisely those instances that are always correctly classified by the machine learning approach. This suggests that a hybrid approach combining both machine learning and exact search may be the solution to a perfect real-time artificial Klaverjas agent.
Word Sense Induction Using Word Sketches

We present three methods for word sense induction based on Word Sketches. The methods are being developed a part of an semiautomatic dictionary creation system, providing annotators with the summarized semantic behavior of a word. Two of the methods are based on the assumption of a word having a single sense per collocation. We cluster the Word Sketch based collocations by their co-occurrence behavior in the first method. The second method clusters the collocations using word embedding model. The last method is based on clustering of Word Sketch thesauri. We evaluate the methods and demonstrate their behavior on representative words.
Concept Integration to Develop Next Generation of Technology-Enhanced Learning Systems

This paper offers an overview on the personalization of learning process components to different learners’ characteristics using ontology-based frameworks. Diversification of ontology knowledge sources is a promising solution to support interoperability between the components, achieve effective personalization, improve learning process and support of a precise and richer e-learning system structure. Ontological components can use open data, published ontologies and domain knowledge to construct a domain ontology consisting of common constructs, concepts and instances. The paper describes a set of requirements that context modelling and reasoning techniques in education area should meet and highlight how the currently most prominent approach to context modelling and reasoning is rooted in ontology-based frameworks.
Cloud Security Service Level Agreements: Representation and Measurement

Service Level Agreements (SLAs) are commonly used to negotiate the computation and performance requirements between a service provider and user. In commercial cloud computing environments, SLAs are commonly used to negotiate performance guarantees. However there are no standardized mechanisms for defining and enforcement of security SLAs in cloud computing. In this paper, we consider the problem of defining security SLAs in cloud computing environment. We divide these SLAs into three categories as availability, security and integrity related SLAs. We provide a mechanism to formally describe SLAs and a method for run-time evaluation of these SLAs through a trusted Third Party Auditor (TPA). TPA collects necessary evidence in the form of logs from the cloud on a cloud user's behalf and evaluates SLA's compliance against collected evidence. We implement few sample SLAs of each category in a cloud testbed and show that TPA can evaluate and enforce the security requirements.