Reviewerly/exHarmony huggingface dataset:
“Situation polygon” as an intelligent environment for strategic decision-making support in development of Russian energy sector

For justification and support of strategic decisions, it is feasible to use intelligent information technologies. For development of intelligent environments for strategic decision-making support on development of Russian energy sector, we propose to use concepts of situational management and semantic modelling. This article considers “Situation polygon” - an intelligent environment for strategic decision-making support in development of Russian energy sector which is based on these concepts and agent-service approach for construction of typical multiagent system. Brief description of these concepts is provided. Main attention is paid to the architecture of this environment shown and its main components. Also, an example of user’s interaction with the system is provided with detailed description. This example is based on threat of temperature decrease which is significant for Russian energy sector, especially Siberian region.
Understanding Learner and Instructor Attitudes Toward and Use of Mobile-Assisted Language Learning

This chapter offers insights into the benefits and drawbacks of adopting mobile learning in language education, both from a theoretical as well as a practical point of view. A survey was designed to explore pedagogically sound practices and provide a better understanding of the current and future role of MALL on language learning, specifically in higher education (HE). Comparison between the two target groups (learners and teachers) produced results to aid in aligning and narrowing distances between the learners' independent usage of MALL in informal learning and the instructors' perception of how mobile apps can or should be integrated into the language curriculum. The chapter proposes a number of research areas that require further exploration in MALL and with a set of recommendations in terms of embracing MALL practices in language learning and teaching.
Evolving Game State Evaluation Functions for a Hybrid Planning Approach

Real-time games often require a combination of long-term and short-term planning as well as interleaved planning and execution. In our previous work, we introduced a hybrid planning and execution approach, in which high-level strategical planning is performed by a Hierarchical Task Network Planner and micro-management is done through Monte Carlo Tree Search. We use evaluation functions that represent weighted sums of selected game features as an interface between the two hierarchy levels.In this work, we present a way of automatically evolving the weights of these evaluation functions in order to improve the efficiency of the execution of high-level tasks. We compare the agent using the evolved evaluation functions with the one using manually created evaluation functions against state-of-theart controllers in the Real Time Strategy game environment microRTS.
Combining Multi-classifier with CNN in Detection and Classification of Breast Calcification

Breast calcification or microtumors screening can early detect breast cancer that can make the disease easier to treat. At present, the segmentation of breast calcifications relies on the delineate by doctors. The process is time-consuming, and the benefits are not readily apparent. None of the paper has been discussed on combining automatically delineate and classify the breast calcifications to benign or malignant in previous research. According to the above reasons, we proposed an approach on combining Cascade Adaboost with CNN to delineate breast calcifications in mammogram and classify breast calcifications to benign or malignant by the CNN we trained. The ability of classification in Cascade Adaboost algorithm is better than Adaboost algorithm, it can significantly reduce the time cost by classification in CNN and speed up the process time. In this paper, we compare our method with the architecture of R-CNN combining CNN, and the experimental results show that by using Cascade Adaboost combined with CNN can detect calcification more accurately and classify it into benign or malignant. We hope that by using the approach in this work can help doctors to detect and diagnose breast calcifications in less time.
Mask R-CNN Method for Dashboard Feature Extraction in Eye Tracking

The traditional information extraction technology of dashboard is easily affected by external factors, and the robustness is poor. To improve the safety of the pilot’s performance on the dashboard, this paper proposes a way for extracting the dashboard feature information in eye tracking, which acquires the line of sight point in simulated dashboard. It then uses the Mask R-CNN method to detect the gaze area and then extracts the target feature information. Finally, it fuses two sets of data to get the result of the pilot who extracts the target gaze area in the scene. Experiment results show that the method of new dashboard information extraction proposed in this paper has a better accuracy.
A Compact Recursive Dense Convolutional Network for image classification

In general, the deeper the convolutional network is, the higher the accuracy will be. However, when the depth of the network increases, the number of parameters (weights and bias) will increase quickly, which requires a lot of storage space when it is applied in real life. Considering the parameters’ efficiency for the deep network, we propose an improved deep convolutional network, named Recursive Dense Convolutional Network (RDenseNet), inspired by the recursive idea. The proposed RDenseNet still utilizes the dense block in Dense Convolutional Network (DenseNet) as a basic structure, but different from the cascade structure of DenseNet, our proposed RDenseNet uses the output of the dense block as input for the current block again. That is, our proposed RDenseNet turns the cascade structure in DenseNet into a recursive structure, which can reduce the number of parameters that actually need to be stored. Experiments illustrate that our proposed RDenseNet can still keep or exceed the accuracy of some state-of-the-art deep networks with much less parameters.
A Novel Semantic Segmentation Algorithm Using a Hierarchical Adjacency Dependent Network

Recent semantic segmentation networks mainly focus on how to fuse multi-level features from classification networks to improve segmentation accuracy. Some researches evenly emphasize the correlation of pixels in a global region, such as conditional random field (CRF). However, the strong correlation feature of pixels in a limited region is less considered in the previous researches and the remedy ability of the correlation of local pixels in semantic segmentation is severely ignored. To deal with this problem, we introduce a hierarchical adjacency dependent network (HadNet), in which an adjacency dependency module (ADM) is constructed by calculating and utilizing the impact fact of the pixel in different directions to classify the pixel. We explored the correlation of adjacent pixels and feature coverage in different feature levels to improve the segmentation accuracy. We evaluate our method on the popular Pascal VOC 2012 test set, and achieve a comparable result of mIOU accuracy of 79.8% with the state of art methods, such as DeepLabv3+ and Exfuse. Further, we discuss and analyze the data distribution of COCO dataset for deeply understanding the feature correlation and coverage in semantic segmentation.
On the Bias of Directed Information Estimators

When estimating the directed information between two jointly stationary Markov processes, it is typically assumed that the recipient of the directed information is itself Markov of the same order as the joint process. While this assumption is often made explicit in the presentation of such estimators, a characterization of when we can expect the assumption to hold is lacking. Using the concept of d-separation from Bayesian networks, we present sufficient conditions for which this assumption holds. We further show that the set of parameters for which these conditions are not also necessary has Lebesgue measure zero. Given the strictness of these conditions, we introduce a notion of partial directed information, which can be used to bound the bias of directed information estimates when the directed information recipient is not itself Markov. Lastly we estimate this bound on simulations in a variety of settings to assess the extent to which the bias should be cause for concern.
Policy Based Inference in Trick-Taking Card Games

Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions. This makes the number of histories exponentially large in the action sequence length, as well as creating extremely large information sets. As a result, these games become too large to solve. To deal with these issues many algorithms employ inference, the estimation of the probability of states within an information set. In this paper, we demonstrate a Policy Based Inference (PI) algorithm that uses player modelling to infer the probability we are in a given state. We perform experiments in the German trick-taking card game Skat, in which we show that this method vastly improves the inference as compared to previous work, and increases the performance of the state-of-the-art Skat AI system Kermit when it is employed into its determinized search algorithm.
Improved Iterative Decoding of Product Codes Based on Trusted Symbols

Iterative decoding of product codes constructed from constituent generalized Reed-Solomon codes (or subcodes thereof, such as Bose-Chaudhuri-Hocquenghem codes) is considered. In each iteration, the received symbols are updated according to horizontal and vertical decoding steps using a bounded distance decoder for the constituent codes. Symbols that were static for by a certain number of past iterations are considered trustworthy and it is assumed that they coincide with the originally transmitted codeword symbols. This enables list decoding of the constituent codes under partial codeword knowledge as proposed just recently. As a result, lower symbol error rates after decoding can be obtained.
Active Eye-in-Hand Data Management to Improve the Robotic Object Detection Performance

Adding to the number of sources of sensory information can be efficacious in enhancing the object detection capability of robots. In the realm of vision-based object detection, in addition to improving the general detection performance, observing objects of interest from different points of view can be central to handling occlusions. In this paper, a robotic vision system is proposed that constantly uses a 3D camera, while actively switching to make use of a second RGB camera in cases where it is necessary. The proposed system detects objects in the view seen by the 3D camera, which is mounted on a humanoid robot’s head, and computes a confidence measure for its recognitions. In the event of low confidence regarding the correctness of the detection, the secondary camera, which is installed on the robot’s arm, is moved toward the object to obtain another perspective of the object. With the objects detected in the scene viewed by the hand camera, they are matched to the detections of the head camera, and subsequently, their recognition decisions are fused together. The decision fusion method is a novel approach based on the Dempster–Shafer evidence theory. Significant improvements in object detection performance are observed after employing the proposed active vision system.
Blockchain Technologies and Artificial Intelligence

The blockchain is authorizing digital information to be scattered, but not copied, and it also created the backbone of a new type of Internet. It is an open infrastructure with blocks of information connected mutually that contain references to the preceding block. It is a decentralized and distributed system like an open ledger that stores a registry of assets and transactions in a peer-to-peer network (i.e., P2P). Artificial Intelligence is a part of computer science sector that emphasizes the design of intelligent machines that work, things, and reacts like human beings. It is the theory and practice of constructing machines capable of performing tasks that seem to require intelligence. It includes machine learning, artificial neural networks, and deep learning concepts. Blockchain and merger of AI into mainstream products and its related services can generate plenty of opportunities for enterprises. This chapter discusses the capabilities recognized at the intersection of AI and Blockchain and also discuss about the standard definitions, benefits, and challenges of this alliance.
Mean estimation for entangled single-sample distributions

We consider the problem of estimating the common mean of univariate data, when independent samples are drawn from non-identical symmetric, unimodal distributions. This captures the setting where all samples are Gaussian with different unknown variances. We propose an estimator that adapts to the level of heterogeneity in the data, achieving near-optimality in both the i.i.d. setting and some heterogeneous settings, where the fraction of “low-noise" points is as small as log n n . Our estimator is a hybrid of the modal interval, shorth, and median estimators from classical statistics. The rates depend on the percentile of the mixture distribution, making our estimators useful even for distributions with infinite variance.
Virtual Dialogue Agent for Supporting a Healthy Lifestyle of the Elderly

In this paper, we introduce a dialogueOno, Risako systemNishizeki, Yuki that supports a healthy daily life of the elderly. This dialogue system proposes actions to be taken by the user from various types of supportive information. ToAraki, Masahiro respond to the diverse needs of the elderly, this system has various user adaptation functions, including a grasp of the users’ interest through a chat dialogue. As a result, it can be used for long periods as well as continuous health monitoring of the elderly.
Privacy-Preserving and Publicly Verifiable Protocol for Outsourcing Polynomials Evaluation to a Malicious Cloud

As cloud computing provides affordable and scalable computational resources, delegating heavy computing tasks to the cloud service providers is appealing to individuals and companies. Among different types of specific computations, the polynomial evaluation is an important one due to its wide usage in engineering and scientific fields. Cloud service providers may not be trusted, thus, the validity and the privacy of such computation should be guaranteed. In this article, the authors present a protocol for publicly verifiable delegations of high degree polynomials. Compared with the existing solutions, it ensures the privacy of outsourced functions and actual results. And the protocol satisfies the property of blind verifiability such that the results can be publicly verified without learning the value. The protocol also improves in efficiency.
Similarity detection method of abnormal data in network based on data mining

With the continuous progress of network technology, some abnormal data are often confused in network data flow, which affects network security. In order to grasp the abnormal degree of abnormal data in networks and detect the similarity of abnormal data, an optimized genetic data mining algorithm i s used to mine abnormal data in network, obtain the initial population of abnormal data mining and optimize genetic operation. On this basis, the network data type and the number of network data types are adaptively adjusted to obtain the optimal abnormal data mining results. Based on Euclidean distance, the similarity value of abnormal data in network is calculated, and the greater the similarity value is, the greater the similarity of abnormal data is and vice versa. The experimental results show that the average standard deviation of detection error and energy consumption of the proposed method are 0.00865 and 398J, respectively. This method is a reliable and energy-saving method for similarity detection of abnormal data in network, which provides an effective basis for grasping the anomaly degree of network data.
A Web Based Interactive System to Promote ICT Education in Bangladesh

This paper presents the design and the implementation of a web based interactive system to promote (teaching and learning) ICT education (for both teachers and students) of the secondary and higher secondary levels in Bangladesh. It can be categorized as a web-based educational information storing and self-evaluating software. The system has been designed in such a manner that takes care of all the needs of students as well as teachers of both rural and urban areas and provides facilities to solve their problems. This interactive learning system will help the teachers to prepare their lessons and the students to help themselves learning the ICT subjects of SSC and HSC levels. This system will be accessible via the Internet and available to the target population through laptop, PC, mobile or other devices with 3G or broadband connection. The system includes content designing, web portal development and an Android mobile application development.
Maxout Neural Network for Big Data Medical Fraud Detection

Globally, health care losses due to fraud rise every year, and for this reason fraud detection is an active research area that, in the U.S. alone, can potentially save billions of dollars. We explore the performance of multiple maxout activation variants on the big data medical fraud detection task using neural networks. Maxout networks have gained great success in many computer vision tasks, but there is limited work on other classification tasks. Our experiments compare Rectified Linear Unit, Leaky Rectified Linear Unit, Scaled Exponential Linear Unit, and hyperbolic tangent to four maxout variants. We evaluate the effectiveness of the activation functions on four U.S. Centers for Medicare and Medicaid Services datasets. Throughout this paper, we found that maxout networks are considerably slower to train compared to traditional activation functions. We find that on average, across all datasets, Scaled Exponential Linear Unit's classification performance is better than any maxout activation, and reported the lowest training time.
Quantum State Transfer via Multi‐Passage Landau–Zener–Stückelberg Interferometry in a Three‐Qubit System

In quantum information processing, it is necessary to transfer quantum state between different systems. Herein, a scheme to transfer quantum state using multi‐passage Landau–Zener–Stückelberg interferometry in three superconducting qubits is proposed. A periodic triangle waveform quickly drives the system across the energy level anticrossing multiple times, inducing multiphoton Rabi oscillations. Using the 0‐photon Rabi oscillations, the dynamical quantum state transfer with probability close to 100% is realized. The fidelity of the state transfer is insensitive to local distortion of the driving waveform. This scheme can be extended to a system where multiple qubits are coupled to a frequency‐tunable qubit, as a quantum medium. It provides a useful tool for scalable quantum computing.
ANOVA Based Significance Testing of Non-functional Requirements in Software Engineering

Non-functional requirements (NFRs) demonstrate how the software system works, though functional requirements represent the tasks of the system (software system). It does not indicate that the latter is more significant, but a majority of requirement collecting approaches emphasis on functional requirements. Therefore, due to the subjective nature and complexity of NFRs, it is relatively impractical to focus on each NFRs. The primary objective of this article is to model NFRs in the form of a Soft Goal Interdependency Digraph (SID). The SID is based on the Interpretive Structural Modeling (ISM) technique which in turn employs the Matrices Impacts Cruise's Multiplication Applique a UN Casement (MICMAC) and Analytic Hierarchy Process (AHP) techniques for the identification of significant NFRs. Therefore, the proposed method allows the analysts and developers to monitor and select best possible trade off selections between NFRs. To evaluate the significance of the proposed technique over others, Analysis of variance (ANOVA) based significance testing is also implemented.
Plaintext-Verifiably-Checkable Encryption

The notion of plaintext-checkable encryption (PCE) has recently emerged in the application of search on encrypted data only by plaintexts. We observe that existing PCE schemes are not sufficient to guarantee check correctness in the case of a malicious encryptor. To address this concern, we put forth the concept of plaintext-verifiably-checkable encryption (PVCE), which captures the basic requirement of output correctness: If M is thought to be the plaintext for a ciphertext $$\textsf {ct}$$ by the Check algorithm, $$\textsf {ct}$$ is actually a valid encryption of M. In other words, it does not exist any maliciously generated ciphertext could succeed in plaintext checking. This property guarantees a meaningful notion of correctness and is crucial in several applications. We propose a PVCE construction using pairing-friendly smooth projective hash function with modified language representation and prove it to be unlink-cca security in the standard model. This is the first verifiable plaintext-checkable encryption that provides both verifiable checkability and the most desirable security in the standard model. To this end, we show a PVCE instantiation from k-MDDH assumption.
Evaluating the Complexity of Players’ Strategies using MCTS Iterations

Monte Carlo Tree Search (MCTS) does not require any prior knowledge about a game to play, except for its legal moves and end conditions. Thus, the same MCTS player can be applied (almost) as it is to a wide variety of games. Accordingly, MCTS may be used as a touchstone to evaluate artificial players on different games. In this paper, we propose to use MCTS to qualitatively evaluate the strength of artificial players as the minimum number of iterations that MCTS needs to perform equivalently to the target player. We define this value as the "MCTS complexity" of the target player. We introduce a bisection procedure to compute the MCTS complexity of a player and present experiments to evaluate the proposed approach on three games: Connect4, Awari, and Othello. Initially, we apply our approach to compute the MCTS complexity of players implemented using MCTS with a known number of iterations, next to players using different strategies. Our preliminary results show that our approach can identify the number of iterations used by MCTS target players. When applied to players implementing unknown strategies, it produces results that are coherent with the underlying players’ strength, assigning higher values of MCTS complexity to stronger players. Our results also suggest that, by using iterations to evaluate the strength of players, we may be able to compare the strength of algorithms that would be incomparable in practice (e.g. a greedy strategy for Connect4 and alpha-beta pruning for Awari).
Automatic Judgement of Neural Network-Generated Image Captions

Manual evaluation of individual results of natural language generation tasks is one of the bottlenecks. It is very time consuming and expensive if it is, for example, crowdsourced. In this work, we address this problem for the specific task of automatic image captioning. We automatically generate human-like judgements on grammatical correctness, image relevance and diversity of the captions obtained from a neural image caption generator. For this purpose, we use pool-based active learning with uncertainty sampling and represent the captions using fixed size vectors from Google's Universal Sentence Encoder. In addition, we test common metrics, such as BLEU, ROUGE, METEOR, Levenshtein distance, and n-gram counts and report F1 score for the classifiers used under the active learning scheme for this task. To the best of our knowledge, our work is the first in this direction and promises to reduce time, cost, and human effort.