Reviewerly/exHarmony huggingface dataset:
Adopting configuration management principles for managing experiment materials in families of experiments

Replication is a key component of experimentation for verifying previous results and findings. Experiment replication requires products like documentation describing the baseline experiment and a version of the experimental material. When replicating an experiment, changes may have to be made to some of the products, leading to new or modified versions of materials. After the replication has been conducted, part of or all the materials should be added to the family history or to the baseline experiment documentation. As the number of replications increases, more versions of the materials are generated. This can lead to product management chaos in replications sharing the same protocol. The aim of this paper is to adopt configuration management principles to manage experimental materials. We apply and validate these principles in a code inspection technique comparison experiment and a personality quasi-experiment. The study was conducted within a research group with lengthy experience in experiment replication. This research group has had trouble with the management of the materials used to run some of the experiments replicated by other colleagues. This is a suitable context for applying action research. We used action research to adopt the configuration management principles and build a materials management framework. We generated the instances of an experiment and a quasi-experiment, identifying the status and traceability of the materials. Additionally, we documented the workload required for instantiation in person-hours. We also checked the ease of use and understanding of the framework for instantiating the personality quasi-experiment configuration plan executed by researchers who did not develop the framework, as well as its usefulness for managing the experimental materials. The experimental materials management framework is useful for establishing the status and traceability of the experimental materials. Additionally, it improves the storage, search, location and retrieval of the experimental material versions.
Manipulating Skeptical and Credulous Consequences When Merging Beliefs

Automated reasoning techniques for multi-agent scenarios need to address the possibility that procedures for collective decision making may fall prey to manipulation by self-interested agents. In this paper we study manipulation in the context of belief merging, a framework for aggregating agents’ positions, or beliefs, with respect to a set of issues represented by propositional atoms. Within this framework agents submit their positions as propositional formulas that are to be aggregated into a single formula. To reach a final decision, we employ well-established acceptance notions and extract the skeptical and credulous consequences (i.e., atoms true in all and, respectively, at least one model) of the resulting formula. We find that, even in restricted cases, most aggregation procedures are vulnerable to manipulation by an agent acting strategically, i.e., one that is able to submit a formula not representing its true position. Our results apply when the goal of such an agent is either that of (i) affecting an atom’s skeptical or credulous acceptance status, or (ii) improving its satisfaction with the result. With respect to latter task, we extend existing work on manipulation with new satisfaction indices, based on skeptical and credulous reasoning. We also study the extent to which an agent can influence the outcome of the aggregation, and show that manipulation can often be achieved by submitting a complete formula (i.e., a formula having exactly one model), yet, the complexity of finding such a formula resides, in the general case, on the second level of the polynomial hierarchy.
Secure Brokered Delegation Through DelegaTEE

DelegaTEE allows users to delegate rights and resources in existing services safely and selectively to others—without ever revealing access credentials to third parties. DelegaTEE enables contextually rich delegation policies that were previously unachievable.
The Minimization of Empirical Risk Through Stochastic Gradient Descent with Momentum Algorithms

The learning problems are always affected with a certain amount of risk. This risk is measured empirically through various risk functions. The risk functional's empirical estimates consist of an average over data points' tuples. With this motivation in this work, the prima face is towards presenting any stochastic approximation method for solving problems involving minimization of risk. Considering huge datasets scenario, gradient estimates are achieved through taking samples of data points' tuples with replacement. Based on this, a mathematical proposition is presented here which account towards considerable impact for this strategy on prediction model's ability of generalization through stochastic gradient descent with momentum. The method reaches optimum trade-off with respect to accuracy and cost. The experimental results on maximization of area under the curve (AUC) and metric learning provides superior support towards this approach.
The Advisable Technology of Key-Point Detection and Expression Recognition for an Intelligent Class System

The intelligent classroom system has a very wide range of application scenarios in modern education. With the continuous breakthrough of artificial intelligence technology, a realtime intelligent system that can judge students' performance in class has a large demand market. In this paper, the real-time detection technology needed by the smart classroom system is studied from the aspects of behavior detection and expression recognition. In terms of behavior detection, this paper combines the two key point detection technologies CPM (Convolutional Pose Machines) and CMU (Carnegie Mellon University) OPENPOSE [1] [2]. In terms of expression recognition, in order to detect the position of the face in real time, this paper studies several popular target detection algorithms and improves the traditional CNN network, and proposes a real-time image classification network architecture [8].
Forgery image detection via mask filter banks based CNN

In this paper, we present a new image forgery detection method via a mask filter banks which is consisted with the designed mask filters to extract the features of different channels of image and a modified a ResNet to classify the input image is tempered or not. The proposed model is proved to be capable for copy-move forgery and splicing image detection. In the mask filter layer, we first convert the image from spatial domain to frequency domain, then extract the image edge information of each channel by element-wise with the designed mask matrix. Finally, edge and noise information features of different channels were fused as feature vectors fed to a trained ResNet to do classification. Experiments on three standard datasets: the copy-move forgery image datasets MICC-F220 and MICC-F2000, splicing image manipulation datasets Columbia demonstrate that proposed method get better results than the original colour image as input method and also outperform some existing works.
Passing the Buck and Firing Fibonacci: Adventures with the Stochastic Abacus

A “pass-the-buck” game begins with n players arranged as the vertices in a graph. One player has a dollar bill. The game is played in turns where, at random, the current bill-holder either wins outright or else passes the bill to a player on an adjacent vertex. If the vertex of the bill-holder has degree d, then each of the d + 1 outcomes is equally likely. We analyze this class of game using Arthur Engel’s stochastic abacus—a chip firing algorithm—revealing a natural relationship with the Fibonacci numbers.
Digital Literacy and Strategy of PR-education in Russia and Kazakhstan: Comparative Analysis

The paper presents results of comparative research in the field of PR-education development in Russia and Kazakhstan. After the disintegration of the USSR, the paths of education development in former soviet republics divided. However, there are still many similarities due to the inter-government cooperation and integration projects such as the Eurasia Economic Union. The study aims to understand how PR-education in Russia and Kazakhstan is developing. In particular, the study compares BA and MA education programs comparing courses in common and not in common. This project uses both quantitative and qualitative data collection tools, but is rooted in a qualitative epistemological position that recognizes the importance of locating the research within a particular social, cultural, and historical context. The authors place an emphasis on the competences in the sphere of digital public relations, assuming that skills in information technologies and digital literacy are crucial for success in the future. In order to understand dynamics of PR-education development, the authors compare statistics on student submission to the institutions of the higher education both in Russia and Kazakhstan. The numbers show that there is a big demand in PR-education in terms of students submitted and in terms of competition. In order to indicate what digital skills is needed a survey method is applied. The authors have conducted a survey with 60 BA and MA students. The survey shows that students consider their decision to become a PR-specialists as `strategically right', but the opinion on digital literacy was controversial.
Fraud Detection with Multi-Modal Attention and Correspondence Learning

Deep learning based recognition systems have shown high performances in various tasks. Most of them are single-modality based, using camera inputs only, thus are vulnerable to look-alike fraud inputs. Fraud inputs may frequently be abused when rewards are given to the users, such as in reverse vending machines. Joint use of multi-modal inputs can be a solution to fraud inputs since modalities contain different information about the target task. In this work, we propose a deep neural network that utilizes multi-modal inputs with an attention mechanism and a correspondence learning scheme. With an attention mechanism, the network can learn better feature representation for multiple modalities; with the correspondence learning scheme, the network learns intermodal relationships and thus can detect fraud inputs where modalities do not correspond to each other. We investigate the proposed approach in a reverse vending machine system, where the task is to perform classification among 3 given classes (can, PET bottles, glass bottles), and reject any suspicious input. Three different modalities (image, ultrasound, and weight) are used. As a result, we show that our proposed model can effectively learn to detect fraud inputs while maintaining a high accuracy for the given classification task.
Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java

Summary This work attempts to analyze the limits of Weka Data Miner in executing the Simple K‐Means algorithm and makes an attempt to identify how much data is too much data for the Weka Data Miner to execute the algorithm. This work is further based on developing a distributed processing model to offer a better solution in handling large datasets. The required features are implemented using the RMI Call back Server. The Euclidean Distance measure is considered for calculating the distance.
Communication Technologies in the Training of IT Specialists in the Digital Economy

This paper introduces some issues of the need for changes in the training of IT-specialists in view of the development and impact of the digital economy on the learning process. The development of knowledge management in organizations makes it necessary for IT professionals to form new high-level cognitive skills at the core of digital competencies. New teaching methods based on communication technologies and project management technologies can contribute to the formation of digital competencies among students in the early stages of study at the university. The results of the collective project activities of students are given on the example of studying new areas of knowledge and modeling information content in a blended/integrated learning environment.
Algorithm Selection for Paracoherent Answer Set Computation

Answer Set Programming (ASP) is a well-established AI formalism rooted in nonmonotonic reasoning. Paracoherent semantics for ASP have been proposed to derive useful conclusions also in the absence of answer sets caused by cyclic default negation. Recently, several different algorithms have been proposed to implement them, but no algorithm is always preferable to the others in all instances. In this paper, we apply algorithm selection techniques to devise a more efficient paracoherent answer set solver combining existing algorithms. The effectiveness of the approach is demonstrated empirically running our system on existing benchmarks.
Learning 3D Semantic Reconstruction on Octrees

We present a fully convolutional neural network that jointly predicts a semantic 3D reconstruction of a scene as well as a corresponding octree representation. This approach leverages the efficiency of an octree data structure to improve the capacities of volumetric semantic 3D reconstruction methods, especially in terms of scalability. At every octree level, the network predicts a semantic class for every voxel and decides which voxels should be further split in order to refine the reconstruction, thus working in a coarse-to-fine manner. The semantic prediction part of our method builds on recent work that combines traditional variational optimization and neural networks. In contrast to previous networks that work on dense voxel grids, our network is much more efficient in terms of memory consumption and inference efficiency, while achieving similar reconstruction performance. This allows for a high resolution reconstruction in case of limited memory. We perform experiments on the SUNCG and ScanNetv2 datasets on which our network shows comparable reconstruction results to the corresponding dense network while consuming less memory.
The high-speed rotorcraft unmanned aerial vehicle path planning based on the biogeography-based optimization algorithm

A three-dimensional path planning method based on the biogeography-based optimization algorithm is presented for high-speed rotorcraft unmanned aerial vehicle which has a maximum speed of 20 m/s. In this study, a novel exponential artificial potential field is used to prevent saturation, for its peculiarity of smoothness and boundness. Several parameters, which can make a large influence to the result of path planning, are picked out and used to improve the process of planning. Biogeography-based optimization algorithm is first used to optimize the parameters of artificial potential fields, for its best performance in stability and speed. The algorithm has good stability and fast operation speed, which can realize the global path planning of multi-rotor aircraft well, verified by the simulation results.
Hybrid Models of Solving Optimization Tasks on the Basis of Integrating Evolutionary Design and Multiagent Technologies

The paper is devoted to the problem of building hybrid intelligent systems for solving multi-objective optimization problems. The authors present the definition of a hybrid system, and the main problems and tasks of its development. The main idea is that integration of methods of computational intelligence and multiagent systems (MAS) can be promising and useful for developing intelligent systems. The paper describes the concepts of designing agents, multi-agent systems, and the design process with elements of self-organization (interaction, crossing, adaptation to the environment, etc.). The authors propose a method of forming child agents as a result of the interaction of parent agents, develop various types of crossover operators, and present the idea of creating agencies (families) as units of the MAS evolving. To implement the proposed ideas, hybrid fuzzy-evolutionary models of forming agents and agencies based on the use of fuzzy coding principles are created and described in the paper. The authors developed a software system to support evolutionary design of agents and multi-agent systems for estimating the effectiveness of the hybrid approach. The results demonstrate the effectiveness of the proposed approach.
The Applications of the Edge Detection on Medical Diagnosis of Lungs

With the development of science and technology,medical images have become important assistant means of diagnosis and therapy. However there are many inevitable defects in biomedical images. In order to improve the readability of images and make the doctor to adopt more effective observation and diagnoses way to anatomy structure and pathological part of the patient, it's necessary to study the medical images processing. Edge detection is an important part of the processing. And the purpose of the edge detection is to enforce the effect of the edge and contour of images, and act on enlarge the contrast of images' grayscale. Firstly, the edge detection algorithm and detector are summarized and analyzed. Then the paper introduces the method of edge detection in vc++ MFC. Finally, this paper describes the realization process of edge detection in medical images. The results show that edge detection can enforce the readability of medical image that can help doctors make better diagnose to patient.
An improved ORB feature point image matching method based on PSO

ORB (Oriented FAST and Rotated BRIEF) algorithm is widely used in feature point matching with images. However, the randomness of the threshold of search strategy makes the matching result inaccurate. The matching result of ORB algorithm is lack of robustness. In this paper, we proposed an improved ORB algorithm based on PSO (Particle Swarm Optimization) algorithm. Firstly, ORB algorithm was used to detect image feature points. Secondly, distance similarity measurement is applied to ORB and orientation constraint was added to reduce mismatching rate. Finally, particle swarm optimization algorithm was used to optimize the threshold of search strategy. Experimental results showed that the improved algorithm can effectively improve the accuracy of image matching and expand the scope of application of the algorithm.
Parallel Image Captioning Using 2D Masked Convolution

Automatically generating a novel description of an image is a challenging and important problem that brings together advanced research in both computer vision and natural language processing. In recent years, image captioning has significantly improved its performance by using long short-term memory (LSTM) as a decoder for the language model. However, despite this improvement, LSTM itself has its own shortcomings as a model because the structure is complicated and its nature is inherently sequential. This paper proposes a model using a simple convolutional network for both encoder and decoder functions of image captioning, instead of the current state-of-the-art approach. Our experiment with this model on a Microsoft Common Objects in Context (MSCOCO) captioning dataset yielded results that are competitive with the state-of-the-art image captioning model across different evaluation metrics, while having a much simpler model and enabling parallel graphics processing unit (GPU) computation during training, resulting in a faster training time.
Community Connections: Advocating for Libraries through Effective Brand Advocacy

Information professionals strive to understand the needs of communities in order to discover ways to connect and engage them in library resources and services. Positive word-of-mouth communication remains one of the most powerful tools used to develop mutually rewarding connections with the community. This article argues that libraries can harness the power of brand advocacy to transform their patrons into the most enthusiastic sharers of information, or informal brand advocates. Moreover, it emphasizes how libraries can carefully build and maintain mutually rewarding community connections through effective brand advocacy by utilizing the STEPPS framework – Social Currency, Triggers, Emotion, Public, Practical Value, Stories. Each principle in this framework can help an organization to effectively spread important brand messages. While the social currency principle encourages people to talk about topics that make them appear more favorable to others, triggers provide everyday cues that remind people of the library brand. Emotion focuses on appealing to feelings and the public principle calls for initiatives that advertise the brand. Finally, practical value motivates people to spread useful information and the stories principle explains that people share their experiences through memorable narratives. The article calls for adopting a holistic approach toward brand advocacy in applying the STEPPS framework in libraries.
Webpage Recommendation System Using Interesting Subgraphs and Laplace Based k-Nearest Neighbor

An interesting research area that permits the user to mine the significant information, called frequent subgraph, is Graph-Based Data Mining (GBDM). One of the well-known algorithms developed to extract frequent patterns is GASTON algorithm. Retrieving the interesting webpages from the log files contributes heavily to various applications. In this work, a webpage recommendation system has been proposed by introducing Chronological Cuckoo Search (Chronological-CS) algorithm and the Laplace correction based k-Nearest Neighbor (LKNN) to retrieve the useful webpage from the interesting webpage. Initially, W-Gaston algorithm extracts the interesting subgraph from the log files and provides it to the proposed webpage recommendation system. The interesting subgraphs subjected to clustering with the proposed Chronological-CS algorithm, which is developed by integrating the chronological concept into Cuckoo Search (CS) algorithm, provide various cluster groups. Then, the proposed LKNN algorithm recommends the webpage from the clusters. Simulation of the proposed webpage recommendation algorithm is done by utilizing the data from MSNBC and weblog database. The results are compared with various existing webpage recommendation models and analyzed based on precision, recall, and F-measure. The proposed webpage recommendation model achieved better performance than the existing models with the values of 0.9194, 0.8947, and 0.86736, respectively, for the precision, recall, and F-measure.
Blockchain-based Intelligent Hospital Security and Data Privacy Construction

With the rapid development of medical information services, the construction of intelligent hospitals is opening up a new mode of medical treatment in the health care industry. Medical data is gradually becoming more and more important, while it also faces some challenges, among which the most urgent problem to be solved is data security and privacy protection. In the construction of intelligent hospitals, the safety issues among the basic information of patients, the protection of medical information and inter-institutional information sharing have become the focus at this stage. Blockchain technology, with highly security, reliable architecture and algorithm design have operated stably in the financial industry for more than seven years. The related innovative technologies such as distributed ledgers, smart contracts, symmetric encryptions and consensus mechanisms are used widely in many fields. This paper will take the demonstration construction of Chongqing Intelligent Hospital as an example, which tries to combine the blockchain, homomorphic encryption and zero-knowledge proof technology to carry out the research on the security construction and data privacy protection of intelligent hospitals.