Reviewerly/exHarmony huggingface dataset:
Siamese Network for Pedestrian Group Retrieval: A Benchmark

In many artificial intelligence applications such as security field, it is important to identify if a specific group of pedestrians has been observed over a network of other surveillance cameras, which ascribes to the pedestrian group retrieval problem. To address this issue, this paper contributes a novel dataset for the pedestrian group retrieval named “SYSU-Group”. We collect diverse images with various pose from every camera for each group, which brings kinds of realistic challenges to the dataset, such as viewpoint variations, illumination changes and internal exchanges of group members. Moreover, we propose the Siamese Verification-Identification based Group Retrieval (SVIGR) method, which combines verification and identification modules in a Siamese network to extract person feature and follows the principle of minimum distance matching to measure the distance among pedestrian groups, and eventually gets the ranking of each query pedestrian group image. Experimental results demonstrate the superiority of SVIGR on the proposed group retrieval dataset.
Query Expansion Using Information Fusion for Effective Retrieval of News Articles

In this paper, we propose an information fusion based approach for query expansion for news articles retrieval. In this approach, we attempt to expand the user query using fusion of context oriented information collected from the underlying corpus statistics and topic diversity of news documents. The proposed methodology first collects graph based statistics and topic diversity to derive a list of additional terms and then uses fusion based approach to select a subset of terms from the derived list which are then used to expand the given query. Then the expanded query is used to perform news document retrieval. The proposed approach is tested on a specific collection of news articles collected from various news sources. The experimental results show that the proposed approach performs effective retrieval of news documents pertaining to the actual user intent.
A distributed smart camera apparatus to enable scene immersion

In this paper, we investigate the potential of an immersion technology system implemented on embedded devices. Our system consists of distributed smart cameras with overlapping views, covering numerous viewpoints of a monitored scene so that each smart camera knows the position of its neighbors. The system provides an on-demand panoramic field-of-view (FOV) in real-time. To generate the panoramic view, we design and implement an image stitching system using images captured from a subset of adjacent embedded cameras. We verify the effectiveness of our method in terms of quality of result (QoR) and computation efficiency. Initial results show up 12 FPS with 8MP cameras.
Nuclei Perception Network for Pathology Image Analysis

Nuclei segmentation is a challenge task in medical image analysis. A digital microscopic tissue image may contain hundreds or even thousands nuclear. Its morphological information provides the biological basis for the diagnosis and classification of diseases. The task requires to detect every nuclear of cells in a densely packed scene and get the segmentation of them for further pathological analysis. Nuclei segmentation can also be described as an instance segmentation task in densely packed scene. In this article, we propose a novel anchor-free dense instance segmentation framework to alleviate the issues. The network detects nuclears and segment them simultaneously. Then the nuclear segmentation mask is aggregated as nuclear instance guided by the offset map generated from the network. The network works by combining target location with pixel-by-pixel classification to distinguish crowded objects. The proposed method performs well on nuclear segmentation dataset.
The Benefits of Using Bar Charts in Company Websites

Abstract This article discusses the use of bar charts on company sites. A bar chart or bar graph is a chart or graph that presents categorical data with a rectangular bars with heights or lengths proportional to the values they represent. The purpose of this research is to discover the benefits of using the bar chart so it will becomes a consideration for institutions and companies that need informative data. With a qualitative approach, the data were analyzed descriptively. The results showed that the use of bar charts speeds up the user’s reading of the data presented. In addition, bar charts are an aesthetic element that facilitates and motivates users who wants to read data or information compared to text data or tables.
Detection of Potential Skin Cancer Lentigo Maligna Melanoma and Nodular Melanoma with Expert System Using Variable-Centered Intelligent Rule System (VCIRS) Method

Melanoma is one of the skin cancers that attacks the cells of melanocytes that produce skin-forming pigments. Melanoma is one of many kinds, but the type of melanoma that will be discussed in this research are Lentigo Maligna Melanoma & Nodular Melanoma only. Lentigo Maligna Melanoma is detected based on Solar Lentigines while Nodular Melanoma detected based on melanocytic nevus. The intelligent system that will be discussed in this paper is the Variable-Centered Intelligent Rule System (VCIRS). VCIRS is a combination of the Rule Based System (RBS) and Ripple Down Rules (RDR) methods with their respective advantages complementing each other with Variable Usage Rates (VUR), Node Usage Rate (NUR) and Rule Usage Rate (RUR) inside. This intelligent system application is an Android based system where users are able to input symptoms that occurs and then the system will process inputs from users. Later, the system will determine whether the user has the disease or not.
Dynamic Path Costs Update Method reflecting Delivery Tendencies for Multi-Agent Delivery Tasks

In multi-agent delivery tasks, Dijkstra's Algorithm, which identifies and plans the shortest path based on costs, is very popular. Specifically, using dynamic path costs, namely updating each path cost to reflect the number of agents having visited it over time, is an efficient method for accomplishing multi-agent delivery tasks. Indeed, it can consider the following logic: “a path that is jammed is not likely to be selected in the routing process”. Suppose tasks that can train with similar delivery tendencies in advance, optimizing the update path cost rule set would lead to more efficient agent path planning. Indeed, it would consider that “the paths that will be jammed are not likely to be selected in the routing process”. Thus, we propose a dynamic path cost update method that reflects delivery tendencies. We design the update path cost if-then rule (what path costs should be added, and under what conditions?) as a current partial agent distribution around an observation point for the if-part, and the amount of added (or subtracted) path cost for the then-part. In one delivery task, each observation point is tied to partial agents' distribution. If a distribution is collated with an update rule's if-part, the corresponding path of the observation point is updated given the update path costs that the collated rule's then-part indicates. This update continues during a task at each observation point at an interval. The set of update path cost if-then rules is optimized with Learning Classifier Systems. We evaluated our method with a multi-agent delivery task that simulates a warehouse swarm robot's Pickup and Delivery task. The results showed our method accomplished the tasks faster than a previous approach. In addition, we evaluated the superiority range of the proposed method when the number of agents used changes. Finally, we compared the path cost transition tendency. It revealed our method was more likely to drastically alter path costs.
Real-Time Visual Object Tracking Based on Reinforcement Learning with Twin Delayed Deep Deterministic Algorithm

Object tracking as a low-level vision task has always been a hot topic in computer vision. It is well known that Challenges such as background clutters, fast object motion and occlusion et al. affect a lot the robustness or accuracy of existing object tracking methods. This paper proposes a reinforcement learning model based on Twin Delayed Deep Deterministic algorithm (TD3) for single object tracking. The model is based on the deep reinforcement learning model, Actor-Critic (AC), in which the Actor network predicts a continuous action that moves the target bounding box in the previous frame to the object position in the current frame and adapts to the object size. The Critic network evaluates the confidence of the new bounding box online to determine whether the Critic model needs to be updated or re-initialized. In further, in our model we use TD3 algorithm to further optimize the AC model by using two Critic networks to jointly predict the bounding box confidence, and to obtain the smaller predicted value as the label to update the network parameters, thereby rendering the Critic network to avoid excessive estimation bias, accelerate the convergence of the loss function, and obtain more accurate prediction values. Also, a small amount of random noise with upper and lower bounds are added to the action in the Actor model, and the search area is reasonably expanded in offline learning to improve the robustness of the tracking method under strong background interference and fast object motion. The Critic model can also guide the Actor model to select the best action and continuously update the state of the tracking object. Comprehensive experimental results on the OTB-2013 and OTB-2015 benchmarks demonstrate that our tracker performs best in precision, robustness, and efficiency when compared with state-of-the-art methods.
Scheduling the cluster server node shutdown based on the hierarchical and k-means clustering combinations

Abstract The problem of server clusters is the use of electrical power. The data center in Indonesia consumed 1.5% of the national generating capacity in 2014. To solve this problem is to turn off the server node on the server cluster. With the turn off server nodes on server clusters scheduling, it’s expected to determine the time and duration of turning off server nodes. When the server cluster is running, data retrieval takes one, five and 15 minutes average load which is done every minute. Then the collected data is clustered using a combination of average linkage hierarchical clustering and K-Means clustering. The results of this clustering produce three clusters load averages that are “low”, “medium” and “high”. Load averages that included into the “low” category are sorted by the time of data retrieval to get the time and duration to turn off at each node. The results of the research result in scheduling turning off node one is 14.09 - 14.28, at node two is 14.47 - 06.57 and at node three is 06.57 - 07.18. Turn off server node scheduling is reduces the use of electrical power from 2,528 kWh to 2,519 kWh and doesn’t affect server quality parameters.
Application of NER and MC in Answers Extraction of Factoid Questions

Question answering (QA) system is intended to be fast, simple and accurate to give feedback to the user's natural language questions. By studying answers extraction in the Chinese QA system, this paper adopted the method of combination of the Named Entity Recognition (NER) and the theory of Metric Cluster (MC) to extract answers about factoid questions. These answers extraction experiments used the standard of TREC evaluation, and the factoid questions' average MRR value was 0.6883. These results show that the method had a higher MRR value and good effect on answers extraction.
Integrating digital stewardship into library instruction: An argument for student (and librarian) success

Faculty learning communities (FLCs) are year-long professional development opportunities available at many higher education institutions in the United States. While the literature reflects some librarian engagement with FLCs, it seems limited primarily to areas of traditional librarian expertise such as information literacy and outreach. This article describes a case study of a librarian-facilitated FLC focused on data literacy, which resulted in the development of a teaching toolkit, library-led data literacy instruction, and ongoing collaborations between librarians and faculty. The FLC structure proved to be a valuable framework that facilitated collaborative learning in topics relevant to both disciplinary faculty and librarians. In addition, the tangible work products produced by the FLC serve to advance the strategic, curricular goals of the university while giving the library an opportunity to showcase its value in the academic lifecycle.
Some Notes on the Possibile Role of Cognitive Architectures in Serious Games

This paper provides a preliminary analysis of the possible role of cognitive architectures in the field of Serious Games. The seminal works that explore the use of Cognitive Architecture in games will be analyzed in the light of an emerging perspective of the games AI research area. Finally, an initial proposal of application of Cognitive Architecture for the design and implementation of non-player characters will be presented.
Improving ResNet-based Feature Extractor for Face Recognition via Re-ranking and Approximate Nearest Neighbor

This paper proposes a framework for face recognition based on feature extractor from ResNet, together with other steps for performance improvement, including face detection, face alignment, face verification/identification, and re-ranking via Approximate Nearest Neighbor Search (ANNS). First, we evaluate two face detection algorithms, MTCNN, and FaceBoxes on three common face detection benchmarks, and then summarize the best usage scenario for each approach. Second, with certain preprocessing and postprocessing, our system selects the ResNet-based feature extractor, which achieves 99.33% verification accuracy on the LFW benchmark. Third, we use the penalty curve to determine the best configuration and obtain improved results of face verification. Based on the proposed preprocessing and post-processing, our method not only boosts accuracy from 84.3% to 86.5% in large inter-class variation datasets (CASIA - WebFace) but improves Rank-l accuracy from 86.6% to 87.7% in large intra-class variation datasets (FG-NET).
Web-based design for lecturer performance reporting applications

Abstract A lecturer as an educator in a college institution obtains professional appreciation for their work through certification of lecturer who is rewarded in the form of lecturer allowance. To obtain a lecturer allowance, lecturers are required to report their academic activities. The purpose of this study is to describe the design of web-based lecturer performance reporting applications. In this study the application is modelled with UML modelling (Unified Modelling Language) that describes a system design. The object of research that used as a user of the application is a lecturer of Private Islamic Higher Education. The results of application design show that the system can work well; can process data of lecturer activities among others: education, research, community service, and supporting which then converted to semester unit value (SKS). This lecture performance reporting application is user friendly, easy access, good system security, and have a good data base.
Knowledge Management System Architecture Design

Abstract This study proposes a model of Knowledge Management System (KMS) design as the solution to the problems that occur in a software company especially knowledge management project which is the core business of the company. The main problem why is documents and knowledge spread is the employee turnover rate is quite high among employees who transfer from one project to another or when the employee leave the company so that each individual’ knowledge had come to move or disappear. Based on these problems, it can be concluded that PT. XYZ requires knowledge management to improve the performance of the company. Research conducted are using 6 steps from the 10 Step method Knowledge Management Roadmap to manufacture a blueprint. Research supported by the Knowledge Management Assessment Kit (KMAK) tools that serve to facilitate the research stages. The result of the sixth step is a Knowledge Management System blueprint which fulfil the seven layers of infrastructure. With this result, the design of KMS model is adjusted to the company’s needs, designed as a centralized architecture to support KMS.
Brain Emotional Learning Networks with Applications

Emotion plays a vital role in human learning, memory and decision-making, which have attracted wide attention in various research fields. To improve the learning speed and performance, a novel Brain Emotional Learning Network (BELN) is developed in this paper, which adds the affective neurons into the model of brain emotional learning and ameliorates the structure of the model. The newly proposed BELN enjoys lower computational complexity, in the sense that two affective coefficients named “anxiety” and “confidence” are added to simulate the changes of emotion in human learning process, which improves the online learning speed of the network. In order to verify the effectiveness of the network, a numerical example and the predictive control of petroleum heating process in a co-current tubular heat exchanger have been studied. The results show that compared with the BP neural network, BELN has better learning speed and recognition ability.
Bayesian nonparametric estimation in the current status continuous mark model

Abstract We consider the current status continuous mark model where, if an event takes place before an inspection time T a “continuous mark” variable is observed as well. A Bayesian nonparametric method is introduced for estimating the distribution function of the joint distribution of the event time ( X ) and mark variable ( Y ). We consider two histogram‐type priors on the density of . Our main result shows that under appropriate conditions, the posterior distribution function contracts pointwisely at rate if the true density is ‐Hölder continuous. In addition to our theoretical results we provide efficient computational methods for drawing from the posterior relying on a noncentered parameterization and Crank–Nicolson updates. The performance of the proposed methods is illustrated in several numerical experiments.
Design and implementation of pension management system based on Internet of things technology

Abstract The old-age management system based on the Internet of things has integrated the health management information resources of the residents, allowing residents to enjoy medical monitoring services and intelligent health services at anytime and anywhere, and provide unified standardized, specialized and affectionate health inspection, analysis and assessment for the residents of the community. In this paper, the feasibility analysis of system construction is carried out, the system requirements are analyzed from the aspects of system feasibility, business function and business process, and the overall design of the system function is carried out. Finally, the effect of the system is expounded.
Research of CouchDB Storage Plugin for Big Data Query Engine Apache Drill

Currently, the document-oriented database supported by Apache Drill is only MongoDB. However, due to the lack of data model, application interface, security and usability of MongoDB, Apache Drill is limited in querying and processing document data. CouchDB is an emerging document-oriented database. Compared to MongoDB, CouchDB has the advantage of supporting triggers, running in Android and BSD environments, rendering in JSON format, and supporting any language that supports HTTP requests, but CouchDB has low query performance and does not support standard SQL queries. Therefore, the research on the CouchDB storage plugin for Apache Drill makes sense. This paper first researches the basic architecture of CouchDB and Apache Drill and the query flow of Apache Drill, and the ValueVector data structure, then designs and implements CouchDB storage plugin based on Apache Drill’s storage plugin specification and CouchDB’s application programming interface. With a simple configuration, users can use CouchDB as a data source for the Apache Drill query engine. Experiments show that the CouchDB Storage Plugin not only further enhances Apache Drill’s query and management capabilities for document-oriented data, but also enables quick query of CouchDB with SQL and greatly improves CouchDB’s query performance.
DRAV: Detection and repair of data availability violations in Internet of Things

The application of the Internet of Things has produced large amounts of data in different scenarios, which are accompanied with problems, such as consistency and integrity violations. Existing research on dealing with data availability violations is insufficient. In this work, the detection and repair of data availability violations (DRAV) framework is proposed to detect and repair data violations in Internet of Things with a distributed parallel computing environment. DRAV uses algorithms in the MapReduce programming framework, and these include detection and repair algorithms based on enhanced conditional function dependency for data consistency violation, MapJoin, and ReduceJoin algorithms based on master data for k-nearest neighbor–based integrity violation detection, and repair algorithms. Experiments are conducted to determine the effect of the algorithms. Results show that DRAV improves data availability in Internet of Things compared with existing methods by detecting and repairing violations.
Comparison of naive Bayes classifier and C4.5 algorithms in predicting student study period

Abstract This study aims to compare Naive Bayes Classifier algorithm with C4.5 in predicting the study period of student. The data for this study is collected from Department of Informatics UIN Sunan Gunung Djati Bandung. Software development method that used is Rational Unified Process (RUP). Comparison of both algorithms is the accuracy of predicting student study period and the speed of data processing. The attributes that used are student id, name, gender, GPA, entry point, tahfidz (cited the holy Quran as a special attribute on the case study), previous school, and extra activities during lecture. The result of this study shows that Naive Bayes Classifier and C4.5 algorithm can be applied to predict the study period of students well and accurate enough. The accuracy of Naive Bayes Classifier algorithm is around 88% better slightly than the C4.5 algorithm that has accuracy around 87%. However, the processing time of algorithm C4.5 is better than the algorithm Naive Bayes Classifier, C4.5 has processing time 0.003 nanosecond and the Naive Bayes Classifier has 12.7 nanosecond.
Audio Replay Attack Detection for Speaker Verification System Using Convolutional Neural Networks

An audio replay attack is one of the most popular spoofing attacks on speaker verification systems because it is very economical and does not require much knowledge of signal processing. In this paper, we investigate the significance of non-voiced audio segments and deep learning models like Convolutional Neural Networks (CNN) for audio replay attack detection. The non-voiced segments of the audio can be used to detect reverberation and channel noise. FFT spectrograms are generated and given as input to CNN to classify the audio as genuine or replay. The advantage of the proposed approach is, because of the removal of the voiced speech, the feature vector size is reduced without compromising the necessary features. This leads to significant amount of reduction on training time of the networks. The ASVspoof 2017 dataset is used to train and evaluate the model. The Equal Error Rate (EER) is computed and used as a metric to evaluate model performance. The proposed system has achieved an EER of 5.62% on the development dataset and 12.47% on the evaluation dataset.