Reviewerly/exHarmony huggingface dataset:
Lexical content as a cooperation aide: A study based on Java software

Collaborative development is a paradigm shift in software development. Loosely coupled developers coordinate their work via distributed versioning systems (SVN, Git, and others), code reviews and priority-led bug tracking systems. This development approach allows many different developers to input additional source code to the same source artifact. This article focuses on the lexical content of the source code produced in a collaborative environment. The lexical content is described as the ‘dictionary’ of the key terms contained within a source artifact. We posit that the lexical content of a Java class will increase as long as more developers add more content to the same class. We analyse the 100 top-ranked GitHub applications (at the time of the sampling) written in Java. Each of their classes is reduced to its lexical content, its size (in LOCs) recorded, as well as the number of different developers who contributed to its source code. Our results show that (i) the lexical content of Java classes is bounded in size, (ii) more developers make the size of the lexical content larger, and (iii) the lexical content of a system’s classes might increase with more developers, but depending on its application domain. The implications for practitioners are two-fold: (i) classes with a large set of lexical content should be split in multiple classes, to minimize the need for further maintenance; and (ii) classes developed by many developers should adhere to specific guidelines so that its lexical content does not increase boundlessly. We tested our results in a tailored case study and we confirmed our findings: larger-than-threshold class corpora tend to deteriorate the class cohesion.
Analysis of AdaBoost-Based Face Detection Algorithm

AdaBoost-based face detection algorithm plays a crucial role in the field of face detection, which is based on the optimization of boosting. And its advantage is that it can determine the sample weight in the next training according to the previous sample training results. In such an algorithm, the leading research focuses on the Haar-like feature, and the algorithm is also closely related to the fundamental diagram. By extracting the portrait features using Haar-like features, calculating the eigenvalues using the Integral Image, and filtering out the weak classifier and strong classifier through the weighted voting method, the algorithm ideally improves the detection speed and accuracy of the classifier. Therefore, it has several excellent advantages, including a lower rate of generalization errors, no need for parameter adjustment, and higher stability.
An Ontology-Based Development of Activity Knowledge and System Design

This paper describes an ontology-based development of activity knowledge on a domain and the system we developed to support it. To understand human activities, it is important to explicitly describe the knowledge of each domain. However, there are some issues of knowledge development: the establishment of the efficient method and process, the improvement of the readability for humans and machines, and the regular improvement of knowledge after development. We thus introduced a process of knowledge development, which uses two different types of knowledge representation (activity knowledge and domain ontology) on a domain that requires technical skills. In this study, we practiced the process in the music field to investigate the effects of developing activity knowledge based on a domain ontology. The results showed that it enables deep understanding and extension of knowledge. Furthermore, we designed a system to help the ontology-based development of activity knowledge. We rewrote the activity knowledge using the system and received preliminary results on term control.
Labanotation Generation Based On BLSTM-CTC Network With Continuous Motion Capture Data

Labanotation is an effective way to represent and record human body movements. The existing methods of generating Labanotation can only complete recognition after segmentation, which are less suitable for real scenes. We proposed a novel method to process continuous motion capture data, which can simultaneously implement segmentation and recognition in this paper. Firstly, we extract the Line feature which contains the location and direction information. The Line feature fully reflects the relative spatial relationship between adjacent joint points, thus it shows good spatial characteristic. Secondly, especially for long time series, the Bidirectional Long Short Term Memory has good time characteristics and long-term memory function, which can learn the further history and future information on the timeline. Therefore, BLSTM can catch more important information with continuous motion capture data on the timeline. Thirdly, Connectionist Temporal Classification is applicable for the case when the length of the input and output sequence are not aligned. Our dataset happens to be the case that the input and output sequence are not aligned. Thus CTC is suitable for processing continuous motion sequences of variable length. According to the experiments, this method based on BLSTM-CTC shows good performance in simultaneously completing segmentation and recognition of the continuous motion sequences of different length. Compared with other methods, our experiment results achieved a better result, which demonstrates the effectiveness of our pro-posed method.
Improv Performance with Wi-Fi Enabled Costume Lights

Costuming an Improv dancer with wearable, color changing lights enables a dimension of creativity where the observer controls the look of the performance in real time. The technical challenges include communication, power, and wiring; these are placed within the further costuming constraints of low bulk, durability, and ease of wear. We selected the Raspberry Pi Zero W for its small form factor that adds a mere 5 mm to the fabric thickness. We chose to use conductive thread in place of wires to limit bulk and increase ease of wear. The Raspberry Pi Zero W was not designed to be used with conductive thread, but leveraging the mounting holes for cable management allowed the substitution to succeed. As part of the Denison Fringe Festival 2019, our prototype was exhibited by Vaval Victor during the show “Evening of Readings and Dance”.
Improving the Learning Accuracy of the Mega Trend Diffusion Technique for Small Data Sets

In the real world, collecting enough data to achieve an effective learning task is sometimes difficult for researchers. The learning results are thus often unsatisfactory, since most of machine learning algorithms are basically developed under the assumption that training sets can fully represent population properties. Once training sets do not contain enough population properties, knowledge could be biased. In the past few decades, virtual sample generation approaches have shown to be an effective way to improve the knowledge acquisition capability of small data sets. In this paper, a better version of mega-trend-diffusion technique is proposed to estimate a more proper domain ranges of small data sets. A data set related to bladder cancer is taken to validate the effectiveness of the proposed approach, and the experiments show better results.
"Exploratory search: user behaviour and search engine adaptation" by Alan Medlar and Dorota Głowacka with Martin Vesely as coordinator

Exploratory searches are open-ended search tasks where users are learning, investigating or otherwise trying to acquire new knowledge. Exploratory search encapsulates many common search activities, such as conducting a scientific literature review or planning a vacation. However, few information retrieval systems are capable of distinguishing exploratory search from traditional lookup search and, therefore, cannot provide adaptive support for both scenarios. In this article, we briefly outline the behavioural differences between users performing exploratory search and lookup search, and describe how search engines can use this information to transparently tailor search results to support both types of search task.
Sketch to image using CNN and GAN

Visual media has been one of the primary sources of human communication. We often have a propensity to rely on logos, images and other visual media to better represent, communicate, elucidate our thoughts. This proves vexing, as with any field that requires immense fecundity and originality in imagination. In this paper, we present a scintillating technique that alleviates human effort and time in creating new images. The technique contains essences of both Image Recognition and Image Generation. With the synergy of the models generation and recognition the system gains efficacy and a superior accuracy when compared to existing GANs. The system is able stimulate image of considerable resemblance to real images.
Processing and Analyzing Big Data Generated from Data Communication and Social Networks: In-terms of Performance Speed and Accuracy

A multiple layer architecture for sentiment analysis has been discussed in the proposed work which achieves a best accuracy of 89.61% using SVM ML classifier. Obtained results conclude that, a better accuracy has been achieved in the proposed scheme compared to existing schemes in the literature. Predictive analytics has been done against tweets collected during IPL10 cricket 20 overs final match. Existing literature highlight conceptual relationships, physicaal locations or topical changes but the textual is not being visualized primarily. Proposed scheme works on exploring hidden semantics which helps in the visualization of real text content. Also, another work has been done where data processing environment is developed using Apache SPARk which is deployed above an existing YARN cluster. With the proposed setup and tuning of resource allocation strategies, we could analyze 85GB of network trace data within 78 seconds by using a distributed 32 node cluster, having a capacity of 1 terabyte RAM and 256 CPU cores. If processing the same amount of data in traditional systems, it will take around 30 minutes.
Analysis of activity detection data pre-processing

Nowadays collecting sensor data is not just a trend, is a necessity. The increasing occurrence of smart home devices, which are now a permanent part of our homes and life, makes this process of gathering, storing and using this data crucial. One of the most important aspects is data pre-processing, as the process and methods involved must be carefully chosen to obtain the best results. We aim to improve data pre-processing for PIR based activity sensors. Our chosen activity detection system is CASAS, a complete system for detecting and storing activity, where we have access to raw data. The pre-processing is done in MatLab and we tackle the option to divide the main activity into multiple sub-activities or even segment the activity data into suitable windows. After that, we look over the data-labeling and feature selection process. Also, we analyze the possibility of making possible predictions based on existing data.
History visualization using augmented reality

Abstract Augmented reality is an interactive digital media where objects in real-world environment is visualized through a computer-generated image. This research aims at developing an application to visualize abstract historical objects into more concrete objects so that it can be used as an instructional media. The development was done using Web Aurasma Based Studio and Reveal Mobile in six stages including concept, design, material collecting, assembly, testing and distribution. The results showed that the developed augmented reality application was highly effective in visualizing historical facts so as to motivate students in studying historical facts in a more real and concrete way.
Fast Global Active Contour Model with Local Information

Based on the edgeless active contour (CV) model, an improved model is proposed in this paper. Because CV model only uses global intensity information to complete image segmentation process, so the processing of inhomogeneous images is not good, and even there will be error segmentation. In view of the disadvantage of CV model, we consider adding local gray information items to the energy function of CV model. In order to speed up the evolution of the model, we improve the global and local terms according to the method of reference [9]. At the same time, the energy penalty term is added to the energy function to avoid the re-initialization of CV model. Finally, in order to verify the practicability and validity of the improved model, some images are selected for MATLAB simulation experiments. The experimental results of the improved model are compared with those of the original CV model and the model in reference [8]. It is concluded that the improved model has higher segmentation accuracy and efficiency for uneven gray images.
Fast and Accurate Learning of Knowledge Graph Embeddings at Scale

Knowledge Graph Embedding (KGE) is used to represent the entities and relations of a KG in a low dimensional vector space. KGE can then be used in a downstream task such as entity classification, link prediction and knowledge base completion. Training on large KG datasets takes a considerable amount of time. This work proposes three strategies which lead to faster training in distributed setting. The first strategy is a reduced communication approach which decreases the All-Gather size by sparsifying the Sparse Gradient Matrix (SGM). The second strategy is a variable margin approach that takes advantage of reduced communication for lower margins but retains the accuracy as obtained by the best fixed margin. The third strategy is called DistAdam which is a distributed version of the popular Adam optimization algorithm. Combining the three strategies results in reduction of training time for the FB250K dataset from twenty-seven hours on one processing node to under one hour on thirty-two nodes with each node consisting of twenty-four cores.
Investigation of Reference Sample Reduction Methods for Ensemble Output with Fuzzy Logic-Based Systems

One of the main methods in data reduction processes is the instance selection method. Reducing the dataset has two main objectives: reducing the requirements for computing resources, and the time for processing the learning task. The paper studies the problem of reducing the size of a reference set of points (reference sample) in collective decision making. In the paper, the reference sample refers to the sample that is used in ensemble output based on fuzzy logic system. The fuzzy controller makes a decision which agent should be used for each point from a test set. The nearest point from the reference sample is determined for any point from a test set. Depending on the distance to the object from a test set and the successfulness of the algorithm on this object, the confidence of the algorithm on this test point is determined. Also, it is proposed to apply the instance selection to choose instances for the reference set from the training set when solving regression problems based on such methods as genetic algorithms (GA), the k-means clustering algorithm, and the random instance selection (RIS). Computational experiments show that effective instance selection in the reference set can significantly reduce the computational costs while maintaining the accuracy of the result.
SPARK: Secure Pseudorandom Key-based Encryption for Deduplicated Storage

Deduplication is a widely used technology to reduce the storage and communication cost for cloud storage services. For any cloud infrastructure, data confidentiality is one of the primary concerns. Data confidentiality can be achieved via user-side encryption. However, conventional encryption mechanism is at odds with deduplication. Developing a user-side encryption mechanism with deduplication is a vital research topic. Existing state-of-the-art solutions in security of deduplication are vulnerable to dictionary attacks and tag inconsistency anomaly. In this paper, we present SPARK, a novel approach for secure pseudorandom key-based encryption for deduplicated storage. SPARK achieves semantic security along with deduplication. Security analysis proves that SPARK is secure against dictionary attacks and tag inconsistency anomaly. As a proof of concept, we implement SPARK in realistic environment and demonstrate its efficiency and effectiveness.
Visualizing Authority Patterns over Space and Time

Unidimensional measures of democracy fail to account for the complex and varied nature of political systems. This article disaggregates the concept of democracy and proposes a multidimensional conceptualization to account for this variation in institutional cofingurations. Three theoretically informed dimensions are featured: participation, electoral contestation, and constraints on the executive. The three dimensions constitute a cube covering all regime types, in which we place countries using V-Dem data from 1789 to 2018. The cube of democracy patterns reveals several interesting observations. We trace historical patterns of democratization and autocratization, and discuss how global and regional developments take different paths at different times. Our conceptualization makes it clear that political systems with a similar score along a unidimensional scale can in fact be quite distinct. In addition, across the globe over 200 years, certain configurations of political institutions never occur. In other words, incompatible institutional pairs do not exist or are extremely short lived. This multidimensional conceptualization ultimately opens up a new field of research in which institutional change can be studied in greater detail across countries and over time.
Quantitative Volume Measurement of Brain Tumor for Treatment and Planning

The disease progression of malignant tumors can be effectively indicated from their quantitative analysis. This article presents a faster and an accurate quantification of brain tumor volume from glioma MRI images. The tumor volume is a prime presaging factor for treatment of the disease and also to fix the treatment plans according to the severity of the disease. In our experimentation, we used T1-weighted glioma MRI images obtained from the hospital clinics. The first phase of the work comprising of the segmentation of tumor portion using semi-automatic methods like seeded region growing and morphological operations based algorithms. Secondly, the area of tumor portion of all tumor affected slices is calculated and this value is multiplied with the slice thickness to obtain the gross tumor volume. It should be noted that the slice thickness and pixel resolution depends on the MRI machine under test. The speed and accuracy for the volume measurement is analysed using the two mentioned segmentation algorithms. Results show that the morphological operations based method yields good segmented region compared to the region growing method. It is also observed that morphological operations based method is faster in segmenting the effective tumor region and hence to calculate the volume.
A 2.5D Thinning Algorithm for Human Skeleton Extraction from a Single Depth Image

This paper presents a novel unsupervised approach to human skeleton extraction from a single depth image. In this approach, a 2. 5D thinning line detecting algorithm is proposed to extract thinning lines of human body in the depth data. Based on the thinning lines, we detect the outer and inner layer benchmark joints of skeletons following the anthropotomy theory. We extract skeletons in two steps, the hypothesis generation and the hypothesis validation. In hypothesis generation step, we construct a human kinematic model with 16 skeleton joints, and initialize the model in accordance with human body proportions. Next, in hypothesis validation step, the final skeleton can be achieved by fitting a standard skeleton model to benchmark joints. We evaluated the proposed algorithm on the Stanford database, and compared our results with the skeletons captured by Microsoft Kinect SDK. The experiment results prove that the proposed 2. 5D thinning algorithm can solve self-occlusion problems. Meanwhile, as the proposed approach needs no training and tracking, it is able to extract skeletons more conveniently and faster than other motion information based approaches.
Modus Operandi in Cybercrime

Even though we are not aware of it most of the time, we often recognize our opposite because of his/her way or method of doing something, also called modus operandi, before we can see his/her face. Especially in the field of crime studies, the modus operandi represents an important concept for learning about offenders and their approach. While it is already a well-known method to analyze the modus operandi of offenders in traditional crime investigations, this is not yet commonly used in cybercrime. The most important reason for this omission currently is the lack of insight into the approach of a cybercriminal. In this article, the term “modus operandi in cybercrime” will be discussed. The objectives of this article are (1) to demonstrate why a valid definition has to take into account the highly interdisciplinary nature of the field, (2) to explain how the modus operandi in cybercrime could look like in practice, and (3) to present recommendations and a conclusion on what should be kept in mind when talking about the modus operandi in the context of cybercrime.
When Deep Learning Meets the Edge: Auto-Masking Deep Neural Networks for Efficient Machine Learning on Edge Devices

Deep neural network (DNN) has demonstrated promising performance in various machine learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring DNN models directly on edge devices trends the development of intelligent systems, like self-driving cars, smart Internet-of-Things (IoTs) and autonomous robotics. The on-device DNN model is obtained by expensive training via vast volumes of high-quality training data in the cloud datacenter, and then deployed into these devices, expecting it to work effectively at the edge. However, edge device always deals with low-quality images caused by compression or environmental noise pollutions. The well-trained model, though could work perfectly on the cloud, cannot adapt to these edge-specific conditions without remarkable accuracy drop. In this paper, we propose an automated strategy, called "AutoMask", to embrace effective machine learning and accelerate DNN inference on edge devices. AutoMask comprises end-to-end trainable software strategies and cost-effective hardware accelerator architecture to improve the adaptability of the device without compromising the constrained computation and storage resources. Extensive experiments, over ImageNet dataset and various state-of-the-art DNNs, show that AutoMask achieves significant inference acceleration and storage reduction while maintains comparable accuracy level on embedded Xilinx Z7020 FPGA, as well as NVIDIA Jetson TX2.
Accelerating Nash Q-Learning with Graphical Game Representation and Equilibrium Solving

Traditional Nash Q-learning algorithm generally accepts a fact that agents are tightly coupled, which brings huge computing burden. However, many multi-agent systems in the real world have sparse interactions between agents. In this paper, sparse interactions are divided into two categories: intra-group sparse interactions and inter-group sparse interactions. Previous methods can only deal with one specific type of sparse interactions. Aiming at characterizing the two categories of sparse interactions, we use a novel mathematical model called Markov graphical game. On this basis, graphical game-based Nash Q-learning is proposed to deal with different types of interactions. Experimental results show that our algorithm takes less time per episode and acquires a good policy.