Reviewerly/exHarmony huggingface dataset:
Using Structural and Semantic Information to Identify Software Components

Component Based Software Engineering (CBSE) seeks to promote the reuse of software by using existing software modules into the development process. However, the availability of such a reusable component is not immediate and is costly and time consuming. As an alternative, the extraction from preexisting OO software can be considered.In this work, we evaluate two community detection algorithms for the task of software components identification. Considering `components' as `communities', the aim is to evaluate how independent, yet cohesive, the components are when extracted by structurally informed algorithms.We analyze 412 Java systems and evaluate the cohesion of the extracted communities using four document representation techniques. The evaluation aims to find which algorithm extracts the most semantically cohesive, yet separated communities.The results show a good performance in both algorithms, however, each has its own strengths. Leiden extracts less cohesive, but better separated, and better clustered components that depend more on similar ones. Infomap, on the other side, creates more cohesive, slightly overlapping clusters that are less likely to depend on other semantically similar components.
A Visualization Tool for Relationship between Source Code and Parse Tree Using VR

Software development process is composed of several activities such as planning, specification development, module design, algorithm design, coding, and test. Various documents are created through these processes, which are related each other as software configuration. Beginners such as student cannot understand mutual relationship within the software configuration well. Considering this situation, we propose and develop a visualization tool named VRale-SCM which shows these documents in 3D virtual reality space. The tool visualizes mutual relationship among the documents so that students can navigate the documents through the relationship. Students are thus expected to clearly understand the relationships by this tool. In this paper, we focus on the coding process. VRale-SCM visualizes relationship between Java source code and the parse tree. Name table is utilized to visualize correspondence among definition and reference of the same name within the source code and the corresponding parse tree.
Human Detection/Tracking System for Video Surveillance With Noise Removal

Object detection and tracking have been extensively used in many applications including security and surveillance. This chapter addresses the problem of human detection and tracking in surveillance videos with noise. The system proposed deals with video processing utilizing Kalman filtering to enhance the process in the presence of challenging weather conditions. To support the target idea, some experiments are introduced to measure the deviation between both the noiseless and the noisy videos and to study the effect of each filter within each noise type of video disturbance. The most efficient masking system is used to enhance the outcome in the presence of video degradations. The proposed tracking and detection technique achieves achieve an average correct assigned tracks accuracy of about 95% for noiseless video. This is due to the challenging video degradation depending on the type and level of noise. In case of noisy videos by applying the correct filter mask, the accuracy of tracking in the presence of a median filter comes to around 90%.
On Ubiquitous Technology, a Digital World and their Influence on People’s Feeling and Control of Presence in Everyday Life

Ubiquitous technology, e.g., smartphones or tablets, has created a continuously available digital world, drastically changing our feeling of being in the here and now – named presence. We thus increasingly shift between the real and the digital world, ranging from losing awareness of real surroundings to cutting out the digital world to truly be in the real one. In this work, we aim to explore the middle ground in between. We move beyond classic VR research on presence and look at everyday ubiquitous technology and its influence on presence in the real and digital world. By means of a focus group (N=6) and a subsequent online survey (N=36), we gathered individual notions of presence as well as experiences and situations in which people move between the worlds. We discuss the need to further explore presence and its dynamics across the real and digital worlds in everyday life.
Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking

This paper develops a robust extended-target multisensor multitarget multi-Bernoulli (ET-MS-MeMBer) filter for enhancing the unsatisfactory quality of measurement partitions arising in the classical ET-MS-MeMBer filter due to increased clutter intensities. Specifically, the proposed method considers the influence of the clutter measurement set by introducing the ratio of the target likelihood to the clutter likelihood. With the constraint of the clutter measurement set, it can obtain better multisensor measurement partitioning results under the original two-step greedy partitioning mechanism. Subsequently, the single-target multisensor likelihood function for the clutter case is derived. Simulation results reveal a favorable comparison to the ET-MS-MeMBer filter in terms of accuracy in estimating the target cardinality and target state under conditions with increased clutter intensities.
Under the Spell of Deadlines

Deadlines are constitutional aspects of research life that the CHI community frequently observes. Despite their importance, deadlines are understudied. Here we bring a mixed art and science perspective on deadlines, which may find broader applications as a starter methodology. In a field study, we monitored four academics at the office, two days before a deadline and two regular days, after the deadline had passed. Based on face video, questionnaire, and interview data we constructed their profiles. We added a dose of fictionalization to these profiles, composing anonymized comic stories that are as humorous as they are enlightening. In the stressful and lonely days towards deadlines, the only common presence in all cases is the researchers' computer. Accordingly, this work aspires to prompt an effort for a deeper understanding of "deadline users'', in support of designing much needed affective interfaces.
Robot Motion Control Using OpenPose

Machine learning, computer vision, and artificial intelligence are an emerging topic in the field of robotics. Human cation recognition comes under computer vision and artificial intelligence which help to estimate the human body position. OpenPose is an open-source tool developed by Carnegie Mellon University that estimates human body parts by detecting 135 anatomical key points like eyes, lips, and hands. With this, it can estimate the human position in real-time motion by using a simple camera and it can reduce the usage of wearable sensors and the installation costs, then it produces a skeleton structure to represent the human pose. We use key points to get the data from an RGB image built on the location of key points. To classify and recognize the pose from an image, we use long short-term memory cells (LSTM) in recurrent neural network (RNN). The extracted data in the form of a coordinate system can be transferred into the humanoid robots to control its motion and to train its work. This model can detect the position with higher accuracy. Since the data can be obtained just by using simple cameras, this human pose detection model can be used in different industrial processes and domestic processes without being expert in Robotics.
Cascade Attention Fusion for Fine-Grained Image Captioning Based on Multi-Layer LSTM

The conventional visual attention-based image captioning approaches typically use image information to guide caption generation. Results from these models tend to be coarse and ignore the details in the image, such as objects, attributes and the distinguishing aspects of each image. In this paper, we propose a visual and semantic fusion network with a margin-based training guidance mechanism to generate fine image descriptions that depict more objects, attributes and other distinguishing aspects of images. In our model, the visual attention layer introduces more low-level visual information, the semantic attention layer provides more high-level semantic attributes. Furthermore, the proposed margin-based loss encourages our model to produce more discriminative descriptions. Extensive experiments are conducted on COCO and Flickr30K image captioning datasets to validate our method, and the results show its superior performance at captioning. Our method achieves a state-of-the-art 70.6 CIDEr-D on the Flickr30K dataset, and a competitive 123.5 CIDEr-D on the MS-COCO dataset.
Rotation Invariance Analysis of Local Convolutional Features in Image Retrieval

Recently, local features computed using convolutional neural networks (CNNs) show good performance to image retrieval. However, the local convolutional features obtained by the CNNs (LC features) are inherently sensitive to rotation perturbations. This leads to miss-judgements in retrieval tasks. In this work, our objective is to enhance the robustness of LC features against image rotation. To do this, we conduct a thorough experimental evaluation of two candidate anti-rotation strategies (in-model data augmentation, and post-model feature augmentation), over two kinds of rotation attacks (dataset attack and query attack). We end up a series of good practices with steady quantitative supports, which lead to the best strategy for computing LC features with high rotation invariance in image retrieval.
The Implementation of Virtual Reality Technology in Education: the Perspective of Learning Environment

Since Virtual Reality (VR) was first mentioned in the 1950s, the research interest into the technology has been widely concerned. Virtual Reality (VR) provides immersion and interactivity with reality. Since their incorporation into classrooms and labs in over a hundred countries, visual innovations have significantly enhanced the availability of advanced concepts to be challenged in a manner that portrays them more realistically than the terms that they mean. Virtual reality enables specialists and technicians to conduct their duties more efficiently by acquiring a greater awareness of their strategic placement. In this study, databases of Google scholars, Scopus, web of science etc. were used for extensive literature retrieval, and excel was used for analysis. The results show that the advantages of incorporating haptic feedback device into the VR systems and how these integrated systems can allow students to compete realistically in the complex machine-assisted learning and experimental process. we need to admit the importance of educating people about how simple it is for scientists to perform chemical simulations using Virtual Reality.
Intelligent Decision Support Systems of Medicinal Forest Plants for Skin Disease

The richness of Borneo's biodiversity with the potential for indigenous tribal knowledge, one of which is using various types of medicinal plants used in traditional local ethnic medicine, especially those around forest areas. This study aims to develop Borneo's medicinal forest plants decision-making system for skin diseases based on the International Classification of Diseases (ICD-10 version 2016). The determination of medicinal plants for the treatment of skin diseases is based on criteria types of plant species, how to process, how to use, and plant parts used. This research resulted in a decision support system intelligence software product for determining of 94 dataset medicinal forest plants for skin diseases using the AHP method for weight determination (priority), and WASPAS for preference. The implementation of the AHP-WASPAS method in case studies of medicinal forest plants decision shows that the user's subjectivity in weighting and decision-making criteria affects the recommended preference values. Furthermore, the multi-criteria analysis method approach to decision making in applied case studies is less objective because the knowledge base of alternatives and criteria is complex.
Design of 2D Systolic Array Accelerator for Quantized Convolutional Neural Networks

Quantization techniques have been studied to reduce the computing and memory requirement of deep neural networks. The full precision floating-point numbers are quantized into integer representation with lower bit-width. In this work, we quantize both activations and weights in CNN to 8-bit integers and apply our quantization method to the hardware accelerator. The accelerator is designed with a systolic-based structure, which can support both the convolutional layers and the fully-connected layers for various network models. By the proposed quantization scheme, there is only 1.68% mAP loss on YOLOv3-tiny model compared to the floating-point model. Benchmarked with AlexNet and VGG-16, the external memory access of convolutional layers is reduced by 1.63x and 1.79x compared with Eyeriss, and the internal memory access is also reduced by 7.31x and 17.48x.
A Cell Augmentation Tool for Blood Smear Analysis

We propose a tool to add parasite-infected red blood cells to blood smear images for malaria screening based on a Mask R-CNN network. The number of infected and uninfected red blood cells in blood smears of malaria patients is typically unbalanced. Our proposed tool extracts cells, augments them by rotation, and then pastes the augmented cells back into empty spaces between existing cells. To paste augmented cells back into an image, the underlying algorithm computes a set of candidate locations that are far away from existing cells by applying a distance transform to a cell mask. Among these candidate locations, the final locations are selected by choosing the locations that are far from each other and distributed across the image to avoid cell clusters and touching cells.For evaluation, we augment 165 blood smear images from 33 patients and run tests on 800 blood smear images from 160 patients. In the training set, the number of infected red blood cells is much smaller than the number of uninfected cells, 1,142 vs. 33,071. The tool adds 8,208 rotated copies of infected cells across all images, increasing the total number of infected cells to 9,350. We then use Mask R-CNN to detect infected cells, before and after augmentation, and find that our tool improves the recall, precision, and F1 measure for the detection of infected cells by about 6%.
Preserving Conversations with Contemporary Holocaust Witnesses

Conversations with Holocaust survivors are an integral part of education at schools and universities as well as part of the German memory culture. The goal of interactive stereoscopic digital Holocaust testimonies is to preserve the effects of meeting and interacting with these contemporary witnesses as faithfully as possible. These virtual humans are non-synthetic. Instead, all their actions, such as answers and movements, are pre-recorded. We conducted a preliminary study to gauge how people perceive this first German-speaking digital interactive Holocaust testimony. The focus of our investigation is the ease-of-use, the accuracy and relevance of the answers given as well as the authenticity and emotiveness of the virtual contemporary witness, as perceived by the participants. We found that digital 3D testimonies can convey emotions and lead to enjoyable experiences, which correlates with the frequency of correctly matched answers.
A Novel Keystone Correction Method Using Camera-Based Touch Interface for Ultra Short Throw Projector

Ultra-short throw projectors (USTPs) that reduce throw distance greatly with a high resolution have been widely developed. However, the hardware characteristics of the USTP make projected image distorted easily comparing to the typical long throw projectors (LTPs). Therefore, keystone correction is an important feature to ensure the usability of the USTPs. Although many automatic keystone correction methods have been presented for the LTP, they are limited to be directly applied to the USTP. Due to this limitation, the commercial USTPs adopted a manual keystone correction. The manual method requires users to be familiar with keystone distortion and correction mechanism, otherwise it takes lots of time to complete and results are unsatisfactory. To solve this problem, we propose a novel keystone correction method with the touch interface. As a prerequisite of keystone correction, we need to know the optimal projected shape without distortion. In the proposed method, the user-specified shape which obtained from the touch interface is used as the optimal shape. In addition, we present the projection model which can explain the cause and the solution of the keystone distortion. Experimental results show that the proposed method outperforms the manual keystone correction in terms of correction performance and usability.
Robustness-aware 2-bit quantization with real-time performance for neural network

Quantized neural networks (NN) with reduced bit precision are practical solutions to minimize computational and memory resource requirements and play a vital role in machine learning. However, it is still challenging to avoid significant accuracy degradation due to numerical approximation and lower redundancy. In this paper, a novel robustness-aware 2-bit quantization scheme (RAQ) is proposed for NN, based on binary NN and generative adversarial networks (GAN), which improve performance by enriching binary NN information, extracting the structural information and considering the robustness of the quantized NN. Specifically, using a shift-add operation to replace the multiply-accumulate in the quantization process can speed the NN. A structural loss is proposed to represent the difference between the original NN and quantized NN, such that the structural information of data is preserved after quantization. The structural information learned from NN plays an important role in improving the performance and allows for further fine-tuning of the quantized NN by applying the Lipschitz constraint to the structural loss. For the first time, we consider the robustness of the quantized NN and propose a non-sensitive perturbation loss function by introducing an extraneous term of the spectral norm. The experiments were conducted on CIFAR-10, SVHN and ImageNet datasets with popular NN (such as MobileNetV2, ResNet20, etc.). Extensive experiments show that our new 2-bit quantization scheme is more efficient than the state-of-the-art quantization methods. Our scheme effectively reduced the latency by 2 × and the accuracy decline by 1–4%. Meanwhile, the experimental results also demonstrate that the RAQ is robust with adversarial attacks, we not only eliminate the robustness gap between full-precision and quantized models, but also improve the robustness over full-precision ones by 10%.
SM9 Digital Signature with Non-repudiation

SM9 is an identity-based cryptography algorithm published by the State Cryptography Administration of China. With SM9, a user's private key for signing is generated by a central system called key generation center (KGC). When the owner of the private key wants to shirk responsibility by denying that the signature was generated by himself, he can claim that the operator of KGC forged the signature using the generated private key. To address this issue, in this paper, two schemes of SM9 digital signature with non-repudiation are proposed. With the proposed schemes, the user's private key for signing is collaboratively generated by two separate components, one of which is deployed in the private key service provider's site while the other is deployed in the user's site. The private key can only be calculated in the user's site with the help of homomorphic encryption. Therefore, only the user can obtain the private key and he cannot deny that the signature was generated by himself. The proposed schemes can achieve the non-repudiation of SM9 digital signature.
Cross-Content Evaluations in the Subjective Quality Assessment of Light Field Images

Plenoptic modalities, in general, have seen a steady increase in research activity in recent years. Advances in this field pose additional demands on the development of new compression algorithms, which could be used for efficient delivery and storage of the acquired data. Subjective assessments are a key part of the development process. In this work, we examine the differences between two passive methodologies for the subjective assessment of light field images; a rating methodology, introduced in our previous work, and a ranking methodology. Additionally, we analyse the impact and viability of cross-content evaluations on the results of the assessment. The resulting data are made publicly available and can be used to design new compression algorithms, subjective assessment methodologies, or objective quality metrics.
Federated Algorithm with Bayesian Approach: Omni-Fedge

In this paper, we consider the problem of Federated Learning (FL) under non-i.i.d data setting. We provide an improved estimate of the empirical loss at each node by using a weighted average of losses across nodes with a penalty term. These uneven weights to different nodes are assigned by taking a novel Bayesian approach to the problem where the problem of learning for each device/node is cast as maximizing the likelihood of a joint distribution. This joint distribution is for losses of nodes obtained by using data across devices for a given neural network of a node. We then provide a PAC learning guarantee on the objective function which reveals that the true average risk is no more than the proposed objective and the error term. We leverage this guarantee to propose an algorithm called Omni-Fedge. Using MNIST and Fashion MNIST data-sets, we show that the performance of the proposed algorithm is significantly better than existing algorithms.
Research on Link Prediction under the Structural Features of Attention Stream Network

The attention flow network is an important branch of network science, and the research on the link prediction algorithm of attention flow network has important theoretical and application value. The traditional link prediction algorithm based on the neighbor hypothesis cannot learn the global structure information of the network, while the traditional graph neural network algorithm needs to combine external information and has a large amount of calculation. Based on the structural characteristics and the advantages of the MLP neural network, this paper proposes a stru2vec-based algorithm. The link prediction SMLP algorithm is compared with the existing DeepWalk, Line, Node2vec, Hope, NetMF and other algorithms. It is found that the SMLP algorithm proposed in this paper improves on the four evaluation indicators of Precision, Recall, Accuracy and F1 by about 15.8&#x0025; on average. The average increase in AUC index is about 12.1&#x0025;, and the performance is better on the ROC curve, and the algorithm proposed in this paper focuses on learning the network structure, and has a good learning effect for the network with missing node label information and sparse self-characteristic attributes.