Reviewerly/exHarmony huggingface dataset:
NeoCyberKG: Enhancing Cybersecurity Laboratories with a Machine Learning-enabled Knowledge Graph

The hands-on lab is a critical component of cybersecurity education. There lacks of a coherent way to manage existing labs to provide a practical learning plan for learners in the cybersecurity area. Previous studies utilized the word embedding technologies to construct a knowledge graph and adopt it as a learning guide for students, but this approach has its limitations. In this paper, we present a new approach based on latent semantic analysis (LSA) method to replace word embedding in previous studies as it is more appropriate in a small-size corpus, and it is also able to create a mapping that connects both the topic of each lab and concepts contained in each lab. We use LSA to identify relevant semantic relations, extract relevant lab problems, and construct knowledge graphs from lab contents related to cybersecurity topics. We utilize the output of this study by establishing a web-based lab environment for students that: 1. providing lab index and searching, which contains concepts and knowledge extract from each lab. 2.building a recommendation/guidance system for cybersecurity labs and suggesting more relevant labs based on users learning preferences and past lab history to maximize learning outcomes. To measure the effectiveness of the proposed solution, we conducted a use case study and collected survey data from a graduate-level cybersecurity class at a public university. Our study shows that users tend to gain enhanced learning outcomes and express more interest in the cybersecurity area by leveraging the knowledge graph as a learning guide.
Combining BRIEF and AD for Edge-Preserved Dense Stereo Matching

Overlap between the patches from which descriptors of adjacent pixels are extracted results in edge fattening. Edge fattening can be addressed by adjusting aggregation techniques such as binary aggregation, that are proved successful in intensity-based matching, for use with descriptors. However, binary aggregation through applying masks risks descriptiveness. Therefore, an intensity-based metric such as AD (Absolute Difference) is proposed to supplement the masked binary descriptor such as BRIEF (Binary Robust Independent Elementary Features). The proposed matching cost function adaptively weights the contribution of each metric according to the patch’s content. The proposed hybrid metric reduces the error by up to 2.86% without adding computational complexity by re-using values that are already computed. The latest version of Middlebury stereo dataset and evaluation SDK (Software Development Kit) are used to evaluate the results .
Insight to the geological character of the Early Triassic strata in the Western Salt Range, Pakistan: A comparative study

The sedimentary strata along the Permian–Triassic Boundary in the Western Salt Range of the sub‐Himalayas were analysed to gain an insight of its geological character regarding palaeoenvironments, palaeoclimate, and sequence stratigraphic attributes. The Early Triassic Mianwali Formation is dominated by shallow marine facies with transgressive phases, differentiation in facial character, and palaeowater depth among different sections. The study suggests that compared to the siliciclastic‐dominated Nammal Gorge Section, the Zaluch Nala Section is more carbonate‐dominated, representing algal‐laminated limestones, dolomitic marls, and siliciclastics – mainly restricted to the Mittiwali Member. There is a general decrease in ammonoids and increase in thickness from east to west, that is believed to be due to algal‐induced rapid sedimentation in the shallow marine environments as compared to deeper marine in the east. The Mianwali Formation has been deposited in two transgressive systems tracts, two highstand systems tracts, and one falling‐stage regressive systems tract. Two flooding surfaces, one each in the Mittiwali and Narmia members, have been recognized. The maximum regressive surfaces at the bottom (with the Chhidru Formation) and top (with the Tredian Formation) mark second‐order eustatic falls. The study also advocates for existence of a low‐energy platform in the temperate waters of the Palaeo‐Tethys. The sediments have similarities with various sections of Gondwanaland having the same trend of shallow marine facies and presence of algal‐laminated deposits. In the Middle Triassic, the gradual loss of sediment binding organisms shifted the low‐energy meandering patterns to high‐energy braided systems.
Wall Detection Via IMU Data Classification In Autonomous Quadcopters

An autonomous drone flying near obstacles needs to be able to detect and avoid the obstacles or it will collide with them. In prior work, drones can detect and avoid walls using data from camera, ultrasonic or laser sensors mounted either on the drone or in the environment. It is not always possible to instrument the environment, and sensors added to the drone consume payload and power - both of which are constrained for drones.This paper studies how data mining classification techniques can be used to predict where an obstacle is in relation to the drone based only on monitoring air-disturbance. We modeled the airflow of the rotors physically to deduce higher level features for classification. Data was collected from the drone’s IMU while it was flying with a wall to its direct left, front and right, as well as with no walls present. In total 18 higher level features were produced from the raw data. We used an 80%, 20% train-test scheme with the RandomForest (RF), K-Nearest Neighbor (KNN) and GradientBoosting (GB) classifiers. Our results show that with the RF classifier and with 90% accuracy it can predict which direction a wall is in relation to the drone.
A Neural Network Approach to Subjective Human Face Perception Classification based on Social Characteristics

Human can perceive rich information from a given face quickly and effortlessly. However, the detailed computational mechanism of face perception is still unclear. Psychologists have studied the face perception using psychophysics and neuroimaging methods for decades, but are still far from forming a converged view of classifying the perception of facial characteristics. One reason is that the facial characteristics are somewhat intertwined but the degree to which is not fully clarified. On the other hand, although having agreements on the association between facial traits and perceived social characteristics, people still perceive faces with a certain level subjectivity. In this study, the authors take the advantage of neural network to propose an algorithm for human face perception classification. The performance of this algorithm is evaluated based on the elements of a database consisting of human face social characteristics, and the experimental results confirm its classification accuracy.
Detailed Investigation and Comparison of Various Binarization Algorithms for Hashing

The fact that the number of images stored continues to increase day by day makes content-based image retrieval (CBIR) systems more important. Considering that almost all of the datasets encountered today are large, it is evident that using the exact nearest neighbor (ENN) search will be inefficient and time-consuming. Therefore, hashing approach, which is seen as the most effective approximate nearest neighbor (ANN) method, is preferred in nearly all retrieval tasks. To learn these binary hash codes, feature extraction, dimension reduction, and binarization steps are generally applied. In this study, the effects of the approaches used in the binarization section on retrieval success are analyzed comparatively. For this purpose, the Kvasir dataset (includes gastrointestinal tract images) and 630 pyramid histograms of oriented gradients (PHOG) features of this dataset are used. First, the dimension of PHOG features is reduced to 16, 32, and 64 bits (usually hash code lengths) using principal component analysis (PCA). Then these different length feature vectors are converted into hash codes by binarization process. Five different threshold methods are used for the binarization process. Binarization is performed by means of techniques such as hard threshold, mean value threshold, adaptive threshold, line mean value, class mean value, total mean value. Finally, the retrieval performances of the hash codes are tested. The performances of the binarization methods are presented comparatively.
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics.It can decompose an original matrix into central tensors (containing the core information) and auxiliary tensors (with only a small proportion of parameters).With the decomposed MPO structure, we propose a novel fine-tuning strategy by only updating the parameters from the auxiliary tensors, and design an optimization algorithm for MPO-based approximation over stacked network architectures.Our approach can be applied to the original or the compressed PLMs in a general way, which derives a lighter network and significantly reduces the parameters to be fine-tuned.Extensive experiments have demonstrated the effectiveness of the proposed approach in model compression, especially the reduction in finetuning parameters (91% reduction on average).The code to reproduce the results of this paper can be found at https://github.com/RUCAIBox/MPOP.
Research on Knowledge Storage and Query Technology Based on General Graph Data Processing Framework

With the development of the Semantic Web, more and more data is currently managed in the form of knowledge graphs. Different knowledge storage and query modes have their own advantages, but also have shortcomings, and there is no unified standard. Aiming at the current deficiencies in knowledge storage and knowledge query technology, this paper proposes a knowledge storage and query scheme based on TinkerPop graph computing framework, a general graph data processing framework that combines Neo4j massive graph data storage capabilities and SPARQL semantic query capabilities.
Beyond VVC: Towards Perceptual Quality Optimized Video Compression Using Multi-Scale Hybrid Approaches

In this paper, we propose a perceptual quality optimization oriented video compression framework using hybrid approaches. The proposed framework, which is built on the top of recently-published Versatile Video Coding (VVC), contains multi-scale optimized coding techniques. Specifically, three major aspects of efforts from coding unit level to video sequence level have been dedicated to obtain substantial compression efficiency improvement. We first propose a block-level rate-distortion optimization (RDO) method with the consideration of block artifacts removal. Subsequently, we propose frame-level perceptual quality optimized convolutional neural networks for the post-processing of each compressed image, within which the channel attention mechanism has been employed to capture and restore the crucial detail in subjective evaluation. We additionally model the bit allocation as sequence-level dynamic programming problem such that optimal perception and bitrate tradeoff could be obtained. Experimental results show that the proposed method achieves 0.98658 MS-SSIM on the validation set in video track of CLIC-2021.
Collaborative Augmented Reality for Cultural Heritage, Tourist Sites and Museums: Sharing Visitors’ Experiences and Interactions

In this chapter, synergies between augmented reality (AR) and shared experiences are explored. The shared experiences (such as visiting a cultural site or a museum) can benefit from computer-supported collaborative work done in the context of maintenance, training, and simulation. With the wide availability and use of AR-enabled smartphones, it is now possible to create collaborative experiences built upon the knowledge gained from such industry-oriented training applications. This novel approach allows for a greater user engagement fostered by the collaborative nature of an AR shared space. Recent AR works allow for many users to observe the same superimposed object as the group and interact collaboratively with it, as users can see not only the other users but their actions and effects in the virtual elements. This methodology allows for group interaction around tangible cultural points of interest at different scales (such as ruins or buildings but also statues and paintings).
sssnake: a remote multiplayer toy for children

sssnake is a soft toy that allows multi-user play over distance as well as single users playing alone. It has a haptic interface and can connect children (especially during the COVID-19 pandemic) while avoiding the usage of screen-based media. It combines social interaction inspired from online gaming with a playful way to learn about music and instruments. We present a handcrafted prototype manufactured from e-textiles, wool and grape seeds. The sssnake prototype was evaluated in a user study with ten children aged between one and eight years. The study showed that online-connected soft toys can be an extension to traditional possibilities of socializing and play.
Education and the dislike society: The impossibility of learning in filter bubbles

As we begin to witness a new phase in the integration of digital social media platforms with educational institutions, we ought to ask how learning exchanges may be altered as a result. Looking to transformations in knowledge exchanges outside of formal education, we find that these technologies have already modified the ways in which communities engage with each other. Gerlitz and Helmond explain that the Like Economy built into all major social media platforms flattens exchanges between users to engagement metrics. With online communities increasingly isolated from each other thanks to inscrutable recommendation algorithms, the most frequent cross-community exchanges manifest in outbursts of rage, producing a so-called ‘Dislike Society’. Practitioners would rather briefly unite to tear down other ways of living than to build new ones. Within the Dislike Society, any form of knowledge that could shape new communities is lost to the governance of algorithms in what Bernard Stiegler calls ‘proletarianisation’. To re-apply knowledge to the improvement of life, as suggested by Whitehead, the attention of learners needs to be shaped in response to new technological conditions. This is best achieved within the educational institutions that now face a reorganisation by the same companies that brought us the Dislike Society.
Fuzzy Modeling and Reasoning

In recent years, the construction of subway systems and underground utilities has increased dramatically due to population pressure and a lack of surface space. The tunnel boring machine (TBM) has been observed with widespread applications in tunnel construction, which can be used for excavating tunnels for nearly all types of rock and geological conditions.
Putting ICT in the Hands of the Seafarers

This paper proposes ethnographic action research on expanding participation in an ICT project in the maritime domain. Concepts from actor network theory are derived to provide a useful perspective on the description and analysis of the case. By analyzing two workshops, the paper discusses how power relations are balanced in a new heterogeneous network of actors, whose dynamical features align different actants' interests toward a common sense. The relevance of the findings is discussed, related to how we might think about the role of researchers in the empowerment of marginalized groups. In this paper, I posit that the creation of a research and development culture to extend participation is helpful and relevant to making a well-organized technological ecosystem to the detriment of seafarers' community well-being.
A survey on face forgery detection of Deepfake

With the development of internet technology, artificial intelligence shows its rapid growth in recent year as well. More and more people pay attention to this field, including criminals inevitably. It is noted that one technology called "Deepfake" appeared on the internet at the end of 2017. As the name suggests, it is a portmanteau of "deep learning" and "fake". In essence, Deepfake is a deep-learning framework in the field of image composite and replacement, which swap faces in images or videos. However detection of face forgery is still in its early stages, due to its novelty and complexity. In this paper we will demonstrate some dataset for deepfake forensics firstly, and then describe various existing detection methods. Those methods are reviewed in two parts: detection based on frame sequences and detection based on single frame. The former is implemented by differences between frames including human features, optical flow, timeline and so on, while the latter is based on features of single frame including extracted features and fusion boundary. Various convolutional Neural Networks (abbreviated as CNN) will be illustrated in this paper. Accordingly, performances of above algorithms are likely to be demonstrated and compared, and a further explanation will be given regarding on their applicable dataset. Finally, further research of face forgery detection of deepfake including methods and applications will be discussed.
Adaptive epipolar distance transform and adaptive matching cost for stereo matching

This paper presents a new algorithm that robustly performs stereo matching for textureless regions in stereo images. To this end, we design an adaptive matching cost which employs a special term. This term can assign distinguishable values to pixels adaptively according to the texture information. Specifically, first, we improve the epipolar distance transform by utilizing a linear expansion function and obtain an adaptive epipolar distance transform (AEDT); second, we propose an adaptive matching cost utilizing the AEDT to deal with textureless region problems. Experiments on the Middlebury benchmark demonstrate that the proposed method can perform accurate stereo matching on textureless regions. Moreover, the experiments show that the proposed adaptive matching cost can be directly utilized to other methods to improve the disparity results in textureless regions.
Visual Analytics Review: An Early and Continuing Success of Convergent Research With Impact

The growth of the field of visual analytics reflects a sustained, long-term commitment to applying convergent research principles to computer science research. Charting the future research directions that the field of visual analytics will evolve toward requires an understanding of the field's rich, productive history of collaboration and a discussion of current challenges.
Topic-extended Emotional Conversation Generation Model Based on Joint Decoding

The research on the expression of emotion in human-computer dialogue can greatly improve the user experience. Existing research has paid a lot of attention to how to generate specific emotional content and how to improve the extraction rate of emotions, while ignoring the reduction of emotion expression caused by factors such as topics and emotions added to the encoder. This paper proposes a novel Topic-extended Emotional Conversation Generation Model Based on Joint Decoding (TECM-JD). The model embeds the specified emotion category as an additional input into the emotional independent unit of the decoder, in order to reduce the expression of the content affected by adding emotion into the model. The joint attention mechanism is used to obtain the input sequence content and the input sequence topic word content obtained by the Twitter LDA model, which ensures that the output topic and the input are under the same topic. The experimental results show that the proposed model can generate richer emotional content related to the topic and have good performance and are superior to traditional dialogue models.
Automatic Language Identification from Spectorgam Images

The main idea of the work is to apply CNN and LSTM algorithms on the preprocessed audio data converted to spectrograms. In the experiment 7 languages are used English, Kazakh, French, German, Italian, Russian and Spanish to test that algorithm which show over 99% training accuracy and the maximum of 94.28% accuracy on the test set. In the end there is a discussion of the 100% of classification of the Kazakh language and the possible influences of dataset on the result.
A Microservice-Based Architecture for Performance and Energy Benchmarking of Docker-Host Linux Distributions on Internet-of-Things Devices

Containers are rapidly being adopted in several areas of the information technology industry. A major area is edge and embedded Internet-of-Things systems. In this paper, we present a microservice-based architecture for performance analysis and energy consumption of Internet-of-Things "Docker host" Linux distributions. Our methodology builds on previous container benchmarking work, with analysis of performance metrics such as processing, memory, and disk throughput. Furthermore, our methodology introduces container-engine performance metrics related to container lifecycle operations, and concurrent container performance. We demonstrate by comparing four Linux distributions in this domain: BalenaOS, HypriotOS, RancherOS, and Raspbian Lite. All source code is provided.
Towards a Holistic Controller

The increased use of cloud and other large scale datacenter IT services and the associated power usage has put the spotlight on more energy-efficient datacenter management. In this paper, a simple model was developed to represent the heat rejection system and energy usage in a small DC setup. The model was then controlled by a reinforcement learning agent that handles both the load balancing of the IT workload, as well as cooling system setpoints. The main contribution is the holistic approach to datacenter control where both facility metrics, IT hardware metric and cloud service logs are used as inputs. The application of reinforcement learning in the proposed holistic setup is feasible and achieves results that outperform standard algorithms. The paper presents both the simplified DC model and the reinforcement learning agent in detail and discusses how this work can be extended towards a richer datacenter model.
Forensics Analysis of Private Web Browsing Using Android Memory Acquisition

Modern smartphones can perform and display nearly as much of the internet as personal computers, including web browsing and video streaming. Nowadays, all users rely on web browsers to access, display, and manipulate information from Internet on their mobile devices. However, almost all of user browsing activities flow through a web browser, thereby threatening the privacy preservation by third-party trackers or browsers' providers. Therefore, there is a demand to make web browsers private for users with the most intensive concentrate on keeping their data safe. In this paper, we perform a thorough digital forensics analysis on the smartphone's volatile memory with the aim at investigating the data privacy on various web browsers. Memory acquisitions is methodically applied in private and non-private modes on Android platforms to examine which of the user artifacts are being protected or exposed from web history or email communications. Comprehensive experiments are conducted on the four popular browsers Google Chrome, Mozilla Firefox, Dolphin and Opera under various scenarios. The experimental results show that the Chrome web browser was the lowest secure browser in which all the inspected data have been retrieved even with a private mode enabled. The other browsers have shown a partial privacy preservation. Such findings emphasize the importance of conducting such forensics analysis and warning users to keep their browsing practices safe from prying eyes.