Reviewerly/exHarmony huggingface dataset:
Folksonomy Based Fuzzy Filtering Recommender System

As the choice of items on the World Wide Web increases, the difficulty to choose the right item also increases. Fast and effective recommender systems help to overcome this difficulty by automatically using algorithms and data to recommend items like movies, webpages, videos, etc. Conventional collaborative filtering recommender systems use the rating data to find the neighborhood of users in terms of their preferences. However, these recommender systems do not incorporate the domain knowledge in their recommendations. In this paper we propose a hybrid folksonomy-based fuzzy collaborative filtering recommender system which uses domain knowledge of the items and combines them with the collaborative process. To further enhance the performance of the system we use fuzzy clusters to represent the neighborhood in a probabilistic manner which makes the recommendation process more accurate as verified by our experimental results
The Influence of Learning Style on Biology Teaching in AR Learning Environment

Augmented Reality (AR) can solve some problems with experimental teaching, such as expensive devices, high risks and difficult to observe. This research has developed an exploratory AR software related to biological experiments and explored different Felder-Silverman learning styles students' improvement of the knowledge level in AR learning environment. We carry out teaching demonstration with the AR software, then conducts questionnaires on sixty-two participated high school students. The research finds that the balanced students have the highest level of improvement in the information processing dimension, and visual students in the information input dimension. It is shown that AR learning environments pay attention to both teamwork ability and independent thinking ability; and compared to verbal, AR learning environments are more conducive to visual students. Combining the advantages and disadvantages reflected in the data results and the suggestions by the teachers in the interviews, this research proposes four suggestions: “Don't be virtual if it can be real”, “Hands-on to explore”, “Multiple combination” and “Explore flexibly and not afraid of mistakes”.
Topic-Attentive Encoder-Decoder with Pre-Trained Language Model for Keyphrase Generation

Keyphrase annotation task aims to retrieve the most representative phrases that express the essential gist of documents. In reality, some phrases that best summarize documents are often absent from the original text, which motivates researchers to develop generation methods, being able to create phrases. Existing generation approaches usually adopt the encoder-decoder framework for sequence generation. However, the widely-used recurrent neural network might fail to capture long-range dependencies among items. In addition, intuitively, as keyphrases are likely to correlate with topical words, some methods propose to introduce topic models into keyphrase generation. But they hardly leverage the global information of topics. In view of this, we employ the Transformer architecture with the pre-trained BERT model as the encoder-decoder framework for keyphrase generation. BERT and Transformer are demonstrated to be effective for many text mining tasks. But they have not been extensively studied for keyphrase generation. Furthermore, we propose a topic attention mechanism to utilize the corpus-level topic information globally for keyphrase generation. Specifically, we propose BertTKG, a keyphrase generation method that uses a contextualized neural topic model for corpus-level topic representation learning, and then enhances the document representations learned by pre-trained language model for better keyphrase decoding. Extensive experiments conducted on three public datasets manifest the superiority of BertTKG.
CNN-Based Deblurring of THz Time-Domain Images

In recent years, terahertz (THz) time-domain imaging attracted significant attention and become a useful tool in many applications. A THz time-domain imaging system measures amplitude changes of the THz radiation across a range of frequencies so the absorption coefficient of the materials in the sample can be obtained. THz time-domain images represent 3D hyperspectral cubes with several hundred bands corresponding to different wavelengths i.e., frequencies. Moreover, a THz beam has a non-zero beam waist and therefore introduces band-dependent blurring effects in the resulting images accompanied by system-dependent noise. Removal of blurring effects and noise from the whole 3D hyperspectral cube is addressed in the current work. We will start by introducing THz beam shape effects and its formulation as a deblurring problem, followed by presenting a convolutional neural network (CNN)-based approach which is able to tackle all bands jointly. To the best of our knowledge, this is the first time that a CNN is used to remove the THz beam shape effects from all bands jointly of THz time-domain images. Experiments on synthetic images show that the proposed approach significantly outperforms conventional model-based deblurring methods and band-by-band approaches.
Privacy Preserving Federated RSRP Estimation for Future Mobile Networks

Leveraging location information for machine learning applications in mobile networks is challenging due to the distributed nature of the data and privacy concerns. Federated Learning (FL) helps to tackle these issues and is a big step towards enabling privacy-aware distributed model training; however still prone to sophisticated privacy attacks such as membership inference. In this paper, we implement an FL approach to estimate Reference Signal Received Power (RSRP) values using geographical location information of the user equipment. We propose a privacy-preserving mechanism using differential privacy to protect against privacy attacks and demonstrate the impacts and the privacy-utility trade-off via privacy accounting measures.
IB2P: An image-based privacy-preserving blockchain model for financial services

Decentralized finance has grown up dramatically in recent years, covering various cryptocurrency or blockchain financial applications. With the support of blockchain technology, people have shown great enthusiasm and trust in decentralized financial services. However, due to sharing personal information with other institutions, users' privacy and data security will be difficult to be guaranteed. Moreover, various encryption methods such as homomorphic encryption focus on protecting the data stored on the blockchain, but few enhance client-side data security. Motivated by this condition, we develop an image-based privacy-preserving scheme to store users' financial service data as images. Transferring the numerical service data into images ensures that users can comprehend the content and prevent data recognized by the machine. We evaluate our scheme through conducting numerical evaluations from image attack effectiveness and smart contract functions' efficiency analysis.
HEALS: A Parallel eALS Recommendation System on CPU/GPU Heterogeneous Platforms

Alternating Least Square (ALS) is a classic algorithm to solve matrix factorization widely used in recommendation systems. Existing efforts focus on parallelizing ALS on multi-/many-core platforms to handle large datasets. Recently, an optimized ALS variant called eALS was proposed, and it yields significantly lower time complexity and higher recommending accuracy than ALS. However, it is challenging to parallelize eALS on modern parallel architectures (e.g., CPUs and GPUs) because: 1) eALS’ data dependence prevents it from fine-grained parallel execution, thus eALS cannot fully utilize GPU's massive parallelism, 2) the sparsity of input data causes poor data locality and unbalanced workload, and 3) its large memory usage cannot fit into GPU's limited on-device memory, particularly for real-world large datasets. This paper proposes an efficient CPU/GPU heterogeneous rec-ommendation system based on fast eALS for the first time (called HEALS) that consists of a set of system optimizations. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. HEALS also presents a CPU/GPU collaboration model that can explore both task parallelism and data parallelism. HEALS also optimizes this collaboration model with data communication overlapping and dynamic workload partition between CPU and GPU. Moreover, HEALS is further enhanced by various parallel techniques (e.g., loop unrolling, vectorization, and GPU parallel reduction). Evaluation results show that HEALS outperforms other state-of-the-art baselines in both performance and recom-mendation quality. Particularly, HEALS achieves up to 5.75 x better performance than a state-of-the-art ALS GPU library. This work also demonstrates the possibility of conducting fast recommendations on large datasets with constrained (or relaxed) hardware resources, e.g, a single CPU/GPU node.
Enhanced Transaction Confirmation Performances without Gas by Using Ethereum Blockchain

Blockchain is the distributed decentralized application for developing many of the use cases. In blockchain, the applications are transparent to all the nodes in the decentralized network and the application may be private or public depends upon the use cases. The nodes in the distributed network shares all the data to the other nodes without any modification in the blockchain technology. The transaction by the node is secured through the digital signatures. In this paper, we implemented the application by confirming the transaction without paying the gas fee by deploying the smart contract. The solidity is a JavaScript programming language for writing our smart contract. We explained the gas optimization technique used by the users and the gas station network in a decentralized application and also explained about the parameters in the Ethereum transactions. We used the open zeppelin library, which allows us to run our 𝐷𝑎𝑝𝑝 to implement the gasless transaction.
How Construction Projects Can Be Agile

There is currently the growing interest of business in the problem of shorten the total length of the construction projectConstruction project life cycle. At the same time, the number of dispersed teamsTeams increased. The level of technology development, including BIM, allows AEC teamsTeams to collaborate effectively. Due to environment turbulence and uncertainty, developers (customers) need to have an opportunity to react on market tendencies during the projectProject execution and implement changes in the projectProject. The group of adaptive PM approaches (agileAgile, hybrid) may help to solve this problem. The paper presents the comparison of typical construction projectConstruction project design phase and new product development. The system hybrid project managementProject management approach with mix of agileAgile and waterfall project managementProject management is proposed.
2D Cubic Spline Transformation With 3D Binary Image for a New Watermarking Algorithm

Watermarking is a new technique proposed to protect digital content from intellectual infringement in recent years. It modifies the original text and adds signature extracts as proof of ownership if needed. This paper uses a two-dimensional cubic spline function with the binary image as the third dimension. The cover image breaks down into three hues, red, and converts into three-dimensional binary information. The locations of pixels in the binary image third dimension (z-plain) are determined using 2D-cubic spline interpolation. To hide the watermarking information in the defined regions, we use direct correlations. The results of simulations utilizing PSNR and BER using this technique show excellent fidelity (92.238 dB) and a low bit error rate (0.0003). Watermark detection accomplishes without the use of the source image. Even when subjected to spatial problems, the simulation results suggest obtaining great realism and robustness.
ResUnet++ for Sparse Samples-based Depth Prediction

Depth prediction based on sparse measurements is a very effective way to obtain high-quality dense depth maps. However, most of them use a network design similar to the RGB-based approach. This results in a complex structure that does not fully exploit the sparse information. To further improve the accuracy of depth prediction, we utilize dense long and short skip connections, by expanding Unet++ to with residual block (denoted as ResUnet++), to extract more useful information from sparse depth measurements. Then, we combine deeply-supervised technique and provide a new method of lightweight network design for fast depth estimation. Experimental results verify the effectiveness of ResUnet++ on NYU-depth-v2 datasets. Combing with deeply-supervised technology, this network structure we propose can be divided into a number of subnets, which can then be used in a really flexible way to maximize the efficiency of the task.
SRACR: Semantic and Relationship-aware Online Course Recommendation

With the development of educational technology, more and more people are learning and gaining ability through online courses. The excessive number of courses has brought about the problem of information overload, making it necessary to recommend suitable courses for students in online interactive way. Some traditional course recommendation schemes ignore or only roughly exploit the semantics of courses, which may result in sub-optimal recommendation. Moreover, most course recommendation schemes didn't take into account the relationships between courses (e.g., two courses are taught by a teacher, etc.). To solve the above issues, this article proposes a semantic and relationship-aware online course recommendation scheme, SRACR, to recommend favorite courses for students. Specifically, Latent Dirichlet Allocation (LDA) is used to extract the fine-grained semantics of each course represented as the topic vector of the course, and then knowledge graph embedding is adopted to map the course relationships to the course knowledge vector. Then, the course feature vector is obtained through combining the course topic vector and knowledge vector. Finally, treating the feature vector of courses as context, we use a contextual multi-armed bandit-based algorithm to estimate students' preference and recommend courses to students through balancing exploration and exploitation. The experiment results on real educational dataset demonstrate the effectiveness of our proposed method.
Real-Time Virtual Fitness Tracker and Exercise Posture Correction

The steady improvement of new advances has introduced a brilliant platter for inactive living. In the current scenario of the pandemic, people hesitate to visit a gym or find it difficult to keep themselves fit and healthy. A traditional gym instructor prescribes exercise programs that can help them lower the risk of non-communicable lifestyle diseases. However, gym instructors often come at a cost and are not always affordable, available, or accessible at all times. This paper presents a two-stage method to effectively analyze the exercise posture, providing repetitions count and the recommendation for the incorrect posture. The first stage involves the real-time body tracking model, extracting a total of 21 body coordinates. Furthermore, these body coordinates are passed through a proposed statistical algorithm which provides exercise count and posture correction recommendations to an individual. The system will not only allow to regulate the exercise count but also monitor the body posture and hence helping in staying fit and healthy.
Biomechanics and Engineering Applications of Race Walking

The purpose of this study is to summarize the application of mechanics and engineering in race walking. The representative literature was summarized in tabular form using the literature method and induction method. The literature draws conclusions about changes in acquisition equipment and filming methods during race walking in terms of kinematics, kinetics, and electromyography. Filming has changed from the beginning of a single low-speed camera (two-dimensional filming) to a single high-speed camera, to a three-dimensional two-camera high-speed camera, and finally to a computer combined with a high-speed camera to process the data directly on site. Research methods have become more scientific and rationalized, from simple of image analysis to standard 3D motion analysis to different models of human inertial parameters.
EtherLearn: Decentralizing Learning via Blockchain

In institutes of higher learning, most of the time course material development and delivery follow a centralized model which is fully lecturer-controlled. In this model, engaging students as partners in learning is a challenging problem as: 1) students are usually hesitant to contribute due to the fear of getting it wrong, 2) not much incentive for them to put in the extra effort, and 3) current online learning systems lack adequate facilities to support seamless and anonymous interactions between students. In this work, we propose EtherLearn, a blockchain based peer-learning system to distribute the control of how course material and formative assessments could be developed and delivered over the set of stakeholders in the particular course. EtherLearn leverages features of the rising blockchain technology, e.g., decentralization, anonymity, transparency and security to address the aforementioned concerns in university learning environments. To this end, we have successfully implemented a proof of concept for EtherLearn based on the Ethereum blockchain network. We have also conducted preliminary evaluations to demonstrate that it can be useful in decentralizing learning resource creation and student sharing in an encouraging teaching and learning environment.
AGSS: An Airline Ground Service Supervising System Based on Blockchain

Air-ground service systems can effectively dispatch vehicles, thus improving the quality and efficiency of air service and avoiding flight delays. However, the current system lacks oversight and traceability. To be specific, when a dispatching accident happened, it is difficult to find out the root cause of the accident and the one in charge of the accident. Therefore, we construct an airline ground service supervising system based on blockchain named AGSS. In AGSS, each step requires agreements of most organizations. The event and the organizations in charge are recorded in blockchain immutably. It's easy to know what happened and the one responsible when needed. First, AGSS is easy to use with a layered design. Users only need to know the application layer, and other layers are available for the developer to modify low-level code. Then Hyperledger Fabric — a permissioned blockchain framework is adopted by us to implement AGSS. In addition, we carried out experiments about the time cost and throughput. The results show that AGSS can supervise and trace events with low overhead.
DLS: A Fast and Flexible Neural Network Training System With Fine-grained Heterogeneous Device Orchestration

Neural network accelerators (e.g., TPUs) have become mainstream devices in computing systems. Unfortunately, the existing accelerator-based systems for neural networks fail to fully leverage the acceleration opportunities due to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">limited flexibility</i> . Specifically, the majority of the accelerators focus on only the compute-intensive operations of neural networks (e.g., convolution and fully-connected layers). However, we identify that sub-optimal handling of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auxiliary operations</i> such as embedding and compression can incur non-trivial loss in terms of accuracy, training speed, and adaptability to new domains. The problem persists considering that recent advancements in neural networks often come from auxiliary operations. To effectively handle rapidly evolving auxiliary operations and maximize acceleration opportunities, we propose DLS, a holistic neural network acceleration system using heterogeneous computing devices. The key idea is to distribute compute-intensive operations on highly specialized ASICs for maximum performance, and auxiliary operations on flexible devices (e.g., FPGA, GPU) for better adaptability. We emphasize that a naïve integration of different devices fails to deliver high performance due to high communication overheads. To address this communication inefficiency, we propose an efficient FPGA-based device orchestration utilizing direct device-to-device communication and fine-grained operation scheduling. In this way, our system alleviates the communication overhead between heterogeneous devices by removing expensive kernel stack traversal and leveraging computation units and communication links in parallel. The evaluation using popular neural networks with emerging auxiliary operations shows that our system achieves both flexibility and high performance for various cases from single-accelerator training to distributed training (2.6–8.9× speedup).
Analysis of the Effectiveness of a General Education Course in Personality Education Centered on Ethics of Life Practice －Case Study in S University

The purpose of this study is to examine the effects of a general education course in personality education centered on ethics of life practice within personality education at universities. The subject of the S Honor Code course attempted to cultivate global citizens with a good personality to adapt well to the future society by examining ethical norms to be observed in college life and daily life and practicing them in real life. The subject areas covered in this course are distinguished from other universities' personality education in the areas of life, such as cyber ethics, study ethics, human rights ethics, family ethics (parent-child relations, marital relations), interpersonal ethics (dating relationships, communication), and Christian ethics. The ethics in life were practiced in the students’real life.&lt;/br&gt;The effect of improving personality and core competency was confirmed based on the response data of 86 students taking the S Honorary Code course and 29 students taking the elective course. This study confirmed that the S Honor Code course, centered on ethics of life practice, had a positive effect on the students' personality development in all areas of cognition, emotion, and behavior. It also contributed to the improvement of core competencies in critical thinking/problem solving, creative thinking, communication, cooperation, and learning capabilities.&lt;/br&gt;Personality education centered on life ethics not only fosters knowledge of personality virtues but also provides opportunities to practice ethics in school, at home, and in society. Thus, it can be applied as a new form of personality education and provide implications for how personality education develops in the future.