Reviewerly/exHarmony huggingface dataset:
A More Accurate and Robust Binary Ring-LWE Decryption Scheme and Its Hardware Implementation for IoT Devices

Learning with error (LWE) over the ring based on binary distribution (ring-BinLWE) has become a potential Internet-of-Things (IoT) confidentiality solution with its anti-quantum attack properties and uncomplicated calculations. Compared with ring-LWE based on discrete Gaussian distribution, the decryption scheme of ring-LWE based on binary distribution needs to be re- determined due to the asymmetry of the error distribution. The direct application of the ring-LWE decryption function based on discrete Gaussian distribution can cause serious misjudgment. In this article, we propose a more accurate and robust decryption scheme for ring-BinLWE based on 2’s complement ring. Compared with the previous decryption function, the re- derived decryption function significantly improves the decoding rate by 50%. Furthermore, based on the proposed decryption function, high-performance, and lightweight hardware architectures for terminal devices in IoT are, respectively, proposed, which are scalable and can be easily adapted to ring-BinLWE hardware deployment with other parameter sets. When the parameter set is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\,\,=$ </tex-math></inline-formula> 256, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q\,\,=$ </tex-math></inline-formula> 256, the high-performance implementation consumes 7.6k LUTs, 6.2k FFs, and 2.3k SLICEs on Spartan 6 field-programmable gate array (FPGA) platform. Compared with the previous implementation, our resource overhead increases by only 23% while the decryption accuracy is significantly improved by 50%. The lightweight implementation for parameter set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\,\,=$ </tex-math></inline-formula> 256, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q\,\,=$ </tex-math></inline-formula> 256 consumes only 230 LUTs, 338 FFs, and 84 SLICEs on the Spartan 6 FPGA platform. Compared with the previous work, the area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> time (AT) is reduced by 47.8%, which is more suitable for deployment on resource-constrained IoT nodes.
Multiobjective Deep Reinforcement Learning for Recommendation Systems

Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start problem from collaborative filtering, it also vulnerable to highly sparse environment, while the evolutionary algorithm suffers from premature convergence and curse of dimensionality. These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. In this study, the performances of proposed DRL approaches that based on Deep Q-Network in MO recommendation problem were investigated. The approaches were evaluated with movie recommendation dataset by using three conflicting metrics, namely precision, novelty, and diversity. The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multi-objective approach based on evolutionary algorithm (PMOEA). Although PMOEA algorithm secured higher average value in precision, it has lower values of novelty and diversity than the proposed DRL approaches. The DRL approaches surpassed the benchmark results in average of maximum novelty and the average of mean diversity metrics, the optimization between accuracy and non-accuracy metrics is inevitable. In addition, the experiments revealed that incorporation of user latent features enhanced the recommendation quality.
Medical Text Prediction and Suggestion Using Generative Pretrained Transformer Models with Dental Medical Notes

Generative pretrained transformer (GPT) models are one of the latest large pretrained natural language processing models that enables model training with limited datasets and reduces dependency on large datasets, which are scarce and costly to establish and maintain. There is a rising interest to explore the use of GPT models in health care.We investigate the performance of GPT-2 and GPT-Neo models for medical text prediction using 374,787 free-text dental notes.We fine-tune pretrained GPT-2 and GPT-Neo models for next word prediction on a dataset of over 374,000 manually written sections of dental clinical notes. Each model was trained on 80% of the dataset, validated on 10%, and tested on the remaining 10%. We report model performance in terms of next word prediction accuracy and loss. Additionally, we analyze the performance of the models on different types of prediction tokens for categories. For comparison, we also fine-tuned a non-GPT pretrained neural network model, XLNet (large), for next word prediction. We annotate each token in 100 randomly sampled notes by category (e.g., names, abbreviations, clinical terms, punctuation, etc.) and compare the performance of each model by token category.Models present acceptable accuracy scores (GPT-2: 76%; GPT-Neo: 53%), and the GPT-2 model also performs better in manual evaluations, especially for names, abbreviations, and punctuation. Both GPT models outperformed XLNet in terms of accuracy. The results suggest that pretrained models have the potential to assist medical charting in the future. We share the lessons learned, insights, and suggestions for future implementations.The results suggest that pretrained models have the potential to assist medical charting in the future. Our study presented one of the first implementations of the GPT model used with medical notes.
A COMPARATIVE STUDY OF THREE CORNER FEATURE BASED MOVING OBJECT DETECTION USING AERIAL IMAGES

Corner feature based moving objects detection is an essential and fundamental research problem in the broader aspects of computer vision and pattern recognition research domain. Performance of various corner features based aerial types image processing specially for moving objects detection is still an unsolved issue due to up and down of performance which makes it difficult to choose the appropriate corner features for detection purpose. The core part mentioned in this research is to categorize significant corner characteristics of the objects using various corner features based detection methods in image extracted from aerial video. This research demonstrated three kinds of corner features, i.e. Moravec, Susan and Harris corners due to capability of these corner features to interpret high and low intensity various for aerial types of images. Standard datasets were used to evaluate each of the corner feature based detection. Based on comprehensive experimental analysis, Harris corner was observed performing efficiently comparing with Moravec and Susan corner based detection for both datasets considered by this research. Experimental results reveals the capacity of each corner characteristics based detection methodology in terms with the effectiveness using various performance metrics for moving object detection using aerial images.
Analysis of Factors Affecting Customer Satisfaction at PT.OSO Gallery

This study aims to identify factors affecting costumer satisfaction in PT.OSO sekuritas cabang galeri Universitas MH. Thamrin and describe it based on customer perceptions. This research uses qualitative approach with case study strategy. Data collection techniques used are interviews, observation, and document review. The participants of this study 25 customer with different background (student or employee). The result of study shows that factors affecting costumer satisfaction in PT.OSO sekuritas cabang galeri Universitas MH. Thamrin Branch are the excellent of the relationship between the employee and the costume, accurancy in satisfying customer desires or demand and adequate facilities in the investment gallery. This strategy will always improved by the employees to provide customer satisfaction in the long term.
An Analysis of the Learning Effects and Differences of College Students Using English Vocabulary APP

With the gradual popularization of mobile learning, it has become a trend for college students to use learning applications (APPs) for learning, but the learning effect has always been a concern. Since college students have different learning purposes, strategies, skills, and habits, responses to these differences have been an urgent problem. This paper takes college students using an English vocabulary APP to study as the survey object. It sets up the influencing factors model of the English vocabulary learning effect based on UTAUT2. Then, we analyzed the factors influencing the learning effect of using an English vocabulary APP and studied its mechanism. The results show that on the one hand, the influence degree of influencing factors is habit, facilitation condition, price value, effort expectancy, and performance expectancy from high to low, and all the above factors have a significant positive impact on the learning effect of college students using English vocabulary APP. On the other hand, gender, grade, and major have a moderating effect on English vocabulary learning, and there are differences among different genders, grades, and majors. Finally, suggestions are put forward from the perspective of APP construction and students’ differences to enhance and improve the learning effect of English vocabulary.
Construction and Analysis of Indicators of the Labor Education System Based on the Gray Correlation Degree Model

Labor education is a complex concept whose value is not only the sum of labor + education. It plays an extremely important role in the growth education of students. Its fundamental purpose is to cultivate students' good technical literacy, improve their practical skills, and form innovative thinking. The monitoring data show that the path of labor education in schools is good, but there are also problems such as unbalanced development of labor practice, insufficient leading role of schools, insufficient basic role of families, and serious lack of social support. Responsibility index, learning motivation, motor health, and self-awareness are significantly and positively correlated with labor practice index. Based on the gray relationship theory, this paper selected relevant data of Chinese students, calculated the comprehensive gray relationship degree between each factor and students' labor reeducation level, and analyzed the variables; the new connotation of education, the construction of labor education evaluation index system, and the construction of labor education support system were studied.
A General Family of MSRD Codes and PMDS Codes with Smaller Field Sizes from Extended Moore Matrices

We construct six new explicit families of linear maximum sum-rank distance (MSRD) codes, each of which has the smallest field sizes among all known MSRD codes for some parameter regime. Using them and a previous result of the author, we provide two new explicit families of linear partial maximum distance separable (PMDS) codes with smaller field sizes than previous PMDS codes for some parameter regimes. Our approach is to characterize evaluation points that turn extended Moore matrices into the parity-check matrix of a linear MSRD code. We then produce such sequences from codes with good Hamming-metric parameters. The six new families of linear MSRD codes with smaller field sizes are obtained using MDS codes, Hamming codes, Bose--Chaudhuri--Hocquenghem codes, and three algebraic-geometry codes. The MSRD codes based on Hamming codes, of minimum sum-rank distance 3, meet a recent bound by Byrne, Gluesing-Luerssen, and Ravagnani [IEEE Trans. Inform. Theory, 67 (2021), pp. 6456--6475].
CNN-LSTM-Based Late Sensor Fusion for Human Activity Recognition in Big Data Networks

The technological advancement in sensor technology and pervasive computing has brought smart devices into our daily life. Due to the continuous connectivity of the internet with our everyday devices, researchers can deploy IoT sensors to health care and other applications, such as human activity recognition. Most of the state-of-the-art sensor-based human activity recognition systems can detect basic activities (such as standing, sitting, and walking), but they cannot accurately distinguish similar activities (ascending stairs or descending stairs). Such systems are not efficient for critical healthcare applications having complex activity sets. This paper proposes two sensor fusion approaches, i.e., position-based early and late sensor fusion using convolutional neural network (CNN) and convolutional long-short-term memory (CNN-LSTM). The performance of our proposed models is evaluated on two publicly available datasets. We also evaluated the effect of different normalization techniques on recognition accuracy. Our results show that the CNN-LSTM-based late sensor fusion model also improves the recognition accuracy of similar activities.
An Effective Measure of Uncertainty of Basic Belief Assignments

This paper presents a new effective measure of un-certainty (MoU) of basic belief assignments. This new continuous measure is effective in the sense that it satisfies a small number of very natural and essential desiderata. Our new simple math-ematical definition of MoU captures well the interwoven link of randomness and imprecision inherent to basic belief assignments. Its numerical value is easy to calculate. This new effective MoU characterizes efficiently any source of evidence used in the belief functions framework. Because this MoU coincides with Shannon entropy for any Bayesian basic belief assignment, it can be also interpreted as an effective generalization of Shannon entropy. We also provide several examples to show how this new MoU works.
Consistency Analysis of Distributed Ledgers in Fog-Enhanced Blockchains

Both revolutionary technologies of Fog Computing (FC) and Blockchain (BC) serve as enablers for enhanced, people-centric trusted applications, and they do meet in the provision of higher standards and expectations. In this paper, we address the reliability of fog-enhanced BC systems by analyzing the forking phenomenon under different conditions, and provide a reliable Distributed Ledger (DL) consistency assessment. We use the FoBSim tool that is specifically designed to mimic and emulate realistic FC-BC integration, in which we deploy the Proof-of-Work (PoW) consensus algorithm and analyze the forking probability under fluctuating conditions. Based on our results, we propose an inconsistency formula, which can quantitatively describe how consistent the DL in a BC system can be. Finally, we show how to deploy this formula in a decision making model for indicating optimal deployment features of a BC network in a Fog-enhanced system.
Policy impacts of statistical uncertainty and privacy

Funding formula reform may help address unequal impacts of uncertainty from data error and privacy protections
Design and Application of Collaborative Experiment Management Platform for Innovation and Entrepreneurship Education Based on an Intelligent Sensor Network

Innovation and entrepreneurship is the soul of entrepreneurship, and realizing the integration of innovation and entrepreneurship education and collaborative experiment platform in colleges and universities is an important way to promote the development of entrepreneurship and facilitate higher quality employment. In this paper, the authors propose the 3D-IVF intelligent sensor overlay algorithm wisdom laboratory system structure and application mode through in-depth investigation of collaborative laboratory, build the macroenvironment and construction demand of collaborative experiment management platform of innovation and entrepreneurship education, comprehensively analyze the current situation, problems, and effectiveness of its integration of innovation and entrepreneurship education and professional education. In addition, we explore the operation mechanism of its organizational system, curriculum system, management system, and platform construction in the practice of integrating innovation and entrepreneurship education with professional education. The average coverage rate of the 3D-IVF-based 3D coverage algorithm is 92.15%, and the deployment time is 2.5857 s; the average coverage rate is improved by 0.76%, and the deployment time is reduced by 0.1712 s. Design FIR filter using MATLAB and Hilbert transform filter, generate HDL code, and implement it on FPGA. Use the designed low-pass and band-pass filters to filter the 100 KHz square wave signal sampled by the ADC. When the number of iterations of the algorithm reaches 30, the coverage rates of the four-node deployment strategies will stabilize, i.e., the forces between the nodes of the 3D-VF-based 3D coverage algorithm of this paper. The equilibrium state is reached. We use resources and real-time experimental data sharing to realize collaborative design among team members, further optimize the curriculum structure of innovation and entrepreneurship education, adopt diversified forms of curriculum implementation, and implement a diversified curriculum evaluation system, etc.
Multiparty GV: functional multiparty session types with certified deadlock freedom

Session types have recently been integrated with functional languages, bringing message-passing concurrency to functional programming. Channel endpoints then become first-class and can be stored in data structures, captured in closures, and sent along channels. Representatives of the GV (Wadler's "Good Variation") session type family are of particular appeal because they not only assert session fidelity but also deadlock freedom, inspired by a Curry-Howard correspondence to linear logic. A restriction of current versions of GV, however, is the focus on binary sessions, limiting concurrent interactions within a session to two participants. This paper introduces Multiparty GV (MPGV), a functional language with multiparty session types, allowing concurrent interactions among several participants. MPGV upholds the strong guarantees of its ancestor GV, including deadlock freedom, despite session interleaving and delegation. MPGV has a novel redirecting construct for modular programming with first-class endpoints, thanks to which we give a type-preserving translation from binary session types to MPGV to show that MPGV is strictly more general than binary GV. All results in this paper have been mechanized using the Coq proof assistant.
Investigating Search Among Physical and Virtual Objects Under Different Lighting Conditions

By situating computer-generated content in the physical world, mobile augmented reality (AR) can support many tasks that involve effective search and inspection of physical environments. Currently, there is limited information regarding the viability of using AR in realistic wide-area outdoor environments and how AR experiences affect human behavior in these environments. Here, we conducted a wide-area outdoor AR user study ($n=48$) using a commercially available AR headset (Microsoft Hololens 2) to compare (1) user interactions with physical and virtual objects in the environment (2) the effects of different lighting conditions on user behavior and AR experience and (3) the impact of varying cognitive load on AR task performance. Participants engaged in a treasure hunt task where they searched for and classified virtual target items (green “gems”) in an augmented outdoor courtyard scene populated with physical and virtual objects. Cognitive load was manipulated so that in half the search trials users were required to monitor an audio stream and respond to specific target sounds. Walking paths, head orientation and eye gaze information were measured, and users were queried about their memory of encountered objects and provided feedback on the experience. Key findings included (1) Participants self-reported significantly lower comfort in the ambient natural light condition, with virtual objects more visible and participants more likely to walk into physical objects at night; (2) recall for physical objects was worse than for virtual objects, (3) participants discovered more gems hidden behind virtual objects than physical objects, implying higher attention on virtual objects and (4) dual-tasking modified search behavior. These results suggest there are important technical, perceptual and cognitive factors that must be considered if the full potential of “anywhere and anytime mobile AR” is to be realized.
H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.