Reviewerly/exHarmony huggingface dataset:
Privacy Threat Modeling in Personalized Search Systems

Personalized search systems simplify information access on the Web by providing adapted results for each user's query according to his preferences and needs. Such systems depend mainly on collecting user data to create profiles that represent user interests. While collecting user data is essential for improving the quality of the returned results, it has also raised serious concerns regarding user privacy. Giving the user a personalized browsing experience usually comes at the cost of his privacy. Thus, most people are afraid of using such applications. Current search engines lack dedicated privacy-preserving features and do not fulfill people’s expectations in terms of privacy. Researchers have been investigating solutions to overcome this issue and create privacy-aware personalized systems. In this paper, we present an overview of the current privacy threats in personalized search systems with a threat modeling approach. Furthermore, we examine techniques and solutions for user data security and privacy protection.
Learning Objectives, Insights, and Assessments: How Specification Formats Impact Design

Despite the ubiquity of communicative visualizations, specifying communicative intent during design is ad hoc. Whether we are selecting from a set of visualizations, commissioning someone to produce them, or creating them ourselves, an effective way of specifying intent can help guide this process. Ideally, we would have a concise and shared specification language. In previous work, we have argued that communicative intents can be viewed as a learning/assessment problem (i.e., what should the reader learn and what test should they do well on). Learning-based specification formats are linked (e.g., assessments are derived from objectives) but some may more effectively specify communicative intent. Through a large-scale experiment, we studied three specification types: learning objectives, insights, and assessments. Participants, guided by one of these specifications, rated their preferences for a set of visualization designs. Then, we evaluated the set of visualization designs to assess which specification led participants to prefer the most effective visualizations. We find that while all specification types have benefits over no-specification, each format has its own advantages. Our results show that learning objective-based specifications helped participants the most in visualization selection. We also identify situations in which specifications may be insufficient and assessments are vital.
New Framework of Digital Entrepreneurship Model Based on Artificial Intelligence and Cloud Computing

With the rapid development of entrepreneurial enterprises and the widespread application of emerging technologies, the commercialization of new technologies for entrepreneurial enterprises is particularly important. This research mainly discusses the new framework of digital entrepreneurship model based on artificial intelligence and cloud computing. Through artificial intelligence technology, the products provided by existing competitors only have the characteristics of one-way value; that is, data is only collected and displayed, and the application of artificial intelligence technology in products makes the value of products develop in two directions; that is, the machine can self-identify faults and errors are resolved and reported. Let customers experience the convenience, accuracy, and safety brought by technology through intelligent acquisition equipment hardware with artificial intelligence algorithm analysis and camera hardware with artificial intelligence image analysis. Customers can pay flexibly according to their needs. This model greatly enhances the high possibility of artificial intelligence companies landing. Use big data analysis and cloud computing technology to provide customers with a series of solutions such as warehouse management, sales forecasting, big data analysis, and financial management. In the SaaS market, in terms of market segmentation, there are no domestic enterprises with scale and brand effect; the incentive and welfare module will focus on the outsourcing and outsourcing of employee benefits. Relevant value-added services and derivative services are the core business, which can give play to the competitive advantages of specialization, scale, and platform. From 2018 to 2020, the cash paid to employees shows a gradual increase, and the taxes and fees paid are also increasing year by year. The cash paid for other operating activities reached a maximum of 12303 million yuan in 2018. This research will promote the innovation of new types of enterprises.
Borders of spot diagrams are always caustic surfaces and/or marginal rays

In this paper, it is proven that for any optical system with a circular exit pupil and wavefronts affected by any aberrations, the borders of all leaving rays are caustic surfaces and/or marginal rays. Several examples are shown for wavefronts affected by linear combinations of Zernike aberrations.
Dual Sequential Recommendation Integrating High-order Collaborative Relations via Graph Attention Networks

Sequential recommendation aims to predict users' preferences in the future based on the interactions that they have done recently. Since users only interact with a small part of items, there is a problem of data sparsity in real-world datasets. Some studies leverage collaborative filtering (CF) to model collaborative relations to alleviate data sparsity. However, CF focuses on modeling first-order relations and ignores high-order collaborative relations. Meanwhile, most of the models assume the attributes of items are static, and only consider users' dynamics. In this paper, we propose Dual Sequential Recommendation with Collaborative Relations (DSRCR) which aggregates high-order collaborative features on the user-user graph and item-item graph via graph attention network as the input of dual sequential models and utilizes the attention-based recurrent neural network to model both user-side and item-side sequences. The contribution of different hidden states to user preferences or item attributes can be captured by the attention mechanism. Each user (item) is represented as the composition of the embedding with high-order collaborative features and the embedding that obtains from sequential models. Extensive experiments on three datasets demonstrate the superiority of our proposed model compared with other state-of-the-art baselines.
TC-Net: Detecting Noisy Labels Via Transform Consistency

It is crucial to distinguish mislabeled samples for dealing with noisy labels. Previous methods such as “Co-teaching” and “JoCoR” introduce two different networks to select clean samples out of the noisy ones and only use these clean samples to train the deep models. Different from these methods which require to train two networks simultaneously, we propose a simple and effective method to identify clean samples only using one single network. We discover that the clean samples prefer to reach consistent predictions for the original images and the transformed images while noisy samples usually suffer from inconsistent predictions. Motivated by this observation, we propose a noisy label detection approach, named Transform Consistency Network (TC-Net), which constrains the transform consistency ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> category consistency and visual attention consistency) between the original images and the transformed images for network training. Then we can select small-loss samples to update the parameters of the network. Furthermore, in order to mitigate the negative influence of noisy labels, we design a classification loss by using the off-line hard labels and on-line soft labels to provide more reliable supervisions for training a robust model. We conduct comprehensive experiments on CIFAR-10, CIFAR-100 and Clothing1M datasets. Compared with the clean sample selection baselines, we achieve the state-of-the-art performance. Especially, in most cases, our proposed method outperforms the baselines by a large margin.
Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines

In this paper, we propose the Convolutional Restricted Boltzmann Machine (CRBM) as a tool for detecting concept drift in time-varying data streams. Recently, it was demonstrated that the Restricted Boltzmann Machine (RBM) can be successfully applied to this task. A properly learned RBM contains information about the data probability distribution. Trained on one part of the stream it can be used to detect possible changes in the incoming data. In this work we replace the fully-connected layer in the standard RBM with the convolutional layer, composing the CRBM. We show that it is more suitable for the drift detection task regarding the image data. Preliminary experimental results demonstrate the usefulness of the CRBM as a tool for drift detection in data streams with such type of data.
Learning Better Visual Data Similarities via New Grouplet Non-Euclidean Embedding

In many computer vision problems, it is desired to learn the effective visual data similarity such that the prediction accuracy can be enhanced. Deep Metric Learning (DML) methods have been actively studied to measure the data similarity. Pair-based and proxy-based losses are the two major paradigms in DML. However, pair-wise methods involve expensive training costs, while proxy-based methods are less accurate in characterizing the relationships between data points. In this paper, we provide a hybrid grouplet paradigm, which inherits the accurate pair-wise relationship in pair-based methods and the efficient training in proxy-based methods. Our method also equips a non-Euclidean space to DML, which employs a hierarchical representation manifold. More specifically, we propose a unified graph perspective — different DML methods learn different local connecting patterns between data points. Based on the graph interpretation, we construct a flexible subset of data points, dubbed grouplet. Our grouplet doesn’t require explicit pair-wise relationships, instead, we encode the data relationships in an optimal transport problem regarding the proxies, and solve this problem via a differentiable implicit layer to automatically determine the relationships. Extensive experimental results show that our method significantly outperforms state-of-the-art baselines on several benchmarks. The ablation studies also verify the effectiveness of our method.
Confidence Estimation via Wrist Movement

Recent advancements have shown that physiological sensing techniques such as eye-gaze and handwriting can be used to classify user confidence when answering questions. While accurately classifying confidence is important for providing feedback to learners, simplifying and streamlining the process is important for real-world applications. The aim of this study is to use a single accelerometer on the wrist to estimate confidence, thus reducing the burden of device orientation and cost for users. We present a study of 10 participants who wrote answers while wearing a wrist accelerometer. Using SVM, we achieved an accuracy rate of 83.8% for participant-dependent and 77.0% for participant-independent classification when the answer given by the user was correct and 84.4% and 71.9% when the answer was incorrect. The results indicate that using a simple wrist accelerometer is effective for providing confidence estimation.
FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot

Conveyor belts are key pieces of equipment for bulk material transport, and they are of great significance to ensure safe operation. With the development of belt conveyors in the direction of long distances, large volumes, high speeds, and high reliability, the use of inspection robots to perform full inspections of belt conveyors has not only improved the efficiency and scope of the inspections but has also eliminated the dependence of the traditional method on the density of sensor arrangement. In this paper, relying on the wireless-power-supply orbital inspection robot independently developed by the laboratory, aimed at the problem of the deviation of the belt conveyor, the methods for the diagnosis of the deviation of the conveyor belt and FPGA (field-programmable gate array) parallel computing technology are studied. Based on the traditional LSD (line segment detection) algorithm, a straight-line extraction IP core, suitable for an FPGA computing platform, was constructed. This new hardware linear detection algorithm improves the real-time performance and flexibility of the belt conveyor diagnosis mechanism.
Python Code Smell Refactoring Route Generation Based on Association Rule and Correlation

Code smell is a software quality problem caused by software design flaws. Refactoring code smells can improve software maintainability. While prior works mostly focused on Java code smells, only a few prior researches detect and refactor code smells of Python. Therefore, we intend to outline a route (i.e. sequential refactoring operation) for refactoring Python code smells, including LC, LM, LMC, LPL, LSC, LBCL, LLF, MNC, CCC and LTCE. The route could instruct developers to save effort by refactoring the smell strongly correlated with other smells in advance. As a result, more smells could be resolved by a single refactoring. First, we reveal the co-occurrence and the inter-causation between smells. Then, we evaluate the smells’ correlation. Results highlight seven groups of smells with high co-occurrence. Meanwhile, 10 groups of smells correlate with each other in a significant level of Spearman’s correlation coefficient at 0.01. Finally, we generate the refactoring route based on the association rules, we exploit an empirical verification with 10 developers involved. The results of Kendall’s Tau show that the proposed refactoring route has a high inter-agreement with the developer’s perception. In conclusion, we propose four refactoring routes to provide guidance for practitioners, i.e. {LPL [Formula: see text] LLF}, {LPL [Formula: see text] LBCL}, {LPL [Formula: see text] LMC} and {LPL [Formula: see text] LM [Formula: see text] LC [Formula: see text] CCC [Formula: see text] MNC}.
Comparison between Calculation Methods for Semantic Text Similarity based on Siamese Networks

In the era of information explosion, people are eager to obtain contents that meet their own needs and interests from massive amounts of information. Therefore, how to understand the needs of Internet users correctly and effectively is one of the urgent problems to be solved. In this case, semantic text similarity task is useful in many application scenarios. To measure semantic text similarity based on text matching model, several Siamese networks are constructed in this paper. Specifically, we firstly use the Stsbenchmark dataset, regarding the GloVe, BERT and DistilBERT as initial models, and add deep neural networks to train and fine-tune, fully utilizing the advantages of the existing models. Next, we test several similarity calculation methods to quantify the semantic similarity of sentence pairs. Moreover, the Pearson and Spearman correlation coefficients are used as evaluation indicators to compare the sentence embedding effects of different models. Finally, experiment result shows the Siamese network based on BERT model has the optimal effect among all, with the highest accuracy rate up to 84.5%. While among several similarity calculation methods, the Cosine Similarity usually obtain the best accuracy rate. In the future, this model can be appropriately used in semantic text similarity tasks, through matching texts between users’ needs and knowledge base. In this way, we can improve machines' language understanding ability as well as meeting the diverse needs of users.
Construction of Some Good Binary Linear Codes Using Hadamard Matrix and BCH Codes

This paper presents a new method of constructing good binary linear codes. The proposed method is first based on the BCH codes parity matrix and Hadamard’s matrix, and after on a GUAVA-based computer search, which allowed us to find several good codes with rates >0.5.
Chatbot to Map Medical Prognosis and Symptoms Using Machine Learning

Computer-aided system is a subject of great importance and extensive requirement. Nowadays, deep learning and machine learning have gained quite a knack in people’s eye and widely used among them. Gone are the occasions when the products were utilized for complex count issues or graphical portrayal alone. And Chatbots are proven revolutionary in our day-to-day lives where they are present in health, career, insurance and customer care support. In this paper, we have built up a Health-Bot using RNN network and Keras classifier. During such a pandemic period when there is an enormous crowd present in hospitals, people can get themselves checked at their homes with this interactive language system. Neural network adds more exactness to our work and reactions. And we further implemented our model on StreamLit which is an open-source framework for machine learning and deep learning.
Separation of Memory and Processing in Dual Recurrent Neural Networks

We explore a neural network architecture that stacks a recurrent layer and a feedforward layer, both connected to the input. We compare it to a standard recurrent neural network. When noise is introduced into the recurrent units activation function, the two networks display binary activation patterns that can be mapped into the discrete states of a finite state machine. But, while the former is equivalent to a Moore machine, the latter can be interpreted as a Mealy machine. The additional feedforward layer reduces the computational load on the recurrent layer, which is used to model the temporal dependencies only. The resulting models are simpler and easier to interpret when the networks are trained on different sample problems, including the recognition of regular languages and the computation of additions in different bases.
Blockchain Based Electronic Health Record Management System for Data Integrity

Integrity of electronic health record (EHR) is essential to prevent harm due to incorrect past treatment records of patients and to ensure improvement in patient health by correct medication and treatment. Generally, healthcare organizations manage such information in centralized systems which are prone to various cyber crimes like ransomeware, DDOS, etc. Blockchain technology has potential of sustaining the integrity of EHRs due to unique characteristics like collusion resistance, transaction visibility, tamperproof and security. The aim of this work is to develop EHR management system for data integrity using distributed ledger technology. This can prove to be a very useful tool to maintain integrity of EHRs for various purposes such as better diagnosis of patient, insurance claim, drug discovery, etc. Developed system permits healthcare organizations to send EHRs to the patient over blockchain network and also allows EHR requesters to access it with the consent of patient. In designed system, unique IDs are computed for each EHR by universally unique identifier (UUID), and the hash generation of each EHR block is performed using secure hash algorithm(SHA)-256. The designed model comprises four building modules which are EHR uploading, EHR mining, EHR addition and EHR retrieval. All four modules of the developed prototype are executed effectively, and few execution outcomes are illustrated in the result section. Execution results shows that developed system is appropriate for EHR management.
Warp-centric K-Nearest Neighbor Graphs construction on GPU

Recent advances and applications of machine learning algorithms are becoming more common in different fields. It is expected that some applications require the processing of large datasets with those algorithms, which leads to high computational costs. Massively parallel GPU methods can be applied to surpass this limitation and reduce the execution time of these algorithms. The construction of approximate K-Nearest Neighbor Graphs (K-NNG) is frequently required for similarity search or other applications such as the t-SNE dimensionality reduction technique. The K-NNG represents the K closest points (neighbors) for each point in a set. In this paper, we propose and analyze an all-points K-Nearest Neighbor Graph construction algorithm on GPU called Warp-centric K-NNG (w-KNNG), which is based on the Random Projection Forest method. Usually, the construction or search for k-NN sets for high dimensional points presents challenges for its implementation on many-core processing units, due to the space limitation in maintaining these sets in high speed shared memory. We present three warp-centric approaches for our algorithm that efficiently search and maintain the k-NN high dimensional point sets in global memory. In our experiments, the new methods allows the algorithm to achieve up to 639% faster execution when compared to the state-of-the-art FAISS library, considering an equivalent accuracy of approximate K-NNG. One of the new strategies (w-KNNG atomic) is more successful when applied to a smaller number of dimensions, while the tiled w-KNNG approach was successful in general scenarios for higher dimensional points.
Life on Wings

This paper presents a novel board game called Life on Wings, designed to create an experience and awareness about the life of birds in an urban landscape. The game lets users experience the life of six tropical urban birds across three seasons of a year. By performing different activities of birds, players learn about the challenges that an ever-changing urban environment creates for bird species. We reflect on our design process and describe the key design decisions that led to the development of our game. We also present insights of a playtesting session that was conducted with 11 participants to evaluate the design aspects of the game. Based on the study insights we present three implications on collaboration over competition, local game movement and longitudinal first-person perspective. Through this work, we aim to inspire more playful explorations on human-wildlife cohabitation.
Multi-source flexible grid disaster recovery smart plan optimization evaluation

The traditional power grid disaster recovery plan has been unable to adapt to today's multi-source feeding new power grid structure. Based on the indicators of the traditional disaster recovery plan, this paper adds new indicators that adapt to the characteristics of the new power grid structure, and builds a new disaster recovery plan evaluation system that adapts to the contemporary power grid. It also proposes a comprehensive weight modeling method that combines the D-AHP and Poisson distribution methods to determine the index weight and time weight. Finally, the TOPSIS method is used to solve the problem of a new regional power grid. Sort the programs. The results of calculation examples show that the method proposed in this paper can effectively evaluate the scientific nature of the new power grid disaster recovery plan.
VROOM - Measurement and Design Research for Virtual Reality in Automated Vehicles

The increasing ubiquity and mobility of VR devices has introduced novel use cases, one of which is using VR while in an automated vehicle (AV). Hence, there is a need to examine the perceptual, cognitive, and behavioral aspects of both the driving experience and VR immersion, and how they influence each other. This project is a step towards this goal, through (1) developing and fully validating a series of novel measures to fill critical gaps in the researchers’ toolbox; and (2) leveraging these tools to study the challenging but highly important task of using VR in a vehicle.