Reviewerly/exHarmony huggingface dataset:
Effective Anomaly Detection in Smart Home by Integrating Event Time Intervals

Smart home IoT systems and devices are susceptible to attacks and malfunctions. As a result, users' concerns about their security and safety issues arise along with the prevalence of smart home deployments. In a smart home, various anomalies (such as fire or flooding) could happen due to cyber attacks, device malfunctions, or human mistakes. These concerns motivate researchers to propose various anomaly detection approaches. Existing works on smart home anomaly detection focus on checking the sequence of IoT devices' events but leave out the temporal information of events. This limitation prevents them from detecting anomalies that cause delay rather than missing/injecting events. To fill this gap, in this paper, we propose a novel anomaly detection method that takes the inter-event intervals into consideration. We propose an innovative metric to quantify the temporal similarity between two event sequences. We design a mechanism for learning the temporal patterns of event sequences of common daily activities. Delay-caused anomalies are detected by comparing the sequence with the learned patterns. We collect device events from a real-world testbed for training and testing. The experiment results show that our proposed method achieves accuracies of 93%, 88%, and 89% for three daily activities.
A Regional Necessity Based Multi-agent Target Search Strategy for Post-earthquake Rescue

The damage caused by the earthquake is huge. And the purpose of post-earthquake rescue is to reduce casualties. So it is necessary to improve the efficiency of rescue as much as possible. This paper focuses on multi-agent cooperative target search in the post-earthquake rescue and how to exploit the prior information of post-disaster building to improve the searching efficiency. A regional necessity based searching strategy is proposed which utilizes pre-disaster building information and adapts to dynamic environments. Firstly, the target probability map is estimated according to the distribution information of pre-disaster buildings. Then, the regional necessity map is constructed by integrating the target probability map and the perception of the environment during the search process. Finally, the search strategy is proposed based on the regional necessity map and the particle swarm optimization algorithm. Combining the gradient direction of regional necessity and local optimal velocity, the agent dynamically selects the search direction. By comparing with other search strategies in simulation experiments, the proposed strategy could shorten the searching time and improve the searching efficiency in the small and medium-scale task environment. Meanwhile, the search strategy can adapt to the post-earthquake dynamic environment, which is confirmed by dynamic experiments.
User Role Discovery Method in Virtual Community Based on DBSCAN Clustering

With the development of computer network and the wide use of smart phones, people can easily share the knowledge and information they are interested in to each other, and care for each other as if they were friends, thus a virtual community has emerged. How to discover users' social attributes in virtual communities is an important issue of interest to community managers. In this paper, we propose a method to discover user roles from user check-in data in virtual community, the method consist of two parts, the first part vectorizes user feature from user check-in data, and the second part using DBSCAN cluster algorithm to discover user roles from their features of vectorization. An experiment was conducted on a public available data set, and 16 user roles from 3 kind of user feature were discovered, which have varified the effectiveness of the method.
Prediction of Bitcoin Price Since COVID-19 by Using Neural Network Models

After Covid-19 swept the globe and bitcoin prices suddenly soared, machine learnings were used to predict the trend of bitcoin prices, but these studies were lack of performance analysis in different time-scale span. In this paper, three neural network models are designed and used to forecast the price of bitcoin after the outbreak of COVID-19. The models A uses the high/low price, open/close price of four-days of bitcoin as input variables and the close price of the fifth day as target variable, the models B uses same variable as the model A and uses optimal weights, and the model C uses same structure as the model B, but adds the trading volume to the input variables. The results show that the model C may lower the difference between actual and calculated outputs, thus boosting the prediction accuracy. Also, it is found that the models that can work well when predicting bitcoin prices in a short time span can be obviously less precise when it comes to predicting bitcoin prices in a longer time span.
Weighted Contiguous Sequential Pattern Mining

In real-life, big data is contiguous, such as traffic flow and network flow, and thus some contiguous mining algorithms have been developed. It has been noticed that the significance of data (e.g., DNA sequences) is often different, and the real data may have various weights. However, the continuity of the mined data is not fully considered in the existing weighted mining algorithms. In this study, we are the first to formulate the problem of mining weighted contiguous sequential patterns and propose a new algorithm named WCSpan. Based on the usage of modified prefix pattern expansion and a tight weighted upper-bound model, we proved that WCSpan can efficiently mine the weighted contiguous sequential patterns. Experimental results showed that compared with existing similar algorithms, the proposed algorithm has advantages in execution time and memory usage. Besides, the integrity of the outcome patterns of WCSpan is preserved while data omission is avoided. In addition, the generation of patterns by the WCSpan method is faster than other methods, where the weighted upper-bound model can prune redundant candidates precisely to save memory. Both of them significantly improve the performance of WCSpan.
Developing Relationships: A Heterogeneous Graph Network with Learnable Edge Representation for Emotion Identification in Conversations

Emotion recognition in conversations (ERC) aims to predict the emotion of utterances. Modeling context dependencies is the critical challenge of the task. Existing efforts in ERC are mainly based on the sequence and graph models. The graph models can better capture structured information than the sequence models. Unfortunately, there are few suitable aggregation strategies for ERC models based on high-dimensional edge features. Moreover, the adjustment of edge representation in graph-based models has been ignored for a long time. Based on this, we propose a learnable edge message-passing model based on a heterogeneous dialog graph. The model first calculates the attention weights between utterance nodes and between nodes and edges separately and then learns contextual utterance representations through these learnable edge representations. Additionally, we conducted our experiment on four public datasets and achieved advanced results.
CDI-E

We live in the gilded age of data-driven computing. With public clouds offering virtually unlimited amounts of compute and storage, enterprises collecting data about every aspect of their businesses, and advances in analytics and machine learning technologies, data driven decision making is now timely, cost-effective, and therefore, pervasive. Alas, only a handful of power users can wield today's powerful data engineering tools. For one thing, most solutions require knowledge of specific programming interfaces or libraries. Furthermore, running them requires complex configurations and knowledge of the underlying cloud for cost-effectiveness. We decided that a fundamental redesign is in order to democratize data engineering for the masses at cloud scale. The result is Informatica Cloud Data Integration - Elastic (CDI-E). Since the early 1990s, Informatica has been a pioneer and industry leader in building no-code data engineering tools. Non-experts can express complex data engineering tasks using a graphical user interface (GUI). Informatica CDI-E is built to incorporate the simplicity of GUI in the design layer with an elastic and highly scalable run time to handle data in any format without little to no user input using automated optimizations. Users upload their data to the cloud in any format and can immediately use them in conjunction with their data management and analytic tools of choice using CDI-E GUI. Implementation began in the Spring of 2017, and Informatica CDI-E has been generally available since the Summer of 2019. Today, CDI-E is used in production by a growing number of small and large enterprises to make sense of data in arbitrary formats. In this paper, we describe the architecture of Informatica CDI-E and its novel no-code data engineering interface. The paper highlights some of the key features of CDI-E: simplicity without loss in productivity and extreme elasticity. It concludes with lessons we learned and an outlook of the future.
Implementation “TRINGO” Philosophy for Multi Literations’ Digital Learning Design

This study aims to develop a Tringo-based multiliteracy learning design in learning to write exposition texts. The research method used is research and development, while the Dick and Carrey development model is the development model used. In this research and development, it is only limited to the expert validation test stage due to research limitations. Learning tools that become the output of this learning design are lesson plans (RPP), student worksheets (LKPD), learning e-modules and guidelines for using learning designs. The results of the development of this learning design were validated by material experts and media experts with the average number of all devices developed obtaining a percentage of 89.76%. The testing phase for students was carried out with individual trials, scoring 89%, small group trials of 81%, and field trials of 89%. The results of the validation and testing on the students showed that the learning design developed was feasible or valid to be used in the learning process and got a good response from the students.
Semantic Pivoting Model for Effective Event Detection

Event Detection, which aims to identify and classify mentions of event instances from unstructured articles, is an important task in Natural Language Processing (NLP). Existing techniques for event detection only use homogeneous one-hot vectors to represent the event type classes, ignoring the fact that the semantic meaning of the types is important to the task. Such an approach is inefficient and prone to overfitting. In this paper, we propose Semantic Pivoting Model for Effective Event Detection (SPEED), which explicitly incorporates prior information during training and captures more semantically meaningful correlation between input and events. Experimental results show that our proposed model achieves the state-of-the-art performance and outperforms the baselines in multiple settings without using any external resources.
Enabling Personal Fabrication for a Sustainable Built Environment

Despite the potential of personal fabrication to provide sustainable living standards via desktop manufacturing technologies, they have not been widely implemented. Adopting personal fabrication practices in line with circular and distributed production and suitable for lay citizens face environmental, socioeconomic, and sociotechnical barriers. This article aims to identify critical factors that support a citizen-driven transition to alternative ways of production and consumption capable of creating sustainable built environments. Findings suggest that opportunities lie in developing enabling tools to empower users, improve the embedded sustainability in open-source resources, increase appropriateness to local contexts, and facilitate knowledge transfer between makers and the general public.
ENCLYZER: Automated Analysis of Transient Data Leaks on Intel SGX

Trusted Execution Environment (TEE) is the cornerstone of confidential computing. Among other TEEs, Intel® Secure Guard Extensions (Intel® SGX) is the most prominent solution that is frequently used in the public cloud to provide confidential computing services. Intel® SGX promotes runtime confidentiality and integrity of enclaves with minimal modifications to existing CPU microarchitectures. However, Transient Execution Attacks, such as L1 Terminal Fault (L1TF), Microarchitectural Data Sampling (MDS), and Transactional Asynchronous Abort (TAA) have exposed certain vulnerabilities within Intel® SGX solution. Over the past few years, Intel has developed various countermeasures against most of these vulnerabilities via microcode updates and hardware fixes. However, arguably, there are no existing tools nor studies that can measurably verify the effectiveness of these countermeasures. In this paper, we introduce an automated analysis tool, called ENCLYZER, to evaluate Transient Execution Vulnerabilities on Intel® SGX. We leverage ENCLYZER to comprehensively analyze a set of processors, with multiple versions of their microcode, to verify the correctness of these countermeasures. Our empirical analysis suggests that most countermeasures are effective in preventing attacks that are initiated from the same CPU hyperthread, but less effective for cross-thread attacks. Therefore, the application of the latest microcode patches and disabling hyperthreading is warranted to enhance the security of Intel® SGX-enabled systems. Security Configurations like hyperthreading disabled/enabled are attestable on Intel® SGX platform to provide user with increased confidence in making decision on system trustworthiness. Note that the Security Configurations cannot be modified without a system reboot.
MIME: Minority Inclusion for Majority Group Enhancement of AI Performance

Several papers have rightly included minority groups in artificial intelligence (AI) training data to improve test inference for minority groups and/or society-at-large. A society-at-large consists of both minority and majority stakeholders. A common misconception is that minority inclusion does not increase performance for majority groups alone. In this paper, we make the surprising finding that including minority samples can improve test error for the majority group. In other words, minority group inclusion leads to majority group enhancements (MIME) in performance. A theoretical existence proof of the MIME effect is presented and found to be consistent with experimental results on six different datasets. Project webpage: https://visual.ee.ucla.edu/mime.htm/ .
Classification of house buildings based on land size using the K-nearest neighbor algorithm

Residential is housing and areas built in a balanced manner with a certain composition in single houses, simple houses, medium-sized houses, and luxurious houses or forms of flats. When building a large, small, or medium-sized residential house, you must pay attention to the size of the land building area and the building area of the house you want to design. If you ignore this, it will impact building the house, such as the plans that have been made are not suitable. The problem that is quite important from various problems exists in densely populated settlements where the land area is minimal and sequentially will impact the building area, where each area will not fit indoors. The standard for broad needs in building simple housing in Indonesia has been regulated in SNI 03-1733-2004, the minimum area for a simple house is 36m2 or 9m2 per person (Indonesian National Standard Agency 2004). This study uses the K-Nearest Neighbor Algorithm to classify house buildings based on land size. The results of classification testing using the K-Nearest Neighbor Algorithm on the WEKA application have the highest accuracy, precision, and recall values, namely testing on a dataset of 80% (66 data) from training data and 20% (16 data) from testing data with accuracy values. 100% precision, 1,000 precision and 1,000 recall.
Investigating Academic Performance Using an AR-based Learning Environment with Retrospective Confidence Judgments

Augmented reality (AR) is being used extensively in education in order to improve academic performance. However, everyone might have a different learning capability when they are exposed to an AR environment. Retrospective confidence judgment (RCJ) is a good metacognitive monitoring tool used in a computer-based learning system to make restudy decisions. In this study, we created an AR-based learning environment with RCJ to investigate academic performance. The Microsoft HoloLens provided a realistic AR environment for students to learn engineering content. Group one was the control group learning in class, and group two was learning AR-based content. Group two was divided into two subgroups, the academic performance of within groups was compared with RCJs and without RCJs. The study revealed the advantages of using an AR learn-ing environment to prompt students’ academic performance in the sagittal plane (U = 680, p &lt; 0.05). The findings of this research advance our understanding of human-computer interactions between students and the learning contents in an AR environment with RCJs. Besides, this research provides new guidelines on how to generate more effective engineering learning content in an AR learning environment.
Abstractive Dialogue Summarization Based on Dynamic Pattern Exploiting Training

Pre-trained language models (PLMs) have shown remarkable performance in natural language processing tasks, whereas these approaches often require a massive amount of data. Due to the lack of sufficient training instances, it is challenging for existing PLMs to achieve good results on dialogue summarization. In this paper, we propose DynamicPET, a pattern-exploiting training (PET) based method for abstractive dialogue summarization, which leverages the recent prompt learning paradigm to boost the performance of PLMs. In contrast to PET, our method does not rely on any task-specific unlabeled data, but obtains strong performance on two dialogue summarization datasets, especially in the few-shot scenarios.
A Study on the Construction of Electronic Teaching Resources for International Students in China's National Conditions Course Based on Word Co-occurrence Network Analysis

This study attempts to solve the problems of insufficient teaching resources and lagging material content in China's national conditions course in international Chinese education. This study selects the content of the “Outline of the Five-Year Plan for National Economic and Social Development of the People's Republic of China” as the basic analysis text. Firstly, by analyzing the high-frequency words and gradient words of the text, the word index table can be formed. Then, based on the “frequency-semantic distance” algorithm and the “node importance-semantic distance” algorithm, a word co-occurrence network with a teaching recommendation order can be constructed. At the same time, the recommended order of vocabulary teaching is established. Finally, based on the “Outline of the Five-Year Plan for National Economic and Social Development of the People's Republic of China” and the text content of the People's Daily for two years as the corpus, an electronic teaching resource library is constructed. The repository has a conditional search function. According to the index words, it can return various teaching content. It is hoped that it can provide effective supplements to the teaching materials of the Chinese national conditions course for international students. The research ideas of this research have reference value for the construction of electronic resources for teaching materials in other fields.
Real-Time Criminal Face Identification Based on Haar-Cascade and Lbph, with Automatic Message Delivery to Whatsapp

Using a machine learning approach, a number of algorithms for automatic face identification of offenders depending on specified goals have recently been developed. Various algorithm has been proposed for face detection such as Eigenface using PCA (Principal Compound Analysis), Fisherface using Linear Discriminative method, Local Binary Pattern, active appearance, 3D shape models. For face recognition, the Local Binary Pattern Histogram technique is employed in this research. The data augmentation is also done, which provides the better performance in training. Haar cascade is used for extracting the features like eyes, nose length, cheek, lips, etc. Initially using the web camera, the individual persons cropped grayscale images are collected as database. Then the classifier trains the images, the data augmentation is used when datasets of the person is not enough. Using the web camera, the images are detected and recognized the person and that information a message will be sent to WhatsApp.
Considering Multiple Stakeholders Perspectives for interval-based Goal Oriented Requirements Prioritization in agile development

Addressing the importance of the requirements prioritization, recent research introduced ‘A value-based approach for reasoning with goal models’, which allows stakeholders to set an importance estimate for each element in a goal model, that relates the requirements with the business objectives. These estimates are then propagated by means of their relationships (dependencies, contributions and decompositions) to get a prioritized list of all requirements.In this paper, a methodology that extends the aforementioned technique is presented. It uses Goal Oriented Requirements Language (GRL) to prioritize requirements in an agile environment, taking into consideration the opinion of many Stakeholders. As the static value of the estimate is never accurate and does not give the stakeholders enough flexibility, the proposed methodology enables each stakeholder to provide for each requirement its importance as an estimated interval instead of a static value. Then, for each requirement the mean value of the estimation interval is calculated, the information asymmetry and confidence level per requirement are demonstrated, so that a prioritized list can then be generated. The analyst can choose to use the prioritized list based on one of these criteria. This methodology is demonstrated on a case study.
Incremental Learning with Differentiable Architecture and Forgetting Search

As progress is made on training machine learning models on incrementally expanding classification tasks (i.e., incremental learning), a next step is to translate this progress to industry expectations. One technique missing from incremental learning is automatic architecture design via Neural Architecture Search (NAS). In this paper, we show that leveraging NAS for incremental learning results in strong performance gains for classification tasks. Specifically, we contribute the following: first, we create a strong baseline approach for incremental learning based on Differentiable Architecture Search (DARTS) and state-of-the-art incremental learning strategies, outperforming many existing strategies trained with similar-sized popular architectures; second, we extend the idea of architecture search to regularize architecture forgetting, boosting performance past our proposed baseline. We evaluate our method on both RF signal and image classification tasks, and demonstrate we can achieve up to a 10% performance increase over state-of-the-art methods. Most importantly, our contribution enables learning from continuous distributions on real-world application data for which the complexity of the data distribution is unknown, or the modality less explored (such as RF signal classification).