Reviewerly/exHarmony huggingface dataset:
Mutexion: Mutually Exclusive Compression System for Mitigating Compression Side-Channel Attacks

To enhance the performance of web services, web servers often compress data to be delivered. Unfortunately, the data compression technique has also introduced a side effect called compression side-channel attacks (CSCA) . CSCA allows eavesdroppers to unveil secret strings included in the encrypted traffic by observing the length of data. A promising defense technique called Debreach was recently proposed to mitigate CSCA by excluding all secret data in a web page during the compression process. Although Debreach has proven to be safe against CSCA and outperforms other approaches, the exclusion of all secret data from compression eventually resulted in a decreased compression efficiency. In this paper, we present a highly efficient CSCA mitigation system called “Mutexion” ( Mut ually ex clusive compress ion ) which allows us to fully take advantage of compression over an entire web page, including secret data. The key idea behind Mutexion is to fully take advantage of all the matching subsequences within a web page except only for those between secret data and user-controlled data (potentially controlled by an attacker) during the compression process. This approach of Mutexion effectively prevents side-channel leaks of secret data under CSCA misusing user-controlled data in a web page while minimizing the degradation in compression efficiency. It is required for our compressor to trace both secret data and user-controlled data in its compression process of web pages. To meet this requirement, we provide techniques to enable automated annotation of secret and user-controlled data in web pages. We implemented Mutexion as a fully working system to test live web pages and evaluated its performance with respect to security and compression efficiency. Our evaluation results demonstrated that Mutexion effectively prevents CSCA and also achieves almost the same compression ratio as the original zlib, which is vulnerable to CSCA, with a slight increase (0.032 milliseconds (7.9%) on average) in execution time.
A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

Abstract Cloud-native database systems have started to gain broad support and popularity due to more and more applications and systems moving to the cloud. Various cloud-native databases have been emerging in recent years, but their developments are still in the primary stage. At this stage, database developers are generally confused about improving the performance of the database by applying AI technologies. The maturity model can help database developers formulate the measures and clarify the improvement path during development. However, the current maturity models are unsuitable for cloud-native databases since their architecture and resource management differ from traditional databases. Hence, we propose a maturity model for AI-empowered cloud-native databases from the perspective of resource management. We employ a systematic literature review and expert interviews to conduct the maturity model. Also, we develop an assessment tool based on the maturity model to help developers assess cloud-native databases. And we provide an assessment case to prove our maturity model. The assessment case results show that the database’s development direction conforms to the maturity model. It proves the effectiveness of the maturity model.
Electromagnetic Fingerprinting of Memory Heartbeats

This paper presents MemScope, a system that fingerprints devices via electromagnetic sensing of their memory heartbeats, i.e., the clock signal that synchronizes memory and memory controller. MemScope leverages the enhanced resolution and security of memory heartbeat fingerprint, which has enriched spectral features thanks to the spread spectrum generation of memory clock, and cannot be concealed as long as the device accesses its memory during computing. MemScope employs signal processing algorithms that allow it to hear the memory heartbeats of devices from a distance, in the presence of noise, and in crowded environments where multiple devices coexist. It then fingerprints memory heartbeats using machine learning tools. Measurements on a set of 65 devices over a month validate the robustness of fingerprint against time variation, and show a high precision and recall. We then use the neural network to build a detector to defend against possible replay attacks. Finally, we further demonstrate the effectiveness of MemScope in two application scenarios, (i) detecting wireless identity spoofing and (ii) identifying and localizing unauthorized hidden cameras.
Clustering and Subsequent Contour and Motion Estimation of Automotive Objects Using a Network of Cooperative Radar Sensors

A method for collaborative target list processing of multiple radar sensors simultaneously estimating multiple extended targets is presented and discussed. A scheme to cluster target points from multiple radar sensors jointly is described that deals with overclustering and separation of objects. Algorithms to estimate the contour (i.e., dimensions) and motion of these objects are proposed and evaluated. Additionally, the influence of the number of sensors on the results is investigated and compared to single sensor estimates. The processing of measurements with three radar sensors and two vehicles shows the advantages of using more than one sensor, as well as the proposed algorithms.
Three Stage Drawing Transfer

This project creates a visual-mental-physical circuit between a Generative Adversarial Network (GAN), a co-robotic arm, and a five-year-old child. From training images to the latent space of a GAN, through pen on paper to a live human collaborator, it establishes a series of translational stages between humans and non-humans played out through the medium of drawing. Trained on a subset of the Rhoda Kellogg Child Art Collection, the neural network at the center of this piece learns its own representations of these images. The generated results, synthetic children's drawings, are of interest both for being outside of adult conventions and learned expression-like Dubuffet's art brut or Surrealist automatism-and for how they align machine learning with the human act of learning to draw. The project layers many kinds of agency and embodiment: from the thousands of anonymous children who produced the original artwork used as training data, through the co-robot drawing from GAN-generated imagery, to the human child's active perception and graphic response to the robot. These questions of where we search for the other; when we attribute autonomy and intelligence; and why we might wish to escape our human subjectivities speak to core issues in the design and use of AI systems. This project is one attempt to think through those questions in an embodied way.
Frisson Waves

Frisson is the feeling and experience of physical reactions such as shivers, tingling skin, and goosebumps. Using entrainment through facilitating interpersonal transmissions of embodied sensations, we present "Frisson Waves" with the aim to enhance live music performance experiences. "Frisson Waves" is an exploratory real-time system to detect, trigger and share frisson in a wave-like pattern over audience members during music performances. The system consists of a physiological sensing wristband for detecting frisson and a thermo-haptic neckband for inducing frisson. In a controlled environment, we evaluate detection (n=19) and triggering of frisson (n=15). Based on our findings, we conducted an in-the-wild music concert with 48 audience members using our system to share frisson. This paper summarizes a framework for accessing, triggering and sharing frisson. We report our research insights, lessons learned, and limitations of "Frisson Waves".
Use It-No Need to Shake It!

Implicit authentication for traditional objects, such as doors and dumbbells, has rich applications but is rarely studied. An ongoing trend is that traditional objects are retrofitted to smart environments; for instance, a contact sensor is attached to a door to detect door opening (but cannot tell "who is opening the door"). We present the first accurate implicit-authentication system for retrofitted everyday objects, named MoMatch. It makes an authentication decision based on a single natural object use, unlike prior work that requires shaking objects. MoMatch is built on the observation that an object has a motion typically because a human hand moves it; thus, the object's motion and the legitimate user's hand movement should correlate. The main challenge is, given the small amount of data collected during one object use, how to measure the correlation accurately. We convert the correlation measurement problem into an image comparison problem and resolve it using neural networks successfully. MoMatch does not need to profile the user's biometric information and is resilient to mimicry attacks.
Evaluating Three Touch Gestures for Moving Objects across Folded Screens

Foldable screens are gaining popularity on mobile platforms as it features novel design schemes for view management and screen extension. However, challenges for performing touch input arises when it is folded in various angles and held in various ways. This paper is among the first to systematically evaluate how touch gestures perform under the influence of various fold angles and holding postures. Three gestures for moving objects on touch screens, namely Direct Drag, Hold &amp; Tap, and Throw &amp; Catch, were adopted from previous works and compared in a controlled experiment, where five fold angles and portrait vs landscape modes were varied. Results provide an in-depth understanding of how orientation, fold angle, target distance and direction affects the performance of each technique. Overall Direct Drag was the most accurate but highly inefficient and tiring. Hold &amp; Tap was the most efficient technique with comparable accuracy, except being much more error-prone in Landscape than Portrait. Throw &amp; Catch had a more balanced trade-off between efficiency and accuracy.
Swin Transformer with Feature Pyramid Networks for Scene Text Detection of the Secondary Circuit Cabinet Wiring

The scene text of the secondary circuit cabinet wiring site in the substation includes various bending, occlusion, lighting, and it is difficult to achieve satisfactory detection results using previous approaches. To solve this problem, we propose a method called Swin-FPN, which integrate the advantages of Swin Transformer into the feature pyramid networks (FPN) to effectively improve the performance of FPN. Specifically, we firstly extract the global self-attention contexts of each level of the FPN using Swin Transformer. Then, all levels of the FPN are concatenated by upsampling to produce the feature fusion and project module. Finally, this module is used to predict the score map and keypoints map of the text regions, and thus obtain the final detection result. To evaluate the performance of our method, we construct a scene text dataset with various shapes and occlusion from the substation secondary circuit cabinet wiring site (SSCCWS). We train our Swin-FPN network on public datasets, and then evaluate the performance on our SSCCWS dataset. Experiments demonstrate that the proposed method can achieve better detection performance for SSCCWS scene text compared with state-of-the-art approaches. Thus, our proposed method lays a good foundation for the intelligent inspection of substations.
Comparison of Scrum Maturity Between Internal and External Software Development: A Case Study at One of the State-Owned Banks in Indonesia

Bank XYZ, a state-owned bank in Indonesia, has just begun its digital transformation and employed scrum methodology for only one year. Lack of resources in the internal IT division (Div-IT) led to the necessity to create a separate unit (Div-Ex) to expedite several digital transformation initiatives to be able to support Bank XYZ in competing with other banks. The purpose of this research is to measure and compare the maturity level of both Div-Ex and Div-IT. This research was conducted under a mix-method approach. Team observation was done for both Div-Ex and Div-IT. Subsequently, questionnaires with 69 questions were circulated online to both divisions. Maturity level was assessed by combining Scrum Maturity Method (SMM) to measure current state and Agile Maturity Method (AMM) to evaluate each key process area. Div-IT reached 72,5% largely achieved Maturity Level 2 - Managed, 72.05% largely achieved Maturity Level 3 - Defined, 50% partially achieved Maturity Level 4 - Quantitatively Managed, and 63.89% largely achieved Maturity Level 5 - optimized. Whereas Div-Ex reached 91,25% fully achieved Maturity Level 2, 91.25% fully achieved Maturity Level 3, 50% partially achieved Maturity Level 4, and 77.78% largely achieved Maturity Level 5. In summary, Div-Ex is overall more mature with maturity level 3 whereas Div-IT only maturity level 2. Recommendations for improvement based on Scrum Guide, SBOK Guide, and Praise for Essential Scrum are provided to Bank XYZ to improve the maturity level of the Div-IT.
Cervical cell classification based on attention mechanism and multi-resolution feature fusion

Liquid-based thin-layer cell smears are very important for the early screening and prevention of cervical cancer, and computer-aided diagnosis can reduce the workload of pathologists. The cell classification method based on deep learning can process data efficiently. However, most classification methods are based on a single resolution for recognition. When the resolution is low, the processing speed of the whole slide image is faster, but lack of picture details, which makes the identification inaccurate. When the resolution is high, it takes more time to process the whole slide image, but with more image detail. To this end, we propose a model based on Attention Mechanism and Multi-resolution Feature Fusion Module (AMFM), which combine the advantages of various resolutions to classify cervical cells. Experiments show that the accuracy is increased by 3.93% and the AUC is improved by 0.022 on the four-classification task of the cervical cell compared to the model based on a single resolution.
Breast Cancer Prediction Using Random Forest and Gaussian Naïve Bayes Algorithms

Breast cancer is the second deadliest cancer after lung cancer. In 2021, ASCO-American Society of Clinical Oncology states that female invasive breast cancer increased by half a percent from 2008 to 2017. Breast cancer is induced by a misspelling of a cell, which causes the cell to become uncontrollable. If the problem is not treated soon within a few months, a large number of cells containing the wrong instructions can be detected as cancer. Machine learning has been widely used for developing breast cancer prediction models. Unfortunately, the problem of imbalanced datasets tends to have little to no attention in previous research using machine learning. This research aimed to develop breast cancer prediction models using Random Forest and Gaussian Naïve Bayes Classifier. Borderline Synthetic Minority Oversampling Technique (BSM) is applied to handle the imbalanced dataset problem; meanwhile, machine learning algorithms such as Random Forest and Gaussian Naïve Bayes algorithms were used to build the prediction models. Using UCI Machine Learning Wisconsin Breast Cancer Dataset (WBCD), the combination of BSM and Random Forest algorithm showed the highest recall score, approximately around 99.8%. Meanwhile, the BSM and Gaussian Naïve Bayes Classifier combination provided the lowest recall score among generated models, 78.2%.
In Image Classification of Skin Cancer Sufferers: Modification of K-Nearest Neighbor with Histogram of Oriented Gradients Approach

Classification in data mining is one technique in recognizing all types of data. Where data can be in the form of text, numeric, images and others. One of the superior classification techniques is the KNN algorithm. The KNN algorithm is a distance search using Euclidean distance. image data classification using the HOG process is needed to modify the KNN. The purpose of this paper is to classify patients with classifying skin cancer patients using the KNN method where the Histogram of Oriented Gradients (HOG) process is used to assist in extracting data for skin cancer patients, which consists of benign and malignant cancers. However, in this paper, the images included in this article are pictures of skin cancer sufferers, which consist of malignant and benign. The data obtained were 660 datasets of which 630 were used as training data and 30 were used as test data. The training and testing went well, this was shown by getting a MAPE of O.06705477%. So that the classification process can be accepted because it shows a small validity.
Software Practices For Agile Developers: A Systematic Literature Review

Software development is one of the work practices in a company’s startup, academics, and industries. Agile is a software development methodology that is currently quite popular. Agile development practices existing methodologies include Test-Driven Development (TDD), Behavior-Driven Development (BDD), Domain-Driven Design (DDD), and Model-Driven Development (MDD). Each software development practice is unique as it could hinder or contribute greatly to the creation of said software. Agile is a consideration for developers when selecting appropriate development practices due to the breadth of available practices. Thus, this study identifies the characteristics and effectiveness of Agile methods. Our opinion is that this study can help software developers to better understand the challenges of each agile practices and implement them to create better optimized softwares.
Artificial Intelligence Governance: A Study on the Ethical and Security Issues that Arise

artificial intelligence (AI) is rapidly becoming integrated into every facet of society. However, its use in current governance is lacking. Across major global powers, attempts are currently being made to regulate AI with the aim of protecting citizens, corporations and political realms. Despite this, there have been several serious incidents involving AI recently. The current gaps in governance could give way to even greater future problems as AI becomes a more pervasive part of society. This research examines both current attempts at governance and common concerns to address using both a case study on Amazon’s Alexa and a previously conducted survey of U.S. public opinion on AI. The results of these studies show that AI is not necessarily always secure and that citizens’ concerns over AI risks are growing. Overall, this research highlights some of the key areas governing bodies should seek to address.
Research on Combined Forecasting Method of Energy Demand Potential of Regional Data Center based on Neural Network Model

This paper presents a combined forecasting method of demand potential of regional data center based on neural network model. Firstly, analyze and quantify the influencing factors of the demand of the regional data center, collect the key factor data affecting the demand potential of the regional data center, and solve the problem that there are many factors affecting the demand potential of the data center and it is difficult to predict; Secondly, the collected index data is brought into the dimensionless processing formula for processing; Then predict the index data of the future trend, and then bring the predicted index data into the combined neural network model to predict the demand of the data center, which can improve the accuracy of predicting the regional development potential of the data center. This prediction method can provide theoretical reference for power grid planning and construction.
Recommender System Using Transformer Model: A Systematic Literature Review

Online transactions are significant in the pandemic era. Using online transactions can minimize the risk of physical contact with disease transmission between buyers and sellers. However, with so many choices of items, it becomes challenging for users to decide which item suits their needs. For this reason, the recommender system was created as a handy tool. Recommender systems can help provide ratings, compare with other user data, use personal transaction history, use current events, or combine the above methods. Currently, computer science experts are constantly trying to improve recommender systems. In 2017 a new method emerged that uses transformers as one of the deep learning models. The combination of recommender systems and transformers can process extensive data, create different weights for each input data, and process data without sequentially allowing parallel processing and reducing training time significantly. Many papers in various countries are continuously trying to improve this methodology. In this literature review, we try to analyze the technology used, the dataset used, and the area where the technology is implemented. In this case, we carry out collecting papers, then filtering, classifying and analyzing, and making conclusions.
Learning to navigate on the rough terrain: A multi-modal deep reinforcement learning approach

How to enable safe navigation of unmanned vehicles on complex and rough terrain is challenging and meaningful research. In this paper, we propose an end-to-end reinforcement learning local navigation method with multi-modal data fusion, which effectively combines the intrinsic perception, such as Inertial Measurement Unit (IMU) measurements, and the extrinsic perception, such as Three-dimensional (3D) point clouds and images, of an unmanned vehicle. A specific feature extraction network is constructed for each modal data, and the total network is effectively trained using a modal separation learning method. The experimental results show that the proposed method can effectively address various obstacles such as rough roads, vegetation obstacles, and water pool disturbances to achieve autonomous and safe navigation of unmanned vehicles in off-road scenarios.
Detecting Fake Yelp Reviews Using a Positional LSTM / K-L Divergence Ensemble Approach

Online reviews of products and services often have a significant impact on future purchases. Unfortunately, this invites opportunities for fraud –to provide fake reviews to improve a company’s reputation in the eyes of consumers or to disparage the reputation of a competitor. We combine two methods to detect fraud in Yelp restaurant reviews. First, we develop and apply a bi-directional Long-Short Term Memory (LSTM), a type of recurrent neural network, to take advantage of the positional relevancy of comments within reviews. LSTMs are well-suited to examine linguistic features in text and evaluating different regions of text within the review enhances our model’s accuracy. To this component, we apply a Kullback-Leibler (K-L) divergence technique to examine the discrepancy in the term rankings between real and fake Yelp reviews. This ensemble is used to discriminate fake reviews from real ones, achieving an Average Precision (AP) of 0.5402 and an Area Under the Curve (AOC) of 0.866. which is an improvement over other state-of-the-art techniques on the same YelpNYC dataset.
Parallel Optimization of Super Pixel Algorithm SLIC

Super pixel algorithm SLIC uses K-means mean clustering method to effectively generate super pixels. Compared with other super pixel algorithms, it is more efficient and improves the segmentation performance. In order to further improve its performance, the program is optimized from five major directions: compilation optimization, data structure optimization, loop vectorization, OpenMP parallel optimization and algorithm optimization.