Reviewerly/exHarmony huggingface dataset:
Poster

Deep neural network (DNN) services have been widely deployed in many different domains. For instance, a client may send its private input data (e.g., images, texts and videos) to the cloud for accurate inferences with pre-trained DNN models. However, significant privacy concerns would emerge in such applications due to the potential data or model sharing. Secure inferences with cryptographic techniques have been proposed to address such issues, and the system can perform secure two-party inferences between each client and cloud. However, most of existing cryptographic systems only focus on DNNs for extracting 2D features for image inferences, which have major limitations on latency and scalability for extracting spatio-temporal (3D) features from videos for accurate inferences. To address such critical deficiencies, we design and implement the first cryptographic inference system, Crypto3D, which privately infers videos on 3D features with rigorous privacy guarantees. We evaluate Crypto3D and benchmark with the state-of-the-art systems on privately inferring videos in the UCF-101 and HMDB-51 datasets with C3D and I3D models. Our results demonstrate that Crypto3D significantly outperforms existing systems (substantially extended to inferences with 3D features): execution time: 186.89x vs. CryptoDL (3D), 63.75x vs. HEANN (3D), 61.52x vs. MP-SPDZ (3D), 45x vs. E2DM (3D), 3.74x vs. Intel SGX (3D), and 3x vs. Gazelle (3D); accuracy: 82.3% vs. below 70% for all of them.
Secure Auctions in the Presence of Rational Adversaries

Sealed bid auctions are used to allocate a resource among a set of interested parties. Traditionally, auctions need the presence of a trusted auctioneer to whom the bidders provide their private bid values. Existence of such a trusted party is not an assumption easily realized in practice. Generic secure computation protocols can be used to remove a trusted party. However, generic techniques result in inefficient protocols, and typically do not provide fairness -- that is, a corrupt party can learn the output and abort the protocol thereby preventing other parties from learning the output.
Poster

LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing of the surrounding environment. The recent rapid deployment of autonomous driving (AD) has highly benefited from the advancement of LiDARs. At the same time, the safety-critical application strongly motivates its security research. Recent studies demonstrate that they can manipulate the LiDAR point cloud and fool object detection by shooting malicious lasers against LiDAR scanning. However, prior efforts focus on limited types of LiDARs and object detection models, and their threat models are not clearly validated in the real world. To fill the critical research gap, we plan to conduct the first large-scale measurement study on LiDAR spoofing attacks against a wide variety of LiDARs with major object detectors. To perform this measurement, we first significantly improved the LiDAR spoofing capability (30x more spoofing points than the prior attack) with more careful optics and functional electronics, which allows us to be the first to clearly demonstrate and quantify key attack capabilities assumed in prior works. In this poster, we present our preliminary results on VLP-16 and our research plan.
Improving ML-based information retrieval software with user-driven functional testing and defect class analysis

Machine Learning (ML) has become the cornerstone of information retrieval (IR) software, as it can drive better user experience by leveraging information-rich data and complex models. However, evaluating the emergent behavior of ML-based IR software can be challenging with traditional software testing approaches: when developers modify the software, they cannot often extract useful information from individual test instances; rather, they seek to holistically verify whether—and where—their modifications caused significant regressions or improvements at scale. In this paper, we introduce not only such a holistic approach to evaluate the system-level behavior of the software, but also the concept of a defect class, which represents a partition of the input space on which the ML-based software does measurably worse for an existing feature or on which the ML task is more challenging for a new feature. We leverage large volumes of functional test cases, automatically obtained, to derive these defect classes, and propose new ways to improve the IR software from an end-user’s perspective. Applying our approach on a real production Search-AutoComplete system that contains a query interpretation ML component, we demonstrate that (1) our holistic metrics successfully identified two regressions and one improvement, where all 3 were independently verified with retrospective A/B experiments, (2) the automatically obtained defect classes provided actionable insights during early-stage ML development, and (3) we also detected defect classes at the finer sub-component level for which there were significant regressions, which we blocked prior to different releases.
UTANGO: untangling commits with context-aware, graph-based, code change clustering learning model

During software evolution, developers make several changes and commit them into the repositories. Unfortunately, many of them tangle different purposes, both hampering program comprehension and reducing separation of concerns. Automated approaches with deterministic solutions have been proposed to untangle commits. However, specifying an effective clustering criteria on the changes in a commit for untangling is challenging for those approaches. In this work, we present UTango, a machine learning (ML)-based approach that learns to untangle the changes in a commit. We develop a novel code change clustering learning model that learns to cluster the code changes, represented by the embeddings, into different groups with different concerns. We adapt the agglomerative clustering algorithm into a supervised-learning clustering model operating on the learned code change embeddings via trainable parameters and a loss function in comparing the predicted clusters and the correct ones during training. To facilitate our clustering learning model, we develop a context-aware, graph-based, code change representation learning model, leveraging Label, Graph-based Convolution Network to produce the contextualized embeddings for code changes, that integrates program dependencies and the surrounding contexts of the changes. The contexts and cloned code are also explicitly represented, helping UTango distinguish the concerns. Our empirical evaluation on C# and Java datasets with 1,612 and 14k tangled commits show that it achieves the accuracy of 28.6%– 462.5% and 13.3%–100.0% relatively higher than the state-of-the-art commit-untangling approaches for C# and Java, respectively.
Leveraging test plan quality to improve code review efficacy

In modern code reviews, many artifacts play roles in knowledge- sharing and documentation: summaries, test plans, and comments, etc. Improving developer tools and facilitating better code reviews require an understanding of the quality of pull requests and their artifacts. This is difficult to measure, however, because they are often free-form natural language and unstructured text data. In this paper, we focus on measuring the quality of test plans at Meta. Test plans are used as a communication mechanism between the author of a pull request and its reviewers, serving as walkthroughs to help confirm that the changed code is behaving as expected. We collected developer opinions on over 650 test plans from more than 500 Meta developers, then introduced a transformer-based model to leverage the success of natural language processing (NLP) tech- niques in the code review domain. In our study, we show that the learned model is able to capture the sentiment of developers and reflect a correlation of test plan quality with review engagement and reversions: compared to a decision tree model, our proposed transformer-based model achieves a 7% higher F1-score. Finally, we present a case study of how such a metric may be useful in experiments to inform improvements in developer tools and experiences.
CodeMatcher: a tool for large-scale code search based on query semantics matching

Due to the emergence of large-scale codebases, such as GitHub and Gitee, searching and reusing existing code can help developers substantially improve software development productivity. Over the years, many code search tools have been developed. Early tools leveraged the information retrieval (IR) technique to perform an efficient code search for a frequently changed large-scale codebase. However, the search accuracy was low due to the semantic mismatch between query and code. In the recent years, many tools leveraged Deep Learning (DL) technique to address this issue. But the DL-based tools are slow and the search accuracy is unstable.
Demo --- SPoKE

We present a Web survey system demo that computes aggregates of sensitive data while protecting individual contributions using a novel secure aggregation algorithm implemented in a Web browser.
Towards Generic Power/EM Side-Channel Attacks: Memory Leakage on General-Purpose Computers

Today’s power/EM side-channel analysis is limited by the complexity of the target hardware. We investigate the feasibility of power/EM side-channel analysis of general-purpose computers. This paper makes a step towards this goal by analyzing memory operations of Raspberry Pi 3 Model B, a widely used general-purpose IoT device that is capable of running an operating system, and shows that it is possible to extract information about the data field of memory operations from near-field EM measurements.
Asynchronous technical interviews: reducing the effect of supervised think-aloud on communication ability

Software engineers often face a critical test before landing a job—passing a technical interview. During these sessions, candidates must write code while thinking aloud as they work toward a solution to a problem under the watchful eye of an interviewer. While thinking aloud during technical interviews gives interviewers a picture of candidates’ problem-solving ability, surprisingly, these types of interviews often prevent candidates from communicating their thought process effectively. To understand if poor performance related to interviewer presence can be reduced while preserving communication and technical skills, we introduce asynchronous technical interviews—where candidates submit recordings of think-aloud and coding. We compare this approach to traditional whiteboard interviews and find that, by eliminating interviewer supervision, asynchronicity significantly improved the clarity of think-aloud via increased informativeness and reduced stress. Moreover, we discovered asynchronous technical interviews preserved, and in some cases even enhanced, technical problem-solving strategies and code quality. This work offers insight into asynchronous technical interviews as a design for supporting communication during interviews, and discusses trade-offs and guidelines for implementing this approach in software engineering hiring practices.
Cross Domain Movie Recommendation System using Personalized Preference Transfer

Recommendation System has become more customized than ever. However, it suffers from cold start and data sparsity issues. Cross Domain Recommendation (CDR) has been proposed as a promising and feasible approach which yields recommendations in the destination domain based on the characteristic and personalized preferences of the users in the source domain. Since the fondness varies for each user, their taste bridge would also be different. Most of the well-known existing models make use of common preference bridge when it comes to transferring preferences for all the users in the system. Personalized Preference Transfer (PPT) for personalizing user preferences using Characteristic Encoder (CE) where user characteristic is mapped to initializing maps is pro-posed for cold start users and from books for warm start users. In this model, a Meta net-work with user attributes is used to attain the bridge preferences for individual users. The Deep Neural Network (DNN) which is a neural network model is used to provide optimized recommendations to the users. The accuracy and efficiency of personalized user preferences transfer is determined based on the comparisons from different models and train/test ratios, making use of two evaluation metrics MAE (Mean Absolute Error) and RMSD (Root Mean Squared Deviation).
Poster

The internal clock of the CPU uses oscillators made from quartz crystals. Small changes in these crystals can cause small but measurable differences in the clock frequency. Under a low CPU load, the function execution time distribution follows the Pareto distribution. However, the function execution time distribution no longer follows the Pareto distribution when the CPU load is high, and the CPU clock fingerprint becomes invalid. In view of this problem, this paper proposes an adaptive Pareto principle that adaptively adjusts the distribution according to the CPU load. Based on this, the robust CPU Clock Fingerprint Model based on the Adaptive Pareto Principle (CFMAP) is proposed. Via a KNN-based fingerprint recognition method, CFMAP solves the instability of existing CPU clock fingerprints under a high CPU load. Experiments show that the average recognition rate of CFMAP fingerprints is 96.82%. Moreover, they are highly robust against CPU load attacks and virtual machine attacks.
Secret-Shared Joins with Multiplicity from Aggregation Trees

We present novel protocols to compute SQL-like join operations on secret shared database tables with non-unique join keys. Previous approaches to the problem had the restriction that the join keys of both the input tables must be unique or had quadratic overhead. Our work lifts this restriction, allowing one or both of the secret shared input tables to have an unknown and unbounded number of repeating join keys while achieving efficient O(n log n) asymptotic communication/computation and O(log n) rounds of interaction, independent of the multiplicity of the keys.
Multi-Port Shared Cloud Framework of College Student Education Management Information System under the Background of Micro -Data Dynamic Network

Cloud computing and big data, as new products of the development of the times, have achieved outstanding results in the application of various industries in society. It can be seen that Ben Zhang pointed out the multi-port shared cloud framework based on the school-based network platform to realize resource sharing, communication, teaching and research training, electronic file management and other education information management, and the school must do a good job in this work. The multi-faceted work is committed to the multi-campus student management work, so that students can manage themselves, position themselves, and improve themselves.
Towards a Principle-Based Approach for Case-Based Reasoning

Case-based reasoning (CBR) is an experience-based approach to solving problems; it adapts previously successful cases to new problems following the key assumption: the more similar the cases, the more similar their solutions. Despite its popularity, there are few works on foundations, or properties, that may underlie CBR models. This paper bridges this gap by defining various notions capturing the above assumption, and proposing a set of principles that a CBR system would satisfy. We discuss their properties and show that the principles that are founded on the CBR assumption are incompatible with some axioms underlying non-monotonic reasoning (NMR). This shows that CBR and NMR are different forms of reasoning, and sheds light on the reasons behind their disagreements.
Exploring the under-explored terrain of non-open source data for software engineering through the lens of federated learning

The availability of open source projects on platforms like GitHub has led to the wide use of the artifacts from these projects in software engineering research. These publicly available artifacts have been used to train artificial intelligence models used in various empirical studies and the development of tools. However, these advancements have missed out on the artifacts from non-open source projects due to the unavailability of the data. A major cause for the unavailability of the data from non-open source repositories is the issue concerning data privacy. In this paper, we propose using federated learning to address the issue of data privacy to enable the use of data from non-open source to train AI models used in software engineering research. We believe that this can potentially enable industries to collaborate with software engineering researchers without concerns about privacy. We present the preliminary evaluation of the use of federated learning to train a classifier to label bug-fix commits from an existing study to demonstrate its feasibility. The federated approach achieved an F1 score of 0.83 compared to a score of 0.84 using the centralized approach. We also present our vision of the potential implications of the use of federated learning in software engineering research.
Recommendation Algorithm Driven by Difficulty Coefficient of E-learning Materials

With the rapid development of Internet technology, online learning has become one of the main ways for people to acquire knowledge. How students can study independently and efficiently through the Internet has become a problem which is worthy of attention and consideration. Firstly, a material difficulty coefficient prediction algorithm was proposed. Secondly, the difficulty coefficient of an e-Iearning material is predicted by using multiple linear regression and Backpropagation neural network model on the data, and the collaborative filtering recommendation of learning materials is carried out by combining students' usual learning ability. The experimental results show that the training duration and prediction accuracy of this recommendation method are better than the simple collaborative filtering algorithm based on user or content.
The further implementation of robots in welfare

Here we present the development of a new robot application. Using a user-centered approach, the aim is to enable human-robot interaction and promote patient safety in medication counselling relevant to emergency contraceptive pill. Emergency contraceptive pills seemed like a suitable choice because the primary pharmacy customers, being young, are comfortable with technology, no prescription is needed to buy the pills in Finland, and the robot can provide non-judgmental interaction to the costumers. We suggest the use of field study methods in future research in which both qualitative and quantitative data analyses are combined to expose the challenges and/or opportunities inherent to the design of robot applications for use in human-robot interaction.
Designing Accessible Digital Services for the Future Public Transportation: Towards Lighter Testbed (LiTe)

This paper represents the design process of the LiTe - Lighter Testbed of a tram stop and tram car to facilitate user evaluations in a realistic context. According to previous studies, people using public transportation seek services that improve travel experiences with the help of novel technology. In the future, accessible digital services will be integrated into all modes of transportation to provide a seamless experience. There is a need to offer physical and local access to innovative co-creation processes providing an environment and support for enabling the testing of novel solutions in an open but safe environment. This way, we can ensure the usability, accessibility, effectiveness, and safety of public transportation services in the Tampere area and further expand it to other cities.
Applying participatory design principles to collaborative art-creating sessions

Participatory design (PD) is an established design practice that is particularly widely used in the field of Human Computer Interaction (HCI) and is based on involving all stakeholders in design process. Considering the popularity of this approach, one might wonder if the same design principles can be applied in other domains, for instance, visual and fine arts. For this study, a small sample of people, who usually would be just passive spectators of art, was invited to participate in a creative workshop organized by PD principles and to create a fine art themselves. This way, people with only past or little artistic ambitions became “users, taking control” rather than passive consumers of an end-product. The participants reflected and documented their feelings before and after the event. Preliminary results suggest that this hybrid approach positively contributes to the relationship dynamics within a formed team and evokes an interest in individual creative activities.
FPGA-Based Acceleration of Convolutional Neural Network for Gesture Recognition Using mm-Wave FMCW Radar

Convolutional Neural Networks (CNNs) have revo-lutionized many applications in recent years, especially in image classification, video processing, and pattern recognition. Another exciting area where CNNs are becoming popular is studying the interaction between humans and computers, also known as human-computer interaction (HCI). Our study uses CNNs to classify hand gestures (a common non-contact-based HCI) obtained using mmWave Frequency-Modulated Continuous Wave (FMCW) radar. For each gesture, features such as range, velocity, and angle, are obtained and fused in one feature map, forming one single input to the CNN model. The proposed CNN model has achieved exceptional accuracy of 99.33% on the test set. Additionally, we have discussed the complete workflow from radar data preprocessing and model compression (quantization and pruning) to the final implementation of the CNN accelerator on FPGAs. With a minimal accuracy degradation, the most optimized CNN accelerator can process 64474.53 samples per second and consume 11.1 times fewer DSP units, 1.27 times fewer BRAMs, 1.37 times fewer flip flops, and 2.52 times reduction in 100kup tables (LUTs) compared to its baseline model.
Blockchain based Electronic Voting Machine

Technology is extremely important in everyday life. With the rising use of technology, new invention and breakthroughs have brought about a significant transformation in democracy. Elections influence a country’s future. Even the world’s most powerful democracies are burdened by a defective voting system. The biggest flaws in the existing voting system include election rigging, hacking, manipulation, and polling booth capture. Blockchain is a new, decentralized, and distributed technology that focuses on high security. Implementing voting through blockchain might remove current voting system issues. Blockchain is a strong tool because of its smart contracts and several features that outperform older systems. Smart contracts are significant bits of code that must be inserted into blockchain and implemented according to the plan in all phases of the blockchain update process. Online voting has become a talk and subject in the world of internet services. The blockchain appears to be a promising option for application in the construction of safer, more secure voting system. A voting application system has been created as a smart transaction in this project to examine the use of blockchain to construct distributed electronic voting systems.