Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on 10-year Beast Cancer Survival Prediction Model Based on Mixed Feature Selection

On the basis of the breast cancer data from 1973 to 2015 in the SEER database, the optimal feature selection is based on the hybrid feature selection algorithm. Hybrid feature selection algorithm is a combination of filtering method and heuristic search algorithm. First, chi-square test (chi) is used to filter redundant or irrelevant features, and then an improved genetic algorithm is used to search to find the best combination of features. Mainly improved the formulation of fitness and improved roulette selection. Then the XGBoost classification algorithm is used to establish a 10-year survival prediction model for breast cancer patients. The experimental result show that the data is reduced from 22-dimensional features to 6-dimensional by using hybrid feature selection method, and in terms of five indicators, the model established by this method is better than the model established by all features. The accuracy, precision and AUC of this model are 0.8468, 0.8385, and 0.8181 respectively, which is superior to of all other models.
How Much and for Whom?: A Multi-Wave Study of the Impact of Self-Regulated Learning Scaffolds on MOOC Student Academic Performance

With an effort to ameliorate the high dropout rates and the low performance in the massive open online courses (MOOCs), this paper presents multi-wave, experimental studies with a longitudinal intervention of self-regulated learning scaffolds on 24 MOOCs for 2650 learners. The self-regulated learning user interface (SRLUI) is designed based on Zimmerman's SRL cyclical model with a learning dashboard, interactive user interface, nudging effect. SRLUI is designed with two goals: 1. manifesting learner self-regulated learning behavior 2. enhancing learning outcomes. In this study, our primary research question is, "What is the marginal effect of SRLUI on learning as evidenced by final course grade? " To answer the research question, we employed a multilevel Bayesian beta regression modeling approach, first to each intervention and then across all three interventions in the aggregate. Our findings were mixed. We found some evidence of enhanced learning for passing and non-passing students across some of the individual interventions. On the whole, we determined that there was no statistical evidence of positive impacts on learning for students who pass a given course, though there is evidence of a small positive impact on learning for students who did not ultimately pass a given course. We discuss potential reasons for this differential impact and its implication for future course design and research.
Fidas: fortifying the cloud via comprehensive FPGA-based offloading for intrusion detection: industrial product

Network intrusion detection systems (IDS) are crucial for secure cloud computing, but they are also severely constrained by CPU computation capacity as the network bandwidth increases. Therefore, hardware offloading is essential for the IDS servers to support the ever-growing throughput demand for packet processing. Based on the experience of large-scale IDS deployment, we find the existing hardware offloading solutions have fundamental limitations that prevent them from being massively deployed in the production environment. In this paper, we present Fidas, an FPGA-based intrusion detection offload system that avoids the limitations of the existing hardware solutions by comprehensively offloading the primary NIC, rule pattern matching, and traffic flow rate classification. The pattern matching module in Fidas uses a multi-level filter-based approach for efficient regex processing, and the flow rate classification module employs a novel dual-stack memory scheme to identify the hot flows under volumetric attacks. Our evaluation shows that Fidas achieves the state-of-the-art throughput in pattern matching and flow rate classification while freeing up processors for other security-related functionalities. Fidas is deployed in the production data center and has been battle-tested for its performance, cost-effectiveness, and DevOps agility.
CraterLake: a hardware accelerator for efficient unbounded computation on encrypted data

Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due to its prohibitive overheads, about 10,000X over unencrypted computation. Recent FHE accelerators have made strides to bridge this performance gap. Unfortunately, prior accelerators only work well for simple programs, but become inefficient for complex programs, which bring additional costs and challenges. We present CraterLake, the first FHE accelerator that enables FHE programs of unbounded size (i.e., unbounded multiplicative depth). Such computations require very large ciphertexts (tens of MBs each) and different algorithms that prior work does not support well. To tackle this challenge, CraterLake introduces a new hardware architecture that efficiently scales to very large cipher-texts, novel functional units to accelerate key kernels, and new algorithms and compiler techniques to reduce data movement. We evaluate CraterLake on deep FHE programs, including deep neural networks like ResNet and LSTMs, where prior work takes minutes to hours per inference on a CPU. CraterLake outperforms a CPU by gmean 4,600X and the best prior FHE accelerator by 11.2X under similar area and power budgets. These speeds enable realtime performance on unbounded FHE programs for the first time.
Hydra: enabling low-overhead mitigation of row-hammer at ultra-low thresholds via hybrid tracking

DRAM systems continue to be plagued by the Row-Hammer (RH) security vulnerability. The threshold number of row activations (TRH) required to induce RH has reduced rapidly from 139K in 2014 to 4.8K in 2020, and TRH is expected to reduce further, making RH even more severe for future DRAM. Therefore, solutions for mitigating RH should be effective not only at current TRH but also at future TRH. In this paper, we investigate the mitigation of RH at ultra-low thresholds (500 and below). At such thresholds, state-of-the-art solutions, which rely on SRAM or CAM for tracking row activations, incur impractical storage overheads (340KB or more per rank at TRH of 500), making such solutions unappealing for commercial adoption. Alternative solutions, which store per-row metadata in the addressable DRAM space, incur significant slowdown (25% on average) due to extra memory accesses, even in the presence of metadata caches. Our goal is to develop scalable RH mitigation while incurring low SRAM and performance overheads. To that end, this paper proposes Hydra, a Hybrid Tracker for RH mitigation, which combines the best of both SRAM and DRAM to enable low-cost mitigation of RH at ultra-low thresholds. Hydra consists of two structures. First, an SRAM-based structure that tracks aggregated counts at the granularity of a group of rows, and is sufficient for the vast majority of rows that receive only a few activations. Second, a per-row tracker stored in the DRAM-array, which can track an arbitrary number of rows, however, to limit performance overheads, this tracker is used only for the small number of rows that exceed the tracking capability of the SRAM-based structure. We provide a security analysis of Hydra to show that Hydra can reliably issue a mitigation within the specified threshold. Our evaluations show that Hydra enables robust mitigation of RH, while incurring an SRAM overhead of only 28 KB per-rank and an average slowdown of only 0.7% (at TRH of 500).
Partial Functional Linear Regression Model Based on Beijing PM2.5 Data Set

In view of the problem that missing PM2.5 data in Beijing, a partial functional linear regression model with missing data was proposed to fit the data to explore how wind speed and temperature affect PM2.5 concentration. By using the method of functional principal component analysis, the estimator of the bivariate parameter function is obtained, and the empirical logarithm likelihood ratio statistics of the linear partial parameters are constructed by using the inverse probability weighting method. The index function R(t) was used to access the fitting results of the data set, and it was concluded that the partial functional linear regression model with missing data had a better fitting effect on the data set, could deal with PM2.5 missing data more flexibly and accurately, and could specifically explain the practical significance of the data.
Identification of stimulus-response relations for cultured neuronal networks using features of multiple temporal resolution levels

In recent years cultured neuronal networks have been used with the aim of unraveling how biological information transmits between neurons. Investigating the evoked activities of cultured neuronal networks helps acquire a better understanding of neural decoding. However, it is still challenging to quantitatively describe and predict evoked patterns for them. This study focuses on evoked patterns of cultured neuronal networks and aims to identify stimulus-response relations with extracted feature sets including spike-based and rate-based features. The majority of neural information is encoded in evoked activities of the post-stimulus intervals. By partitioning post-stimulus intervals, features with multiple temporal resolution levels were constructed. This study investigates the impact of temporal resolution level on accuracy in recognizing stimulus-response relations.
VoiceFind: Noise-Resilient Speech Recovery in Commodity Headphones

Robust speech enhancement is a key requirement for many emerging applications. It is challenging to recover clear speech in commodity devices, especially in noisy real-world scenarios. In this paper, we propose VoiceFind, which uses only two microphones to spatial filter the desired speech from all interference. Furthermore, to improve the intelligibility of the speech after filtering, we design a Conditional Generative Adversarial Network (cGAN) to reconstruct the desired speech from environmental noises and interference speeches. This is an early attempt to explore this direction. Results from simulation and real-world experiments show promise.
Where are the Large N Studies in Education?: Introducing a Dataset of Scientific Articles and NLP Techniques

Research, especially in Education, is hampered by scale and our aim is to help shift this dynamic by tracking relevant studies from major scientific venues. The current version of our dataset considers top conferences and journals from the domain (N = 33,531). Several heuristics using advanced text searches and regular expressions, together with NLP techniques ranging from part of speech tagging, syntactic dependency parsing, to semantic models, are employed to extract relevant studies. Our method achieves an F1 score of . 633 when employed on a manually annotated subset of 1,000 articles. When applied to the entire dataset, the total number of articles with large N was around 10%, with a positive trend in the last years. L@S was by far the venue with the highest density of identified articles, thus arguing for its emphasis on scale. Further filtering of the articles is required when focusing only on learning, as the articles span across multiple domains and have multiple interests. Nonetheless, the order of magnitude raises current problems, namely that these studies are scarce and future endeavors should emphasize the importance of scale. Our dataset, including the validation subset, and the corresponding code for data crawling, PDF processing, and large N extraction mechanisms, have been open-sourced to further support the initiative and stimulate new analyses.
Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network

Graph neural network (GNN) is a promising emerging application for link prediction, recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM-GNN), however, the enterprises usually adopt huge graph with large-scale distributed GNN (LSD-GNN) that has to be carried out with distributed in-memory storage. The LSD-GNN is very different from SM-GNN in terms of system architecture demand, workflow and operators, and hence characterizations. In this paper, we first quantitively characterize the LSD-GNN with industrial-grade framework and application, summarize that its challenges lie in graph sampling, including distributed graph access, long latency, and underutilized communication and memory bandwidth. These challenges are missing from previous SM-GNN targeted researches. We then propose a customized hardware architecture to solve the challenges, including a fully pipelined access engine architecture for graph access and sampling, a low-latency and bandwidth-efficient customized memory-over-fabric hardware, and a RISC-V centric control system providing good programma-bility. We implement the proposed architecture with full software support in a 4-card FPGA heterogeneous proof-of-concept (PoC) system. Based on the measurement result from the FPGA PoC, we demonstrate a single FPGA can provide up to 894 vCPU's sampling capability. With the goal of being profitable, programmable, and scalable, we further integrate the architecture to FPGA cloud (FaaS) at hyperscale, along with the industrial software framework. We explicitly explore eight FaaS architectures that carry out the proposed accelerator hardware. We finally conclude that off-the-shelf FaaS.base can already provide 2.47× performance per dollar improvement with our hardware. With architecture optimizations, FaaS.comm-opt with customized FPGA fabrics pushes the benefit to 7.78×, and FaaS.mem-opt with FPGA local DRAM and high-speed links to GPU further unleash the benefit to 12.58×.
Accelerating database analytic query workloads using an associative processor

Database analytic query workloads are heavy consumers of data-center cycles, and there is constant demand to improve their performance. Associative processors (AP) have re-emerged as an attractive architecture that offers very large data-level parallelism that can be used to implement a wide range of general-purpose operations. Associative processing is based primarily on efficient search and bulk update operations. Analytic query workloads benefit from data parallel execution and often feature both search and bulk update operations. In this paper, we investigate how amenable APs are to improving the performance of analytic query workloads. For this study, we use the recently proposed Content-Addressable Processing Engine (CAPE) framework. CAPE is an AP core that is highly programmable via the RISC-V ISA with standard vector extensions. By mapping key database operators to CAPE and introducing AP-aware changes to the query optimizer, we show that CAPE is a good match for database analytic workloads. We also propose a set of database-aware microarchitectural changes to CAPE to further improve performance. Overall, CAPE achieves a 10.8× speedup on average (up to 61.1×) on the SSB benchmark (a suite of 13 queries) compared to an iso-area aggressive out-of-order processor with AVX-512 SIMD support.
LEGenT: Localizing Errors and Generating Testcases for CS1

In a CS1 course, testcases are the most common way of providing feedback. However, manually designed testcases, even if carefully crafted, may miss out on certain crucial corner cases. These testcases are only generated once for the whole class and do not take into account errors generated by specific students. This paper presents LEGenT, an automated tool that generates personalized testcases for student submission. LEGenT first localizes a statement in the program that causes deviation from the expected behaviour. Then it generates testcases that expose the deviation to the student. Our premise is that such a targeted test would help students identify one of the early reasons for the deviation. LEGenT works by separating buggy programs from the correct ones using an off-the-shelf formal equivalence checker. It uses another off-the-shelf tool to cluster buggy as well as correct programs. It aligns an incorrect program cluster with a nearby correct program cluster to generate relevant testcases for the incorrect program. We have evaluated our technique on 26082 (2764 correct + 23318 buggy) real student submissions across 11 different programming problems. LEGenT was able to localize an erroneous statement and generated testcases for 7696 buggy submissions. LEGenT fails to generate a testcase when the buggy submission is nowhere close to any correct submission. For the cases where LEGenT successfully generated testcases, we use a novel validation method to prove its accuracy, that LEGenT correctly identifies a cause of deviation from the correct behaviour.
Anticipating and eliminating redundant computations in accelerated sparse training

Deep Neural Networks (DNNs) are the state of art in image, speech, and text processing. To address long training times and high energy consumption, custom accelerators can exploit sparsity, that is zero-valued weights, activations, and gradients. Proposed sparse Convolution Neural Network (CNN) accelerators support training with no more than one dynamic sparse convolution input. Among existing accelerator classes, the only ones supporting two-sided dynamic sparsity are outer-product-based accelerators. However, when mapping a convolution onto an outer product, multiplications occur that do not correspond to any valid output. These Redundant Cartesian Products (RCPs) decrease energy efficiency and performance. We observe that in sparse training, up to 90% of computations are RCPs resulting from the convolution of large matrices for weight updates during the backward pass of CNN training. In this work, we design a mechanism, ANT, to anticipate and eliminate RCPs, enabling more efficient sparse training when integrated with an outer-product accelerator. By anticipating over 90% of RCPs, ANT achieves a geometric mean of 3.71× speed up over an SCNN-like accelerator [67] on 90% sparse training using DenseNet-121 [38], ResNet18 [35], VGG16 [73], Wide ResNet (WRN) [85], and ResNet-50 [35], with 4.40× decrease in energy consumption and 0.0017mm2 of additional area. We extend ANT to sparse matrix multiplication, so that the same accelerator can anticipate RCPs in sparse fully-connected layers, transformers, and RNNs.
Integrating Dynamic Supports into an Equity Teaching Simulation to Promote Equity Mindsets

Implementing high-quality professional learning on diversity, equity, and inclusion (DEI) issues is a massive scaling challenge. Integrating dynamic support using natural language processing (NLP) into equity teaching simulations may allow for more responsive, personalized training in this field. In this study, we trained machine learning models on participants' text responses in an equity teaching simulation (494 users; 988 responses) to detect certain text features related to equity. We then integrated these models into the simulation to provide dynamic supports to users during the simulation. In a pilot study (N = 13), we found users largely thought the feedback was accurate and incorporated the feedback in subsequent simulation responses. Future work will explore replicating these results with larger and more representative samples.
The effect of ethanol on B-DNA in salt solution through Molecular Dynamics Simulations

DNA is an important biomacromolecule in biological cell. The researches of DNA have a significant meaning in the fields of DNA drugs and DNA nanotechnology. The differences of solution environment will have great effect on the DNA. In pure water, B-DNA is the stable structure of DNA. To explore the effect of ethanol on B-DNA in salt solution, a series of molecular dynamics simulations have been performed on 171D DNA with the sequence of (CGCGAATTCGCG). The results show that ethanol molecules have a great effect on the DNA structural stability in the MgCl2 salt solution. The stability of DNA structure is gradually declined with the increasing of the concentration of ethanol. The DNA structural changes always occur at the both ends of DNA chain, rather than the central part. In high concentration of ethanol, the combination between metal ions and phosphate groups is great stronger which affect the interaction between electropositive metal ions and the electronegative phosphate groups of DNA. For multiple DNA chains in salt solution, the presence of ethanol makes a contribution to the aggregation of DNA chains in solution.
Food, Mood, Context: Examining College Students’ Eating Context and Mental Well-being

Deviant eating behavior such as skipping meals and consuming unhealthy meals has a significant association with mental well-being in college students. However, there is more to what an individual eats. While eating patterns form a critical component of their mental well-being, insights and assessments related to the interplay of eating patterns and mental well-being remain under-explored in theory and practice. To bridge this gap, we use an existing real-time eating detection system that captures context during meals to examine how college students’ eating context associates with their mental well-being, particularly their affect, anxiety, depression, and stress. Our findings suggest that students’ irregularity or skipping meals negatively correlates with their mental well-being, whereas eating with family and friends positively correlates with improved mental well-being. We discuss the implications of our study in designing dietary intervention technologies and guiding student-centric well-being technologies.
Re-envisioning a K-12 Early Warning System with School Climate Factors

The Every Student Succeeds Act (ESSA) prescribes holistic measures of schools for student success and well-being. However, many early warning systems rely exclusively on the "Attendance, Behavior, Course" (ABC) taxonomy, which misses potentially crucial determinants such as school climate and students' socioemotional learning. We report early findings from a larger project that aims to apply machine learning methods to improve an early warning system by incorporating factors related to school climate and socioemotional learning. These preliminary analyses suggest that a culturally inclusive, socially supportive, and emotionally and physically safe school climate is related to academic success and fewer engagement/behavior problems at the school level. They suggest the promise of integrating these features into early warning systems to help schools change their practices to better support student well-being.
Securing GPU via region-based bounds checking

Graphics processing units (GPUs) have become essential general-purpose computing platforms to accelerate a wide range of workloads, such as deep learning, scientific, and high-performance computing (HPC) applications. However, recent memory corruption attacks, such as buffer overflow, exposed security vulnerabilities in GPUs. We demonstrate that out-of-bounds writes are reproducible on an Nvidia GPU, which can enable other security attacks. We propose GPUShield, a hardware-software cooperative region-based bounds-checking mechanism, to improve GPU memory safety for global, local, and heap memory buffers. To achieve effective protection, we update the GPU driver to assign a random but unique ID to each buffer and local variable and store individual bounds information in the bounds table allocated in the global memory. The proposed hardware performs efficient bounds checking by indexing the bounds table with unique IDs. We further reduce the bounds-checking overhead by utilizing compile-time bounds analysis, workgroup/warp-level bounds checking, and GPU-specific address mode. Our performance evaluations show that GPUShield incurs little performance degradation across 88 CUDA benchmarks on the Nvidia GPU architecture and 17 OpenCL benchmarks on the Intel GPU architecture with a marginal hardware overhead.