OpenRaiser/Intern-Atlas huggingface dataset:
The Impact of Remote Pair Programming in an Upper-Level CS Course
Venue: arXiv cs.CY 2022
Year: 2022

Pair programming has been highlighted as an active learning technique with several benefits to students, including increasing participation and improving outcomes, particularly for female computer science students. However, most of the literature highlights the effects of pair programming in introductory courses, where students have varied levels of prior programming experience and thus may experience related group issues. This work analyzes the effect of pair programming in an upper-level computer science course, where students have a more consistent background education, particularly in languages learned and best practices in coding. Secondly, the effect of remote pair programming on student outcomes is still an open question and one of increasing importance with the advent of Covid-19. This work utilized split sections with a control and treatment group in a large, public university. In addition to comparing pair programming to individual programming, results were analyzed by modality (remote vs. in person) and by gender, focusing on how pair programming benefits female computer science students in confidence, persistence in the major, and outcomes. We found that pair programming groups scored higher on assignments and exams, that remote pair programming groups performed as well as in person groups, and that female students increased their confidence in asking questions in class and scored 12\% higher in the course when utilizing pair programming.
Faster Pattern Matching under Edit Distance
Venue: arXiv cs.DS 2022
Year: 2022

We consider the approximate pattern matching problem under the edit distance. Given a text $T$ of length $n$, a pattern $P$ of length $m$, and a threshold $k$, the task is to find the starting positions of all substrings of $T$ that can be transformed to $P$ with at most $k$ edits. More than 20 years ago, Cole and Hariharan [SODA'98, J. Comput.'02] gave an $\mathcal{O}(n+k^4 \cdot n/ m)$-time algorithm for this classic problem, and this runtime has not been improved since. Here, we present an algorithm that runs in time $\mathcal{O}(n+k^{3.5} \sqrt{\log m \log k} \cdot n/m)$, thus breaking through this long-standing barrier. In the case where $n^{1/4+\varepsilon} \leq k \leq n^{2/5-\varepsilon}$ for some arbitrarily small positive constant $\varepsilon$, our algorithm improves over the state-of-the-art by polynomial factors: it is polynomially faster than both the algorithm of Cole and Hariharan and the classic $\mathcal{O}(kn)$-time algorithm of Landau and Vishkin [STOC'86, J. Algorithms'89]. We observe that the bottleneck case of the alternative $\mathcal{O}(n+k^4 \cdot n/m)$-time algorithm of Charalampopoulos, Kociumaka, and Wellnitz [FOCS'20] is when the text and the pattern are (almost) periodic. Our new algorithm reduces this case to a new dynamic problem (Dynamic Puzzle Matching), which we solve by building on tools developed by Tiskin [SODA'10, Algorithmica'15] for the so-called seaweed monoid of permutation matrices. Our algorithm relies only on a small set of primitive operations on strings and thus also applies to the fully-compressed setting (where text and pattern are given as straight-line programs) and to the dynamic setting (where we maintain a collection of strings under creation, splitting, and concatenation), improving over the state of the art.
Fluently specifying taint-flow queries with fluentTQL
Venue: arXiv cs.PL 2022
Year: 2022

Previous work has shown that taint analyses are only useful if correctly customized to the context in which they are used. Existing domain-specific languages (DSLs) allow such customization through the definition of deny-listing data-flow rules that describe potentially vulnerable taint-flows. These languages, however, are designed primarily for security experts who are knowledgeable in taint analysis. Software developers consider these languages to be complex. This paper presents fluentTQL, a query language particularly for taint-flow. fluentTQL is internal Java DSL and uses a fluent-interface design. fluentTQL queries can express various taint-style vulnerability types, e.g. injections, cross-site scripting or path traversal. This paper describes fluentTQL's abstract and concrete syntax and defines its runtime semantics. The semantics are independent of any underlying analysis and allows evaluation of fluentTQL queries by a variety of taint analyses. Instantiations of fluentTQL, on top of two taint analysis solvers, Boomerang and FlowDroid, show and validate fluentTQL expressiveness. Based on existing examples from the literature, we implemented queries for 11 popular security vulnerability types in Java. Using our SQL injection specification, the Boomerang-based taint analysis found all 17 known taint-flows in the OWASP WebGoat application, whereas with FlowDroid 13 taint-flows were found. Similarly, in a vulnerable version of the Java PetClinic application, the Boomerang-based taint analysis found all seven expected taint-flows. In seven real-world Android apps with 25 expected taint-flows, 18 were detected. In a user study with 26 software developers, fluentTQL reached a high usability score. In comparison to CodeQL, the state-of-the-art DSL by Semmle/GitHub, participants found fluentTQL more usable and with it they were able to specify taint analysis queries in shorter time.
AutoCOR: Autonomous Condylar Offset Ratio Calculator on TKA-Postoperative Lateral Knee X-ray
Venue: arXiv cs.CV 2022
Year: 2022

The postoperative range of motion is one of the crucial factors indicating the outcome of Total Knee Arthroplasty (TKA). Although the correlation between range of knee flexion and posterior condylar offset (PCO) is controversial in the literature, PCO maintains its importance on evaluation of TKA. Due to limitations on PCO measurement, two novel parameters, posterior condylar offset ratio (PCOR) and anterior condylar offset ratio (ACOR), were introduced. Nowadays, the calculation of PCOR and ACOR on plain lateral radiographs is done manually by orthopedic surgeons. In this regard, we developed a software, AutoCOR, to calculate PCOR and ACOR autonomously, utilizing unsupervised machine learning algorithm (k-means clustering) and digital image processing techniques. The software AutoCOR is capable of detecting the anterior/posterior edge points and anterior/posterior cortex of the femoral shaft on true postoperative lateral conventional radiographs. To test the algorithm, 50 postoperative true lateral radiographs from Istanbul Kosuyolu Medipol Hospital Database were used (32 patients). The mean PCOR was 0.984 (SD 0.235) in software results and 0.972 (SD 0.164) in ground truth values. It shows strong and significant correlation between software and ground truth values (Pearson r=0.845 p<0.0001). The mean ACOR was 0.107 (SD 0.092) in software results and 0.107 (SD 0.070) in ground truth values. It shows moderate and significant correlation between software and ground truth values (Spearman's rs=0.519 p=0.0001412). We suggest that AutoCOR is a useful tool that can be used in clinical practice.
The Distressing Ads That Persist: Uncovering The Harms of Targeted Weight-Loss Ads Among Users with Histories of Disordered Eating
Venue: arXiv cs.HC 2022
Year: 2022

Targeted advertising can harm vulnerable groups when it targets individuals' personal and psychological vulnerabilities. We focus on how targeted weight-loss advertisements harm people with histories of disordered eating. We identify three features of targeted advertising that cause harm: the persistence of personal data that can expose vulnerabilities, over-simplifying algorithmic relevancy models, and design patterns encouraging engagement that can facilitate unhealthy behavior. Through a series of semi-structured interviews with individuals with histories of unhealthy body stigma, dieting, and disordered eating, we found that targeted weight-loss ads reinforced low self-esteem and deepened pre-existing anxieties around food and exercise. At the same time, we observed that targeted individuals demonstrated agency and resistance against distressing ads. Drawing on scholarship in postcolonial environmental studies, we use the concept of slow violence to articulate how online targeted advertising inflicts harms that may not be immediately identifiable. CAUTION: This paper includes media that could be triggering, particularly to people with an eating disorder. Please use caution when reading, printing, or disseminating this paper.
BIMxAR: BIM-Empowered Augmented Reality for Learning Architectural Representations
Venue: arXiv cs.HC 2022
Year: 2022

Literature review shows limited research investigating the utilization of Augmented Reality (AR) to improve learning and understanding architectural representations, specifically section views. In this study, we present an AR system prototype (BIMxAR), its new and accurate building-scale registration method, and its novel visualization features that facilitate the comprehension of building construction systems, materials configuration, and 3D section views of complex structures through the integration of AR, Building Information Modeling (BIM), and physical buildings. A pilot user study found improvements after students studied building section views in a physical building with AR, though not statistically significant, in terms of scores of the Santa Barbara Solids Test (SBST) and the Architectural Representations Test (ART). When incorporating time as a performance factor, the ART timed scores show a significant improvement in the posttest session. BIMxAR has the potential to enhance the students spatial abilities, particularly in understanding buildings and complex section views.
Transformer-Based Language Models for Software Vulnerability Detection
Venue: arXiv cs.CR 2022
Year: 2022

The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the transferability of the knowledge gained by these models in one domain to other related domains, and the closeness of natural languages to high-level programming languages, such as C/C++, this work studies how to leverage (large) transformer-based language models in detecting software vulnerabilities and how good are these models for vulnerability detection tasks. In this regard, firstly, a systematic (cohesive) framework that details source code translation, model preparation, and inference is presented. Then, an empirical analysis is performed with software vulnerability datasets with C/C++ source codes having multiple vulnerabilities corresponding to the library function call, pointer usage, array usage, and arithmetic expression. Our empirical results demonstrate the good performance of the language models in vulnerability detection. Moreover, these language models have better performance metrics, such as F1-score, than the contemporary models, namely bidirectional long short-term memory and bidirectional gated recurrent unit. Experimenting with the language models is always challenging due to the requirement of computing resources, platforms, libraries, and dependencies. Thus, this paper also analyses the popular platforms to efficiently fine-tune these models and present recommendations while choosing the platforms.
Accelerating Attention through Gradient-Based Learned Runtime Pruning
Venue: arXiv cs.CL 2022
Year: 2022

Self-attention is a key enabler of state-of-art accuracy for various transformer-based Natural Language Processing models. This attention mechanism calculates a correlation score for each word with respect to the other words in a sentence. Commonly, only a small subset of words highly correlates with the word under attention, which is only determined at runtime. As such, a significant amount of computation is inconsequential due to low attention scores and can potentially be pruned. The main challenge is finding the threshold for the scores below which subsequent computation will be inconsequential. Although such a threshold is discrete, this paper formulates its search through a soft differentiable regularizer integrated into the loss function of the training. This formulation piggy backs on the back-propagation training to analytically co-optimize the threshold and the weights simultaneously, striking a formally optimal balance between accuracy and computation pruning. To best utilize this mathematical innovation, we devise a bit-serial architecture, dubbed LeOPArd, for transformer language models with bit-level early termination microarchitectural mechanism. We evaluate our design across 43 back-end tasks for MemN2N, BERT, ALBERT, GPT-2, and Vision transformer models. Post-layout results show that, on average, LeOPArd yields 1.9x and 3.9x speedup and energy reduction, respectively, while keeping the average accuracy virtually intact (<0.2% degradation)
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Venue: arXiv cs.NE 2022
Year: 2022

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations, based on network prior knowledge, induce different search behaviors with similar characteristics to various perspectives. Specifically, it is difficult for users to determine a suitable transformation a priori. This article proposes a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and to avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted via: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, and 3) selecting the final output seed set containing all the transformation's knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. The experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed at https://github.com/xiaofangxd/MTEFIM.
Multi-Sample $ζ$-mixup: Richer, More Realistic Synthetic Samples from a $p$-Series Interpolant
Venue: arXiv cs.LG 2022
Year: 2022

Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information. A popular recent method, mixup, uses convex combinations of pairs of original samples to generate new samples. However, as we show in our experiments, mixup can produce undesirable synthetic samples, where the data is sampled off the manifold and can contain incorrect labels. We propose $ζ$-mixup, a generalization of mixup with provably and demonstrably desirable properties that allows convex combinations of $N \geq 2$ samples, leading to more realistic and diverse outputs that incorporate information from $N$ original samples by using a $p$-series interpolant. We show that, compared to mixup, $ζ$-mixup better preserves the intrinsic dimensionality of the original datasets, which is a desirable property for training generalizable models. Furthermore, we show that our implementation of $ζ$-mixup is faster than mixup, and extensive evaluation on controlled synthetic and 24 real-world natural and medical image classification datasets shows that $ζ$-mixup outperforms mixup and traditional data augmentation techniques.
Enabling All In-Edge Deep Learning: A Literature Review
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers. Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important in mission-critical applications, e.g., health care. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism and split learning, which facilitate DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented.
Implementing a Real-Time, YOLOv5 based Social Distancing Measuring System for Covid-19
Venue: arXiv cs.CV 2022
Year: 2022

The purpose of this work is, to provide a YOLOv5 deep learning-based social distance monitoring framework using an overhead view perspective. In addition, we have developed a custom defined model YOLOv5 modified CSP (Cross Stage Partial Network) and assessed the performance on COCO and Visdrone dataset with and without transfer learning. Our findings show that the developed model successfully identifies the individual who violates the social distances. The accuracy of 81.7% for the modified bottleneck CSP without transfer learning is observed on COCO dataset after training the model for 300 epochs whereas for the same epochs, the default YOLOv5 model is attaining 80.1% accuracy with transfer learning. This shows an improvement in accuracy by our modified bottleneck CSP model. For the Visdrone dataset, we are able to achieve an accuracy of upto 56.5% for certain classes and especially an accuracy of 40% for people and pedestrians with transfer learning using the default YOLOv5s model for 30 epochs. While the modified bottleneck CSP is able to perform slightly better than the default model with an accuracy score of upto 58.1% for certain classes and an accuracy of ~40.4% for people and pedestrians.
Energy Consumption and Performance of Heapsort in Hardware and Software
Venue: arXiv cs.SE 2022
Year: 2022

In this poster abstract we will report on a case study on implementing the Heapsort algorithm in hardware and software and comparing their time and energy consumption. Our experiment shows that the Hardware implementation is more energy efficient, but slower than the Software implementation due to a low clock frequency. It also indicate that the optimal degree of parallelization differs when optimizing for time compared to optimizing for time.
On the Price of Locality in Static Fast Rerouting
Venue: arXiv cs.DC 2022
Year: 2022

Modern communication networks feature fully decentralized flow rerouting mechanisms which allow them to quickly react to link failures. This paper revisits the fundamental algorithmic problem underlying such local fast rerouting mechanisms. Is it possible to achieve perfect resilience, i.e., to define local routing tables which preserve connectivity as long as the underlying network is still connected? Feigenbaum et al. (ACM PODC'12) and Foerster et al. (SIAM APOCS'21) showed that, unfortunately, it is impossible in general. This paper charts a more complete landscape of the feasibility of perfect resilience. We first show a perhaps surprisingly large price of locality in static fast rerouting mechanisms: even when source and destination remain connected by a linear number of link-disjoint paths after link failures, local rerouting algorithms cannot find any of them which leads to a disconnection on the routing level. This motivates us to study resilience in graphs which exclude certain dense minors, such as cliques or a complete bipartite graphs, and in particular, provide characterizations of the possibility of perfect resilience in different routing models. We provide further insights into the price of locality by showing impossibility results for few failures and investigate perfect resilience on Topology Zoo networks.
DeepXPalm: Tilt and Position Rendering using Palm-worn Haptic Display and CNN-based Tactile Pattern Recognition
Venue: arXiv cs.RO 2022
Year: 2022

Telemanipulation of deformable objects requires high precision and dexterity from the users, which can be increased by kinesthetic and tactile feedback. However, the object shape can change dynamically, causing ambiguous perception of its alignment and hence errors in the robot positioning. Therefore, the tilt angle and position classification problem has to be solved to present a clear tactile pattern to the user. This work presents a telemanipulation system for plastic pipettes consisting of a multi-contact haptic device LinkGlide to deliver haptic feedback at the users' palm and two tactile sensors array embedded in the 2-finger Robotiq gripper. We propose a novel approach based on Convolutional Neural Networks (CNN) to detect the tilt and position while grasping deformable objects. The CNN generates a mask based on recognized tilt and position data to render further multi-contact tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm and the preset mask, tilt, and position recognition by users is increased from 9.67% using the direct data to 82.5%.
Literature Review on Image Compression, Tracking, Adaptive Training and 3D Data Transmission
Venue: arXiv cs.MM 2022
Year: 2022

The literature review presented below on Image Compression, Transmission of 3D data over wireless networks and tracking of objects is the in depth study of Research Papers done in Multimedia lab. Most of the papers presented in this literature review have tackled the problems present in the conventional system and offered an optimal and practical solution.