Reviewerly/exHarmony huggingface dataset:
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks

We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparsedense matrix multiplication under a single operation called FusedMM. By using user-defined functions, FusedMM can capture almost all computational patterns needed by popular graph embedding and GNN approaches.FusedMM is an order of magnitude faster than its equivalent kernels in Deep Graph Library. The superior performance of FusedMM comes from the low-level vectorized kernels, a suitable load balancing scheme and an efficient utilization of the memory bandwidth. FusedMM can tune its performance using a code generator and perform equally well on Intel, AMD and ARM processors. FusedMM speeds up an end-to-end graph embedding algorithm by up to 28 x on different processors. The source code is available at https://github.com/HipGraph/FusedMM.
Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

Power and energy is the first-class design constraint for multi-core processors and is a limiting factor for future-generation supercomputers. While modern processor design provides a wide range of mechanisms for power and energy optimization, it remains unclear how software can make the best use of them. This article presents a novel approach for runtime power optimization on modern multi-core systems. Our policy combines power capping and uncore frequency scaling to match the hardware power profile to the dynamically changing program behavior at runtime. We achieve this by employing reinforcement learning (RL) to automatically explore the energy-performance optimization space from training programs, learning the subtle relationships between the hardware power profile, the program characteristics, power consumption and program running times. Our RL framework then uses the learned knowledge to adapt the chip's power budget and uncore frequency to match the changing program phases for any new, previously unseen program. We evaluate our approach on two computing clusters by applying our techniques to 11 parallel programs that were not seen by our RL framework at the training stage. Experimental results show that our approach can reduce the system-level energy consumption by 12 percent, on average, with less than 3 percent of slowdown on the application performance. By lowering the uncore frequency to leave more energy budget to allow the processor cores to run at a higher frequency, our approach can reduce the energy consumption by up to 17 percent while improving the application performance by 5 percent for specific workloads.
Deepfake Detection Using Robust Spatial and Temporal Features from Facial Landmarks

Most current deepfake detectors may suffer the decrease of detection accuracy under common video processing like compression. To deal with this issue, we proposed a new way of using biometric features for robust deepfake detection. Our biometric features are derived from a set of selected facial landmarks. We first presented a metric to select robust facial landmarks, and then constructed facial feature vectors with the selected landmarks. The spatial angles and the temporal rotation angles are introduced to facilitate the construction of the SVM feature vector. In essence, we use the spatial angles and temporal rotation angles to characterize the inherent consistency of facial landmarks at both frame level and video level. Experimental results have demonstrated that our detector has the best robustness compared with 6 current methods. It also has good scores in AUC (Area Under the Receiver Operating Characteristic Curve) and detection accuracy.
Semi-synthesis: A fast way to produce effective datasets for stereo matching

Stereo matching is an important problem in computer vision which has drawn tremendous research attention for decades. Recent years, data-driven methods with convolutional neural networks (CNNs) are continuously pushing stereo matching to new heights. However, data-driven methods require large amount of training data, which is not an easy task for real stereo data due to the annotation difficulties of per-pixel ground-truth disparity. Though synthetic dataset is proposed to fill the gaps of large data demand, the fine-tuning on real dataset is still needed due to the domain variances between synthetic data and real data. In this paper, we found that in synthetic datasets, close-to-real-scene texture rendering is a key factor to boost up stereo matching performance, while close-to-real-scene 3D modeling is less important. We then propose semi-synthetic, an effective and fast way to synthesize large amount of data with close-to-real-scene texture to minimize the gap between synthetic data and real data. Extensive experiments demonstrate that models trained with our proposed semi-synthetic datasets achieve significantly better performance than with general synthetic datasets, especially on real data benchmarks with limited training data. With further fine-tuning on the real dataset, we also achieve SOTA performance on Middlebury and competitive results on KITTI and ETH3D datasets.
Delta Sampling R-BERT for limited data and low-light action recognition

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset [60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.
A Gendered Perspective on Making from an Autoethnography in Makerspaces

The purpose of this pictorial is to provide a unique perspective, through an autoethnographic account, of the experiences a woman of colour may have, as she seeks to enter into the world of makerspaces. As an intersectional woman, conducting this autoethnography is a means to assess the cultural and gender diversity of makerspaces in an Australian metro city, and their practices to foster inclusivity. Over a period of 6 months, the lead author explored 4 different types of makerspaces. The autoethnography found cultural and gendered roles and biases in the physical and socio-cultural environments of each makerspace, as well as in terminologies, tools, materials and learning pathways that constrained her involvement. The research identifies important factors that could be amplified to increase the involvement of intersectional women and the foundations for new guidelines and activities that would enable makerspaces to improve their diversity and inclusivity.
Best Match Graphs with Binary Trees

Best match graphs (BMG) are a key intermediate in graph-based orthology detection and contain a large amount of information on the gene tree. We provide a near-cubic algorithm to determine whether a BMG can be explained by a fully resolved gene tree and, if so, to construct such a tree. Moreover, we show that all such binary trees are refinements of the unique binary-resolvable tree (BRT), which in general is a substantial refinement of the also unique least resolved tree of a BMG.
Development and application of hybrid teaching-learning genetic algorithm in fuel reloading optimization

The problems of fuel reloading optimization is a complex multi-objective discrete optimization problem with multiple local optimums. In recent years, great process has been made in solving these problems by using meta-heuristic optimization algorithms. Teaching-learning based optimization algorithm (TLBO) is one kind of novel meta-heuristic optimization algorithms. However, it is seldom used to solve the problems of fuel reloading optimization, because its original purpose is to solve continuous optimization problems. In this paper, a hybrid teaching-learning genetic algorithm (HTLGA) is developed, which could be directly applied to solve the problems of fuel reloading optimization. This hybrid algorithm takes TLBO as main part, combines three operators of genetic algorithms (GA) which are coding, crossover and mutation. The optimization solutions which are represented as students in TLBO are further divided into top students, ordinary students and poor students in HTLGA. The calculation phases “Teacher phase” and “Learner phase” in TLBO are improved into “Teacher phase”, “Discussion phase” and “Self-study phase” in HTLGA. For testing the optimization ability of HTLGA, it is applied to solve the problems of fuel reloading optimization for the 1/6 core of thorium-based block-type HTGRs. The results showed that the developed HTLGA has more powerful optimization ability than TLBO and GA.
Efficient GPU Sharing for Serverless Workflows

Serverless computing has emerged as a new cloud computing paradigm, where an application consists of individual functions that can be separately managed and executed. However, the function development environment of all serverless computing frameworks at present is CPU-based. In this paper, we propose to extend the open-sourced KNIX high-performance serverless framework so that it can execute functions on shared GPU cluster resources. We have evaluated the performance impacts on the extended KNIX system by measuring overheads and penalties incurred using different deep learning frameworks.
Taxonomy of cyber security metrics to measure strength of cyber security

Cyber security is guarding computer systems, data, network and other resources from unauthorized access and malicious users. There are no direct methods of measuring strength of cyber security. As they say, “You can’t manage what you can’t measure”. One can easily track the efforts taken for security through cyber security metrics. Being a quantifiable measure, metric can be utilized in tracking the status of a specific process and assess its outcomes along with its strength. This work aims to provide taxonomy of cyber security metrics with five basic metrics, along with the tools under Multi Criteria Decision making approach can be used in evaluation of cyber security strength.
Escaping the Time Pit: Pitfalls and Guidelines for Using Time-Based Git Data

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents the first survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 690 technical track and data papers published in MSR 2004-2020, we saw at least 35% of papers utilized time-based data. We then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty commit timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories.
The Topological Mu-Calculus: completeness and decidability

We study the topological μ-calculus, based on both Cantor derivative and closure modalities, proving completeness, decidability and FMP over general topological spaces, as well as over T0 and TD spaces. We also investigate relational μ-calculus, providing general completeness results for all natural fragments of μ-calculus over many different classes of relational frames. Unlike most other such proofs for μ-calculus, ours is modeltheoretic, making an innovative use of a known Modal Logic method (-the 'final' submodel of the canonical model), that has the twin advantages of great generality and essential simplicity.
On the Application of Binary Neural Networks in Oblivious Inference

This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the model parameters. We make two contributions to this field. First, we devise light-weight cryptographic protocols designed specifically to exploit the unique characteristics of BNNs. Second, we present dynamic exploration of the runtime-accuracy tradeoff of BNNs in a single-shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under different computational budgets. Compared to Crypt-Flow2, the state-of-the-art in oblivious inference of non-binary DNNs, our approach reaches 2× faster inference at the same accuracy. Compared to XONN, the state-of-the-art in oblivious inference of binary networks, we achieve 2× to 11× faster inference while obtaining higher accuracy.
Anomaly detection of power line insulator from aerial imagery with attribute self-supervised learning

Unmanned aerial vehicles (UAVs) can conveniently capture the insulator images in aerial scenes. With the aerial insulator images, we can effectively conduct insulator status inspections. However, the insulator anomaly detection is challenging with low accuracy due to the complex background in insulator images as well as the insulator variety. To cope with this problem, we propose a novel insulator anomaly detection method. Specifically, we first use the Generative Adversarial Network (GAN) to coarsely detect insulator defects based on the reconstruction error of insulator area. Then, we incorporate the foreground attribute learning and structure attribute learning to dynamically improve our model’s sensitivity for detecting the insulator defects. The foreground attribute learning aims to highlight the foreground regions of aerial insulator images, which makes the image features more robust to the background interference. Also, by using the structure attribute learning method, our model can learn normal structure pattern of insulators more effectively, increasing the ability to distinguish the abnormal sample. With these strategies, the proposed model reduces the influence of background interference and becomes more discriminative to the insulator defects. Extensive experiments on one real-world UAV images dataset have demonstrated the effectiveness of the proposed method for insulator anomaly detection.
A generative adversarial network for single and multi-hop distributional knowledge base completion

Knowledge bases (KBs) inherently lack reasoning ability, limiting their effectiveness for tasks such as question–answering and query expansion. Machine-learning is hence commonly employed for representation learning in order to learn semantic features useful for generalization. Most existing methods utilize discriminative models that require both positive and negative samples to learn a decision boundary. KBs, by contrast, contain only positive samples, necessitating that negative samples are generated by replacing the head/tail of predicates with randomly-chosen entities. They are thus frequently easily discriminable from positive samples, which can prevent learning of sufficiently robust classifiers. Generative models, however, do not require negative samples to learn the distribution of positive samples; stimulated by recent developments in Generative Adversarial Networks (GANs), we propose a novel framework, Knowledge Completion GANs (KCGANs), for competitively training generative link prediction models against discriminative belief prediction models. KCGAN thus invokes a game between generator-network G and discriminator-network D in which G aims to understand underlying KB structure by learning to perform link prediction while D tries to gain knowledge about the KB by learning predicate/triplet classification. Two key challenges are addressed: 1) Classical GAN architectures’ inability to easily generate samples over discrete entities; 2) the inefficiency of softmax for learning distributions over large sets of entities. As a step toward full first-order logical reasoning we further extend KCGAN to learn multi-hop logical entailment relations between entities by enabling G to compose a multi-hop relational path between entities and D to discriminate between real and fake paths. KCGAN is tested on benchmarks WordNet and FreeBase datasets and evaluated on link prediction and belief prediction tasks using MRR and [email protected] 10, achieving best-in-class performance.
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling

We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs. Despite recent advances in single person motion transfer, prior methods often require a large number of training images and take long training time. One promising direction is to perform few-shot human motion transfer, which only needs a few of source images for appearance transfer. However, it is particularly challenging to obtain satisfactory transfer results. In this paper, we address this issue by rendering a human texture map to a surface geometry (represented as a UV map), which is personalized to the source person. Our geometry generator combines the shape information from source images, and the pose information from 2D keypoints to synthesize the personalized UV map. A texture generator then generates the texture map conditioned on the texture of source images to fill out invisible parts. Furthermore, we may fine-tune the texture map on the manifold of the texture generator from a few source images at the test time, which improves the quality of the texture map without over-fitting or artifacts. Extensive experiments show the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively. Our code is available at https://github.com/HuangZhiChao95/FewShotMotionTransfer.
SapiMouse: Mouse Dynamics-based User Authentication Using Deep Feature Learning

The increasing interest in the analysis of mouse-based human-computer interaction may be attributed to prominent applications, such as user authentication and bot detection based on mouse dynamics. The aim of our paper is to present the SapiMouse dataset that can be used for training and evaluation of both user authentication and bot detection systems. In this paper we present the tools and protocols used for data collection, as well as the exploratory analysis of this new dataset. In addition, we also present user authentication results on this new dataset. Instead of using handcrafted features, our system learns the features directly from raw data using a convolutional neural network. The performance of our system is 0.94 AUC for 15 seconds of data.
Healthcare Chatbot System using Artificial Intelligence

This paper presents Healthcare Chatbot using Artificial Intelligence that can make a human-system interaction to resolve basic queries regarding health parameters before consulting a doctor. The actual purpose behind this work is to work on the user's symptoms and to provide medical suggestions according to it, to reduce the time and cost required for the process. It is a system that communicates with users using Natural Language processing rather we can say it is a computer program that uses NLP for interaction. The chatbot works on provided input by the user, It takes sentence keywords and makes decisions to solve the user's query and answers it accordingly. Some of these calculations like Rank calculation and sentence similarity are performed with the help of TF-IDF, Stemming, n-grams and cosine similarity. Basically, our system uses machine learning techniques to solve user's queries regarding healthcare by using suitable algorithms. The system allows users to create their profile to specify their symptoms, to suggest doctors and dosage reminders. This chatbot can be used by normal humans in any kind of emergency situation where it can play the role of adviser to people about primary care before consulting a doctor or sometimes it will work as a doctor for minor and short term health issues like cold, headache, etc. Along with this chatbot will be a support for the needy who require urgent solutions. A user can be able to recognize the actual disease by providing symptoms of it. As if a person will also know about the solutions or we can say precautions and remedies that they should take accordingly.
Strategy for Implementing Immersive Technologies in the Professional Training Process of Future Designers

Abstract The article is devoted to the formulation of a strategy for studying immersive technologies in the professional training process of future designers. To formulate the strategy, we reviewed current research in the field of design education on the use of augmented or virtual reality. Based on the analysis of scientific and methodological literature, the features of implementing the study of immersive technologies in the process of professional training of designers are described. There were reviewed educational programs of leading Ukrainian and foreign higher educational institutions in the specialty “design” using SWOT analysis. The strategy for studying augmented reality technology is formulated on the example of the educational program “Graphic Design”. Based on the results of the study, it is concluded that immersive technologies can be successfully implemented in higher education institutions with a developed infrastructure, qualified personnel and students with sufficiently developed multimedia competence. The future prospects of the study are connected with the conduction of an experimental study in order to find out the effectiveness of applying the proposed strategy for the implementation of immersive technologies on the example of the educational program “Graphic Design”.
Collecting Geospatial Data with Local Differential Privacy for Personalized Services

Geospatial data provides a lot of benefits for personalized services. However, since the geospatial data contains sensitive information about personal activities, collecting the raw data has a potential risk of leaking private information from the collectors. Recently, local differential privacy (LDP), which protects the privacy of users without trusting the collector, has been adopted to preserve privacy in many real applications. However, most of existing LDP algorithms focus on obtaining aggregated values such as mean and histogram from the collected data. In this paper, we investigate the problem of collecting the locations of individual users under LDP, and propose a perturbation mechanism designed carefully to reduce the error of each perturbed location according to the privacy budget and the domain size. In addition, we show the effectiveness of the proposed algorithm through experiments on various real datasets.