Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Classifiers for Criminal Identification System Using Face Recognition

Computer vision and object detection software systems have evolved to solve problems that are hard to tackle. Many advances and research has been conducted in the field of object detection, facial tracking, face recognition etc. Security systems and biometrics remain an essential area where object detection and other techniques can be used. The main intention of this study is to develop an effective criminal identification system employing face detection and alignment using Multitask cascaded convolution neural networks, face embeddings using the application of “FaceNet” which harnesses the power of Convolution neural networks trained on a loss function and optimised using SGD method and finally a comparison of the performance of various Machine Learning classifiers to verify the identity of criminals. The dataset used here is the NIST mugshot dataset, which includes mugshots of offenders and lawbreakers. Tools such as precision, recall, accuracy and f1 scores are used to show the efficiency and working of the various models.
Multi-scale Mixed Reality Collaboration for Digital Twin

In this poster, we present a digital twin-based mixed reality system for remote collaboration with the size-scaling of the user and the space. The proposed system supports collaboration between an AR host user and a VR remote user by sharing a 3D digital twin of the AR host user. To enhance the coarse authoring of a shared digital twin environment, we provide a size scaling of the digital twin environment with the world-in-miniature view. Also, we enable scaling the size of the VR user’s avatar to enhance both coarse (size-up) and fine-grained (size-down) authoring of the digital twin environment. We describe the system setup, input methods, and interaction methods for scaling space and user.
Reverse Attack: Black-box Attacks on Collaborative Recommendation

Collaborative filtering (CF) recommender systems have been extensively developed and widely deployed in various social websites, promoting products or services to the users of interest. Meanwhile, work has been attempted at poisoning attacks to CF recommender systems for distorting the recommend results to reap commercial or personal gains stealthily. While existing poisoning attacks have demonstrated their effectiveness with the offline social datasets, they are impractical when applied to the real setting on online social websites. This paper develops a novel and practical poisoning attack solution toward the CF recommender systems without knowing involved specific algorithms nor historical social data information a priori. Instead of directly attacking the unknown recommender systems, our solution performs certain operations on the social websites to collect a set of sampling data for use in constructing a surrogate model for deeply learning the inherent recommendation patterns. This surrogate model can estimate the item proximities, learned by the recommender systems. By attacking the surrogate model, the corresponding solutions (for availability and target attacks) can be directly migrated to attack the original recommender systems. Extensive experiments validate the generated surrogate model's reproductive capability and demonstrate the effectiveness of our attack upon various CF recommender algorithms.
Bespoke Cache Enclaves: Fine-Grained and Scalable Isolation from Cache Side-Channels via Flexible Set-Partitioning

Cache partitioning is a principled defense against side-channel attacks on shared last-level caches (LLCs). Such defenses allocate isolated cache regions to distrusting applications and prevent a spy from monitoring the cache accesses of a victim. But current solutions have severe practical limitations. Way-partitioning is not scalable as the number of partitions is limited by cache associativity and page-coloring is inflexible as it requires coupled DRAM and LLC allocations in the same ratio. For cache partitioning to be practical, we need a scheme that can scale to a large number of fine-grained partitions and places no restrictions on DRAM allocations.This paper proposes Bespoke Cache Enclaves (BCE), a secure cache partitioning substrate that is scalable in supporting hundreds of isolated cache partitions and is flexible in allocating cache space independent of memory allocations. BCE allocates cache space at the granularity of a cluster, a group of a few sets (e.g., 64 KB in size). The key insight of BCE is a configurable cache indexing function (determining the line to set mapping) that guides cache lines of a domain to only the allocated cache sets, enabling flexible set-partitioning independent of memory allocations. BCE achieves this by modifying the cache indexing hardware to include a Cluster-Indirection Module (CIM), which maps logical-to-physical clusters of a domain and a Load-Balancing Hash (LBH), which uniformly distributes lines of a domain among its clusters. Our implementation of BCE with a 32MB 16-way LLC scalably supports up to 512 isolated partitions while incurring negligible storage overheads (<2%) and slowdown (1% on average) compared to a non-secure unpartitioned LLC.
Three birds, one stone: A novel translation based framework for joint entity and relation extraction

Joint entity and relation extraction is an important task in natural language processing and knowledge graph construction. Existing studies mainly focus on three issues: redundant predictions, overlapping triples and relation connections. However, as far as we know, none of them is able to solve the three problems simultaneously in a unified architecture. To address this issue, in this paper, we propose a novel translation based unified framework. Specifically, the proposed framework contains two components: an entity tagger and a relation extractor. The former is used to recognize all candidate head entities and tail entities respectively. The latter predicts relations for every entity pair dynamically through ranking with translation mechanism. To show the superiority of the proposed framework, we instantiate it through the simplest binary entity tagger and TransE algorithm. Extensive experiments over two widely used datasets demonstrate that, even with the simplest components, the proposed framework can still achieve competitive performance with most previous baselines. Moreover, the framework is flexible. It enjoys further performance boost when employing more powerful entity tagger and knowledge graph embedding algorithm.
Polynomial-based graph convolutional neural networks for graph classification

Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggregating scheme, to compute representations of graphs. The most common convolution operators only exploit local topological information. To consider wider topological receptive fields, the mainstream approach is to non-linearly stack multiple graph convolutional (GC) layers. In this way, however, interactions among GC parameters at different levels pose a bias on the flow of topological information. In this paper, we propose a different strategy, considering a single graph convolution layer that independently exploits neighbouring nodes at different topological distances, generating decoupled representations for each of them. These representations are then processed by subsequent readout layers. We implement this strategy introducing the polynomial graph convolution (PGC) layer, that we prove being more expressive than the most common convolution operators and their linear stacking. Our contribution is not limited to the definition of a convolution operator with a larger receptive field, but we prove both theoretically and experimentally that the common way multiple non-linear graph convolutions are stacked limits the neural network expressiveness. Specifically, we show that a graph neural network architecture with a single PGC layer achieves state of the art performance on many commonly adopted graph classification benchmarks.
Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir

The focus of this work is to design a deeply quantized anomaly detector of oil leaks that may happen at the junction between the wind turbine high-speed shaft and the external bracket of the power generator. We propose a block-based binary shallow echo state network (BBS-ESN) architecture belonging to the reservoir computing (RC) category and, as we believe, it also extends the extreme learning machines (ELM) domain. Furthermore, BBS-ESN performs binary block-based online training using fixed and minimal computational complexity to achieve low power consumption and deployability on an off-the-shelf micro-controller (MCU). This has been achieved through binarization of the images and 1-bit quantization of the network weights and activations. 3D rendering has been used to generate a novel publicly available dataset of photo-realistic images similar to those potentially acquired by image sensors on the field while monitoring the junction, without and with oil leaks. Extensive experimentation has been conducted using a STM32H743ZI2 MCU running at 480 MHz and the results achieved show an accurate identification of anomalies, with a reduced computational cost per image and memory occupancy. Based on the obtained results, we conclude that BBS-ESN is feasible on off-the-shelf 32 bit MCUs. Moreover, the solution is also scalable in the number of image cameras to be deployed and to achieve accurate and fast oil leak detections from different viewpoints.
Underwater image restoration by color compensation and color-line model

The images captured underwater suffer from low contrast and color distortion owing to the harsh underwater imaging environment. An underwater image restoration algorithm is introduced in this paper based on color compensation and color-line model. Firstly, color compensation method is proposed to compensate the attenuated color information and improve the accuracy of estimated color lines. Secondly, the relationship of three-channel transmission is derived from the association between the global background light and the inherent optical characteristics. Then, an underwater image local transmission optimization model is established in light of the color line law to acquire local transmission map. Finally, the restoration images are performed by inverting the form of the simplified imaging model. Experimental results show that our method is superior to other methods in subjective evaluation, objective evaluation, color accuracy tests and application tests. This method can obtain images with significantly enhanced visibility and higher color fidelity.
On-shelf utility mining from transaction database

As an important technique for dealing with transaction database in the field of data mining, utility-driven mining can be used to discover useful patterns (i.e., itemsets, sequences) which have a high utility. However, it has a bias towards the item/object combinations which have more exhibition period since they have more opportunity to generate a high utility. To address this, the on-shelf time period of items need to be considered, thus on-shelf utility mining (OSUM) can be applied in the application which is more closer to the actual situation. Currently several models have been proposed to deal with the OSUM problem, but they still suffer from the requirement that it needs to maintain a massive candidates in memory and to scan database many times. In this paper, we propose two effective one-phase algorithms named OSUMI (On-Shelf Utility Mining from transactIon database) and OSUMI+ (the improve version of OSUMI). Both OSUMI and OSUMI+ search all itemsets as a set-enumeration tree and discover the on-shelf itemsets with high utility in a more practical way. More precisely, in order to avoid the problems of high memory consumption, two algorithms apply some properties of the concept of on-shelf utility. Besides, two upper-bounds named subtree utility and local utility are applied to early filter out unpromising patterns and then prune the search space. Finally, an extensive experimental study on several real on-shelf datasets shows that our proposed algorithms can be significantly faster than the state-of-the-art algorithm.
WaC

Commercial machine learning accelerators like Intel neural Compute Stick 2 (NCS2) enable efficient inference on otherwise low resource edge devices. However, these accelerators are also exposed to new threats leveraging physical access. In this paper, we present the first results demonstrating practical electromagnetic side-channel attack on NCS2, allowing secret weight recovery from executed models.
Detecting Missed Security Operations Through Differential Checking of Object-based Similar Paths

Missing a security operation such as a bound check has been a major cause of security-critical bugs. Automatically checking whether the code misses a security operation in large programs is challenging since it has to understand whether the security operation is indeed necessary in the context. Recent methods typically employ cross-checking to identify deviations as security bugs, which collects functionally similar program slices and infers missed security operations through majority-voting. An inherent limitation of such approaches is that they heavily rely on a substantial number of similar code pieces to enable cross-checking. In practice, many code pieces are unique, and thus we may be unable to find adequate similar code snippets to utilize cross-checking.
A Study of Machine Translation Approaches for Gujarati to English Translation

In the world of computer research, machine translation is a hot topic. It is fundamental in NLP. Language translation is very important in India because it is a country with myriad languages. The aim of this paper is to look at several various types of machine translation techniques and see how they are used in different translation systems. We begin with some tried-and-true methods before moving on to more modern and hybrid ways that are still being perfected. We also compare these studies based on the following parameters such as the machine translation approaches used, the language pair taken into research and the papers key features. We then offer a language translation model using Hybrid Machine Translation systems to facilitate us in translating from Gujarati to English.
U-Shaped Attention Connection Network for Remote-Sensing Image Super-Resolution

In recent years, deep learning-based remote-sensing image super-resolution (SR) methods have made significant progress, and these methods require a large number of synthetic data for training. To obtain sufficient training data, researchers often generate synthetic data via fixed bicubic downsampling methods. However, the synthesized data cannot reflect the complex degradation process of real remote-sensing images. Thus, performance will dramatically reduce when these methods work in real low-resolution (LR) remote-sensing images. This letter proposes a U-shaped attention connection network (US-ACN) for remote-sensing image SR to solve this issue. Our US-ACN does not rely on any synthetic external dataset for training and merely requires one LR image to complete the training. The US-ACN utilizes remote-sensing images’ strong internal feature repetitiveness and fully learns this internal repetitive feature through a well-designed US-ACN to achieve the remote-sensing image SR. In addition, we design a 3-D attention module to generate effective 3-D weights by modeling channel and spatial attention weights, which is more helpful for the learning of internal features. Through the U-shaped connection among attention modules, context information propagation and attention weights learning are fully utilized. Many experiments show that our US-ACN adequately adapts to the remote-sensing image SR in various situations and performs advanced performance.
GHTRec: A Personalized Service to Recommend GitHub Trending Repositories for Developers

GitHub is one of the largest hosting service platforms for software development, which contains more than 40 million users and 100 million software repositories. GitHub provides a trending page to help software developers discover potential repositories during a period of time. Also, GitHub introduces a feature named “topic” to label repositories. However, GitHub does not explicitly provide user preference information. It is difficult for software developers to find personalized GitHub trending repositories satisfying their preferences. In this paper, we propose a service named GHTRec to recommend personalized GitHub trending repositories for software developers. First, we use a deep-learning method to predict topics for GitHub repositories. Next, we leverage the historical repositories committed by software developers to make recommendation of GitHub trending repositories. Then we evaluate our topic prediction model and recommendation service, and results show that our GHTRec service could recommend trending repositories satisfying developers' topic preferences.
Cleaning the NVD: Comprehensive Quality Assessment, Improvements, and Analyses

Vulnerability databases are vital sources of information on emergent software security concerns. Security professionals, from system administrators to developers to researchers, heavily depend on these databases to track vulnerabilities and analyze security trends. However, are these databases reliable and accurate? In this article, we explore this question with the National Vulnerability Database (NVD), the U.S. government's repository of vulnerability information that arguably serves as the industry standard. Through a systematic investigation, we uncover inconsistent or incomplete data in the NVD that can impact its practical uses, affecting information such as the vulnerability publication dates, applications affected by the vulnerability, their severity scores, and their high level type categorization. We explore the extent of these discrepancies and identify methods for their automated corrections. Finally, we demonstrate the impact that these data issues can pose by comparing analyses using the original and our rectified versions of the NVD. Ultimately, our investigation of the NVD not only produces an improved source of vulnerability information, but also provides important insights and guidance for the security community on the curation and use of such data sources.
Low Resource Neural Machine Translation: Assamese to/from Other Indo-Aryan (Indic) Languages

Machine translation (MT) systems have been built using numerous different techniques for bridging the language barriers. These techniques are broadly categorized into approaches like Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). End-to-end NMT systems significantly outperform SMT in translation quality on many language pairs, especially those with the adequate parallel corpus. We report comparative experiments on baseline MT systems for Assamese to other Indo-Aryan languages (in both translation directions) using the traditional Phrase-Based SMT as well as some more successful NMT architectures, namely basic sequence-to-sequence model with attention, Transformer, and finetuned Transformer. The results are evaluated using the most prominent and popular standard automatic metric BLEU (BiLingual Evaluation Understudy), as well as other well-known metrics for exploring the performance of different baseline MT systems, since this is the first such work involving Assamese. The evaluation scores are compared for SMT and NMT models for the effectiveness of bi-directional language pairs involving Assamese and other Indo-Aryan languages (Bangla, Gujarati, Hindi, Marathi, Odia, Sinhalese, and Urdu). The highest BLEU scores obtained are for Assamese to Sinhalese for SMT (35.63) and the Assamese to Bangla for NMT systems (seq2seq is 50.92, Transformer is 50.01, and finetuned Transformer is 50.19). We also try to relate the results with the language characteristics, distances, family trees, domains, data sizes, and sentence lengths. We find that the effect of the domain is the most important factor affecting the results for the given data domains and sizes. We compare our results with the only existing MT system for Assamese (Bing Translator) and also with pairs involving Hindi.
Prediction Using Knowledge Growing System: A Cognitive Artificial Intelligence Approach

In the Artificial Intelligence (AI) world, prediction is a means for recognizing a phenomenon. Three approaches have been used for this task, namely supervised, semi-supervised, and unsupervised. Data annotation or labeling is a must in the first two methods, while the last method clusters the inputted data without knowing the labels. The most basic for those methods is a training data set has to be provided at first and a massive amount of data for the supervised one. This paper used a new means called Cognitive Artificial Intelligence (CAI) Knowledge Growing System (KGS) for the prediction task. KGS is characterized by its cognitive learning capability to learn by interacting directly with the phenomenon. Therefore, it is prospective to overcome the existing methods in the context of past data providing and data recognizing through just-in-time labeling. We also show that KGS succeeds in predicting some data with 100% perfect accuracy.
A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization

The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that allows state-of-the-art alternating-type algorithms. To overcome this issue, we first split the decision variable and transform the symmetric NMF to a penalized nonsymmetric one, paving the way for designing efficient alternating-type algorithms. We then show that solving the penalized nonsymmetric reformulation returns a solution to the original symmetric NMF. Moreover, we design a family of alternating-type algorithms and show that they all admit strong convergence guarantee: the generated sequence of iterates is convergent and converges at least sublinearly to a critical point of the original symmetric NMF. Finally, we conduct experiments on both synthetic data and real image clustering to support our theoretical results and demonstrate the performance of the alternating-type algorithms.
Conformer-Based Hybrid ASR System For Switchboard Dataset

The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on different datasets. To our best knowledge, the impact of using conformer acoustic model for hybrid ASR is not investigated. In this paper, we present and evaluate a competitive conformer-based hybrid model training recipe. We study different training aspects and methods to improve worderror-rate as well as to increase training speed. We apply time downsampling methods for efficient training and use transposed convolutions to upsample the output sequence again. We conduct experiments on Switchboard 300h dataset and our conformer-based hybrid model achieves competitive results compared to other architectures. It generalizes very well on Hub5’01 test set and outperforms the BLSTM-based hybrid model significantly.