Reviewerly/exHarmony huggingface dataset:
A Hybrid Artificial Bee Colony Algorithmic Approach for Classification Using Neural Networks

Artificial neural networks are an integral component of most corporate and research functions across different platforms. However, depending upon the nature of the problem and quality of initialisation values, the usage of standard stochastic gradient descent always risks the possibility of getting trapped in local minima and saddle points for smaller neural networks in particular. One way to overcome this is by using algorithms with proven global search capabilities to train the network. This allows the neural net to reach the optimum values for weights regardless of the initialisation parameters used during training. Two algorithms are proposed based on modifications to the original artificial bee colony algorithm, and their performances are analysed extensively on three benchmark datasets of increasing complexity. The first (NMABC) employs neural network appropriate initialisation and linear search space expansion. This is integrated into the second (LHABC) which incorporates stochastic gradient descent into the employed phase of the bees for faster convergence. It is found that the proposed algorithms consistently outperform standard approaches in all cases.
Packed Multiplication: How to Amortize the Cost of Side-Channel Masking?

Higher-order masking countermeasures provide strong provable security against side-channel attacks at the cost of incurring significant overheads, which largely hinders its applicability. Previous works towards remedying cost mostly concentrated on "local" calculations, i.e., optimizing the cost of computation units such as a single AND gate or a field multiplication. This paper explores a complementary "global" approach, i.e., considering multiple operations in the masked domain as a batch and reducing randomness and computational cost via amortization. In particular, we focus on the amortization of $$\ell $$ parallel field multiplications for appropriate integer $$\ell > 1$$ , and design a kit named packed multiplication for implementing such a batch. For $$\ell +d\le 2^m$$ , when $$\ell $$ parallel multiplications over $$\mathbb {F}_{2^{m}}$$ with d-th order probing security are implemented, packed multiplication consumes $$d^2+2\ell d + \ell $$ bilinear multiplications and $$2d^2 + d(d+1)/2$$ random field variables, outperforming the state-of-the-art results with $$O(\ell d^2)$$ multiplications and $$\ell \left\lfloor d^2/4\right\rfloor + \ell d$$ randomness. To prove d-probing security for packed multiplications, we introduce some weaker security notions for multiple-inputs-multiple-outputs gadgets and use them as intermediate steps, which may be of independent interest. As parallel field multiplications exist almost everywhere in symmetric cryptography, lifting optimizations from "local" to "global" substantially enlarges the space of improvements. To demonstrate, we showcase the method on the AES Subbytes step, GCM and TET (a popular disk encryption). Notably, when $$d=8$$ , our implementation of AES Subbytes in ARM Cortex M architecture achieves a gain of up to $$33\%$$ in total speeds and saves up to $$68\%$$ random bits than the state-of-the-art bitsliced implementation reported at ASIACRYPT 2018.
Improving the Designs of Nearest Neighbour Quantum Circuits for 1D and 2D Architectures

In recent years, the semiconductor industry is increasingly considering alternative computation schemes and in this conjuncture quantum computing has come up as a promising alternative due its ability to perform parallel computations (by exploiting superposition) and capacity to solve some of the intractable problems exponentially faster. But one of the criteria in the realization of quantum circuits is that corresponding descriptions demand a so-called nearest neighbour (NN) constraint to be satisfied. One of the possible solutions towards satisfying such constraints is through the insertion of SWAP gates that makes the qubit interactions within a gate adjacent. But since the incorporation of SWAP gates increases the cost of the circuits, strategies should be employed which satisfy the NN constraint while placing SWAP gates so that the cost are reduced. In this work, we propose improved design strategies to realize low-cost NN-compliant quantum circuits. Two approaches are introduced, where the first approach relies on a graph traversal process and uses heuristics in critical decision-making conditions to design a 1D NN-compliant representation. The second approach considers qubit-to-qubit interactions and realizes the circuits in a 2D platform based on some weighted metric. Both the designs are evaluated over a wide spectrum of benchmarks and results have been compared with related works. In those comparisons, we have seen that our approaches perform fairly better than the reported ones.
A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain

The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation.
Constrained Manifold Learning for Videos

Automatic image manipulation can be used to make subtle changes at the pixel level resulting in morphism from one domain to another. This is desirable in tasks such as creating mock expressions for an individual or dynamic scene generation in autonomous driving. This type of morphism can be achieved using an adversarial model where the generator and the discriminator compete to produce fake images of the target domain. Due to high variance among the images, it is difficult to learn an optimal loss function. Previously, manifold matching of clusters in the source domain with labeled samples and the target domain that is generated was used to overcome this limitation. To generate videos it is common to use three-dimensional convolution however, such a model has very high complexity. Instead, in this paper we use manifold constrained model selection to do a constrained clustering of the combined manifold with fixed start and end images for the morphism. We show that each step in the principal path connecting the centroids is analogous to a single time delay in the video sequence. Hence, we can construct a cascade of models using samples from a pair of connected centroids such that one model is used to initialize the next. We apply the model to smile generation from neutral face expression and for predicting the next few frames while driving on real roads. We are able to outperform the baselines in the quality of images generated and the computational cost for training the model.
Video Summarization with Anchors and Multi-Head Attention

Video summarization is a challenging task that will automatically generate a representative and attractive highlight movie from the source video. Previous works explicitly exploit the hierarchical structure of video to train a summarizer. However, their method sometimes uses fixed-length segmentation, which breaks the video structure or requires additional training data to train the segmentation model. In this paper, we propose an Anchor-Based Attention RNN (ABA-RNN) for solving the video summarization problem. ABA-RNN provides two contributions. One is that we attain the frame-level and clip-level features by the anchor-based approach, and the model only needs one layer of RNN by introducing subtraction manner used in minus-LSTM. We also use multi-head attention to let the model select suitable lengths of segments. Another contribution is that we do not need any extra video preprocessing to determine shot boundaries and our architecture is end-to-end training. In experiments, we follow the standard datasets SumMe and TVSum and achieve competitive performance against the state-of-the-art results.
Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor

It is an effective strategy for the multi-person pose tracking task in videos to employ prediction and pose matching in a frame-by-frame manner. For this type of approach, uncertainty-aware modeling is essential because precise prediction is impossible. However, previous studies have relied on only a single prediction without incorporating uncertainty, which can cause critical tracking errors if the prediction is unreliable. This paper proposes an extension to this approach with Sequential Monte Carlo (SMC). This naturally reformulates the tracking scheme to handle multiple predictions (or hypotheses) of poses, thereby mitigating the negative effect of prediction errors. An important component of SMC, i.e., a proposal distribution, is designed as a probabilistic neural pose predictor, which can propose diverse and plausible hypotheses by incorporating epistemic uncertainty and heteroscedastic aleatoric uncertainty. In addition, a recurrent architecture is introduced to our neural modeling to utilize time-sequence information of poses to manage difficult situations, such as the frequent disappearance and reappearances of poses. Compared to existing baselines, the proposed method achieves a state-of-the-art MOTA score on the PoseTrack2018 validation dataset by reducing approximately 50% of tracking errors from a state-of-the art baseline method.
"Union is power"

A goal model family is a set of related goal models that conform to the same metamodel, with commonalities and variabilities between models. Such families stem from the evolution of initial models into several versions over time and/or the variation of models over the space dimension (e.g., products). In contexts where there are several versions/variations of a goal model, analyzing a set of related models with typical similarities, one model at a time, often involves redundant computations and may require repeated user assistance (e.g., for interactive analysis) and laborious activities. This paper proposes the use of union models as first-class artifacts to analyze families of goal models, in order to improve performance of language-specific analysis procedures. The paper empirically evaluates the performance gain resulting from adapting (or lifting) an existing analysis technique specific to the Goal-oriented Requirement Language (GRL) to a family of GRL models, all at once using a union model, compared to analyzing individual models. Our experiments show, based on the use of the IBM CPLEX optimizer, the usefulness and performance gains of using union models to perform a computationally expensive analysis, namely quantitative backward propagation, on families of GRL models.
Cross Entropy Attack on Deep Graph Infomax

Graph embedding has been widely used to process various downstream tasks on large-scale graphs, i.e. node classification, community detection and link prediction. Among various embedding methods, Deep Graph Infomax (DGI) is a newly proposed method which achieves excellent performance in node classification. However, such outstanding achievement may cause the over-mining issue of user privacy and the robustness of this embedding method is still unexplored. In this paper, we investigate how to disturb the node classification accuracy of DGI from an attacker's perspective. We propose a novel attack method called Cross Entropy Attack (CEA), which aims to make target nodes be misclassified by DGI model with only limited edges being modified. By slightly changing the topological structure of a graph, CEA can successfully interfere with the accuracy of node classification in an unsupervised manner. Experiment results show that the proposed CEA obviously outperforms two baseline methods in terms of both Misclassified Rate (MR) and Average Modified of Edge (AME).
Infrared-Visible Person Re-Identification Via Cross-Modality Batch Normalized Identity Embedding And Mutual Learning

Cross-Modality Infrared-Visible Person Re-identification (IV-REID) is an important application in intelligent video surveillance. Compared to traditional single-modality person re-identification (re-ID) task, IV-REID aims at matching pedestrian images across different spectrum camera views. In this work, we propose a simple but effective framework to reduce the modality discrepancy. First, a batch normalized cross-modality Identity (ID) embedding method is designed to ease the vanishing gradient problem in IVREID. Second, we introduce a novel mutual learning strategy for different single-modality ID Embedding method to further learn discriminative representations under different modalities. Albeit simple, extensive experiments show that our method outperforms the state-of-the-art on RegDB and SYSU-MMOI datasets. Source code is publicly available at: https://giChub.com/linyq17/IV-REID.
Mosaicing of Dynamic Mesentery Video with Gradient Blending

In biomedical imaging using video microscopy, understanding large tissue structures at cellular and finer resolution poses many image acquisition challenges including limited field-of-view and tissue dynamics during imaging. Automated mosaicing or stitching of live tissue video microscopy enables the visualization and analysis of subtle morphological structures and large scale vessel network architecture in tissues like the mesentery. But mosacing can be challenging if there are deformable, motion-blurred, textureless, feature-poor frames. Feature-based methods perform poorly in such cases for the lack of distinctive keypoints. Standard single block correlation matching strategies might not provide robust registration due to deformable content. In addition, the panorama suffers if there is motion blur present in a sequence. To handle these challenges, we propose a novel algorithm, Deformable Normalized Cross Correlation (DNCC) image matching with RANSAC to establish robust registration. Besides, to produce seamless panorama from motion-blurred frames we present gradient blending method based on image edge information. The DNCC algorithm is applied on Frog Mesentery sequences. Our result is compared with PSS/AutoStitch [1, 2] to establish the efficiency and robustness of the proposed DNCC method.
Experimentation for Business-to-Business Mission-Critical Systems

Continuous experimentation (CE) refers to a group of practices used by software companies to rapidly assess the usage, value and performance of deployed software using data collected from customers and the deployed system. Despite its increasing popularity in the development of web-facing applications, CE has not been discussed in the development process of business-to-business (B2B) mission-critical systems.
Multi-field of View Aggregation and Context Encoding for Single-Stage Nucleus Recognition

Automated nucleus/cell recognition is a very challenging task, especially for differentiating tumor nuclei from non-tumor nuclei in Ki67 immunohistochemistry (IHC) stained images. Convolutional neural networks and their variants have been recently introduced to identify different types of nuclei and have achieved state-of-the-art performance. However, previous nucleus recognition approaches do not explicitly encode contextual information in the images, which can be very helpful for network representation learning. In this paper, we propose a novel multi-field-of-view context encoding method for single-stage nuclei identification in Ki67 IHC stained images. Specifically, we learn a deep structured regression model that takes multi-field of views of images as input and conducts feature aggregation on the fly for representation learning; then, we design a context encoding module to explicitly explore the multi-field-of-view contextual information and enhance the model’s representation power. In order to further improve nucleus recognition, we also introduce a novel deep regression loss that can emphasize specific channels of the prediction map with category-aware channel suppression. The proposed method can be learned in an end-to-end, pixel-to-pixel manner for single-stage nucleus recognition. We evaluate our method on a large-scale pancreatic neuroendocrine tumor image dataset, and the experiments demonstrate the superior performance of our method in nucleus recognition.
Grouped Temporal Enhancement Module for Human Action Recognition

Temporal information is a significant cue for recognizing human actions from videos. Different from 2D CNN which can only capture spatial information in an efficient way, 3D CNN is good at capturing both spatial and temporal information at the expense of high computational cost. Beyond both methods, this paper presents a Grouped Temporal Enhancement (GTE) module which even outperforms 3D CNN, meanwhile only needs similar low computational cost as 2D CNN. The GTE module firstly decomposes an input video into spatial and temporal groups along channel dimension, and then uses a learnable temporal shift (LTS) operation for efficient temporal modeling. Finally, a 2D convolution filter is used to enhance the ability of LTS for spatial modeling. Extensive experiments on three benchmark datasets validate the effect of our method.
Lean UX research at scale

In recent years, as technology engineering production and release cycles have sped up, user experience (UX) research practices have correspondingly become more Lean to best match those cycles. On top of that, the increasing interest in incorporating user feedback into product development keeps pushing the limit of resources that UX research teams can allocate. Is this seemingly unstoppable appetite for user insights sustainable? How can UX researchers properly support their product teams' growing needs? We argue that, to be successful at scaling up Lean UX research, product teams need to become active actors in the process, while researchers need to use their expertise to provide guidance and training as required. As an example of this approach, we describe our experience organizing "Developer Day", a yearly event aimed at scaling up the Lean UX research practices of Microsoft's Developer Division. Developer Day has also extended the reach of the division's UX Research team for impacting decisions about the company's software developer tools and services.
Boa Views

Mining Software Repositories (MSR) has recently seen a focus toward ultra-large-scale datasets. Several tools exist to support these efforts, such as the Boa language and infrastructure. While Boa has seen extensive use, in its current form it is not always possible to perform the entire analysis task within the infrastructure, often requiring some post-processing in another language. This limits end-to-end reproducibility and limits sharing/re-use of MSR queries. To address this problem, we use the notion of views from the relational database field and designed a query language and runtime infrastructure extension for Boa that we call materialized views. Materialized views provide output reuse to Boa users, so that the results of prior Boa queries can be easily reused by others. This allows for computing results not previously possible within Boa and provides for increased sharing and reuse of MSR queries. To evaluate views, we performed two partial reproductions of prior MSR studies utilizing Boa's dataset and infrastructure and compare our results to the prior studies. This shows the usability of the new infrastructure, allowing analyses in Boa that were not previously possible as well as providing a previously hand created gold dataset for identifier splitting as a reusable view for other MSR researchers. We also verified the caching behavior using the queries from one of the case studies. The results show that caching works as expected and can drastically improve runtime performance.
A New Three-stage Curriculum Learning Approach for Deep Network Based Liver Tumor Segmentation

Automatic segmentation of liver tumors in medical images is crucial for computer-aided diagnosis and therapy. It is a challenging task, since the tumors are notoriously small against the background voxels. This paper proposes a new three-stage curriculum learning approach for training deep networks to tackle this small object segmentation problem. The learning in the first stage is performed on the whole input volume to obtain an initial deep network for tumor segmentation. Then the second stage of learning focuses on the tumor-specific features by continuing training the network on the tumor patches. Finally, we retrain the network on the whole input volume in the third stage, in order that the tumor-specific features and the global context can be integrated to improve the final segmentation accuracy. With this approach, we can employ a single network to segment the tumors directly without the need of liver segmentation. We evaluate our approach on a clinical dataset from the hospital and the public MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge dataset. In the experiments, our approach exhibits significant improvement compared with the commonly used cascade counterpart.
Deep Sub-pJ/Bit Low-Area Energy-Security Scalable SIMON Crypto-Core in 40 nm

This paper describes an energy-security scalable crypto-core for private-key cryptography in low-end sensor nodes based on SIMON cipher. Energy and area footprints are reduced through techniques at the algorithm, microarchitectural and gate level. At the algorithm level, multiple encryption is introduced to dynamically expand the key length from 64 to 256 bits at minimal reconfiguration complexity, allowing shorter keys and lower energy when lower level of security is demanded. The 1-round parallel architecture with short 32-bit datapath narrows the traditionally large gap between the conventional crypto-core data (e.g., 128 bits) and low-end processors (32 bit or lower), mitigating or eliminating the need for area- and energy-hungry FIFO buffers at their interface. At the gate level, the adoption of multi-bit pulsed latch-based pipelines with internal clock buffer sharing reduces the dominant clock power/area contribution of sequential elements. Tunable clock duty cycle allows time borrowing for improved variation resilience at ultra-low voltages at low hold-fix buffer count, leveraging the inherent margin against hold time violations enabled by relatively uniform pipestage delays. A 40 nm testchip shows energy down to 0.31 pJ/bit at 0.45 V with 64-bit key and 0.79E6 F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area (F = process minimum feature size). The proposed crypto-core is well suited for ubiquitous security in energy/area-constrained platforms (e.g., low-end sensor nodes, RFIDs), while preserving full 256-bit security when necessary.