Reviewerly/exHarmony huggingface dataset:
Assessing Coding Metrics for Parallel Programming of Stream Processing Programs on Multi-cores

From the popularization of multi-core architectures, several parallel APIs have emerged, helping to abstract the programming complexity and increasing productivity in application development. Unfortunately, only a few research efforts in this direction managed to show the usability pay-back of the programming abstraction created, because it is not easy and poses many challenges for conducting empirical software engineering. We believe that coding metrics commonly used in software engineering code measurements can give useful indicators on the programming effort of parallel applications and APIs. These metrics were designed for general purposes without considering the evaluation of applications from a specific domain. In this study, we aim to evaluate the feasibility of seven coding metrics to be used in the parallel programming domain. To do so, five stream processing applications implemented with different parallel APIs for multi-cores were considered. Our experiments have shown COCOMO II is a suitable model for evaluating the productivity of different parallel APIs targeting multi-cores on stream processing applications while other metrics are restricted to the code size.
Genuine Reversible Data Hiding Technique for H.264 Bitstream Using Multi-Dimensional Histogram Shifting Technology on QDCT Coefficients

Video has become the most important medium for communication among people. Video has become the most important medium for communication among people. Therefore, reversible data hiding technologies for video have been developed so that information can be hidden in the video without damaging the original video in order to be used in the copyright protection and distribution field of video. This paper proposes a practical and genuine reversible data hiding method by using a multi-dimensional histogram shifting scheme on QDCT coefficients in the H.264/AVC bitstream. The proposed method defines the vacant histogram bins as a set of n-dimensional vectors and finds the optimal vector space, which gives the best performance, in a 4 × 4 QDCT block. In addition, the secret message is mapped to the optimal vector space, which is equivalent to embedding the information into the QDCT block. The simulation results show that the data hiding efficiency is the highest among the compared five existing methods. In addition, the image distortion and maximum payload capacity are measured quite high.
Single-stage Face Detection under Extremely Low-light Conditions

Face detection has been well studied for many years. One remaining challenge is to detect faces from low-light images. The brightness of the image captured under extremely low-light conditions could be very low and the contrast will be severely reduced. It is easy to cause confusion during feature extraction and affects the performance of face detection. In this paper, we propose a single-stage low-light face detection method. First, we design an improved MSRCR method to increase the image quality under the condition of ensuring that the colors of the image are not distorted. It shows better enhancement effect than other methods in the DARK FACE dataset, especially the low-resolution face details are well preserved. There are a number of small, blurred and partially occluded faces. To address this, the Pyramidbox algorithm is a very effective face detection algorithm. Moreover, we conduct multi-scale tests to further develop the performance of the model and integrated the results through Soft-NMS method to obtain final results. Integrating these techniques, this paper has achieved high accuracy and obtained excellent results in the face detection task of the DARK FACE dataset.
Simultaneous multi-person tracking and activity recognition based on cohesive cluster search

We present a bootstrapping framework to simultaneously improve multi-person tracking and activity recognition at individual, interaction and social group activity levels. The inference consists of identifying trajectories of all pedestrian actors, individual activities, pairwise interactions, and collective activities, given the observed pedestrian detections. Our method uses a graphical model to represent and solve the joint tracking and recognition problems via three stages: (i) activity-aware tracking, (ii) joint interaction recognition and occlusion recovery, and (iii) collective activity recognition. This full-stack problem induces great complexity in learning the representations for the sub-problems at each stage, and the complexity increases as with more stages in the system. Our solution is to make use of symbolic cues for inference at higher stages, inspired by the observations of cohesive clusters at different stages. This also avoids learning more ambiguous representations in the higher stages. High-order correlations among the visible and occluded individuals, pairwise interactions, groups, and activities are then solved using the cohesive cluster search within a Bayesian framework. Experiments on several benchmarks show the advantages of our approach over the existing methods.
Model Predictive Path Planning for Autonomous Parking Based on Projected C-Space

This paper proposes a model predictive path planning method with collision avoidance in narrow parking space. Objects are expressed as rectangle collision area, and obstacle avoidance constraints are defined by inequalities in a projected C-space which projects the rectangle collision area into the circle. Switching of driving forward and backward is planned automatically in the proposed method without a limitation on the switching. Numerical simulations were carried out to validate the performance with strict obstacle avoidance in a narrow environment assuming the reverse parking and the parallel parking tasks. The Achievability of the parking task and the computation time were compared between the proposed method and the conventional method with the collision avoidance constraints derived from Farkas' lemma.
Hybrid Quantum-Classical Circuit Simplification with the ZX-Calculus

We present a complete optimization procedure for hybrid quantum-classical circuits with classical parity logic. While common optimization techniques for quantum algorithms focus on rewriting solely the pure quantum segments, there is interest in applying a global optimization process for applications such as quantum error correction and quantum assertions. This work, based on the pure-quantum circuit optimization procedure by Duncan et al., uses an extension of the formal graphical ZX-calculus called Open image in new window as an intermediary representation of the hybrid circuits to allow for granular optimizations below the quantum-gate level. We define a translation from hybrid circuits into diagrams that admit the graph-theoretical focused-gFlow property, needed for the final extraction back into a circuit. We then derive a number of gFlow-preserving optimization rules for Open image in new window diagrams that reduce the size of the graph, and devise a strategy to find optimization opportunities by rewriting the diagram guided by a Gauss elimination process. Then, after extracting the circuit, we present a general procedure for detecting segments of circuit-like Open image in new window diagrams which can be implemented with classical gates in the extracted circuit. We have implemented our optimization procedure as an extension to the open-source python library PyZX.
The Comparison of the Use of Microsoft Academic, Google Scholar, and ScienceDirect as Search Engines in Obtaining Library Resources for Students

Obtaining library resources is the main task that carried out by students in attending lectures. The student learning experience will be achieved maximally if it is supported by complete library resources. The existence of strong development of information and communication technology makes everyone tend to seek information through the internet. For that reason, search engines are an easy way to get sufficient library resources. Many types of search engines are available, but which ones are effective and efficient are not studied in depth. Hence, it is necessary to research to find an effective search engine. In this study, three types of engines were taken from that were mostly used by the students, namely Microsoft Academic, Google Scholar, and ScienceDirect. A total of 134 doctoral program students were taken as research samples. The results showed that students used Google Scholar more in finding library sources, followed by ScienceDirect, and Microsoft Academic. There are differences in the advantages, disadvantages, and availability of information obtained through the three search engines.
cPCA++: An efficient method for contrastive feature learning

In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data. This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a "target" dataset may be obscured by high variance components during traditional PCA. By analyzing what is referred to as a "background" dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the "target" dataset. Similar to another recently proposed algorithm called "contrastive PCA" (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings. However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient. Several experiments were conducted in order to compare the proposed method to state-of-the-art methods. These experiments show that the proposed method achieves performance that is similar to or better than that of the other methods, while being more efficient.
AntOpt

The orderly operation and development of any system are indivisible from decision optimization. Several issues in life are applicable to the thought of optimization problems to resolve. In this digital age, the size of information and data is obtaining larger and the potency of problem determination is changing into more demanding. Though there're many solvers for specific optimization problems, in the face of large-scale scenarios, there's no single platform that concurrently addresses usability, solvers' uniformity, and computing efficiency. In this demo, we present AntOpt, a decision optimization platform that integrates large-scale distributed computing engines, optimization algorithm solvers and productized services.
Contact Tracing: Ensuring Privacy and Security

Faced with the biggest virus outbreak in a century, world governments at the start of 2020 took unprecedented measures to protect their healthcare systems from being overwhelmed in the light of the COVID-19 pandemic. International travel was halted and lockdowns were imposed. Many nations adopted measures to stop the transmission of the virus, such as imposing the wearing of face masks, social distancing, and limits on social gatherings. Technology was quickly developed for mobile phones, allowing governments to track people’s movements concerning locations of the virus (both people and places). These are called contact tracing applications. Contact tracing applications raise serious privacy and security concerns. Within Europe, two systems evolved: a centralised system, which calculates risk on a central server, and a decentralised system, which calculates risk on the users’ handset. This study examined both systems from a threat perspective to design a framework that enables privacy and security for contact tracing applications. Such a framework is helpful for App developers. The study found that even though both systems comply with the General Data Protection Regulation (GDPR), Europe’s privacy legislation, the centralised system suffers from severe risks against the threats identified. Experiments, research, and reviews tested the decentralised system in various settings but found that it performs better but still suffers from inherent shortcomings. User tracking and re-identification are possible, especially when users report themselves as infected. Based on these data, the study identified and validated a framework that enables privacy and security. The study also found that the current implementations using the decentralised Google/Apple API do not comply with the framework.
Strategies to simulate dephasing-assisted quantum transport on digital quantum computers

Abstract Simulating charge and energy transfer in extended molecular networks requires an effective model to include the environment because it significantly affects the quantum dynamics. A prototypical effect known as environment-assisted quantum transport (ENAQT) consists in the enhancement of the transfer efficiency by the interaction with an environment. A simple description of this phenomenon is obtained by a quantum master equation describing a quantum walk over the molecular network in the presence of inter-site decoherence. We consider the problem of simulating the dynamics underlying ENAQT in a digital quantum computer. Two different quantum algorithms are introduced, the first one based on stochastic Hamiltonians and the second one based on a collision scheme. We test both algorithms by simulating ENAQT in a small molecular network on a quantum computer emulator and provide a comparative analysis of the two approaches. Both algorithms can be implemented in a memory efficient encoding with the number of required qubits scaling logarithmically with the size of the simulated system while the number of gates increases quadratically. We discuss the algorithmic quantum trajectories generated by the two simulation strategies showing that they realize distinct unravellings of the site-dephasing master equation. In our approach, the non-unitary dynamics of the open system is obtained through effective representations of the environment, paving the way to digital quantum simulations of quantum transport influenced by structured environments.
Multi-task Learning for Dependability Assessment of Industrial Wireless Communication Systems

Wireless communication systems for the industrial domain will rapidly grow in the forthcoming years, which is bringing a strong need for quantitative dependability assessment. Empirical formulas and artificial intelligence have been applied in dependability assessment for years. However, these methods always focused on a single task, while multiple tasks are common and have inner relationships, such as dependability scoring and prediction. These relationships are useful and can improve every task performance. Hence, we propose the multi-task learning model to solve three tasks: dependability scoring, prediction, and scoring prediction, where we design a denoising sequence to sequence neural network with a decay attention. To validate the novelty, we measured and collected a dataset from a realistic system, and then we compared the multi-task learning results of three tasks with benchmarks, which indicates superiority.
Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications

We compare the potential of neural network (NN)-based channel estimation with classical linear minimum mean square error (LMMSE)-based estimators, also known as Wiener filtering. For this, we propose a low-complexity recurrent neural network (RNN)-based estimator that allows channel equalization of a sequence of channel observations based on independent time- and frequency-domain long short-term memory (LSTM) cells. Motivated by Vehicle-to-Everything (V2X) applications, we simulate time- and frequency-selective channels with orthogonal frequency division multiplex (OFDM) and extend our channel models in such a way that a continuous degradation from line-of-sight (LoS) to non-line-of-sight (NLoS) conditions can be emulated. It turns out that the NN-based system cannot just compete with the LMMSE equalizer, but it also can be trained w.r.t. resilience against system parameter mismatch. We thereby showcase the conceptual simplicity of such a data-driven system design, as this not only enables more robustness against, e.g., signal-to-noise-ratio (SNR) or Doppler spread estimation mismatches, but also allows to use the same equalizer over a wider range of input parameters without the need of re-building (or re-estimating) the filter coefficients. Particular attention has been paid to ensure compatibility with the existing IEEE 802.11p piloting scheme for V2X communications. Finally, feeding the payload data symbols as additional equalizer input unleashes further performance gains. We show significant gains over the conventional LMMSE equalization for highly dynamic channel conditions if such a data-augmented equalization scheme is used.
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Active learning maximizes the performance of the current learning model by soliciting benefits from unlabeled data. In its early stage with insufficient labels, finding representations that maintain a consistent hypothesis with the entire unlabeled pool is an intelligent paradigm. From a matrix perspective, this paradigm can be transformed into a sparse representation of the input matrix by the ℓ2,1-norm constraint. However, the ℓ2,1-norm constraint is used in the loss function, which may lead to the mean accumulation problem, resulting in sub-optimal mean-centering and low robustness to outliers. In this paper, to solve the aforementioned problems, we generalize the ℓ2,1-norm into the ℓ2,p-norm constraint, where 0<p≤1. Specifically, when p=1, the ℓ2,1 constraint derives the optimal mean-centering, avoiding biased feature transformations, and when 0<p<1, ℓ2,p regulates the sparsity of the loss function and regularization terms in the matrix representation, yielding robust active representations. Experiment results on benchmark databases demonstrate that our method outperforms other state-of-the-art active learning methods, particularly for outlier perturbations.
A New Hardware Architecture for FPGA Implementation of Feed Forward Neural Networks

Artificial neural networks are very popular and fast-growing machine learning algorithms today. There exist a large number of ways for implementing ANN into reality. Generally, the main two techniques are neuromorphic programming and neural networks. This paper presents an overview of such methods. Nowadays machine learning chips are available with a high level of parallel designs, but deep neural network requires flexible and efficient hardware structure that can be perfect for any type of neural networks. Also, varieties of hardware topologies are available for FPGA implementation. This paper explains those architectural variations and suggests a new topology. The proposed architecture adopts the systolic structure and applies to any feed forward neural networks such as Multi-Layer Perceptron (MLP), Auto Encoder (AE) and, Logic Regression (LR). Unlike other hardware neural network structures, this architecture implements a single activation function block and the largest layer only. This paper also includes the implementation of a feed-forward neural network for digit recognition (0 to 9) in the Zynq-7000 board with MNIST as the dataset. Different activation functions and different parameters of each activation function are used for the network. Changes and improvements are mentioned in this paper based on Accuracy, Operating frequency and, Resource usage. Logistic Sigmoidal functions can achieve more accuracy and performance as compared with others.
Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation

Several video-based 3D pose and shape estimation algorithms have been proposed to resolve the temporal inconsistency of single-image-based methods. However it still remains challenging to have stable and accurate reconstruction. In this paper, we propose a new framework Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D human pose and mesh from RGB videos. We reformulate the task as a multi-modality problem that fuses RGB and optical flow for more reliable estimation. In order to fully utilize both sensory modalities (RGB or optical flow), we train a two-stream temporal network based on transformer to predict SMPL parameters. The supplementary modality, optical flow, helps to maintain temporal consistency by leveraging motion knowledge between two consecutive frames. The proposed algorithm is extensively evaluated on the Human3.6 and 3DPW datasets. The experimental results show that it outperforms other state-of-the-art methods by a significant margin.
Convolutional Neural Networks Hyperparameters Optimization Using Sine Cosine Algorithm

The most challenging task in the machine learning domain is optimizing the hyperparameters in convolutional neural networks. This task is representative of NP-hard problems, and consequently, it is not possible to solve it by applying standard deterministic approaches in an acceptable amount of time. Additionally, convolutional neural networks’ hyperparameters must be optimized for each particular problem, as there is no solution that fits all possible applications. Swarm intelligence metaheuristics have been established as efficient optimizers, and this paper proposes the enhanced sine cosine algorithm to address the task of hyperparameters optimization. The experiments conducted in this research were executed with the CIFAR-10 benchmark dataset. The experimental results were analyzed and validated against other proven metaheuristics approaches, and it can be concluded that the presented enhanced sine cosine approach outperformed other methods included in this research.Bacanin, Nebojsa Zivkovic, Miodrag Salb, Mohamed Strumberger, Ivana Chhabra, Amit
Toward Neural-Network-Guided Program Synthesis and Verification

We propose a novel framework of program and invariant synthesis called neural network-guided synthesis. We first show that, by suitably designing and training neural networks, we can extract logical formulas over integers from the weights and biases of the trained neural networks. Based on the idea, we have implemented a tool to synthesize formulas from positive/negative examples and implication constraints, and obtained promising experimental results. We also discuss an application of our method for improving the qualifier discovery in the framework of ICE-learning-based CHC solving, which can in turn be applied to program verification and inductive invariant synthesis. Another potential application is to a neural-network-guided variation of Solar-Lezama’s program synthesis by sketching.