Miracle-dz/UniFAR-FTUs huggingface dataset:
One-way multimodal image-to-image translation for heterogeneous face recognition

Abstract. Cross-domain face matching is known as heterogeneous face recognition (HFR), which is more valuable than traditional face recognition and plays a key role in public safety. It remains a challenging problem due to insufficient heterogeneous data and large domain discrepancies. Recent work has focused on learning common features from different visual domains and synthesizing face images from other domains into the visible domain to reduce domain discrepancy. The former performs domain transformation during face recognition, which requires additional runtime. The latter has poorer performance limited by insufficient heterogeneous data. We propose a one-way multimodal image-to-image translation (OMIT) to tackle the problem of insufficient heterogeneous data. Specifically, we learn the generation of heterogeneous data from small-scale paired heterogeneous training data, and an identity-preserving loss is imposed on the generated heterogeneous images to ensure their identity consistency. Then, we transform large-scale visible data of abundant identity information into paired heterogeneous data, which addresses the lack of identity diversity due to small-scale heterogeneous data. The generated pairwise heterogeneous data can be directly used to train the HFR network to improve recognition accuracy and precision. Finally, our method exhibits generated quality improvements on BUAA, CASIA NIR–VIS 2.0, Oulu NIR–VIS, and Tufts face database. Heterogeneous datasets generated by OMIT improve the performance of HFR networks.
Photonic crystal broadband y-shaped 1 × 2 beam splitter inversely designed by genetic algorithm

Abstract. A novel two-dimensional photonic crystal broadband Y-shaped 1 × 2 beam splitter is proposed. Broadband performance of the 1 × 2 beam splitter can be greatly improved by optimizing the z-axis offset of the dielectric rods adjacent to the input and output waveguides, and the offset along the V-shaped oblique waveguide of the dielectric rods at the junction area. To enhance the optimization efficiency and achieve excellent splitting performance of the 1 × 2 beam splitter, the genetic algorithm is applied to inversely design the above offsets of the 1 × 2 beam splitter. The results show that, in the bandwidth range of 1515 to 1587 nm, the highest and lowest transmittance of the 1 × 2 beam splitter are 99.6% and 97.9%, respectively, the additional loss is <0.1 dB, and the uniformity tends to 0 dB. Finally, the tolerance analysis of the 1 × 2 beam splitter is carried out. When the deviation of each variable reaches ±10 nm, the total transmittance of the 1 × 2 beam splitter in the bandwidth range of 1515 to 1587 nm is still higher than 95.2%, with the additional loss <0.22 dB. Due to the virtues of broad operating bandwidth, high transmittance, good uniformity, and strong robustness, the proposed 1 × 2 beam splitter will have great application prospects in the field of photonic integrated circuits, all-optical communication network, and so on.
Geometrical positioning surveying-based features for BEOL line-end-pull-back modeling using regression-based machine-learning

Abstract. Background Line-end-pull-back (LEPB) is a well-known systematic defect in BEOL metal layers, where a line-end (LE) tip is pulled back from its desired location due to lithography (litho) process effects. Severe LEPB directly affects BEOL connectivity and may lead to partial or total metal-via disconnection. Aim LEPB can be characterized through model-based litho simulations but at the cost of high computational resource consumption. This study aims to provide a fast and accurate approximation of computationally expensive litho simulations through regression-based machine learning (ML) modeling. Approach LEPB modeling is approached through the LightGBM model. Input features were approached using density pixels, density concentric circle area sampling (CCAS), and geometrical positioning surveying (GPS), which is an edge-based engine that provides a direct one-to-one mapping between model features and geometrical measurements between the LE as a point-of-interest and its surrounding contextual patterns. The importance of LightGBM features by splits was employed to reduce features across the used approaches. Results The reduced features of GPS produced almost the same modeling quality (training: RMS = 0.571 nm, δEWD = 0.297 nm, and R2 % = 98.74 % , and testing: RMS = 0.643 nm, δEWD = 0.344 nm, and R2 % = 98.40 % ) with −22.22 % fewer number of features and less feature extraction runtime compared to the full features set of density pixels and density CCAS approaches. Conclusions Compared to model-based litho simulations, the obtained calibrated ML models can be used to provide fast, yet accurate predictions of the amounts of pull-back or extensions introduced at LEs near vias, eliminating a major contributor to systematic IC yield loss.
Detection of adversarial attacks on machine learning systems

One of the major issues limiting the adoption of machine learning (ML) in applications where accuracy is critical is failure of an otherwise accurate system. Recent work has developed tools to independently measure the competency of machine learning models and the conditions that drive these competency differences. The purpose of this paper is to explore ways to detect and mitigate adversarial attacks by using the same tools to assess competency. We will introduce BAE System’s MindfuL software which assesses ML competency under varying environmental conditions. We then consider a few different types of adversarial attacks and describe detection experiments. We examine the predicted performance and strategy and use that information to detect adversarial attacks. We will present the results of these experiments and discuss the implications of this work and potential future directions for this research.
FM-YOLOv7: an improved detection method for mine personnel helmet

Abstract. Due to the complex underground environment and the small object of the helmet, the detection accuracy is low when the original YOLOv7 algorithm is used to detect whether the mine personnel wears the helmet, which cannot be applied to the actual operation site. In response to this problem, we proposed an FM-YOLOv7 mine personnel helmet detection. First, to improve the feature extraction ability of the shallow network and enhance the representation ability of the model on the helmet, we propose the fused-MBCA (fused-MBConv with the coordinate attention) module. Second, to improve the detection ability of small objects, enable the fused features to obtain high-level semantic information and low-level details from different scales, and have more extensive receptive fields, we propose the multi-scale feature fusion efficient layer aggregation networks. Finally, to accelerate the convergence of the model and improve the regression accuracy, we use efficient intersection over union as the bounding box regression loss function. These experiments are based on the self-built mine personnel safety helmet dataset. The results show that the FM-YOLOv7 model outperformed the other six algorithms. The mAP@0.5 of the proposed model can reach 85.7%, which is 1.4% higher than the original YOLOv7 model. Also, the improved YOLOv7 model achieves 91 frames per second in detection speed, which detects whether the mine personnel wears a safety helmet in real time.
Real-time monitoring of cement clinker hydration via a remote fiber optic Raman probe

Hydration reactions of cement-based materials are of great significance to their properties and durability. Various technologies have been investigated to study the hydration processes regarding reaction heat, chemical changes, or microstructures. As a non-destructive chemical analysis technique, Raman spectroscopy provides detailed information about chemical structures, which takes advantage of tracking and monitoring the chemical change during the hydration reaction. In this study, a novel in situ fiber optic Raman probe was utilized to continuously monitor the long-term hydration process of cement clinker stages, from early to late hydration stages and from fresh to hardened state of paste samples. With the remarkable capability of this technique for dry or moist, crystalline or amorphous samples, the hydration process of tricalcium silicate (C3S) pastes with different water-to-solid (w/s) ratios can be monitored from the beginning of the hydration reaction. The main hydration products, especially C-S-H and silicate (CH), have been successfully identified, and there in situ quantitative changes have been continuously monitored. The effect of the w/s ratio on the hydration process of C3S slurries is also discussed. Moreover, the X-Ray Powder Refraction (XRD) results strongly correlate with the Raman spectra of the hydration products, demonstrating the technique's reliability. By comparing with the existing in situ fiber optic Raman spectroscopy technique, the proposed sensor performs a significantly better signal-to-noise ratio (SNR), providing essential aid for future use in the construction field for monitoring and assessing the health and performance of concrete structures.
Multiparameter encrypted orbital angular momentum multiplexed holography based on multiramp helicoconical beams

Abstract. Optical orbital angular momentum (OAM) multiplexed holography has been implemented as an effective method for information encryption and storage. Multiramp helicoconical-OAM multiplexed holography is proposed and experimentally implemented. The mode selectivity of the multiramp mixed screw-edge dislocations, constant parameter K, and normalized factor are investigated, respectively, which demonstrates that those parameters can be used as additional coding degrees of freedom for holographic multiplexing. The combination of the topological charge and the other three parameters can provide a four-dimensional multiplexed holography and can enhance information capacity.
Providing predictions of adversary movements in a gridworld environment to a human-machine team improves teaming performance

Knowing the future states of an adversary in an adversary-avoidance game can impart a survival advantage. To assess how predictive modeling helps agents achieve goals and avoid adversaries, we tested the efficacy of three predictive algorithms within a gridworld-based game. For one predictive algorithm, model predictions of adversary moves furnished to an agent helped the agent avoid capture compared to a case without predictions. A human-machine team scenario also benefited from model predictions, while humans alone experienced a ceiling effect. We investigated the efficacy of two additional predictive algorithms and present a maritime vessel pursuit scenario.
Underwater polarization image restoration based on a partition method

Abstract. The quality of underwater polarization imaging is mainly affected by the polarization properties of the target and impurity particles. Traditional methods often assume uniform polarization characteristics of the target, which make it difficult to address the restoration issues of complex targets. In response, a partition-based method for recovering underwater polarization images is proposed. The method involves pre-processing the image using the Gaussian curvature filtering algorithm, partitioning the image based on polarization information. In addition, a joint image evaluation method is used to achieve restoration of complex polarized characteristic targets. The method estimates the value of the reflected light polarization of one partition to estimate the value of the next partition and links the polarization values of each partition. Our approach achieves clear restoration results for multiple targets or complex structural objects underwater. Achieving significant improvement in image quality in multi-target underwater scenes, our method is highly effective for complex underwater environments. Experimental results show that our method, when compared with three other newer methods on multiple images of different targets and under varying scattering conditions, achieves an average increase of 617% in the standard deviation image evaluation index and a 61% optimization in the natural image quality evaluator index. Furthermore, our method is robust for different degrees of water turbidity.
Fabrication of optically transparent millimeter-wave micromesh structures

Global trends towards mobility and miniaturization along with increasing demands to improve the performance and efficiency of sensor systems, especially in autonomous systems, have led to a need to decrease their size, weight, and power (SWaP). For millimeter wave (mmWave) and optical sensing, optically transparent mmWave antennas provide a solution to meet this need by offering a multi-modal, single aperture system with applications in vehicle windshields, solar cells on satellites, and drone camera lenses, among others. However, integrating these sensing systems while optimizing both the mmWave properties and optical sensing capabilities forces competing design and fabrication requirements. Currently, there also exists limited research and literature on a fabrication procedure for these micromesh structures. This paper presents a photolithographic procedure for fabricating micro-scale optically transparent mmWave transmission lines (TLs) and antennas suitable for use in the design optimization processes. It investigates the fabrication of varying line width versus line gap dimensions as well as the fabrication of different mesh unit cell designs. The fabrication procedure was designed to achieve down to 1-2 μm features and is replicable for both single-sided and dual-sided designs. Fabricated micro-mesh structures exhibit strong performance in both operating regimes, with optical transmission ranging from 80- 95% transmittance in the visible spectrum and high conductivity with sheet resistance less than 1 ohm-per-square.
A frequency-tunable dual-band polarization conversion metasurface

In this paper, a simple dual-band polarization conversion metasurface(PCM) is proposed. This polarization conversion metasurface can be used in 5G applications such as satellites and radar. The polarization conversion is achieved by etching trapezoidal slots on the upper surface of the metasurface unit, resulting in a change in the distribution of the surface current. To achieve a dual-band, an additional resonant frequency is excited by loading two metal vias and two slots on either side of the metal via. In addition, the tuning of the frequency and the control of the operating bandwidth can be achieved by changing the radius size of the meta via and the slot width of the metasurface unit. The simulation results show that the line polarization(LP) conversion to line polarization(LP) is achieved at 37.6~59.5 GHz and 63~75.6 GHz with the average polarization conversion ratio of 94% and 96% in the two bands, respectively, and the lowest polarization conversion ratio of 90%.
Performance analysis of hybrid geometric-probabilistic shaped 8/12 quadrature amplitude modulation for coherent optical communication system

Abstract. A hybrid shaping (HS) scheme based on geometric shaping (GS) and probabilistic shaping (PS) in a coherent optical communication system is proposed. A particle swarm optimization algorithm and Maxwell-Boltzmann distribution are employed to sequentially implement GS and PS. The results demonstrate that hybrid shaped 8/12-ary quadrature amplitude modulation (HS-8/12QAM) is superior to regular-8/12QAM (R-8/12QAM) in terms of reducing the bit error rate (BER) and increasing the generalized mutual information (GMI). HS-8QAM achieves a 2 dB optical signal-to-noise ratio (OSNR) gain and 0.45 bits / symbol GMI gain compared with R-8QAM. Meanwhile, HS-12QAM achieves 1.9 dB OSNR gain and 0.68 bits/symbol GMI gain compared with R-12QAM. In addition, HS-8/12QAM is better than R-8/12QAM in terms of transmission distance and data rate.
Delivering on the promise of autonomous agents in the battlefield

As the Army begins to deliver on the promise of autonomous agents including unmanned ground and air vehicles and other intelligent agents the focus has shifted from functionality to robustness and resilience. Intelligent systems must not only be capable of performing specific tasks, but they must also do it in a manner that is resilient to the dynamism of real-world environments and adapt to the unexpected including adversaries. This paper surveys some of the recent research and breakthroughs towards robust and resilient autonomy, some of the keys to achieving systems that learn rapidly in new environments, and that demonstrate the ability deal with uncertainty through various means including leveraging human knowledge to greatly speed up the learning process.
Introducing Computational Thinking at Vocational High Schools

The Technovation Girls program (TG) is an after-school program, where teams of female school students follow a software ideation process to address one of the UN's Sustainable Development goals. Concretely, they prototype a mobile application which they later pitch to judges. The TG curriculum gives students the tools they need to carry out this challenge, with activities focusing on entrepreneurship, product ideation and pitching, as well as coding. Previous evaluations of this program have focused on student and mentor perceptions and attitudes, collected through surveys, observation, focus groups and interviews. In this paper, we evaluate the effect that the coding part of the TG curriculum has on students' computational thinking skills, specifically in Chile, where the TG curriculum was expanded to a year to account for differences in the school curriculum. Also, in this experience, the TG program was carried out during class hours, and at both single- and mixed-gender vocational high schools. We applied an existing computational thinking test to the 2020 cohort (approx. 1000 students), before and after participating in the program, analyzing the results by school, gender and age. We found a slight improvement in test scores, with a more positive effect in male students.
Privacy Concerns and Antecedents of Building Data Sharing: A Privacy Calculus Perspective

In recent years, machine learning (ML) based building analytics have been developed for diverse building services. Yet, the widespread sharing of building data, which underpins the establishment of ML models, is not a common practice in the buildings industry today. Clearly, there are privacy concerns. There are studies on protecting building data, e.g., to k-anonymize building data; yet these studies are computational methods. The root causes of why building operators are or are not willing to share data are unclear. In this paper, we study the problem of willingness to share building data. First, we justify our study by investigating the field to show that data sharing is indeed limited. Second, we examine the issue of the willingness to share building data from the perspective of a social science study. We observe that the intention to disclose (i.e., decision making on data sharing) is not only based on perceived risks, but also on perceived benefits. We leverage the privacy calculus theory and present a systematic study. We develop hypotheses, design a questionnaire, conduct a survey involving 95 building operators and service providers around the world, and analyze the results, wherein we quantify how various factors influence the willingness to share building data. We further enhance our results by a small scale interview. Third, we use trust, an important factors to the intention to disclose, to develop a trust model with differentiable trust levels. Such model provides building operators a mechanism to share data besides a 0-and-1 choice. We present a case study where we enhance an existing building data anonymization platform, PAD with the trust model. We show that the enhanced PAD has a substantially smaller computation workloads.
Using Micro Parsons Problems to Scaffold the Learning of Regular Expressions

Regular expressions (regex) are a text processing method widely used in data analysis, web scraping, and input validation. However, students find regular expressions difficult to create since they use a terse language of characters. Parsons problems can be a more efficient way to practice programming than typing the equivalent code with similar learning gains. In traditional Parsons problems, learners place mixed-up fragments with one or more lines in each fragment in order to solve a problem. To investigate learning regex with Parsons problems, we introduce micro Parsons problems, in which learners assemble fragments in a single line. We conducted both a think-aloud study and a large-scale between-subjects field study to evaluate this new approach. The think-aloud study provided insights into learners' perceptions of the advantages and disadvantages of solving micro Parsons problems versus traditional text-entry problems, student preferences, and revealed design considerations for micro Parsons problems. The between-subjects field study of 3,752 participants compared micro Parsons problems with text-entry problems as an optional assignment in a MOOC. The dropout rate for the micro Parsons condition was significantly lower than the text-entry condition. No significant difference was found for the learning gain on questions testing comprehensive regex skills between the two conditions, but the micro Parsons group had a significantly higher learning gain on multiple choice questions which tested understanding of regex characters.
A Case Study on When and How Novices Use Code Examples in Open-Ended Programming

Many students rely on examples when learning to program, but they often face barriers when incorporating these examples into their own code and learning the concepts they present. As a step towards designing effective example interfaces that can support student learning, we investigate novices' needs and strategies when using examples to write code. We conducted a study with 12 pairs of high school students working on open-ended game design projects, using a system that allows students to browse examples based on their functionality, and to view and copy the example code. We analyzed interviews, screen recordings, and log data, identifying 5 moments when novices request examples, and 4 strategies that arise when students use examples. We synthesize these findings into principles that can inform the design of future example systems to better support students.
Color Transformation Based on the Hue Division for Color Scanners

The color transformation from the RGB values of a scanner to the CIE colorimetric values is used for color capture correctly.The relationships between them were built by the fitting in six color spaces respectively,based on the division of hue into red,yellow,green,cyan,blue,and magenta.Polynomials consisting of 10 terms were introduced,and the average conversion CIEL*a*b* color differences for IT8.7/2 color target on two scanners were all about 1,and the maximal color differences were less than 4.And the same results were got in the case of the test target.It was also showed that the transformation from the RGB to the CIEXYZ has a higher accuracy than that to the CIEL*a*b*.
Influence of foot posture focused racing shoes on running motion

To enhance running performance of elite runners development of lightweight shoes, resilient midsole cushioning materials, and high bending stiffness midsole plate are in progress (Hoogkamer et al., 2017). In addition to these factors, we focused on forefoot strike pattern (FFS) and midfoot strike pattern (MFS). We hypothesized that FFS and MFS could use Stretch-Shortening Cycle, which has been suggested as an element to enhance running performance (Paavolaien et al., 1985). FFS and MFS can take advantage of the spring-like behavior of the muscles and tendons, but eccentric contraction of the triceps surae (TS) placing strain on the muscle.