Reviewerly/exHarmony huggingface dataset:
What Evidence We Would Miss If We Do Not Use Grey Literature?

Context: Multivocal Literature Reviews (MLR) search for evidence in both Traditional Literature (TL) and Grey Literature (GL). Despite the growing interest in MLR-based studies, the literature assessing how GL has contributed to MLR studies is still scarce. Objective: This research aims to assess how the use of GL contributed to MLR studies. By contributing, we mean, understanding to what extent GL is providing evidence that is indeed used by an MLR to answer its research question. Method: We start by conducting a tertiary study to identify MLR studies published between 2017 and 2019, selecting nine of them. We then identified the GL used in these studies and assessed to what extent the GLs are providing evidence that help these studies to answer their research questions. Results: Our analysis identified that 1) GL provided evidence not found in TL, 2) most of the GL sources were used to provide recommendations to solve problems, explain a topic, and classify the findings, and 3) 19 different GL types were used in the studies; these GLs were mainly produced by SE practitioners (including blog posts, slides presentations, or project descriptions). Conclusions: We evidence how GL contributed to MLR studies. We observed that if these GLs were not included in the MLR, several findings would have been omitted or weakened. We also described the challenges involved when conducting this investigation, along with potential ways to deal with them, which may help future SE researchers.
In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces

We present a method for reconstructing the 3D shape of underwater environments from a single, stationary camera placed above the water. We propose a novel differentiable framework, which, to our knowledge, is the first single-camera solution that is capable of simultaneously retrieving the structure of dynamic water surfaces and static underwater scene geometry in the wild. This framework integrates ray casting of Snell’s law at the refractive interface, multi-view triangulation and specially designed loss functions.Our method is calibration-free, and thus it is easy to collect data outdoors in uncontrolled environments. Experimental results show that our method is able to realize robust and quality reconstructions on a variety of scenes, both in a laboratory environment and in the wild, and even in a salt water environment. We believe the method is promising for applications in surveying and environmental monitoring.
Towards Generating High Definition Face Images from Deep Templates

Face recognition based on deep convolutional neural networks (CNN) has manifested superior accuracy. Despite the high discriminability of deep features generated by CNN, the vulnerability of the deep feature is often overlooked and leads to the security and privacy concerns, particularly the risks of reconstructing face images from the deep templates. In this paper, we propose a method to generate high definition (HD) face images from deep features. To be specific, the deep features extracted from CNN are mapped to the input (latent vector) of the pre-trained StyleGAN2 using a regression model. Subsequently, HD face images can be generated based on the latent vector by the pre-trained StyleGAN2 model. To evaluate our method, we derived the face features from the generated HD face images and compared them against the bona fide face features. In the sense of face image reconstruction, our method is simple, yet the experimental results suggest the effectiveness, which achieves an attack performance as high as SAR=46.08% (18.30%) @ FAR=0.1 threshold under type-I (type-II) attack settings. Besides, experiment results also indicate that 50.7% of the generated HD face images can pass one commercial off-the-shelf (COTS) liveness detection.
Blockchain in a Nutshell

Blockchain gets its name from being a series of blocks that are linked together to form a chain. Once the information has been added to the chain, it cannot be changed. There are several consensus protocols, and each of them is chosen based on the type of blockchain and the system requirements. With the rapid urbanization of the world, several economic, social, and environment-related issues have been raised. Smart cities are an emerging concept that holds the solution to these urban problems. Blockchain is such an innovation that can promote the development of smart cities. Along with its application in the internet of things, smart cities, and logistics, blockchain truly is state-of-the-art technology. Here, the authors aim to provide an in-depth look into this relatively new technology, beginning with blockchain's fundamentals and then covering the applications, issues, and future scope.
Light-Weight Deformable Registration Using Adversarial Learning With Distilling Knowledge

Deformable registration is a crucial step in many medical procedures such as image-guided surgery and radiation therapy. Most recent learning-based methods focus on improving the accuracy by optimizing the non-linear spatial correspondence between the input images. Therefore, these methods are computationally expensive and require modern graphic cards for real-time deployment. In this paper, we introduce a new Light-weight Deformable Registration network that significantly reduces the computational cost while achieving competitive accuracy. In particular, we propose a new adversarial learning with distilling knowledge algorithm that successfully leverages meaningful information from the effective but expensive teacher network to the student network. We design the student network such as it is light-weight and well suitable for deployment on a typical CPU. The extensively experimental results on different public datasets show that our proposed method achieves state-of-the-art accuracy while significantly faster than recent methods. We further show that the use of our adversarial learning algorithm is essential for a time-efficiency deformable registration method. Finally, our source code and trained models are available at https://github.com/aioz-ai/LDR_ALDK.
Exploring the Current Practices, Costs and Benefits of FAIR Implementation in Pharmaceutical Research and Development: A Qualitative Interview Study

The findable, accessible, interoperable, reusable (FAIR) principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines. Research and development (R&amp;D) in the pharmaceutical industry is becoming increasingly data driven, but managing its data assets according to FAIR principles remains costly and challenging. To date, little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&amp;D. This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&amp;D in seven pharmaceutical businesses. Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification, and the elements that influence the decision-making process for FAIRifying legacy data sets. Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings. Implementation costs, however, were still considered a barrier by participants, with the need for considerable expenditure in terms of resources, and cultural change. How decisions were made about FAIRification was influenced by legal and ethical considerations, management commitment, and data prioritisation. The findings have significant implications for those in the pharmaceutical R&amp;D industry who are engaged in driving FAIR implementation, and for external parties who seek to better understand existing practices and challenges.
The Effect of Customers’ Attitudes Towards Chatbots on their Experience and Behavioural Intention in Turkey

Chatbots are a recent technology that brands and companies adopt to provide 24/7 customer service.However, some customers have several concerns regarding technology, and therefore, prefer talking to humans rather than chatbots.Brands must improve their chatbots based on customer experience because customers satisfied with chatbots are more likely to use them to contact brands/companies.Therefore, this article investigated the effect of perceived ease of use, usefulness, enjoyment, and risk factors on customer experience and behavioral intention regarding chatbots.The study also looked into the impact of customer experience on behavioral intention.The sample consisted of 211 chatbot users of Turkish recruited using non-probability convenience sampling.Data were analyzed using the Statistical Package for Social Sciences (SPSS) and SmartPLS3.The results showed that perceived ease of use and usefulness affected behavioral intention, but perceived risk had no impact on customer experience and behavioral intention regarding chatbots.Perceived enjoyment affected only customer experience.Lastly, customer experience affected behavioral intention.
Syn-STELLAR: An EM/Power SCA-Resilient AES-256 With Synthesis-Friendly Signature Attenuation

Mathematically secure cryptographic algorithms leak meaningful side-channel information in the form of correlated power and electromagnetic (EM) signals, leading to physical side-channel analysis (SCA) attacks. Circuit-level countermeasures against power/EM SCA include a current equalizer, IVR, non-linear LDOs, enhancing protection up to 10M traces, and current-domain signature attenuation (CDSA), and randomized NL-LDO cascaded with arithmetic countermeasures achieved protection up to >1B. This work embraces the concept of analog CDSA but makes it easily scalable over technology nodes with digital-friendly current sources, digital control loop, and digital bleed path to increase the MTD from 10M to 250M ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25\times $ </tex-math></inline-formula> improvement, using a single digital countermeasure). Ring oscillator (RO) used as the bleed path to bypass encryption-dependent leakage acts as local negative feedback (LNFB). Besides, based on RO oscillation frequency, AES node voltage can be tuned at startup, PVT, or frequency variation. Thus, RO acts as integrated LNFB and global feedback for the digital signature attenuation circuit (DSAC). Another circuit technique, namely, the time-varying transfer function (TVTF), removes the requirement of dc bias in the current-domain equalizer (best switch capacitor-based countermeasure till date) to make it digital and utilizes switch cap-based circuit for time-domain obfuscation to achieve enhanced security. This work, namely, Syn-STELLAR: SYNthesis-friendly Signature aTtenuation Embedded crypto with Low-Level metAl Routing, combines both DSAC and TVTF techniques to achieve an MTD > 1.25B for both EM and power SCA, which is 25% higher than the existing state of the art. The 65-nm CMOS test chip contains unprotected and both the protected (DSAC and DSAC-TVTF) parallel AES-256 implementation. This implementation is the first synthesis-friendly countermeasure, which converges two analog-type strong protections (signature attenuation and switched cap current equalizer) in a digital-friendly solution and achieves > 1.25B MTD with power and area overheads comparable to previous circuit-level countermeasures.
Improving random walker segmentation using a nonlocal bipartite graph

The classical random walk segmentation explores merely local affinity among neighboring pixels for cutting out objects, which falls short of effectiveness when handling distant repetitive patterns. Meanwhile, the running efficiency is also limited by solving a large scale linear system. To alleviate the quandary, in this paper, we first propose to introduce nonlocal affinity among distant pixels with similar local features in the underlying segmentation graph, which enables label propagation among disconnected foregrounds and thus multiple repetitive patterns can be segmented jointly. Secondly, the segmentation graph is extended to a multi-scale superpixel based bipartite graph, where random walker segmentation is performed to transfer labeling information from annotated seeds to superpixels and further to unlabeled pixels. We show such a bipartite graph based approach can considerably save the computational cost without sacrificing the segmentation accuracy. Extensive experiments are conducted on multiple datasets, demonstrating the effectiveness of the proposed method.
Grammatical structure detection by Instinct Plasticity based Echo State Networks with Genetic Algorithm

A novel model called Instinct Plasticity Echo State Network with New Weights Selection Method, which is Optimized by Genetic Algorithm, is proposed (IP-NESN-GA). There are three proposed methods that are employed in the conventional ESN. New weights selection method provides a novel approach to replace the random weights of ESN, which obtains much better performance than the conventional ESN. At the same time, Instinct Plasticity is successfully applied in ESN, which enhances the connection among reservoir states and increase the accuracy. Finally, Genetic Algorithm is applied to seek the most suitable parameters. The detecting ability of our proposed model is assessed on two kinds of distinct grammatical constructions data. It not only performs significantly better than the baselines in the meaning error and sentence error, but it also visualizes the expected probabilities for each of the possible thematic roles.
Psychology in Virtual Reality: Toward a Validated Measure of Social Presence

OPINION article Front. Psychol., 04 October 2021 | https://doi.org/10.3389/fpsyg.2021.705448
56 GBaud PAM-4 100 Km Transmission System With Photonic Processing Schemes

Analog photonic computing has been proposed and tested in recent years as an alternative approach for data recovery in fiber transmission systems. Photonic reservoir computing, performing nonlinear transformations of the transmitted signals and exhibiting internal fading memory, has been found advantageous for this kind of processing. In this work, we show that the effectiveness of the internal fading memory depends significantly on the properties of the signal to be processed. Specifically, we demonstrate two experimental photonic post-processing schemes for a 56 GBaud PAM-4 experimental transmission system, with 100 km uncompensated standard single-mode fiber and direct detection. We show that, for transmission systems with significant chromatic dispersion, the contribution of a photonic reservoir's fading memory to the computational performance is limited. In a comparison between the data recovery performances between a reservoir computing and an extreme learning machine fiber-based configuration, we find that both offer equivalent data recovery. The extreme learning machine approach eliminates the necessity of external recurrent connectivity, which simplifies the system and increases the computation speed. Above 31 dB OSNR, the photonics-based equalization exhibits a lower BER than the respective offline DSP-based KK receiver.
Product Graph Learning From Multi-Domain Data With Sparsity and Rank Constraints

In this paper, we focus on learning product graphs from multi-domain data. We assume that the product graph is formed by the Cartesian product of two smaller graphs, which we refer to as graph factors. We pose the product graph learning problem as the problem of estimating the graph factor Laplacian matrices. To capture local interactions in data, we seek sparse graph factors and assume a smoothness model for data. We propose an efficient iterative solver for learning sparse product graphs from data. We then extend this solver to infer multi-component graph factors with applications to product graph clustering by imposing rank constraints on the graph Laplacian matrices. Although working with smaller graph factors is computationally more attractive, not all graphs readily admit an exact Cartesian product factorization. To this end, we propose efficient algorithms to approximate a graph by a nearest Cartesian product of two smaller graphs. The efficacy of the developed framework is demonstrated using several numerical experiments on synthetic and real data.
A visual persistence model for image captioning

Object-level features from Faster R-CNN and attention mechanism have been used extensively in image captioning based on Encoder-Decoder frameworks. However, most existing methods feed the average pooling of object features as the global representation to the captioning model and recalculate the attention weights of object regions when generating a new word without considering the visual persistence like humans. In this paper, we respectively build Visual Persistence modules in encoder and decoder: The visual persistence module in encoder seeks the core object features to replace the image global representation; the visual persistence module in decoder evaluates the correlation between previous attention results and current attention results, and fuses them as the final attended feature to generate a new word. The experimental results on MSCOCO validate the effectiveness and competitiveness of our Visual Persistence Model (VPNet). Remarkably, VPNet also achieves competitive scores in most metrics on MSCOCO online test server compared to the existing state-of-the-art methods.
Video Geo-Localization Employing Geo-Temporal Feature Learning and GPS Trajectory Smoothing

In this paper, we address the problem of video geo-localization by proposing a Geo-Temporal Feature Learning (GTFL) Network to simultaneously learn the discriminative features for the query video frames and the gallery images for estimating the geo-spatial trajectory of a query video. Based on a transformer encoder architecture, our GTFL model encodes query and gallery data separately, via two dedicated branches. The proposed GPS Loss and Clip Triplet Loss exploit the geographical and temporal proximity between the frames and the clips to jointly learn the query and the gallery features. We also propose a deep learning approach to trajectory smoothing by predicting the outliers in the estimated GPS positions and learning the offsets to smooth the trajectory. We build a large dataset from four different regions of USA; New York, San Francisco, Berkeley and Bay Area using BDD driving videos as query, and by collecting corresponding Google StreetView (GSV) Images for gallery. Extensive evaluations of proposed method on this new dataset are provided . Code and dataset details is publicly available at https://github.com/kregmi/VTE.
A new 3D MRI segmentation method based on Generative Adversarial Network and Atrous Convolution

Although many algorithms have been proposed to segment brain structures in MRI scans, comparison of different algorithms in the same data set is rarely done. Many methods still run on privately held data and include comparisons with previous versions. The purpose of this study is to introduce a new Generative Adversarial Network (GAN) based segmentation architecture. Brain tissues on 3D-MRI scans with T1w modality were segmented into three (GM, WM, CSF) and eight (CGM, BG, WM, WMH, CF, VE, CE ve BS) parts. The proposed approach (Vol2SegGAN) consists of two parts: preprocessing and segmentation. The preprocessing includes extraction of the brain region, editing of labels in the datasets, MNI152 registration, clipping/sampling. The segmentation part is carried out by the collaboration of a generator (includes ACFP and PAM modules) and a discriminator (distinguishes real/fake) architectures. In the three part segmentation process, the proposed method showed the best segmentation success in CSF (Dice = 0.739, VS = 0.967), GM (Dice = 0.878, HD = 2.378) and WM (HD = 2.105) tissues, and the second best segmentation success in WM (Dice = 0.793, VS = 0.972) tissue according to Dice and VS metrics. Similarly, in the segmentation of eight parts, it is seen that it has the best success according to VS and HD metrics and the second best according to Dice metric. Vol2SegGAN, which has fewer parameters (6,883 mil.) than existing architectures, has an average of 11–12 s (CPU) or 0–1 s (GPU) to segment a sample 3D-MRI. Implementation codes of the proposed architecture are available on the github page1.
ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients

Videos take a lot of time to transport over the network, hence running analytics on the live video on embedded or mobile devices has become an important system driver. Considering such devices, e.g., surveillance cameras or AR/VR gadgets, are resource constrained, although there has been significant work in creating lightweight deep neural networks (DNNs) for such clients, none of these can adapt to changing runtime conditions, e.g., changes in resource availability on the device, the content characteristics, or requirements from the user. In this article, we introduce ApproxNet, a video object classification system for embedded or mobile clients. It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under changing runtime conditions. It achieves this by enabling two approximation knobs within a single DNN model rather than creating and maintaining an ensemble of models, e.g., MCDNN [MobiSys-16]. We show that ApproxNet can adapt seamlessly at runtime to these changes, provides low and stable latency for the image and video frame classification problems, and shows the improvement in accuracy and latency over ResNet [CVPR-16], MCDNN [MobiSys-16], MobileNets [Google-17], NestDNN [MobiCom-18], and MSDNet [ICLR-18].
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning

Learning new skills by observing humans’ behaviors is an essential capability of AI. In this work, we leverage instructional videos to study humans’ decision-making processes, focusing on learning a model to plan goal-directed actions in real-life videos. In contrast to conventional action recognition, goal-directed actions are based on expectations of their outcomes requiring causal knowledge of potential consequences of actions. Thus, integrating the environment structure with goals is critical for solving this task. Previous works learn a single world model will fail to distinguish various tasks, resulting in an ambiguous la-tent space; planning through it will gradually neglect the desired outcomes since the global information of the future goal degrades quickly as the procedure evolves. We address these limitations with a new formulation of procedure planning and propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning. Experiments conducted on real-world instructional videos show that our method can achieve state-of-the-art performance in reaching the indicated goals. Furthermore, the learned contextual information presents interesting features for planning in a latent space.