Reviewerly/exHarmony huggingface dataset:
Characterizing the transfer of program comprehension in onboarding: an information-push perspective

Many software developers struggle to understand code written by others, leading to increased maintenance costs. Research on program comprehension to date has primarily focused on individual developers attempting to understand code. However, software developers also work together to share and transfer understanding of their codebases. This is common during the onboarding process, when a new developer has joined a project or a company. The work reported here uses a Grounded Theory approach to explore the different types of information passed from experts to newcomers during onboarding, and the perceived value of these types. The theory is grounded in field-study data collected during twelve in-situ onboarding sessions, across eight organizations, with a design based on two pilot studies that were carried out in advance. The field-study data was supplemented and validated with interviews and questionnaires. It provides a description of four views through which the experts represent their code to the newcomers, revealing several interesting aspects of expert-led program comprehension. In particular, it provides evidence that extends current thinking on the temporal aspect of code: where experts discuss changes that have been made to the code-base, changes that are currently being made to the code-base (including temporary fixes) and changes intended for the code-base in the future. In addition, a rationale-based view of the code-base is emphasized in the findings, making explicit the system's functional/non-functional requirements, and their impact on the system's design. This information was perceived as highly valued by the newcomers. Additionally, Structural and Algorithmic views, which have already been firmly established in program comprehension literature, were also noted in these onboarding sessions.
Energy Transfer and Interference by Collective Electromagnetic Coupling

The physics of collective optical response of molecular assemblies, pioneered by Dicke in 1954, has long been at the center of theoretical and experimental scrutiny. The influence of the environment on such phenomena is also of great interest due to various important applications in, e.g., energy conversion devices. In this Letter, we demonstrate both experimentally and theoretically the spatial modulations of the collective decay rates of molecules placed in proximity to a metal interface. We show in a very simple framework how the cooperative optical response can be analyzed in terms of intermolecular correlations causing interference between the response of different molecules and the polarization induced on a nearby metallic boundary and predict similar collective interference phenomena in excitation energy transfer between molecular aggregates.
Challenges and Research Directions for Blockchains in the Internet of Things

In this paper, we analyze the state of the art in distributed ledger technologies and blockchains and investigate potential applications in the Internet of Things (IoT) domain. Afterwards, we discuss interoperability of blockchains, and their use in smart contracts, and artificial intelligence.
Scene Parsing via Integrated Classification Model and Variance-Based Regularization

Scene Parsing is a challenging task in computer vision, which can be formulated as a pixel-wise classification problem. Existing deep-learning-based methods usually use one general classifier to recognize all object categories. However, the general classifier easily makes some mistakes in dealing with some confusing categories that share similar appearances or semantics. In this paper, we propose an integrated classification model and a variance-based regularization to achieve more accurate classifications. On the one hand, the integrated classification model contains multiple classifiers, not only the general classifier but also a refinement classifier to distinguish the confusing categories. On the other hand, the variance-based regularization differentiates the scores of all categories as large as possible to reduce misclassifications. Specifically, the integrated classification model includes three steps. The first is to extract the features of each pixel. Based on the features, the second step is to classify each pixel across all categories to generate a preliminary classification result. In the third step, we leverage a refinement classifier to refine the classification result, focusing on differentiating the high-preliminary-score categories. An integrated loss with the variance-based regularization is used to train the model. Extensive experiments on three common scene parsing datasets demonstrate the effectiveness of the proposed method.
On Schatten-q quasi-norm induced matrix decomposition model for salient object detection

Salient object detection is not only important but also challenging tasks in the study of computer vision. In this paper, different from existing approaches, we propose a novel regularization model for the salient object detection, which integrates a weighted group sparsity with the convex Schatten-1 or the non-convex Schatten-2/3 and Schatten-1/2 norm, respectively. A weighted group sparsity induced norm developed in this paper is shown to be able to make the foreground being consistent within the same image patches by effectively absorbing the image geometrical structure as well as the feature similarity. The Schatten quasi-norm is successfully used to capture the lower rank of background via factorization technique, and an alternative non-convex formulation for nuclear norm is studied. The corresponding alternative direction method of multiplier (ADMM) with derived solutions are discussed in detail, and the convergence of algorithm is validated. Extensive experiments on the six widely used datasets show that the proposed approach has capacity in outperforming most of state-of-the-art models in current literature.
Automatic Vehicle Counting for IoT based Smart Traffic Management System for Indian urban Settings

For smart city management, efficient handling of road traffic is one of the key aspects. Traffic congestion can be managed effectively, if the numbers of vehicles that are to pass through a crowded junction can be pre-estimated in time. The proposed method presents a framework, which has the capability to continuously convey the vehicle count and generate an alarm in case of large vehicle gathering to the controlling station in the Chandigarh or alike urban Indian cities. The number of vehicles passing through a location well before the required traffic junction can be estimated using the help of image processing techniques. Further, the monitoring details can be shared to a distant controlling centre situated anywhere in the city through internet usage. The performed experiments demonstrate the effectiveness of this Internet of Things (IoT) based technology.
Co-designing platform collectivism

We report a codesign effort to envision a community water quality data platform with local stakeholders. We employed interviews, field observations, and hackathons through a 9-month study (ongoing). This codesign effort helps to distinguish our proposal of platform collectivism, in which local resources and stakeholders become more visible, accessible, and engaged with one another through the development and use of a community-based open data platform. In addition, we synthesize key insights for codesign. In our project, local stakeholders have an significant stake in the research and design work. They pose research questions, set goals, and establish parameters for collaboration with us.
Keep Others from Peeking at Your Mobile Device Screen!

People use their mobile devices anywhere and anytime to run various apps, and the information shown on their device screens can be seen by nearby (unauthorized) parties, called shoulder surfers. To mitigate this privacy threat, we have developed HideScreen by utilizing the human vision and optical system properties to hide the users' on-screen information from the shoulder surfers.
Measuring and Assessing Augmented Reality Potential for Educational Purposes: SmartMarca Project

Augmented and Virtual reality proved to be valuable solutions to convey contents in a more appealing and interactive way. Their use is nearly embracing several domains like medicine, geospatial applications, industry, tourism and so on. But among the others, the one that might benefit the most by their use is the Cultural Heritage. In fact, given the improvement of mobile and smart devices in terms of both usability and computational power, contents can be easily conveyed with a realism level never reached in the past. However, despite the tremendous number of researches related with the presentation of new fascinating applications of ancient goods and artifacts augmentation, few papers are focusing on the real effect that these tools have on learning. In fact, whether a disposable use of such tools seems to have a great benefit in terms of visual impact for the users, the same cannot be said about the long-term effect they have on the users, especially for education purposes. Within the framework of SmartMarca project, that will be briefly explained in these pages, this paper focuses on assessing the potential of AR applications specifically designed for Cultural Heritage. More specifically, tests have been conducted on an Augmented Reality experience upon different paintings. For evaluating the benefits of such technology in terms of learning, we have performed our experiment on classrooms of teenagers. By testing different learning approaches, we were able to evaluate and assess the effectiveness of using these technologies for the education process. The paper will even argue on the necessity of developing new tools to enable users to become producers of contents of AR/VR experiences, since up to now there no exists a platform specifically designed for an agile creation, even for not skilled programmers.
Moving Target Tracking and Obstacle Avoidance for a Mobile Robot Using MPC

Target tracking and obstacle avoidance are the most challenging tasks for autonomous non-holonomic mobile robots moving in unknown environments. In this paper a constrained model predictive controller is designed to track a moving target in an environment with static unknown obstacles. In the proposed controller both linear and angular velocities of the mobile robot are computed to control the next position and orientation of the robot. The design of tracking control problem is then formulated as a constraint quadratic programming (QP) problem. Solving the QP problem gives the vector of incremental control action, which will be used to calculate the optimal control input. The obstacle avoidance is also considered in designing the controller using a new switching algorithm and applying some changes in definition of the desired orientation. The proposed scheme guarantees the robot to track a moving target without collision with obstacles. The cost function has been defined as the Lyapunov function which proves stability of the system. The simulation results show a good performance for robot in tracking a moving target while avoiding some obstacles.
A Novel Vision-based Approach for Dietary Assessment using Deep Learning View Synthesis

Dietary assessment system has proven as an effective tool to evaluate the eating behavior of patients suffering from diabetes and obesity. To assess the dietary intake, the traditional method is to carry out a 24-hour dietary recall (24HR), a structured interview aimed at capturing information on food items and portion size consumed by participants. However, unconscious biases are developed easily due to individual's subjective perception in this self-reporting technique which may lead to inaccuracy. Thus, this paper proposed a novel vision-based approach for estimating the volume of food items based on deep learning view synthesis and depth sensing techniques. In this paper, a point completion network is applied to perform 3D reconstruction of food items using a single depth image captured from any convenient viewing angle. Compared to previous approaches, the proposed method has addressed several key challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity. Experiments have been carried out to examine this approach and showed the feasibility of the algorithm in accurate estimation of food volume.
Z-Order Recurrent Neural Networks for Video Prediction

We present a Z-Order RNN (Znet) for predicting future video frames given historical observations. There are two main contributions respectively in deterministic and stochastic modeling perspective. First, we propose a new RNN architecture for modeling the deterministic dynamics, which updates hidden states along a z-order curve to enhance the consistency of the features of mirrored layers. Second, we introduce an adversarial training approach to two-stream Znet for modeling the stochastic variations, which forces the Znet-Predictor to imitate the behavior of the Znet-Probe. This two-stream architecture enables the adversarial training to be conducted in the feature space instead of the image space. Our model achieves the state-of-the-art prediction accuracy on two video datasets.
A Relation Network Embedded with Prior Features for Few-Shot Caricature Recognition

Caricature is a simple and abstract description of a person using her/his exaggerated characteristics. Due to amplified facial variations in the caricatures and significant differences among caricature and real face modalities, building vision models for recognizing each other between these modalities is an extremely challenging task. In addition, it is not easy to collect abundant samples of real faces and corresponding caricatures for training vision models, which makes the recognition more difficult. In this paper, we propose a novel relation network via meta learning to address the problem of few-shot caricature face recognition. In particular, we present a deep relation network to capture and memorize the relation among different samples. To employ the prior knowledge, we combine learned deep and handcrafted features to form the hybrid-prior representation via joint meta learning. Final recognition is derived from our relation network by learning to compare between the hybrid-prior features of samples. Experimental results on three caricature datasets of WebCaricature, IIIT-CFW, and Caricature-207 demonstrate that our method performs better than many existing ones for few-shot caricature recognition.
Visual querying and analysis of temporal fiscal networks

We present TeFNet, a new system for the visual analysis of temporal networks in the fiscal domain, aimed to contrast tax evasion, fiscal frauds, and money laundering. The design of TeFNet has been driven by domain experts (tax officers) and the system is currently used by the Italian revenue agency, Agenzia delle Entrate. TeFNet is based on a powerful visual query language that allows users to easily define suspicious patterns involving temporal constraints. To efficiently execute graph pattern matching algorithms on large networks, TeFNet exploits modern graph database technologies. Besides its visual query language, TeFNet is equipped with graph visualization techniques to quickly convey time-varying information during an interactive exploration of the subgraphs that match a desired pattern. Both the query language and the visualization techniques rely on a suitable timeline approach, which maps the time dimension to the space dimension. The system has been tested in a real working environment. We demonstrate its effectiveness by discussing use cases on real suspicious patterns and the results of experiments conducted with expert tax officers on real data. Furthermore, we performed a qualitative evaluation to get additional insights about advantages and limits of our system.
CNNP-v2:An Energy Efficient Memory-Centric Convolutional Neural Network Processor Architecture

An energy efficient memory-centric convolutional neural network (CNN) processor architecture is proposed for smart devices such as wearable devices or internet of things (IoT) devices. To achieve energy-efficient processing, it has 2 key features: First, 1-D shift convolution PEs with fully distributed memory architecture achieve 3.1TOPS/W energy efficiency. Compared with conventional architecture, even though it has massively parallel 1024 MAC units, it achieve high energy efficiency by scaling down voltage to 0.46V due to its fully local routed design. Next, fully configurable 2-D mesh core-to-core interconnection support various size of input features to maximize utilization. The proposed architecture is evaluated 16mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> chip which is fabricated with 65nm CMOS process and it performs real-time face recognition with only 9.4mW at 10MHz and 0.48V.
Image Super-Resolution using a Improved Generative Adversarial Network

In recent years, there have been a variety of learning methods applied to single image super-resolution problems (SISR). Generative adversarial network (GAN) for image super-resolution which can infer photo-realistic natural images for 4× upscaling factors has been proposed. Images generated from SRGAN have sharper details, but some texture will be distorted and deformed. In this situation, the image looks unsatisfactory. In order to generate clearer, more eye-catching picture, we improved the SRGAN network. We proposed a encoder block in the generator to extract more crucial features. In this condition, we can generate more clear and natural image.
Call-by-need is clairvoyant call-by-value

Call-by-need evaluation, also known as lazy evaluation, provides two key benefits: compositional programming and infinite data. The standard semantics for laziness is Launchbury’s natural semantics DBLP:conf/popl/Launchbury93, which uses a heap to memoise the results of delayed evaluations. However, the stateful nature of this heap greatly complicates reasoning about the operational behaviour of lazy programs. In this article, we propose an alternative semantics for laziness, clairvoyant evaluation , that replaces the state effect with nondeterminism, and prove this semantics equivalent in a strong sense to the standard semantics. We show how this new semantics greatly simplifies operational reasoning, admitting much simpler proofs of a number of results from the literature, and how it leads to the first denotational cost semantics for lazy evaluation.
Albatross – An optimistic consensus algorithm

Distributed ledgers have the potential to revolutionize the financial landscape by removing trusted third parties. At the heart of most distributed ledgers is their consensus protocol. The consensus protocol describes the way participants in a distributed network interact with each other to obtain and agree on a shared state. While classical Byzantine fault tolerant (BFT) algorithms are designed to work in closed, size-limited networks only, modern distributed ledgers - and blockchains in particular - often focus on open, permissionless networks. In this paper, we present a novel blockchain consensus algorithm for open, permissionless networks, called Albatross, inspired by speculative BFT algorithms. We argue that the protocol is secure under regular PBFT security assumptions and has a theoretical performance close to the maximum for single-chain Proof-of-Stake consensus algorithms. We conclude by describing our future work related to Albatross.
Sparse Matrix Masking-Based Non-Interactive Verifiable (Outsourced) Computation, Revisited

A Privacy-preserving Verifiable (outsourced) Computation (PVC) protocol enables a resource-constrained client to outsource expensive and sensitive workloads to computationally powerful but possibly untrusted service providers (called workers) and to verify the correctness of the results. In a PVC protocol, the inputs and outputs of the computation are hidden, so that the worker is unable to determine them. This is referred to as the privacy property. A Non-interactive PVC (NPVC) protocol is a PVC protocol without communications between a client and worker, apart from distributing the workloads and results. In the literature, Sparse Matrices (SMs) have been used in NPVC protocols to efficiently hide the inputs and outputs from the worker. However, to the best of our knowledge, how the low density of an SM affects privacy in such NPVC protocols has not been formally analyzed. In this work, we first propose a formal definition of the privacy property of an NPVC protocol with respect to matrix density. We use this definition to demonstrate that all of the SM masking-based NPVC protocols that we know of do not hold this privacy property under the ciphertext-only attack model. We then propose an SM masking construction to modify two of those protocols, chosen because they are state-of-the-art, and prove that the modified protocols hold the privacy property under the chosen-plaintext attack model. Our modifications do not require the client operating matrix inversion, and they are able to keep the same level of high performance and all other properties as the originals.
Serialized lightweight SHA-3 FPGA implementations

In this article, we extend our study of lightweight FPGA implementations of SHA-3 published at ReConFig 2016. We use the shallow pipeline optimization technique for the slice-oriented SHA-3 architecture developed previously and examine additional aspects. Firstly, we adapt the implementation to the state organization proposed by Winderickx et al. based on shift register primitives available on Xilinx FPGA platforms. Secondly, we study the usage of block RAM instead of distributed RAM for the original designs. The shallow pipeline optimization already has reduced the area to about 90 slices for both Virtex-5 and Virtex-6 FPGAs. This is a significant improvement over the previous state of the art. On the one hand, our additional results show that the optimized state representation by Windericks et al. using shift registers does not improve the performance at all, compared to the solution based on distributed RAM. The main reason for this is the implementation of the ρ function, which requires different offsets for the rotations to be implemented and also larger shift registers for most lanes than the 64 bits of a lane. Together, this leads to a higher than expected area consumption for the shift register approach, which leads to a very similar total area consumption than the RAM based approach. On the other hand, the block RAM solution shows a considerable reduction of the slice utilization from about 88 to only 54 slices at the expense of 13 to 14 block RAMs. However, at the same time the achievable maximum clock frequency is considerably lower, because of the additional routing delays from and to the block RAM.
Modeling and simulation of practical quantum secure communication network

As the quantum key distribution (QKD) technology supporting the point-to-point application matures, the need to build the quantum secure communication network (QSCN) to guarantee the security of a large scale of nodes becomes urgent. Considering the project time and expense control, it is the first choice to build the QSCN based on an existing classical network. Suitable modeling and simulation are very important to construct a QSCN successfully and efficiently. In this paper, a practical QSCN model, which can reflect the network state well, is proposed. The model considers the volatile traffic demand of the classical network and the real key generation capability of the QKD devices, which can enhance the accuracy of simulation to a great extent. In addition, two unique QSCN performance indicators, ITS (information-theoretic secure) communication capability and ITS communication efficiency, are proposed in the model, which are necessary supplements for the evaluation of a QSCN except for those traditional performance indicators of classical networks. Finally, the accuracy of the proposed QSCN model and the necessity of the proposed performance indicators are verified by plentiful simulation results.