Reviewerly/exHarmony huggingface dataset:
Removing Raindrops from a Single Image using Synthetic Data

We simulated the exact features of raindrops on a camera lens and conducted an experiment to evaluate the performance of a network trained to remove raindrops using synthetic raindrop data. Although research has been conducted to precisely evaluate methods to remove raindrops, with some evaluation networks trained on images with real raindrops and others trained on images with synthetic raindrops, there have not been any studies that have directly compared the performance of two networks trained on each respective kind of image. In a previous study wherein images with synthetic raindrops were used for training, the network did not work effectively on images with real raindrops because the shapes of the raindrops were simulated using simple arithmetic expressions. In this study, we focused on generating raindrop shapes that are closer to reality with the aim of using these synthetic raindrops in images to develop a technique for removing real-world raindrops. After categorizing raindrops by type, we further separated each raindrop type into its constituent elements, generated each element separately, and finally combined the generated elements. The proposed technique was used to add images with synthetic raindrops to the training data, and when we evaluated the model, we confirmed that the technique's precision exceeded that of when only images with actual raindrops were used for training. The evaluation results proved that images with synthetic raindrops can be used as training data for real-world images.
The Design of COVID Alert NY in Six Parables

A multi-disciplinary team of volunteers collaborated with New York State to design the COVID contact tracing app for its residents (COVID Alert NY). Facing a myriad of constraints and conflicting forces, we conducted 13 evaluation studies, using 5 UX methods, in 10 weeks. These studies revealed over 150 usability and attitudinal issues, many of which were prioritized and addressed before launch on Oct 1, 2020. We selected six stories in which to convey some of the lessons we learned: parables, if you will, for UX practice.
PASTA: Synthesizing Object State Transformers for Dynamic Software Updates

Object transformation (upgrading heap objects to their new-version counterparts) is a crucial step in dynamic software update (DSU). However, providing non-trivial object transformers for complex software updates can be difficult for software developers and upgrade maintainers. This paper presents the design and implementation of PASTA, a tool for automatic object transformer synthesis.
Design and Execution of ETL Process to Build Topic Dimension from User-Generated Content

Latest research studies on multi-dimensional design have combined business data with User-Generated Content (UGC). They have integrated new analytical aspects, such as user’s behavior, sentiments, opinions or topics of interest, to ameliorate decisional analysis. In this paper, we deal with the complexity of designing topics dimension schema due to the dynamicity and heterogeneity of its hierarchies. Researchers addressed partially this issue by offering technical solutions to topics detection without focusing on the Extraction, Transformation and Loading (ETL) process allowing their integration in multi-dimensional schema. Our contribution consists in modeling ETL steps generating valid topic dimension hierarchies referring to UGC informal texts. In this research work, we propose a generic ETL4SocialTopic process model defining a set of operations executed following a specific order. The implementation of these steps offers a set of customized jobs simplifying the ETL designer’s work by automating a large part of the process. Experimentation results show the consistency of ETL4SocialTopic to design valid topic dimension schemas in several contexts.
Potentials of Augmented Reality – Insights into Industrial Practice

With emerging technologies, research is increasingly focusing on the digitalization of the working world and the resulting potentials, but also risks, for employees. People and technology are beginning to grow together in industrial environments. New technologies can support skills and improve results of the daily work, but the most important factor is still the human being. Assistance systems based on Augmented Reality (AR) are increasingly attract the attention of companies. Currently, there are not many implementation reports and applications in companies. This paper discusses the findings based on observations from industrial site inspections in the areas of training, commissioning of goods, and the manual assembly process. Inspections took place at three pioneering companies in the automotive industry, online distance selling, and the energy sector. When it comes to the use of assistance systems, the main goals of these companies that are very open to innovation are to improve efficiency and quality, e.g., minimize errors and optimize speed. Qualitative content analysis was used to evaluate the interview transcripts, the collected transcripts and notes from the guided tours, and the brief participations in these industries. The results are presented in the form of user experiences with AR addressing their advantages and disadvantages in the context of use. In addition, these were categorized into a predefined classification of influencing factors and challenges.
Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera

Robot detection, recognition, positioning, and other applications require not only real-time video image information but also the distance from the target to the camera, that is, depth information. This paper proposes a method to automatically generate any monocular camera depth map based on RealSense camera data. By using this method, any current single-camera detection system can be upgraded online. Without changing the original system, the depth information of the original monocular camera can be obtained simply, and the transition from 2D detection to 3D detection can be realized. In order to verify the effectiveness of the proposed method, a hardware system was constructed using the Micro-vision RS-A14K-GC8 industrial camera and the Intel RealSense D415 depth camera, and the depth map fitting algorithm proposed in this paper was used to test the system. The results show that, except for a few depth-missing areas, the results of other areas with depth are still good, which can basically describe the distance difference between the target and the camera. In addition, in order to verify the scalability of the method, a new hardware system was constructed with different cameras, and images were collected in a complex farmland environment. The generated depth map was good, which could basically describe the distance difference between the target and the camera.
A survey on : Online Social Networking Attacks Detection Techniques

Today's due the popularity of internet number of users are increase on every social media platform. In recent research found that 80% of youth depend on social media to make new friends , share photos. Through this they get popularity and large number of user base and become influencers . Most of the social media platform are providing different privacy and security . Still attacker find out the way to breech the security, privacy and confidently of users and companies or organizations using several techniques . This paper highlight the major security issues phasing by many social networking web applications. Also identify the solution based on attacks in different literature . At last, we discuss open research issues
A Vehicle Accident Detection and Driver Surveillance

Abstract Perpetual increase in the number of deadly traffic accidents around the world are often considerablyminimized if contemporaryproficiency is integrated inside of the car towards observe the fitness of the driving force onconsistentinterims over the vehicle runningand precautionary measures predictably taken for the safety of all anxiouspersonnel, either within the vehicle or outside. The Raspberry Pi controller constantly records every one of the parameters of the vehicle based on the heartbeat, drowsiness and the level of alcohol consumption of the driver, for anticipation and recognition of mishap. In the unfortunate case of occurrence of an accident, the details and location of the vehicle is intimated to the emergency contacts via message immediately in order to seek first aid so as to prevent the avoidable loss of lives.
Technical Debt in Large-Scale Distributed Projects: An Industrial Case Study

Technical debt (TD) is a metaphor that reflects the technical compromises that sacrifice the long-term health of a software product to achieve short term benefit. It is important to manage TD to avoid software degradation. In large-scale distributed projects, technical debt management (TDM) becomes more complex and challenging. There is a lack of empirical studies on the TD accumulation in large-scale distributed projects. Then, to address this gap, we conducted a case study in Ericsson (a European Telecom Company) to identify the relationship between TD accumulation and factors such as task complexity, lead time, total of developers, and task scaling. We used data from 33 projects extracted from managerial documents to conduct a regression analysis. We also conducted interviews with seniors developers of the team to interpret the results. We found out that Task Complexity has a strong relationship (p-value = 5.69 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> ) with Technical Debt, while Global Distance was mentioned by the interviewees as a relevant factor for TD accumulation (although not statistically significant in our regression analysis). Practitioners should consider avoiding complex/big tasks, breaking down big tasks into small ones (if possible). We also plan to analyze other projects in this company to confirm our findings further.
Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image

We present a novel method for synthesizing both temporally and geometrically consistent street-view panoramic video from a single satellite image and camera trajectory. Existing cross-view synthesis approaches focus on images, while video synthesis in such a case has not yet received enough attention. For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view. As for synthesis in the 3D space, we implement a cascaded network architecture with two hourglass modules to generate point-wise coarse and fine features from semantics and per-class latent vectors, followed by projection to frames and an up-sampling module to obtain the final realistic video. By leveraging computed correspondences, the produced street-view video frames adhere to the 3D geometric scene structure and maintain temporal consistency. Qualitative and quantitative experiments demonstrate superior results compared to other state-of-the-art synthesis approaches that either lack temporal consistency or realistic appearance. To the best of our knowledge, our work is the first one to synthesize cross-view images to videos..
Background Invariance by Adversarial Learning

Convolutional neural networks are shown to be vulnerable to changes in the background. The proposed method is an end-to-end method that augments the training set by introducing new backgrounds during the training process. These backgrounds are created by a generative network that is trained as an adversary to the model. A case study is explored based on overhead power line insulators detection using a drone - a training set is prepared from photographs taken inside a laboratory and then evaluated using photographs that are harder to collect from outside the laboratory. The proposed method improves performance by over 20% for this case study.
A Study on the Reader Reception of English Translations of The Analects-Data Analysis with Python

Based on Python data processing technology, this study collects reader comments on the English translations of The Analects on the mainstream western social book platforms to construct a review database so as to analyze readers' emotional attitudes, recognition of the works' ideological values, focal dimensions of translation quality, gains and losses of word translation, and the reflected image of China. The quantitative and qualitative analyses find that the overall reader acceptance of the English translations of The Analects is good and that China, with its philosophy and culture, is attractive and brilliant, which also arise their interest and enthusiasm in getting to know Chinese culture and elements. Hopefully, this study will provide insight for relevant research on the positioning and influence of foreign translation of Chinese classics.
Research on teaching reform of electrical and electronic training innovative talents training based on computer technology

The idea of network education based on computer technology is a supplement to the cultivation of innovative talents. There is no central point in the structure of network technology based on computer technology. Its structure determines that its inner spirit is decentralized and equal. Its structure determines that the inner spirit is decentralized and equal. In order to adapt to and innovate the arrival of the Internet era, the teaching understanding and thinking are changed, and the teaching structure is changed, and the improvement of teaching methods is promoted. On this basis, this paper expounds and summarizes the practical training process of electrical and Electronic Training and innovation and entrepreneurship teaching for college students.
Internet-based services to obtain information on science and technology according to the degree of interest

Some trial events such as "Science Cafés" have been established to promote scientific and technological (S&T) literacy. However, these trials likely gather only highly motivated citizens. In this study, we surveyed two aspects of media that were effective in approaching those with a lower interest in S&T: first, we determined the types of media used by people to obtain information on S&T;; second, the types of Internet-based services used, both one-way and two-way. We conducted an online questionnaire survey and analyzed the answers according to the degree of interest in S&T. Our survey indicated that Wiki, a one-way service, was the preferred outlet for scientific information. Moreover, general websites might be also useful for those showing potential interest in S&T. As for two-way services, social networking had the potential to introduce less motivated people to S&T, although its reliability requires further examination.
Extensible, Extendable, Expandable, Extractable: The 4E Design Approach for Reconfigurable Displays

We introduce “4E,” a new design approach of reconfigurable displays that can change their form factors by capitalizing on four quality properties inspired by applied material: extensibility, extendability, expandability, and extractability. This approach is applicable to both fixed and portable displays. We define and exemplify each property, highlighting the key differences in how reconfigurable displays can change their form factors to accommodate more screen real estate for more users, applications, and functionality. To demonstrate the 4E approach, we conduct a targeted literature review and introduce E3Screen, a prototype that enhances any flat screen (e.g., of a tablet, laptop, monitor) with two slidable, rotatable, and foldable lateral displays. We report results from a controlled experiment with N=103 participants, conducted to collect, analyze, and understand end users’ preferences for display configurations permitted by the extendability, expandability, and extractability of E3Screen. Our results structure future research and development in reconfigurable displays and multi-display collaborative workspaces.
Learning Neural Textual Representations for Citation Recommendation

With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming. While several approaches for automated citation recommendation have been proposed in the recent years, effective document representations for citation recommendation are still elusive to a large extent. For this reason, in this paper we propose a novel approach to citation recommendation which leverages a deep sequential representation of the documents (Sentence-BERT) cascaded with Siamese and triplet networks in a submodular scoring function. To the best of our knowledge, this is the first approach to combine deep representations and submodular selection for a task of citation recommendation. Experiments have been carried out using a popular benchmark dataset - the ACL Anthology Network corpus - and evaluated against baselines and a state-of-the-art approach using metrics such as the MRR and F1@ k score. The results show that the proposed approach has been able to outperform all the compared approaches in every measured metric.
Calibration Data-based CNN Filter Pruning for Efficient Layer Fusion

Convolutional Neural Network (CNN) optimization is critical to reduce the inference latency on computing devices like CPU and GPU. The most important step in efficiently executing these algorithms involves combining multiple operations within a single convolutional layer through a process called Layer Fusion. We first form a correlation between Layer Fusion and data management on computing platforms like CPUs and GPUs, and analyze its significance for different networks under different model compression techniques, e.g., Pruning and Quantization. Weight Pruning removes redundant parameters in the network thereby shrinking the model size. This method, however, creates sparse matrices which can hamper the performance on CPU and GPU devices. Several node/filter pruning algorithms have been developed to resolve the bottlenecks of irregular pruning and reduce the inference time. Although symmetric pruning techniques reduce the forward path computation time, the weights and activations are still executed in floating point precision which can be further optimized by Quantization. This method reduces the bit-width of individual CNN parameter from high precision (Float32) to lower precision (Int8). Even though several model compression (pruning and quantization) techniques have been developed to improve performance, their integrated study with Layer Fusion has not been performed. In today's scenario, not all CPUs and GPUs explicitly support Int8 multiplication. Hence, we analyze the performance of optimized implementation of Int8 Quantization on such devices like Intel's Skylake and Nvidia's Tesla V100. We develop a novel Node Pruning algorithm to remove redundant filters which can aid in efficient implementation of Layer Fusion/Quantized Networks. We compare the execution time of our combined pruning and quantization implementation with a traditional node pruning algorithm which achieved a mean speedup of 3.5 times on Skylake CPU.
Inductive Benchmarks for Automated Reasoning

We present a large set of benchmarks for automated theorem provers that require inductive reasoning. Motivated by the need to compare first-order theorem provers, SMT solvers and inductive theorem provers, the setting of our examples follows the SMT-LIB standard. Our benchmark set contains problems with inductive data types as well as integers. In addition to SMT-LIB encodings, we provide translations to some other less common input formats.
Performance Evaluation of Background Subtraction Techniques for Video Frames

The fundamental working of background subtraction is to identify the moving region by taking pixel-wise difference of the current frame from the previous one. The proposed study presents the comparison and implementation of different background subtraction techniques i.e., frame-difference method, mixture of Gaussian model 2 (MOG2) and k-nearest neighbor (KNN) for background subtraction. For all the three techniques, prior to segmentation, background modeling and then features extraction steps are performed. It is investigated that on the same dataset, frame-difference technique outperforms both MOG2 and KNN and achieve accuracy of 89.98%, recall of 94.43% precision 79.55% and F1-score of 81.42%.
Attention-Guided Second-Order Pooling Convolutional Networks

Recently, channel attention-guided convolutional networks (ConvNets) have shown great advance on visual recognition tasks. However, they mainly exploit coarse first-order statistics to characterize holistic image and rarely focus on long-range feature dependencies, which limits the representation power in a certain. To handle above limitations, this paper proposes a novel attention-guided second-order pooling convolutional network (ASP-Net). ASP-Net introduces bilinear pooling that captures pairwise feature interactions to model second-order statistics. Meanwhile, it explicitly collects long-range dependencies via non-local operations, thus providing a global view in lower layers. Then, the second-order statistics and non-local context features are fused to obtain the enhanced representation for predicting channel-wise attention map and scaling convolution features. Experiment results on three commonly used datasets illuminate that ASP-Net outperforms its counterparts and achieves competitive performance. The source code is available at https://github.com/ShannanChen/ASPNet.
From Detectables to Inspectables: Understanding Qualitative Analysis of Audiovisual Data

Audiovisual recordings of user studies and interviews provide important data in qualitative HCI research. Even when a textual transcription is available, researchers frequently turn to these recordings due to their rich information content. However, the temporal, unstructured nature of audiovisual recordings makes them less efficient to work with than text. Through interviews and a survey, we explored how HCI researchers work with audiovisual recordings. We investigated researchers’ transcription and annotation practice, their overall analysis workflow, and the prevalence of direct analysis of audiovisual recordings. We found that a key task was locating and analyzing inspectables, interesting segments in recordings. Since locating inspectables can be time consuming, participants look for detectables, visual or auditory cues that indicate the presence of an inspectable. Based on our findings, we discuss the potential for automation in locating detectables in qualitative audiovisual analysis.
Scooter &amp; Sidecar: a domain-specific approach to writing secure database migrations

Web applications often handle large amounts of sensitive user data. Modern secure web frameworks protect this data by (1) using declarative languages to specify security policies alongside database schemas and (2) automatically enforcing these policies at runtime. Unfortunately, these frameworks do not handle the very common situation in which the schemas or the policies need to evolve over time---and updates to schemas and policies need to be performed in a carefully coordinated way. Mistakes during schema or policy migrations can unintentionally leak sensitive data or introduce privilege escalation bugs. In this work, we present a domain-specific language (Scooter) for expressing schema and policy migrations, and an associated SMT-based verifier (Sidecar) which ensures that migrations are secure as the application evolves. We describe the design of Scooter and Sidecar and show that our framework can be used to express realistic schemas, policies, and migrations, without giving up on runtime or verification performance.