Reviewerly/exHarmony huggingface dataset:
Towards Improving the Organization of Hybrid Development Approaches

Agile methods were proposed to address the problems of traditional or plan-based software development, e.g., late customer feedback or resistance to change. However, unlike plan-based methods, they are not designed for long-term planning or to cope with large projects. Software companies want the ability of a fast reaction to changes but also the ability of long-term planning. To profit from the strength of both approaches, software companies often use a combination of agile and plan-based methods, called hybrid development approaches. These approaches depend on the respective context of each company. Therefore, the companies have to properly arrange and connect the phases, activities, roles, and artifacts from plan-based and agile approaches individually in their hybrid development approach. This is considered as the organization of hybrid development approaches. However, the organization of hybrid approaches is often difficult for the companies. Until now, research considers only the chosen development methods without the organization of hybrid development approaches. With my work, I want to strengthen the understanding of how hybrid approaches are organized and get a detailed picture of the challenges when organizing hybrid approaches. Based on my findings, I want to develop measures to support practitioners while organizing their development approach.
Comparing Degenerate Strings

Uncertain sequences are compact representations of sets of similar strings. They highlight common segments by collapsing them, and explicitly represent varying segments by listing all possible options. A generalized degenerate string (GD string) is a type of uncertain sequence. Formally, a GD string Ŝ is a sequence of n sets of strings of total size N, where the ith set contains strings of the same length ki but this length can vary between different sets. We denote by W the sum of these lengths k0, k1, . . . , kn-1. Our main result is an 𝒪(N + M)-time algorithm for deciding whether two GD strings of total sizes N and M, respectively, over an integer alphabet, have a non-empty intersection. This result is based on a combinatorial result of independent interest: although the intersection of two GD strings can be exponential in the total size of the two strings, it can be represented in linear space. We then apply our string comparison tool to devise a simple algorithm for computing all palindromes in Ŝ in 𝒪(min{W, n2}N)-time. We complement this upper bound by showing a similar conditional lower bound for computing maximal palindromes in Ŝ. We also show that a result, which is essentially the same as our string comparison linear-time algorithm, can be obtained by employing an automata-based approach.
RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments

We present a realtime tracking algorithm, Road-Track, to track heterogeneous road-agents in dense traffic videos. Our approach is designed for dense traffic scenarios that consist of different road-agents such as pedestrians, two-wheelers, cars, buses, etc. sharing the road. We use the tracking-by-detection approach where we track a road-agent by matching the appearance or bounding box region in the current frame with the predicted bounding box region propagated from the previous frame. Roadtrack uses a novel motion model called the Simultaneous Collision Avoidance and Interaction (SimCAI) model to predict the motion of road-agents by modeling collision avoidance and interactions between the road-agents for the next frame. We demonstrate the advantage of RoadTrack on a dataset of dense traffic videos and observe an accuracy of 75.8% on this dataset, outperforming prior state-of-the-art tracking algorithms by at least 5.2%. RoadTrack operates in realtime at approximately 30 fps and is at least 4× faster than prior tracking algorithms on standard tracking datasets.
Passive Authentication Image Forgery Detection Using Multilayer CNN

With the development of various image editing tools and techniques, the forgery has become a common aspect in the image domain, nowadays. We can now insert, delete, or transform a small part of an image very easily. We can also copy-paste an image into some other image or the same image frequently. The image forgery mainly focuses on passive-based approach, because it does not involve the addition of any information into the image unlike active-based approach. Our proposed method mainly focuses on copy-move forgery and splicing forgery detection which are types of passive authentication techniques. The proposed method uses four convolution and pooling layers in a succession with different parameters. A filter is applied on each convolution layer, and all the layers are initialized with some weights and a bias. Using a hidden layer after four convolutions and pooling layer and finally using output layer for decision making with a result of 86.4% accuracy, precision of 81%, recall of 79%, and F-measure of 84% as compared with state-of-the-art approaches.
Toward Architecting the Future of System Security

Abstract Cybersecurity is not just a technical decision regarding cost, benefit, and performance. Cybersecurity interweaves with civil liberties (opting out of the digital world), privacy (the right to be unobserved and the right to be forgotten), automated resolution of moral dilemmas (autonomous vehicle choosing who to hit), financial security (wealth representation being bits on a hard drive), socio‐political deception (detecting fake news on which we base life decisions), and physical safety (loss of life and property from adverse cyber‐physical events). As we look to the future of system security and system security engineering, we have hard choices to make that effect the way we live. This paper examines many of those choices and establishes architectural premises toward architecting the future of system security.
Romeo: Conversion and Evaluation of HDL Designs in the Encrypted Domain

As cloud computing becomes increasingly ubiquitous, protecting the confidentiality of data outsourced to third parties becomes a priority. While encryption is a natural solution to this problem, traditional algorithms may only protect data at rest and in transit, but do not support encrypted processing. In this work we introduce ROMEO, which enables easy-to-use privacy-preserving processing of data in the cloud using homomorphic encryption. ROMEO automatically converts arbitrary programs expressed in Verilog HDL into equivalent homomorphic circuits that are evaluated using encrypted inputs. For our experiments, we employ cryptographic circuits, such as AES, and benchmarks from the ISCAS'85 and ISCAS'89 suites.
Inference-Based Posteriori Parameter Distribution Optimization

Encouraging the agent to explore has always been an important and challenging topic in the field of reinforcement learning (RL). Distributional representation for network parameters or value functions is usually an effective way to improve the exploration ability of the RL agent. However, directly changing the representation form of network parameters from fixed values to function distributions may cause algorithm instability and low learning inefficiency. Therefore, to accelerate and stabilize parameter distribution learning, a novel inference-based posteriori parameter distribution optimization (IPPDO) algorithm is proposed. From the perspective of solving the evidence lower bound of probability, we, respectively, design the objective functions for continuous-action and discrete-action tasks of parameter distribution optimization based on inference. In order to alleviate the overestimation of the value function, we use multiple neural networks to estimate value functions with Retrace, and the smaller estimate participates in the network parameter update; thus, the network parameter distribution can be learned. After that, we design a method used for sampling weight from network parameter distribution by adding an activation function to the standard deviation of parameter distribution, which achieves the adaptive adjustment between fixed values and distribution. Furthermore, this IPPDO is a deep RL (DRL) algorithm based on off-policy, which means that it can effectively improve data efficiency by using off-policy techniques such as experience replay. We compare IPPDO with other prevailing DRL algorithms on the OpenAI Gym and MuJoCo platforms. Experiments on both continuous-action and discrete-action tasks indicate that IPPDO can explore more in the action space, get higher rewards faster, and ensure algorithm stability.
AR Precision Navigation System Based on Baidu Map API

To solve the problems of traditional navigation software, such as early stop before arrival, inaccurate location of destination and so on, an intelligent and accurate navigation system based on AR was designed using Android studio as development environmentusing, Java as development language. Baidu map AR Identification SDK provides service interfaces for the system and it was modified properly to improve the applicability of the interface. High-precision continuous positioning technology was used to determine the location of the destination and ensure the accuracy of navigation. AR augmented reality technology can achieve the suspension display of the target point in the three-dimensional, so as to show the location of the target point in space more clearly and conveniently. Based on POI retrieval techno-logy,multifunction AR navigation selection was provided for different application scenarios and user usage habits,such as realizing intelligent identification of buildings, exploring the surrounding life services and the guiding function of scenic spots inside the scenic area.
The Impact of Environmental Pollution on Leisure Sports Activities of Urban Residents

Yang, J.P., 2020. The impact of environmental pollution on leisure sports activities of urban residents. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 913–916. Coconut Creek (Florida), ISSN 0749-0208.With the rapid development of the economy, the problem of environmental pollution is becoming more and more prominent, and has a multi-faceted impact on the lives of urban residents. According to the research of scholars at home and abroad, environmental pollution has had a negative impact on leisure and sports activities of urban residents. Based on the current situation of environmental pollution in China, this paper analyzes the specific factors that lead to the negative impact of environmental pollution on the leisure activities of urban residents, and puts forward corresponding suggestions for improvement. It is hoped to provide a new research perspective for the study of the effect of leisure sports activities of urban residents.
The Role of Blockchain Technology in Financial Engineering

This research work is to study and list out the processes and operational areas where Blockchain Technology (BCT) is playing a greater role as a tool in the process of financial engineering (FE) in the insurance business. We studied the use of BCT as one of the InsurTech tools in the design and development of financially engineered insurance products. Here, the development of insurance products covers the design of new and innovative insurance policy models, its attractive features as per the needs and requirements of concerned target customers. Insurance processes cover the management and administration of insurance business i.e., marketing, sales and distribution, the underwriting process and claims management, etc. Financial Engineering is a process of creating a new and innovative insurance model by merging existing policy models OR creating a new and innovative insurance model. FE uses the tools and techniques of Statistics, Financial Mathematics, Econometrics, ICTs which includes, FinTech tools, InsurTech tools like Blockchain Technology, Artificial Intelligence, etc. In this research work, we used descriptive cum explorative research methodology. We have studied the role of BCT as an effective tool in the financial engineering process of the insurance business.
A Survey of Sentiment Analysis Based on Machine Learning

Every day, Facebook, Twitter, Weibo and other social network sites and major e-commerce sites generate a large number of online reviews with emotions. The analysing people’s opinions from these reviews can assist a variety of decision-making processes in organisations, products, and administrations. Therefore, it is practically and theoretically important to study how to analyse online reviews with emotions. To help researchers study sentiment analysis, in this paper, we survey the machine learning based method for sentiment analysis of online reviews. These methods are main based on Support Vector Machine, Neural Networks, Naïve Bayes, Bayesian network, Maximum entropy, and some hybrid methods. In particular, we point out the main problems in the machine learning based methods for sentiment analysis and the problems to be solved in the future.
Artificial GAN Fingerprints: Rooting Deepfake Attribution in Training Data

Photorealistic image generation is progressing rapidly and has reached a new level of quality, thanks to the invention and breakthroughs of generative adversarial networks (GANs). Yet the dark side of such deepfakes, the malicious use of generated media, never stops raising concerns of visual misinformation. Existing research works on deepfake detection demonstrate impressive accuracy, while it is accompanied by adversarial iterations on detection countermeasure techniques. In order to lead this arms race to the end, we investigate a fundamental solution on deepfake detection, agnostic to the evolution of GANs in order to enable a responsible disclosure or regulation of such double-edged techniques. We propose to embed artificial fingerprints into GAN training data, and show a surprising discovery on the transferability of such fingerprints from training data to GAN models, which in turn enables reliable detection and attribution of deepfakes. Our empirical study shows that our fingerprinting technique (1) holds for different state-of-the-art GAN configurations, (2) turns more effective along with the development of GAN techniques, (3) has a negligible side effect on the generation quality, and (4) stays robust against image-level and model-level perturbations. When we allocate each GAN publisher a unique artificial fingerprint, the margins between real data and deepfakes, and the margins among different deepfake sources are fundamentally guaranteed. As a result, we are able to evidence accurate deepfake detection/attribution using our fingerprint decoder, which makes this solution stand out from the current arms race.
Resilient Custody of Crypto-Assets, and Threshold Multisignatures

Ensuring safe custody of crypto-assets, while at the same time allowing a user to easily access and transfer them, is one of the biggest challenges of this nascent industry. This is even harder considering also the multiple technological implementations behind each crypto-asset. Here we present a survey of the various solutions for this custody problem, comparing advantages and disadvantages. Then we delve deeper into some interesting schemes based on secure multiparty computation, which give a blockchain-agnostic solution that balances security, safety, and transactional ease of use, and in particular, a protocol that enhances practicality by exploiting a party that may stay offline during the key generation.
VIMES

The advancement of artificial intelligence and wearable computing triggers the radical innovation of cognitive applications. In this work, we propose VIMES, an augmented reality-based memory assistance system that helps recall declarative memory, such as whom the user meets and what they chat. Through a collaborative method with 20 participants, we design VIMES, a system that runs on smartglasses, takes the first-person audio and video as input, and extracts personal profiles and event information to display on the embedded display or a smartphone. We perform an extensive evaluation with 50 participants to show the effectiveness of VIMES for memory recall. VIMES outperforms (90% memory accuracy) other traditional methods such as self-recall (34%) while offering the best memory experience (Vividness, Coherence, and Visual Perspective all score over 4/5). The user study results show that most participants find VIMES useful (3.75/5) and easy to use (3.46/5).
Context-Aware Time Series Imputation for Multi-Analyte Clinical Data

Clinical time series imputation is recognized as an essential task in clinical data analytics. Most models rely either on strong assumptions regarding the underlying data-generation process or on preservation of only local properties without effective consideration of global dependencies. To advance the state of the art in clinical time series imputation, we participated in the 2019 ICHI Data Analytics Challenge on Missing Data Imputation (DACMI). In this paper, we present our proposed model: Context-Aware Time Series Imputation (CATSI), a novel framework based on a bidirectional LSTM in which patients' health states are explicitly captured by learning a "global context vector" from the entire clinical time series. The imputations are then produced with reference to the global context vector. We also incorporate a cross-feature imputation component to explore the complex feature correlations. Empirical evaluations demonstrate that CATSI obtains a normalized root mean square deviation (nRMSD) of 0.1998, which is 10.6% better than that of state-of-the-art models. Further experiments on consecutive missing datasets also illustrate the effectiveness of incorporating the global context in the generation of accurate imputations.
LG-VTON: Fashion Landmark Meets Image-Based Virtual Try-On

Current leading algorithms of the image-based virtual try-on systems mainly model the deformation of clothes as a whole. However, the deformation of different clothes parts can change drastically. Thus the existing algorithms fail to transfer the clothes to the proper shape in cases, such as self-occlusion, complex pose, and sophisticated textures. Based on this observation, we propose a Landmark-Guided Virtual Try-On Network (LG-VTON), which explicitly divides the clothes into regions using estimated landmarks, and performs a part-wise transformation using the Thin Plate Spline (TPS) for each region independently. The part-wise TPS transformation can be calculated according to the estimated landmarks. Finally, a virtual try-on sub-network is introduced to estimate the composition mask to fuse the wrapped clothes and person image to synthesize the try-on result. Extensive experiments on the virtual try-on dataset demonstrate that LG-VTON can handle complicated clothes deformation and synthesize satisfactory virtual try-on images, achieving state-of-the-art performance both qualitatively and quantitatively.
Balancing Constraints and Rewards with Meta-Gradient D4PG

Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly set due to the complex nature of a system or the inability to verify the thresholds offline (e.g, no simulator or reasonable offline evaluation procedure exists). This results in solutions where a task cannot be solved without violating the constraints. However, in many real-world cases, constraint violations are undesirable yet they are not catastrophic, motivating the need for soft-constrained RL approaches. We present a soft-constrained RL approach that utilizes meta-gradients to find a good trade-off between expected return and minimizing constraint violations. We demonstrate the effectiveness of this approach by showing that it consistently outperforms the baselines across four different MuJoCo domains.
Unified programming concepts for unobtrusive integration of cloud-based and local parallel computing

The growth in the data and computation need of today's operations has led to technical solutions that distribute workload over several entities for better performance. To facilitate such a paradigm, research studies have been investigating efficient approaches for combining high performance computing in shared-memory with distributed-memory environments. Meanwhile, the benefits of cloud computing and its modern enhancements have created potentials for applications to leverage the powerful, ubiquitous and cheap resources of cloud infrastructures. Yet, a small portion of the work in this area addresses the programming aspects of cloud-related technologies. Despite the extensive improvements in the fundamental mechanisms of this realm, programming environments offer little support for incorporating the high performance mechanisms of shared-memory computing with cloud computing. This study proposes a solution for an unobtrusive definition and integration of cloud-based and shared-memory parallel computing, in order to further simplify the application of cloud capabilities in local systems. It does so by implementing the proposed concepts in @PT (Annotation Parallel Task), a parallel-programming environment that utilizes native Java annotations as its language constructs. The experimental evaluations discussed here demonstrate that the proposed approach facilitates achieving the potential benefits of cloud computing for performance and energy consumption in local devices.
Effective Use of the Machine Learning Approaches on Different Clouds

Exponential and rapid growth is seen in the field of cloud computing these days. This phenomenon is getting its demands day by day. Numerous companies are moving over the cloud for adopting various features that are supported by cloud. In parallel to this machine learning is also gaining its importance in every field. Machine learning has minimized the work of human and has made life easier. Many researches are being done in clubbing these two strong technologies i.e fusion of machine and cloud computing. In this paper detailed study about cloud computing is briefed with their comparative analysis over different clouds. Also benefits of machine learning in cloud computing and field of cloud machine learning services has also been the part of the paper. Also applicability of machine learning techniques on different cloud types is being explained in detail. The fusion of these two strong technologies helps in solving complex real time problem with minimum efforts.
A Time Domain-Based Iterative Method for Helicopter Maritime Search Area Planning and Construction of the Simulation Environment

Helicopters are widely used in maritime search and rescue (SAR) missions. To improve the probability of success (POS) of SAR missions, search areas should be carefully planned. However, the search area is usually determined based on the survivors' probable locations at a given moment by existing planning methods, while the effects of the relative motion between the helicopter and the search objects are ignored, possibly leading to a significant decrease in the POS. To minimize the impact of search object motion, a time domain-based iterative planning (TIP) method is proposed in this article to obtain the optimal search areas. The survivors' probable locations and mean drift direction are updated iteratively, while the probability map is developed by taking survivors' mean drift direction as a reference. Then, the optimal search area is determined by an iterative search method starting from the cell with the highest probability of containment. To evaluate the effectiveness of a search plan, an agent-based simulation environment of a maritime search mission is constructed based on the AnyLogic simulation platform. Taking a capsizing case as an example, the simulation results show that the novel TIP method minimizes the impact of search object motion on the search effectiveness and obtains higher POS values than those obtained by other methods.