Reviewerly/exHarmony huggingface dataset:
QuadroNet: Multi-Task Learning for Real-Time Semantic Depth Aware Instance Segmentation

Vision for autonomous driving is a uniquely challenging problem: the number of tasks required for full scene understanding is large and diverse; the quality requirements on each task are stringent due to the safety-critical nature of the application; and the latency budget is limited, requiring real-time solutions. In this work we address these challenges with QuadroNet, a one-shot network that jointly produces four outputs: 2D detections, instance segmentation, semantic segmentation, and monocular depth estimates in real-time (>60fps) on consumer-grade GPU hardware. On a challenging real-world autonomous driving dataset, we demonstrate an increase of+2.4% mAP for detection, +3.15% mIoU for semantic segmentation, +5.05% mAP@0.5 for instance segmentation and +1.36% in δ <; 1.25 for depth prediction over a baseline approach. We also compare our work against other multi-task learning approaches on Cityscapes and demonstrate state-of-the-art results.
Comparative analysis of decision tree algorithms: Random forest and C4.5 for airlines customer satisfaction classification

Abstract This article aims to a comparative analysis of decision tree algorithms between random forest and C4.5 for airlines customer satisfaction classification. The comparative study predicts both algorithms have better accuracy, precision, recall AUC (area under the curve) for analyzing data set of customer satisfaction on airlines, which are useful for later if have some same kind set of data set and problem. In this particular comparative analysis, first, need to select the dataset and transform so it can be used for data mining technique classification after choosing the algorithm to analyze the data set. The analyzing of the dataset it will go through validation, testing, and also result for each algorithm used. Then will compare the result from each algorithm, to determine which algorithm are best to use in this particular dataset or problem for customer satisfaction for airlines. The results of the comparative analysis are the best alternative algorithm choice for use in airline customer satisfaction classifications. For this comparison, the Random forest algorithm has a better result than the C4.5 algorithms.
Efficient video annotation with visual interpolation and frame selection guidance

We introduce a unified framework for generic video annotation with bounding boxes. Video annotation is a longstanding problem, as it is a tedious and time-consuming process. We tackle two important challenges of video annotation: (1) automatic temporal interpolation and extrapolation of bounding boxes provided by a human annotator on a subset of all frames, and (2) automatic selection of frames to annotate manually. Our contribution is two-fold: first, we propose a model that has both interpolating and extrapolating capabilities; second, we propose a guiding mechanism that sequentially generates suggestions for what frame to annotate next, based on the annotations made previously. We extensively evaluate our approach on several challenging datasets in simulation and demonstrate a reduction in terms of the number of manual bounding boxes drawn by 60% over linear interpolation and by 35% over an off-the-shelf tracker. Moreover, we also show 10% annotation time improvement over a state-of-the-art method for video annotation with bounding boxes [25]. Finally, we run human annotation experiments and provide extensive analysis of the results, showing that our approach reduces actual measured annotation time by 50% compared to commonly used linear interpolation.
Topological recovery for non-rigid 2D/3D registration of coronary artery models

Background and Objective: Intra-operative X-ray angiography, the current standard method for visualizing and diagnosing cardiovascular disease, is limited in its ability to provide essential 3D information. These limitations are disadvantages in treating patients. For example, it is a cause of lowering the success rate of interventional procedures. Here, we propose a novel 2D-3D non-rigid registration method to understand vascular geometry during percutaneous coronary intervention. Methods: The proposed method uses the local bijection pair distance as a cost function to minimize the effect of inconsistencies from center-line extraction. Moreover, novel cage-based 3D deformation and multi-threaded particle swarm optimization are utilized to implement real-time registration. We evaluated the proposed method for 154 examinations from 10 anonymous patients by coverage percentage, comparing the average distance of the 2D extracted center-line with that of the registered 3D center-line. Results: The proposed 2D-3D non-rigid registration method achieved an average distance of 1.98 mm with a 0.54 s computation time. Additionally, in aiming to reduce the uncertainty of XA images, we used the proposed method to retrospectively visualize the connections between 2D vascular segments and the distal part of occlusions. Conclusions: Ultimately, the proposed 2D/3D non-rigid registration method can successfully register the 3D center-line of coronary arteries with corresponding 2D XA images, and is computationally sufficient for online usage. Therefore, this method can improve the success rate of such procedures as a percutaneous coronary intervention and provide the information necessary to diagnose cardiovascular diseases better.
Data tag replacement algorithm for data integrity verification in cloud storage

Data integrity verification is an important technical method to detect the integrity of remote cloud storage data. At present, the existing data integrity verification algorithms generate data tags for outsourced data blocks before verification, and then use these data tags to verify the data integrity proofs provided by the data storage provider and determine whether the corresponding data blocks are intact. Unfortunately, most of these algorithms do not check the reliability of data tags. Once the data tags are compromised due to the exposure of the private key of data owner, the corrupted data can easily bypass subsequent verification, thereby invalidating the verification. To solve this issue, we propose a secure and reliable data tag replacement algorithm DTRA. The proposed algorithm uses proactive and reactive replacement to update the data owner's private key. In addition, the algorithm applies homomorphic encryption to implement data tag replacement directly in the remote storage environment, avoids data owner downloading outsourced data from remote storage space, and reduces the data transmission overhead caused by data tag replacement. Theoretical analysis and experimental results indicate that the algorithm can achieve data tag replacement safely and reliably at a low cost.
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations

A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations
Simplified decision making in the belief space using belief sparsification

In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as decision making in a belief space, over a possibly high-dimensional state space. Typically, to solve a decision problem, one should identify the optimal action from a set of candidates, according to some objective. We claim that one can often generate and solve an analogous yet simplified decision problem, which can be solved more efficiently. A wise simplification method can lead to the same action selection, or one for which the maximal loss in optimality can be guaranteed. Furthermore, such simplification is separated from the state inference and does not compromise its accuracy, as the selected action would finally be applied on the original state. First, we present the concept for general decision problems and provide a theoretical framework for a coherent formulation of the approach. We then practically apply these ideas to decision problems in the belief space, which can be simplified by considering a sparse approximation of their initial belief. The scalable belief sparsification algorithm we provide is able to yield solutions which are guaranteed to be consistent with the original problem. We demonstrate the benefits of the approach in the solution of a realistic active-SLAM problem and manage to significantly reduce computation time, with no loss in the quality of solution. This work is both fundamental and practical and holds numerous possible extensions.
High-Performance Computation in Residue Number System Using Floating-Point Arithmetic

Residue number system (RNS) is known for its parallel arithmetic and has been used in recent decades in various important applications, from digital signal processing and deep neural networks to cryptography and high-precision computation. However, comparison, sign identification, overflow detection, and division are still hard to implement in RNS. For such operations, most of the methods proposed in the literature only support small dynamic ranges (up to several tens of bits), so they are only suitable for low-precision applications. We recently proposed a method that supports arbitrary moduli sets with cryptographically sized dynamic ranges, up to several thousands of bits. The practical interest of our method compared to existing methods is that it relies only on very fast standard floating-point operations, so it is suitable for multiple-precision applications and can be efficiently implemented on many general-purpose platforms that support IEEE 754 arithmetic. In this paper, we make further improvements to this method and demonstrate that it can successfully be applied to implement efficient data-parallel primitives operating in the RNS domain, namely finding the maximum element of an array of RNS numbers on graphics processing units. Our experimental results on an NVIDIA RTX 2080 GPU show that for random residues and a 128-moduli set with 2048-bit dynamic range, the proposed implementation reduces the running time by a factor of 39 and the memory consumption by a factor of 13 compared to an implementation based on mixed-radix conversion.
Startups Transitioning from Early to Growth Phase - A Pilot Study of Technical Debt Perception

Context: Software startups are software-intensive early phase companies with high growth rates. Previous researchers regarded startups’ time to market as short and decisive in establishing the product/service success. This led to shortcuts in software engineering decisions. Researchers in previous investigations documented a high accumulation of technical debt (TD) in early startup phases. However, we found little evidence in the literature concerning TD when startups transition to the growth phase. Aim: Our goal was to evaluate how the transition from early to growth phases affects TD perception in software startups. Methodology: We conducted a pilot study guided by semi-structured interviews from multiple software startup cases. Results: We identified the four following dimensions: (1) managing, (2) accepting, (3) avoiding, and (4) ignoring TD. Contribution: Our study will allow practitioners to address TD in growth-phase software startups. Future researchers can benefit from our findings by conducting exploratory studies and providing educated recommendations.
Item Recommendation from Implicit Feedback

The task of item recommendation is to select the best items for a user from a large catalogue of items. Item recommenders are commonly trained from implicit feedback which consists of past actions that are positive only. Core challenges of item recommendation are (1) how to formulate a training objective from implicit feedback and (2) how to efficiently train models over a large item catalogue. This chapter formulates the item recommendation problem and points out its unique characteristics. Then different training objectives are discussed. The main body deals with learning algorithms and presents sampling based algorithms for general recommenders and more efficient algorithms for dot product models. Finally, the application of item recommenders for retrieval tasks is discussed.
Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction

This study evaluates semantic healthcare procedure code embeddings on a Medicare fraud classification problem using publicly available big data. Traditionally, categorical Medicare features are one-hot encoded for the purpose of supervised learning. One-hot encoding thousands of unique procedure codes leads to high-dimensional vectors that increase model complexity and fail to capture the inherent relationships between codes. We address these shortcomings by representing procedure codes using low-rank continuous vectors that capture various dimensions of similarity. We leverage publicly available data from the Centers for Medicare and Medicaid Services, with more than 56 million claims records, and train Word2Vec models on sequences of co-occurring codes from the Healthcare Common Procedure Coding System (HCPCS). Continuous-bag-of-words and skip-gram embed-dings are trained using a range of embedding and window sizes. The proposed embeddings are empirically evaluated on a Medicare fraud classification problem using the Extreme Gradient Boosting learner. Results are compared to both one-hot encodings and pre-trained embeddings from related works using the area under the receiver operating characteristic curve and geometric mean metrics. Statistical tests are used to show that the proposed embeddings significantly outperform one-hot encodings with 95% confidence. In addition to our empirical analysis, we briefly evaluate the quality of the learned embeddings by exploring nearest neighbors in vector space. To the best of our knowledge, this is the first study to train and evaluate HCPCS procedure embeddings on big Medicare data.
Dynamic Workload Allocation for Edge Computing

Artificial intelligence models implemented in power-efficient Internet-of-Things (IoT) devices have accuracy degradation due to limited power consumption. To mitigate the accuracy loss on IoT devices, an edge-server joint inference system is introduced. On the edge-server inference system, allocate more workloads to the server end can mitigate accuracy loss, but data transmission contributes to the power consumption of the edge device. Thus, in this article, we present a novel two-stage method to allocate workloads to the server or the edge to maximize inference accuracy under a power constraint. In the first stage, we present a clusterwise threshold-based method for estimating the trustworthiness of a prediction made at the edge. In the second stage, we further determine the workload allocation of a trustworthy image based on the probability of the top 1 prediction and the power constraint. In addition, we propose a fine-tuning process to the pretrained model at the edge for achieving better accuracy. In the experiments, we apply the proposed method to several well-known deep neural network models. The results show that the proposed method can improve inference accuracy up to 3.93% under a specific power constraint compared to previous methods.
A Literature Review On Sentiment Analysis Techniques Involving Social Media Platforms

Sentiment analysis refers to the active field of Natural language processing that extracts the attitude and emotion of a human being. With the growth of social media, more people are using online platforms such as Twitter, Facebook, Y ouTube, etc. to express their opinions. Twitter is considered to be the purest platform to express one's views. Mostly all personalities from diverse backgrounds use twitter. Therefore, it becomes a need of the hour to study public opinion. This provides us valuable information and helps organizations and governments to contemplate mass public opinion and take better decisions accordingly. In this review paper, an extensive and exhaustive guide to the subfield of Natural language processing (NLP), focusing precisely on sentiment analysis on twitter dataset, has been presented. It highlights three main approaches to analyze the sentiment. We have summarized and compared the approaches on different metrics opted by various researchers in the field of sentiment analysis using the twitter dataset. With so much active work in this field, this review paper would assist all future researchers.
MACCAI LiTS17 Liver Tumor Segmentation Using RetinaNet

Automatic liver tumor detection is an important task and is more challenging in Computed Tomography (CT) due to its size, location, and irregular shape. Detection of small tumors in the liver is problematic because the liver area is mostly covered by the right rib cage. Recent work employs two-stage object detectors that are trained with bounding box annotations. In our work, we apply a simpler and faster one stage detector RetinaNet for localization of liver tumor on LiTS17, and our proposed method precisely detects one or more tumors. This model resolves the class imbalance problem between foreground and background by evaluating the focal loss. Our results on liver tumor CT images from LiTS17 dataset yields strong detection and scored mAP of 96%.
A Practical Guide to Updating Beliefs From Contradictory Evidence

We often make high stakes choices based on complex information that we have no way to verify. Careful Bayesian reasoning—assessing every reason why a claim could be false or misleading—is not feasible, so we necessarily act on faith: we trust certain sources and treat claims as if they were direct observations of payoff relevant events. This creates a challenge when trusted sources conflict: Practically speaking, is there a principled way to update beliefs in response to contradictory claims? I propose a model of belief formation along with several updating axioms. An impossibility theorem shows there is no obvious best answer, while a representation theorem delineates the boundary of what is possible.
A rotation and scale invariant approach for multi-oriented floor plan image retrieval

An automatic system for analysis and retrieval of building floor plans images is helpful for the architects while designing new projects and providing recommendations to the buyers. For such systems, query by example is preferred over query by keyword, for which user's requirements must be available in digital image form. Floor plans are converted to digital form by scanning and often get rotated slightly by a certain degree of angle during digitization. In this paper, we have proposed a geometric feature-based approach for floor plan image retrieval and our key contribution is to handle different kinds of rotation and scale while retrieving similar floor plan from the database. Our framework is divided into three phases, namely outer shape feature extraction, internal object feature extraction, followed by matching and retrieval. For our experimentation, we have rotated images of ROBIN dataset as currently no rotated floor plan dataset was available. Our experiment shows that the proposed methodology outperforms recent competing methods.
Modeling search and session effectiveness

Many information needs cannot be resolved with a single query, and instead lead naturally to a sequence of queries, issued as a search session. In a session test collection, each topic has an associated query sequence, with users assumed to follow that sequence when reformulating their queries. Here we propose a session-based offline evaluation framework as an extension to the existing query-based C/W/L framework, and use that framework to devise an adaptive session-based effectiveness metric, as a way of measuring the overall usefulness of a search session. To realize that goal, data from two commercial search engines is employed to model the two required behaviors: the user conditional continuation probability, and the user conditional reformulation probability. We show that the session-extended C/W/L framework allows the development of new metrics with associated user models that give rise to greater correlation with observed user behavior during search sessions than do previous session metrics, and hence provide a richer context in which to compare retrieval systems at a session level.
Image Steganography: 2-Bit XOR Algorithm Used In YCbCr Color Model With Crypto-algorithm

Steganography is a study of invisible communication that typically addresses how the message is concealed. Protection is required when we want to send data across any medium, this is why steganography has been built to safely send data in an image where the human eyes cannot identify it. This paper integrates cryptography and steganography through an image processing technique. YCbCr color model based on the 2-bit XOR LSB image steganography is proposed here in. The proposed scheme is a very safe technique for data concealed in the spatial field for image steganography, which converts an image from the color of RGB to the YCbCr space, and then secret data is concealed inside in the Cr color space component using 2-bit XOR. Before concealed the data in an image, our work introduces a simple crypto algorithm to a text file for getting unreadable format. For the crypto algorithm, we are using Rail Fence Cipher with key 2 for the encryption and decryption of text files. A detailed study has been carried out in the proposed 2-bit XOR substitution algorithm for YCbCr image. Finally, the Mean Square Errors (MSE) and Peak Signal to Noise Ratio (PSNR) were contrasted with various encryption techniques. The proposed method is high secure, high PSNR with low MSE values, which indicates that this technique is very effective to conceal data in an image.
On the Use of Parsing for Named Entity Recognition

Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task.
Corporate Capture of Blockchain Governance

Abstract We develop a theory of blockchain governance. In our model, the proof-of-work system, the most common set of rules for validating transactions in blockchains, creates an industrial ecosystem with specialized suppliers of goods and services. We analyze the interactions between blockchain governance and the market structure of the industries in the blockchain ecosystem. We show that the proof-of-work system may lead to a situation in which some large firms in the blockchain industrial ecosystem—blockchain conglomerates—capture the governance of the blockchain. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.