Reviewerly/exHarmony huggingface dataset:
Scaled Fast Nested Key Equation Solver for Generalized Integrated Interleaved BCH Decoders

The generalized integrated interleaved BCH (GII-BCH) codes are among the best error-correcting codes for next-generation terabit/s memories. The key equation solver (KES) in the nested decoding of GII codes limits the achievable clock frequency. Recently, by polynomial scalar pre-computation, the critical path of the nested KES for Reed-Solomon (RS)-based GII codes has been reduced to one multiplier. However, for GII-BCH codes, the nested KES has more complicated formulas in order to skip the odd iterations and hence prior techniques do not directly extend. This paper proposes novel reformulations of the nested BCH KES to enable scalar pre-computation. Additionally, polynomial scaling is incorporated to enable complexity reduction. As a result, the critical path of the nested BCH KES with odd iterations skipped is reduced to one multiplier. For an example GII-BCH code over GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> ), the proposed design reduces the average nested BCH KES latency to around a half with similar silicon area compared to the best prior design.
Styling Words: A Simple and Natural Way to Increase Variability in Training Data Collection for Gesture Recognition

Due to advances in deep learning, gestures have become a more common tool for human-computer interaction. When implementing a large amount of training data, deep learning models show remarkable performance in gesture recognition. Since it is expensive and time consuming to collect gesture data from people, we are often confronted with a practicality issue when managing the quantity and quality of training data. It is a well-known fact that increasing training data variability can help to improve the generalization performance of machine learning models. Thus, we directly intervene in the collection of gesture data to increase human gesture variability by adding some words (called styling words) into the data collection instructions, e.g., giving the instruction "perform gesture #1 faster" as opposed to "perform gesture #1." Through an in-depth analysis of gesture features and video-based gesture recognition, we have confirmed the advantageous use of styling words in gesture training data collection.
SVM-based anomaly detection in remote working: Intelligent software SmartRadar

Increasing the productivity of flexible working during working hours is a common concern for employers. Employees working in different sectors use various tools to carry out their tasks and work in different working environments. With the Covid-19 pandemic that began in the beginning of 2020, remote working has become an essential part of life. However, one of the biggest problems faced by managers and employers is how to control remote workers. In this study, SmartRadar software was developed to track employee computer use behavior and detect anomalous behavior. Anomalous behavior is defined as computer-based activities or processes carried out during work time which are not related to the tasks for which the employee is responsible. Clicking, mouse wheel scrolling, copying and other similar actions by the user are processed, a summary of the data is generated, and a multi-dimensional dataset is created. Anomalous behavior can then be detected using support vector machines. The proposed software has been shown to detect anomalous computer use behavior by employees with a high degree of accuracy. The favorable results of the study show that the proposed method and the software could be used for tracking and reporting purposes both in workplaces and in flexible working conditions.
Towards combining deep learning, verification, and scenario-based programming

Deep learning (DL) [4] is dramatically changing the world of software. The rapid improvement in deep neural network (DNN) technology now enables engineers to train models that achieve superhuman results, often surpassing algorithms that have been carefully hand-crafted by domain experts [19, 20]. There is even an intensifying trend of incorporating DNNs in safety-critical systems, e.g. as controllers for autonomous vehicles and drones [1, 12].
Quatemary Hermitian linear complementary dual codes with small distance

Massey introduced a class of linear code which is called linear complementary dual (LCD) codes. It has been known that LCD codes play a significant role in improving the security of the information processed by sensitive device. In this paper, we constructed 166 quaternary Hermitian linear complementary dual codes with small distance, where 154 codes are new constructions. Besides, it includes 145 Hermitian optimal LCD codes and 9 Hermitian near optimal LCD codes.
RPT: Effective and Efficient Retrieval of Program Translations from Big Code

Program translation is a growing demand in software engineering. Manual program translation requires programming expertise in source and target language. One way to automate this process is to make use of the big data of programs, i.e., Big Code. However, existing code retrieval techniques lack the design to cover cross-language code retrieval. Other data-driven approaches require human efforts in constructing cross-language parallel datasets to train translation models. In this paper, we present RPT, a novel code translation retrieval system. We propose a lightweight but informative program representation, which can be generalized to all imperative PLs. Furthermore, we present our index structure and hierarchical filtering mechanism for efficient code retrieval from a Big Code database.
Natural Language Processing Technique for Generation of SQL Queries Dynamically

Natural Language Processing is being used in every field of human to machine interaction. Database queries although have a confined set of instructions, but still found to be complex and dedicated human resources are required to write, test, optimize and execute structured query language statements. This makes it difficult, time-consuming and many a time inaccurate too. Such difficulties can be overcome if the queries are formed dynamically with standard procedures. In this work, parsing, lexical analysis, synonym detection and formation processes of the natural language processing are being proposed to be used for dynamically generating SQL queries and optimization of them for fast processing with high accuracy. NLP parsing of the user inputted text for retrieving, creation and insertion of data are being proposed to be created dynamically from English text inputs. This will help users of the system to generate reports from the data as per the requirement without the complexities of SQL. The proposed system will not only generate queries dynamically but will also provide high accuracy and performance.
Speaker Identification and Its Application to Social Network Construction for Chinese Novels

Character is one of the three elements of a novel, and conversation is an important way to describe characters. The personality, emotion, and interpersonal relationships of characters are reflected in conversations. Thus, extracting conversations, speakers and other conversation elements from novels are crucial for character analysis and content understanding. We start with Jin Yong’s novels, annotate the largest Chinese corpus for speaker identification with 9721 quotes, and analyze language styles of different characters based on quotes. We then propose a machine learning-based speaker identification method, and design feature templates that show a good performance. For the application of speaker identification, we construct the social network of characters in Jin Yong’s novels based on dialog chain, which lays the foundation for the analysis of the relationship between characters in novels.
Hercule: Representing and Reasoning About Norms as a Foundation for Declarative Contracts Over Blockchain

Current blockchain approaches for business contracts are based on smart contracts, namely, software programs placed on a blockchain that are automatically executed to realize a contract. However, smart contracts lack flexibility and interfere with the autonomy of the parties concerned. We propose Hercule, an approach for declaratively specifying blockchain applications in a manner that reflects business contracts. Hercule represents a contract via regulatory norms that capture the involved parties’ expectations of one another. It computes the states of norms (hence, of contracts) from events in the blockchain. Hercule’s novelty and significance lie in that it operationalizes declarative contracts over semistructured databases, the underlying representation for practical blockchain such as Hyperledger Fabric and Ethereum. Specifically, it exploits the map–reduce capabilities of such stores to compute norm states. We demonstrate that our implementation over Hyperledger Fabric can process thousands of events per second, sufficient for many applications.
Domain adaptation of object detector using scissor-like networks

When the training data and the test data do not obey the same distribution, the performances of many object detection methods always decrease greatly. Naturally, domain adaptation methods at feature level are proposed. The basic idea is to adapt the feature extraction network such that the feature distributions of the source and target domains match. We propose a new method that is built directly on the Faster R-CNN model, which not only aligns the source and target data features, but also forces their generated features closer together to further align the source and target domains. Moreover, compared with previous approaches, we construct a more powerful discriminator and a simple generator to solve the domain adaptation problem. The model works like a pair of scissors, so we call it Scissors Networks (SN). We conduct extensive experiments on popular datasets, including Cityscapes, Foggy Cityscapes, SIM10k and KITTI. The experimental results demonstrate that our algorithm is superior to the state-of-the-art deep learning based domain adaptation approaches.
Fast Multi-Level Foreground Estimation

Alpha matting aims to estimate the translucency of an object in a given image. The resulting alpha matte describes pixel-wise to what amount foreground and background colors contribute to the color of the composite image. While most methods in literature focus on estimating the alpha matte, the process of estimating the foreground colors given the input image and its alpha matte is often neglected, although foreground estimation is an essential part of many image editing workflows. In this work, we propose a novel method for foreground estimation given the alpha matte. We demonstrate that our fast multi-level approach yields results that are comparable with the state-of-the-art while outperforming those methods in computational runtime and memory usage.
Computing stable resultant-based minimal solvers by hiding a variable

Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e., solving minimal problems, in a RANSAC-style framework. Minimal problems often result in complex systems of polynomial equations. The existing state-of-the-art methods for solving such systems are either based on Gröbner bases and the action matrix method, which have been extensively studied and optimized in the recent years or recently proposed approach based on a resultant computation using an extra variable. In this paper, we study an interesting alternative resultant-based method for solving sparse systems of polynomial equations by hiding one variable. This approach results in a larger eigenvalue problem than the action matrix and extra variable resultant-based methods; however, it does not need to compute an inverse or elimination of large matrices that may be numerically unstable. The proposed approach includes several improvements to the standard sparse resultant algorithms, which significantly improves the efficiency and stability of the hidden variable resultant-based solvers as we demonstrate on several interesting computer vision problems. We show that for the studied problems, our sparse resultant based approach leads to more stable solvers than the state-of-the-art Gröbner basis as well as existing resultant-based solvers, especially in close to critical configurations. Our new method can be fully automated and incorporated into existing tools for the automatic generation of efficient minimal solvers.
Simple Upper and Lower Bounds on the Ultimate Success Probability for Discriminating Arbitrary Finite-Dimensional Quantum Processes

We consider the problem of discriminating finite-dimensional quantum processes, also called quantum supermaps, that can consist of multiple time steps. Obtaining the ultimate performance for discriminating quantum processes is of fundamental importance, but is challenging mainly due to the necessity of considering all discrimination strategies allowed by quantum mechanics, including entanglement-assisted strategies and adaptive strategies. In the case in which the processes to be discriminated have internal memories, the ultimate performance would generally be more difficult to analyze. In this paper, we present a simple upper bound on the ultimate success probability for discriminating arbitrary quantum processes. In the special case of multi-shot channel discrimination, it can be shown that the ultimate success probability increases by at most a constant factor determined by the given channels if the number of channel evaluations increases by one. We also present a lower bound based on Bayesian updating, which has a low computational cost. Our numerical experiments demonstrate that the proposed bounds are reasonably tight. The proposed bounds do not explicitly depend on any quantum phenomena, and can be readily extended to a general operational probabilistic theory.
DreamStore: A Data Platform for Enabling Shared Augmented Reality

Unlike traditional object stores, Augmented Reality (AR) query workloads possess several unique characteristics, such as spatial and visual information. Such workloads are often keyed on a variety of attributes simultaneously, such as device orientation and position, the scene in view, and spatial anchors. The natural mode of user-interaction in these devices triggers queries implicitly based on the field in the user's view at any instant, generating data queries in excess of the device frame rate. Ensuring a smooth user experience in such a scenario requires a systemic solution exploiting the unique characteristics of the AR workloads. For exploration in such contexts, we are presented with a view-maintenance or cache-prefetching problem; how do we download the smallest subset from the server to the mixed reality device such that latency and device space constraints are met? We present a novel data platform - DreamStore, that considers AR queries as first-class queries, and view-maintenance and large-scale analytics infrastructure around this design choice. Through performance experiments on large-scale and query-intensive AR workloads on DreamStore, we show the advantages and the capabilities of our proposed platform.
Regression with Deep Learning for Sensor Performance Optimization

Neural networks with at least two hidden layers are called deep networks. Recent developments in AI and computer programming in general has led to development of tools such as Tensorflow, Keras, NumPy etc. making it easier to model and draw conclusions from data. In this work we re-approach non-linear regression with deep learning enabled by Keras and Tensorflow. In particular, we use deep learning to parametrize a non-linear multivariate relationship between inputs and outputs of an industrial sensor with an intent to optimize the sensor performance based on selected key metrics.
Quantum-like Gaussian mixture model

A new concept of a quantum-like mixture model is introduced. It describes the mixture distribution with the assumption that a point is generated by each Gaussian at the same time. The quantum-like mixture Gaussian improves the classification accuracy in machine learning by indicating that the uncertain points should not be assigned to any class. It increases the accuracy of the mixture Gaussian model on the iris data set from 96.67 to 99.24%.
GazeMAE: General Representations of Eye Movements using a Micro-Macro Autoencoder

Eye movements are intricate and dynamic events that contain a wealth of information about the subject and the stimuli. We propose an abstract representation of eye movements that preserve the important nuances in gaze behavior while being stimuli-agnostic. We consider eye movements as raw position and velocity signals and train separate deep temporal convolutional autoencoders. The autoencoders learn micro-scale and macroscale representations that correspond to the fast and slow features of eye movements. We evaluate the joint representations with a linear classifier fitted on various classification tasks. Our work accurately discriminates between gender and age groups and outperforms previous works on biometrics and stimuli classification. Further experiments highlight the validity and generalizability of this method, bringing eye-tracking research closer to real-world applications.
SLFC: Scalable Light Field Coding

Light field imaging enables some post-processing capabilities like refocusing, changing view perspective, and depth estimation. As light field images are represented by multiple views, they contain a huge amount of data that makes compression inevitable. Although there are some proposals to efficiently compress light field images, their main focus is on encoding efficiency. However, some important functionalities such as viewpoint and quality scalabil- ities, random access, and uniform quality distribution have not been addressed adequately. In this paper, an efficient light field image compression method based on a deep neural network is proposed, which classifies multiple views into various layers. In each layer, the target view is synthesized from the available views of previously encoded/decoded layers using a deep neural network. This synthesized view is then used as a virtual reference for the target view inter-coding. In this way, random access to an arbitrary view is provided. Moreover, uniform quality distribution among multiple views is addressed. In higher bitrates where random access to an arbitrary view is more crucial, the required bitrate to access the requested view is minimized.
A Design of Greenhouse Monitoring System Based on Low-Cost Mesh Wi-Fi Wireless Sensor Network

Agricultural environment monitoring has become a crucial field of control and protection providing a real-time system with the physical world. This paper is proposed the design and implementation of a wireless agricultural monitoring system platform based on the Mesh Wi-Fi Networking with low-cost and resource-limited hardware-ESP8266 Platform. The system includes a root, 3 stations-repeaters, and 6 nodes, each node has three sensors to measure parameters: soil moisture and soil temperature, the environmental temperature and environmental humidity of the farm, and light intensity. The collected data are transmitted from the nodes to the root and updated on the cloud database and displayed on the dashboard. The website based on the MEAN stack will be built for the owners to track real-time information and save historical data.
System- and Software-level Architecting Harmonization Practices for Systems-of-Systems : An exploratory case study on a long-running large-scale scientific instrument

The problems caused by the gap between system- and software-level architecting practices, especially in the context of Systems of Systems where the two disciplines inexorably meet, is a well known issue with a disappointingly low amount of works in the literature dedicated to it. At the same time, organizations working on Systems of Systems have been developing solutions for closing this gap for many years now. This work aims to extract such knowledge from practitioners by studying the case of a large-scale scientific instrument, a geographically distributed radio telescope to be more specific, developed as a sequence of projects during the last two decades. As the means for collecting data for this study we combine online interviews with a virtual focus group of practitioners from the organization responsible for building the instrument. Through this process, we identify persisting problems and the best practices that have been developed to deal with them, together with the perceived benefits and drawbacks of applying the latter in practice. Some of our major findings include the need to avoid over-reliance on the flexibility of software to compensate for incomplete requirements, hidden assumptions, as well as late involvement of system architecting, and to facilitate the cooperation between the involved disciplines through dedicated architecting roles and the adoption of unifying practices and standards.
Instilling Knowledge Claims of Personas from 346 Research Articles

Our research goal is to summarize the body of persona knowledge by identifying knowledge claims. This can aid HCI researchers to (a) navigate persona knowledge to form an understanding of what is known about personas quickly, (b) identify central research gaps of what is not known (or said) about personas, and (c) identify claims that are not substantiated with strong empirical evidence and warrant future work. To this end, we use computational and manual techniques to extract 130 knowledge claims based on 9139 sentences from 346 persona articles and analyze whether the existing literature supports these claims. The results, clustered into four groups (“Definition”, “Creation”, “Evaluation”, and “Use”), indicate that claims regarding persona definition are characterized by a higher degree of consensus. In contrast, persona creation and use contain a high proportion of unverified claims. There are few claims concerning evaluation. Empirical research should address unverified claims and develop the ontological understanding on persona evaluation.
DPSA: A Brief Review for Design Pattern Selection Approaches

The software design phase is important and challenging due to its high impact on other phases of software development life cycle. Design pattern is a proven solution based on software developers' experience to solve recurring problems, which used to acquire quality software design. However, the large number of design patterns has made it difficult to select the right one for a particular design problem. To overcome this difficulty, several approaches with different methods have been proposed to automate the design pattern selection process. The purpose of this paper is to suggest a framework called "DPSA" which includes the classification of existing approaches, a comparison between approaches based on provided criteria, and also analyzing each approach based on these criteria. DPSA helps future research to a) employing the existing approaches taking into account the specification of each one and b) comparing the current works with the future.