Reviewerly/exHarmony huggingface dataset:
Visual Hand Tracking on Depth Image using 2-D Matched Filter

Hand detection has been the central attention of human-machine interaction in recent researches. In order to track hand accurately, traditional methods mostly involve using machine learning and other available libraries, which requires a lot of computational resource on data collection and processing. This paper presents a method of hand detection and tracking using depth image which can be conveniently and manageably applied in practice without the huge data analysis. This method is based on the two-dimensional matched filter in image processing to precisely locate the hand position through several underlying codes, cooperated with a Delta robot. Compared with other approaches, this method is comprehensible and time-saving, especially for single specific gesture detection and tracking. Additionally, it is friendly-programmed and can be used on variable platforms such as MATLAB and Python. The experiments show that this method can do fast hand tracking and improve accuracy by selecting the proper hand template and can be directly used in the applications of human-machine interaction. In order to evaluate the performance of gesture tracking, a recorded video on depth image model is used to test theoretical design, and a delta parallel robot is used to follow the moving hand by the proposed algorithm, which demonstrates the feasibility in practice.
Hierarchical and view-invariant light field segmentation by maximizing entropy rate on 4D ray graphs

Image segmentation is an important first step of many image processing, computer graphics, and computer vision pipelines. Unfortunately, it remains difficult to automatically and robustly segment cluttered scenes, or scenes in which multiple objects have similar color and texture. In these scenarios, light fields offer much richer cues that can be used efficiently to drastically improve the quality and robustness of segmentations. In this paper we introduce a new light field segmentation method that respects texture appearance, depth consistency, as well as occlusion, and creates well-shaped segments that are robust under view point changes. Furthermore, our segmentation is hierarchical, i.e. with a single optimization, a whole hierarchy of segmentations with different numbers of regions is available. All this is achieved with a submodular objective function that allows for efficient greedy optimization. Finally, we introduce a new tree-array type data structure, i.e. a disjoint tree, to efficiently perform submodular optimization on very large graphs. This approach is of interest beyond our specific application of light field segmentation. We demonstrate the efficacy of our method on a number of synthetic and real data sets, and show how the obtained segmentations can be used for applications in image processing and graphics.
Improved iterative decoding of QC-MDPC codes in the McEliece public key cryptosystem

We improve iterative decoding of the moderate density parity-check codes, recently suggested as code candidates in the McEliece public key cryptosystem. In case of bit-flipping (BF) decoder failure, the code parity-check matrix is extended by adding auxiliary variable nodes based on reliability information from the BF decoder. Then iterative decoding is applied to the extended parity-check matrix. The proposed decoding algorithm is analyzed and its frame error rate performance is compared to the same performance of both the best implementations of BF decoding and its modifications. It is demonstrated an improved performance for the iterative decoding step in decryption, which allows to increase the resistance against recent attacks based on taking advantage of the somewhat large failure probability of the BF algorithm.
G-SpAR: GPU-Based Voxel Graph Pathfinding for Spatial Audio Rendering in Games and VR

The influx of investment in virtual and augmented reality in recent years has brought with it a renewed interest in spatial audio. Spatial audio in virtual environments and games has been neglected previously due in part due to computationally expensive nature of the processing involved. Here we introduce G-SpAR, a GPU-based spatial audio renderer for the Unity3D game engine. The system develops a pathfinding method that shortens occlusion and obstruction, increasing performance in-game.
Comparative Evaluation of AVIRIS-NG and Hyperion Hyperspectral Image for Talc Mineral Identification

The advancement and progressive development in hyperspectral remote sensing technology enhance the capability to measure the minor variations in spectral feature on the small and big scale of measurement. The remote sensing images provide information of earth’s features according to the capability of regional coverage and larger synoptic view, but hyperspectral image provides detailed, subtle variation in the spectral resolution. In this study, Hyperion and AVIRIS-NG data were used for identification of talc mineral in and around the Jahazpur city, Chabhadiya village, India. The Hyperion hyperspectral data show absorption features of talc minerals but AVIRIS-NG minerals show the multiple absorption features with iron-bearing talc at the same location. In comparison, observed result shows that AVIRIS-NG has much accurate capability for identification of different minerals in the visible region along with the short-wave infrared portion of the electromagnetic spectrum.
Anomaly Detection with HMM Gauge Likelihood Analysis

This paper describes a new method, HMM gauge likelihood analysis, or GLA, of detecting anomalies in discrete time series using Hidden Markov Models and clustering. At the center of the method lies the comparison of subsequences. To achieve this, they first get assigned to their Hidden Markov Models using the Baum-Welch algorithm. Next, those models are described by an approximating representation of the probability distributions they define. Finally, this representation is then analyzed with the help of some clustering technique or other outlier detection tool and anomalies are detected. Clearly, HMMs could be substituted by some other appropriate model, e.g. some other dynamic Bayesian network. Our learning algorithm is unsupervised, so it doesn't require the labeling of large amounts of data. The usability of this method is demonstrated by applying it to synthetic and real-world syslog data.
A3 Architecture Views – A Project Management Tool?

Abstract This paper explores the benefits of using A3 Architecture Overviews (A3AO) as a tool for project management and interdisciplinary knowledge sharing in a development project. The goal of the development project was to create an autonomous subsea inspection systems for the oil and gas industry. Sharing of implicit system knowledge early in the systems life cycle is a challenge, yet important for successful development of new technology. Through expert interviews and reverse architecting we made A3AO for two new system that were sequential steps into a fully autonomous solution. Through the research we applied these overviews in meetings with HSEQ, finance, senior management, and engineering. The immediate responses were positive, with comments like, “now I understand what we are trying to achieve ”, and “ Can I please include the A3AO in my document for the management?” Project personnel, with limited system knowledge, quickly got an enhanced understanding of the project goals by viewing the A3AO. We also experienced that senior management used the illustrative tool as a supplement to support a business case for the board of directors. Clients were happy to see models and schematics as a supplement to textural documents when being presented with new technology. Inclusion of industry terminology and support from management would enhance adoption of A3AO within the oil and gas industry. Overall, our research strongly supports the use of A3 Architectural Overviews as a project management tool when developing new complex systems.
Simulation of Mooring Lines Based on Position-Based Dynamics Method

The Position-Based Dynamics (PBD) method considers the rope as a series of particles in rope simulation.This paper introduces the PBD method and simulates mooring line in ship mooring simulation training system with four constraints, which are distance constraint, bending constraint, Long Range Attachment (LRA) constraint and pin constraint.In this paper, the LRA constraint method is applied to solve the overstretch problem of mooring line simulation caused by distance constraint and bending constraint.A pin constraint which combines two particles into one is put forward to achieve the connection effect between cable and the eye splice as well as the interaction between anchor and chain.Moreover, the method of Continuous Collision Detection (CCD) constraint is proposed to realize the collision detection between mooring lines and other objects.Compared with Newton's method, our adopted method is more efficient and robustness.Since it has sufficient visual plausibility and can realize real-time visualization, the simulation system developed by PBD method has a good training effect for crew.
Homelessness and Open City Data: Addressing a Global Challenge

Homelessness is a global issue that continues to affect the developed and developing world. The absence of comprehensive data collection and measurement of homelessness has led to a general lack of understanding of the global homeless. Improving our knowledge of homelessness requires information that reflects the reality of homelessness and housing exclusion. In this chapter, we review homeless data that is openly available from 14 global cities. With the use of ISO 37120 and open city data, we analysed the extent to which homeless data is standardized, consistent and comparable and the validity of the ISO 37120 indicators in the context of this homeless data. The availability, accessibility and formatting of the open city data are directly compared using ISO 37120 shelter-themed city indicators (ISO 37120. ISO37120: Sustainable development of communities—Indicators for city services and quality of life, 2014) and the City Indicator Data Openness Measure (CIDOM). We found that the majority of the homeless data could be placed into two general categories: homeless profiles and homeless services. There is no standard data model between cities, and periodicity of data collection varied. When directly compared using CIDOM, Toronto came out as the city with the most comprehensive dataset for each of the ISO 37120 shelter indicators. ISO 37120 shelter indicators do provide a standardized means to measure the homeless and unsheltered population of a city, but city Open Data lacks the robustness and utility to produce indicators. Underlying data is not fully published, raising questions of indicator value validity. In the global context, open city homeless data is currently not standardized, consistent or comparable.
A Common Framework for the Extraction of ILD Patterns from CT Image

Majority of the pulmonary diseases and their identification rely on geometric progression of lung spaces. Most common types of lung diseases include abnormalities categorised as Interstitial lung diseases (ILD) like sarcoidosis, idiopathic pulmonary fibrosis (IPF), malignant nodules, extrinsic allergic alveolitis (EAA) and honey comb structures from the infectious disorders is a very difficult task for diagnosis. For clinical practices, images are accumulated and stored in digital representation like MRI and CT to facilitate corresponding diagnosis. Some of the physicians can't provide inadequacy in image parts which are known as (ROI) region of interest. Researchers converse at focusing on ROI coding to guarantee the use of multiple and randomly shaped ROI's in image depicting the importance of ROI confining the background regions that can be exhibited by varying the levels of quality. This paper highlights the medical image ROI segmentation that delineates the diseased part using morphological algorithm. This paper addresses working on reliable methods for diagnosis and prognosis of the pulmonary diseases. Segmentation of ROI for the detection of CT lung pattern abnormalities likely nodules, sarcoidosis, IPF and honeycomb are done based on morphology in this research work. The techniques used to decoct medical information helps the radiologists for early diagnosis of ILD to figure out appropriate treatment.
Statistical Analysis of Survival Models Using Feature Quantification on Prostate Cancer Histopathological Images

Grading of prostatic adenocarcinoma is based on the Gleason scoring system and the more recently established prognostic grade groups. Typically, prostate cancer grading is performed by pathologists based on the morphology of the tumor on hematoxylin and eosin (H and E) slides. In this study, we investigated the histopathological image features with various survival models and attempted to study their correlations.Three texture methods (speeded-up robust features, histogram of oriented gradient, and local binary pattern) and two convolutional neural network (CNN)-based methods were applied to quantify histopathological image features. Five survival models were assessed on those image features in the context with other prostate clinical prognostic factors, including primary and secondary Gleason patterns, prostate-specific antigen levels, age, and clinical tumor stages.Based on statistical comparisons among different image features with survival models, image features from CNN-based method with a recurrent neural network called CNN-long-short-term memory provided the highest hazard ratio of prostate cancer recurrence under Cox regression with an elastic net penalty.This approach outperformed the other image quantification methods listed above. Using this approach, patient outcomes were highly correlated with the histopathological image features of the tissue samples. In future studies, we plan to investigate the potential use of this approach for predicting recurrence in a wider range of cancer types.
Research on enterprise management integration mechanism and information platform by internet of things

In the era of the Internet of Things, the way of information dissemination and the speed of information computing have changed dramatically. New generation technologies such as big data, IOTs, cloud computing, and mobile internet have rapidly developed, and the information resources in the process of using customers have promoted to value creation activities. The guiding force, therefore, the service model must also follow the changes. This paper is based on the enterprise management integration mechanism and information platform research of the Internet of Things environment, and analyses its conceptual model, the relationship between the platform and related subjects, the value creation mechanism, the core business functions and the profit model, which have certain guiding significance for practice. The enterprise management integration mechanism and information platform research in the Internet of Things environment requires the parties to interact, share, and cooperate with each other to achieve common value creation. In addition, enterprises effectively research internal resource integration and information sharing, rationally streamline enterprise management institutions, thereby improving the quality and efficiency of business operations, giving full play to the role of internal control and risk management, so that enterprises can develop steadily.
Evaluating the Impact of Data Representation on EHR-Based Analytic Tasks.

Different analytic techniques operate optimally with different types of data. As the use of EHR-based analytics expands to newer tasks, data will have to be transformed into different representations, so the tasks can be optimally solved. We classified representations into broad categories based on their characteristics, and proposed a new knowledge-driven representation for clinical data mining as well as trajectory mining, called Severity Encoding Variables (SEVs). Additionally, we studied which characteristics make representations most suitable for particular clinical analytics tasks including trajectory mining. Our evaluation shows that, for regression, most data representations performed similarly, with SEV achieving a slight (albeit statistically significant) advantage. For patients at high risk of diabetes, it outperformed the competing representation by (relative) 20%. For association mining, SEV achieved the highest performance. Its ability to constrain the search space of patterns through clinical knowledge was key to its success.
Loong: a new IND-CCA-secure code-based KEM

Based on hardness of the rank support learning problem which is as hard as the rank syndrome problem, we introduce a new IND-CPA-secure public-key encryption scheme- Loong.CPAPKE. By applying a variant of the Fujisaki-Okamoto transform to Loong.CPAPKE, a new IND-CCA-secure key encapsulation mechanism (KEM for short) can be constructed. We also make comparison on parameter sets between our scheme and some NIST submissions.
Interledger Smart Contracts for Decentralized Authorization to Constrained Things

We present models that utilize smart contracts and interledger mechanisms to provide decentralized authorization for constrained IoT devices. The models involve different tradeoffs in terms of cost, delay, complexity, and privacy, while exploiting key advantages of smart contracts and multiple blockchains that communicate with interledger mechanisms. These include immutably recording hashes of authorization information and policies in smart contracts, resilience through the execution of smart contract code on all blockchain nodes, and cryptographically linking transactions and IoT events recorded on different blockchains using hash and time-lock mechanisms. The proposed models are evaluated on the public Ethereum testnets Rinkeby and Ropsten, in terms of execution cost (gas), delay, and reduction of data that needs to be sent to the constrained IoT devices.
Culture as a Sensor? A Novel Perspective on Human Activity Recognition

Human Activity Recognition (HAR) systems are devoted to identifying, amidst the sensory stream provided by one or more sensors located so that they can monitor the actions of a person, portions related to the execution of a number of a-priori defined activities of interest. Improving the performance of systems for Human Activity Recognition is a long-standing research goal: solutions include more accurate sensors, more sophisticated algorithms for the extraction and analysis of relevant information from the sensory data, and the enhancement of the sensory analysis with general or person-specific knowledge about the execution of the activities of interest. Following the latter trend, in this article we propose the association and enhancement of the sensory data analysis with cultural information, that can be seen as an estimate of person-specific information, relieved of the burden of a long/complex setup phase. We propose a culture-aware Human Activity Recognition system which associates the recognition response provided by a state-of-the-art, culture-unaware HAR system with culture-specific information about where and when activities are most likely performed in different cultures, encoded in an ontology. The merging of the cultural information with the culture-unaware responses is done by a Bayesian Network, whose probabilistic approach allows for avoiding stereotypical representations. Experiments performed offline and online, using images acquired by a mobile robot in an apartment, show that the culture-aware HAR system consistently outperforms the culture-unaware HAR system.
UTCPredictor: An uncertainty‐aware novel teaching cases predictor

Abstract Teaching cases are crucial for computer science (e.g., software testing) teaching. With the fast development of computer science, old and outdated teaching cases cannot meet the requirements of teaching. Therefore, teachers need to update teaching case repository continually and timely. However, teaching case development is an extremely time‐consuming work. Given today's complex and fast‐moving environment of computer science, teachers often feel blind about about what types of cases should be added for teaching. This paper presents UTCPredictor—an automated approach of predicting novel teaching cases from real production by identifying the most uncertain data, which are always with new features that reflect the latest developments and trends in the field. The implementation of UTCPredictor is based on the idea of interactive machine learning as well as several text mining techniques. To evaluate the effectiveness of UTCPredictor, we take bug report case building in software testing teaching as an example, using UTCPredictor to perform 10‐fold cross‐validation on an existing teaching case set. The performance in terms of indicators; Recall, Precision, and F1‐score, achieved three very competitive values—0.91, 0.94, and 0.85, respectively. We further evaluate the effectiveness of UTCPredictor through a user study and a questionnaire. The results are very positive; the user study indicates that educators can build a teaching case set from latest bug report repository by spending only 8.16%–18.11% time costs compared with traditional manual approach; the responses from 2,000 students for the questionnaire show that the teaching cases built with UTCPredictor are very popular among students.
Project Thyia: A Forever Gameplayer

The space of Artificial Intelligence entities is dominated by conversational bots. Some of them fit in our pockets and we take them everywhere we go, or allow them to be a part of human homes. Siri, Alexa, they are recognised as present in our world. But a lot of games research is restricted to existing in the separate realm of software. We enter different worlds when playing games, but those worlds cease to exist once we quit. Similarly, AI game-players are run once on a game (or maybe for longer periods of time, in the case of learning algorithms which need some, still limited, period for training), and they cease to exist once the game ends. But what if they didn't? What if there existed artificial game-players that continuously played games, learned from their experiences and kept getting better? What if they interacted with the real world and us, humans: live-streaming games, chatting with viewers, accepting suggestions for strategies or games to play, forming opinions on popular game titles? In this paper, we introduce the vision behind a new project called Thyia, which focuses around creating a present, continuous, `always-on', interactive game-player.
Lexical Semantics of Mandarin Carry and Hold Verbs: A Frame-Based Constructional Analysis of ná 拿 and wò 握

This study aims to identify the lexical semantic distinctions of Mandarin carry and hold verbs by comparing their syntactic and semantic behaviors from a frame-based constructional approach. The two commonly used verbs ná 拿 and wò 握 are intensively examined in terms of their collocational distributions in BCC. It is found that ná 拿 behaves like caused motion verbs ‘carry’ as it is more likely to co-occur with directional markers. Whereas, wò 握 behaves more like hold verbs, which tends to collocate with static-durative markers. This shows that although both verbs pertain to hand movement, ná 拿 is lexicalized with a purpose of moving, while wò 握 typically denote ‘prolonged contact’. This study is significant in providing a pilot study of the two verbs representing two basic semantic frames with clearly defined semantic-to-syntactic distinctions, and the findings are feasible to the construction of Mandarin VerbNet.