Reviewerly/exHarmony huggingface dataset:
The effect of orthognathic surgery on facial recognition algorithm analysis

The purpose of this study was to evaluate the ability of a publicly available facial recognition application program interface to calculate similarity scores for presurgical and postsurgical photographs of patients who underwent orthognathic surgeries. Our primary objective was to identify which surgical procedure(s) had the greatest effect(s) on the similarity score.Existing photographs for 25 orthodontic-orthognathic patients were analyzed using the application program interface to calculate similarity scores between the presurgical and postsurgical photographs. Photographs from 2 presurgical timepoints were compared as controls. Both relaxed and smiling photographs were included to assess the added impact of a facial pose. Patient characteristics and surgery types were recorded for statistical analysis. Nonparametric Kruskal-Wallis rank-sum tests were performed to analyze the relationship between patient characteristics and similarity scores. Multiple comparisons Wilcoxon rank-sum tests were performed on the statistically significant characteristics.Recognition scores were significantly lower after orthognathic surgery at rest (P = 0.009) and smiling (P <0.001). Patients receiving both LeFort I and bilateral sagittal split osteotomy (BSSO) surgeries had a lower median similarity score compared with those that received only BSSO (P = 0.009) when comparing relaxed photographs before and after surgery. Similarly, for the score comparing presurgical relaxed photographs to postsurgical smiling photographs, patients that received both surgeries were found to have lower similarity scores compared with those receiving only BSSO (P = 0.036).Two-jaw surgeries were associated with a statistically significant decrease in similarity score when compared with 1-jaw procedure. Pose was also found to be a factor influencing similarity scores, especially when comparing presurgical relaxed photographs to postsurgical smiling photographs.
CAFM: A 3D Morphable Model for Animals

We present Cat-like Animals Facial Model (CAFM) - a 3D Morphable Model (3DMM) constructed from 50 samples, including lion, tiger, puma, American Shorthair, Abyssinian cat, etc. To the best of our knowledge, CAFM is the first animal morphable model ever constructed. New animal face images can be registered automatically by fitting pose and shape parameters of CAFM. Moreover, the parametric model regulates the naturalness of the generated animal faces avoiding unreasonable appearance.Computer vision has recently experienced great advances in automatic facial landmark detection. In this paper, to demonstrate CAFM's application to 3D reconstruction of cat face images, and to put effort towards uniform annotation scheme of immense databases and fair experimental comparison of cat-like animals' facial landmark systems, we improve the labeled cat face data set of10,000 images with 15 landmarks. Besides, we propose an algorithm matching our model to the input cat face images. With the projection parameters and shape parameter of CAFM, we can generate corresponding 3D meshes.
Application of Blockchain News Production Based on Digital Encryption Technology

Blockchain technology provides a new logic for digital content production. At present, the Internet is a “one center, multi node” technology architecture. Blockchain constructs the underlying structure of “multi center, multi node”. The block chain's digital encryption, backtracking, distributed account book and time stamp can solve the problems that the network data is easy to tamper with and not easy to save. Meanwhile, it provide a new technical support for the verification of news data. On the basis of Hasche Algorithm, the information after user's signature and authorization can be disclosed in the network, which can ensure the security of user's personal information. On the other hand, the application of blockchain in the field of news production can reshape the trust mechanism between communicators and users. This study uses observation and induction to describe the application scenarios of blockchain technology in the media industry. The application of blockchain technology will change the existing mode of news production and distribution. Specifically, Based on the main chain and parallel chain of DAG account book, this study considers that blockchain technology has a strong application value in the fields of news copyright protection, news source verification, advertising traffic fraud control and so on.
Exploring Photography in Rural Kenyan Households: Considering "Relational Objects" in CSCW and HCI

Domestic and personal photography are topics of longstanding interest to CSCW and HCI researchers. In this paper, we explore these topics in Bungoma County, Kenya. We used interview and observation methods to investigate how photographs are taken, displayed, organized, and shared, in 23 rural households. To more deeply understand participants' photography practices, we also gave them digital cameras, observed what they did with them, and asked them to engage in a photo-elicitation exercise. Our findings draw attention to the ways photographs are "relational objects" - that is, material objects that support the maintenance, reproduction, and transformation of social relations. We then describe these relations: economic (i.e., working as cameramen producing and distributing printed images); family (i.e., parents and children using printed images to tell family histories); and community (i.e., people using printed images to present an idealized self). The introduction of digital cameras into these households did not appear to change these practices; instead, it reinforced them. We discuss how considering relational objects in CSCW/HCI is useful for balancing the technologically determinist perspectives that are the basis of many prior studies of photography in these fields. In particular, we detail how considering the concept provides new perspectives on materiality, as well an alternative to the individualistic perspective, which underlies these communities' understanding of photography.
Dynamic forecasting-based load control of an autonomous photovoltaic installation

The use of renewable energy sources in non-urban regions has become increasingly important thanks to its great contribution to enhancing human life. Up to now, standalone Photovoltaic Generator (PVG) installation connected to a storage bank is used extensively. However, climactic changes may cause supply disturbances for the installation. The present paper proposes an algorithm of loads operation planning, by means of a relay unit, of a PVG/Battery installation composed of three load types: light, water pump and cold room. The planning strategy was based on the PVG generation dynamic forecast using least squares approach. Starting at sunrise, the generated energy was forecasted based on the outcome of the last 15 days. Then, the energy prediction process was adjusted every 15 min depending on the amount of energy being really generated. The planning algorithm was meant first to guarantee the continuous supply of power for the cold room and second to insure the dispatching of the rest of the energy supply to support lighting (at night-time) or the water pump (during the daytime). Optimization criteria were considered in order to ensure battery protection and a maximum utilization of the generated energy. The obtained results proved the respect of the pre-fixed criteria during both perturbed and stable days, a battery safety with an SOC above 28%, and a cold room being constantly supplied.
Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions

This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution.
Multi-Thread Frame Tiling Model in Concurrent Real-Time Object Detection for Resources Optimization in YOLOv3

In the process of object detection, You Look Only Once, version 3 (YOLOv3) approach has been applied as a more efficient solution than Convolutional Neural Network (CNN) methods in accuracy and speed. In some environments, there is a need to process multiple real-time object detection algorithms concurrently where each object detection algorithm receives a live stream from a camera. In this research, we aim to optimize the system resources including the Graphics Processing Unit(GPU) and the Central Processing Unit(CPU) where accuracy indicated using frames per second (FPS) has no change in the process of detection. We propose improvements in terms of architectural models for real-time object detection. In this way, a Multi-thread Frame Tiling model is proposed to optimize GPU and CPU usage per YOLO object to handle concurrent surveillance video streams. Thus, it can be covered a large scale of surveillance cameras on multiple live streams concurrently. To evaluate the proposed model, we investigate the efficiency of GPU and CPU system resources. The experiment results show that the detection algorithm with the Multi-thread Frame Tiling model is by an average of 116.6 in FPS for all video qualities (480p, 720p, and 1080p resolution) compared to the Original YOLOv3 by an average of 27.1 while there is no change in accuracy in the detection process by YOLOv3.
RICERCANDO: Data mining toolkit for mobile broadband measurements

Increasing reliance on mobile broadband (MBB) networks for communication, vehicle navigation, healthcare, and other critical purposes calls for improved monitoring and troubleshooting. While recent advances in monitoring with crowdsourced and network infrastructure-based methods allow us to tap into a number of performance metrics from all layers of networking, huge swaths of data remain poorly explored due to a lack of tools suitable for fast, interactive, and rigorous MBB data analysis. In this paper we present RICERCANDO, a solution that enables rapid exploration of large heterogeneous MBB measurement data as well as the identification and explanation of unusual patterns detected in such data. RICERCANDO consists of a preprocessing module ensuring that time-series data is stored in the most appropriate form for mining, a rapid exploration module enabling iterative analysis of time-series and geomobile data to detect and single-out anomalies, and the advanced mining module that lets the analyst deduce root causes of observed anomalies. We implement and release RICERCANDO in open-source, and validate its usability on case studies from a pan-European MBB measurement testbed.
A dataset for exploring gaze behaviors in text summarization

Automatic text summarization has been a hot research topic for years. Though most of the existing studies only use the content itself to generate the summaries, researchers believe that an individual's reading behaviors have much to do with the summaries s/he generates, usually regarded as the ground truth. However, such research is limited by the lack of a dataset that provides the connection between people's reading behaviors and the summaries provided by them. This paper fills in this gap by providing a dataset covering 50 individuals' gaze behaviors collected by a high-accurate eye tracking device (that generates 100 gaze points per second) when they are reading 100 articles (from 10 popular categories) and composing the corresponding summaries for each article. Collected in a controlled environment, our dataset with 157 million gaze points in total, provides not only the basic gaze behaviors when different people read an article and compose its corresponding summary, but also the connections between different behavior patterns and the summaries they will provide. We believe such a dataset will be valuable for a wide range of studies, and we also provide sample use cases of the dataset.
Intelligent Robot for Early Childhood Education

The paper introduces a preschool robot with a face tracking module based on the application of artificial intelligence in early childhood education. The face recognition function based on the convolutional neural network and k-Nearest Neighbor classification method allows the early childhood robot to recognize a specified face. The two-degree-of-freedom PTZ can be automatically adjusted based on the PID (proportion-integralderivative) control to track a face. Preschool robots actively interact with young children and send their video information to parents in real-time, effectively establishing a communication bridge between the family and the campus. Because the preschool robot can track the specified face ID and send the video information directly to the parents, this avoids the wrong tracking of non-children and protects privacy to a large extent.
Using Geomatics Techniques to Produce a Geospatial Database System for Geological Hazards in the Al-Salt Area

In the Al-Salt area of Jordan, natural geological hazard events occur frequently because the area northeast of the Dead Sea is recognized as an active seismic zone. Therefore, using the cutting edge technology represented in this study by the geomatics techniques of remote sensing, GPS and GIS were become necessary and sufficient, that because there are many variables a several features to be mapped and store. This study was focused on faults and landslides because these are the dominant events in the study area. Satellite images, geological maps and topographic maps were used to produce GIS layers describing geological hazard elements. The mapped elements included faults, landslides and many other spatial features including lineaments, drainage patterns, road networks, vegetation, contour lines, slopes, aspects and residential areas. Faults, lineaments and landslide maps were extracted from the published geological map of the Al-Salt area (1:50 000 scale). Other features were extracted either from a topographic map or from satellite images of the study area. Digital image-processing techniques were performed on satellite images to enhance the required spatial features (i.e., faults and landslides). Several techniques were applied to the digital images, including false-color composite band ratio analysis, principal-component analysis and high-pass filtering. A Garmin hand-held GPS (model GPSMAP60CSx) was used to track and to map the city's recently constructed main ring road. The hand-held GPS was also used to locate positions of the faults and landslides in the study area. Finally, the produced GIS layers and their attributes were stored in a spatial GIS database using the Arc GIS software package. These layers could be used for retrieving data on the geological hazard elements and for producing a geological hazards map (or any other thematic map) for the Al-Salt area in Jordan.
Kinetic AR: A Framework for Robotic Motion Systems in Spatial Computing

We present Kinetic AR, a holistic user experience framework for visual programming of robotic motion systems in Augmented Reality. The Kinetic AR framework facilitates human-robot collaboration in a co-located environment. Our goal is to present a deployable guide for the creation and visualization of manifold robotic interfaces while maintaining a low entry barrier to complex spatial hardware programming. A two phase validation process has been conducted to assess our work. As an initial phase, we have performed a set of interviews with robotics experts. Based on these interviews we have established three main areas that our framework tackles in different time domains. In a second phase, we have developed a set of prototypes using mobile Augmented Reality that apply the principles of Kinetic AR to multiple hardware actors including an AGV, a robotic arm, and a prosthetic system. Additional feedback from experts indicate the potential of the Kinetic ARframework.
Towards Effective Categorization of Weather Images using Deep Convolutional Architecture

In the era of Industry 4.0, when various industries are coming together to achieve commercial enterprise goals, factors like weather plays an important role in enterprise growth. Weather conditions not only influence our daily lives, but it's also a big factor in various industries like logistics, retails, construction, etc. Apart from that, it also influences the functionality of many visual systems, like vehicle assistance driving systems, outdoor video surveillance, etc. Foggy weather condition is a vital threat to car accidents. The effect of harsh weather conditions can also be seen in India. There are loads of research that has been going on in the field of `Weather Analytics' but it nevertheless wants a lot of research to follow artificial intelligence in this domain. This paper is presenting a precise work to fulfill the gap. The study investigates the applications of a deep learning algorithm (convolutional neural networks) in weather detection problems. The traditional ways of detecting weather conditions rely on expensive sensors. To minimize the cost, this work presents a computer vision approach to detect live weather condition.
The Romantic App: Augmented Reality in Fine Art Education

This chapter explores The Romantic App an ongoing project in the School of the Art Institute of Chicago. It is a custom application for smartphone or tablet made especially for the nineteenth and twentieth century paintings of the European Painting and Sculpture wing in the Art Institute of Chicago. Art Institute students designed and executed The Romantic App in the Fall semester of 2015, with a second iteration produced in Spring, 2019. It is intended both as a teaching vehicle but also as an art work, allowing young people to engage collaboratively with mentors from another time and period. Augmented Reality was chosen as a medium for this project because it functions in parallel to the grand painting tradition of the nineteenth century, that moment when photography had its first dizzying impact on Parisian culture. Like so many of the optical viewing devices of that époque, The Romantic App also reveals illusions and phantasmagoria hidden beneath the surface of the visible world. When the app is open, one can glimpse visions and optical illusions through the interface of a device, integrated into a curated selection of paintings scattered throughout the Museum. Using one’s phone as a kind of magical mirror or optical lens, users are encouraged to examine individual paintings, to see hidden animations, enactments and other kinds of moving pictures subtly mixed into their surfaces.
Towards Open-Set Forensic Source Grouping on JPEG Header Information

Image provenance, i.e., information on the model and make of the device that was used to produce an image, is a helpful cue in many digital investigations. Such information can, for example, help refute the hypothesis that an illegal photograph found on the Internet was produced using the personal device of a suspect. Grouping images by provenance can be done in different ways. Based on the encouraging insights from previous works, we consider the grouping of JPEG images by their file headers where previous work performed image classification on a closed-set of devices. However, due to the ongoing development of new camera models and the practical difficulty to keep a model database up-to-date, we propose to treat image provenance as an open-set classification problem with the goal to predict the make of a previously unseen camera model. We show that such a prediction can performe remarkably well, with median accuracies beyond 90% for each make. In a more challenging experiment with images that were postprocessed, we achieve a median accuracy of about 55% and 75% for desktop software and for smartphone apps, respectively.
Maxson: Reduce Duplicate Parsing Overhead on Raw Data

JSON is a very popular data format in many applications in Web and enterprise. Recently, many data analytical systems support the loading and querying JSON data. However, JSON parsing can be costly, which dominates the execution time of querying JSON data. Many previous studies focus on building efficient parsers to reduce this parsing cost, and little work has been done on how to reduce the occurrences of parsing. In this paper, we start with a study with a real production workload in Alibaba, which consists of over 3 million queries on JSON. Our study reveals significant temporal and spatial correlations among those queries, which result in massive redundant parsing operations among queries. Instead of repetitively parsing the JSON data, we propose to develop a cache system named Maxson for caching the JSON query results (the values evaluated from JSONPath) for reuse. Specifically, we develop effective machine learning-based predictor with combining LSTM (long shortterm memory) and CRF (conditional random field) to determine the JSONPaths to cache given the space budget. We have implemented Maxson on top of SparkSQL. We experimentally evaluate Maxson and show that 1) Maxson is able to eliminate the most of duplicate JSON parsing overhead, 2) Maxson improves end-to-end workload performance by 1.5-6.5×.
Joint non-negative and fuzzy coding with graph regularization for efficient data clustering

Non-negative matrix factorization (NMF) is an effective model in converting data into non-negative coefficient representation whose discriminative ability is usually enhanced to be used for diverse pattern recognition tasks. In NMF-based clustering, we often need to perform K-means on the learned coefficient as postprocessing step to get the final cluster assignments. This breaks the connection between the feature learning and recognition stages. In this paper, we propose to learn the non-negative coefficient matrix based on which we jointly perform fuzzy clustering, by viewing that each column of the dictionary matrix as a concept of each cluster. As a result, we formulate a new fuzzy clustering model, termed Joint Non-negative and Fuzzy Coding with Graph regularization (G-JNFC), and design an effective optimization method to solve it under the alternating direction optimization framework. Besides the convergence and computational complexity analysis on G-JNFC, we conduct extensive experiments on both synthetic and representative benchmark data sets. The results show that the proposed G-JNFC model is effective in data clustering.