Reviewerly/exHarmony huggingface dataset:
Color-based template selection for detection of gastric abnormalities in video endoscopy

Computer-aided diagnosis of gastric diseases from endoscopy frames is an important task. It facilitates both the patient and gastroenterologist in terms of time, money and most important health. Colors are the basic visual features of endoscopic images and also provide clues about abnormal regions in endoscopy frames. A variety of color spaces available for representation of color frames. However, we are not certain about which color space is more suitable for representing color features of gastric images. This paper presents a comparison of color features in different color spaces for detection of abnormal areas in chromoendoscopy (CH) frames. In addition, the CH images are segmented by using an existing color-difference based segmentation method Delta E (ΔE). A framework for automatic segmentation is presented for endoscopy images by selecting a template image in ΔE by using trained models. For classification, colors features are also merged with texture descriptors. The support vector machine (SVM) classifier is trained on color features and also the hybrid color combined texture characteristics. Then the trained classifier is used to group CH frames into abnormal and normal classes. ΔE with manual template selection has achieved 57.44% accuracy and 56.88% accuracy with the automated process. Moreover, the suggested method achieves 86.6% accuracy and 0.91 area under the curve for the classification of gastric lesions.
VeriSketch

We present VeriSketch, a security-oriented program synthesis framework for developing hardware designs with formal guarantee of functional and security specifications. VeriSketch defines a synthesis language, a code instrumentation framework for specifying and inferring timing-sensitive information flow properties, and uses specialized constraint-based synthesis for generating HDL code that enforces the specifications. We show the power of VeriSketch through security-critical hardware design examples, including cache controllers, thread schedulers, and system-on-chip arbiters, with formal guarantee of security properties such as absence of timing side-channels, confidentiality, and isolation.
Property Directed Inference of Relational Invariants

Property Directed Reachability (PDR) is an efficient and scalable approach for solving systems of symbolic constraints, also known as Constrained Horn Clauses (CHC). In the case of non-linear CHCs, which may arise, e.g., from relational verification tasks, PDR aims to infer an inductive invariant for each uninterpreted predicate. However, in many practical cases, this reasoning is not successful, as invariants need to be discovered for groups of predicates, as opposed to individual predicates. We contribute a novel algorithm that identifies such groups automatically and complements the existing PDR technique. The key feature of the algorithm is that it does not require a possibly expensive synchronization transformation over the system of CHCs. We have implemented the algorithm on top of a state-of the-art CHC solver SPACER. Our experimental evaluation shows that for some CHC systems, on which existing solvers diverge, our tool is able to discover relational invariants.
A moving target defense against adversarial machine learning

Adversarial Machine Learning has become the latest threat with the ubiquitous presence of machine learning. In this paper we propose a Moving Target Defense approach to defend against adversarial machine learning, i.e., instead of manipulating the machine learning algorithms, we suggest a switching scheme among machine learning algorithms to defend against adversarial attack. We model the problem as a Stackelberg game between the attacker and the defender. We propose a switching strategy which is the Stackelberg equilibrium of the game. We test our method against rational, and boundedly rational attackers. We show that designing a method against a rational attacker is enough in most scenarios. We show that even under very harsh constraints, e.g., no attack-cost, and availability of attacks which can bring down the accuracy to 0, it is possible to achieve reasonable accuracy in the context of classification. This work shows, that in addition to switching among algorithms, one can think of introducing randomness in tuning parameters, and model choices to achieve better defense against adversarial machine learning.
ARP

Review rating prediction is an important task in data mining and natural language processing fields, and has wide applications. Users usually express opinions towards many aspects in their reviews, and the overall review rating is a synthesis of these opinions. However, most existing review rating prediction methods ignore users' opinions on aspects, which is insufficient. In this paper, we propose a neural aspect-aware rating prediction approach for Chinese reviews. In our approach we propose a collaborative learning framework to jointly train review-level rating predictor and multiple aspect-level rating predictors. In our framework different rating predictors share the same review encoder model to exploit the inherent relatedness between them, but have different attention networks to focus on different informative texts for each task. The final review representation for rating prediction is a concatenation of the review representations from all predictors. Since word segmentation of Chinese reviews is usually inaccurate, we propose a multi-view learning model to learn review representations from both words and characters. Extensive experiments on real-world dataset validate the effectiveness of our approach.
Practice the higher-order thinking skills in optic topic through physics worksheet equipped with augmented reality

Physics learning requires critical thinking skills. This skill is generally not optimal because there are no media that supports it. This study produces physics student worksheets in optic topic equipped with Augmented Reality (AR) as a medium to practice students' higher-order thinking skills. The method used in this research is research and development method with Dick and Carey approach. The result is a student worksheet that integrated with AR applications that can display video. The content of the student worksheet consists of three stages, namely, introduction, practicum, and post-practicum. The introduction stage contains illustrations that integrate with the AR video for an explanation of the initial question. AR integration for the practicum phase contains a video explanation of the practical tools and procedures. The application of physics concepts to everyday life is explained through AR videos at the post-practicum stage. This student worksheet has stages that can build critical thinking skills. These skills break down into three stages, namely: analyzing, evaluating, and creating. The validation results provide that this AR worksheet is materially feasible with a rating scale of 77.87%, which is categorized as good and in the media with a rating scale of 94.57% which is categorized very good.
Understanding Enjoyment in VR Games with GameFlow

In this paper, we report on a work in progress project that aims to understand affordances and inhibiters of enjoyment in virtual reality (VR) video games. We apply the GameFlow model to review and analyse VR and non-VR versions of the same games to identify differences in enjoyment. Our approach includes conducting expert reviews using the GameFlow model, as well as conducting qualitative analysis on video game reviews, using GameFlow as a conceptual foundation. In this paper, we report our initial findings for the game Superhot. Our ongoing work evaluates a selection of games to map opportunities and pitfalls when designing games for VR.
Uncertainty analysis of impact factors of eco-environmental vulnerability based on cloud theory

This paper studied water source area of the Central Line Project of South-to-North Water Diversion. It built an assessment indicators system on eco-environmental vulnerability of water source area which includes four levels target layer, effect layer, state layer and response layer. Aiming at the uncertainties of experts score on effect layer, state layer and response layer in the analysis and evaluation of eco-environmental vulnerability in water source areas, it built a cloud model-based scale criterion for eco-environmental vulnerability analysis of water source areas based on the index scale. The discreteness, ambiguity and randomness were considered in the calculation process. The cloud model weights, a calculation results of the ecological environment vulnerability analysis in the water source area, were calculated by constructing matrix that is a two-by-two judgment matrix through using the cloud scale criterion. The results were got by calculating three numerical characteristics which includes the Expected Value(Ex), the Entropy(En) and the Hyper Entropy(He) of the cloud model. The results showed that the six factors of the Ex is 0.058–0.282, the En is 0.081–0.297 and the He is 0.081–0.297 where they stayed in the state layer in the evaluation index system of ecological environment vulnerability in the water source. Then, the results showed that the sixteen factors of the Ex is 0.008–0.177, the En is 0.013–0.171 and the He is 0.013–0.171 where they stayed in the response layer. The dense and relative small data showed that all these factors are micro-sensitive factors which indicated that the impact of a single factor on eco-environmental vulnerability of water source area is not obvious. And the consequence was caused by the long-term interaction of multiple factors. The algorithm provided a new idea for the assessment of eco-environmental vulnerability in water source areas.
Accessing the Pokélayer: Augmented Reality and Fantastical Play in Pokémon Go

The mobile gaming phenomenon Pokémon Go is one of the first successful mainstream games to feature augmented reality (AR) components, giving players the feeling of hunting Pokémon in real-life settings. Pokémon Go offers players access to “Pokélayer,” a game layer laid on top of the real world, combining GPS and an AR mode. This ability to turn daily activities into gameplay affords players the opportunity to live out their fantasy of being a Pokémon Trainer. The authors present findings from a phenomenological study with 30 participants showing the variant ways in which the Pokélayer and augmented reality’s capability for fantasy play emerged.
Drone Image Stitching Based on Compactly Supported Radial Basis Function

Image stitching stitches multiple overlapping images into a seamless image according to the corresponding geometric relationship between the reference and source images. In this article, a parallax-tolerant image stitching method based on robust elastic warping is integrated in the stitching of drone images, and locality preserving feature matching is used to effectively remove outliers from a set of putative feature correspondences. Our method can avoid the ghost issue in traditional methods when the scene suffers large view point changes. The method can be divided into three stages, namely, locality preserving feature matching, robust elastic warping, and global projectivity preservation. First, a set of high-precision matched feature points are provided for a pair of drone images, where the locality preserving matching is used. Second, the robust elastic warping function eliminates the parallax error, and the input image is distorted according to the calculated deformation on the nongrid plane. Finally, the global projectivity preserving method is applied to obtain high-precision resultant panoramas. Experiments on several sets of drone images demonstrate that our method can generate better panoramas over the state-of-the-art competitors.
A Dynamic Product-aware Learning Model for E-commerce Query Intent Understanding

Query intent understanding is a fundamental and essential task in searching, which promotes personalized retrieval results and users' satisfaction. In E-commerce, query understanding is particularly referring to bridging the gap between query representations and product representations. In this paper, we aim to map the queries into the predefined tens of thousands of fine-grained categories extracted from the product descriptions. The problem is very challenging in several aspects. First, a query may be related to multiple categories and to identify all the best matching categories could eventually drive the search engine for high recall and diversity. Second, the same query may have dynamic intents under various scenarios and there is a need to distinguish the differences to promote accurate categories of products. Third, the tail queries are particularly difficult for understanding due to noise and lack of customer feedback information. To better understand the queries, we firstly conduct analysis on the search queries and behaviors in the E-commerce domain and identified the uniqueness of our problem (e.g. longer sessions). Then we propose a Dynamic Product-aware Hierarchical Attention (DPHA) framework to capture the explicit and implied meanings of a query given its context information in the session. Specifically, DPHA automatically learns the bidirectional query-level and self-attentional session-level representations which can capture both complex long range dependencies and structural information. Extensive experimental results on a real E-commerce query data set demonstrate the effectiveness of the proposed DPHA compared to the state-of-art baselines.
Sign Language Recognition Based on CBAM-ResNet

As we are aware, there are millions of deaf-mutes around the world. It is a necessity to conduct research into sign language recognition as it is of massive significance to helping normal people and deaf-mute people communicate smoothly with others. A behavior recognition method was proposed in this paper to address the sign language recognition issue. Inspired by Multi-Fiber Networks, CBAM-ResNet neural network that extended the network structure of ResNet into 3D convolution was proposed and convolutional block attention module was added. In the fifth layer of the network, the structure unit of 3D-Res2Net was used to preserve the advantages possessed by the network structure of Multi-Fiber Networks, which could compensate for the deficiency of multi-Fiber Networks channel information fusion. A comparison was drawn of it with the models adding convolutional block attention module, Convolution Long Short-Term Memory, optical flow and other methods at the same time. The proposed method achieved an accuracy rate of 83.3% without optical flow on Chinese Sign Language Recognition Dataset, which was about 9% higher than that of Multi-Fiber Networks.
Exploring privileged information from simple actions for complex action recognition

Complex action recognition is an important yet challenging problem in computer vision. Sufficient labeled training data are required for learning a robust model. However, labeling complex actions is often time-consuming and expensive. Considering that each complex action is composed of a sequence of simple actions, we propose a new learning framework for complex action recognition by using a sequence of existing simple actions. A matrix is first designed as the probability matrix by manual annotation, which encodes the occurrence of simple actions in complex actions. As the probability matrix depends on label information, it is only available for the training data and is regarded as privileged information. A new framework is proposed, which is called latent task learning with privileged information (LTL-PI). An efficient algorithm is also presented for solving the proposed LTL-PI formulation, which obtains the optimal sparse weight parameters. To validate the proposed LTL-PI algorithm, extensive experiments are carried out on two challenging complex action datasets: the Olympic Sports dataset and the UCF50 dataset. Experimental results show that LTL-PI is a promising development that improves the performance of complex action recognition, and the designed privileged information can offer promising enhancement.
Privacy Preserving Approximate K-means Clustering

Privacy preserving computation is of utmost importance in a cloud computing environment where a client often requires to send sensitive data to servers offering computing services over untrusted networks. Eavesdropping over the network or malware at the server may lead to leaking sensitive information from the data. To prevent this, we propose to encode the input data in such a way that, firstly, it should be difficult to decode it back to the true data, and secondly, the computational results obtained with the encoded data should not be substantially different from those obtained with the true data. Specifically, the computational activity that we focus on is the K-means clustering, which is widely used for many data mining tasks. Our proposed variant of the K-means algorithm is capable of privacy preservation in the sense that it requires as input only binary encoded data, and is not allowed to access the true data vectors at any stage of the computation. During intermediate stages of K-means computation, our algorithm is able to effectively process the inputs with incomplete information seeking to yield outputs relatively close to the complete information (non-encoded) case. Evaluation on real datasets show that the proposed methods yields comparable clustering effectiveness in comparison to the standard K-means algorithm on image clustering (MNIST-8M dataset), and in fact outperforms the standard K-means on text clustering (ODPtweets dataset).
Are we Asking the Right Questions in MovieQA?

Joint vision and language tasks like visual question answering are fascinating because they explore high-level understanding, but at the same time, can be more prone to language biases. In this paper, we explore the biases in the MovieQA dataset and propose a strikingly simple model which can exploit them. We find that using the right word embedding is of utmost importance. By using an appropriately-trained word embedding, about half the Question-Answers (QAs) can be answered by looking at the questions and answers alone, completely ignoring narrative context from video clips, subtitles, and movie scripts. Compared to the best published papers on the leaderboard, our simple question+answer only model improves accuracy by 5% for video + subtitle category, 5% for subtitle, 15% for DVS and 6% higher for scripts.
Azimuth-Height Background Filtering Method for Roadside LiDAR Data

LiDAR becomes a promising sensor in intelligent transportation for traffic data collection. Unlike the onboard LiDAR sensors which only focus on the surroundings of the vehicle, we set up LiDAR sensors at the infrastructure side to cover a wider detection range. In order to extract real-time movement status of road users, background filtering is the initial step to reduce a large amount of unrelated information. Since the LiDAR sensors are fixed at roadside, the detected background objects at a specific location are unchanged, which is different from dynamic backgrounds detected by onboard LiDAR sensors in a moving vehicle. This paper proposed an innovative real-time Azimuth-Height background filtering method for roadside LiDAR systems by comparing the heights between raw LiDAR data and background objects based on laser channels and azimuth angles. A case study showed more than 98% background data were successfully filtered and target (non-background) data were kept well. The proposed filtering process is performed while parsing original data stream and only target data are stored, thus the speed of processing LiDAR data in real time is increased, and computer memory is saved. The roadside LiDAR sensing systems provide a new approach to extracting pedestrian and vehicle trajectories.
Matrix PRFs: Constructions, Attacks, and Applications to Obfuscation

We initiate a systematic study of pseudorandom functions (PRFs) that are computable by simple matrix branching programs; we refer to these objects as "matrix PRFs". Matrix PRFs are attractive due to their simplicity, strong connections to complexity theory and group theory, and recent applications in program obfuscation. Our main results are:
A Hopping Umbrella for Fuzzy Joining Data Streams From IoT Devices in the Cloud and on the Edge

Internet of Things (IoT) is a new technology that changes the image of the current world, yielding new possibilities, but also proliferating data. IoT devices may constantly produce enormous amounts of data as data streams that can be analyzed in real time and also collected for further exploration in data lakes in huge data centers. Due to their scaling capabilities, these data centers are frequently located in the Cloud. However, recent rapid growth in the number of IoT devices and their applications in manufacturing, transport, and health care motivates moving the burden of data processing and analysis to the Edge. One of the phases of data processing is combining data streams from two (or more) IoT devices that monitor the same object and work asynchronously. Since they generate sensor readings at various moments of time, their data streams must be properly combined in order to obtain a complete image of the monitored object or process. In this article, we present the idea of a hopping umbrella which fuzzifies timestamps from sensor readings while joining data streams from asynchronous IoT devices in a flexible way. In contrast to processing data at rest, the hopping umbrella implements the fuzzy join operation in time windows for data streams (data in motion). By using fuzzy sets, the hopping umbrella not only allows combining asynchronous events from multiple sensors, but also facilitates evaluation of the degree of matching of the combined sensor readings, and consequently allows for reduction of the output stream size. Our experiments performed in Cloud and on Edge devices proved that with the use of this idea, we are able to properly join the best matching sensor readings and in some scenarios, reduce the number of data transferred to the Cloud data center without significant overhead in resource utilization of stream processing units.