Reviewerly/exHarmony huggingface dataset:
A dynamic classification unit for online segmentation of big data via small data buffers

In many segmentation processes, we assign new cases according to a model that was built on the basis of past cases. As long as the new cases are “similar enough” to the past cases, segmentation proceeds normally. However, when a new case is substantially different from the known cases, a reexamination of the previously created segments is required. The reexamination may result in the creation of new segments or in the updating of the existing ones. In this paper, we assume that in big and dynamic data environments it is not possible to reexamine all past data and, therefore, we suggest using small groups of selected cases, stored in small data buffers, as an alternative to the collection of all past data. We present an incremental dynamic classifier that supports real-time unsupervised segmentation in big and dynamic data environments. In order to reduce the computational effort of unsupervised clustering in such environments, the suggested model performs calculations only on the relevant data buffers that store the relevant representative cases. In addition, the suggested model can serve as a dynamic classification unit (DCU) that can act as an autonomous agent, as well as collaborate with other DCUs. The evaluation is presented by comparing three approaches: static, dynamic, and incremental dynamic.
The Many AI Challenges of Hearthstone

Since the inception of artificial intelligence, games have benchmarked algorithmic advances. Recent success in classic board games such as Chess and Go have left space for video games that pose related yet different sets of challenges. With this shifted focus, the set of AI problems associated with video games has expanded from simply playing these games to win, to include playing games in particular styles, generating game content, modeling players, etc. Different games pose different challenges for AI systems, and several such AI challenges can typically be addressed in the same game. In this article we analyze the popular collectible card game Hearthstone published by Blizzard in 2014, and describe a varied set of interesting AI challenges it poses. Despite their popularity and associated interesting challenges, collectible card games are relatively understudied in the AI community. By analyzing a single game in-depth, we get a glimpse of the entire field of AI and games through the lens of a single game, discovering a few new variations on existing research topics.
Peace Through Superior Puzzling: An Asymmetric Sybil Defense

A common tool to defend against Sybil attacks is proof-of-work, whereby computational puzzles are used to limit the number of Sybil participants. Unfortunately, current Sybil defenses require significant computational effort to offset an attack. In particular, good participants must spend computationally at a rate that is proportional to the spending rate of an attacker. In this paper, we present the first Sybil defense algorithm which is asymmetric in the sense that good participants spend at a rate that is asymptotically less than an attacker. In particular, if T is the rate of the attacker's spending, and J is the rate of joining good participants, then our algorithm spends at a rate f O(√(TJ) + J). We provide empirical evidence that our algorithm can be significantly more efficient than previous defenses under various attack scenarios. Additionally, we prove a lower bound showing that our algorithm's spending rate is asymptotically optimal among a large family of algorithms.
Towards precise spatio-temporal control of scents and air for olfactory augmented reality

Scent presentation in conjunction with virtual reality (VR) inherently has a property of augmented reality (AR), as humans always take breath and we cannot block the surrounding air. In this paper, we first categorize various olfactory display techniques in analogy with visual displays for AR. Then we report our recent work on spatial scent presentation. Our approach is a counterpart of spatial AR, that overlays information onto the real world. In order to cover various aspects of olfactory experiences, we make use of multiple methods: wind and vortex rings. We investigated the human sensitivity on the wind direction to show the design requirement of wind display. Also, we considered to use a vortex ring to transfer the scent encapsulated in it. By letting two vortex rings collide and collapse at the target point, a local scent distribution can be generated in the air. However, this approach has been suffered from the uncertainty of vortex collision. For stable generation of mid-air scent distribution, we explored various aspects in expelling vortex rings.
A Hybrid Pipelined Architecture for High Performance Top-<i>K</i> Sorting on FPGA

We present a hybrid pipelined sorting architecture capable of finding and producing as its output the K largest elements from an input sequence. The architecture consists of a bitonic sorter and L cascaded sorting units. The sorting unit is designed to output P elements during every cycle with the aim of increasing the throughput and lowering the latency. The function of the bitonic sorter is to generate a segmented ordered sequence. The sorting unit processes this sequence to identify and output the P largest elements. Hence, the K=PL largest elements are obtained after the segmented ordered sequence proceeds through L cascaded sorting units. Variable-length and continuous sequences are supported by the proposed sorting architecture. The results of the implementation show that the sorting architecture can achieve a throughput of 22.88 GB/s with P=16 on a state-of-the-art Field Programmable Gate Array (FPGA).
Runtime Monitoring and Resolution of Probabilistic Obstacles to System Goals

Software systems are deployed in environments that keep changing over time. They should therefore adapt to changing conditions to meet their requirements. The satisfaction rate of these requirements depends on the rate at which adverse conditions prevent their satisfaction. Obstacle analysis is a goal-oriented form of risk analysis for requirements engineering (RE), whereby obstacles to system goals are identified, assessed, and resolved through countermeasures. The selection of effective countermeasures relies on environment assumptions and on the assessed likelihood and criticality of the corresponding obstacles. Those various factors estimated at RE time may, however, evolve at system runtime. To meet the system’s goals under changing conditions, this article proposes to defer obstacle resolution to system runtime. Techniques are presented for monitoring obstacle satisfaction rates; deciding when adaptation should be triggered; and adapting the system on-the-fly to countermeasures that are more effective. The approach relies on a model where goals and obstacles are refined and specified in a probabilistic linear temporal logic. The techniques allow for monitoring the satisfaction rate of probabilistic leaf obstacles; determining the severity of obstacle consequences on goal satisfaction rates computed from the monitored obstacle satisfaction rates; and shifting to countermeasures that better meet the required goal satisfaction rates. Our approach is evaluated on fragments of an ambulance dispatching system.
A Multi-Scale Position Feature Transform Network for Video Frame Interpolation

Given a video sequence, video frame interpolation aims to synthesize an in-between frame of two consecutive frames. In this paper, we propose a multi-scale position feature transform (MS-PFT) network for video frame interpolation where two parallel prediction networks and one optimization network are designed to predict the features of target frame and generate the final interpolation result, respectively. To increase the fidelity of the synthesised frames, we propose to apply a position feature transform (PFT) layer in the residual blocks of the prediction networks to estimate scaling factors which help evaluate different degrees of the importance of deep features around a target pixel. A PFT layer utilizes optical flow to extract and generate position features and then adjusts the learning process of our model. We further extend our model into a multi-scale structure in which each scale of the network shares the same parameters to maximise the efficiency of our network with model size unchanged. The experiments show that our method can handle the challenging scenarios like occlusion and large motion effectively and the proposed method outperforms those state-of-the-art approaches on different datasets.
Detecting Spear-phishing Emails Based on Authentication

In this paper, stylometric features, gender features and personality features are introduced in the phishing detection. Experimental results show that the detection accuracy reaches 95.05%, which is about 10% higher than that only using the stylometric features. This results indicates that the algorithm has detection effect.
Deep attention network for person re-identification with multi-loss

Person re-identification (person re-ID) is one of the most challenging tasks in the computer vision area as it involves large variations in human appearances, human poses, background illuminations, camera views, etc. In particular, images for person re-ID are mostly low resolution due to the long-range deployment of the cameras and the cropping operation from the surveillance system. In this paper, we present a novel deep Siamese person re-ID network equipped with an attention mechanism, constrained by a multi-loss function. The attention mechanism enhances the discriminability of the network by emphasizing effective features and suppressing the less useful ones. The purpose of the multi-loss function is to diminish distances of identical persons and at the same time expand distances between dissimilar persons in the learned feature space. Extensive comparative evaluations demonstrate that the proposed method significantly outperforms a number of state-of-the-art approaches, including both conventional and deep network based ones, on the challenging Market1501 and CUHK03 data sets.
Towards a real-time unsupervised estimation of predictive model degradation

Automating predictive machine learning entails the capability of properly triggering the update of the trained models. To this aim, the degradation of predictive models has to be continuously evaluated over time to detect data distribution drifts between the original training set and the new data. Traditionally, prediction performance is used as a degradation metric. However, prediction quality indices require ground-truth class labels to be known for the newly classified data, making them unsuitable for real-time applications, as ground-truth labels might be totally absent or be available only later.
Examining Effectiveness and User Experiences in 3D Mobile based Augmented Reality for MSU Virtual Tour

A campus tour is a time-consuming event and human resource intensive task. It is not always possible for visitors to visit all the facilities on a one-day tour due to the time limit or some activities on campus that cannot be disturbed. Along with technological advancements, some universities began to use multimedia technology as a supporting media for campus tour activities. This paper presents the mobile augmented reality and virtual tour application for Management and Science University (MSU). This application will able to help visitors in the process of obtaining such information on MSU in a very simple way by augmented reality feature, and let the visitors become familiar with MSU's environment by virtual tour feature. This project also presents the main use cases covered by the application and describes the main feature components. Furthermore, the evaluation of the application based on user testing is also explained.
Portable System for Box Volume Measurement Based on Line-Structured Light Vision and Deep Learning

Portable box volume measurement has always been a popular issue in the intelligent logistic industry. This work presents a portable system for box volume measurement that is based on line-structured light vision and deep learning. This system consists of a novel 2 × 2 laser line grid projector, a sensor, and software modules, with which only two laser-modulated images of boxes are required for volume measurement. For laser-modulated images, a novel end-to-end deep learning model is proposed by using an improved holistically nested edge detection network to extract edges. Furthermore, an automatic one-step calibration method for the line-structured light projector is designed for fast calibration. The experimental results show that the measuring range of our proposed system is 100-1800 mm, with errors less than ±5.0 mm. Theoretical analysis indicates that within the measuring range of the system, the measurement uncertainty of the measuring device is ±0.52 mm to ±4.0 mm, which is consistent with the experimental results. The device size is 140 mm × 35 mm × 35 mm and the weight is 110 g, thus the system is suitable for portable automatic box volume measurement.
A Maximum Margin Matrix Factorization based Transfer Learning Approach for Cross-Domain Recommendation

Recommender systems usually employ techniques like collaborative filtering for providing recommendations on items/services. Maximum Margin Matrix Factorization (MMMF) is an effective collaborative filtering approach. MMMF suffers from the data sparsity problem, i.e., the number of items rated by the users are very small as compared to the very large item space. Recently, techniques like cross-domain collaborative filtering (transfer learning) is suggested for addressing the data sparsity problem. In this paper, we propose a model for transfer learning in collaborative filtering through MMMF to address the data sparsity issue. The latent feature matrices involved in MMMF are clustered and combined to generate a cluster-level rating pattern called codebook and a codebook transfer is used for transfer of information. Transferring of codebook and finding the predicted rating matrix is done in a novel way by introducing a softness constraint into the optimization function. We have experimented our methods with different levels of sparsity using benchmark datasets. Results from experiments show that our model approximates the target matrix well.
NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems

Convolutional Neural Networks (CNNs) suffer from energy-hungry implementation due to their computation and memory intensive processing patterns. This problem is even more significant by the proliferation of CNNs on embedded platforms. To overcome this problem, we offer NeuroPower as an automatic framework that designs a highly optimized and energy efficient set of CNN architectures for embedded systems. NeuroPower explores and prunes the design space to find improved set of neural architectures. Toward this aim, a multi-objective optimization strategy is integrated to solve Neural Architecture Search (NAS) problem by near-optimal tuning network hyperparameters. The main objectives of the optimization algorithm are network accuracy and number of parameters in the network. The evaluation results show the effectiveness of NeuroPower on energy consumption, compacting rate and inference time compared to other cutting-edge approaches. In comparison with the best results on CIFAR-10/CIFAR-100 datasets, a generated network by NeuroPower presents up to 2.1x/1.56x compression rate, 1.59x/3.46x speedup and 1.52x/1.82x power saving while loses 2.4%/−0.6% accuracy, respectively.
Iterative Projection and Matching: Finding Structure-Preserving Representatives and Its Application to Computer Vision

The goal of data selection is to capture the most structural information from a set of data. This paper presents a fast and accurate data selection method, in which the selected samples are optimized to span the subspace of all data. We propose a new selection algorithm, referred to as iterative projection and matching (IPM), with linear complexity w.r.t. the number of data, and without any parameter to be tuned. In our algorithm, at each iteration, the maximum information from the structure of the data is captured by one selected sample, and the captured information is neglected in the next iterations by projection on the null-space of previously selected samples. The computational efficiency and the selection accuracy of our proposed algorithm outperform those of the conventional methods. Furthermore, the superiority of the proposed algorithm is shown on active learning for video action recognition dataset on UCF-101; learning using representatives on ImageNet; training a generative adversarial network (GAN) to generate multi-view images from a single-view input on CMU Multi-PIE dataset; and video summarization on UTE Egocentric dataset.
Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery

Maneuvering target detection in satellite images is difficult due to their small sizes, blurred appearances under various illuminations and shadows, and occlusion by trees and buildings. Recently, a fully convolutional regression network (FCRN) was proposed and achieved by the state-of-the-art performance in the Munich vehicle database. However, such a one-phase approach often makes mistakes at difficult places because of its swift glance and rejecting any second check. In this letter, a new object spatial density building net (SDBN) was designed, and a two-phase detection approach was proposed. It used the first SDBN to generate candidate regions and the second SDBN to proceed with a meticulous check on the object categories. Experiments on four maneuvering target databases, the Munich vehicle database, the Open Vehicle Database of San-Francisco (OVDS), the Overhead Imagery Research Data Set (OIRDS), and the Open Aircraft Database (OAD) show that the proposed method outperforms FCRN by an obvious margin. In addition, the accurate geometrical parameters (positions, orientations, and lengths) of all the objects were computed based on the spatial density maps, and the published experimental result of FCRN in OIRDS was pointed out and the corrected result was given. All source codes and databases are available at http://www.github.com/cxy177/SDBN.
Should we embed? A study on the online performance of utilizing embeddings for real-time job recommendations

In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian job platform Studo Jobs, we evaluate two popular recommendation scenarios: (i) providing similar jobs and, (ii) personalizing the job postings that are shown on the homepage. Our results show that for recommending similar jobs, we achieve the best online performance in terms of Click-Through Rate when we employ embeddings based on the most recent interaction. To personalize the job postings shown on a user's homepage, however, combining embeddings based on the frequency and recency with which a user interacts with job postings results in the best online performance.
To Complete or to Estimate, That is the Question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation

Robust three-dimensional scene understanding is now an ever-growing area of research highly relevant in many realworld applications such as autonomous driving and robotic navigation.In this paper, we propose a multi-task learningbased model capable of performing two tasks:-sparse depth completion (i.e.generating complete dense scene depth given a sparse depth image as the input) and monocular depth estimation (i.e.predicting scene depth from a single RGB image) via two sub-networks jointly trained end to end using data randomly sampled from a publicly available corpus of synthetic and real-world images.The first subnetwork generates a sparse depth image by learning lower level features from the scene and the second predicts a full dense depth image of the entire scene, leading to a better geometric and contextual understanding of the scene and, as a result, superior performance of the approach.The entire model can be used to infer complete scene depth from a single RGB image or the second network can be used alone to perform depth completion given a sparse depth input.Using adversarial training, a robust objective function, a deep architecture relying on skip connections and a blend of synthetic and real-world training data, our approach is capable of producing superior high quality scene depth.Extensive experimental evaluation demonstrates the efficacy of our approach compared to contemporary state-of-the-art techniques across both problem domains.
Performance Evaluation of CP-ABE Schemes under Constrained Devices

Recently, Internet of Things devices (IoT) have become a hot spot for researchers. Their industrial importance is growing exponentially day after day. Statistics show that the number of IoT devices will reach fifty billions by 2020. In addition, IoT applications are backed through the Cloud where data is stored and processed by gigantic processing systems. However, since the Cloud is honest but curious, sensitive information belonging to the IoT devices owners might be accessed and used beyond the intended purpose. Therefore, data privacy on Cloud servers should be preserved, which means that they should not reveal any piece of personally identifiable information (PII). Attribute Based Encryption (ABE) is a new form of public key encryption. ABE is a good candidate to achieve privacy and fine-grained access control for IoT applications running on Cloud servers. However, performing all the related cryptographic operations on such devices is practically infeasible because of their resource constraints. For alleviating all the computation burden on these resource-limited devices, several schemes have been proposed. In this paper, we investigate different ABE schemes, we implement and provide a performance evaluation in order to compare two relevant CP-ABE schemes: a fully encryption Vs a delegation based alternative.
Applying Blockchain to Improve the Integrity of the Software Development Process

Software development is a complex endeavor that encompasses application and implementation layers with functional (refers to what is done) and non-functional (how is done) aspects. The efforts to scale agile software development practices are not wholly able to address issues such as integrity, which is a crucial non-functional aspect of the software development process. However, if we consider most software failures are Byzantine failures (i.e., where components may fail and there is imperfect information on which a component has failed.) that might impair the operation but do not completely disable the production line. In this paper, we assume software practitioners who cause defects as Byzantine participants and claim that most software failures can be mitigated by viewing software development as the Byzantine Generals Problem. Consequently, we propose a test-driven incentive mechanism based on a blockchain concept to orchestrate the software development process where production is controlled by a similar infrastructure based on the working principles of blockchain. We discuss the model that integrates blockchain with the software development process, and provide some recommendations for future work to address the issues while orchestrating software production.