Reviewerly/exHarmony huggingface dataset:
Prediction of Pathological Complete Response after Neoadjuvant Chemotherapy for breast cancer using ensemble machine learning

Neoadjuvant Chemotherapy is administered intravenously during the treatment of breast cancer. Prior to surgery, doctors recommend chemotherapy to shrink the large size of an invasive tumor. This research work proposes a Deux Machine Learning framework, implementing a double ensemble of Machine Learning algorithms for building an optimized and efficient solution to predict a complete pathological response of patients after Neoadjuvant Chemotherapy. Unlike algorithms which focus on the accuracy of prediction, the performance of the Deux Machine Learning framework is measured using a multi criteria decision-making technique known as weighted simple additive weighting (WSAW). The WSAW comprehensive performance score is calculated by considering ten evaluation metrics, namely, Accuracy, Mean Absolute Error, Root Mean Square Error, TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, and ROC. The results are validated using the k-fold cross validation technique, achieving an accuracy of 99.08%. When the performance of the proposed framework is compared with the performance of state-of-the-art classifiers such as SVM and random forest, the results are quite promising. With the growing trend of the applications of Artificial Intelligence in Cancer research, Machine Learning has an important future in prognostication and decision-making.
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-Identification

Vehicle re-identification (reID) is to identify a target vehicle in different cameras with non-overlapping views. When deploy the well-trained model to a new dataset directly, there is a severe performance drop because of differences among datasets named domain bias. To address this problem, this paper proposes an domain adaptation framework which contains an image-to-image translation network named vehicle transfer generative adversarial network (VTGAN) and an attention-based feature learning network (ATTNet). VTGAN could make images from the source domain (well-labeled) have the style of target domain (unlabeled) and preserve identity information of source domain. To further improve the domain adaptation ability for various backgrounds, ATTNet is proposed to train generated images with the attention structure for vehicle reID. Comprehensive experimental results clearly demonstrate that our method achieves excellent performance on VehicleID dataset.
Mining high occupancy itemsets

Frequent itemset mining has been extensively studied in data mining for over the last two decades because of its numerous applications. However, the classic support-based mining framework used by most previous studies is not suitable for some real-world applications, such as the travel landscapes recommendation, where occupancy besides support also plays a key role in evaluating the interestingness of an itemset. In this paper, we propose a new kind of tasks based on occupancy, namely high occupancy mining, by introducing occupancy into the support-based mining framework. An efficient algorithm, HEP (abbreviation for High Efficient algorithm for mining high occupancy itemsets), is developed to discover all high occupancy itemsets. HEP use a structure, named occupancy-list, to store the occupancy information about an itemset and employs an iterative level-wise approach to mine high occupancy itemset via a pruning strategy based on upper bound of occupancy. Substantial experiments on both synthetic and real datasets show that HEP is efficient for mining high occupancy itemsets and is at least one order of magnitude faster than the baseline algorithm.
Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multi-hop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labeled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.
An encoder–decoder switch network for purchase prediction

Users in e-commerce tend to click on items of their interest. Eventually, the more frequently an item is clicked by a user, the more likely the item will be purchased by the user after all. However, what if a user clicked on every item only once before purchases? This is a frequently observed user behavior in reality, but predicting which of the clicked items will be purchased is a challenging task. This paper addresses a practical yet widely overlooked task of predicting purchase items within a non-duplicate click session, i.e., a session in which every item is clicked only once. We propose an encoder–decoder neural architecture to simultaneously model users’ click and purchase behaviors. The encoder captures a user’s intent contained in the user’s click session, and the decoder, which is equipped with pointer network via a switch gate, extracts relevant clicked items for future purchase candidates. To the best of our knowledge, our work is the first to address the task of purchase prediction given non-duplicate click sessions. Experiments demonstrate that our proposed method outperforms the state-of-the-art purchase prediction methods by up to 18% in terms of recall.
Evaluating the Effectiveness of Redirected Walking with Auditory Distractors for Navigation in Virtual Environments

Many virtual locomotion interfaces allowing users to move in virtual reality have been built and evaluated, such as redirected walking (RDW), walking-in-place (WIP), and joystick input. RDW has been shown to be among the most natural and immersive as it supports real walking, and many newer methods further adapt RDW to allow for customization and greater immersion. Most of these methods have been demonstrated to work with vision, in this paper we evaluate the ability for a general distractor-based RDW framework to be used with only auditory display. We conducted two studies evaluating the differences between RDW with auditory distractors and other distractor modalities using distraction ratio, virtual and physical path information, immersion, simulator sickness, and other measurements. Our results indicate that auditory RDW has the potential to be used with complex navigational tasks, such as crossing streets and avoiding obstacles. It can be used without designing the system specifically for audio-only users. Additionally, sense of presence and simulator sickness remain reasonable across all user groups.
Energy-Aware Temporal Logic Motion Planning for Mobile Robots

This paper presents a methodology for synthesizing a motion plan for a mobile robot to ensure that the robot never gets depleted with battery charge while carrying out its mission successfully. The specification of the robot is provided in the form of an LTL (Linear Temporal Logic) formula. A trajectory satisfying an LTL formula may contain a loop whose repetitive execution causes the depletion of battery charge in the robot. The motion plan generated by our methodology ensures that the robot visits the charging station periodically in such a way that it never gets depleted with battery charge while carrying out its mission optimally. Given a set of potential charging station locations and an LTL specification, our algorithm also finds the best location for the charging station along with the optimal trajectory for the robot. We encode the motion planning problem as an SMT (Satisfiability Modulo Theory) solving problem and use the off-the-shelf SMT solver Z3 to solve the constraints to find the location of the charging station and generate an optimal trajectory for the robot. We apply our methodology to synthesize energy-aware trajectories for robots with different dynamics in various workspaces and for various LTL specifications.
EST-TSA: An effective search tendency based to tree seed algorithm

As a population-based stochastic search algorithm for continuous optimization problems, tree−seed algorithm (TSA) fails to balance capability of the exploitation and exploration of the algorithm effectively. Therefore, TSA has certain defects in local search capability. This paper proposes improvements of TSA in local search capability. Although TSA can achieve good performance in general, it does not fully consider the role of the current optimal position of the population in solving optimization problems. This slows down the convergence speed of the algorithm. This paper redesigns the balance rule between exploitation and exploration and improves the original position updating formula of TSA. EST-TSA can converge to the global optimal solution more effectively in the search process, which greatly strengthens the local search capability. Experimental results show that EST-TSA has better local search capability compared with the basic TSA. The improvements proposed in this paper are significant for TSA performance.
Ontology of Metrics for Cyber Security Assessment

Development of metrics that are valuable for assessing security and decision making is an important element of efficient counteraction to cyber threats. The paper proposes an ontology of metrics for cyber security assessment. The developed ontology is based on determining the concepts and relations between primary features of initial security data and forming a set of hierarchically interconnected security metrics. The paper describes the main classes of the proposed ontology, the revealed relations, the involved security metrics, and the used data sources. The publicly available sources of security data are analyzed to get primary security metrics. Application of the approach is shown on a case study. The main feature of the proposed ontology is representation of security metrics as separate instances of ontology. It allows using the relations between the concepts of ontology for calculating integral metrics reflecting the security state.
On the Adversarial Robustness of Subspace Learning

In this paper, we study the adversarial robustness of subspace learning problems. Different from the assumptions made in existing work on robust subspace learning where data samples are contaminated by gross sparse outliers or small dense noises, we consider a more powerful adversary who can first observe the data matrix and then intentionally modify the whole data matrix. We first characterize the optimal rank-one attack strategy that maximizes the subspace distance between the subspace learned from the original data matrix and that learned from the modified data matrix. We then generalize the study to the scenario without the rank constraint and characterize the corresponding optimal attack strategy. Our analysis shows that the optimal strategies depend on the singular values of the original data matrix and the adversary's energy budget. Finally, we provide numerical experiments and practical applications to demonstrate the efficiency of the attack strategies.
Analyzing AWS Spot Instance Pricing

Many cloud computing vendors offer a preemptible class of service for rented virtual machines. In November 2017, Amazon.com changed the pricing mechanism for its preemptible "spot instances" so that prices would change more "smoothly." This paper analyzes the effect of this change on spot instance prices. It examines the prices immediately before and after the mechanism change to determine the extent to which prices themselves changed. It then compares the 90-day period immediately after the change in mechanism to the next 90-day period. Finally, it compares the two most recent 90-day periods (ending on October 15, 2018). Our results indicate that in addition to smoothing prices, the mechanism change introduced generally higher prices which is a trend that continues.
Crystal: A scalable and fault-tolerant Archimedean-based server-centric cloud data center network architecture

The explosive increase in adopting cloud services leads to the need for accommodating millions to even billions of servers in the supporting infrastructure of data center networks. Thus, providing highly scalable and noticeably fault-tolerant interconnecting architectures with great network capacity, limited delay, and reasonable energy consumption has become of paramount importance. In this paper, Crystal, a server-centric and recursively constructed structure, is presented. Crystal is made out of building blocks inspired by vertex configuration pattern of Archimedean solids and is intrinsically fault-tolerant mainly due to the adoption of multihoming mechanism in the building block. Crystal also scales double exponentially owing to its recursively defined architecture. Furthermore, to increase the routing efficiency and manage different kinds of failures in the network, two exclusively designed routing algorithms are proposed. Theoretical analysis and simulation results all witness that Crystal is highly fault-tolerant and can provide good network capacity and low latency to support delay-sensitive and data-intensive applications. Generally, Crystal satisfies a reasonable trade-off between all the major goals of designing a cloud data center network.
Augmenting Virtual Reality with Near Real World Objects

Pure virtual reality headsets lack support for user awareness of real world objects. We show how to augment a virtual environment with real world objects by incorporating color and stereo information from the front-mounted stereo camera system of the HTC Vive Pro. With an adjustable amount of virtual and real elements by specifying the depth range of interest for real objects these can be either always augmented on top of the virtual reality or embedded in the virtual world with correct occlusion. Possible use cases are training with appliances that require you to see both the appliance as well as your hands, collaborating in virtual reality with the real users instead of avatars, as well as static and moving obstacle detection. Objects of interest are cut out depending on depth values of the stereo matching system's depth image. The depth image matches only one of both camera sensors so a second depth image is derived to match the other camera sensor as well. We overlay the rendered virtual reality images for both eyes with the corresponding cut-out camera images. Furthermore we address the problem of missing depth data especially for very near objects with an integral image based depth-dependent foreground mask expansion algorithm. Since a comfortable mixed reality experience requires high framerates of optimally 90 frames per second we utilize the GPU for optimized implementations of the algorithms and computations involved.
Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environments

This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real environments, such as urban streets or crowded areas, the efficient and robust incorporation of temporal dependencies into otherwise static occupancy models remains a challenge. We propose a method to capture the uncertainty of moving objects and incorporate this uncertainty information into a continuous occupancy map represented in a rich high-dimensional feature space. This data-efficient model not only allows us to learn the occupancy states incrementally, but also makes predictions about what the future occupancy states will be. Experiments performed using 2D and 3D laser data collected from crowded unstructured outdoor environments show that the proposed methodology can accurately predict occupancy states for areas of around 1000 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at 10 Hz, making the proposed framework ideal for online applications under real-time constraints.
Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding

The design of reward functions in reinforcement learning is a human skill that comes with experience. Unfortunately, there is not any methodology in the literature that could guide a human to design the reward function or to allow a human to transfer the skills developed in designing reward functions to another human and in a systematic manner. In this paper, we use Systematic Instructional Design, an approach in human education, to engineer a machine education methodology to design reward functions for reinforcement learning. We demonstrate the methodology in designing a hierarchical genetic reinforcement learner that adopts a neural network representation to evolve a swarm controller for an agent shepherding a boids-based swarm. The results reveal that the methodology is able to guide the design of hierarchical reinforcement learners, with each model in the hierarchy learning incrementally through a multi-part reward function. The hierarchy acts as a decision fusion function that combines the individual behaviours and skills learnt by each instruction to create a smart shepherd to control the swarm.
Conversation is multimodal

In this paper we try to provoke by teasing the question "if conversational user interfaces should be multimodal?". Of course they should! In decades of research in multimodal HCI excellent arguments can be found. We substantiate our perspective with an example showing how conversational interaction becomes more robust and efficient through the use of multimodality.
A Novel Multi-layer Framework for Tiny Obstacle Discovery

For tiny obstacle discovery in a monocular image, edge is a fundamental visual element. Nevertheless, because of various reasons, e.g., noise and similar color distribution with background, it is still difficult to detect the edges of tiny (b) obstacles at long distance. In this paper, we propose an obstacle-aware discovery method to recover the missing contours of these obstacles, which helps to obtain obstacle proposals as much as possible. First, by using visual cues in monocular images, several multi-layer regions are elaborately inferred to reveal the distances from the camera. Second, several novel obstacle-aware occlusion edge maps are constructed to well capture the contours of tiny obstacles, which combines cues from each layer. Third, to ensure the existence of the tiny obstacle proposals, the maps from all layers are used for proposals extraction. Finally, based on these proposals containing tiny obstacles, a novel obstacle-aware regressor is proposed to generate an obstacle occupied probability map with high confidence. The convincing experimental results with comparisons on the Lost and Found dataset demonstrate the effectiveness of our approach, achieving around 9.5% improvement on the accuracy than FPHT and PHT, it even gets comparable performance to MergeNet. Moreover, our method outperforms the state-of-the-art algorithms and significantly improves the discovery ability for tiny obstacles at long distance.
Non-interactive Non-malleability from Quantum Supremacy

We construct non-interactive non-malleable commitments without setup in the plain model, under well-studied assumptions. First, we construct non-interactive non-malleable commitments w.r.t. commitment for $$\epsilon \log \log n$$ tags for a small constant $$\epsilon > 0$$ , under the following assumptions: Second, as our key technical contribution, we introduce a new tag amplification technique. We show how to convert any non-interactive non-malleable commitment w.r.t. commitment for $$\epsilon \log \log n$$ tags (for any constant $$\epsilon >0$$ ) into a non-interactive non-malleable commitment w.r.t. replacement for $$2^n$$ tags. This part only assumes the existence of sub-exponentially secure non-interactive witness indistinguishable (NIWI) proofs, which can be based on sub-exponential security of the decisional linear assumption. Interestingly, for the tag amplification technique, we crucially rely on the leakage lemma due to Gentry and Wichs (STOC 2011). For the construction of non-malleable commitments for $$\epsilon \log \log n$$ tags, we rely on quantum supremacy. This use of quantum supremacy in classical cryptography is novel, and we believe it will have future applications. We provide one such application to two-message witness indistinguishable (WI) arguments from (quantum) polynomial hardness assumptions.
Dynamic Structural Health Monitoring using a DIC-enabled drone

The structural assessment of infrastructure components, such as bridges and railroad tracks, is crucial for their safe operation. The process is expensive and time-consuming given the large amount of structures that require systematic inspection. Over the last decades, non-contact measurement techniques have been developed that allow remote evaluation of a structure that is accurate and not labor intensive. One of the techniques in this field is Digital Image Correlation (DIC), that has proven to be an accurate method to measure 2D and 3D shape and deformation fields in structures. In current applications, DIC makes use of a stationary stereo vision camera system that is rigidly placed close to the inspected object. In this paper, an autonomous system of a drone equipped with a DIC camera system, that successfully performs remote structural evaluation of a railroad tie, is presented. This development significantly increases the portability of DIC, resulting in faster deployment of DIC/Drone measurement systems that can reach remote locations and perform fast and accurate structural health monitoring.