Reviewerly/exHarmony huggingface dataset:
Dealing with complexity: How to use a hybrid approach to incorporate complexity in health behavior interventions

In the process of developing interventions targeting health behaviors (e.g., smoking cessation, safe sex, healthy nutrition), intervention planners interact with multiple types of information coming from various sources (e.g. theory, previous research, input from stakeholders). Intervention planners then need to meaningfully integrate this information into an analytical framework that guides the construction of the intervention components. Commonly used frameworks often oversimplify human behaviors despite acknowledging their inherent complexity. For instance, frameworks include chains of lists (in logic models) instead of richer structures considering loops and the dynamicity of relations between determinants. There is thus a lack of systematic approaches to effectively form such rich structures in health behavior interventions. While integrating many aspects of complexity in logic models may be possible, it also runs the risk of producing artifacts that become equally complex instead of equipping intervention planners and health researchers with decision-making tools to estimate the effects of interventions. Consequently, effective intervention development requires (i) techniques of systematic integration of information of different types and from various sources, which can account for (ii) the complex structure and functioning of interactions between the identified behavioral determinants, and (iii) will support the testing of intervention scenarios. In this paper, we show how a hybrid approach of Fuzzy Cognitive Maps and machine learning may support various aspects of complexity in intervention design by meeting all three requirements.
Detecting Morphing Attacks through Face Geometry Features

Face-morphing operations allow for the generation of digital faces that simultaneously carry the characteristics of two different subjects. It has been demonstrated that morphed faces strongly challenge face-verification systems, as they typically match two different identities. This poses serious security issues in machine-assisted border control applications and calls for techniques to automatically detect whether morphing operations have been previously applied on passport photos. While many proposed approaches analyze the suspect passport photo only, our work operates in a differential scenario, i.e., when the passport photo is analyzed in conjunction with the probe image of the subject acquired at border control to verify that they correspond to the same identity. To this purpose, in this study, we analyze the locations of biologically meaningful facial landmarks identified in the two images, with the goal of capturing inconsistencies in the facial geometry introduced by the morphing process. We report the results of extensive experiments performed on images of various sources and under different experimental settings showing that landmark locations detected through automated algorithms contain discriminative information for identifying pairs with morphed passport photos. Sensitivity of supervised classifiers to different compositions on the training and testing sets are also explored, together with the performance of different derived feature transformations.
Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces

Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as k-medoids work under the assumption that the data points are close to a few cluster centroids, and cannot handle the case where data lie close to a union of subspaces. This paper proposes a new exemplar selection model that searches for a subset that best reconstructs all data points as measured by the l1 norm of the representation coefficients. Geometrically, this subset best covers all the data points as measured by the Minkowski functional of the subset. To solve our model efficiently, we introduce a farthest first search algorithm that iteratively selects the worst represented point as an exemplar. When the dataset is drawn from a union of independent subspaces, our method is able to select sufficiently many representatives from each subspace. We further develop an exemplar based subspace clustering method that is robust to imbalanced data and efficient for large scale data. Moreover, we show that a classifier trained on the selected exemplars (when they are labeled) can correctly classify the rest of the data points.
Transcoherent States: Optical States for Maximal Generation of Atomic Coherence

Quantum technologies are built on the power of coherent superposition. Atomic coherence is typically generated from optical coherence, most often via Rabi oscillations. However, canonical coherent states of light create imperfect resources; a fully quantized description of “π/2 pulses” shows that the atomic superpositions generated remain entangled with the light. We show that there are quantum states of light that generate coherent atomic states perfectly, with no residual atom-field entanglement. These states can be found for arbitrarily short times and approach slightly-number-squeezed π/2 pulses in the limit of large intensities; similar ideal states can be found for any (2k+1)π/2 pulses, requiring more number squeezing with increasing k. Moreover, these states can be repeatedly used as “quantum catalysts” to successfully generate coherent atomic states with high probability. From this perspective we identify states that are “more coherent” than coherent states.Received 15 August 2020Accepted 12 October 2020DOI:https://doi.org/10.1103/PRXQuantum.1.020306Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical coherenceQuantum coherence & coherence measuresQuantum gatesTechniquesJaynes-Cummings modelRabi modelGeneral PhysicsInterdisciplinary PhysicsQuantum InformationAtomic, Molecular & Optical
Change detection using weighted features for image-based localization

Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled to achieve long-term robot autonomy. In vision-based localization and navigation methods, one of the major issues is the scene dynamics. The autonomous operation of the robot may become unreliable if the changes occurring in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects and other changes make many conventional methods fail. To deal with these challenges, we present a novel method for change detection based on weighted local visual features. The core idea of the algorithm is to distinguish the valuable information in stable regions of the scene from the potentially misleading information in the regions that are changing. We evaluate the change detection algorithm in a visual localization framework based on feature matching by performing a series of long-term localization experiments in various real-world environments. The results show that the change detection method yields an improvement in the localization accuracy, compared to the baseline method without change detection. In addition, an experimental evaluation on a public long-term localization data set with more than 10 000 images reveals that the proposed method outperforms two alternative localization methods on images recorded several months after the initial mapping.
Deterministic Secure Quantum Communication on the BB84 System

In this paper, we propose a deterministic secure quantum communication (DSQC) protocol based on the BB84 system. We developed this protocol to include quantum entity authentication in the DSQC procedure. By first performing quantum entity authentication, it was possible to prevent third-party intervention. We demonstrate the security of the proposed protocol against the intercept-and-re-send attack and the entanglement-and-measure attack. Implementation of this protocol was demonstrated for quantum channels of various lengths. Especially, we propose the use of the multiple generation and shuffling method to prevent a loss of message in the experiment.
Exploring Visuo-haptic Feedback Congruency in Virtual Reality

Visuo-haptic feedback is an important aspect of virtual reality experiences, with several previous works investigating its benefits and effects. A key aspect of this domain is congruency of crossmodal feedback and how it affects users. However, an important sub-domain which has received surprisingly little focus is visuo-haptic congruency in an interactive multisensory setting. This is especially important given that multisensory integration is crucial to player immersion in the context of virtual reality video games. In this paper, we attempt to address this lack of research. To achieve this, a total of 50 participants played a virtual reality racing game with either congruent or incongruent visuo-haptic feedback. Specifically, these users engaged in a driving simulator with physical gear shift interfaces, with one treatment group using a stick-shift gearbox, and the other using a paddle-shift setup. The virtual car they drove (A Formula Rookie race car) was only visually congruent with the stick-shift setup. A motion simulator was also used to provide synchronous vestibular cues and diversify the range of modalities in multisensory integration. The racing simulator used was Project CARS 2, one of the world's most popular commercial racing simulators. Our findings showed no significant differences between the groups in measures of user presence or in-game performance, counter to previous work regarding visuo-haptic congruency. However, the Self-evaluation of Performance PQ subscale was notably close to significance. Our results can be used to better inform games and simulation developers, especially those targeting virtual reality.
Political factors affecting the survival of SMEs case study: An empirical study in Tehran Grand Bazaar

Today, the basic role of small and medium-sized enterprises (SMEs) in determining the economic growth of developed and developing countries is obvious for all governments. In the present study, the political factors refer to the application of a nested set of macroeconomic and political policies in practice without the intended profit or theoretically transparent and profitable but rent-seeking in the implementation method, which is detrimental to the small businesses from the micro perspective and the economy from a macro perspective. Considering the economic problems of Iran in the field of SMEs, the present study investigated the political factors affecting the survival of SMEs. In this regard, more than 300 individuals were selected from those with SMEs in Tehran Grand Bazaar in 2018 by using a binary model. The logit and probit regression models were used to analyze the data, and Excel 2016 and SHAZAM 11 software packages were used to analyze the results. The results indicated that among the variables and indicators used in the model as political factors, interactive policies (LIPs), the rule of law policies (RLPs), and the international tension-releasing policies (IPs) significantly influence the factor of SMEs survival policy in Tehran's Grand Bazaar.
Sentiment Analysis using Ensemble Classification Technique

Sentiment Analysis of Twitter data is a way to predict the emotional tone of the tweets. There are various challenges faced in the process of predicting the correct sentiment of the tweets. The data is pre-processed using the (Term Frequency-Inverse Document Frequency Vectorizer) TF -IDF Vectorizer to extract the features of the tweets. Afterwards various classification techniques like Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, and SGD Classifier are used to classify the data. After classification, the models are passed into Voting Classification technique of Ensemble Classification methodology.
A Statistical Analysis Framework for ICS Process Datasets

In recent years, several schemes have been proposed to detect anomalies and attacks on Cyber-Physical Systems (CPSs) such as Industrial Control Systems (ICSs). Based on the analysis of sensor data, unexpected or malicious behavior is detected. Those schemes often rely on (implicit) assumptions on temporally stable sensor data distributions and invariants between process values. Unfortunately, the proposed schemes often perform not optimally with Recall scores lower than 70% (e.g., missing 3 alarms every 10 anomalies) for some ICS datasets, with unclear root issues.
Method for Constructing Artificial Intelligence Player With Abstractions to Markov Decision Processes in Multiplayer Game of<i>Mahjong</i>

In this article, we propose a method for constructing artificial intelligence (AI) player of Mahjong, which is a multiplayer imperfect information game. Since the size of the game tree is huge, constructing an expert-level AI player of Mahjong is challenging. We define multiple Markov decision processes (MDPs) as abstractions of Mahjong to construct effective search trees. We also introduce two methods of inferring state values of the Mahjong using these MDPs. We evaluated the effectiveness of our method using gameplays vis-à-vis the current strongest AI player.
Secure and Efficient Medical Data Storage and Sharing Scheme Based on Double Blockchain

In the digital era, electronic medical record (EMR) has been a major way for hospitals to store patients' medical data.The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data sharing.The storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low throughput.To address these issues, we propose a secure and efficient medical data storage and sharing scheme based on double blockchain.In our scheme, we encrypt the original EMR and store it in the cloud.The storage blockchain stores the index of the complete EMR, and the shared blockchain stores the index of the shared part of the EMR.Users with different attributes can make requests to different blockchains to share different parts according to their own permissions.Through experiments, it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain, but also greatly reduced the risk of leakage of the original EMR.Content Extraction Signature (CES) combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original EMR.The symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) not only ensures the safe storage of data in the cloud, but also achieves the consistency and convenience of data update, avoiding redundant backup of data.Safety analysis and performance analysis verified the feasibility and effectiveness of our scheme.
Adversarial Active Learning for Named Entity Recognition in Cybersecurity

Owing to the continuous barrage of cyber threats, there is a massive amount of cyber threat intelligence.However, a great deal of cyber threat intelligence come from textual sources.For analysis of cyber threat intelligence, many security analysts rely on cumbersome and time-consuming manual efforts.Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence.As the foundation for constructing cybersecurity knowledge graph, named entity recognition (NER) is required for identifying critical threat-related elements from textual cyber threat intelligence.Recently, deep neural network-based models have attained very good results in NER.However, the performance of these models relies heavily on the amount of labeled data.Since labeled data in cybersecurity is scarce, in this paper, we propose an adversarial active learning framework to effectively select the informative samples for further annotation.In addition, leveraging the long short-term memory (LSTM) network and the bidirectional LSTM (BiLSTM) network, we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM encoderdecoder.With the selected informative samples annotated, the proposed NER model is retrained.As a result, the performance of the NER model is incrementally enhanced with low labeling cost.Experimental results show the effectiveness of the proposed method.
Effect of Cognitive Load on Curvature Redirected Walking Thresholds

To allow users to perform real walking in a virtual environment larger than the physical space, redirected walking (RDW) techniques could be employed. Users do not notice this manipulation and immersion remains intact when RDW is applied within certain thresholds. Although many studies on RDW detection thresholds exists, in none of these studies, users were performing an additional task during the threshold identification process. These existing thresholds could be only conservative estimates and the potential of RDW may not be fully utilized.
Automatic assessment of students' software models using a simple heuristic and machine learning

Software models are increasingly popular. To educate the next generation of software engineers, it is important that they learn how to model software systems well, so that they can design them effectively in industry. It is also important that instructors have the tools that can help them assess students' models more effectively. In this paper, we investigate how a tool that combines a simple heuristic with machine learning techniques can be used to help assess student submissions in model-driven engineering courses. We apply our proposed technique to first identify submissions of high quality and second to predict approximate letter grades. The results are comparable to human grading and a complex rule-based technique for the former and surprisingly accurate for the latter.
Governance landscapes for emerging technologies: The case of cryptocurrencies

Abstract Scholars call for “adaptive governance” to balance concern about technology uncertainty with the need for innovation in the governance of emerging technologies. Yet, empirical assessment of such governance systems remains sparse. Do the actors interested in the potential regulation of an emerging technology focus on their own interests and opportunities, or do they think in terms of the collective interest? We focus on the actors who embed such systems from a novel landscape perspective that combines two dimensions of governance: the nature of the regulatory target, and the nature of the process they advocate for governing the target. We present data from over 70 actors in the area of cryptocurrencies for evaluating this new logic.
EyeShopper

Recent advances in machine and deep learning allow for enhanced retail analytics by applying object detection techniques. However, existing approaches either require laborious installation processes to function or lack precision when the customers turn their back in the installed cameras. In this paper, we present EyeShopper, an innovative system that tracks the gaze of shoppers when facing away from the camera and provides insights about their behavior in physical stores. EyeShopper is readily deployable in existing surveillance systems and robust against low-resolution video inputs. At the same time, its accuracy is comparable to state-of-the-art gaze estimation frameworks that require high-resolution and continuous video inputs to function. Furthermore, EyeShopper is more robust than state-of-the-art gaze tracking techniques for back head images. Extensive evaluation with different real video datasets and a synthetic dataset we produced shows that EyeShopper estimates with high accuracy the gaze of customers.
Three-Dimensional Obstacle Avoidance for Autonomous Underwater Robot

Obstacle avoidance in the 3-D region for an underwater robot such as autonomous underwater vehicle is still a difficult research topic. A novel 3-D obstacle avoidance algorithm for an autonomous underwater robot with a sphere cross section method is discussed in this letter. The proposed mechanism utilizes a Dubins-like curve to design a continuous path in a 3-D underwater region while adjusting its heading angle with coordinate system rotation and sphere cross section methods if there is one convex polygon obstacle along the designed trajectory. Numerical simulation results verify that the proposed algorithm can avoid obstacles while maintaining a continuous and smooth cruising trajectory.
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning

Learning-based approaches for solving large sequential decision making problems have become popular in recent years. The resulting agents perform differently and their characteristics depend on those of the underlying learning approach. Here, we consider a benchmark planning problem from the reinforcement learning domain, the Racetrack, to investigate the properties of agents derived from different deep (reinforcement) learning approaches. We compare the performance of deep supervised learning, in particular imitation learning, to reinforcement learning for the Racetrack model. We find that imitation learning yields agents that follow more risky paths. In contrast, the decisions of deep reinforcement learning are more foresighted, i.e., avoid states in which fatal decisions are more likely. Our evaluations show that for this sequential decision making problem, deep reinforcement learning performs best in many aspects even though for imitation learning optimal decisions are considered.
Contrastive Rendering for Ultrasound Image Segmentation

Ultrasound (US) image segmentation embraced its significant improvement in deep learning era. However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation. Previous methods often resort to global context, multi-scale cues or auxiliary guidance to estimate the boundaries. It is hard for these methods to approach pixel-level learning for fine-grained boundary generating. In this paper, we propose a novel and effective framework to improve boundary estimation in US images. Our work has three highlights. First, we propose to formulate the boundary estimation as a rendering task, which can recognize ambiguous points (pixels/voxels) and calibrate the boundary prediction via enriched feature representation learning. Second, we introduce point-wise contrastive learning to enhance the similarity of points from the same class and contrastively decrease the similarity of points from different classes. Boundary ambiguities are therefore further addressed. Third, both rendering and contrastive learning tasks contribute to consistent improvement while reducing network parameters. As a proof-of-concept, we performed validation experiments on a challenging dataset of 86 ovarian US volumes. Results show that our proposed method outperforms state-of-the-art methods and has the potential to be used in clinical practice.