Reviewerly/exHarmony huggingface dataset:
User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement Learning

Personalized image aesthetic assessment (PIAA) has recently become a hot topic due to its wide applications, such as photography, film, television, e-commerce, fashion design, and so on. This task is more seriously affected by subjective factors and samples provided by users. In order to acquire precise personalized aesthetic distribution by small amount of samples, we propose a novel user-guided personalized image aesthetic assessment framework. This framework leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users. It mainly consists of two stages. In the first stage, personalized aesthetic ranking is generated by interactive image enhancement and manual ranking, meanwhile, two policy networks will be trained. These two networks will be trained iteratively and alternatively to facilitate the final personalized aesthetic assessment. In the second stage, these modified images are labeled with aesthetic attributes by one style-specific classifier, and then the personalized aesthetic distribution is generated based on the multiple aesthetic attributes of these images, which conforms to the aesthetic preference of users better. Compared with other existing methods, our approach has achieved new state-of-the-art in the task of personalized image aesthetic assessment on the public AVA and FLICKR-AES datasets.
GooAQ: Open Question Answering with Diverse Answer Types

While day-to-day questions come with a variety of answer types, the current open questionanswering (QA) literature represents isolated efforts on niche response types, with a heavy focus on specific kinds of short responses (people, places, etc.).To address this gap, we present GOOAQ, a large-scale dataset collected from Google questions and answers, containing 3 million questions with diverse answer types ranging from factual short answers to snippets to collections.Our human evaluation shows that 94% of the mined answers are accurate, enabling fine-tuning a pre-trained language model for answering GOOAQ questions.We use this dataset to study inherent differences between models producing different answer types, and observe interesting trends.For example, in line with recent work, LM's strong performance on GOOAQ's shortanswer questions heavily benefits from annotated data.However, their surprisingly high quality in generating coherent and accurate answers for questions requiring long responses (such as 'how' and 'why' questions) is less reliant on observing annotated data and mainly supported by their pre-training.Moreover, we show that GOOAQ is a valuable training resource, resulting in strong performance on the recent ELI5 long-answers dataset.We release GOOAQ to facilitate further research on improving QA with diverse response types. 1 Question: how many calories burned 30 minutes crossfit?Answer(short: from the snippet): 260 calories Answer(snippet): According to the American Council on Exercise, a 115-pound person running for 30 minutes at a slow-to-moderate pace (a 10-minute mile) would burn about 260 calories.Question: what are the steps for decision making?Answer(collection): [Step 1: Identify the decision You realize that you need to make a decision., Step 2: Gather relevant information.Step 3: Identify the alternatives.Step 4: Weigh the evidence.Step 5: Choose among alternatives.Step 6: Take action.Step 7: Review your decision & its consequences.
Multi-Modality Video Representation for Action Recognition

Nowadays, action recognition is widely applied in many fields. However, action is hard to define by single modality information. The difference between image recognition and action recognition is that action recognition needs more modality information to depict one action, such as the appearance, the motion and the dynamic information. Due to the state of action evolves with the change of time, motion information must be considered when representing an action. Most of current methods define an action by spatial information and motion information. There are two key elements of current action recognition methods: spatial information achieved by sampling sparsely on video frames’ sequence and the motion content mostly represented by the optical flow which is calculated on consecutive video frames. However, the relevance between them in current methods is weak. Therefore, to strengthen the associativity, this paper presents a new architecture consisted of three streams to obtain multi-modality information. The advantages of our network are: (a) We propose a new sampling approach to sample evenly on the video sequence for acquiring the appearance information; (b) We utilize ResNet101 for gaining high-level and distinguished features; (c) We advance a three-stream architecture to capture temporal, spatial and dynamic information. Experimental results on UCF101 dataset illustrate that our method outperforms other previous methods.
Knowledge Management Model for Smart Campus in Indonesia

The application of smart campuses (SC), especially at higher education institutions (HEI) in Indonesia, is very diverse, and does not yet have standards. As a result, SC practice is spread across various areas in an unstructured and uneven manner. KM is one of the critical components of SC. However, the use of KM to support SC is less clearly discussed. Most implementations and assumptions still consider the latest IT application as the SC component. As such, this study aims to identify the components of the KM model for SC. This study used a systematic literature review (SLR) technique with PRISMA procedures, an analytical hierarchy process, and expert interviews. SLR is used to identify the components of the conceptual model, and AHP is used for model priority component analysis. Interviews were used for validation and model development. The results show that KM, IoT, and big data have the highest trends. Governance, people, and smart education have the highest trends. IT is the highest priority component. The KM model for SC has five main layers grouped in phases of the system cycle. This cycle describes the organization’s intellectual ability to adapt in achieving SC indicators. The knowledge cycle at HEIs focuses on education, research, and community service.
Challenges in Detoxifying Language Models

Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang. Findings of the Association for Computational Linguistics: EMNLP 2021. 2021.
Deep Learning Approaches for Robust Time of Arrival Estimation in Acoustic Emission Monitoring

In this work, different types of artificial neural networks are investigated for the estimation of the time of arrival (ToA) in acoustic emission (AE) signals. In particular, convolutional neural network (CNN) models and a novel capsule neural network are proposed in place of standard statistical strategies which cannot handle, with enough robustness, very noisy scenarios and, thus, cannot be sufficiently reliable when the signal statistics are perturbed by local drifts or outliers. This concept was validated with two experiments: the pure ToA identification capability was firstly assessed on synthetic signals for which a ground truth is available, showing a 10× gain in accuracy when compared to the classical Akaike information criterion (AIC). Then, the same models were tested via experimental data acquired in the framework of a localization problem to identify targets with known coordinates on a square aluminum plate, demonstrating an overreaching precision under significant noise levels.
A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems

Water Distribution System (WDS) threats have significantly grown following the Maroochy shire incident, as evidenced by proofed attacks on water premises. As a result, in addition to traditional solutions (e.g., data encryption and authentication), attack detection is being proposed in WDS to reduce disruption cases. The attack detection system must meet two critical requirements: high accuracy and near real-time detection. This drives us to propose a two-stage detection system that uses self-supervised and unsupervised algorithms to detect Cyber-Physical (CP) attacks. Stage 1 uses heuristic adaptive self-supervised algorithms to achieve near real-time decision-making and detection sensitivity of 66% utilizing Boss. Stage 2 attempts to validate the detection of attacks using an unsupervised algorithm to maintain a detection accuracy of 94% utilizing Isolation Forest. Both stages are examined against time granularity and are empirically analyzed against a variety of performance evaluation indicators. Our findings demonstrate that the algorithms in stage 1 are less favored than those in the literature, but their existence enables near real-time decision-making and detection reliability. In stage 2, the isolation Forest algorithm, in contrast, gives excellent accuracy. As a result, both stages can collaborate to maximize accuracy in a near real-time attack detection system.
Projected cutting guides using an augmented reality system to improve surgical margins in maxillectomies: A preclinical study

Positive margins have been reported up to 80% in advanced maxillary cancers. Intraoperative navigation (IN) aims to improve margins, but provides a two-dimensional view of a registered instrument without anticipating any cutting directions, and the information is displayed in monitors outside surgical field. Augmented Reality (AR) can delineate margins while addressing the gaze-toggling drawback of IN. In a preclinical setting, we implemented preoperative-planned osteotomies needed for maxillectomies and projected this information on the surgical field using AR. We aimed to improve negative margin rates while retaining the benefits of AR.Five maxillary tumor models were built. Five fellowship-trained surgeons completed virtual unguided and AR-guided maxillectomies. Comparisons in terms of intratumoral cuts, close, adequate, and excessive distances from the tumor were performed. Differences between "ideal" cutting-plan and the AR-guided virtual osteotomies was obtained. Workload questionnaires to evaluate the technology were completed.115 virtual osteotomies were analyzed. Intra-tumoral and "close" margins were lower for the AR-assisted osteotomies (0.0% vs 1.9% p < 0.0001 and 0.8% vs 7.9% p < 0.0001). Proportion of "adequate" margins were higher in the AR simulations (25.3% vs 18.6%, p = 0.018). The AR osteotomies had high similarity with the pre-planned with interclass correlation index close to 1 in "adequate" margins 0.893 (95% CI: 0.804-0.949). Workload scores were better for AR-guided simulations for the domains of mental demand, performance, effort and frustration.The projector-based AR method improved margin delineation, and preoperative planning was accurately translated to the simulations. Clinical translation will aim to consolidate our preclinical findings to improve outcomes.
Multi-robot cooperation and path planning for stick transporting using improved Q-learning and democratic robotics PSO

The highlight of this paper is to propose an innovative method for solving the problem of path planning and synchronization of robot pairs in a known static and complex environment. The problem addresses the stick transportation by robot pairs from the predefined starting position to the pre-assumed goal position. This paper describes the method of computing collision-free paths for multiple numbers of paired robots called twins using a hybrid algorithm with balanced local exploitation and global exploration. The hybrid algorithm is designed by combining the individual benefits of the improved Q-Learning (IQ) and democratic robotics particle swarm optimization (DRPSO). The main intention of IQ is to produce the best position with collision avoidance by executing the best state-action pair. DRPSO algorithm computes the optimal trajectory for the twin robot using the distributed democratic approach and enhances the intensification capability. The twin robots are synchronized to the number of steps, turns, the direction of movement, and distance between both of them in the proposed approach. Computer simulation results reveal the efficacy of the proposed algorithm in both paths travelled and runtime. The experimental and VREP simulation results illustrate that the proposed algorithm offers better synchronization, cooperation, and performance in terms of several parameters such as path length, path deviation, run time, and path smoothness as a replica of the number of turns made. The comparative study with artificial bee colony optimization (ABCO) and imperialist competitive firefly algorithm (ICFA) witnesses the superiority of the hybrid IQ-DRPSO algorithm. Experiments that have been conducted through Arduino bots in a real environment prove the efficiency of the proposed approach. Results obtained from simulation and experiment reveal the supremacy of the proposed algorithm for synchronization, cooperation, and path traveled.
Twelve Million Phones, One Dataset, Zero Privacy *

Every minute of every day, everywhere on the planet, dozens of companies—largely unregulated, little scrutinized—are logging the movements of tens of millions of people with mobile phones and storing the information in gigantic data files. The Times Privacy Project obtained one such file, by far the largest and most sensitive ever to be reviewed by journalists. Each piece of information in this file represents the precise location of a single smartphone over a period of several months in 2016 and 2017. Location data companies generally downplay the risks of collecting such revealing information at scale. For many Americans, the only real risk they face from having their information exposed would be embarrassment or inconvenience.
Leveraging blockchain technology to control contextualized business risks: Evidence from China

The complexity and diversity of socio-economic environments call for a more nuanced consideration of contextualized risks confronting enterprises operating in these environments. By dissecting six cases that successfully adopted blockchain technology in China, we present findings of a grounded theory study into the deployment of blockchain for managing contextualized risks and opportunistic risks. Findings reveal that applying blockchain can augment risk management by controlling opportunistic risk, the latter of which denotes the variability arising from opportunistic practices of internal agents or external parties afforded by their immediate socio-economic environment. Particularly, we not only identify credibility, predatory, and compliance risk as three distinct types of opportunistic risk stemming from the unique socio-economic environment of China, but we also illustrate how blockchain could be leveraged to deal with such opportunistic risks through trust-evoking mechanisms. We discovered that blockchain could evoke trust between individuals-to-organization, organization-to-organization, and organization-to-individuals by bolstering competency, fostering benevolence, and gauging integrity in individual, inter-organizational and intra-organizational contexts, respectively.
A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations

Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020-March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.
Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID's good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.
A Novel, Modular Robot for Educational Robotics Developed Using Action Research Evaluated on Technology Acceptance Model

This research evaluates a novel, modular, open-source, and low-cost educational robotic platform in Educational Robotics and STEM Education. It is the sequel of an action research cycle on which the development of this robot is based. The impetus for the need to develop this came from the evaluation of qualitative and quantitative research data collected during an educational robotics event with significant participation of students in Athens, which showed an intense interest in students in participating in educational robotics activities, but—at the same time—recorded their low involvement due to the high cost of educational robots and robotic platforms. Based on the research’s findings, this robot was designed to suit the whole educational community; its specifications came from its members’ needs and the processing and analysis of qualitative and quantitative data. This paper presents an evaluation of the robot using the Technology Acceptance Model. The robot was exposed to 116 undergraduate students attending a pedagogical university department to evaluate its handling according to the model’s factors. Research results were promising and showed a high degree of acceptance of the robot by these students and future teachers, providing the impetus for further research.
Identification of Disease or Symptom terms in Reddit to Improve Health Mention Classification

In a user-generated text such as on social media platforms and online forums, people often use disease or symptom terms in ways other than to describe their health. In data-driven public health surveillance, the health mention classification (HMC) task aims to identify posts where users are discussing health conditions rather than using disease and symptom terms for other reasons. Existing computational research typically only studies health mentions in Twitter, with limited coverage of disease or symptom terms, ignore user behavior information, and other ways people use disease or symptom terms. To advance the HMC research, we present a Reddit health mention dataset (RHMD), a new dataset of multi-domain Reddit data for the HMC. RHMD consists of 10,015 manually labeled Reddit posts that mention 15 common disease or symptom terms and are annotated with four labels: namely personal health mentions, non-personal health mentions, figurative health mentions, and hyperbolic health mentions. With RHMD, we propose HMCNET that combines a target keyword (disease or symptom term) identification and user behavior hierarchically to improve HMC. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods with an F1-Score of 0.75 (an increase of 11% over the state-of-the-art) and shows that our new dataset poses a strong challenge to the existing HMC methods.
Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm

Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. This is because placing data in an image can cause permanent distortion after data mining. Therefore, a reversible data hiding (RDH) technique is required. One of the well-known designs of RDH is the difference expansion (DE) method. In the DE-based RDH method, finding spaces that create less distortion in the marked image is a significant challenge, and has a high insertion capacity. Therefore, the smaller the difference between the selected pixels and the more correlation between two consecutive pixels, the less distortion can be achieved in the image after embedding the secret data. This paper proposes a multilayer RDH method using the multilevel thresholding technique to reduce the difference value in pixels and increase the visual quality and the embedding capacity. Optimization algorithms are one of the most popular methods for solving NP-hard problems. The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. In the proposed method, the SMA is applied to the host image for optimal multilevel thresholding of the image pixels. Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. Experimental results show that the proposed method is better than comparable methods regarding the degree of distortion, quality of the marked image, and insertion capacity.
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?

Recently, self-supervised learning (SSL) has demonstrated strong performance in speaker recognition, even if the pre-training objective is designed for speech recognition. In this paper, we study which factor leads to the success of self-supervised learning on speaker-related tasks, e.g. speaker verification (SV), through a series of carefully designed experiments. Our empirical results on the Voxceleb-1 dataset suggest that the benefit of SSL to SV task is from a combination of mask speech prediction loss, data scale, and model size, while the SSL quantizer has a minor impact. We further employ the integrated gradients attribution method and loss landscape visualization to understand the effectiveness of self-supervised learning for speaker recognition performance.