Reviewerly/exHarmony huggingface dataset:
Investigating effects of emoji on neutral narrative text: Evidence from eye movements and perceived emotional valence

Digital images of faces such as emoji in virtual communication have become increasingly popular, but current research findings are inconsistent regarding their emotional effects on perceptions of text. Similarly, emoji effects on reading behaviours are largely unknown and require further examination. The present study (N = 41) investigated how the position and emotional valence of emoji in neutral narrative sentences influenced eye movements during reading and perceptions of sentence valence. Participants read neutral narrative sentences containing smiling or frowning emoji in sentence-initial or sentence-final positions and rated the perceived emotional valence of the sentence. Results from linear mixed-effects models demonstrated significantly longer fixations on sentence-final emoji and longer sentence reading times when emoji were in sentence-final positions. These findings are comparable to sentence 'wrap-up' effects witnessed in the processing of lexical units during sentence reading, providing new evidence towards the way readers integrate emoji into contextual processing. However, no impact of emoji valence or position on first-pass target word processing or sentence-valence ratings were found. This would refute previous suggestions that digital faces influence text valence, raising questions about reader preference for emoji or sentence sentiment, the influence of sentence formatting, and delivery/display mechanism on these effects.
Doo bee doo bee doo

Abstract We explore the design and implementation of Frank, a strict functional programming language with a bidirectional effect type system designed from the ground up around a novel variant of Plotkin and Pretnar’s effect handler abstraction. Effect handlers provide an abstraction for modular effectful programming: a handler acts as an interpreter for a collection of commands whose interfaces are statically tracked by the type system. However, Frank eliminates the need for an additional effect handling construct by generalising the basic mechanism of functional abstraction itself. A function is but the special case of a Frank operator that interprets no commands. Moreover, Frank’s operators can be multihandlers which simultaneously interpret commands from several sources at once, without disturbing the direct style of functional programming with values. Effect typing in Frank employs a novel form of effect polymorphism which avoids mentioning effect variables in source code. This is achieved by propagating an ambient ability inwards, rather than accumulating unions of potential effects outwards. With the ambient ability describing the effects that are available at a certain point in the code, it can become necessary to reconfigure access to the ambient ability. A primary goal is to be able to encapsulate internal effects, eliminating a phenomenon we call effect pollution . Moreover, it is sometimes desirable to rewire the effect flow between effectful library components. We propose adaptors as a means for supporting both effect encapsulation and more general rewiring. Programming with effects and handlers is in its infancy. We contribute an exploration of future possibilities, particularly in combination with other forms of rich type systems.
Rethinking Query Expansion for BERT Reranking

Recent studies have shown promising results of using BERT for Information Retrieval with its advantages in understanding the text content of documents and queries. Compared to short, keywords queries, higher accuracy of BERT were observed on long, natural language queries, demonstrating BERT’s ability in extracting rich information from complex queries. These results show the potential of using query expansion to generate better queries for BERT-based rankers. In this work, we explore BERT’s sensitivity to the addition of structure and concepts. We find that traditional word-based query expansion is not entirely applicable, and provide insight into methods that produce better experimental results.
Many-To-Many Voice Conversion Using Conditional Cycle-Consistent Adversarial Networks

Voice conversion (VC) refers to transforming the speaker characteristics of an utterance without altering its linguistic contents. Many works on voice conversion require to have parallel training data that is highly expensive to acquire. Recently, the cycle-consistent adversarial network (CycleGAN), which does not require parallel training data, has been applied to voice conversion, showing the state-of-the-art performance. The CycleGAN based voice conversion, however, can be used only for a pair of speakers, i.e., one-to-one voice conversion between two speakers. In this paper, we extend the CycleGAN by conditioning the network on speakers. As a result, the proposed method can perform many-to-many voice conversion among multiple speakers using a single generative adversarial network (GAN). Compared to building multiple CycleGANs for each pair of speakers, the proposed method reduces the computational and spatial cost significantly without compromising the sound quality of the converted voice. Experimental results using the VCC2018 corpus confirm the efficiency of the proposed method.
Towards a Framework for Monitoring and Analyzing High Performance Computing Environments Using Kubernetes and Prometheus

The challenge of monitoring a computational center grows as the center deploys larger and more diverse systems. As system size grows, it becomes harder to discern the problem from the noise. Staff often experience alert fatigue, an occurrence when so many alerts come in that the actual problem is obscured by false alarms or by alarms for issues that are symptoms of the core problem. The National Energy Research Scientific Computing Center (NERSC) at the Lawrence Berkeley National Laboratory (LBNL) has begun to address this issue by ensuring that most alerts are actionable and that multiple alerts for common problems, such as node outages, do not arise. However, more work is needed for these solutions to be extensible to emerging extreme-scale systems. In this paper, we propose a framework for proactively monitoring and managing data center operations, capable of scaling to accommodate the heterogeneity and complexity of next-generation systems. We describe a new architecture for the Operations Monitoring and Notification Infrastructure (OMNI) at NERSC that enables proactive monitoring and management at scale by integrating state-of-the-art technology, such as Kubernetes, Prometheus, Grafana, and other predictive platforms with data from metrics, sensors, and analytics engines. The system will support the operation of the upcoming Perlmutter HPC system, to be delivered in late 2020, as well as NERSC's successive computational system deployments. This comprehensive infrastructure will assist in centrally orchestrating services and deployments, automatically analyzing streaming data, correlating multiple-sourced data, and thresholding alerts to identify core issues from a single view.
Enforcing social norms: The morality of public shaming

Abstract Public shaming plays an important role in upholding valuable social norms. But, under what conditions, if any, is it morally justifiable? Our aim in this paper is systemically to investigate the morality of public shaming, so as to provide an answer to this neglected question. We develop an overarching framework for assessing the justifiability of this practice, which shows that, while shaming can sometimes be morally justifiable, it very often is not. In turn, our framework highlights several reasons to be concerned about the increasingly widespread phenomenon of online public shaming.
Hierarchical Temporal Modeling With Mutual Distance Matching for Video Based Person Re-Identification

Comparing to image-based person re-identification (re-ID) problems, video-based person re-ID can take advantage of more cues from appearance and temporal information, and therefore receives widespread attention recently. However, due to the different pose, occlusion, misalignment and multi-granularity in video sequences, those consequent inter-sequence variations and intra-sequence variations, inevitably makes the feature learning and matching in videos more difficult. Under this circumstance, it is necessary to design an effective discriminative representation learning mechanism, as well as a matching solution, to tackle these variations in video-based person re-ID. To this end, this paper introduces a multi-granularity temporal convolution network and a mutual distance matching measurement, aiming at alleviating the intra-sequence variation and the inter-sequence variation, respectively. Particularly, in the feature learning stage, we model different temporal granularities by hierarchically stacking temporal convolution blocks with different dilation factors. In the feature matching stage, we propose a clip-level probe-gallery mutual distance measurement and consider the most convincing clip pairs by top-k selection. We validate that our proposed method can achieve state-of-the-art results on three video-based person re-ID benchmarks, more than that, we conduct extensive ablation study to demonstrate conciseness and effectiveness of our method in video re-ID tasks.
A Multi-Class Multi-Movement Vehicle Counting Framework for Traffic Analysis in Complex Areas Using CCTV Systems

Traffic analysis using computer vision techniques is attracting more attention for the development of intelligent transportation systems. Consequently, counting traffic volume based on the CCTV system is one of the main applications. However, this issue is still a challenging task, especially in the case of complex areas that involve many vehicle movements. This study performs an investigation of how to improve video-based vehicle counting for traffic analysis. Specifically, we propose a comprehensive framework with multiple classes and movements for vehicle counting. In particular, we first adopt state-of-the-art deep learning methods for vehicle detection and tracking. Then, an appropriate trajectory approach for monitoring the movements of vehicles using distinguished regions tracking is presented in order to improve the performance of the counting. Regarding the experiment, we collect and pre-process the CCTV data at a complex intersection to evaluate our proposed framework. In particular, the implementation indicates the promising results of our proposed method, which achieve accuracy around 80% to 98% for different movements for a very complex scenario with only a single view of the camera.
ALRESCHA: A Lightweight Reconfigurable Sparse-Computation Accelerator

Sparse problems that dominate a wide range of applications fail to effectively benefit from high memory bandwidth and concurrent computations in modern high-performance computer systems. Therefore, hardware accelerators have been proposed to capture a high degree of parallelism in sparse problems. However, the unexplored challenge for sparse problems is the limited opportunity for parallelism because of data dependencies, a common computation pattern in scientific sparse problems. Our key insight is to extract parallelism by mathematically transforming the computations into equivalent forms. The transformation breaks down the sparse kernels into a majority of independent parts and a minority of data-dependent ones and reorders these parts to gain performance. To implement the key insight, we propose a lightweight reconfigurable sparse-computation accelerator (Alrescha). To efficiently run the data-dependent and parallel parts and to enable fast switching between them, Alrescha makes two contributions. First, it implements a compute engine with a fixed compute unit for the parallel parts and a lightweight reconfigurable engine for the execution of the data-dependent parts. Second, Alrescha benefits from a locally-dense storage format, with the right order of non-zero values to yield the order of computations dictated by the transformation. The combination of the lightweight reconfigurable hardware and the storage format enables uninterrupted streaming from memory. Our simulation results show that compared to GPU, Alrescha achieves an average speedup of 15.6x for scientific sparse problems, and 8x for graph algorithms. Moreover, compared to GPU, Alrescha consumes 14x less energy.
Achieving Efficient Secure Deduplication With User-Defined Access Control in Cloud

Cloud storage as one of the most important services of cloud computing which significantly facilitates cloud users to outsource their data to the cloud for storage and share them with authorized users. In cloud storage, secure deduplication has been widely investigated as it can eliminate the redundancy over the encrypted data to reduce storage space and communication overhead. Regarding the security and privacy, many existing secure deduplication schemes generally focus on achieving the following properties: data confidentiality, tag consistency, access control, and resistance to brute-force attacks. However, as far as we know, none of them can achieve these four requirements at the same time. To overcome this shortcoming, in this article, we propose an efficient secure deduplication scheme that supports user-defined access control. Specifically, by allowing only the cloud service provider to authorize data access on behalf of data owners, our scheme can maximally eliminate duplicates without violating the security and privacy of cloud users. Detailed security analysis shows that our authorized secure deduplication scheme achieves data confidentiality and tag consistency while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme outperforms the existing competing schemes, in terms of computational, communication and storage overheads as well as the effectiveness of deduplication.
Learning Innovation Using the Zahir Application in Improving Understanding of Accounting Materials

Abstract The purpose of this study in order to innovate in accounting learning activities using zahir accounting application tools. This is done because based on observations of student learning outcomes it can be said that the level of understanding of accounting material is still low. Especially if the student’s background comes from high school from the majors of natural sciences, languages, and not from social science principles. With this learning innovation, it will be easier for lecturers to provide material so that teaching and learning activities are more centered on students who will impact on the absorption and ultimately will increase the understanding of accounting material. The method in this study uses a quasi-experimental time series design type. Population and sample using probability sampling techniques The results of this study will be used as a benchmark to improve student safety and serve as a reference for educators to be able to innovate in developing learning resources.
Uncertainty-aware consistency regularization for cross-domain semantic segmentation

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Most existing methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model. Besides, the local regional consistency in UDA has been largely neglected, and only extracting the global-level pattern information is not powerful enough for feature alignment due to the abuse use of contexts. To this end, we propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation. Firstly, we introduce an uncertainty-guided consistency loss with a dynamic weighting scheme by exploiting the latent uncertainty information of the target samples. As such, more meaningful and reliable knowledge from the teacher model can be transferred to the student model. We further reveal the reason why the current consistency regularization is often unstable in minimizing the domain discrepancy. Besides, we design a ClassDrop mask generation algorithm to produce strong class-wise perturbations. Guided by this mask, we propose a ClassOut strategy to realize effective regional consistency in a fine-grained manner. Experiments demonstrate that our method outperforms the state-of-the-art methods on four domain adaptation benchmarks, i.e., GTAV → Cityscapes, SYNTHIA → Cityscapes, Virtual KITTI ⟶ KITTI and Cityscapes ⟶ KITTI.
Real-Time Prediction of Docker Container Resource Load Based on a Hybrid Model of ARIMA and Triple Exponential Smoothing

More and more enterprises are beginning to use Docker containers to build cloud platforms. Predicting the resource usage of container workload has been an important and challenging problem to improve the performance of cloud computing platform. The existing prediction models either incur large time overhead or have insufficient accuracy. This article proposes a hybrid model of the ARIMA and triple exponential smoothing. It can accurately predict both linear and nonlinear relationships in the container resource load sequence. To deal with the dynamic Docker container resource load, the weighting values of the two single models in the hybrid model are chosen according to the sum of squares of their predicted errors for a period of time. We also design and implement a real-time prediction system that consists of the collection, storage, prediction of Docker container resource load data and scheduling optimization of CPU and memory resource usage based on predicted values. The experimental results show that the predicting accuracy of the hybrid model improves by 52.64, 20.15, and 203.72 percent on average compared to the ARIMA, the triple exponential smoothing model and ANN+SaDE model respectively with a small time overhead.
A novel coordinated motion planner based on capability map for autonomous mobile manipulator

With the development of the robotic technology, Autonomous Mobile Manipulator (AMM) is increasingly used in more applications. Reasonable motion planning for AMM to maintain high manipulation capability is the prerequisite for the success of the mobile manipulation task. In this paper, the Capability Map (CM) of AMM that gives the distribution of the manipulability in cartesian space is first built. Then given the path of the end effector, we design a novel path planner for the mobile robot by querying CM online so that AMM keeps high manipulability. Moreover, a task-priority coordinated motion controller is developed to control the mobile robot and the manipulator to track their trajectories. In this controller, the trajectory of the manipulator is used as the primary task, and the trajectory of the mobile robot is treated as the constrained task. Simulation results show that the path of the mobile robot can be found online, and AMM follows the trajectories well.
Immune genetic algorithm based multi-UAV cooperative target search with event-triggered mechanism

In this paper, a strategy is designed to address the problem of closed trajectory cooperative target search for multiple UAVs, with the flight range and the initial and terminal heading constrains. The strategy is composed of two related phases, cooperative target searching phase and flight path planning phase for UAV returning to the nearest base. In the first phase, an Immune Genetic Algorithm (IGA) is proposed to improve the target search efficiency of UAVs in uncertain environment. An immune operator related to the problem is introduced to enhance the robustness of the algorithm, and expedite its convergent rate to the optimal solution. In the second phase, a Divide-and-conquer and Deterministic Path Optimization Algorithm (DDPOA) is designed to generate an optimal path for each UAV from the position of event trigger time instant to the nearest return base, with the initial and terminal velocity vector constraints. Simulations results verify the effectiveness of the algorithms.
The aesthetics of reality media

In this article, the authors examine the aesthetics of immersion in two emerging media forms: 360° video and 3D VR. Their goal is to move beyond addressing technical affordances, to consider the techniques and choices that producers of 360° video and 3D VR are making to exploit these affordances, and what resulting effects those viewing experiences have. They discuss the tension between transparency and reflectivity in two contrasting examples, in particular: the Danish company Makropol’s Anthropia (2017) and Arora and Unseld’s The Day the World Changed (2018). The authors argue that technical affordances are part of a complex process of mediation that includes both experimentation with the technology at hand and a reliance on earlier media forms. It is critical, they argue, to understand the creative tension between established forms and new ones that underscore new aesthetic and narrative experiences in VR and 360° formats.
Strong Artificial Intelligence and Consciousness

In the last 10 years, Artificial Intelligence (AI) has seen successes in fields such as natural language processing, computer vision, speech recognition, robotics and autonomous systems. However, these advances are still considered as Narrow AI, i.e. AI built for very specific or constrained applications. These applications have its usefulness in improving the quality of human life; but it is not good enough to do highly general tasks like what the human can do. The holy grail of AI research is to develop Strong AI or Artificial General Intelligence (AGI), which produces human-level intelligence, i.e. the ability to sense, understand, reason, learn and act in dynamic environments. Strong AI is more than just a composition of Narrow AI technologies. We proposed that it has to be a holistic approach towards understanding and reacting to the operating environment and decision-making process. The Strong AI must be able to demonstrate sentience, emotional intelligence, imagination, effective command of other machines or robots, and self-referring and self-reflecting qualities. This paper will give an overview of current Narrow AI capabilities, present the technical gaps, and highlight future research directions for Strong AI. Could Strong AI become conscious? We provide some discussion pointers.
Deep Multi Agent Reinforcement Learning for Autonomous Driving

Deep Learning and back-propagation have been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative environment. In this work, we present techniques for centralized training of Multi-Agent Deep Reinforcement Learning (MARL) using the model-free Deep Q-Network (DQN) as the baseline model and communication between agents. We present two novel, scalable and centralized MARL training techniques (MA-MeSN, MA-BoN), which achieve faster convergence and higher cumulative reward in complex domains like autonomous driving simulators. Subsequently, we present a memory module to achieve a decentralized cooperative policy for execution and thus addressing the challenges of noise and communication bottlenecks in real-time communication channels. This work theoretically and empirically compares our centralized and decentralized training algorithms to current research in the field of MARL. We also present and release a new OpenAI-Gym environment which can be used for multi-agent research as it simulates multiple autonomous cars driving on a highway. We compare the performance of our centralized algorithms to existing state-of-the-art algorithms, DIAL and IMS based on cumulative reward achieved per episode. MA-MeSN and MA-BoN achieve a cumulative reward of at-least $$263\%$$ of the reward achieved by the DIAL and IMS. We also present an ablation study of the scalability of MA-BoN showing that it has a linear time and space complexity compared to quadratic for DIAL in the number of agents.