Reviewerly/exHarmony huggingface dataset:
Iterative Human-in-the-Loop Discovery of Unknown Unknowns in Image Datasets

Automatic predictions (e.g., recognizing objects in images) may result in systematic errors if certain classes are not well represented by training instances (these errors are called unknowns). When a model assigns high confidence scores to these wrong predictions (this type of error is called unknown unknowns), it becomes challenging to automatically identify them. In this paper, we present the first work on leveraging human intelligence to discover unknown unknowns (UUs) in an iterative way. The proposed methodology first differentiates the feature space generated by crowd workers labelling instances (e.g., images) in an active learning fashion from the space learned by the prediction model over a batch training phase, and thus identifies the predictions most likely to be UUs. Next, we add crowd labels collected for these discovered UUs to the training set and re-train the model with this extended dataset. This process is then repeated iteratively to discover more instances of both unknown and under-represented classes. Our experimental results show that the proposed methodology is able to (1) efficiently discover UUs, (2) significantly improve the quality of model predictions, and (3) to push UUs into known unknowns (i.e., the model makes mistakes but at least its classification confidence on those instances is low so those predictions can be discarded or post-processed) for further investigation. We additionally discuss the trade-off between prediction quality improvements and the human effort required to achieve those improvements. Our results bear implications on building cost-effective systems to discover UUs with humans in the loop.
Chronic Kidney Disease Diagnosis System using Sequential Backward Feature Selection and Artificial Neural Network

The number of factors that can be categorized into the diagnosis of Chronic Kidney Disease (CKD) at an early stage makes information about the diagnosis of the disease divided into information that has many influences and has little influence. This study aims to select diagnoses in medical records with the most influential information on chronic kidney disease. The first step is to select a diagnosis with much influence by implementing the Sequential Backward Feature Selection (SBFS). This algorithm eliminates features that are considered to have little influence when compared to other features. In the second step, the features of the best diagnoses are used as input to the Artificial Neural Network (ANN) classification algorithm. The results obtained from this study are information in the form of the best diagnoses that have much influence on chronic kidney disease and the accuracy results based on the selected diagnoses. Based on the study results, 15 features are considered the best of the 18 features used to achieve 88% accuracy results. Compared with conventional methods, this method still requires consideration from the medical staff because it is not a final diagnosis for patients.
Public Perceptions Towards Synthetic Voice Technology

Text-to-Speech (TTS) technologies have provided ways to produce acoustic approximations of human voices. However, recent advancements in machine learning (i.e., neural network TTS) have helped move beyond coarse mimicry and towards more natural-sounding speech. With only a small collection of recorded utterances, it is now possible to generate wholly synthetic voices indistinguishable from those of human speakers. While these new approaches to speech synthesis can help facilitate more seamless experiences with artificial agents, they also lower the barrier to entry for those seeking to perpetrate deception. As such, in the development of these technologies, it is important to anticipate potential harms and devise strategies to help mitigate against misuse. This paper presents findings from a 360-person survey that assessed public perceptions of synthetic voices, with a particular focus on how voice type and social scenarios impact ratings of trust. Findings have implications for the responsible deployment of synthetic speech technologies.
Differentiated Performance in NoSQL Database Access for Hybrid Cloud-HPC Workloads

In recent years, the demand for cloud-based high-performance computing applications and services has grown in order to sustain the computational and statistical challenges of big-data analytics scenarios. In this context, there is a growing need for reliable large-scale NoSQL data stores capable of efficiently serving mixed high-performance and interactive cloud workloads. This paper deals with the problem of designing such NoSQL database service: to this purpose, a set of modifications to the popular MongoDB software are presented. The modified MongoDB lets clients submit individual requests or even carry out whole sessions at different priority levels, so that the higher-priority requests are served with shorter response times that exhibit less variance, with respect to lower-priority requests. Experimental results carried out on two big multi-core servers using synthetic workload scenarios demonstrate the effectiveness of the proposed approach in providing differentiated performance levels, highlighting what trade-offs are available between maximum achievable throughput for the platform, and the response-time reduction for higher-priority requests.
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots

Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data.Despite many efforts having been made towards generating impressive fluent sentences by finetuning powerful pre-trained language models, the faithfulness of generated content still needs to be improved.To this end, this paper proposes a novel approach Attend, Memorize and Generate (called AMG), inspired by the text generation process of humans.In particular, AMG (1) attends over the multi-granularity of context using a novel strategy based on table slot level and traditional token-by-token level attention to exploit both the table structure and natural linguistic information; (2) dynamically memorizes the table slot allocation states; and (3) generates faithful sentences according to both the context and memory allocation states.Comprehensive experiments with human evaluation on three domains (i.e., humans, songs, and books) of the Wiki dataset show that our model can generate higher qualified texts when compared with several state-ofthe-art baselines, in both fluency and faithfulness. 1
Research Progress and Prospects of Agricultural Aero-Bionic Technology in China

Accelerating the development of agricultural aviation technology is the need of China’s modern agricultural construction. With the rise of emerging industries such as artificial intelligence, biotechnology, autonomous navigation, and the Internet of Things, agricultural aviation is further developing toward the direction of intelligence to meet the requirements of efficient and sophisticated agricultural aviation operations. Bionics is a multi-discipline and comprehensive border subject. It is produced by the mutual penetration and integration of life science and engineering science. Bionic technology has received more and more attention in recent years, and breakthroughs have been made in the fields of biomedicine and health, military, brain science and brain-like navigation, and advanced manufacturing. This study summarized the research progress of biomimetic technology in the field of agricultural aviation from three aspects of biological perception, biological behavior, and biological intelligence. On this basis, problems of related research and application of agricultural aircraft in real-time obstacle avoidance, path planning, and intelligent navigation were analyzed. Combined with the practice of the rapid development of agricultural aircraft, research and application of bionic technology suitable for agricultural aircraft were then proposed. Finally, prospects of agricultural aero-bionic technology were also discussed from multiple bionic target fusion, three-dimensional spatial information exploration, sensors, and animal brain system mechanism. This review provides a reference for the development of bionic technology in China’s agricultural aviation.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned.

We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and return natural language answers. The aim of the competition was to build systems that can predict correct answers while also satisfying strict on-disk memory budgets. These memory budgets were designed to encourage contestants to explore the trade-off between storing large, redundant, retrieval corpora or the parameters of large learned models. In this report, we describe the motivation and organization of the competition, review the best submissions, and analyze system predictions to inform a discussion of evaluation for open-domain QA.
The Present and Future of Discrete Logarithm Problems on Noisy Quantum Computers

The discrete logarithm problem (DLP) is the basis for several cryptographic primitives. Since Shor&#x2019;s work, it has been known that the DLP can be solved by combining a polynomial-size quantum circuit and a polynomial-time classical post-processing algorithm. The theoretical result corresponds the situation where a quantum device working with a medium number of qubits of very small errors can solve DLP. However, all the quantum devices that we can use have a limited number of noisy qubits, as of the noisy intermediate-scale quantum (NISQ) era. Thus, evaluating the instance size that the latest quantum device can solve, and give a future prediction of the size along the progress of quantum devices are emerging research topics. This paper contains two proposal to discuss the performance of quantum devices against DLP in the NISQ era. (1) A quantitative measure based on the success probability of the post-processing algorithm to determine whether an experiment on a quantum device (or a classical simulator) succeeded. (2) A procedure to modify bit strings observed from a Shor&#x2019;s circuit to increase the success probability of a lattice-based post-processing algorithm. We conducted our experiments with the <monospace>ibm_kawasaki</monospace> device and discovered that the simplest circuit (7 qubits) from a 2-bit DLP instance achieves a sufficiently high success probability to proclaim the experiment successful. Experiments on another circuit from a slightly harder 2-bit DLP instance, on the other hand, did not succeed, and we determined that reducing the noise level by half is required to achieve a successful experiment. Finally, we give a near-term prediction based on required noise levels to solve some selected small DLP and integer factoring instances.
Are Language-Agnostic Sentence Representations actually Language-Agnostic?

With the emergence of pre-trained multilingual models, multilingual embeddings have been widely applied in various natural language processing tasks.Language-agnostic models provide a versatile way to convert linguistic units from different languages into a shared vector representation space.The relevant work on multilingual sentence embeddings has reportedly reached low error rate in cross-lingual similarity search tasks.In this paper, we apply the pre-trained embedding models and the cross-lingual similarity search task in diverse scenarios, and observed large discrepancy in results in comparison to the original paper.Our findings on cross-lingual similarity search with different newly constructed multilingual datasets show not only correlation with observable language similarities but also strong influence from factors such as translation paths, which limits the interpretation of the language-agnostic property of the LASER model.
A Rational Approximation of the Fourier Transform by Integration with Exponential Decay Multiplier

Recently we have reported a new method of rational approximation of the sinc function obtained by sampling and the Fourier transforms. However, this method requires a trigonometric multiplier that originates from shifting property of the Fourier transform. In this work we show how to represent the Fourier transform of a function $f(t)$ in form of a ratio of two polynomials without any trigonometric multiplier. A MATLAB code showing algorithmic implementation of the proposed method for rational approximation of the Fourier transform is presented.
Blockchain Governance: A Dynamic View

The governance of blockchain systems is unique due to its decentralized nature and automatically enforced rules and mechanisms. Moreover, blockchain governance is crucial in achieving success and sustainability. With this study, we aim to advance the theory of blockchain governance and support practitioners by defining blockchain governance from a holistic viewpoint and identifying its building blocks. As a result of a systematic literature review of 75 recent articles focusing on blockchain governance, we propose a dynamic model that deepens the researchers' and practitioners' understanding of blockchain governance. The conceptual model can serve as a reference framework and structured foundation for analyzing, discussing, and developing the governance of blockchain systems.
Diverse data! Diverse schemata?

One of the key value propositions for knowledge graphs and semantic web technologies is fostering semantic interoperability, i.e., integrating data across different themes and domains. But why do we aim at interoperability in the first place? A common answer to this question is that each individual data source only contains partial information about some phenomenon of interest. Consequently, combining multiple diverse datasets provides a more holistic perspective and enables us to answer more complex questions, e.g., those that span between the physical sciences and the social sciences. Interestingly, while these arguments are well established and go by different names, e.g., variety in the realm of big data, we seem less clear about whether the same arguments apply on the level of schemata. Put differently, we want diverse data, but do we also want diverse schemata or a single one to rule them all?
A Novel Hybrid Approach: Instance Weighted Hidden Naive Bayes

Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estimates are often suboptimal. Hidden naive Bayes (HNB) adds a hidden parent to each attribute, which can reflect dependencies from all the other attributes. Compared with other Bayesian network algorithms, it offers significant improvements in classification performance and avoids structure learning. However, the assumption that HNB regards each instance equivalent in terms of probability estimation is not always true in real-world applications. In order to reflect different influences of different instances in HNB, the HNB model is modified into the improved HNB model. The novel hybrid approach called instance weighted hidden naive Bayes (IWHNB) is proposed in this paper. IWHNB combines instance weighting with the improved HNB model into one uniform framework. Instance weights are incorporated into the improved HNB model to calculate probability estimates in IWHNB. Extensive experimental results show that IWHNB obtains significant improvements in classification performance compared with NB, HNB and other state-of-the-art competitors. Meanwhile, IWHNB maintains the low time complexity that characterizes HNB.
Human Face Identification based on Optimal Sparse Features

Security of human being is an important aspect in the context of data communication. To maintain security, technology is being developed from alpha-numeric passwords to biometric scanners. Recent advancement in security is the user authentication using face recognition. But the flaws in existing face recognition systems are yet to be addressed. This paper discusses solutions to the issues encountered by face recognition systems. Sparsity based classification is performed in this work. This method can handle errors occurs due to compress in and occlusion in a robust manner. We suggest a comprehensive classification algorithm characterized by sparse representation and 1l -minimization. In this method, the feature points and selection of features are not critical. The effect of change in occlusion can be easily addressed by using this optimal sparse representation based classification (OSRC) algorithm.
Ultrafast coherent excitation of an ytterbium ion with single laser pulses

Experimental realizations of two qubit entangling gates with trapped ions typically rely on addressing spectroscopically resolved motional sidebands, limiting gate speed to the secular frequency. Fast entangling gates using ultrafast pulsed lasers overcome this speed limit. This approach is based on state-dependent photon recoil kicks from a sequence of counter-propagating, resonant, ultrafast pulse pairs, which can allow sub-microsecond gate speeds. Here, we demonstrate a key component of the ultrafast gate protocol, the coherent excitation of a 171Yb+ ion across the 2S1∕2-2P1∕2 transition with a single near-resonant short optical pulse at 369.53 nm. We achieve a maximum population transfer of 94(1)% using a picosecond pulsed laser that can be tuned across the 2S1∕2-2P1∕2 transition and 42.5(9)% with 190(7) GHz detuning.
Universal single-server blind quantum computation for classical clients

Blind quantum computation allows a client without enough quantum technologies to delegate her quantum computation to a remote quantum server, while keeping her input, output and algorithm secure. In this paper, we propose a universal single-server and almost-classical-client blind quantum computation protocol. In this protocol, the client interfaces with only one server and the only ability of the client required is to get particles from the trusted center and forward them to the server. We present an analysis of this protocol and modify it to a universal single-server and fully-classical-client blind quantum computation protocol by improving the ability of the trusted center. Based on our protocols and recent works, a new "Cloud + Certificate Authority (CA)" style is proposed for the blind quantum computation.
Using STEAM-6E Model in AR/VR Maker Education Teaching Activities to Improve High School Students’ Learning Motivation and Learning Activity Satisfaction

The Ministry of Education has also actively promoted STEM maker education in recent years. As a result, the maker teaching resources in secondary schools have flourished. STEM is composed of Science, Technology, Engineering and Mathematics. This research mainly introduces STEAM & Maker Education into the class and plans a “STEAM-6E Model” set to incorporate AR/VR Maker Education courses. Learn about theory, application, and implementation through courses. Integrate resources near the school and apply the knowledge learned to solve problems for the community practically. The subjects of the study are 60 high school third-year students in two classes. One class is the experimental group using STEAM-6E teaching and the control group uses traditional briefing teaching, conducting experiments. The MSLQ (Motivated Strategies for Learning Questionnaire) and the ASQ (Activity Satisfaction Questionnaire) are applied to acquire quantitative data for examination. The results showed that the experimental group had differences in learning motivation and satisfaction with learning activities.
A High-Efficient and Configurable Hardware Accelerator for Convolutional Neural Network

As convolutional neural network (CNN) achieves better results than traditional algorithms in applications such as style transfer, target detection, CNN has become a research focus in the field of computer vision. In this paper, a self-adaptive channel-wise block strategy is proposed to reduce the on-chip memory size and the access to the external memory for better energy efficiency. Besides, the hardware computing unit utilizes multiplexing and register configuration to increase the throughput and enhance the versatility of the accelerator. Employing these two techniques, a CNN accelerator is designed and verified by simulation, achieving a throughput of 380 giga operations per second (GOPS) and power consumption of 52.4 GOPS/W.
A secure cross‐organizational container deployment approach to enable ad hoc collaborations

Summary When organizations need to collaborate urgently, for example, in the case of an emergency situation, it is needed to deploy software components into the different domains in order to allow crucial data to be exchanged. The ad hoc aspect is important as it does not allow the participating organizations to negotiate entire workflows and/or contracts upfront. To enable these ad hoc cross‐organizational collaborations, a container orchestration platform, like Kubernetes, can be used to quickly deploy pods of containers in a cross‐organizational overlay network, even fully automated. Although this is technically feasible, there may be a trust issue from the perspective of a participating organization when an external organization is capable of deploying any software inside its network domain. This concern is examined and resolved in this article, by proposing an extension to the existing deployment scheme used in vanilla Kubernetes. It allows the participating organizations to assess whether a suggested deployment conforms to the goal of the project and to maintain an overview of all activities related to a single collaboration. This intermediate step prevents an honest organization against potentially malicious behaviour of external entities, either the orchestrator and/or the other organizations, solving the aforementioned trust issue. Evaluation of the implemented prototype shows that a secure collaboration, which requires at most tens of containers, can be attained with sub‐second deployment overheads per container, apart from the required manual interventions for trust management purposes.
Procedural Enemy Generation through Parallel Evolutionary Algorithm

This research presents a Parallel Evolutionary Algorithm (PEA) that generates enemies with diverse characteristics, such as the enemy's health, weapons, and movement. Our PEA aims to create enemies matching their difficulty degrees with the difficulty goal given as input parameter. We designed our algorithm in this way to be future used in an online adaptive generation system. We experimented with a set of generated enemies with an Action-Adventure game prototype as a testbed. The results show that players evaluated our approach positively, successfully creating enemies considered easy, medium, or hard to face, as defined by their original fitness' target value. Besides, the players found the game fun to play for all difficulty levels played, and the perceived challenge rose as the PEA fitness was higher. In terms of performance results, our PEA converged into the input solution in less than a second for most cases, denoting its future use in online adaptive applications.
Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

Various Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user’s sensitive raw streaming data. For this reason, protecting the privacy of the data streams is crucial. In this paper, we explore local differential privacy techniques for streaming data. We compare the techniques and report the advantages and limitations. We also present the effect on component (e.g., smoother, perturber) variations of distribution-based local differential privacy. We find that combining distribution-based noise during perturbation provides more flexibility to the interested entity.