Reviewerly/exHarmony huggingface dataset:
A triz-directed approach in proposing device-oriented ideas that cultivate water-drinking habits among children

This study aims to investigate problems concerning water-drinking habits among children, ongoing efforts to resolve these problems, limitations in these ongoing efforts and recommended ideas to overcome these limitations. The TRIZ approach was used primarily to identify the root causes of the main problem and establish technical contradictions in order to steer the researchers to specific solution models. The solution models generated from the 40 inventive principles of TRIZ were used to recommend ideas that could potentially resolve the technical contradictions. The recommended ideas were then substantiated with existing research, concepts or analogies in order to authenticate their practicalities with regard to the study’s aim. The inventive principles extracted include the universality, preliminary action and segmentation principles. The principles used allowed the proposal of inventive design recommendations at the end of the study. For instance, based on the segmentation principle, the idea to improve water-drinking habits among children can be in the form of a water bottle that is compartmentalised into two parts; the top part being the containment unit for the water and the bottom part being the storage section for a reward beverage (perhaps a sweet drink such as orange juice or syrup). The compartmentalised water bottle idea would entice children to completely consume the water in the bottle before accessing the reward beverage. Although further research and verifications are still required, the proposed ideas can serve as precursory recommendations for researchers and designers in their pursuits to improve the water-drinking habits among children in future.
Regulating Financial Services in an Era of Technological Disruption

Financial regulators are challenged to respond to the innovation opportunities presented by financial technology (fintech). Current rules are not necessarily sufficient or effective to adequately regulate new business models and new products relating to innovations such as crypto assets or digital financial services. Regulators that fail to respond in a timely manner may drive innovation offshore and deprive their markets and consumers of appropriate, new services. To respond to new financial innovation, regulators have been establishing innovation hubs and regulatory sandboxes. Innovation hubs enable them to engage innovators more effectively. Sandboxes allow the products to be tested in a controlled environment and enable to regulator to consider whether existing laws are appropriate to regulate such products and, of not, what measures may be required. Sandboxes are however resource intensive and they hold a number of risks. Financial regulators are, of course, not alone in having to address the regulatory challenges of innovation. This article therefore also considers other non-financial regulatory experiences of innovative products and services, namely automated vehicles; emissions trading in China; and Uber and its clones, to consider whether those experiences hold lessons for financial regulators.&#x0D;
An Experimental and Mathematical investigation of optimal tilt angle and effects of reflectors on PV energy production

The performance of the solar photovoltaic panels relies on its direction and tilt angle with respect to the horizontal to obtain better conversion efficiency. The tilt angle of the PV panel needs to be in proper location and position to obtain maximum power output from photovoltaics. The optimum tilt angle is generally calculated based on global, diffused, and direct radiation on the horizontal surface. This study focuses on the concept of the optimal tilt angle that improves the performance of the PV panel. The paper discusses the MATLAB mathematical modelling and experimental validation conducted at Nitte, India to determine the optimal tilt angle of PV panels of the region for maximum solar radiation. The investigation also includes the effect of three different types of reflectors on the PV panel for the obtained optimal tilt angle. The experimental results show that to get the optimum power output, the tilt angle needs to be changed every month. Hence monthly optimal tilt must be chosen for optimum power output. The results showed a PV panel with a focused mirror reflector produced higher power output compared to aluminum and stainless-steel reflectors.
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks. In this paper, we introduce a simple model-free algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance is just as good as, if not better than, a state-of-the-art model-based algorithm for the MuJoCo benchmark. Moreover, REDQ can achieve this performance using fewer parameters than the model-based method, and with less wall-clock run time. REDQ has three carefully integrated ingredients which allow it to achieve its high performance: (i) a UTD ratio ≫1; (ii) an ensemble of Q functions; (iii) in-target minimization across a random subset of Q functions from the ensemble. Through carefully designed experiments, we provide a detailed analysis of REDQ and related model-free algorithms. To our knowledge, REDQ is the first successful model-free DRL algorithm for continuous-action spaces using a UTD ratio ≫1.
Predicting Memory Compiler Performance Outputs Using Feed-forward Neural Networks

Typical semiconductor chips include thousands of mostly small memories. As memories contribute an estimated 25% to 40% to the overall power, performance, and area (PPA) of a product, memories must be designed carefully to meet the system’s requirements. Memory arrays are highly uniform and can be described by approximately 10 parameters depending mostly on the complexity of the periphery. Thus, to improve PPA utilization, memories are typically generated by memory compilers. A key task in the design flow of a chip is to find optimal memory compiler parametrizations that, on the one hand, fulfill system requirements while, on the other hand, they optimize PPA. Although most compiler vendors also provide optimizers for this task, these are often slow or inaccurate. To enable efficient optimization in spite of long compiler runtimes, we propose training fully connected feed-forward neural networks to predict PPA outputs given a memory compiler parametrization. Using an exhaustive search-based optimizer framework that obtains neural network predictions, PPA-optimal parametrizations are found within seconds after chip designers have specified their requirements. Average model prediction errors of less than 3%, a decision reliability of over 99%, and productive usage of the optimizer for successful, large volume chip design projects illustrate the effectiveness of the approach.
Influencers as socio-technical actors: mapping the paths of influence and the traces of the digital influencers’ system in Italy

In recent years, digital influencers or digital opinion leaders have emerged as a global phenomenon, one providing a rich field of investigation for several disciplines from sociology to marketing. The goal of the paper is to study this phenomenon by adopting an ecological approach, not focusing only on a single dimension but observing the influencer system as an environment with its own rules and subjects, examining the other components of the system and tracing the relationships between them. The original contribution of this paper is to conceptualize influencers as socio-technical actors, i.e. as social subjects that operate within technological platforms, and to adopt an interdisciplinary approach, starting from sociological and marketing studies on the influencer phenomenon and then moving on to STS studies that have focused on the social shaping of technology to shed light on how the influencers are transforming the (social) media system in which they are inserted and then the chain of communication industry. The argument will therefore start by reviewing the theories that address this phenomenon and by sketching a genealogy, tracing its roots in the cultural and social context of the participatory web and web 2.0. It will move, then, to investigate the dynamics of shaping social media platforms, reviewing the studies that have investigated them, from the Social Shaping of Technology approach to platform studies. The paper will apply the analysis to the Italian context, unfolding the dynamics of influence with the support of the case study of ClioMakeUp (the leading beauty creator in Italy), examples, and grey literature from the Italian context. In the final part, the paper will map the traces of the influencer system in Italy and the communication chain, focusing on the processes of mutual shaping between brands, communication agencies, influencer enterprises, regulatory bodies, media, and of course platforms. The STS approach proved to be useful in disentangling the several actors of the system, but as platforms evolved the opportunities for mutual shaping have gradually diminished as the balance of power seems to be tending in favor of the platforms. More research is needed to further understand the concepts of closure, script, and relevant social groups within social media platforms.
THE APPLICATION OF TECHNOLOGY IN MUSEUMS

Alongside with collections in form of historical objects, as well as objects related to human culture, museums have taken an important role in providing education, especially for tourists who visiting a destination. However, at its conditions who have lack of innovation, has implications for declining number in interest of tourists to come and dig deeper into the collections in museum. This research set an objective to answer how could technology applied in museum. Research has been done through Focus Group Discussion (FGD) and depth observation conducted on January 2020, in Jakarta. Nine informants/participants involved during FGD, including 5 museums (public and private), 2 technology providers, and 1 museum users community. Research indicates that strong cooperation between museums and technology providers has been elaborated, in particular in several museums in Jakarta. Augmented Reality and Virtual Reality are two most applications applied. Meanwhile, museums have established several platforms for security and data collection purposes. Both parties will try to add more platforms through video mapping application, projection mapping, virtual tour, and internet of things (IoT) operations. Process of increasing experience and immersion encounters obstacles, to provide additional experience to visitors, so that all five senses that exist in visitors, can enjoy all museum collection. Technology could embrace interaction and immersion to add more experience value on visitors. Thus, tourism industry would be more attractive with the presences of museums.
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning

It is of significance for an agent to autonomously explore the environment and learn a widely applicable and general-purpose goal-conditioned policy that can achieve diverse goals including images and text descriptions. Considering such perceptually-specific goals, one natural approach is to reward the agent with a prior non-parametric distance over the embedding spaces of states and goals. However, this may be infeasible in some situations, either because it is unclear how to choose suitable measurement, or because embedding (heterogeneous) goals and states is non-trivial. The key insight of this work is that we introduce a latent-conditioned policy to provide goals and intrinsic rewards for learning the goal-conditioned policy. As opposed to directly scoring current states with regards to goals, we obtain rewards by scoring current states with associated latent variables. We theoretically characterize the connection between our unsupervised objective and the multi-goal setting, and empirically demonstrate the effectiveness of our proposed method which substantially outperforms prior techniques in a variety of tasks.
The Impact of Information Technology Governance in Reducing Cloud Accounting Information Systems Risks in Telecommunications Companies in the State of Kuwait

This study aims to identify the impact of information technology governance in reducing cloud accounting information systems Risks in Kuwaiti telecommunications companies. The study population represented by all Kuwaiti telecommunications companies, which number (3) companies. The sampling unit consisted of workers in the upper and middle management of Kuwaiti telecommunications companies. The researcher distributed (327) questionnaires electronically, the researcher retrieved (291) questionnaires, of which (269) were valid for statistical analysis. The results indicated that the relative importance of all dimensions of information technology governance. The results demonstrate the importance of the role of information technology governance in reducing cloud accounting information systems risks. Also, that all information technology governance dimensions (Align, Plan and Organize, Build, Acquire and Implement, Deliver, Service and Support, Monitor, Evaluate and Assess) affect the cloud accounting information systems risks reduction in Kuwaiti telecommunications companies.
Real-time Machine Health Monitoring System using Machine Learning with IoT Technology

Machine health monitoring is the main focal point for now as many industries are evolving to industry 4.0. Industry 4.0 is the revolution in industrial that involve the Internet of Things (IoT) and artificial intelligence toward automation and data sharing for production efficiency improvement. The existing established methods for machine health monitoring were not in real-time and there was no real-time correction of data from the load and processing of data on the computer. In tracking machine health efficiency this approach wasn’t very successful. Real-time machine health monitoring can improve overall equipment effectiveness (OEE), reduce electricity consumption, minimize unplanned downtime, and extend machine lifetime. In this research paper, we propose to design a real-time machine health monitoring system using machine learning with IoT technology that can analyze the supply balancing condition on a 3-phase system. This system is built with compact physical hardware and can capture the electrical data from the load then send it to the server. The server will progress data and train the data using machine learning. The system was installed on a blender machine in a factory. In this research, a system which is able to monitor the machine operation and classify the operation stages of the machine was developed. Besides that, the system also capable to monitor the load balancing condition of the machine.
OPPR: An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme with Local Histograms in Cloud Environment

As the wide application of imaging technology, the number of big image data which may containing private information is growing fast.Due to insufficient computing power and storage space for local server device, many people hand over these images to cloud servers for management.But actually, it is unsafe to store the images to the cloud, so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage.However, it is not conducive to the efficient application of image, especially in the Content-Based Image Retrieval (CBIR) scheme.This paper proposes an outsourcing privacypreserving JPEG CBIR scheme.We design a set of JPEG format-compatible encryption method, making no file expansion to JPEG files.We firstly combine multiple adjacent 8 × 8 DCT coefficient blocks into big-blocks.Then, random scrambling and stream encryption are used on the binary code of DCT coefficients to protect the JPEG image privacy.The task of extracting features from encrypted images and retrieving similar images are done by the cloud server.The group index histograms of DCT coefficients are extracted from the encrypted big-blocks, then the global vector is produced to represent the JPEG image with the aid of bagof-words (BOW) model.The security analysis and experimental results show that our proposed scheme has strong security and good retrieval performance.
Quantifying computational advantage of Grover’s algorithm with the trace speed

Despite intensive research, the physical origin of the speed-up offered by quantum algorithms remains mysterious. No general physical quantity, like, for instance, entanglement, can be singled out as the essential useful resource. Here we report a close connection between the trace speed and the quantum speed-up in Grover's search algorithm implemented with pure and pseudo-pure states. For a noiseless algorithm, we find a one-to-one correspondence between the quantum speed-up and the polarization of the pseudo-pure state, which can be connected to a wide class of quantum statistical speeds. For time-dependent partial depolarization and for interrupted Grover searches, the speed-up is specifically bounded by the maximal trace speed that occurs during the algorithm operations. Our results quantify the quantum speed-up with a physical resource that is experimentally measurable and related to multipartite entanglement and quantum coherence.
Efficient Lattice Gadget Decomposition Algorithm With Bounded Uniform Distribution

A gadget decomposition algorithm is commonly used in many advanced lattice cryptography applications which support homomorphic operations over ciphertexts to control the noise growth. For a special structure of a gadget, the algorithm is digit decomposition. If such algorithm samples from a subgaussian distribution, that is, the output is randomized, it gives more benefits on output quality. One of the important advantages is Pythagorean additivity which makes the resulting noise contained in a ciphertext grow much less than naive digit decomposition. Therefore, the error analysis becomes cleaner and tighter than the use of other measures like Euclidean norm and infinity norm. Even though such advantage can also be achieved by use of discrete Gaussian sampling, it is not attractive for practical performance due to a large factor in resulting noise and the complex computation of the exponential function, whereas a more relaxed probability condition is required for a subgaussian distribution. Nevertheless, subgaussian sampling has barely received an attention so far, thus no practical algorithms was implemented before an efficient algorithm is presented by Genis et al., recently. In this paper, we present a practically efficient gadget decomposition algorithm where output follows a subgaussian distribution. We parallelize the existing practical subgaussian gadget decomposition algorithm, using a bounded uniform distribution. Our algorithm is divided into two independent subalgorithms and only one algorithm depends on the input. Therefore, the other algorithm can be considered as precomputation. As an experimental result, our algorithm performs over 50% better than the existing algorithm.
Ambiguity of objective image quality metrics: A new methodology for performance evaluation

Objective image quality metrics try to estimate the perceptual quality of the given image by considering the characteristics of the human visual system. However, it is possible that the metrics produce different quality scores even for two images that are perceptually indistinguishable by human viewers, which have not been considered in the existing studies related to objective quality assessment. In this paper, we address the issue of ambiguity of objective image quality assessment. We propose an approach to obtain an ambiguity interval of an objective metric, within which the quality score difference is not perceptually significant. In particular, we use the visual difference predictor, which can consider viewing conditions that are important for visual quality perception. In order to demonstrate the usefulness of the proposed approach, we conduct experiments with 33 state-of-the-art image quality metrics in the viewpoint of their accuracy and ambiguity for three image quality databases. The results show that the ambiguity intervals can be applied as an additional figure of merit when conventional performance measurement does not determine superiority between the metrics. The effect of the viewing distance on the ambiguity interval is also shown.
Scalable multimode entanglement based on efficient squeezing of propagation eigenmodes

Continuous-variable encoding of quantum information in the optical domain has recently yielded large temporal and spectral entangled states instrumental for quantum computing and quantum communication. We introduce a protocol for the generation of spatial multipartite entanglement based on phase-matching of a propagation eigenmode in a monolithic photonic device: the array of quadratic nonlinear waveguides. We theoretically demonstrate in the spontaneous parametric downconversion regime the generation of large multipartite entangled states useful for multimode quantum networks. Our protocol is remarkably simple and robust as it does not rely on specific values of coupling, nonlinearity, or length of the sample.Received 20 March 2020Accepted 8 January 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013068Published 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 AreasEntanglement productionIntegrated opticsOptical quantum information processingQuantum information processing with continuous variablesQuantum networksQuantum opticsQuantum state engineeringQuantum InformationAtomic, Molecular & OpticalNetworks
Atar: Attention-based LSTM for Arabizi transliteration

A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).