Reviewerly/exHarmony huggingface dataset:
Workload-Aware Performance Tuning for Autonomous DBMSs

Optimal configuration is vital for a DataBase Management System (DBMS) to achieve high performance. There is no one-size-fits-all configuration that works for different workloads since each workload has varying patterns with different resource requirements. There is a relationship between configuration, workload, and system performance. If a configuration cannot adapt to the dynamic changes of a workload, there could be a significant degradation in the overall performance of DBMS unless a sophisticated administrator is continuously re-configuring the DBMS. In this tutorial, we focus on autonomous workload-aware performance tuning, which is expected to automatically and continuously tune the configuration as the workload changes. We survey three research directions, including 1) workload classification, 2) workload forecasting, and 3) workload-based tuning. While the first two topics address the issue of obtaining accurate workload information, the third one tackles the problem of how to properly use the workload information to optimize performance. We also identify research challenges and open problems, and give real-world examples about leveraging workload information for database tuning in commercial products (e.g., Amazon Redshift). We will demonstrate workload-aware performance tuning in Amazon Redshift in the presentation.
CRW-NER: Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Recently, incorporating word information into a character-based model has been proved to be effective for Chinese NER task. However, most existing work ignore the radical information. A novel CRW-NER model is proposed to utilize multiple embeddings in this paper. Besides, the GRU-GatedConv in CRW-NER model is explored to utilize effectively long-distance contextual character information. Experimental results on three public datasets demonstrate the validity of CRW-NER model. The CRW-NER model achieves more excellent performance than the state-of-the-art comparison models.
An Open-World Novelty Generator for Authoring Reinforcement Learning Environment of Standardized Toolkits

Current research in Reinforcement Learning (RL) is based on closed-world learning environment where the environment remains fixed and unchanged throughout the agent’s training and application session. The fixed environment may be prone to failure when the agents incorporate under novel unseen situations. To overcome the drawback of the existing closed-world model, an Open-world learning model is required which can classify the novelty occurring in an environment in a hierarchical manner. The proposed control suite with open world novelty generator is an attempt to augment the machine learning environment for authoring the novelty in actors, interactions, and environment of standardized Reinforcement learning toolkits such as UnityML, OpenAI Gym, and DeepMind Control Suite in real-time. Such a tool will provide an opportunity to the RL researchers to simulate the Open-world learning model and test their algorithms within the standardized closed-world learning environments of the standardized RL toolkits.
Smart Shopping Trolley System using IoT

Abstract Currently, billions of consumers shop at large in shopping mall stores for their everyday necessities. In today’s life, purchasing has become a part of everyday life. On holidays and weekends, malls experience massive crowds. When there are exclusive sales and discounts, the urgency is intensified. Here we will use IOT based technology for people buy various things and place them in a trolley. When they have completed their order, they must pay at the billing desk. The Bills generated by a RFID reader which is a time-consuming procedure that leads to long queues at billing counters. The key objective is to build a framework that can be used in shopping malls to fix the issue described above. Both trolleys would be fitted with the system. It will require an RFID reader; RFID tags would be found in many of the mall’s goods. When a consumer places items in the trolley, the code is read, and the price of certain items is saved in the admin portal. When we add items to the cart, the device depicts the item’s name, number, expiration date, and rate, which is then applied to the overall bill. As a result, billing will take place within the trolley itself. The complete bill data will be passed to online portal Wi-Fi Module with trolley id, and we will be able to pay the balance either online or at bill counter by scanning the RFID tag placed in the trolley. In-season inventory reductions are often applied in transactions through admin, and item stock may be recorded. Intelligent Carts and automated recognition systems are being utilized by merchants to enhance the efficiency of their offerings and to boost market demand, enabling them to save time and money.
On-The-Fly Stochastic Codebook Re-generation for Sources with Memory

This paper proposes a generalized stochastic mechanism for codebook generation in lossy coding settings for sources with memory. Earlier work has shown that the rate-distortion bound can be asymptotically achieved for discrete memoryless sources by a “natural type selection” (NTS) algorithm. In iteration n, the distribution that is most likely to produce the types of a sequence of K codewords of finite length I that “dmatch” a respective sequence of K source words of length I, (i.e., which satisfy the distortion constraint), is used to regenerate the codebook for iteration n+1. The resulting sequence of codebook generating distributions converges to the optimal distribution Q* that achieves the rate-distortion bound for the memoryless source, asymptotically in I, K, and n. This work generalizes the NTS algorithm to account for sources with memory. The algorithm encodes mI-length source words consisting of I vectors (or super-symbols) of length m. We show that for finite m and I, the sequence of codebook reproduction distributions Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0,m,l</sub> , Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,m,l,</sub> ... (each computed after observing a sequence of K d-match events) converges to the optimal achievable distribution Q* <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m,l</sub> (within a set of achievable distributions determined by m and I), asymptotically in K and n. It is further shown that Q* <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m,l</sub> converges to the optimal reproduction distribution Q* that achieves the rate-distortion bound for sources with memory, asymptotically in m and I.
Energy-Efficient Deep Reinforcement Learning Accelerator Designs for Mobile Autonomous Systems

Deep reinforcement learning (DRL) is widely used for autonomous systems including autonomous driving, robots, and drones. DRL training is essential for human-level control and adaptation to rapidly changing environments in mobile autonomous systems. However, acceleration of DRL training has three challenges: 1) large memory access, 2) various data patterns, 3) complex data dependency due to utilization of multiple DNNs. Two CMOS DRL accelerators have been proposed to support high speed, high energy-efficiency DRL training in mobile autonomous systems. One accelerator handles different data patterns with transposable PE architecture and reduces large feature map memory access with top-3 experience compression. The other accelerator supports group-sparse training for weight compression and integrates the on-line DRL task scheduler to support multi-DNNs operations.
Mobile technologies providing educational activity during classes

Abstract Modern mobile technologies have become widespread in our lives. All of us use them every day. The use of mobile phones in education is a topical issue too. This paper presents the methodological features of the use of mobile technologies in the astronomy’s class. Modern methods offer a lot of techniques and innovative forms of teaching methods in order to improve students’ knowledge. These innovations are aimed to increase using activity-based and person-centered approaches to learning and also, they will help to intensify students’ training activities therefore. The including mobile technology in education process will be engage and productive. We used mobile technologies for the organization of practical tasks from astronomy, as well as the possibility of individualization of education is shown. The such tasks help to increase students’ cognitive activity. It is presented in the article, as an example for astronomy studying, there is example of mobile phone using for the Moon research.
System Interoperability for Next Gen Services at Home. A Challenge/Opportunity for Integration

Interoperability is complex non-functional requirement of any today’ system, and it refers the exchanged information. Two systems must offer the possibility to exchange and use the data in order to be named “interoperable”, meaning that the message sent must contain data (in a standard coded format) that can be interpreted by the system that will receive it. The next section describes the challenges encounter during the integration activities from the interoperability point of view for the next gen of systems that offer services at home.
A High-Throughput VLSI Architecture Design of Canonical Huffman Encoder

In this brief, a high-throughput Huffman encoder VLSI architecture based on the Canonical Huffman method is proposed to improve the encoding throughput and decrease the encoding time required by the Huffman code word table construction process. We proposed parallel computing architectures for frequency-statistical sorting and code-size computational sorting. This architecture results in a process of building a tree and assigning symbols that can be completed by scanning the data only once. This solves the problem of the low efficiency of the traditional algorithm, which needs to scan the data twice. Consequently, in addition to the advantages of the high compression ratio inherited from the Canonical Huffman, the proposed architecture has overridden advantages for a high parallelism processing capacity. The experimental results showed that the proposed architecture decreased the encoding time by 26.30% compared to the available Huffman encoder using the standard algorithm when encoding 256 8-bit symbols. Furthermore, the VLSI architecture could further decrease the encoding time when encoding more 8-bit symbols. In particular, when encoding 212,642 8-bit symbols, the proposed VLSI architecture could reduce the encoding time by 87.40%. Thus, compared with the traditional Huffman encoders, this brief achieved the improvement of coding efficiency.
A systematic review of mobile-assisted oral communication development from selected papers published between 2010 and 2019

With the advancement of mobile technology, mobile-assisted language learning (MALL) has significant potential regarding its practical applications and benefits in foreign language learning. Nevertheless, little research was conducted to examine factors regarding the facilitation of oral communication through MALL based on established theories or models. The purpose of this review is to investigate the main constituents conducive to the intended outcomes based on an adapted model from Beatty (2010) and further provide guidelines for enthusiastic stakeholders in the field. Twenty-eight empirical studies were collected and categorized according to four key variables of the design model and analyzed qualitatively, with the key findings identified as follows. Due to the technical affordance of mobile technology, it is found that a student-centered self-regulated learning context is created, where students construct knowledge through self-instruction, self-evaluation, and self-correction. Furthermore, a speaking strategy-driven collaborative-based learning design enhances students’ oral proficiency through strong social connections, interactions, and communication. Finally, given the pedagogical design and practices, high-level cognitive thinking is thereby promoted, with promising affective learning outcomes. In light of the findings, guidelines for educational practitioners, learners, and system designers are provided for pedagogical and practical application in the future.
Low power on-line machine monitoring at the edge

On-line machine monitoring enables more efficient maintenance, by continuous analysis of machine parameters (e.g., vibration, sound, temperature, ...) to enable early identification of malfunction. This task is best implemented as an anomaly detection scheme, as no or limited data is available regarding malfunctioning conditions. This scheme should moreover be integrated on the sensory edge device, to avoid the burden of centralizing the analysis of the raw sensor data on the compute and network infrastructure. In this work we therefore present a workflow for training a quantized deep learning anomaly detection model (Deep SVDD), suitable for edge devices with limited resources (e.g. memory, compute, power). We show that using minimal architectural interventions we achieve a reduction of 50% in terms computational resources when compared to autoencoder based approaches. Our final models run at 4 bits without loss of detection performance in terms AUC.
Applying Deep Learning for Extremism Detection

According to recent research, the use of social media to track the spread of radical ideas and extremist threats has attracted the attention of researchers for more than 10 years. In the last 3 years, there has been a surge in research interest in identifying and predicting based on the analysis of the text content of messages in open social networks as extremists actively use social networks and the number of calls to extremism and the number of recruitment through social networks is growing. In this paper, we consider an important applied problem of using deep learning methods to identify potential extremist and terrorist information on the Internet. It provides an overview of existing solutions and approaches and offers its own method for detecting online extremism. The applicability and effectiveness of the proposed method is demonstrated experimentally on a reference set of real data potentially containing extremist information. The results of the experiment show high accuracy in detecting extremist messages.
Certifying Multilevel Coherence in the Motional State of a Trapped Ion

Quantum coherence is one of the clearest departures from classical physics, exhibited when a system is in a superposition of different basis states. Here the coherent superposition of three motional Fock states of a single trapped ion is experimentally certified, with a procedure provably robust against imperfect operation. As the motional state cannot be directly interrogated, our scheme uses an interference pattern generated by projective measurement of the coupled qubit state. The minimum number of coherently superposed states is inferred from a series of threshold values based on analysis of the interference pattern. This demonstrates that high-level coherence can be verified and investigated with simple, nonideal control methods well-suited to noisy intermediate-scale quantum devices.
3D human tongue reconstruction from single “in-the-wild” images

3D face reconstruction from a single image is a task that has garnered increased interest in the Computer Vision community, especially due to its broad use in a number of applications such as realistic 3D avatar creation, pose invariant face recognition and face hallucination. Since the introduction of the 3D Morphable Model in the late 90's, we witnessed an explosion of research aiming at particularly tackling this task. Nevertheless, despite the increasing level of detail in the 3D face reconstructions from single images mainly attributed to deep learning advances, finer and highly deformable components of the face such as the tongue are still absent from all 3D face models in the literature, although being very important for the realness of the 3D avatar representations. In this work we present the first, to the best of our knowledge, end-to-end trainable pipeline that accurately reconstructs the 3D face together with the tongue. Moreover, we make this pipeline robust in “in-the-wild” images by introducing a novel GAN method tailored for 3D tongue surface generation. Finally, we make publicly available to the community the first diverse tongue dataset, consisting of 1,800 raw scans of 700 individuals varying in gender, age, and ethnicity backgrounds**Project url: www.github.com/steliosploumpis/tongue. As we demonstrate in an extensive series of quantitative as well as qualitative experiments, our model proves to be robust and realistically captures the 3D tongue structure, even in adverse “in-the- wild” conditions.
Descending Order Transformation-based Synthesis of MVL Reversible Circuits

Transformation-based synthesis is a straightforward technique for determining a circuit for a reversible function. Various methods employing the transformation-based approach have been previously proposed. For an r-valued function, the existing methods consider the function specification in ascending order from the input assignment 0,0,...,0 to the input assignment r - 1, r - 1,..., r - 1. In this paper, we extend the transformation-based synthesis approach by showing how the input assignments can be considered in descending order. A useful technique is to synthesize two circuits, one using ascending order and the other using descending order, and to choose the cheaper circuit as the solution. Experimental results considering both reversible gate count and quantum cost for the 9! 3-valued reversible functions and for a selection of larger functions demonstrate the advantages of considering descending as well as ascending order. The paper includes suggestions for further research incorporating the new descending order approach.
A framework for the automation of testing computer vision systems

Vision systems, i.e., systems that enable the detection and tracking of objects in images, have gained substantial importance over the past decades. They are used in quality assurance applications, e.g., for finding surface defects in products during manufacturing, surveillance, but also automated driving, requiring reliable behavior. Interestingly, there is only little work on quality assurance and especially testing of vision systems in general. In this paper, we contribute to the area of testing vision software, and present a framework for the automated generation of tests for systems based on vision and image recognition with the focus on easy usage, uniform usability and expandability. The framework makes use of existing libraries for modifying the original images and to obtain similarities between the original and modified images. We show how such a framework can be used for testing a particular industrial application on identifying defects on riblet surfaces and present preliminary results from the image classification domain.
Linear codes associated to symmetric determinantal varieties: Even rank case

We consider linear codes over a finite field Fq, for odd q, derived from determinantal varieties, obtained from symmetric matrices of bounded ranks. A formula for the weight of a codeword is derived. Using this formula, we have computed the minimum distance for the codes corresponding to matrices upperbounded by any fixed, even rank. A conjecture is proposed for the cases where the upper bound is odd. At the end of the article, tables for the weights of these codes, for spaces of symmetric matrices up to order 5, are given.
Partition crossover for continuous optimization

Partition crossover (PX) is an efficient recombination operator for gray-box optimization. PX is applied in problems where the objective function can be written as a sum of subfunctions fl(.). In PX, the variable interaction graph (VIG) is decomposed by removing vertices with common variables. Parent variables are inherited together during recombination if they are part of the same connected recombining component of the decomposed VIG. A new way of generating the recombination graph is proposed here. The VIG is decomposed by removing edges associated with subfunctions fl(.) that have similar evaluation for combinations of variables inherited from the parents. By doing so, the partial evaluations of fl(.) are taken into account when decomposing the VIG. This allows the use of partition crossover in continuous optimization. Results of experiments where local optima are recombined indicate that more recombining components are found. When the proposed epsilon-PX (ePX) is compared with other recombination operators in Genetic Algorithms and Differential Evolution, better performance is obtained when the epistasis degree is low.
A Novel Statistical and Neural Network Combined Approach for the Cloud Spot Market

The price of virtual machine instances in the Amazon EC2 spot model is often much lower than in the on-demand counterpart. However, this price reduction comes with a decrease in the availability guarantees. Several mechanisms have been proposed to analyze the spot model in the last years, employing different strategies. To our knowledge, there is no work that accurately captures the trade-off between spot price and availability, for short term analysis, and does long term analysis for spot price tendencies, in favor of user decision making. In this work, we propose (a) a utility-based strategy, that balances cost and availability of spot instances and is targeted to short-term analysis, and (b) a LSTM (Long Short Term Memory) neural network framework for long term spot price tendency analysis. Our experiments show that, for r4.2xlarge, 90 percent of spot bid suggestions ensured at least 5.73 hours of availability in the second quarter of 2020, with a bid price of approximately 38 percent of the on-demand price. The LSTM experiments were able to predict spot prices tendencies for several instance types with very low error. Our LSTM framework predicted an average value of 0.19 USD/hour for the r5.2xlarge instance type (Mean Squared Error <inline-formula><tex-math notation="LaTeX">$&lt;10^{-6}$</tex-math></inline-formula> ) for a 7-day period of time, which is about 37 percent of the on-demand price. Finally, we used our combined mechanism on an application that compares thousands of SARS-CoV-2 DNA sequences and show that our approach is able to provide good choices of instances, with low bids and very good availability.
Sustainability means inclusivity: engaging citizens in early stage smart city development

The challenge of how cities can be designed and developed in an inclusive and sustainable direction is monumental. Smart city technologies currently offer the most promising solution for long-term sustainability. However, smart city projects have been criticised for ignoring diverse needs of the local population and increasing social divides. A sustainable urban environment depends as much on creating an inclusive space that is safe, accessible and comfortable for a diverse group of citizens as it does on deploying “smart” technologies for energy efficiency or environmental protection. This is because citizens will be more likely to adopt technologies promoting sustainability if they are well-aligned with their lived needs and experiences. In this paper, we present the rationale behind an ongoing interdisciplinary research project that aims to address exactly the problem outlined above by using a participatory design approach. Focusing on a smart city test site in Sweden where sensors are currently being deployed to collect data on noise, particles, vehicle numbers and types (amongst other), the goal is to bring local residents and government representatives into dialogue with technical developers by adopting a “meet-in-the-middle” approach. This paper comprises a brief presentation of early findings and a reflection on this approach.