Reviewerly/exHarmony huggingface dataset:
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain. This model uses a soft, top-down attention mechanism to create a bottleneck in the agent, forcing it to focus on task-relevant information by sequentially querying its view of the environment. The output of the attention mechanism allows direct observation of the information used by the agent to select its actions, enabling easier interpretation of this model than of traditional models. We analyze different strategies that the agents learn and show that a handful of strategies arise repeatedly across different games. We also show that the model learns to query separately about space and content (`where' vs. `what'). We demonstrate that an agent using this mechanism can achieve performance competitive with state-of-the-art models on ATARI tasks while still being interpretable.
Turning a machine vision camera into a high precision position and angle encoder: nanoGPS-OxyO

Position determination is key in many aspects of automation, production technologies, and QA/QC. Optical technologies are used in several position sensing technologies, such as single-axis incremental encoders based on grating scales and interferometers. A lesser known approach for position sensing is based on imaging systems, where the position scale is based on semi-regular patterns read by a camera equipped with suitable optics. Specific algorithms extract the in-plane coordinates of the patterns that are conjugated with the center of the read head, along with the angle that corresponds to the orientation of the patterns. Consequently, it is possible to turn a machine vision camera into an encoder with 3 degrees of freedom: in-plane position x, y, and in-plane orientation &phi;z. Through a proper design of the 2-dimension scales, proprietary decoding software, and adequate implementation, we have been able to design and demonstrate absolute position determination with the nanoGPS Oxyo technology, with nm resolution on x, y, and accuracy better than 100nm over 80mm. The resolution on the orientation has been found to be better than 10&mu;rad on &phi;z. As an example, we have shown that this system can be extremely efficient for the performance assessment of a microscopy stage. The nanoGPS Oxyo technology can be used to turn any camera into an absolute position encoder. This can be useful for a variety of purposes, such as checking trajectories, or checking the repositioning capabilities, or checking cameras axis alignment are with machine movement axis, etc. These tasks can be performed either on a QA/QC basis (in this case the scale is placed during the QC operation, and removed after), or on a permanent basis (in this case the scale should be integrated to the machine).
Application of Virtual Reality Technology in College Physical Education Teaching and Training

Abstract With the continuous development of social science and technology, the application range of computers is wider and wider. The computer “virtual reality” technology is used in college sports training, which can enrich the content of physical education classrooms, update classroom teaching methods, and improve the quality and efficiency of physical education training. However, in the process of actual application, there are still some problems to be solved.
Toward a Scalable Learning Analytics Solution

Since its founding in 2011, Kidaptive has built customized models that provide adaptivity and/or personalization in online learning environments. We have supported adaptive game-based learning through rule-based and dynamic Bayesian psychometric models, and we have developed behavioral models for online learning and online test preparation environments based on learners’ time management, answer behavior, and test scores. Our models are deployed on a scalable distributed-computing platform that has supported millions of learners, but the human expertise required to build custom models for every learning environment is not scalable. To address this limitation, we have recently been working toward an abstracted version of our psychometric and behavioral models, to be provided as an “out-of-the-box” product offering. This paper describes insights and challenges encountered in this process.
Recursive Sketches for Modular Deep Learning

We present a mechanism to compute a sketch (succinct summary) of how a complex modular deep network processes its inputs. The sketch summarizes essential information about the inputs and outputs of the network and can be used to quickly identify key components and summary statistics of the inputs. Furthermore, the sketch is recursive and can be unrolled to identify sub-components of these components and so forth, capturing a potentially complicated DAG structure. These sketches erase gracefully; even if we erase a fraction of the sketch at random, the remainder still retains the `high-weight' information present in the original sketch. The sketches can also be organized in a repository to implicitly form a `knowledge graph'; it is possible to quickly retrieve sketches in the repository that are related to a sketch of interest; arranged in this fashion, the sketches can also be used to learn emerging concepts by looking for new clusters in sketch space. Finally, in the scenario where we want to learn a ground truth deep network, we show that augmenting input/output pairs with these sketches can theoretically make it easier to do so.
Faster Bootstrapping of FHE over the Integers

In FHE over the integers, decryption function is simplified by sparse subset subset sum problem (SSSP) assumption, which is introduced by Dijk et al. (Eurocrypt 2010), so that bootstrapping can be achieved successfully. Later, Nuida and Kurowasa (Eurocrypt 2015) proposed an advanced method of which the degree is very low and the message space is non-binary. These previous methods require low degree but more than $$O(\lambda ^4)$$ homomorphic multiplications which make them very slow. For a general bootstrapping method in FHE over the integers, the number of homomorphic multiplications and the degree of decryption function are important factors for the efficiency of bootstrapping procedure. In this paper, we propose a new bootstrapping method for FHE over the integers requiring only $$O(\log ^2 \lambda )$$ homomorphic multiplications which is significantly lower than previous methods. Implementing our bootstrapping method on the scale-invariant FHE over the integers called CLT scheme, it takes 6 s for 500-bit message space and 80-bit security on a desktop. We also apply our bootstrapping method to the homomorphic evaluation of AES-128 circuit: It takes about 8 s per 128-bit block and is faster than the previous results of homomorphic AES evaluation using FHEs over the integers without bootstrapping.
A Hybrid Approach for Recognizing Web Crawlers

In recent years, web crawlers have been widely used for collecting data from the Internet. Accurately recognizing web crawlers can help to better utilize friendly crawlers while stopping malicious ones. Existing web crawler recognition researches have difficulties in handling new crawlers, such as distributed crawlers, proxy based crawlers, and browser engine based crawlers. Moreover, it is non-trivial to achieve both high identification accuracy and high response time simultaneously. To tackle these issues, we propose a novel approach to web crawler recognition which combines real-time recognition methods based on heuristic rules and offline recognition methods based on machine learning. The aforementioned problems are well solved in this approach. The advantage of this approach is that both accuracy and efficiency are improved. We build a website and analyze its web access log using the proposed method. According to the results, the proposed approach achieves desirable performance in both accuracy and efficiency.
Universal Unconditional Verifiability in E-Voting without Trusted Parties

In e-voting protocols, cryptographers must balance usability with strong security guarantees, such as privacy and verifiability. In traditional e-voting protocols, privacy is often provided by a trusted authority that learns the votes and computes the tally. Some protocols replace the trusted authority by a set of authorities, and privacy is guaranteed if less than a threshold number of authorities are corrupt. For verifiability, stronger security is demanded. Typically, corrupt authorities that try to fake the tally result must always be detected.To provide verifiability, many e-voting protocols use Non-Interactive Zero-Knowledge proofs (NIZK). Thanks to their non-interactive nature, NIZK allow anybody, including third parties that do not participate in the protocol, to verify the correctness of the tally. Therefore, NIZK can be used to obtain universal verifiability. Additionally, NIZK also improve usability because they allow voters to cast a vote non-interactively.The disadvantage of NIZK is that their security is based on setup assumptions such as the common reference string (CRS) or the random oracle model. The former requires a trusted party to generate a CRS. The latter, though a popular model for secure protocol design, has been shown to be unsound.We address the design of e-voting protocols that provide verifiability without any trust assumptions. We show that Non-Interactive Witness-Indistinguishable proofs can be used for this purpose. Our e-voting protocols are private under the Decision Linear assumption, while perfect individual verifiability, i.e. a fake tally is detected with probability 1, holds unconditionally. Perfect universal verifiability requires a trusted public bulletin board. We remark that our definition of verifiability does not consider eligibility or end-to-end verifiability. First, we present a general construction that supports any tally function. Then, we show how to efficiently instantiate it for specific types of elections through Groth-Sahai proofs.
A Real-Time 17-Scale Object Detection Accelerator With Adaptive 2000-Stage Classification in 65 nm CMOS

Machine learning has become ubiquitous in applications including object detection, image/video classification, and natural language processing. While machine learning algorithms have been successfully used in many practical applications, accurate, fast, and low-power hardware implementations of such algorithms is still a challenging task, especially for mobile systems such as Internet of Things (IoT), autonomous vehicles, and smart drones. This paper presents an energy-efficient programmable ASIC accelerator for object detection. Our ASIC accelerator supports multi-class (e.g., face, traffic sign, car license plate, and pedestrian) that are programmable, many-object (up to 50) in one image with different sizes (17-scale support with 6 down-/11 up-scaling), and high accuracy (AP of 0.87/0.81/0.72/0.76 for FDDB/AFW/BTSD/Caltech datasets). We designed an integral channel detector with 2,000 classifiers for rigid boosted templates, where the number of stages used for classification can be adaptively controlled depending on the content of the search window. This can be implemented with a more modular hardware, compared to support vector machine (SVM) and deformable parts model (DPM) designs. By jointly optimizing the algorithm and the efficient hardware architecture, the prototype chip implemented in 65nm CMOS demonstrates real-time object detection of 20-50 frames/s with low power consumption of 22.5-181.7 mW (0.54-1.75 nJ/pixel) at 0.58-1.1 V supply.
Performance Modeling of the Consensus Mechanism in a Permissioned Blockchain

We consider a permissioned blockchain and analyze the dissemination and commitment processes of blocks among its corresponding miner nodes in the underlying peer-to-peer network. We propose a Markovian non-purging (n, k) fork-join queueing model to analyze the delay performance of the synchronization process among these miner nodes that apply a vote-based consensus procedure. We determine the impact of the most influential design and load parameters on the resulting commitment delay of new blocks that are appended to the blockchain after successful commitment decisions and the approval by the fully distributed consensus procedure. The proposed analysis of a permissioned blockchain is illustrated by means of a simple example of a fully interconnected P2P graph applying mean-value analysis techniques.
The Wave Theory of Sound

Acoustics is the science of sound, including its production, transmission, and effects.
Fingerprinting Big Data: The Case of KNN Graph Construction

We propose fingerprinting, a new technique that consists in constructing compact, fast-to-compute and privacy-preserving binary representations of datasets. We illustrate the effectiveness of our approach on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and show that fingerprinting can drastically accelerate a large range of existing KNN algorithms, while efficiently obfuscating the original data, with little to no overhead. Our extensive evaluation of the resulting approach (dubbed GoldFinger) on several realistic datasets shows that our approach delivers speedups of up to 78.9% compared to the use of raw data while only incurring a negligible to moderate loss in terms of KNN quality.
Methods and tools for extracting personal data from theses abstracts

The problem of extraction of data about a person from scarce data collection is studied. The data collections are public resources on the internet. When these data are collected and parsed they present additional value for users. Collecting such data is problematic because of it’s weak structure restrictions. Thus the system is suggested to automate information gathering and parsing. The initial task is to process personal data from thesis documents publicly available on the internet. This data presents information about scientists which can’t be obtained from other sources. The goal is to be able to make requests to the data having its semantics in mind and not only plain text. The prototype system is developed with PHP and XPath able to collect raw documents from digital repository of National Library of Ukraine by V. I. Vernadskiy. The system also extracts data from the collected documents and stores them locally in RDF data model suitable for specific data and for future exposition to the Semantic Web. The collection of more than 63000 documents was processed to test the system.
Multi-Hop Paragraph Retrieval for Open-Domain Question Answering

This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, respectively.
Synthetic Test Data Generation Using Recurrent Neural Networks: A Position Paper

Testing in production-like test environments is an essential part of quality assurance processes in many industries. Provisioning of such test environments, for information-intensive services, involves setting up databases that are rich-enough to enable simulating a wide variety of user scenarios. While production data is perhaps the gold-standard here, many organizations, particularly within the public sectors, are not allowed to use production data for testing purposes due to privacy concerns. The alternatives are to use anonymized data, or synthetically generated data. In this paper, we elaborate on these alternatives and compare them in an industrial context. Further we focus on synthetic data generation and investigate the use of recurrent neural networks for this purpose. In our preliminary experiments, we were able to generate representative and highly accurate data using a recurrent neural network. These results open new research questions that we discuss here, and plan to investigate in our future research.
Using an Exponential Random Graph Model to Recommend Academic Collaborators

Academic collaboration networks can be formed by grouping different faculty members into a single group. Grouping these faculty members together is a complex process that involves searching multiple web pages in order to collect and analyze information, and establishing new connections among prospective collaborators. A recommender system (RS) for academic collaborations can help reduce the time and effort required to establish a new collaboration. Content-based recommendation system make recommendations based on similarity without taking social context into consideration. Hybrid recommender systems can be used to combine similarity and social context. In this paper, we propose a weighting method that can be used to combine two or more social context factors in a recommendation engine that leverages an exponential random graph model (ERGM) based on historical network data. We demonstrate our approach using real data from collaborations with faculty members at the College of Computer and Information Sciences (CCIS) in Saudi Arabia. Our results demonstrate that weighting social context factors helps increase recommendation accuracy for new users.
YOLO: Speeding Up VM and Docker Boot Time by Reducing I/O Operations

Although this comes as a surprise, the time to boot a Docker-based container can last as long as a virtual machine in high consolidated cloud scenarios. Because this time is critical as boot duration defines how an application can react w.r.t. demands’ fluctuations (horizontal elasticity), we present in this paper the YOLO mechanism (You Only Load Once). YOLO reduces the number of I/O operations generated during a boot process by relying on a boot image abstraction, a subset of the VM/container image that contains data blocks necessary to complete the boot operation. Whenever a VM or a container is booted, YOLO intercepts all read accesses and serves them directly from the boot image, which has been locally stored on fast access storage devices (e.g., memory, SSD, etc.). In addition to YOLO, we show that another mechanism is required to ensure that files related to VM/container management systems remain in the cache of the host OS. Our results show that the use of these two techniques can speed up the boot duration 2–13 times for VMs and 2 times for containers. The benefit on containers is limited due to internal choices of the docker design. We underline that our proposal can be easily applied to other types of virtualization (e.g., Xen) and containerization because it does not require intrusive modifications on the virtualization/container management system nor the base image structure.
Sentimental text processing tool for Russian language based on machine learning algorithms

Several studies have been published to analyze different approaches to traditional text classification methods. Most of these studies cover the application of certain methods of semantic terminology to textual classification to a certain extent. However, they are not specifically aimed at the classification algorithms for semantic text and their advantages over the traditional text classification.
Online Burst Events Detection Oriented Real-Time Microblog Message Stream

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become... | Find, read and cite all the research you need on Tech Science Press
Marine Organisms Tracking and Recognizing Using YOLO

A system that investigates deep sea automatically has never developed. A purpose of this study is developing such a system. We employed a technique of recognition and tracking of multi-objects, called “You Only Look Once: YOLO.” This method provides us very fast and accurate tracker. In our system, we remove the haze, which is caused by turbidity of water, from image. After its process, we apply “YOLO” to tracking and recognizing the marine organisms, which includes shrimp, squid, crab, and shark. Our developed system shows generally satisfactory performance.
Non-trap Artificial Potential Field Based on Virtual Obstacle

This paper first describes the inherent problems of artificial potential field and analyzes their causes. Then, aiming at the problem of local minimum trap, the virtual obstacle method is proposed to repel mobile robot from traps. Next, in order to solve the chattering phenomenon, a geometric method is proposed to ensure that the route is relatively smooth. Finally, computer simulations are carried out to demonstrate the effectiveness of these two methods.
Are ICT good partners for the development of creativity A systematic review of literature

Creativity is considered one of the key skills of the 21st century. Likewise, information and communication technologies (ICT) have been identified as a constant presence in contemporary education, where there is the persistent challenge of developing creative thinking in children and young people who will live and perform in a highly globalised, interconnected, changing and uncertain social and cultural context. To better understand the relationship between the educational use of ICT and the development of creativity, a systematic literature review was carried out on 100 published studies with this subject. The results highlight on the one hand, the coexistence of three different conceptualisations on creativity and on the other hand, the identification of both contributions and barriers of ICT to the development of creative thinking that have more to do with cultural, attitudinal and organisational issues about their educational use than with the very nature of ICT.
A note on science, legal research and artificial intelligence

This paper discusses the principles of scientific research and in turn review legal research that was done using Artificial Intelligence arguing that it is the tools (Artificial Intelligence) that take center stage while the meaning (legal research) is left back stage. In turn, this kind of research does not adhere to the fundamentals of scientific research nor comply with scientific and industry ethical codes.
Density Peak clustering algorithm based on t-SNE Optimization

Aiming at the defect that the fast peak search clustering does not adapt to high-dimensional data sets, a T-DPC optimization algorithm is proposed. The algorithm is based on the t-SNE dimensionality reduction method, and also optimizes the calculation method of Gaussian kernel function, using a uniform metric when solving the density. Finally, the T-DPC algorithm and the DPC algorithm are compared in the artificial data set and the UCI standard data set, respectively, The experimental results show that the T-DPC algorithm not only adapts to the high dimensional dataset, but also improves the efficiency of the DPC algorithm.