Reviewerly/exHarmony huggingface dataset:
Playing First-Person-Shooter Games with A3C-Anticipator Network Based Agents Using Reinforcement Learning

Common built-in bots act upon pre-written scripts to make decisions and actions where sometimes they acquire and take advantage of unfair information, instead of acting flexibly like human players, who make decisions only based on game screens. This paper mainly focuses on studying the applications of deep learning and reinforcement learning in the field of computer game agents. The goal is to create agents that make decisions in human's way and gets rid of relying on unfair information. A game agent is implemented in line to the A3C algorithm. This agent takes the original real-time game screen as an input to the network and then outputs the corresponding discrete actions. The agent can interact with Viz and read the real-time game screen to make decisions for controlling the characters. This paper made an improvement to the A3C algorithm by adding an anticipator network to the original model structure. The goal of doing this is to make the agent act more like human players. It generates anticipation before making decisions, and then combines the real-time game screen with anticipation images together as an input to the network defined by the A3C algorithm. The result shows, that the A3C algorithm with Anticipation performs better than the original A3C algorithmd.
KnobCollector: Custom Device Controller for Dynamic Real-Time Subjective Data Collection in Virtual Reality

Real-time data collection in immersive virtual reality can be difficult due to the visual obstruction to the real-world. Furthermore, subjective data collection is conducted post-task, reducing the information of detailed user responses during a task therefore potentially missing critical events, subtle difference, or issues not obvious to a user. In this paper we present, KnobCollector, a custom device controller for dynamic real-time collection of subjective user data during an experimental. We conducted a user study with the KnobCollector to demonstrate the feasibility of use during a virtual reality task. Our results provide more rich data sets of user feedback during the virtual reality experience and could be used for future user studies in virtual reality environments.
AFF4-L: A Scalable Open Logical Evidence Container

With the proliferation of cloud-based evidence and locked down physical storage logical imaging is increasingly necessary in digital forensics. In practice closed formats are commonly used, however they lack extensibility and expressiveness, are poorly defined, and suffer from limited interoperability. This work proposes and implements an open logical imaging format based on the AFF4 evidence container, supporting scalable arbitrary metadata storage and deduplicated logical image storage.
A Comparative Study of CNN-Based Super-Resolution Methods in MRI Reconstruction

Super-resolution (SR) and MRI reconstruction are two hot topics in the field of computer vision and medical imaging, respectively. Both of them have been attacked by the recent deep learning methodology. This work aims to investigate some typical CNN-based SR algorithms and their applications in MRI reconstruction. By dividing the whole network in MRI reconstruction to be data-consistency sub-network and image prior sub-network, we investigate the reconstruction performance by utilizing various CNN-based SR networks. Experimental results demonstrate that the ResNetlike and DenseNet-like hybrid SR networks can obtain very significantly superior performance than current state-of the-art approaches.
Optimized Speculative Execution Strategy for Different Workload Levels in Heterogeneous Spark Cluster

Spark is a big data processing framework based on MapReduce, whose calculation model requires that all tasks in all parent stages are completed before starting a new stage. Machine service variability or congested network connections caused by partial or intermittent machine failures become a bottleneck for the Spark framework to execute tasks. In this paper, we focus on the design of speculative execution schemes for heterogeneous Spark from an optimization perspective on different loading conditions. First, we derive the load arrival rate threshold for different operating regimes. Second, for the lightly loaded case, we analyze and propose the speculative execution based on task-cloning algorithm (SETC) which reduce the application completion time by maximizing the overall system utility. Then, for the heavily loaded case, we propose the speculative execution based on straggler-detection algorithm(SESD), which aims to mitigate stragglers. Finally, we conduct experiments to verify the performance of SETC and SESD. Results show that our method is faster than Spark-Speculation, LATE, and SCA by16.7%, 8.2%, and 11.7%. Also it outperforms the baseline algorithms in some metric aspect such as the cluster throughput.
Designing Augmented Reality Navigation Visualizations for the Vehicle: A Question of Real World Object Coverage?

Augmented reality head-up displays are emerging in automotive industry. While they have obvious advantages by showing needed information directly within the field of vision and at the exact location, problems as overlaying real world objects could occur that might lead to driver inattention. We introduce relevant design factors for augmented reality navigation visualizations and present a driving simulator study, where different augmented reality navigation designs were evaluated and the degree of coverage was varied. Performance criteria as well as subjective measurements were recorded. We found differences between the designs concerning their subjective acceptance and their navigation performance, but none of those differences could solely be explained by their degree of coverage. Future work is needed to determine effects in terms of coverage.
Investigating the use of linear programming to solve implicit symbolic regression problems

Approaches that attempt to combine feature selection and symbolic feature evolution processes have gained popularity in the recent years to solve symbolic regression problems. However, much of the existing research is applicable/has been applied to solve explicit equations (y = f (x)) only, which cannot be directly applied to discover implicit equations (f (x) = 0). In this paper, we investigate a potential approach that uses Linear Programming to construct meaningful implicit equations out of a set of evolving features.
In the Era of Cybersecurity: Cryptographic Hardware and Embedded Systems

Cybersecurity is a completely new concept, which has attracted the major interest of both academia and industry. New technologies, such as Internet of Things, have shown special interest for cybersecurity systems, in order to fruitful the special demands, of confidentially, authorization, and integrity, for sensitive and personal data. In the cybersecurity era, critical hardware based technologies are latest applied, in order to support efficiently crucial security applications and embedded devices. Filling the security gaps of present and future, for technologies such as IoT, 5G etc, are consider as targets of major importance. Security tokens, privacy services, approaches such as smart cards, and trusted platforms modules, are also focused. Systems' vulnerabilities, are well as security analysis and possible attacks, are considered of major importance, in the cybersecurity era. Cryptographic hardware and embedded systems, are proven powerful and trustworthy solutions in terms of implementation efficiency: timing, throughput, allocated resources, power, energy, always in balance with the security level, each time. Alternative hardware devices and frameworks, can be used alternatively, in order to achieve the best implementation parameters each time.
Analysis of clustering algorithms for group discovery in a web-based intelligent tutoring system

DITUS is a web-based intelligent tutoring system developed and used at our institution as an additional learning platform. To make the system even more adaptive we expanded its architecture with several modules that perform educational data mining tasks such as clustering, to discover groups of students that use the system in a similar manner, and high-utility sequential pattern mining to discover efficient learning paths through the knowledge domain. The results of these modules enable the system to offer hints to students on which knowledge units to learn before or after the currently selected unit. One of the main pre-conditions of the quality of hints is the clustering phase in which we discover groups of students that are using the system in a similar manner in terms of learning activity and efficiency. In this paper, we analyze the results of several well-known clustering algorithms on our datasets, to determine which one is best suited for the needs of our system.
The past, the present, and the future of fitness tracking

The gym of the future will be the quintessence of truly invisible ubiquitous technology that automatically recognizes and tracks the workout progress of each person
Simplified model to correct thermopile pyranometer solar radiation measurements for photovoltaic module yield estimation

This article presents and evaluates the performance of a simplified model to generate 10-min global horizontal synthetic solar radiation data that would correspond to the measurements of a calibrated photovoltaic monocrystalline cell. The model, which only requires global horizontal solar radiation data measured with a thermopile pyranometer as input, is based on the characterization of the relation between the data measured with a thermopile pyranometer and a calibrated cell as a function of the sky condition and the solar elevation. We have used an extensive solar radiation database for the location of Seville (Spain) for the training of the model that has been tested in Seville and Lancaster (USA), showing satisfactory results and suggesting a global applicability with no local adaptation or calibration requirement. The model shows the best results for high levels of solar radiation and solar elevations and decreases its performance on days with high levels of diffuse irradiation and for very low solar elevation angles. We obtain a daily RMSD between measured and synthetic data of 1.9% in Seville and 5.2% in Lancaster. The frequency distribution of the synthetic datasets shows a KSI of 3.7 W/m2 in Seville and 8.6 W/m2 in Lancaster. We also evaluate the ramp rates of measured and synthetic sets through the KSI of the measured and synthetic ramp rates sets, obtaining 0.11 W/m2۰min in Seville and 0.20 W/m2۰min in Lancaster.
A Verifiable Encryption Scheme Supporting Fuzzy Search

Searchable encryption supports retrieval over encrypted data while protecting data privacy. How to prevent non-trusted cloud service providers from performing partial retrieval operations or tampering with or deleting some encrypted data in order to save computing resources and bandwidth, thereby returning partial or erroneous search results. Therefore, we proposed a verifiable encryption scheme supporting fuzzy Search (VESFS). Through principle of matrix operations, VESFS realizes the fuzzy retrieval over encrypted data, and enables the user to verify the correctness of the retrieval results efficiently by applying the RSA accumulator and the proposed challenge response-based search verification method. The security analysis proves that VESFS provides privacy security and verifiable security; the performance analysis and experimental results show that VESFS is effective and feasible.
Grouping the districts in Indonesia based on value of science subjects of National Exam using K-Means clustering method

Abstract Implementation of the National Examination (NE) for senior high schools for each district in Indonesia is done as one way to mapping the success of education in each the district. Mapping can be done by grouping districts based on attribute values obtained on the implementation of the NE, which in this study using the K-means clustering method. The attribute of NE values used are the values for the science majors, which consist of 6 subjects, where from 514 districts only 510 districts own it. The purpose of the study in addition to grouping districts, will also predict the group of 4 districts that have no NE values. Prediction is done by entering the value of the district Human Development Index (HDI) that has no UN value into 95% confidence interval of HDI from groups formed from k-means clustering. Once known where the district is in a group based on the HDI group then the value of its UN will follow the confidence interval of the UN value of the group. The results of this study indicate that the number of clusters (k) selected to classify districts in Indonesia as many as 5 clusters. Group prediction results show that one district belongs to the V cluster, while the other three districts are predicted to be members of the I cluster.
LUIM: New Low-Utility Itemset Mining Framework

High-utility itemset mining (HUIM), which is the detection of high-utility itemsets (HUIs) in a transactional database, provides the decision maker with greater flexibility to exploit item utilities, such as quantity and profits, to extract remarkable and efficient database patterns. However, most prevailing empirical articles have focused on HUIs. Nevertheless, in many practical situations, low-utility itemsets (LUIs) maintain a high level of significance and usage (e.g., in security systems and the low-utility itemsets represent the security system vulnerabilities that need monitoring). Hence, this paper proposes a new association rule mining (ARM) framework named low-utility itemset mining (LUIM) that extracts LUIs. Enhancing the performance of LUIM, the researchers here propose innovative HUI generators, determining the generators based on the itemset transaction weight utility (TWU) rather than the support values used in HUG-Miner and GHUI-Miner. Moreover, this paper offers two efficient algorithms called LUG-Miner and LUIMA. The LUG-Miner extracts high and low-utility generators while LUIMA extracts low-utility itemsets using low-utility generators (LUGs). The experimental results on both dense and sparse datasets illuminated the recommended framework, and the algorithms are efficiently operational.
Multiparty Interactive Communication with Private Channels

A group of n players wants to run a distributed protocol ℘ over a network where communication occurs via private point-to-point channels. Can we efficiently simulate ℘ in the presence of an adversary who knows ℘ and is able to maliciously flip bits on the channels? We show that this is possible, even when L, the number of bits sent in ℘, the average message size α in ℘, and T, the number of bits flipped by the adversary are not known in advance. In particular, we show how to create a robust version of ℘, ℘ such that 1) ℘' fails with probability at most δ, for any δ>0; and 2) ℘' sends O( L (1 + (1/α) łog (n L/δ)) + T) bits. We note that if α is Ω (log (n L/δ), then ℘ sends only O(L+T) bits, and is therefore within a constant factor of optimal. Critically, our result requires that ℘ runs correctly in an asynchronous network and our protocol ℘ must run in a synchronous network.
Immersive game for dental anesthesia training with haptic feedback

Training anesthetic application is a challenge for teaching dentistry, given the complexity of the procedure and the risks involved. Through an immersive virtual reality environment, the system presented here offers a playful way of learning, through game elements. This serious game allows the user to practice anesthesia technique with or without aids (tactile or visual) and to receive scores at different levels of difficulty. An important differential is the possibility of the syringe being driven automatically (tactile aid) in order to reproduce trajectories inserted by experienced instructors. Thus, the student can feel as if the instructor was conducting the learner's hand while guiding her or him.
Towards a Personalized Adaptive Remedial e-Learning Model

Recently, demand for database programming specialists has greatly increased in Kenya. These professionals play a key role in the computing and software development industries. Although database programming skills are key fundamentals for learners in computing disciplines, skills mastery by students is still not easy. For these reasons, this study establishes an adaptive remedial learning model to assist learners in their quest of gaining skills online. The proposed solution adopts the use of fuzzy logic theory to create an appropriate learning path based on the learners' prior concepts miscomprehensions. This technique selects a suitable remedial materials for learners after constructing a learning path based on the learners' preference. After evaluation of the model through conducting several experiments, it is proposed that it can be used to offer a comprehensive and stable remedial learning environment for any LMS. Analysis of the model by learners confirm that it has achieved the effects of remedial and adaptive learning.
SPOC Blended Teaching of Computer Fundamental Course

With the rapid progress of technology, the contents and requirements of computer fundamental course teaching are increasing year by year, while at the same time there are problems caused by the contradiction of the limited class hours and the varied levels of the starting points of the students. In order to improve the teaching level and effectiveness of computer fundamental course, we took advantages of SPOC (Small Private Online Courses) blended advanced teaching idea, combined the teaching concepts with teaching practice closely, and explored on reasonable application and implementation of SPOC model. The main measures included reconstruction of the structure and organization of teaching contents according to professional requirements as well as the needs of students, offering an individualized quality education for every student, online and offline learning and practice in which the students acting as the main body, fully development of student-student and teacher-student interaction. We also tried to solve puzzles from students in time, to explore the potential of students, and to promote their creative ideas and works. Practice showed that by using SPOC blended mode in the computer fundamental course teaching, the students' initiative was stimulated, the performance of course examination was improved obviously, and the learners performed better in extracurricular activities compared with control groups. The experience of the SPOC blended teaching mode reform has been proved to be a good help to promote the quality of engineering teaching and improve the students' ability of information technology.
Investigation of Random Neighborhood Features for Interpretation of MLP Classification Results

In this paper, we propose an investigation of the random neighborhood features and their classification results in order to get explanations for a particular decision and increase the interpretability of the black box model. It is shown (by examples from the known iris and Wisconsin breast cancer datasets), that even if outputs of neural network model seem to be confident, it doesn’t necessarily mean that decision is stable enough. Investigation of statistic properties of decisions near the input feature vector shows the importance of some features over others. After that, we build random forest (as a method with a high level of interpretability) on a set of generated neighbor input vectors. Investigation of separate nodes of solution routes in each decision tree independently shows that it is possible to find counterfactuals of feature vectors, classified by a model as a different class. As a result, the human gets not only the explanation of current classification but also examples of feature vectors representing other classes, that differs from initial input feature vector just with particular features keeping these changes as low as possible.
Collective Named Entity Recognition in User Comments via Parameterized Label Propagation

Named entity recognition (NER) in the past has focused on extracting mentions in a local region, within a sentence or short paragraph. When dealing with user‐generated text, the diverse and informal writing style makes traditional approaches much less effective. On the other hand, in many types of text on social media such as user comments, tweets, or question–answer posts, the contextual connections between documents do exist. Examples include posts in a thread discussing the same topic, tweets that share a hashtag about the same entity. Our idea in this work is utilizing the related contexts across documents to perform mention recognition in a collective manner. Intuitively, within a mention coreference graph, the labels of mentions are expected to propagate from more confidence cases to less confidence ones. To this end, we propose a novel semisupervised inference algorithm named parameterized label propagation . In our model, the propagation weights between mentions are learned by an attention‐like mechanism, given their local contexts and the initial labels as input. We study the performance of our approach in the Yahoo! News data set, where comments and articles within a thread share similar context. The results show that our model significantly outperforms all other noncollective NER baselines.
Rotation and Scale-Invariant Object Detector for High Resolution Optical Remote Sensing Images

Object detection of high-resolution optical remote sensing images is challenging due to two fundamental problems. One is the huge scale variation of objects in images, e.g., small vehicle and cross-sea bridge. The other one is the objects could take on arbitrary orientations because of the high angle shot. In this paper, we propose a Rotation and Scale-invariant Detector (RS-Det) for remote sensing images to solve the above problem in an unified network. Specifically, RS-Det consists of a deformable convolution module to learn spatial transformation (such as rotation, transition, etc) and a feature pyramid architecture for multi-scale feature representation. These two modules enable a better feature learning of convolutional neural network and boost the performance by 3.6% compared with the baseline method. In DOTA, a large-scale dataset for aerial image object detection, our RS-Det achieves the state-of-the-art accuracy, which verifies our method’s superiority.
Totally Looks Like - How Humans Compare, Compared to Machines

Perceptual judgment of image similarity by humans relies on rich internal representations ranging from low-level features to high-level concepts, scene properties and even cultural associations. However, existing methods and datasets attempting to explain perceived similarity use stimuli which arguably do not cover the full breadth of factors that affect human similarity judgments, even those geared toward this goal. We introduce a new dataset dubbed Totally-Looks-Like (TLL) after a popular entertainment website, which contains images paired by humans as being visually similar. The dataset contains 6016 image-pairs from the wild, shedding light upon a rich and diverse set of criteria employed by human beings. We conduct experiments to try to reproduce the pairings via features extracted from state-of-the-art deep convolutional neural networks, as well as additional human experiments to verify the consistency of the collected data. Though we create conditions to artificially make the matching task increasingly easier, we show that machine-extracted representations perform very poorly in terms of reproducing the matching selected by humans. We discuss and analyze these results, suggesting future directions for improvement of learned image representations.