Reviewerly/exHarmony huggingface dataset:
Gesture Recognition Based on Kinect v2 and Leap Motion Data Fusion

This study proposed a method for multiple motion-sensitive devices (i.e. one Kinect v2 and two Leap Motions) to integrate gesture data in Unity. Other depth cameras could replace the Kinect. The general steps in integrating gesture data for motion-sensitive devices were introduced as follows. (1) A method was proposed to recognize the fingertip from depth images for the Kinect v2. (2) Coordinates observed by three motion-sensitive devices were aligned in space in three steps. First, preliminary coordinate conversion parameters were obtained through joint calibration of the three devices. Second, two types of devices were approached to the observed value of the standard Leap Motion by the least squares method twice (i.e. one Kinect and one Leap Motion on the first round, then two Leap Motions on the second round). (3) Data of the three devices were aligned with time by using Unity while applying the data plan. On this basis, a human hand interacted with a virtual object in Unity. Experimental results demonstrated that the proposed method had a small recognition error of hand joints and realized the natural interaction between the human hand and virtual objects.
Toward a Unified Executable Formal Automobile OS Kernel and Its Applications

In automobile industry, it is a common approach to develop automobile real-time operating systems under some standards. For instance, OSEK/VDX is a world-wide adopted open standard. Traditional workflow is to first understand the standard, design and develop a system, then test its conformance to the standard, and finally deploy. There are several issues with the traditional workflow, e.g., ambiguities in standards may lead to incorrect design and implementation of real-world systems; the conformance of real-world systems to standards is difficult to check; and bug fixing after implementation is costly. To remedy the situation, in this paper, we present a unified executable formal automobile kernel under OSEK/VDX standard by defining the operational semantics of the system services in the standard using a rewrite-based executable semantic framework called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {K}$</tex-math></inline-formula> . The formal kernel is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unified</i> in that it serves multiple purposes such as: 1) formal modeling of the OSEK/VDX standard helps detect ambiguities in the standard; 2) the executable kernel is essentially a formal model of the standard, which can be used to verify the correctness of automobile applications; and 3) verified applications can be used as test cases to check the conformance of a real-world automobile operating system against the OSEK/VDX standard. Using the formal kernel, we identify several ambiguities in the OSEK/VDX standard and a potential deadlock vulnerability in an industrial automobile application.
From Recommendation Systems to Facility Location Games

Recommendation systems are extremely popular tools for matching users and contents. However, when content providers are strategic, the basic principle of matching users to the closest content, where both users and contents are modeled as points in some semantic space, may yield low social welfare. This is due to the fact that content providers are strategic and optimize their offered content to be recommended to as many users as possible. Motivated by modern applications, we propose the widely studied framework of facility location games to study recommendation systems with strategic content providers. Our conceptual contribution is the introduction of a mediator to facility location models, in the pursuit of better social welfare. We aim at designing mediators that a) induce a game with high social welfare in equilibrium, and b) intervene as little as possible. In service of the latter, we introduce the notion of intervention cost, which quantifies how much damage a mediator may cause to the social welfare when an off-equilibrium profile is adopted. As a case study in high-welfare low-intervention mediator design, we consider the one-dimensional segment as the user domain. We propose a mediator that implements the socially optimal strategy profile as the unique equilibrium profile, and show a tight bound on its intervention cost. Ultimately, we consider some extensions, and highlight open questions for the general agenda.
Perceptions of Visualizing Physical Activity as a 3D-Printed Object: Formative Study

Background: The UK government recommends that children engage in moderate-to-vigorous physical activity for at least 60 min every day. Despite associated physiological and psychosocial benefits of physical activity, many youth fail to meet these guidelines partly due to sedentary screen-based pursuits displacing active behaviors. However, technological advances such as 3D printing have enabled innovative methods of visualizing and conceptualizing physical activity as a tangible output.
Quantum Metalanguage and The New Cognitive Synthesis

Consideration of artificial intelligence in this regard is fundamentally important, as in this case we are dealing with systems, the structures of which we know.In regard to human thinking, the situation is different -we do not know exactly which structures store information and how such information is processed.This has given rise to the most important problem of what is innate and what is not.Problems of cognition and thinking are at the intersection of different sciences (see for example, Miller, 2003) (Fig. 1).However, the synthesis of these sciences in relation to problems of cognition has not yet taken place.Scientists of various cognitive sciences such as linguistics, epistemology, the philosophy of science, artificial intelligence, etc. do not often refer to one another's work.In particular, there is no single
Quantum Annealing Machines Based on Semiconductor Nanostructures

The development of quantum annealing machines (QAMs) based on superconducting qubits has progressed considerably in recent years and these machines are now widely used in both academia and commerce. On the other hand, QAMs based on semiconductor nanostructures such as quantum dots (QDs) appear to be still at the initial elementary research stage because of the difficulty in controlling the interaction between qubits. In this paper, we review a QAM based on semiconductor nanostructures such as floating gates (FGs) or QDs from the viewpoint of qubit integration. We theoretically propose the use of conventional high-density memories such as NAND flash memories for the QAM rather than the construction of a semiconductor qubit system from scratch. A large qubit system will be obtainable as a natural extension of the miniaturization of commercial-grade electronics, although further effort will likely be required to achieve high-quality qubits.
Advertisement recommendation based on personal interests and ad push fairness

Purpose Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required. Design/methodology/approach The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad’s exposure time into consideration and investigate users’ three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website. Findings The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach. Originality/value Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.
Towards Instance-Optimal Private Query Release

We study efficient mechanisms for the query release problem in differential privacy: given a workload of m statistical queries, output approximate answers to the queries while satisfying the constraints of differential privacy. In particular, we are interested in mechanisms that optimally adapt to the given workload. Building on the projection mechanism of Nikolov, Talwar, and Zhang, and using the ideas behind Dudley's chaining inequality, we propose new efficient algorithms for the query release problem, and prove that they achieve optimal sample complexity for the given workload (up to constant factors, in certain parameter regimes) with respect to the class of mechanisms that satisfy concentrated differential privacy. We also give variants of our algorithms that satisfy local differential privacy, and prove that they also achieve optimal sample complexity among all local sequentially interactive private mechanisms.
Familial monads and structural operational semantics

We propose a categorical framework for structural operational semantics, in which we prove that under suitable hypotheses bisimilarity is a congruence. We then refine the framework to prove soundness of bisimulation up to context, an efficient method for reducing the size of bisimulation relations. Finally, we demonstrate the flexibility of our approach by reproving known results in three variants of the π-calculus.
A high precision data encryption algorithm in wireless network mobile communication

At present, the MD5 based data encryption algorithm for wireless network mobile communication cannot effectively detect the intrusion data in the mobile communication. Redundant data is not removed, the efficiency of data encryption is low, and the overall communication security is poor. In this paper, a MDEA based data encryption algorithm for wireless network mobile communication is proposed. By applying normalization of communication data to DBN model, using the way of changing one parameter while keeping others, the optimal DBN detection model is built to achieve high-precision detection of intrusion data. Using the signal intensity at different times, the speed and process time of the data level movements are estimated. By estimating the results, the redundant data and inappropriate data are removed, and performed the MDEA operation based on the secret data, introduced random numbers and timestamps to prevent the foreign infiltrations. Experiments show that the algorithm can not only improve the detection quality of intrusion data, but also enhance the cleaning effect of redundant data and in the communication, and enhance data security.
Reengineering framework for open source software using decision tree approach

A Software engineering is an approach to software development. Once software gets developed and delivered, it needs maintenance. Changes in software incur due to new requirements of the end-user, identification of bug in software or failure to achieve system objective. It has been observed that successive maintenance in the developed software reduces software quality and degrades the performance of software system. Reengineering is an approach of retaining the software quality and improving maintainability of the software system. But the question arises “when to reengineer the software”. The paper proposed a framework for software reengineering process using decision tree approach which helps decision makers to decide whether to maintain or reengineer the software systems.
Heterogeneous multimedia cooperative annotation based on multimodal correlation learning

Rich multimedia contents are dominating the current Web. In popular social media platforms such as FaceBook, Twitter, and Instagram, there are over millions of multimedia contents being created by users. In the meantime, multimedia data consists of data in multiple modalities, such as text, images, videos, audio, time series sequences, and so on. Many research efforts have been devoted to multimedia annotation to further improve the performance. However, the prevailing methods are designed for single-media annotation task. In fact, heterogeneous media content describes given labels from respective modality and is complementary to each other, and it becomes critical to explore advanced techniques for heterogeneous data analysis and multimedia annotation. Inspired by this idea, this paper presents a new multimodal correlation learning method for heterogeneous multimedia cooperative annotation, named unified space learning, which projects heterogeneous media data into one unified space. We formulate the multimedia annotation task into a semi-supervised learning framework, in which we learn different projection matrices for different media type. By doing so, different media content is aligned cooperatively, and jointly provides a more complementary profile of given semantic labels. Experimental results on data set with images, audio clips, videos and 3D models show that the proposed approach is more effective.
IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs

This paper introduces the IPOMDP-net, a neural network architecture for multi-agent planning under partial observability. It embeds an interactive partially observable Markov decision process (I-POMDP) model and a QMDP planning algorithm that solves the model in a neural network architecture. The IPOMDP-net is fully differentiable and allows for end-to-end training. In the learning phase, we train an IPOMDP-net on various fixed and randomly generated environments in a reinforcement learning setting, assuming observable reinforcements and unknown (randomly initialized) model functions. In the planning phase, we test the trained network on new, unseen variants of the environments under the planning setting, using the trained model to plan without reinforcements. Empirical results show that our model-based IPOMDP-net outperforms the other state-of-the-art modelfree network and generalizes better to larger, unseen environments. Our approach provides a general neural computing architecture for multi-agent planning using I-POMDPs. It suggests that, in a multi-agent setting, having a model of other agents benefits our decision-making, resulting in a policy of higher quality and better generalizability.
Enriching Non-Parametric Bidirectional Search Algorithms

NBS is a non-parametric bidirectional search algorithm proven to expand at most twice the number of node expansions required to verify the optimality of a solution. We introduce new variants of NBS that are aimed at finding all optimal solutions. We then introduce an algorithmic framework that includes NBS as a special case. Finally, we introduce DVCBS, a new algorithm in this framework that aims to further reduce the number of expansions. Unlike NBS, DVCBS does not have any worst-case bound guarantees, but in practice it outperforms NBS in verifying the optimality of solutions.
On Limited Conjunctions and Partial Features in Parameter-Tractable Feature Logics

Standard reasoning problems are complete for EXPTIME in common feature-based description logics—ones in which all roles are restricted to being functions. We show how to control conjunctions on left-hand-sides of subsumptions and use this restriction to develop a parameter-tractable algorithm for reasoning about knowledge base consistency. We then show how the resulting logic can simulate partial features, and present algorithms for efficient query answering in that setting.
Dimension-Free Error Bounds from Random Projections

Learning from high dimensional data is challenging in general – however, often the data is not truly high dimensional in the sense that it may have some hidden low complexity geometry. We give new, user-friendly PAC-bounds that are able to take advantage of such benign geometry to reduce dimensional-dependence of error-guarantees in settings where such dependence is known to be essential in general. This is achieved by employing random projection as an analytic tool, and exploiting its structure-preserving compression ability. We introduce an auxiliary function class that operates on reduced dimensional inputs, and a new complexity term, as the distortion of the loss under random projections. The latter is a hypothesis-dependent data-complexity, whose analytic estimates turn out to recover various regularisation schemes in parametric models, and a notion of intrinsic dimension, as quantified by the Gaussian width of the input support in the case of the nearest neighbour rule. If there is benign geometry present, then the bounds become tighter, otherwise they recover the original dimension-dependent bounds.
testforDEP: An R Package for Modern Distribution-free Tests and Visualization Tools for Independence

This article introduces testforDEP, a portmanteau R package implementing for the first time several modern tests and visualization tools for independence between two variables.While classical tests for independence are in the base R packages, there have been several recently developed tests for independence that are not available in R.This new package combines the classical tests including Pearson's product moment correlation coefficient method, Kendall's τ rank correlation coefficient method and Spearman's ρ rank correlation coefficient method with modern tests consisting of an empirical likelihood based test, a density-based empirical likelihood ratio test, Kallenberg datadriven test, maximal information coefficient test, Hoeffding's independence test and the continuous analysis of variance test.For two input vectors of observations, the function testforDEP provides a common interface for each of the tests and returns test statistics, corresponding p values and bootstrap confidence intervals as output.The function AUK provides an interface to visualize Kendall plots and computes the area under the Kendall plot similar to computing the area under a receiver operating characteristic (ROC) curve.
Analysis of UAV multicopter of air photography in New Yogyakarta International Airports

The higher the quality of the drone, the longer the drone will fly and the better the quality of the drone's photography. Survey of research location in Glagah Indah Beach, preparation of drone at ground, we plan the height of flying drones, then testing drone at ground, we measure camera calibration, and then result capture in the air and images in the air. Vehicle specifications are as follows: Frame: F450; Flight Controller: DJI Naza M-Lite; Propeller: 1045 Prop; motorbike: brushless sunnsky 980 kVa; ESC: Skywalker 40 Ampere 3s; Battery: Ace 3s Gens 5000mAH; Remote: Turnigy 9XR with Frsky Tanseiver; and camera: Xiaomi Yi 4k International edition.This drone type multicopter can penetrate the high of 100 meters to 200 meters and can air for 30 minutes, can take an area of up to 1 km while payload drones multicopter is 2.8 kg.This multicopter drone has a 12 megapixel sensor; maximum flight time of 15 minutes; speed of 20 m/s, maximum take-up speed of 6 m/s, maximum landing speed of 4m/s, temperature range when operating drone 0o to 40oC and maximum image size of 4000x3000.
Distribution-Based Semi-Supervised Learning for Activity Recognition

Supervised learning methods have been widely applied to activity recognition. The prevalent success of existing methods, however, has two crucial prerequisites: proper feature extraction and sufficient labeled training data. The former is important to differentiate activities, while the latter is crucial to build a precise learning model. These two prerequisites have become bottlenecks to make existing methods more practical. Most existing feature extraction methods highly depend on domain knowledge, while labeled data requires intensive human annotation effort. Therefore, in this paper, we propose a novel method, named Distribution-based Semi-Supervised Learning, to tackle the aforementioned limitations. The proposed method is capable of automatically extracting powerful features with no domain knowledge required, meanwhile, alleviating the heavy annotation effort through semi-supervised learning. Specifically, we treat data stream of sensor readings received in a period as a distribution, and map all training distributions, including labeled and unlabeled, into a reproducing kernel Hilbert space (RKHS) using the kernel mean embedding technique. The RKHS is further altered by exploiting the underlying geometry structure of the unlabeled distributions. Finally, in the altered RKHS, a classifier is trained with the labeled distributions. We conduct extensive experiments on three public datasets to verify the effectiveness of our method compared with state-of-the-art baselines.
MemWander: Memory Dynamic Remapping via Hypervisor Against Cache-Based Side-Channel Attacks

Current countermeasures against last level cache (LLC) based non-memory-sharing side-channel attacks (LNSA), which is a powerful and practical cache attack in the cloud, fail in practical due to their shortage of generality or efficiency. Motivated by random cache access, we propose a novel defense method called dynamic remapping which dynamically changes mapping relationship from virtual memory to cache at hypervisor layer. It aims to confuse the attacker about the relationship between observed cache activities and values of demanded secret. In order to guarantee scalability and deployability, we formalize the problem of remapping, and design a sequential classified selection algorithm to decide how to remap all those protected memory. Furthermore, we implement MemWander, a prototype system integrated in Xen and OpenStack which are popular cloud settings. And its security improvement and performance overhead are evaluated on various applications and an apache server as a simulated cloud service. The experimental results show that MemWander not only provides enough security guarantees for general services in cloud, but also induces low performance overhead as no more than 7%, which is tolerable in most scenarios.