Reviewerly/exHarmony huggingface dataset:
Human Action Recognition Using Median Background and Max Pool Convolution with Nearest Neighbor

Discovering human activities on mobile devices is a challenging task for human action recognition. The ability of a device to recognize its user's activity is important because it enables context-aware applications and behavior. Recently, machine learning algorithms have been increasingly used for human action recognition. During the past few years, principal component analysis and support vector machines is widely used for robust human activity recognition. However, with global dynamic tendency and complex tasks involved, this robust human activity recognition (HAR) results in error and complexity. To deal with this problem, a machine learning algorithm is proposed and explores its application on HAR. In this article, a Max Pool Convolution Neural Network based on Nearest Neighbor (MPCNN-NN) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics. The MPCNN-NN framework for HAR consists of three steps. In the first step, for each activity, the features of interest or foreground frame are detected using Median Background Subtraction. The second step consists of organizing the features (i.e. postures) that represent the strongest generic discriminating features (i.e. postures) based on Max Pool. The third and the final step is the HAR based on Nearest Neighbor that postures which maximizes the probability. Experiments have been conducted to demonstrate the superiority of the proposed MPCNN-NN framework on human action dataset, KARD (Kinect Activity Recognition Dataset).
Dark Web Content Analysis and Visualization

The Dark Web, which is a vast array of encrypted online content and websites that can be only be accessed by the use of anonymizing tools such as The Onion Router (Tor), is currently a topic of serious concern because it has become a growth area for clandestine entities catering to forbidden activities and services such as illegal drugs and weapons, child pornography, sensitive information such as stolen credit card information, distributed denial of services (DDoS) tools such as Booter, and other antisocial activities. As a result, it is now being investigated by researchers, law enforcement agencies, security companies, and academic institutions around the world. However, there is not an efficient investigation method and it takes a lot of time. In an effort to gain a better understanding of the Dark Web, we analyzed a large amount of Onion domain name data obtained from using the "Ichidan" search engine and the "Fresh Onions" open source Tor. More specifically, we collected everything we could gather into an acquired Onion domain and then downloaded the top page. The Onion domain was then classified into six categories, including classification not possible, and a new domain was created from the downloaded top page text. Additionally, we implemented a simulator that is a directed graph created from the uncovered hyperlinks and connection states to the Dark Web were achieved with simplicity. We then attempted to determine the relationships and characteristics of each instance Dark Web content by reflecting on the graphed classification results.
Cybersecurity Awareness and Skills of Senior Citizens: A Motivation Perspective

Senior citizens are one of the most vulnerable groups of Internet users who are prone to cyberattacks. Thus, assessing senior citizens’ motivation to acquire cybersecurity skills is critical to help them understand the risks of cyber-attacks. This study investigated a set of constructs that contribute to senior citizens’ motivation to acquire cybersecurity skills and assessed the actual cybersecurity skill level of these individuals. Utilizing a Web-based survey and a hands-on scenario-based app called MyCyberSkills™, this research study measured the constructs of interest before and after cybersecurity awareness training. Study participants were 254 senior citizens with a mean age of approximately 70 years. The results indicated that the cybersecurity awareness training was effective in increasing the cybersecurity skill level of the senior citizens and empowered them with small but significant improvements in the requisite skills to take mitigating actions against cyberattacks. Theoretical and practical implications are discussed.
Deep Learning Algorithm using Virtual Environment Data for Self-driving Car

Recent outstanding progresses in artificial intelligence researches enable many tries to implement self-driving cars. However, in real world, there are a lot of risks and cost problems to acquire training data for self-driving artificial intelligence algorithms. This paper proposes an algorithm to collect training data from a driving game, which has quite similar environment to the real world. In the data collection scheme, the proposed algorithm gathers both driving game screen image and control key value. We employ the collected data from virtual game environment to learn a deep neural network. Experimental result for applying the virtual driving game data to drive real world children's car show the effectiveness of the proposed algorithm.
Joint Deep and Depth for Object-Level Segmentation and Stereo Tracking in Crowds

Tracking multiple people in crowds is a fundamental and essential task in the multimedia field. It is often hindered by difficulties, such as dynamic occlusion between objects, cluttered background, and abrupt illumination changes. To respond to this need, in this paper, we combine deep and depth to build a stereo tracking system for crowds. The core of the system is the fusion of the advantages of deep learning and depth information, which is exploited to achieve object segmentation and improve the multiobject tracking performance in severe occlusion. More specifically, first, to obtain more accurate detection observations in the tracking system, we present a novel object-level segmentation method. This method combines the effective detection results of deep learning with depth information to obtain precise object segmentation results. Then, we integrate the segmentation results and three-dimensional (3-D) information to extract 2-D and 3-D characteristics to represent the target, and design three similarity models to realize a stereo tracking method through data association in crowds. Finally, we build a diverse stereo dataset including various challenging indoor and outdoor scenes. The comprehensive experiments verify the effective and robust tracking performance of our system in various scenarios, and the system has rich output results including segmentation results, target distance, and tracking results. Moreover, the qualitative and quantitative comparison results show that the proposed algorithm not only has good object segmentation performance but also improves the tracking performance of completely and partially occluded objects, which is superior to the tested state-of-the-art tracking approaches.
Smart technologies in education and formation of entrepreneurial competencies

Currently, the universities role in Russia is seriously changing: universities call up to become the locomotives of economic development in the region, using their potential in education and science. Special results are expected in process development through the commercialization of know-how created in higher educational institutions. The commercialization includes the entrepreneurship promotion. In order to increase competitiveness and fulfill a special mission, many Russian universities set such ambitious goals for themselves. In an entrepreneurial university, the commercialization of new knowledge is becoming one of the main goals, while educational activities are aimed at training of scientists, specialists having scientific knowledge, and entrepreneurs. The study purpose is to identify the needs of students learnt in the university in the formation of innovator's competencies; substantiation of the possibility for using Smart-technologies in the development of entrepreneurial competencies in the industrial center of the Far East. As a result of the study, the motives of future graduates to engage in business activities were identified; the factors hindering the development of innovative entrepreneurial activity (IEA) were studied; as well as the need to obtain specific knowledge, abilities and skills at each stage of the IEA. It is shown that the formation of innovator's competencies should be carried out with the help of Smart-education, using the network interaction (based on the triple helix model), a digital format system; student's orientation to the priority trends of intellectual, scientific and process of the country and region development. The study results can be used by leaders of higher educational institutions to develop the entrepreneurial university functions.
Symmetrical Uncertainty-Based Feature Subset Generation and Ensemble Learning for Electricity Customer Classification

The use of actual electricity consumption data provided the chance to detect the change of customer class types. This work could be done by using classification techniques. However, there are several challenges in computational techniques. The most important one is to efficiently handle a large number of dimensions to increase customer classification performance. In this paper, we proposed a symmetrical uncertainty based feature subset generation and ensemble learning method for the electricity customer classification. Redundant and significant feature sets are generated according to symmetrical uncertainty. After that, a classifier ensemble is built based on significant feature sets and the results are combined for the final decision. The results show that the proposed method can efficiently find useful feature subsets and improve classification performance.
A neural model for type classification of entities for text

Entity classification has become an increasingly crucial component in the development of knowledge graphs. Due to the incompleteness of the knowledge graph, the semantic relation features of entities in the knowledge graph are generally incomplete, leading to some entities cannot be complete classified. To overcome the weakness of existing research, in this study, we investigated the problem of classifying entities in knowledge graph from the text and proposed an end-to-end entity classification system based on the neural network model. To be specific, firstly, the mention model used long short-term memory to identify the types of each entity mention from the sentences that it contains. Secondly, we proposed a fusion model to fuse the types of multiple mentions to compensate for the existing systems of entity classification. The experimental results demonstrated the necessity and effectiveness of each module in the system. We believe that our proposed method posed a good complement for the existing systems of entity classification.
Paris and Nice terrorist attacks: Exploring Twitter and web archives

The attacks suffered by France in January and November 2015, and then in the course of 2016, especially the Nice attack, provoked intense online activity both during the events and in the months that followed. The digital traces left by this reactivity and reactions to events gave rise, from the very first days and even hours after the attacks, to a ‘real-time’ institutional archiving by the National Library of France ( Bibliothèque nationale de France, BnF) and the National Audio-visual Institute ( Institut national de l’audiovisuel, Ina). The results amount to millions of archived tweets and URLs. This article seeks to highlight some of the most significant issues raised by these relatively unprecedented corpora, from collection to exploitation, from online stream of data to its mediation and re-composition. Indeed, web archiving practices in times of emergency and crises are significant, almost emblematic, loci to explore the human and technical agencies, and the complex temporalities, of ‘born-digital’ heritage. The cases examined here emphasize the way these ‘emergency collections’ challenge the perimeters and the very nature of web archives as part of our digital and societal heritage, and the guiding visions of its governance and mission. Finally, the present analysis underlines the need for a careful contextualization of the design process – both of original web pages or tweets and of their archived images – and of the tools deployed to collect, retrieve and analyse them.
Exploring and Understanding the Role of Workshop Environments in Personal Fabrication Processes

Growing interest in personal fabrication has resulted in many ways to ideate, design, and prototype, in addition to studies of who a maker is and the challenges they face. Less attention, however, has focused on the role of the environment in fabrication processes. By understanding how interactions with tools, fixtures, materials, and spaces shape workflows, we can better determine how to design the next generation of workshops, design tools, and fabrication equipment to support personal fabrication activities. To build this understanding, site visits and interviews at local makerspaces, fabrication studios, and workshops were conducted. These visits uncovered the rich practices and roadblocks generated by workshops today. The observations identified the importance of spatial layouts, territoriality and occupant agency, distributed knowledge, and organizational flux, among others, to design and fabrication processes. These observations were further synthesized into one possible direction for such spaces: hybrid workshops (i.e., environments that can leverage computation and responsive architecture to enhance a maker's ability to design and fabricate). This work identifies how such spaces could harness the rich practices and eliminate the challenges found with workshops today and discusses the technical innovations and philosophical questions that hybrid workshops will pose to the future of personal fabrication.
Hover: Exploring cognitive maps and mid-air pointing for television control

We introduce "Hover," a new concept and interface for remote TV control based on air pointing in the users' personal, peripersonal, and extrapersonal space and implemented with mid-air spatial shortcuts to viewers' preferred TV channels distributed in the room with no representation other than that created in users' own imagination. Hover exploits memory and spatial orientation skills to enable fast access to TV channels with a mere pointing of the hand in mid-air. A controlled experiment with 17 participants and 9 mid-air loci corresponding to shortcuts to television channels revealed an average user recall rate of 80% and individual rates up to 100% for mid-air loci, pointing accuracy offsets of 10 cm, and 99% recognition rate for air pointing actions. We hope that Hover will inspire researchers and practitioners of user interfaces for smart TVs to consider spatial locations and mid-air as active elements to design new interactive experiences for home entertainment environments centered on the smart TV.
Efficient reversible data hiding in encrypted images based on multi-stage integer wavelet transform

Recently, reversible data hiding in encrypted images has drawn increasing interest. We theoretically analyze the feasibility of reversibly hiding secret data into encrypted images using integer transform and propose an efficient reversible data hiding scheme in encrypted images based on integer wavelet transform (IWT) in this paper. The cover image is first encrypted. Then the encrypted image is decomposed with IWT to get coefficients of four sub-bands. Location maps are generated according to the most significant bit-planes of high frequency coefficients. The location maps are further lossless compressed and embedded with secret data into the most significant bits of high frequency wavelet coefficients by bits replacement. Ratio correction (RC) is applied in the pre-processing phase to make sure the efficiency and validity of the proposed scheme. To achieve higher capacity, multi-stage IWT and multi-level embedding are performed. In addition, we then propose an adaptive correction (AC) algorithm which allows the scheme to accommodate more payload. On the receiving side, a recipient can correctly extract embedded secret message with data hiding key. The original image can be perfectly recovered with both the data hiding key and the encryption key. Experimental results demonstrate that the proposed algorithm significantly improves the embedding capacity and the reconstructed image quality compared with some existing state-of-the-art schemes.
Automated synthesis of application-layer connectors from automata-based specifications

Ubiquitous and Pervasive Computing, and the Internet of Things, promote dynamic interaction among heterogeneous systems. To achieve this vision, interoperability among heterogeneous systems represents a key enabler, and mediators are often built to solve protocol mismatches. Many approaches propose the synthesis of mediators. Unfortunately, a rigorous characterization of the concept of interoperability is still lacking, hence making hard to assess their applicability and soundness. In this paper, we provide a framework for the synthesis of mediators that allows us to: (i) characterize the conditions for the mediator existence and correctness; and (ii) establish the applicability boundaries of the synthesis method.
Deep Neural Architectures for Joint Named Entity Recognition and Disambiguation

Current entity linking methods typically first apply a named entity recognition (NER) model to extract a named entity and classify it into a predefined category, then apply an entity disambiguation model to link the named entity to a corresponding entity in the reference knowledge base. However, these methods ignore the inter-relations between the two tasks. Our work jointly optimizes deep neural models of both NER and entity disambiguation. In the entity disambiguation task, our deep neural model includes a recursive neural network and convolutional neural network with attention mechanism. Our model compares similarities between a mention and candidate entities by simultaneously considering semantic and background information, including Wikipedia description pages, contexts where the mention occurs, and entity typing information. The experiments show that our model can effectively leverage the semantic information of context, and performs competitively to conventional approaches.
A Minimax Game for Generative and Discriminative Sample Models for Recommendation

Recommendation systems often fail to live up to expectations in real situations because of the lack of user feedback, known as the data sparsity problem. A large number of existing recommendation methods resort to side information to gain a performance improvement. However, these methods are either too complicated to follow or time-consuming. To alleviate the data sparsity problem, in this paper we propose UGAN, which is a general adversarial framework for recommendation tasks and consists of a generative model and a discriminative model. In UGAN, the generative model, acts as an attacker to cheat the discriminative model to capture the pattern of the original data input and generate similar user profiles, while the counterpart, the discriminative model aims to distinguish the forged samples from the real data. By competing with each other, two model are alternatively updated like playing a minimax game until the generative model has learned the original data distribution. The experimental results on two real-world datasets, Movielens and Douban, show that the user profiles forged by UGAN can be easily integrated into a wide range of recommendation methods and significantly improve their performance, which provides a promising way to mitigate the adverse impact of missing data.
Q-MAT+: An error-controllable and feature-sensitive simplification algorithm for medial axis transform

The medial axis transform (MAT), as an intrinsic shape representation, plays an important role in shape approximation, recognition and retrieval. Q-MAT is a state-of-the-art algorithm driven by quadratic error minimization to compute a geometrically precise, structurally concise, and compact representation of the MAT for 3D shapes. In this work we extend the technique to make it more robust, controllable, and name it Q-MAT+. Combining shape diameter function (SDF) and other mesh information, Q-MAT+ gets a more complete and accurate initial MAT than Q-MAT, even for extreme thin features, such as wires and sheets. Q-MAT+ could quickly remove insignificant branches while preserving significant ones to get a simple and faithful piecewise linear approximation of the MAT. Moreover, it performs the medial axis simplification with explicit maintenance and the control of Hausdorff error, which is not originally supported in Q-MAT. We further demonstrate the outstanding efficiency and accuracy of our method compared with other existing approaches for MAT generation and simplification.
Quantum Private Comparison Protocols With a Number of Multi-Particle Entangled States

Private comparison allows n(n ≥ 2) participants who do not trust each other to compare whether their secret data are the same, without leaking the secret data of their own.Quantum private comparison (QPC) uses quantum mechanics to accomplish the comparison.In this paper, we present a simple and effective method which can design QPC protocols based on multi-particle entangled states including the genuinely entangled five-qubit state, the generalized Brown state, the genuine six-qubit entangled state, etc.We take the Bell state and the genuinely entangled five-qubit state as examples, respectively, to present two QPC protocols, where a semi-honest third party who assists two participants in implementing the protocols is assumed.A key feature of our protocols is that quantum states are prepared by two participants rather than by the third party, which effectively prevents the third party from preparing fake quantum states, thus improving the security of the protocols.In addition, we use the entanglement properties of multi-particle entangled states and collaborative computing between participants for privacy protection, and we use QKD to ensure the security of the cooperative computing when two participants are in different locations.We show that the security of our protocols towards both outsider and insider attacks can be guaranteed.
Kelp: QoS for Accelerated Machine Learning Systems

Development and deployment of machine learning (ML) accelerators in Warehouse Scale Computers (WSCs) demand significant capital investments and engineering efforts. However, even though heavy computation can be offloaded to the accelerators, applications often depend on the host system for various supporting tasks. As a result, contention on host resources, such as memory bandwidth, can significantly discount the performance and efficiency gains of accelerators. The impact of performance interference is further amplified in distributed learning, which has become increasingly common as model sizes continue to grow. In this work, we study the performance of four production machine learning workloads on three accelerator platforms. Our experiments show that these workloads are highly sensitive to host memory bandwidth contention, which can cause 40% average performance degradation when left unmanaged. To tackle this problem, we design and implement Kelp, a software runtime that isolates high priority accelerated ML tasks from memory resource interference. We evaluate Kelp with both production and artificial aggressor workloads, and compare its effectiveness with previously proposed solutions. Our evaluation shows that Kelp is effective in mitigating performance degradation of the accelerated tasks, and improves performance by 24% on average. Compared to previous work, Kelp reduces performance degradation of ML tasks by 7% and improves system efficiency by 17%. Our results further expose opportunities in future architecture designs.