Reviewerly/exHarmony huggingface dataset:
A knowledge-driven approach for activity recognition in smart homes based on activity profiling

The Internet of Things (IoT) is a technology for seamlessly connecting a large number of small-end devices and enabling the development of many smart applications to control different aspects of our life; shifting us, ever-closer to living in a smart city. IoT makes it possible to convert our homes to smart environments in which sensors are responsible for handling inhabitants' behaviours and monitor their daily activities. Activity Recognition (AR) is a new service within smart homes. It has been introduced as a solution to improve the quality of life of people such as elderly and children. AR is concerned with the assignment of an activity label to a sequence of sensors' events that are generated from the smart infrastructure. To help in effectively recognizing home activities, classification algorithms are applied on segmented sequences that are extracted automatically. Segments are subject to error due to the existence of irrelevant data and difficulties in how segmentation is applied. This negatively affects the accuracy on the classification task. In addition, the data generated from the network is streamed in nature, and big data techniques need to be utilized. In this paper, we propose a model to improve Activity Recognition in smart homes. The proposed technique is based on defining a profile for each activity from training datasets. The profile will be used to induce extra features and will help in distinguishing residents' activities (fingerprinting). To validate our model, real datasets have been used for the experiments, and results show a significant enhancement in accuracy, compared with traditional techniques.
Perceptual Video Hashing for Content Identification and Authentication

Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions.
A GPU-accelerated parallel K-means algorithm

Clustering approaches are widely used methodologies to analyse large data sets. The K-means algorithm is well-known as a procedure too computational-intensive for the large data analytic problem. In this work, we focus on a parallel technique to reduce the execution time when the K-means is used to cluster large dataset. We exploit computational powerful of its design when the Graphic Processor Units (GPUs), a massively parallel architecture, is adopted. We optimize the proposed implementation to handle (i) the space limitation issue of GPUs; (ii) the host-device data transfer time. Experimental results, on real and synthetic data, show how our parallelization approach give good results in terms of execution time and speed-up.
Deep belief networks and cortical algorithms: A comparative study for supervised classification

The failure of shallow neural network architectures in replicating human intelligence led the machine learning community to focus on deep learning, to computationally match human intelligence. The wide availability of increasing computing power coupled with the development of more efficient training algorithms have allowed the implementation of deep learning principles in a manner and span that had not been previously possible. This has led to the inception of deep architectures that capitalize on recent advances in artificial intelligence and insights from cognitive neuroscience to provide better learning solutions. In this paper, we discuss two such algorithms that represent different approaches to deep learning with varied levels of maturity. The more mature but less biologically inspired Deep Belief Network (DBN) and the more biologically grounded Cortical Algorithms (CA) are first introduced to give readers a bird’s eye view of the higher-level concepts that make up these algorithms, as well as some of their technical underpinnings and applications. Their theoretical computational complexity is then derived before comparing their empirical performance on some publicly available classification datasets. Multiple network architectures were compared and showed that CA outperformed DBN on most datasets, with the best network architecture consisting of six hidden layers.
A Classification Based Web Service Selection Approach

Selection of an appropriate web service fulfilling the requirements of the end user is a challenging task. Most of the existing systems use Quality of Service (QoS) as predominant parameter for web service selection, without any preprocessing or filtering. These systems consider all of the candidate web services during selection process and require unnecessary processing of those web services which are far below the expectations of the end user. In this work, an approach for web service selection based on QoS parameters is proposed. The proposed method starts with prefiltering of candidate web services using classification technique. An improved PROMETHEE method, we call it as PROMETHEE Plus, is applied to most eligible web services and Maximizing Deviation Method based hybrid weight evaluation mechanism is adopted. Top-k web services matching closely with the QoS requirements of the end user are selected. Experiments on the dataset of real world web services are conducted. Experimental results show that our approach performs better in terms of end user satisfaction and efficiency with reference to the existing similar approaches.
No-Reference Stereoscopic Image Quality Assessment Based on Image Distortion and Stereo Perceptual Information

In this paper, we propose a novel no-reference three-dimensional (3-D) image quality assessment (NR-SIQA) method, which takes three types of visual perceptual characteristics into consideration, including image distortion, depth perception, and binocular combination perception. The estimation of image distortion is based on the observation that blurriness, noisiness, and blockiness are considered as three important factors that affect image quality. Moreover, the value difference between any two neighboring pixels for any image satisfies a generalized Laplace distribution, which will be changed with various types of distortion existing in the image. Since the major difference between 2-D images and 3-D images is the depth, a disparity search algorithm is a key element for 3-D perception assessment. To address this, a robust disparity search algorithm is first designed based on the Gaussian average SSIM, followed by the generation of cyclopean image, rivalry map, depth map, and weight map. These four maps have taken the visual perceptual characteristics into consideration. Three types of features relating to image distortion, depth perception, and binocular disparity are extracted from these maps and stereo-pairs. In the end, machine learning is utilized to map these features to human perceptual score. Experimental results show that the proposed algorithm outperforms the recent full reference SIQA and NR-SIQA metrics on two publicly available stereoscopic image quality assessment databases.
Scheduling Jobs across Geo-Distributed Datacenters with Max-Min Fairness

It has become routine for large volumes of data to be generated, stored, and processed across geographically distributed datacenters. To run a single data analytic job on such geo-distributed data, recent research proposed to distribute its tasks across datacenters, considering both data locality and network bandwidth across datacenters. Yet, it remains an open problem in the more general case, where multiple analytic jobs need to fairly share the resources at these geo-distributed datacenters. In this paper, we focus on the problem of assigning tasks belonging to multiple jobs across datacenters, with the specific objective of achieving max-min fairness across jobs sharing these datacenters, in terms of their job completion times. We formulate this problem as a lexicographical minimization problem, which is challenging to solve in practice due to its inherent multi-objective and discrete nature. To address these challenges, we iteratively solve its single-objective subproblems, which can be transformed to equivalent linear programming (LP) problems to be efficiently solved, thanks to their favorable properties. As a highlight of this paper, we have designed and implemented our proposed solution as a fair job scheduler based on Apache Spark, a modern data processing framework. With extensive evaluations of our real-world implementation on Amazon EC2 and large-scale simulations, we have shown convincing evidence that max-min fairness has been achieved and the worst job completion time has been significantly improved using our new job scheduler.
HybridORAM: Practical oblivious cloud storage with constant bandwidth

Along with the popularity of outsourcing data to the cloud server, data privacy becomes a central consideration. Because encryption alone has been proved insecure for the leakages of access pattern, Oblivious RAM (ORAM) was proposed to protect where, when and how often the data block has been accessed. However, different types of ORAM implementations have different limitations in terms of significant bandwidth cost or massive storage space, making them impractical for some applications like Internet of Things (IoT). In this paper, we present a practical ORAM, called HybridORAM, with constant bandwidth, which can be applied in wide application scopes. HybridORAM explores a new ORAM design to combine the advantages of layer and tree ORAMs; more specifically, it combines the frequently-accessed small levels of the former to improve the response time, and the small shuffle of the latter to save the storage capacity. Compared to the typical schemes, HybridORAM has an efficient response time reduced by O(log k), low bandwidth cost optimized from O(log N · B) to O(B) and small client storage, where k is level size factor, B is block size, N is the number of real blocks in ORAM. Experiments show that the response time of HybridORAM is 50.3% shorter than OnionORAM and 34.8% shorter than OS-PIR by practical parameters.
Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor

Identification of human movements is crucial for the design of intelligent devices capable to provide assistance. In this work, a Bayesian formulation, together with a sequential analysis method, is presented for identification of sit-to-stand (SiSt) and stand-to-sit (StSi) activities. This method performs autonomous iterative accumulation of sensor measurements and decision-making processes, while dealing with noise and uncertainty present in sensors. First, the Bayesian formulation is able to identify sit, transition and stand activity states. Second, the transition state, divided into transition phases, is used to identify the state of the human body during SiSt and StSi. These processes employ acceleration signals from an inertial measurement unit attached to the thigh of participants. Validation of our method with experiments in offline, real-time and a simulated environment, shows its capability to identify the human body during SiSt and StSi with an accuracy of 100% and mean response time of 50 ms (5 sensor measurements). In the simulated environment, our approach shows its potential to interact with low-level methods required for robot control. Overall, this work offers a robust framework for intelligent and autonomous systems, capable to recognise the human intent to rise from and sit on a chair, which is essential to provide accurate and fast assistance.
SEPDP: Secure and Efficient Privacy Preserving Provable Data Possession in Cloud Storage

Cloud computing is an emergent paradigm to provide reliable and resilient infrastructure enabling the users (data owners) to store their data and the data consumers (users) can access the data from cloud servers. This paradigm reduces storage and maintenance cost of the data owner. At the same time, the data owner loses the physical control and possession of data which leads to many security risks. Therefore, auditing service to check data integrity in the cloud is essential. This issue has become a challenge as the possession of data needs to be verified while maintaining the privacy. To address these issues this work proposes a secure and efficient privacy preserving provable data possession (SEPDP). Further, we extend SEPDP to support multiple owners, data dynamics and batch verification. The most attractive feature of this scheme is that the auditor can verify the possession of data with low computational overhead.
Video-based person re-identification via spatio-temporal attentional and two-stream fusion convolutional networks

Person re-identification aims at matching the identity of a same person that is captured from non-overlapping cameras. Most of the existing person re-identification methods are still focused on image-based solution. The problem of video-based person re-identification is better handled since video includes spatial and temporal information of pedestrians rather than a separate image. In this paper, we propose a novel approach for video-based person re-identification using spatio-temporal attentional and two-stream fusion convolutional networks, which consist of the two-stream fusion convolutional neural networks (TSF-CNN), the long short-term memory networks (LSTM), the spatial attention subnetwork and the temporal attention subnetwork. Specifically, the TSF-CNN learns the temporal and spatial characteristics simultaneously, and performs two fusions to achieve better feature representation. The spatial attention network is to automatically select the important part of pedestrian image in each frame. The temporal attention model assigns different weights according to the importance of different frames. Experiments on benchmark datasets demonstrate that the proposed approach is superior to existing methods of video-based person re-identification.
Facial Expression Recognition with Neighborhood-Aware Edge Directional Pattern (NEDP)

Currently available local feature descriptors used in facial expression recognition at times suffer from unstable feature descriptions, especially in the presence of weak and distorted edges due to noise, limiting their performances. We propose a novel local descriptor named Neighborhood-aware Edge Directional Pattern (NEDP) to overcome such limitations. Instead of relying solely on the local neighborhood to describe the feature around a pixel, as done by the existing local descriptors, NEDP examines the gradients at the target (center) pixel as well as its neighboring pixels to explore a wider neighborhood for the consistency of the feature in spite of the presence of subtle distortion and noise in local region. We introduce template-orientations for the neighboring pixels, which give importance to the gradients in consistent edge directions, prioritizing the specific neighbors falling in the direction of the local edge to represent the shape of the local textures, unambiguously. Moreover, due to the effective management of the featureless regions, no such region is erroneously encoded as a feature by NEDP. Experiments of the performances for person-independent recognition on benchmark expression datasets also show that NEDP performs better than other existing descriptors, and thereby, improves the overall performance of facial expression recognition.
A Semantic-Based Method for Visualizing Large Image Collections

Interactive visualization of large image collections is important and useful in many applications, such as personal album management and user profiling on images. However, most prior studies focus on using low-level visual features of images, such as texture and color histogram, to create visualizations without considering the more important semantic information embedded in images. This paper proposes a novel visual analytic system to analyze images in a semantic-aware manner. The system mainly comprises two components: a semantic information extractor and a visual layout generator. The semantic information extractor employs an image captioning technique based on convolutional neural network (CNN) to produce descriptive captions for images, which can be transformed into semantic keywords. The layout generator employs a novel co-embedding model to project images and the associated semantic keywords to the same 2D space. Inspired by the galaxy metaphor, we further turn the projected 2D space to a galaxy visualization of images, in which semantic keywords and images are visually encoded as stars and planets. Our system naturally supports multi-scale visualization and navigation, in which users can immediately see a semantic overview of an image collection and drill down for detailed inspection of a certain group of images. Users can iteratively refine the visual layout by integrating their domain knowledge into the co-embedding process. Two task-based evaluations are conducted to demonstrate the effectiveness of our system.
3-D Reconstruction of Human Body Shape From a Single Commodity Depth Camera

3-D human body reconstruction is an important research topic in computer vision. A 3-D human body model can be used in sports science, movie industry and personalized entertainment, especially virtual reality games. Most of depth-based 3-D reconstruction algorithms need multiple cameras surrounding the user and require the user to keep a specific pose strictly while capturing depth images. In this paper, we propose an algorithm to reconstruct the 3-D shape of human bodies using a single commodity depth camera. Our algorithm only needs two depth images of the front-facing and back-facing bodies. It also has strong operability since the proposed method is insensitive to the pose variations between the two depth images. We reconstruct 3-D shapes of front-facing and back-facing bodies from the two depth images, respectively, and stitch them together. We also propose a novel registration method, namely, “iterative mid-distance points,” which has fast convergence and robustness to the depth noise. The proposed method enables robust and easy-to-use human body reconstruction, and achieves higher accuracy than state-of-the-art methods.
Uncertain fate of fair sampling in quantum annealing

Recently, it was demonstrated both theoretically and experimentally on the D-Wave quantum annealer that transverse-field quantum annealing does not find all ground states with equal probability. In particular, it was proposed that more complex driver Hamiltonians beyond transverse fields might mitigate this shortcoming. Here, we investigate the mechanisms of (un)fair sampling in quantum annealing. While higher-order terms can improve the sampling for selected small problems, we present multiple counterexamples where driver Hamiltonians that go beyond transverse fields do not remove the sampling bias. Using perturbation theory we explain why this is the case. In addition, we present large-scale quantum Monte Carlo simulations for spin glasses with known degeneracy in two space dimensions and demonstrate that the fair-sampling performance of quadratic driver terms is comparable to standard transverse-field drivers. Our results suggest that quantum annealing machines are not well suited for sampling applications, unless post-processing techniques to improve the sampling are applied.
Semantics-Aware Visual Object Tracking

In this paper, we propose a semantics-aware visual object tracking method, which introduces semantics into the tracking procedure and extends the model of an object with explicit semantics prior to enhancing the robustness of three key aspects of the tracking framework, i.e., appearance model, search scheme, and scale adaptation. We first present a semantic object proposal generation method for video sequences to generate high-quality category-oriented object proposals. Then, a hybrid semantics-aware tracking algorithm with semantic compatibility is proposed. This algorithm takes full advantages of globally sparse semantic object proposal prediction and locally dense prediction with a template model and semantic distractor-aware color appearance model. Furthermore, we propose to exploit semantics to localize object accurately via an energy minimization framework-based scale adaptation method, which jointly integrates dense location prior, instance-specific color, and category-specific semantic information. Extensive experiments are conducted on two widely used benchmarks, and the results demonstrate that our method achieves the state-of-the-art performance.
Structuring the synthesis of heap-manipulating programs

This paper describes a deductive approach to synthesizing imperative programs with pointers from declarative specifications expressed in Separation Logic. Our synthesis algorithm takes as input a pair of assertions---a pre- and a postcondition---which describe two states of the symbolic heap, and derives a program that transforms one state into the other, guided by the shape of the heap. The program synthesis algorithm rests on the novel framework of Synthetic Separation Logic (SSL), which generalises the classical notion of heap entailment $\mathcal{P} \vdash \mathcal{Q}$ to incorporate a possibility of transforming a heap satisfying an assertion $\mathcal{P}$ into a heap satisfying an assertion $\mathcal{Q}$. A synthesized program represents a proof term for a transforming entailment statement $\mathcal{P} \leadsto \mathcal{Q}$, and the synthesis procedure corresponds to a proof search. The derived programs are, thus, correct by construction, in the sense that they satisfy the ascribed pre/postconditions, and are accompanied by complete proof derivations, which can be checked independently. We have implemented a proof search engine for SSL in a form the program synthesizer called SuSLik. For efficiency, the engine exploits properties of SSL rules, such as invertibility and commutativity of rule applications on separate heaps, to prune the space of derivations it has to consider. We explain and showcase the use of SSL on characteristic examples, describe the design of SuSLik, and report on our experience of using it to synthesize a series of benchmark programs manipulating heap-based linked data structures.