Query: Direct Pose Estimation and Refinement

Relevant passages:
Simultaneous Mosaicing and Tracking with an Event Camera <=SEP=> An event camera is a silicon retina which outputs not a sequence of video frames like a standard camera, but a stream of asynchronous spikes, each with pixel location, sign and precise timing, indicating when individual pixels record a threshold log intensity change. By encoding only image change, it offers the potential to transmit the information in a standard video but at vastly reduced bitrate, and with huge added advantages of very high dynamic range and temporal resolution. However, event data calls for new algorithms, and in particular we believe that algorithms which incrementally estimate global scene models are best placed to take full advantages of its properties. Here, we show for the first time that an event stream, with no additional sensing, can be used to track accurate camera rotation while building a persistent and high quality mosaic of a scene which is super-resolution accurate and has high dynamic range. Our method involves parallel camera rotation tracking and template reconstruction from estimated gradients, both operating on an event-by-event basis and based on probabilistic filtering.

Real-time visual odometry from dense RGB-D images <=SEP=> We present an energy-based approach to visual odometry from RGB-D images of a Microsoft Kinect camera. To this end we propose an energy function which aims at finding the best rigid body motion to map one RGB-D image into another one, assuming a static scene filmed by a moving camera. We then propose a linearization of the energy function which leads to a 6×6 normal equation for the twist coordinates representing the rigid body motion. To allow for larger motions, we solve this equation in a coarse-to-fine scheme. Extensive quantitative analysis on recently proposed benchmark datasets shows that the proposed solution is faster than a state-of-the-art implementation of the iterative closest point (ICP) algorithm by two orders of magnitude. While ICP is more robust to large camera motion, the proposed method gives better results in the regime of small displacements which are often the case in camera tracking applications.

On unifying key-frame and voxel-based dense visual SLAM at large scales <=SEP=> This paper proposes an approach to real-time dense localisation and mapping that aims at unifying two different representations commonly used to define dense models. On one hand, much research has looked at 3D dense model representations using voxel grids in 3D. On the other hand, image-based key-frame representations for dense environment mapping have been developed. Both techniques have their relative advantages and disadvantages which will be analysed in this paper. In particular each representation's space-size requirements, their effective resolution, the computation efficiency, their accuracy and robustness will be compared. This paper then proposes a new model which unifies various concepts and exhibits the main advantages of each approach within a common framework. One of the main results of the proposed approach is its ability to perform large scale reconstruction accurately at the scale of mapping a building.

Simultaneous map building and localization for an autonomous mobile robot <=SEP=> In this paper, we discuss a significant open problem in mobile robotics: simultaneous map building and localization, which we define as long-term globally referenced position estimation without a priori information. This problem is difficult because of the following paradox: to move precisely, a mobile robot must have an accurate environment map; however, to build an accurate map, the mobile robot’s sensing locations must be known precisely. In this way, simultaneous map building and localization can be seen to present a question of “which came first, the chicken or the egg?” (The map or the motion?) When using ultrasonic sensing, to overcome this issue we equip the vehicle with multiple servo-mounted sonar sensors, to provide a means in which a subset of environment features can be precisely learned from the robot’s initial location and subsequently tracked to provide precise positioning.

gSLICr: SLIC superpixels at over 250Hz <=SEP=> We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to 83× from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.

IoT-based occupancy monitoring techniques for energy-efficient smart buildings <=SEP=> With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption of buildings via smart HVAC control. In this respect, the major challenges we envision are 1) to achieve occupancy monitoring in a minimally intrusive way, e.g., using the existing infrastructure in the buildings and not requiring installation of any apps in the users' smart devices, and 2) to develop effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources. This paper surveys the existing works on occupancy monitoring and multi-modal data fusion techniques for smart commercial buildings. The goal is to lay down a framework for future research to exploit the spatio-temporal data obtained from one or more of various IoT devices such as temperature sensors, surveillance cameras, and RFID tags that may be already in use in the buildings. A comparative analysis of existing approaches and future predictions for research challenges are also provided.

Quality evaluation of product reviews using an information quality framework <=SEP=> Available online 27 August 2010

Distributed fair scheduling in a wireless LAN <=SEP=> Fairness is an important issue when accessing a shared wireless channel. With fair scheduling, it is possible to allocate bandwidth in proportion to weightsof the packet flows sharing the channel. This paper presents a fully distributed algorithm for fair scheduling in a wireless LAN. The algorithm can be implemented without using a centralized coordinator to arbitrate medium access. The proposed protocol is derived from the Distributed Coordination Function in the IEEE 802.11 standard. Simulation results show that the proposed algorithm is able to schedule transmission such that the bandwidth allocated to different flows is proportional to their weights. An attractive feature of the proposed approach is that it can be implemented with simple modifications to the IEEE 802.11 standard.

Educational Data Mining: A Review of the State of the Art <=SEP=> Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Artificial Vision in Road Vehicles <=SEP=> The last few decades witnessed the birth and growth of a new sensibility to transportation efficiency. In particular, the need for efficient and improved people and goods mobility pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to the (partial) customization of road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application on future road vehicles is discussed.

A Log-Logistic Model-Based Interpretation of TF Normalization of BM25 <=SEP=> The effectiveness of BM25 retrieval function is mainly due to its sub-linear term frequency (TF) normalization component, which is controlled by a parameter k1. Although BM25 was derived based on the classic probabilistic retrieval model, it has been so far unclear how to interpret its parameter k1 probabilistically, making it hard to optimize the setting of this parameter. In this paper, we provide a novel probabilistic interpretation of the BM25 TF normalization and its parameter k1 based on a log-logistic model for the probability of seeing a document in the collection with a given level of TF. The proposed interpretation allows us to derive different approaches to estimation of parameter k1 based solely on the current collection without requiring any training data, thus effectively eliminating one free parameter from BM25. Our experiment results show that the proposed approaches can accurately predict the optimal k1 without requiring training data and achieve better or comparable retrieval performance to a well-tuned BM25 where k1 is optimized based on training data.

A New Paradigm for Collision-free Hashing: Incrementality at Reduced Cost <=SEP=> We present a simple, new paradigm for the design of collision-free hash functions. Any function emanating from this paradigm is incremental. (This means that if a message x which I have previously hashed is modi ed to x0 then rather than having to re-compute the hash of x 0 from scratch, I can quickly \update" the old hash value to the new one, in time proportional to the amount of modi cation made in x to get x.) Also any function emanating from this paradigm is parallelizable, useful for hardware implementation. We derive several speci c functions from our paradigm. All use a standard hash function, assumed ideal, and some algebraic operations. The rst function, MuHASH, uses one modular multiplication per block of the message, making it reasonably e cient, and signi cantly faster than previous incremental hash functions. Its security is proven, based on the hardness of the discrete logarithm problem. A second function, AdHASH, is even faster, using additions instead of multiplications, with security proven given either that approximation of the length of shortest lattice vectors is hard or that the weighted subset sum problem is hard. A third function, LtHASH, is a practical variant of recent lattice based functions, with security proven based, again on the hardness of shortest lattice vector approximation. Dept. of Computer Science & Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. E-Mail: mihir@cs.ucsd.edu. URL: http://www-cse.ucsd.edu/users/mihir. Supported in part by NSF CAREER Award CCR-9624439 and a Packard Foundation Fellowship in Science and Engineering. yMIT Laboratory for Computer Science, 545 Technology Square, Cambridge, MA 02139, USA. E-Mail: miccianc@theory.lcs.mit.edu. Supported in part by DARPA contract DABT63-96-C-0018.

CINCO: a simplicity-driven approach to full generation of domain-specific graphical modeling tools <=SEP=> Even with the help of powerful metamodeling frameworks, the development of domain-specific graphical modeling tools is usually a complex, repetitive, and tedious task, which introduces substantial upfront costs often prohibiting such approaches in practice. In order to reduce these costs, the presented Cinco meta tooling suite is designed to provide a holistic approach that greatly simplifies the development of such domain-specific tools. Our solution is based on the idea to apply the concept of domain specialization also to the (meta-)domain of “domain-specific modeling tools”. Important here is our focus on complex graph-based models, comprising various kinds of nodes and edges together with their individual representation, correlations, and interpretation. This focus allows for high-level specifications of the model structures and functionalities as the prerequisite for push-button tool generation.

Microgrid power electronic converters: State of the art and future challenges <=SEP=> This paper presents a review of the state of the art of power electric converters used in microgrids. The paper focuses primarily on grid connected converters. Different topologies and control and modulation strategies for these specific converters are critically reviewed. Moreover, future challenges in respect of these converters are identified along with their potential solutions.

Parental stress, family quality of life, and family-teacher partnerships: Families of children with autism spectrum disorder. <=SEP=> BACKGROUND
Reducing parental stress and improving family quality of Life (FQOL) are continuing concerns for families of children with autism spectrum disorder (ASD). Family-teacher partnerships have been identified as a positive factor to help parents reduce their stress and improve their FQOL. However, the interrelations among parental stress, FQOL, and family-teacher partnerships need to be further examined so as to identify the possible paths to help parents reduce their stress and improve their FQOL. The purpose of this study was to examine the interrelations among these three variables.


METHOD
A total of 236 parents of school children with ASD completed questionnaires, which included three measures: (a) the Beach Center Family Quality of Life Scale, (b) the Parental Stress Scale, and (c) the Beach Center Family-Professional Partnerships Scale. The structural equation modeling was used to analyze the interrelations among these three variables.


RESULTS
Perceived parental stress had a direct effect on parental satisfaction concerning FQOL and vice versa. Perceived family-teacher partnerships had a direct effect on FQOL, but did not have a direct effect on parental stress. However, family-teacher partnerships had an indirect effect on parental stress through FQOL.


CONCLUSIONS AND IMPLICATIONS
Reducing parental stress could improve FQOL for families of children with ASD and vice versa. Strong family-teacher partnerships could help parents of children with ASD improve their FQOL and indirectly reduce their stress.

Large-vocabulary audio-visual speech recognition by machines and humans <=SEP=> We compare automatic recognition with human perception of audio-visual speech, in the large-vocabulary, continuous speech recognition (LVCSR) domain. Specifically, we study the benefit of the visual modality for both machines and humans, when combined with audio degraded by speech-babble noise at various signal-to-noise ratios (SNRs). We first consider an automatic speechreading system with a pixel based visual front end that uses feature fusion for bimodal integration, and we compare its performance with an audio-only LVCSR system. We then describe results of human speech perception experiments, where subjects are asked to transcribe audio-only and audiovisual utterances at various SNRs. For both machines and humans, we observe approximately a 6 dB effective SNR gain compared to the audio-only performance at 10 dB, however such gains significantly diverge at other SNRs. Furthermore, automatic audio-visual recognition outperforms human audioonly speech perception at low SNRs.

A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering <=SEP=> In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.

Advances in High-Voltage Modulators for Applications in Pulsed Power and Plasma-Based Ion Implantation <=SEP=> Modern pulsed power technology has its roots in the late 1950s and early 1960s, and it was driven overwhelmingly by applications in national defense carried out by several countries, especially the U.S., U.K., Russia, and China. The following decades, particularly the early 1990s, witnessed an increased interest in compact systems with pulse repetition rate that could be used in nondefense applications such as treatment of material surfaces by plasma and beam interactions, treatment of pollutants, food sterilization, medical applications, etc. This spawned a new generation of pulsed power components (solid-state switches) that led to completely solid-state modulators. This paper describes how the pulsed power technology used originally in beam sources and cathodic arcs has converged to produce power sources for plasma-based ion implantation (PBII) and related technologies. The present state of the art is reviewed, and prospects for future advances are described, especially for PBII.

Norepinephrine transporter occupancy by antidepressant in human brain using positron emission tomography with (S,S)-[18F]FMeNER-D2 <=SEP=> Central norepinephrine transporter (NET) is one of the main targets of antidepressants. Although the measurement of NET occupancy has been attempted in humans, the outcomes have been inconclusive. In this study, the occupancy of NET by different doses of an antidepressant, nortriptyline, was measured using positron emission tomography (PET) with (S,S)-[18F]FMeNER-D2. PET scans using (S,S)-[18F]FMeNER-D2 were performed on six healthy men before and after oral administration of a single oral dose of nortriptyline (10–75 mg). After a bolus i.v. injection of (S,S)-[18F]FMeNER-D2, dynamic scanning was performed for 0–90 min, followed by scanning for 120–180 min. The ratio of the thalamus-to-caudate areas under the curve (120–180 min) minus 1 was used as the binding potential (BPND) for NET. NET occupancy was calculated as the percentage reduction of BPND. Venous blood samples were taken to measure the concentrations of nortriptyline just before injection of the tracer and at 180 min after the injection. Mean NET occupancies by nortriptyline were 16.4% at 10 mg, 33.2% at 25 mg, and 41.1% at 75 mg. The mean plasma concentration of nortriptyline was less than the lower limit of detection at 10 mg, 23.7 ng/mL at 25 mg, and 50.5 ng/mL at 75 mg. Estimated ED50 was 76.8 mg of administration dose and 59.8 ng/mL of plasma concentration. NET occupancy by nortriptyline corresponding to the administration dose of 10–75 mg or plasma concentration was observed from 16% to 41%.

Learning to learn with the informative vector machine <=SEP=> This paper describes an efficient method for learning the parameters of a Gaussian process (GP). The parameters are learned from multiple tasks which are assumed to have been drawn independently from the same GP prior. An efficient algorithm is obtained by extending the informative vector machine (IVM) algorithm to handle the multi-task learning case. The multi-task IVM (MTIVM) saves computation by greedily selecting the most informative examples from the separate tasks. The MT-IVM is also shown to be more efficient than random sub-sampling on an artificial data-set and more effective than the traditional IVM in a speaker dependent phoneme recognition task.

A neuromorphic architecture for anomaly detection in autonomous large-area traffic monitoring <=SEP=> The advanced sensing and imaging capability of today's sensor networks enables real time monitoring in a large area. In order to provide continuous monitoring and prompt situational awareness, an abstract-level autonomous information processing framework is developed that is able to detect various categories of abnormal traffic events with unsupervised learning. The framework is based on cogent confabulation model, which performs statistical inference in a manner inspired by human neocortex system. It enables detection and recognition of abnormal target vehicles within the context of surrounding traffic activities and previous events using likelihood-ratio test. A neuromorphic architecture is proposed which accelerates the computation for real-time detection by leveraging memristor crossbar arrays.

Ontogenesis of Agency in Machines : A Multidisciplinary Review <=SEP=> How does agency arise in biocognitive systems? Can the emergence of agency be explained by physical laws? We review these and related questions in philosophy, psychology, biology, and physics. Based on the review we ask the questions: i) Can machines have agency? and, if so, ii) How can we build machines with agency? We examine existing work in artificial intelligence and machine learning on selfmotivated, self-teaching, and self-developing systems with respect to the ontogenesis (“coming into being”) of agency in computational systems. The impact of these “autogenic” systems or machine agency on science, technology, and humanity will be discussed.

CODRA: A Novel Discriminative Framework for Rhetorical Analysis <=SEP=> Clauses and sentences rarely stand on their own in an actual discourse; rather, the relationship between them carries important information that allows the discourse to express a meaning as a whole beyond the sum of its individual parts. Rhetorical analysis seeks to uncover this coherence structure. In this article, we present CODRA— a COmplete probabilistic Discriminative framework for performing Rhetorical Analysis in accordance with Rhetorical Structure Theory, which posits a tree representation of a discourse.CODRA comprises a discourse segmenter and a discourse parser. First, the discourse segmenter, which is based on a binary classifier, identifies the elementary discourse units in a given text. Then the discourse parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing. We present two approaches to combine these two stages of parsing effectively. By conducting a series of empirical evaluations over two different data sets, we demonstrate that CODRA significantly outperforms the state-of-the-art, often by a wide margin. We also show that a reranking of the k-best parse hypotheses generated by CODRA can potentially improve the accuracy even further.

An Access Control scheme for Big Data processing <=SEP=> Access Control (AC) systems are among the most critical of network security components. A system's privacy and security controls are more likely to be compromised due to the misconfiguration of access control policies rather than the failure of cryptographic primitives or protocols. This problem becomes increasingly severe as software systems become more and more complex, such as Big Data (BD) processing systems, which are deployed to manage a large amount of sensitive information and resources organized into a sophisticated BD processing cluster. Basically, BD access control requires the collaboration among cooperating processing domains to be protected as computing environments that consist of computing units under distributed AC managements. Many BD architecture designs were proposed to address BD challenges; however, most of them were focused on the processing capabilities of the “three Vs” (Velocity, Volume, and Variety). Considerations for security in protecting BD are mostly ad hoc and patch efforts. Even with some inclusion of security in recent BD systems, a critical security component, AC (Authorization), for protecting BD processing components and their users from the insider attacks, remains elusive. This paper proposes a general purpose AC scheme for distributed BD processing clusters.

Educational System for the Holy Quran and Its Sciences for Blind and Handicapped People Based on Google Speech API <=SEP=> There is a great need to provide educational environments for blind and handicapped people. There are many Islamic websites and applications dedicated to the educational services for the Holy Quran and Its Sciences (Quran Recitations, the interpretations, etc.) on the Internet. Unfortunately, blind and handicapped people could not use these services. These people cannot use the keyboard and the mouse. In addition, the ability to read and write is essential to benefit from these services. In this paper, we present an educational environment that allows these people to take full advantage of the scientific materials. This is done through the interaction with the system using voice commands by speaking directly without the need to write or to use the mouse. Google Speech API is used for the universal speech recognition after a preprocessing and post processing phases to improve the accuracy. For blind people, responses of these commands will be played back through the audio device instead of displaying the text to the screen. The text will be displayed on the screen to help other people make use of the system.

Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement <=SEP=> Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness1.

Language, music, syntax and the brain <=SEP=> The comparative study of music and language is drawing an increasing amount of research interest. Like language, music is a human universal involving perceptually discrete elements organized into hierarchically structured sequences. Music and language can thus serve as foils for each other in the study of brain mechanisms underlying complex sound processing, and comparative research can provide novel insights into the functional and neural architecture of both domains. This review focuses on syntax, using recent neuroimaging data and cognitive theory to propose a specific point of convergence between syntactic processing in language and music. This leads to testable predictions, including the prediction that that syntactic comprehension problems in Broca's aphasia are not selective to language but influence music perception as well.

Long-term effect of high-intensity laser therapy in the treatment of patients with chronic low back pain: a randomized blinded placebo-controlled trial <=SEP=> The aim of this study was to compare the effect of high-intensity laser therapy (HILT), alone or combined with exercise, in the treatment of chronic low back pain (CLBP). A total of 72 male patients participated in this study, with a mean (SD) age of 32.81 (4.48) years. Patients were randomly assigned into three groups and treated with HILT plus exercise (HILT + EX), placebo laser plus exercise (PL + EX), and HILT alone in groups 1, 2, and 3, respectively. The outcomes measured were lumbar range of motion (ROM), pain level by visual analog scale (VAS), and functional disability by both the Roland Disability Questionnaire (RDQ) and the Modified Oswestry Disability Questionnaire (MODQ). Statistical analyses were performed to compare the differences between baseline and post-treatment measurements. The level of statistical significance was set as P < 0.05. ROM significantly increased after 4 weeks of treatment in all groups, then significantly decreased after 12 weeks of follow-up, but was still significantly more than the baseline value in groups 1 and 2. VAS, RDQ, and MODQ results showed significant decrease post-treatment in all groups, although the RDQ and MODQ results were not significantly different between groups 2 and 3. HILT combined with exercise appears to be more effective in patients with CLBP than either HLLT alone or placebo laser with exercise.

Monte Carlo Tree Search for Asymmetric Trees <=SEP=> We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account. Our first algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree structure uncertainty and leverages it for exploration in a modified UCB formula. Results show vastly improved efficiency in a well-known asymmetric domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about asymmetric termination to the presence of loops in the tree, where the same state appears multiple times in a single trace. An extension to our algorithm (MCTS-T+), which in addition to nonstochasticity assumes full state observability, further increases search efficiency for domains with loops as well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates that our algorithms always perform better than or at least equivalent to standard MCTS, and could be first-choice tree search algorithms for non-stochastic, fully-observable environments.

A Lightweight Multicast Authentication Mechanism for Small Scale IoT Applications <=SEP=> Security is very important for Internet of Things (IoTs). As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. With the analysis of Nyberg's fast one-way accumulator and its security, we discover that it has the property of absorbency in addition to the one-way and quasi-communicative property that makes it very suitable for applications in which accumulated items are dynamic. In this paper, we revise the original Nyberg's fast one-way accumulator and construct a lightweight multicast authentication mechanism for small scale IoT applications. We analyze the security of the mechanism in detail. In addition, we evaluate seven performance aspects of the mechanism.