Miracle-dz/UniFAR-FTUs huggingface dataset:
Gesture Elicitation Studies for Mid-Air Interaction: A Review

Mid-air interaction involves touchless manipulations of digital content or remote devices, based on sensor tracking of body movements and gestures. There are no established, universal gesture vocabularies for mid-air interactions with digital content or remote devices based on sensor tracking of body movements and gestures. On the contrary, it is widely acknowledged that the identification of appropriate gestures depends on the context of use, thus the identification of mid-air gestures is an important design decision. The method of gesture elicitation is increasingly applied by designers to help them identify appropriate gesture sets for mid-air applications. This paper presents a review of elicitation studies in mid-air interaction based on a selected set of 47 papers published within 2011–2018. It reports on: (1) the application domains of mid-air interactions examined; (2) the level of technological maturity of systems at hand; (3) the gesture elicitation procedure and its variations; (4) the appropriateness criteria for a gesture; (5) participants number and profile; (6) user evaluation methods (of the gesture vocabulary); (7) data analysis and related metrics. This paper confirms that the elicitation method has been applied extensively but with variability and some ambiguity and discusses under-explored research questions and potential improvements of related research.
Vision-Based Flying Targets Detection via Spatiotemporal Context Fusion

Deriving from the imperative necessities for developing Sense and Avoid (SAA) capability of Unmanned Aerial Vehicle (UAV), a newly designed flying targets detection algorithm is presented in this paper for enhancing the UAV environment perception ability. Since spatiotemporal context is crucial for insuring the effectiveness of flying targets detection, the algorithm is constructed on the basis of spatiotemporal context fusion. The algorithm proposed in this paper contains three parts, namely the spatial context extraction, temporal context extraction and spatiotemporal context fusion. 1) In order to extract spatial context, dense sampling method is firstly applied to obtain dense image grids, then spatial context is generated via pre-learned conditional random field (CRF) model using a layered structure: dense image patches, bottom feature descriptors, sparse codes, and predicted CRF labels. 2) In order to extract temporal context, the forward and back motion history image (FBMHI) is firstly computed for detecting motion cues, and the adaptive foreground and background isolation is further adopted for acquiring the temporal probability map. 3) The presence probability map of flying targets is finally obtained by spatiotemporal context fusion, and flying targets is therefore picked out by analyzing fused presence probability map. A set of videos containing different drone models are selected for evaluation, and the comparisons against other algorithms demonstrate superiority of the proposed algorithm.
An Evaluation of Game Usability in Shared Mixed and Virtual Environments

Augmented reality (AR) and virtual reality (VR) technologies are increasingly becoming more pervasive and important in the information technology area. Thanks to the technological improvements, desktop interfaces are being replaced by immersive VR devices that offer a more compelling game experience. AR games have started to draw attention of researchers and companies for their ability to exploit both the real and virtual environments. New fascinating challenges are generated by the possibility of designing hybrid games that allow several users to access shared environments exploiting the features of both AR and VR devices. However, the user experience and usability can be affected by several parameters, such as the field of view (FoV) of the employed devices or the realism of the scene. The work presented in this chapter aims to assess the impact of the FoV on the usability of the interfaces in a first-person shooter game. Two players, interacting with AR (first player) and VR (second player) devices, can fight each other in a large game environment. Although we cannot ascertain that different FoVs have affected the game usability, users considered the narrow FoV interfaces to be less usable, even though they could freely move around the real environment.
iLSM-SSD: An Intelligent LSM-Tree Based Key-Value SSD for Data Analytics

Several key-value stores such as RocksDB and MongoDB are implemented on the file system using the Log-Structured Merge-Tree (LSM-tree). The LSM-tree involves high compaction overhead. To minimize this overhead, WiscKey, the state-of-the-art LSM-tree, separates key and value, appends the value to the Value Log file, and LSM-tree manages only the key and Value Log offset. This minimizes the compaction overhead by reducing the number of SSTables managed by the LSM-tree. However, WiscKey still has a high I/O stack overhead that must go through the OS file system and block-layer. Therefore, this paper proposes iLSM-SSD that implements WiscKey in SSD and supports near-data processing. iLSM-SSD has the following features: (i) iLSM-SSD implements a key-value separation based LSM-tree in a limited memory space inside the SSD. (ii) The Value Log offset update management overhead incurred during the Value Log cleaning has a significant performance impact on CPU and memory-constrained SSD environments. To minimize this overhead, iLSM-SSD implements Scattered Logging, which reuses invalidated Value Log pages on the Value Log. (iii) iLSM-SSD manages the data layout internally. This enables iLSM-SSD to eliminate the need for file system interactions to obtain the data layout for in-storage processing on traditional block-interface-based SSDs. We prototyped the iLSM-SSD on the Cosmos+ OpenSSD platform in a Linux environment. Extensive evaluations with synthetic benchmarks have shown that the PUT performance of iLSM-SSD is 1.6-4 times higher than that of WiscKey implemented in RocksDB.
Understanding the combinatorial thinking through the strategy used by students cognitive reflective in solving permutation

Combinatorial thinking is useful for training students in the concept of enumerationand others. Therefore, it is important to understand the students' combinatorial thinking. Thestrategy were used to be able to know the combinatorial way of thinking. This research aimedto determine the strategy and describe the process of using the strategy of the research subject.The data collection was done by giving the permutation problem and interview. The validationwas done in time triangulation. The technique of data analysis was done through three stepsnamely data reduction, data presentation, and conclusion. The students with reflectivecognitive style were chosen based on MFFT to 140 students. There were 49 students withreflective cognitive style were asked to do the permutation problem. There were two groupsnamely 92% of the students used one strategy and 8% of the students used two kinds ofstrategy. The research subjects chosen were only two students. The results showed that twotypes of strategies used are the filling slot and the formula. The students in general used fillingslot. For convincing the truth, some students used another way that is the formula ofpermutation. Finally, the students matched the answers from the two strategies.
Optimal Route Search using Bounded Cost Informative Routes

: As travel is taking more significant part in our life, route recommendation service becomes user interested to visit new spots and the new short rout as well as long route with the interesting new places, a big business and attracts many major players in IT industry. Given a user specified origin and destination, a route recommendation service aims to provide users the routes with the best travelling experience according to criteria such as travelling distance, travelling time, etc. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers. In this work the approach to this challenging problem is with a completely different perspective – leveraging crowds’ knowledge to improve the recommendation quality. The widespread location-aware applications produce a vast amount of spatio-textual data that contains both spatial and textual attributes. To make use of this enriched information for users to describe their preferences for travel routes, a Bounded-Cost Informative Route (BCIR) query is proposed to retrieve the routes that are the most textually relevant to the user-specified query keywords subject to a travel cost constraint. BCIR query is particularly helpful for tourists and city explorers to plan their travel routes. The proposed system will show that BCIR query is an NP-hard problem. To answer BCIR query efficiently, the exact solution is explained with effective pruning techniques and two approximate solutions with performance guarantees. Extensive experiments over real data sets demonstrate that the proposed solutions achieve the expected performance.
An Improved Greedy Forwarding Scheme in MANETs

—Position-based routing protocols are widely accepted eﬃcient solution for routing in MANETs. The main feature of position-based routing protocols is to use greedy forwarding methods to route data. The greedy forwarding methods select a node, either having maximum progress towards destination (distance-based principle) or minimum deviation with line between source and destination (direction-based strategy). The ﬁrst method minimizes the hopcount in a path and on the other hand, second method minimizes the spatial distance between source and destination. The distance-based routing has a great impact on the selection of reliable node and the direction based routing plays a major role to increase the stability of route towards destination. Therefore, in this paper authors propose a weighted forwarding method, which combines both the selection, schemes to select an optimal next forwarding node in a range. The simulation results show that the proposed scheme performs better than existing position-based routing protocols.
Frequent State Transition Patterns of Multivariate Time Series

Sequence pattern discovery is a key issue in multivariate time series analysis. Popular approaches first obtain the pattern of each single-variate time series and then obtain cross-variate associations. In this paper, we consider different variables at the same time during pattern construction, and propose a new type of pattern called State Transition pAttern with Periodic wildcard gaps (STAP). Compared to previous types, STAP reveals stronger cross associations among different variables and provides better interpretability for decision makers. We design an approach with two stages, namely frequent state discovery and pattern synthesis, to obtain frequent STAPs. We propose two pre-pruning and an Apriori-pruning techniques to speed up pattern discovery. We also propose two post-pruning techniques to simplify the output and a visualization way to support expert decision. Experimental results on four real-world datasets demonstrate 1) STAP captures the cross and temporal associations; 2) the five pruning and pattern synthesis techniques are quite effective; and 3) visualization technique greatly increases the readability of STAP.
Developing RC4 Algorithm Using S-Box of Advanced Encryption Standard Cipher

RC4 stream cipher is one of the most significant symmetric cryptosystems, it is simple and used in many commercial products. RC4 uses dynamic permutations and avoids using Linear Feed Back Shift Register (LFSR). It has many weaknesses, such as the tendency in the generated key stream that some key bytes are biased toward different values. This paper presents a new algorithm using S-box of Advanced Encryption Standard (AES) to solve the correlation between public known outputs of the internal state. The state table is filled from S-box values and additional swapping operations are used. The analysis of the proposed algorithm over variable key length produces key byte streams that have no single and double bias. This paper obtains a new algorithm that combines the efficiency of the RC4 and robustness of AES. The results show that the sequences that are generated by the developed RC4 are more random than the sequence that was generated by the RC4. Also, the developed algorithm demands little time more than RC4 execution time. Additionally, the developed algorithm is robust against most attacks, such as distinguishing attack and can be used in different protocols such as Secure Sockets Layer (SSL) Protocol, Oracle Secure SQL, and Wired Equivalent Privacy (WEP) Protocol.
Spark98: Sparse Matrix Kernels for Shared Memory and Message Passing Systems

Abstract : Spark98 is a collection of sparse matrix kernels for shared memory and message passing systems. Our aim is to provide system builders with a set of example sparse matrix codes that are simple, realistic, and portable. Each kernel performs a sequence of sparse matrix vector product operations using matrices that are derived from a family of three dimensional finite element earthquake applications. We describe the computational structure of the kernels, summarize their performance on a parallel system, and discuss some of the insights that such kernels can provide. In particular we notice that efficient parallel programming of sparse codes requires careful partitioning of data references, regardless of the underlying memory system. So on one hand, efficient shared memory programs can be just as difficult to write as efficient message passing programs. On the other hand, shared memory programs are not necessarily less efficient than message passing programs.
Initial benchmarking of the Intel 3D-stacked MCDRAM

Modern, manycore processors are increasingly using high-bandwidth memory (HBM) to provide the necessary memory bandwidth to high core counts. The Knights Landing processor is one example, which is paired with a three-dimensional (3D) stacked memory, and a multi-channel direct random access memory (MCDRAM). The MCDRAM offers high-bandwidth memory, however, it is also important to understand the latency of the memory and how the memory will respond to noncontiguous accesses. This work provides information to programmers about how their applications will perform, and which of the many memory settings that will provide the best performance. These settings are non-trivial and can have a large impact on application performance. In addition to several micro-benchmarks, this work shows that using the MCDRAM can provide speedups up to 3.7x for a congregant gradient application, while giving slowdowns of 3x for certain hash table implementations. Given this range of performance, it is essential for programmers to understand this memory technology and how to use it appropriately.
Lazy scheduling based disk energy optimization method

: Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efﬁciency of high-performance computing systems. We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window, which we call time window-based lazy scheduling. We prove that sorting requests within a single time window can reduce the disk energy consumption, and we discuss the relationship between the size of the time window and the disk energy consumption, proving that the energy consumption is highly likely to decrease with increasing window size. To exploit this opportunity, we propose the Lazy Scheduling based Disk Energy Optimization (LSDEO) algorithm, which adopts a feedback method to periodically adjust the size of the time window, and minimizes the local disk energy consumption by sorting disk requests within each time window. We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm, conﬁrming that increasing the time window increases disk energy savings. When the average request arrival rate is 300 and the threshold of average request response time is 50 ms, LSDEO can yield disk energy savings of 21.5%.
Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks

In this paper, we propose a novel circuit and system to optimize the spacing between optical channels in gridless (also called flexible-grid or elastic) networking. The method will exploit the beginning-of-life link margin by enabling the channel to operate in super-Nyquist dense wavelength division multiplexing mode. We present the work in the context of software-defined networking and high-speed optical flexible-rate transponders. The clock recovery scheme allows the mitigation of jitter by decoupling the contribution of high-jitter noise sources from the clock recovery loop. The method and associated algorithm are experimentally verified where a spectrum gain of up to 2 GHz in spacing between two channels in the Media Channel (MC) is obtained compared to conventional clocking strategies. We showed that the improvement is equivalent to increasing throughput, in a data-center interconnect scenario, by up to 300 giga-bits per second per route.
A Survey on Techniques Adopted in the Prioritization of Test Cases for Regression Testing

Regression testing is testing the software with the intention to confirm that changes made on part of a module do not necessitate other parts of the module. Test case prioritization helps to reduce regression testing cost by ordering the test cases in such a way that it produces optimized results. Code Coverage and Fault detection being the factors behind the prioritization is dealt with techniques like Heuristic method, Meta Heuristic methods and Data mining techniques. The effectiveness of the techniques applied can be evaluated with the metrics like Average Percentage of Fault Detection (APFD) , Average Percentage Block Coverage (APBC), Average Percentage Decision Coverage (APDC) etc . In this paper,, a detailed survey on the various techniques adopted for the prioritization of test cases are presented.
Age-Invariant Face Recognition Using Trigonometric Central Features

Many facial recognition systems must fail because of many influences such as lighting, changes in composition, expression and aging in the face. But the effects of facial aging are the main problem that many algorithms face with precision. Accordingly, this work provides a model based on the extraction of a number of trigonometric features. The proposed model includes three areas connecting and surrounding the main facial features. In addition, aging is recognized by calculating the trigonometric zones extracted. The system compares the query face with the database. Then, the query image is extracted from the original face image. Next, the performance of the proposed model is compared with some of the latest facial recognition techniques. Facial recognition systems in different stages of life prove that the proposed facial recognition system gives enhanced accuracy of 99.80% with very low FAR level of 0.0001.
Optimization and new applications of edge-illumination based X-ray phase contrast CT

The Edge Illumination (EI) method is a phase contrast imaging technique suitable for use with conventional X-ray sources, which has demonstrated its great potential for translation into real-world environments. The increase in image contrast provided by it, in particular for low-absorbing materials, along with its ability to be operated as a computed tomography (CT) modality, make it an especially advantageous technique for biomedical applications. The work presented in this thesis aimed to develop new image acquisition and processing strategies to further advance the current capabilities of EI CT, such that it could be used in a robust way for a wide range of biomedical applications. This was achieved by the development of two retrieval algorithms, which are required for the extraction of quantitative information of various sample properties. The first algorithm targets applications which involve high-resolution, multi-modal lab-based CT scans. It has been shown that, during these long scans, the variation in system parameters across the field-of-view and over time can lead to significant image artefacts in reconstructed CT slices. The proposed algorithm is capable of correcting for both types of variations, leading to the quantitative retrieval of sample properties from long CT scans in non-ideal environments. The second algorithm was aimed at applications which require fast CT scans, possibly at the expense of spatial resolution and quantitative information. A previously developed algorithm enabled a substantial reduction in scan times for homogeneous samples, by reducing the number of required images per angular view. The new algorithm presented here has extended the previous one to non-homogeneous samples, therefore expanding the range of objects which could benefit from a reduction in scan times through use of this approach. In addition, perinatal post-mortem imaging has been identified as a new biomedical application which could benefit from EI CT in the future. Promising results are reported from a proof-of-principle scan carried out using a gold standard X-ray phase contrast imaging method at a synchrotron.
Robust Algorithms for Localizing Moving Nodes in Wireless Sensor Networks

The vivid success of the emerging wireless sensor technology (WSN) gave rise to the notion of localization in the communications field. Indeed, the interest in localization grew further with the proliferation of the wireless sensor network applications including medicine, military as well as transport. By utilizing a subset of sensor terminals, gathered data in a WSN can be both identified and correlated which helps in managing the nodes distributed throughout the network. In most scenarios presented in the literature, the nodes to be localized are often considered static. However, as we are heading towards the 5th generation mobile communication, the aspect of mobility should be regarded. Thus, the novelty of this research relies in its ability to merge the robotics as well as WSN fields creating a state of art for the localization of moving nodes. The challenging aspect relies in the capability of merging these two platforms in a way where the limitations of each is minimized as much as possible. A hybrid technique which combines both the Particle Filter (PF) method and the Time Difference of Arrival Technique (TDOA) is presented. Simulation results indicate that the proposed approach outperforms other techniques in terms of accuracy and robustness.
Main Fault Types and Classification Methods of Metering Box

This article classifies and analyzes the failures of low-pressure metering boxes to ensure accurate and reliable energy metering. Component Critical Assessment (CCA) method was used to evaluate various components of the metering box system. Failure Mode and Effect Analysis (FMEA) method was used for risk assessment of the low pressure metering box. Establish a database of low-pressure metering faults, responsible for the storage, management, and maintenance of fault data. A fault crossover analysis model was established to analyze the strength of the correlation between the impact factors of the low pressure metering box. Optimize the transportation, storage and use of low-pressure metering boxes to reduce the failure of metering boxes.