Although many software engineering studies have been conducted, it is not clear whether they meet the needs of software development practitioners. Some studies evaluated the effectiveness of software engineering research by practitioners, to clarify the research satisfies the needs of the practitioners. We performed replicated study of them, recruiting practitioners who mainly belong to SMEs (small and medium-sized enterprises) to the survey. We asked 16 practitioners to evaluate cutting-edge software engineering studies presented in ICSE 2016. In the survey, we set the viewpoint of the evaluation as the effectiveness for the respondent's own work. As a result, the ratio of positive answers (i.e., the answers were greater than 2 on a 5-point scale) was 33.3%, and the ratio was lower than past studies. The result was not affected by the number of employees in the respondent's company, but would be affected by the viewpoint of the evaluation.

Information-centric networking (ICN) provides an alternative to the traditional end-to-end communication model of the current Internet architecture by focusing on information dissemination and information retrieval. Named Data Networking (NDN) is one of the candidates that implements the idea of ICN on a practical level. Implementing NDN in wireless sensor networks (WSNs) will bring all the benefits of NDN to WSNs, making them more efficient. By applying the NDN paradigm directly to wireless multi-hop ad-hoc networks, various drawbacks are observed, such as packet flooding due to the broadcast nature of the wireless channel. To cope with these problems, in this paper, we propose an Interest called the accumulation-based forwarding scheme, as well as a novel content store architecture to increase its efficiency in terms of storing and searching data packets. We have performed extensive simulations using the ndnSIM simulator. Experimental results showed that the proposed scheme performs better when compared to another scheme in terms of the total number of Interests, the content store search time, and the network lifetime.

Objective interestingness measures play a vital role in association rule mining of a large-scaled database because they are used for extracting, filtering, and ranking the patterns. In the past, several measures have been proposed but their similarities or relations are not sufficiently explored. This work investigates sixty-one objective interestingness measures on the pattern of A → B, to analyze their similarity and dissimilarity as well as their relationship. Three-probability patterns, P(A), P(B), and P(AB), are enumerated in both linear and exponential scales and each measure's values of those conditions are calculated, forming synthesis data for investigation. The behavior of each measure is explored by pairwise comparison based on these three-probability patterns. The relationship among the sixty-one interestingness measures has been characterized with correlation analysis and association rule mining. In the experiment, relationships are summarized using heat-map and association rule mined. As the result, selection of an appropriate interestingness measure can be realized using the generated heat-map and association rules.

In this paper, we propose a packet loss detection mechanism called Interest ACKnowledgement (ACK). Interest ACK provides information on the history of successful Interest packet receptions at a repository (i.e., content provider); this information is conveyed to the corresponding entity (i.e., content consumer) via the header of Data packets. Interest ACKs enable the entity to quickly and accurately detect Interest and Data packet losses in the network. We conduct simulations to investigate the effectiveness of Interest ACKs under several scenarios. Our results show that Interest ACKs are effective for improving the adaptability and stability of CCN with window-based flow control and that packet losses at the repository can be reduced by 10%-20%. Moreover, by extending Interest ACK, we propose a lossy link detection mechanism called LLD-IA (Lossy Link Detection with Interest ACKs), which is a mechanism for an entity to estimate the link where the packet was discarded in a network. Also, we show that LLD-IA can effectively detect links where packets were discarded under moderate packet loss ratios through simulation.

With the spread of map applications, route generation has become a familiar function. Most of route generation methods search a route from a starting point to a destination point with the shortest time or shortest length, but more enjoyable route generation is recently focused on. Particularly, cyclic-route generation for strolling requires to suggest to a user more than one route passing through several POIs (Point-of-Interests), to satisfy the user's preferences as much as possible. In this paper, we propose a multiple cyclic-route generation method with a route length constraint considering POIs. Firstly, our proposed method finds out a set of reference points based on the route length constraint. Secondly, we search a non-cyclic route from one reference point to the next one and finally generate a cyclic route by connecting these non-cyclic routes. Compared with previous methods, our proposed method generates a cyclic route closer to the route length constraint, reduces the number of the same points passing through by approximately 80%, and increases the number of POIs passed approximately 1.49 times.

Peer-to-peer overlay networks can easily achieve a large-scale content sharing system on the Internet. Although unstructured peer-to-peer networks are suitable for finding entire partial-match content, flooding-based search is an inefficient way to obtain target content. When the shared content is semantically specified by a great number of attributes, it is difficult to derive the semantic similarity of peers beforehand. This means that content search methods relying on interest-based locality are more advantageous than those based on the semantic similarity of peers. Existing search methods that exploit interest-based locality organize multiple peer groups, in each of which peers with common interests are densely connected using short-cut links. However, content searches among multiple peer groups are still inefficient when the number of incident links at each peer is limited due to the capacity of the peer. This paper proposes a novel content search method that exploits interest-based locality. The proposed method can organize an efficient peer-to-peer network similar to the semantic small-world random graph, which can be organized by the existing methods based on the semantic similarity of peers. In the proposed method, topology transformation based on local link replacement maintains the numbers of incident links at all the peers. Simulation results confirm that the proposed method can achieve a significantly higher ratio of obtainable partial-match content than existing methods that organize peer groups.

In the field of action recognition, Spatio-Temporal Interest Points (STIPs)-based features have shown high efficiency and robustness. However, most of state-of-the-art work to describe STIPs, they typically focus on 2-dimensions (2D) images, which ignore information in 3D spatio-temporal space. Besides, the compact representation of descriptors should be considered due to the costs of storage and computational time. In this paper, a novel local descriptor named 3D Gradient LBP is proposed, which extends the traditional descriptor Local Binary Patterns (LBP) into 3D spatio-temporal space. The proposed descriptor takes advantage of the neighbourhood information of cuboids in three dimensions, which accounts for its excellent descriptive power for the distribution of grey-level space. Experiments on three challenging datasets (KTH, Weizmann and UT Interaction) validate the effectiveness of our approach in the recognition of human actions.

Content-centric networking (CCN) is an emerging networking architecture that is being actively investigated in both the research and industrial communities. In the latest version of CCN, a large number of interests have to be issued when large content is retrieved. Since CCN routers have to search several tables for each incoming interest, this could cause a serious problem of router workload. In order to solve this problem, this paper introduces a novel strategy of “grouping” multiple interests with common information and “packing” them to a special interest called the list interest. Our list interest is designed to co-operate with the manifest of CCN as its dual. This paper demonstrates that by skipping and terminating several search steps using the common information in the list interest, the router can search its tables for the list interest-based request with dramatically smaller complexity than the case of the standard interest-based request. Furthermore, we also consider the deployment of list interests and design a novel TCP-like congestion control method for list interests to employ them just like standard interests.

In this paper, we first analyze the discriminative power in the Best Match (BM) 25 formula and provide its calculation method from the Bayesian point of view. The resulting, derived discriminative power is quite similar to the exponential inverse document frequency (EIDF) that we have previously proposed [1] but retains more preferable theoretical advantages. In our previous paper [1], we proposed the EIDF in the framework of the probabilistic information retrieval (IR) method BM25 to address the instance search task, which is a specific object search for videos using an image query. Although the effectiveness of our EIDF was experimentally demonstrated, we did not consider its theoretical justification and interpretation. We also did not describe the use of region-of-interest (ROI) information, which is supposed to be input to the instance search system together with the original image query showing the instance. Therefore, here, we justify the EIDF by calculating the discriminative power in the BM25 from the Bayesian viewpoint. We also investigate the effect of the ROI information for improving the instance search accuracy and propose two search methods incorporating the ROI effect into the BM25 video ranking function. We validated the proposed methods through a series of experiments using the TREC Video Retrieval Evaluation instance search task dataset.

Many previous studies on image quality assessment of 3D still images or video clips have been conducted. In particular, it is important to know the region in which assessors are interested or on which they focus in images or video clips, as represented by the ROI (Region of Interest). For multi-view 3D images, it is obvious that there are a number of viewpoints; however, it is not clear whether assessors focus on objects or background regions. It is also not clear on what assessors focus depending on whether the background region is colored or gray scale. Furthermore, while case studies on coded degradation in 2D or binocular stereoscopic videos have been conducted, no such case studies on multi-view 3D videos exist, and therefore, no results are available for coded degradation according to the object or background region in multi-view 3D images. In addition, in the case where the background region is gray scale or not, it was not revealed that there were affection for gaze point environment of assessors and subjective image quality. In this study, we conducted experiments on the subjective evaluation of the assessor in the case of coded degradation by JPEG coding of the background or object or both in 3D CG images using an eight viewpoint parallax barrier method. Then, we analyzed the results statistically and classified the evaluation scores using an SVM.

Due to the recent progress made in camera and network environments, on-line video services enable people around the world to watch or share high-quality HD videos that can record a wider angle without losing objects' details in each image. As a result, users of these services can watch videos in different ways with different ROIs (Regions of Interest), especially when there are multiple objects in a scene, and thus there are few common ways for them to transfer their impressions for each scene directly. Posting messages is currently the usual way but it does not sufficiently enable all users to transfer their impressions. To transfer a user's impressions directly and provide users with a richer video watching experience, we propose a system that enables them to extract their favorite parts of videos as ROI trajectories through simple and intuitive manipulation of their tablet device. It also enables them to share a recorded trajectory with others after stabilizing it in a manner that should be satisfactory to every user. Using statistical analysis of user manipulations, we have demonstrated an approach to trajectory stabilization that can eliminate undesirable or uncomfortable elements due to tablet-specific manipulations. The system's validity has been confirmed by subjective evaluations.

Segmenting indicated objects from natural color images remains a challenging problem for researches of image processing. In this paper, a novel level set approach is presented, to address this issue. In this segmentation algorithm, a contour that lies inside a particular region of the concerned object is first initialized by a user. The level set model is then applied, to extract the object of arbitrary shape and size containing this initial region. Constrained on the position of the initial contour, our proposed framework combines two particular energy terms, namely local and global energy, in its energy functional, to control movement of the contour toward object boundaries. These energy terms are mainly based on graph partitioning active contour models and Bhattacharyya flow, respectively. Its flow describes dissimilarities, measuring correlative relationships between the region of interest and surroundings. The experimental results obtained from our image collection show that the suggested method yields accurate and good performance, or better than a number of segmentation algorithms, when applied to various natural images.

We present a novel point of interest (POI) construction approach based on street-level imagery (SLI) such as Google StreetView. Our method consists of: (1) the creation of a conflation map between an SLI trace and a vector map; (2) the detection of the corresponding buildings between the SLI scene and the conflation map; and (3) POI name extraction from a signboard in the SLI scene by user-interactive text recognition. Finally, a POI is generated through a combination of the POI name and attributes of the building object on a vector map. The proposed method showed recall of 92.99% and precision of 97.10% for real-world POIs.

High-definition (HD) videos become more and more popular on portable devices these years. Due to the resolution mismatch between the HD video sources and the relative low-resolution screens of portable devices, the HD videos are usually fully decoded and then down-sampled (FDDS) for the displays, which not only increase the cost of both computational power and memory bandwidth, but also lose the details of video contents. In this paper, an encoder-unconstrained partial decoding scheme for H.264/AVC is presented to solve the problem by only decoding the object of interest (OOI) related region, which is defined by users. A simplified compression domain tracking method is utilized to ensure that the OOI locates in the center of the display area. The decoded partial area (DPA) adaptation, the reference block relocation (RBR) and co-located temporal Intra prediction (CTIP) methods are proposed to improve the visual quality for the DPA with low complexity. The simulation results show that the proposed partial decoding scheme provides an average of 50.16% decoding time reduction comparing to the fully decoding process. The displayed region also presents the original HD granularity of OOI. The proposed partial decoding scheme is especially useful for displaying HD video on the devices of which the battery life is a crucial factor.

This paper proposes a region-of-interest (ROI) based H.264 encoder and the VLSI architecture of the ROI detection algorithm. In ROI based video coding system, pre-processing unit to detect ROI should only introduce low computational complexity overhead due to the low power requirement. The Macroblocks (MBs) in ROIs are detected sequentially in the same order of H.264 encoding to satisfy the MB level pipelining of ROI detector and H.264 encoder. ROI detection is performed in a novel estimation-and-verification process with an ROI contour template. Proposed architecture can be configured to detect either single ROI or multiple ROIs in each frame and the throughput of single detection mode is 5.5 times of multiple detection mode. 98.01% and 97.89% of MBs in ROIs can be detected in single and multiple detection modes respectively. Hardware cost of proposed architecture is only 4.68 k gates. Detection speed is 753 fps for CIF format video at the operation frequency of 200 MHz in multiple detection mode with power consumption of 0.47 mW. Compared with previous fast ROI detection algorithms for video coding application, the proposed architecture obtains more accurate and smaller ROI. Therefore, more efficient ROI based computation complexity and compression efficiency optimization can be implemented in H.264 encoder.

An advanced interest point detector is proposed to improve the Hessian-Matrix based detector of the SURF algorithm. Round-like shapes are utilized as the filter shape to calculate of the Hessian determinant. Dxy can be acquired from approximate round areas, while the regions for computing Dyy or Dxx are designed with the consideration to symmetry and a balance of pixel number. Experimental results indicate that the proposed method has higher repeatability than the one used in SURF, especially in the aspects of rotation and viewpoint, due to the centrosymmetry of the proposed filter shapes. The results of image matching also show that more precision can be gained with the application of proposed detector.

People are being inundated under enormous volumes of information and they often dither about making the right choices from these. Interactive user support by information service system such as concierge services will effectively assist such people. However, human-machine interaction still lacks naturalness and thoughtfulness despite the widespread utilization of intelligent systems. The system needs to estimate user's interest to improve the interaction and support the choices. We propose a novel approach to estimating the interest, which is based on the relationship between the dynamics of user's eye movements, i.e., the endogenous control mode of saccades, and machine's proactive presentations of visual contents. Under a specially-designed presentation phase to make the user express the endogenous saccades, we analyzed the timing structures between the saccades and the presentation events. We defined resistance as a novel time-delay feature representing the duration a user's gaze remains fixed on the previously presented content regardless of the next event. In experimental results obtained from 10 subjects, we confirmed that resistance is a good indicator for estimating the interest of most subjects (75% success in 28 experiments on 7 subjects). This demonstrated a higher accuracy than conventional estimates of interest based on gaze duration or frequency.

This letter presents a novel robust video watermarking scheme based on space-time interest points. These points correspond to inherent structures of the video so that they can be used as synchronization signals for watermark embedding and extraction. In the proposed scheme, local regions are generated using the space-time interest points, and the watermark is embedded into all the regions by quantization. It is a blind scheme and the watermark can be extracted from any position of the video. Experimental results show that the watermark is invisible and it can robustly survive traditional signal processing attacks and video-oriented attacks.

Conventional interest point based matching requires computationally expensive patch preprocessing and is not appropriate for recognition of plain objects with negligible detail. This paper presents a method for extracting distinctive interest regions from images that can be used to perform reliable matching between different views of plain objects or scene. We formulate the correspondence problem in a Naive Bayesian classification framework and a simple correlation based matching, which makes our system fast, simple, efficient, and robust. To facilitate the matching using a very small number of interest regions, we also propose a method to reduce the search area inside a test scene. Using this method, it is possible to robustly identify objects among clutter and occlusion while achieving near real-time performance. Our system performs remarkably well on plain objects where some state-of-the art methods fail. Since our system is particularly suitable for the recognition of plain object, we refer to it as Simple Plane Object Recognizer (SPOR).

In this paper, a coefficient-parameter embedding method into Motion-JPEG2000 (MJP2) is proposed for invertible deinterlacing with variable coefficients. Invertible deinterlacing, which the authors have developed before, can be used as a preprocess of frame-based motion picture codec, such as MJP2, for interlaced videos. When the conventional field-interleaving is used instead, comb-tooth artifacts appear around edges of moving objects. On the other hand, the invertible deinterlacing technique allows us to suppress the comb-tooth artifacts and also guaranties recovery of original pictures. As previous works, the authors have developed a variable coefficient scheme with a motion detector, which realizes adaptability to local characteristics of given pictures. However, when this deinterlacing technique is applied to a video codec, coefficient parameters have to be sent to receivers for original picture recovery. This paper proposes a parameter-embedding technique in MJP2 and constructs a standard stream which consists both of picture data and the parameters. The parameters are embedded into the LH1 component of wavelet transform domain through the ROI (region of interest) function of JPEG2000 without significant loss in the performance of comb-tooth suppression. Some experimental results show the feasibility of our proposed scheme.