Suspicious Pairs Research Articles

Phonology Analysis Theory of Phonetics

Phonetic features such as phonemes, minimal pairs, suspicious pairs, and procedures are applied in this study. Identifies the analyzed phoneme, interview methods, native speaker involment and methods. Literature studies are for data collection, data analysis, and especially during analysis. Phonemes are completed through multiple processes including preprocessing (pre-process), separation process, merger process (separation procedure).

Proactive Eavesdropping in Massive MIMO-OFDM Systems via Deep Reinforcement Learning

It is challenging to monitor suspicious links in massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with directional beamforming. When the monitor and suspicious user are not in the coverage of the same beam, to implement legitimate monitoring, we propose the proactive eavesdropping scheme in which the monitor acts as a spoofing relay to realize beam misleading and data eavesdropping. In the phase of beam sweep, the beam misleading algorithm induces the transmitter to choose the beam which is beneficial to the monitor by optimizing the relay precoding matrices. In the phase of data transmission, the data eavesdropping algorithm elevates the monitoring rate by optimizing the relay power split factors and power gain factors of all subcarriers. Due to the unknown channel state information between suspicious pairs, we propose to find the optimal precoders and power split factors by the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The simulation results verify the effectiveness and superiority of the proposed eavesdropping scheme.

T-norms or t-conorms? How to aggregate similarity degrees for plagiarism detection

Making correct decisions as to whether code chunks should be considered similar becomes increasingly important in software design and education and not only can improve the quality of computer programs, but also help assure the integrity of student assessments. In this paper we test numerous source code similarity detection tools on pairs of code fragments written in the data science-oriented functional programming language R. Contrary to mainstream approaches, instead of considering symmetric measures of “how much code chunks A and B are similar to each other”, we propose and study the nonsymmetric degrees of inclusion “to what extent A is a subset of B” and “to what degree B is included in A”. Overall, t-norms yield better precision (how many suspicious pairs are actually similar), t-conorms maximise recall (how many similar pairs are successfully retrieved), and custom aggregation functions fitted to training data provide a good balance between the two. Also, we find that program dependence graph-based methods tend to outperform those relying on normalised source code text, tokens, and names of functions invoked.

A pruning strategy to improve pairwise comparison-based near-duplicate detection

Efficient and accurate near-duplicate detection is a trending topic of research. Complications arise from the great time and space complexities of existing algorithms. This study proposes a novel pruning strategy to improve pairwise comparison-based near-duplicate detection methods. After parsing the documents into punctuation-delimited blocks called chunks, it decides between the categories of “near duplicate,” “non-duplicate” or “suspicious” by applying certain filtering rules. This early decision makes it possible to disregard many of the non-necessary computations—on average 92.95% of them. Then, for the suspicious pairs, common chunks and short chunks are removed and the remaining subsets are reserved for near-duplicate detection. Size of the remaining subsets is on average 4.42% of the original corpus size. Evaluation results show that near-duplicate detection with the proposed strategy in its best configuration (CHT = 8, τ = 0.1) has F-measure = 87.22% (precision = 86.91% and recall = 87.54%). Its F-measure is comparable with the SpotSig method with less execution time. In addition, applying the proposed strategy in a near-duplicate detection process eliminates the need for preprocessing. It is also tunable to achieve the intended levels of near duplication and noise suppression.

Keypoint Extraction Using SURF Algorithm for CMFD

Copy-move forgery is a specific type of image tampering where a part of the image is copied and pasted on another part generally to conceal unwanted portions of the image. Hence, the goal in detection of copy-move forgeries is to detect image areas that are same or extremely similar. In this paper we propose a new approach which can efficiently identify the copy move forged areas. For achieving the copied image area first segment the image that is the test image is segmented into different independent patches. These patches are evaluated based on their matching and then the copied images are identified. For patch matching a two level of matching is carried out. In the first stage suspicious pairs of matches is obtained and affine transform matrix is roughly estimated. In-order to confirm the existence of copy move forgery a second level of patch matching with respect to Expectation Maximization algorithm is used. Thus the copy move forged areas are identified.

An efficient forgery detection algorithm for object removal by exemplar-based image inpainting

As a popular image manipulation technique, object removal can be achieved by image-inpainting without any noticeable traces, which poses huge challenges to passive image forensics. The existing detection approach utilizes full search for block matching, resulting in high computational complexity. This paper presents an efficient forgery detection algorithm for object removal by exemplar-based inpainting, which integrates central pixel mapping (CPM), greatest zero-connectivity component labeling (GZCL) and fragment splicing detection (FSD). CPM speeds up suspicious block search by efficiently matching those blocks with similar hash values and then finding the suspicious pairs. To improve the detection precision, GZCL is used to mark the tampered pixels in suspected block pairs. FSD is adopted to distinguish and locate tampered regions from its best-match regions. Experimental results show that the proposed algorithm can reduce up to 90% of the processing time and maintain a detection precision above 85% under different kinds of object-removed images.

Segmentation-Based Image Copy-Move Forgery Detection Scheme

In this paper, we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the keypoints for comparison. The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an Expectation-Maximization-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.

How to Reduce an Unwritten Language to Writing: III. Phonetic Similarity, Suspicious Pairs, and Minimal Pairs

Phonetic analysis can be done with a short list of a few hundred words. First, carefully transcribe the list phonetically. Second, list the inventory of symbols. Third, tabulate the symbols by their articulatory position. Fourth, declare symbols in close articulatory proximity, that is, sounds of great phonetic similarity, as suspicious pairs. Finally, find two words with different meanings that are phonetically identical except for one member of a suspicious pair in each. Finding such a minimal pair implies that we have found a phoneme (writing symbol).