Orientation-Encoding CNN for Point Cloud Classification and Segmentation

With the introduction of effective and general deep learning network frameworks, deep learning based methods have achieved remarkable success in various visual tasks. However, there are still tough challenges in applying them to convolutional neural networks due to the lack of a potential rule structure of point clouds. Therefore, by taking the original point clouds as the input data, this paper proposes an orientation-encoding (OE) convolutional module and designs a convolutional neural network for effectively extracting local geometric features of point sets. By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. Further experiments on diverse datasets show that the proposed method has competitive performance on classification and segmentation tasks of point sets.

Mughal Authority: The View from Below

This chapter views Mughal authority from below, in terms of the entangled relations between the state and social forces. It looks at the state as an activity, ceaselessly reproducing itself in and through complex layers of relations with the local power relations. Looking at the state from the vantage point of the localities, it argues that the state was largely undifferentiated from the networks of social relations. State–society relations were molded by the use of pen and paper, but scribal literacy was intertwined with oral tradition and performative practices. Literacy was not just an instrument of state control, but was also appropriated by social actors to participate in the rule structure. The ordinary subjects negotiated with the state, and incessantly modified the system of rule through such devices as petitions, complaints, handbills, etc. that were routinely presented at the local qazi’s courts.

Children Only 3 Years Can Succeed At Conditional "If, Then" Reasoning, Much Earlier Than Anyone Had Thought Possible

We report results showing success at 3 years on conditional discrimination (CD) -- 12-18 months younger than previously reported. Three-year-olds succeeded when color was a property of the stimulus, rather than a property of the background, as in all past CD testing. Previously, we and others found children succeed on the dimensional change card sort (DCCS) test at 3 years -- 12-24 months earlier than previously reported -- by making color a property of the background, instead of a property of the stimulus, as in standard DCCS testing. Neither the change to CD or DCCS affected the rule structure or reasoning requirements of the task. This double dissociation, with 3-year-olds performing better on CD when color and shape were integrated but better on DCCS when color and shape were separated, indicates that when superficial stimulus properties are modified 3-year-olds can do conditional reasoning and grasp a hierarchical rule structure - but they seem to need perceptual boot-strapping to do that. Children of 3 years evidently have difficulty mentally separating physical dimensions (e.g., color and shape) of the same object and difficulty mentally integrating physical dimensions not part of the same object. These results provide the strongest evidence to date against conceptual accounts of why children of 3 years fail conditional discrimination or card sorting.

Fuzzy Logic based Improvement in Discrete Wavelet Transform Edge Representation in Images.doc

We introduce a rule base fuzzy technique on decomposed wavelet coefficients, to improve the wavelet edge representation. Our algorithm mitigates ‘incorrect’ responses, due primarily to the symmetries of directional derivative filters. Since the Discrete Wavelet Transform (DWT) coefficients are revealed from two dimensional symmetric filter banks and undermine some gradient information. These wavelet coefficients are prearranged into ‘if-then’ rule structure of a fuzzy inference system, to improve the wavelet edge representation.

Fuzzy Logic based Improvement in Discrete Wavelet Transform Edge Representation in Images.doc

We introduce a rule base fuzzy technique on decomposed wavelet coefficients, to improve the wavelet edge representation. Our algorithm mitigates ‘incorrect’ responses, due primarily to the symmetries of directional derivative filters. Since the Discrete Wavelet Transform (DWT) coefficients are revealed from two dimensional symmetric filter banks and undermine some gradient information. These wavelet coefficients are prearranged into ‘if-then’ rule structure of a fuzzy inference system, to improve the wavelet edge representation.

Dissociable Neural Systems Support the Learning and Transfer of Hierarchical Control Structure

Humans can draw insight from previous experiences in order to quickly adapt to novel environments that share a common underlying structure. Here we combine functional imaging and computational modeling to identify the neural systems that support the discovery and transfer of hierarchical task structure. Human subjects completed multiple blocks of a reinforcement learning task that contained a global hierarchical structure governing stimulus-response action mapping. First, behavioral and computational evidence showed that humans successfully discover and transfer the hierarchical rule structure embedded within the task. Next, analysis of fMRI BOLD data revealed activity across a frontal-parietal network that was specifically associated with the discovery of this embedded structure. Finally, activity throughout a cingulo-opercular network and in caudal frontal cortex supported the transfer and implementation of this discovered structure. Together, these results reveal a division of labor in which dissociable neural systems support the learning and transfer of abstract control structures.

“Let’s Pretend You Took Freddy’s Mask Off”: Communicative Strategies and Agency Redistribution in Digitally-informed Children’s Pretend Play

The paper addresses the issue of children’s play within a contemporary digital environment. Building on data collected through participant observation at an afterschool club for primary-school children, the article analyzes digitally-informed pretend play in which the plot and the rule-structure of the “Five Nights at Freddy’s” game have been employed as a resource. Andrew Burn’s scheme for the analysis of computer games’ adaptation to the playground and the sociolinguistic classifications of children’s speech patterns used in a pretend play serve as a conceptual framework for this study. An examination of the way children deploy media-references in their play provides evidence for children’s creative meaning-making and their ability to collectively rethink and adjust media-texts to the actual playground context, as well as to their own play goals and needs—while also relying on cultural resources of different kinds. More importantly, the structural borrowing from a digital game and its adaptation to a pretend play gives participants more opportunities to perform agentive acts than they have otherwise; both compared to the original digital game and “wholly original” pretend play. Agency is realized by using specific verbal structures (“you utterances”). These results contradict the adults’ common concerns about children being passive and not imaginative in the process of consuming digital games.

3C3R, an Image Encryption Algorithm Based on BBI, 2D-CA, and SM-DNA

Color image encryption has enticed a lot of attention in recent years. Many authors proposed a chaotic system-based encryption algorithms for that purpose. However, due to the shortcomings of the low dimensional chaotic systems, similar rule structure for RGB channels, and the small keyspace, many of those were cryptanalyzed by chosen-plaintext or other well-known attacks. A Security vulnerability exists because of the same method being applied over the RGB channels. This paper aims to introduce a new three-channel three rules (3C3R) image encryption algorithm along with two novel mathematical models for DNA rule generator and bit inversion. A different rule structure was applied in the different RGB-channels. In the R-channel, a novel Block-based Bit Inversion (BBI) is introduced, in the G-channel Von-Neumann (VN) and Rotated Von-Neumann (RVN)- based 2D-cellular structure is applied. In the B-channel, a novel bidirectional State Machine-based DNA rule generator (SM-DNA) is introduced. Simulations and results show that the proposed 3C3R encryption algorithm is robust against all well-known attacks particularly for the known-plaintext attacks, statistical attacks, brute-force attacks, differential attacks, and occlusion attacks, etc. Also, unlike earlier encryption algorithms, the 3C3R has no security vulnerability.

Do Bilingual Infants Possess Enhanced Cognitive Skills?

Prior studies have reported that bilingualism enhancescognitive ability due to the regular conflict management oftwo language systems (Bialystok, 2015). Here, we explorewhether infant bilingualism improves cognitive ability at 9.5months. Twenty-four monolingual English and 23 bilingualFrench-English infants were first trained to predict a rewardon the right based on a set of tone-shape rule structure (AABpattern). Infants were later trained to predict a differentreward on the left based on another set of new rule structure(ABB pattern). Correct anticipation of reward locationsindicates successful learning. If bilingualism improvesinfants’ cognitive skills, bilingual infants would be better atlearning a new pattern-reward association. However, we didnot find evidence that bilinguals looked at the correct locationmore than monolinguals or learned the new pattern-rewardassociation faster. Thus, our results suggest bilingualism maynot enhance cognitive ability at 9.5 months, as least using thecurrent paradigm.

Concept Based Censor Production Rules

This paper presents a rule structure called Concept Based Censor Production Rule (CBCPR) that deals with real time cases. CBCPR is an extension of a rule structure called Censored Production Rule (CPR). CPR is a standard rule structure with UNLESS slot, which contains various censor conditions that might rarely happen and prevent the action of the rule to be taken. The more time one has, the more censor conditions one can check. The major extension of CPR is by concentrating on what is called concept. The concept is what about the user needs the decision. Each rule will have a certain concept title that specifies its job. In addition, in every CBCPR structure, at least one slot related to UNLESS part in the rule is existing, where each UNLESS slot is related to a certain category having censor conditions concerned with the concept. The structure will help the system to give more certain answers within the given time for the real-time systems instead of keep checking unnecessary censor conditions for the same concept of different UNLESS categories.