Mining Discriminative Triplets of Patches for Fine-Grained Classification

Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of discriminative regions remains a major challenge. We describe a patch-based framework to address this problem. We introduce triplets of patches with geometric constraints to improve the accuracy of patch localization, and automatically mine discriminative geometrically-constrained triplets for classification. The resulting approach only requires object bounding boxes. Its effectiveness is demonstrated using four publicly available fine-grained datasets, on which it outperforms or achieves comparable performance to the state-of-the-art in classification

Recognizing Visual Categories by Commonality and Diversity

Visual categories refer to categories of objects or scenes in the computer vision literature. Building a well-performing classifier for visual categories is challenging as it requires a high level of generalization as the categories have large within class variability. We present several methods to build generalizable classifiers for visual categories by exploiting commonality and diversity of labeled samples and the cat- egory definitions to improve category classification accuracy. First, we describe a method to discover and add unlabeled samples from auxil- iary sources to categories of interest for building better classifiers. In the literature, given a pool of unlabeled samples, the samples to be added are usually discovered based on low level visual signatures such as edge statistics or shape or color by an unsupervised or semi-supervised learning framework. This method is inexpensive as it does not require human intervention, but generally does not provide useful information for accuracy improvement as the selected samples are visually similar to the existing set of samples. The samples added by active learning, on the other hand, provide different visual aspects to categories and contribute to learning a better classifier, but are expensive as they need human labeling. To obtain high quality samples with less annotation cost, we present a method to discover and add samples from unlabeled image pools that are visually diverse but coherent to cat- egory definition by using higher level visual aspects, captured by a set of learned attributes. The method significantly improves the classification accuracy over the baselines without human intervention. Second, we describe now to learn an ensemble of classifiers that captures both commonly shared information and diversity among the training samples. To learn such ensemble classifiers, we first discover discriminative sub-categories of the la- beled samples for diversity. We then learn an ensemble of discriminative classifiers with a constraint that minimizes the rank of the stacked matrix of classifiers. The resulting set of classifiers both share the category-wide commonality and preserve diversity of subcategories. The proposed ensemble classifier improves recognition accuracy significantly over the baselines and state-of-the-art subcategory based en- semble classifiers, especially for the challenging categories. Third, we explore the commonality and diversity of semantic relationships of category definitions to improve classification accuracy in an efficient manner. Specif- ically, our classification model identifies the most helpful relational semantic queries to discriminatively refine the model by a small amount of semantic feedback in inter- active iterations. We improve the classification accuracy on challenging categories that have very small numbers of training samples via transferred knowledge from other related categories that have a lager number of training samples by solving a semantically constrained transfer learning optimization problem. Finally, we summarize ideas presented and discuss possible future work

Copper-molybdenum sulfide and phosphide electrodes for superior energy storage and conversion

Ternary transition metal sulfide (TTMS) and ternary transition metal phosphide (TTMP) based materials have received great attention as materials for energy storage and generation devices due to several advantages, such as high electrical conductivity, abundant active sites, and synergetic effects between each transition metal. In this project, the copper-molybdenum sulfide and phosphide materials were designed via facile hydrothermal technique and successive hydrothermal and phosphatization methods, respectively. The coppermolybdenum sulfide and phosphide have cotton-like morphology and each electrode showed high electrochemical energy storage and conversion properties. The electrodes displayed high areal-specific capacitance of 3.5 and 5.2 F/cm2 at the current density of 3 mA/cm2. In addition, compared to the first cycle performance, the electrodes exhibited specific capacitance retention of 86.9 and 69.4 % with ~ 100% coulombic efficiency after 4,000 cycles. Moreover, the copper-molybdenum sulfide and phosphide electrodes showed superior catalytic activities and stability towards overall water splitting. Each electrode required the low HER overpotential of 207 mV and 147 mV at 10 mA/cm2 and showed the Tafel slope of 118 and 109 mV/dec, respectively. Furthermore, to obtain the current density of 10 mA/cm2, OER overpotential of 270 mV and 213 mV was necessary, along with a Tafel slope of 82 and 48 mV/dec. Also, the excellent catalytic performance of all electrodes was observed by the comparison of 1st vs 1k activity and 40 hours chronoamperometry measurements. Based on the electrochemical performance, copper-molybdenum sulfide and phosphide can be effective materials for superior energy storage and conversion

Pomegranate: An Eco-Friendly Source for Green Energy Storage Devices

With an increasing demand for energy and concerns about the environment, scientists are trying to find a better way to generate green energy and to efficiently store the generated energy. Biowaste could be an attractive source for the preparation of active materials for energy storing devices. In this project, a shell of pomegranate was used for the preparation of high surface area carbon for supercapacitor applications. The dry powder of a pomegranate was chemically activated using various ratios of pomegranate and activating agent to produce carbon with a range of different properties. The surface area of the pomegranate-based carbon was 40 m2/g, which improved to 1459, 1737, and 2189 m2/g upon chemical activation using, 1:1, 1:2, and 1:3 ratios of pomegranate: activating agent, respectively. The energy storage capacity was calculated using galvanostatic charge-discharge measurements, and the highest specific capacitance of 190 F/g at 1 A/g was observed for PG-2 (1:2 ratio of pomegranate: activating agent) activated pomegranate-based carbon. Using the electrode, the symmetric supercapacitor devices were fabricated utilizing various electrolytes (aqueous, organic, and ionic liquid electrolytes). From the Ragone curve, the highest energy and power density of [8.8 Wh kg-1, 3,950 W kg-1], [39 Wh kg-1, 8,943 W kg-1], and [68 Wh kg-1, 11,316 W kg-1] was obtained for aqueous, organic, and ionic liquid electrolytes, respectively. Our research suggests that pomegranate-based carbon could be an attractive material for the fabrication of energy storage devices

Cyber Blackbox for collecting network evidence

In recent years, the hottest topics in the security field are related to the advanced and persistent attacks. As an approach to solve this problem, we propose a cyber blackbox which collects and preserves network traffic on a virtual volume based WORM device, called EvidenceLock to ensure data integrity for security and forensic analysis. As a strategy to retain traffic for long enough periods, we introduce a deduplication method. Also this paper includes a study on the network evidence which is collected and preserved for analyzing the cause of cyber incident. Then, a method is proposed to suggest a starting point for incident analysis to a forensic practitioner who has to investigate on the vast amount of network traffic collected using the cyber blackbox. Experimental results show this approach is effectively able to reduce the amount of data to search by dividing doubtful flows from normal traffic. Finally, we discuss the results with the forensically meaningful point of view and present further works

Metal-Oxide Frameworks-based Cobalt Oxides as Efficient Electrocatalysts

Green energy production via cost-effective ways is one of the main requirements in current days. Hydrogen as a green fuel source is very attractive for a sustainable future as hydrogen is considered a zero-carbon emission fuel. Hydrogen can be produced via many routes. Among many approaches, hydrogen generation via water splitting is one of the greenest ways to get green fuel. In most cases, hydrogen production via water splitting requires efficient electrocatalysts to reduce the overpotential (extra cost) of this process. Platinum and rareearth-based materials are considered efficient electrocatalysts, however, their high cost is one of the limiting factors. process. In this work, metal oxide framework-based cobalt oxides were synthesized and used as efficient electrocatalysts for water splitting applications. The nanostructured MOF-based cobalt oxides were prepared using a facile method that can be easily adapted for commercial applications. The samples were hoghly porous with a high surface area which acted as active sites for electrocatalytic activities. The materials\u27 properties were tuned by calcining the samples at various temperatures. These materials showed low overpotential in the range of 75 to 137 mV to achieve a current density of 10 mA/cm2 for hydrogen production. Depending on the growth conditions, these materials required an overpotential in the range of 370 to 440 mV for oxygen production. These materials showed stable performance for up to 1,000 cycles of cyclic voltammetric studies suggesting possible commercial applications in fuel cell technology

Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents

Accomplishing household tasks such as 'bringing a cup of water' requires planning step-by-step actions by maintaining knowledge about the spatial arrangement of objects and the consequences of previous actions. Perception models of the current embodied AI agents, however, often make mistakes due to a lack of such knowledge but rely on imperfect learning of imitating agents or an algorithmic planner without knowledge about the changed environment by the previous actions. To address the issue, we propose CPEM (Context-aware Planner and Environment-aware Memory) to incorporate the contextual information of previous actions for planning and maintaining spatial arrangement of objects with their states (e.g., if an object has been moved or not) in an environment to the perception model for improving both visual navigation and object interaction. We observe that CPEM achieves state-of-the-art task success performance in various metrics using a challenging interactive instruction following benchmark both in seen and unseen environments by large margins (up to +10.70% in unseen env.). CPEM with the templated actions, named ECLAIR, also won the 1st generalist language grounding agents challenge at Embodied AI Workshop in CVPR'23.Comment: ICCV 202