Abstract

Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication. These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes. Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions. In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing. Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed. Our system has been developed using CNN and LSTM and outperformed the baseline score.

Abstract

Off-colour humour is a category of humour which is considered by many to be in poor taste or overly vulgar. Most commonly, off-colour humour contains remarks on particular ethnic group or gender, violence, domestic abuse, acts concerned with sex, excessive swearing or profanity. Blue humour, dark humour and insult humour are types of off-colour humour. Blue and dark humour unlike insult humour are not outrightly insulting in nature but are often misclassified because of the presence of insults and harmful speech. As the primary contributions of this paper we provide an original data-set consisting of nearly 15,000 instances and a novel approach towards resolving the problem of separating dark and blue humour from offensive humour which is essential so that free-speech on the internet is not curtailed. Our experiments show that deep learning methods outperforms other n-grams based approaches like SVM’s, Naive Bayes and Logistic Regression by a large margin.

Abstract

In developing the infrastructure facilities such as irrigation canals and road networks, topography acts as a significant enabler or constraint. Contour maps and low resolution DEMs have been used by Irrigation engineers and planners to assess the canal routing options, which is time consuming and requires repeated evaluations of the potential paths. So, there is a need to develop robust path planning algorithms, including least cost routing, that takes the topographic and engineering constraints while providing potential canal routing paths. Some recent works have attempted to develop algorithms on synthetic data sets but have not been scaled up on high-resolution data sets, limiting their practical use. This work develops a generic algorithm to determine the least-cost flow path between two geo-locations, given the grid-based Digital Elevation Models (DEMs) and a unit cost of construction per length. From the numerous paths that are possible between the two points in any given topography, a distinct least-cost path is identified. The proposed approach is evaluated by computing canal routing paths over publicly available real-world datasets across two different resolutions of 1Km and 90-meter from different sources for Indian terrains. This is then compared against a global reference real-world dataset, Digital Chart of the World (DCW) at 1Km at a grid cell level. The results across multiple stretches shows an average accuracy of 82.09% as measured based on the path overlap against the DCW dataset, proving that the proposed algorithm can be useful in practice.

Abstract

This work presents a highly accurate current reference of 66nA for ultra-low power applications. A very low figureof-merit (FOM) of 1.3501ppm/◦C 2 is achieved on consuming a minimal quiescent current of 199.37nA. To cancel out process variations, the current subtraction technique is employed and a β-multiplier is used to compensate for mobility (µ) and threshold voltage (Vth). In addition, curvature compensation technique backed by PTAT and CTAT current cancellation is adopted to attain a lower temperature coefficient (TC). Hence, an imperceptible variation of accuracy with temperature and supply variations across all process corners is attained. An adopted trimming scheme further minimizes the overall process spread to ±1.515% without compromising on accuracy. Accordingly, a TC of 67.04ppm/◦C over a wide temperature range of -50◦C to 100◦C is obtained. Furthermore, 1.413%/V line sensitivity (LS) in the supply range of 1.38V to 3V is observed. Low power consumption of 275.13nW@1.38V facilitates its use in high-performance low power applications. Index Terms—FOM, curvature compensation, Line Sensitivity, Proportional to Absolute Temperature (PTAT), Complementary to Absolute Temperature (CTAT), ZTC

Abstract

This brief presents a CMOS-only ultra low power voltage reference of 0.925V which is designed and simulated in TSMC 180nm technology. Current mixing between SelfBiased Self Cascode MOSFET (SBSCM) and Self Cascode MOSFET (SCM) pair is introduced along with stacking of proportional to absolute temperature (PTAT) and complementary to absolute temperature (CTAT) voltage generators to get second order curvature compensation. Temperature coefficient (TC) of 3.75ppm/◦C over a wide temperature range of -55◦C to 140◦C is achieved. To feed on the power reduction requirement of low power biomedical applications, the entire circuit is designed in sub-threshold region. Therefore, the total power consumption is 27nW @ 1.45V at room temperature (27◦C). Furthermore, a supply independent current bias aids in realizing high PSRR of -91dB @ 10Hz / -50dB @ 100MHz and line regulation of 0.0779%/V over a supply range 1.45V to 3.6V. Besides, an employed trimming technique attenuates the process spread from ±3% to ±0.3%. Moreover, the proposed design occupies an active area of 0.0054mm2 only. Index Terms—Ultra Low Power, Voltage Reference, PSRR, CTAT, PTAT, Biomedical, SBSCM, SCM.

Abstract

This paper presents a systematic review on thermal comfort studies in Indian residential buildings, to identify the present research scenario, data gaps and policy interventions. Majority of the studies are performed in composite climate (ten), followed by warm-humid (seven) and a very few from cold (two) and hot-dry (two) climates. None thermal comfort study is found in the temperate climate. Seven studies have considered multiple climates for assessment of thermal comfort in residential buildings. This shows that thermal comfort studies in Indian residential buildings are scarce, scattered and unorganized. Further, due to differences in socio-cultural set-up and local adaptations, the prodigious variations in occupant’s comfort requirements are reported. This review argues for the dynamic modifications in individual behaviours due to change in cost of building energy services and comfort requirements. Only four studies have partially considered the occupant behaviour regarding control of indoor thermal environment. The results obtained from these studies indicate that there is strong need of localised thermal comfort model that will not only help in improving comfort requirements but also building energy performance. Importantly this review

Abstract

We present the design and implementation of Two-Grid Preconditioned Bundle Adjustment (TPBA), a robust and efficient technique for solving the non-linear least squares problem that arises in bundle adjustment. Bundle adjustment (BA) methods for multi-view reconstruction formulate the BA problem as a non-linear least squares problem which is solved by some variant of the traditional LevenbergMarquardt (LM) algorithm. Most of the computation in LM goes into repeatedly solving the normal equations that arise as a result of linearizing the objective function. To solve these system of equations we use the Generalized Minimal Residual (GMRES) method, which is preconditioned using a deflated algebraic two-grid method. To the best of our knowledge this is the first time that a deflated algebraic twogrid preconditioner has been used along with GMRES, for solving a problem in the computer vision domain. We show that the proposed method is several times faster than the direct method and block Jacobi preconditioned GMRES.

Abstract

A behaviourally transformative experience via a strong theme, narrative, and gameplay is a powerful learning paradigm when presented coherently. The interplay of these factors facilitates learning and allows for an immersive experience. It enables players to explore their options and deliberate on the outcomes of their decisions. In this paper, we focus on the socially relevant theme of human trafficking. With Unlocked, we present a perspective that has not been addressed by similarly themed games so far. Understanding such an outlook is crucial to connect with the realities of human trafficking. Our game demonstrates the obstacles in real-life escapes and rescue operations while using well-established literature on learning as guiding principles to create an effective transformative experience. It allows the player to identify with victims and understand their mindset and thought processes. We measure our game’s efficacy in educating players about factors like socio-economic and cultural predicaments, and latent conditions that influence these crimes. Such factors are often overlooked and are hard to understand until they are experienced first-hand. Our study attempts to show that transformative games can help recognize the role that these factors play by placing the player in the victim’s position and building a strong narrative around it. We evaluate the learning process by having two phases of survey, pre-test, and post-test. The pre-test survey tests the player’s knowledge about the current state of human trafficking. In the post-test survey, players rate the experience, gameplay, and effectiveness of the game in educating them about the trafficking scenario. The game was tested and validated by social activists who work to rescue trafficked women and children. They acknowledged the potential impact our game could have on raising awareness. Our findings establish that transformative games grounded with a strong narrative and built with a meticulous design guided by principles of transformation and learning prove to be highly effective in propagating social messages on the perils of human trafficking. Index Terms—Transformative play, Game based learning, Serious games, Human Trafficking, Role playing

Abstract

As scientific communities grow and evolve, there is emergence of new techniques and decline of old ones. The tremendous amount of research publications available online aims to solve a lot of interesting problems. With time, some of the fields have been studied well and research problems solved to a great extent. However, there are few difficult research problems which are yet not solved completely and interests a lot of researchers. In this paper, we aim to find research fields which are saturated and research fields which need to be explored yet. We first extract research problems in a semi supervised manner using a proven bootstrap framework from scientific literature of the last fifty years. We show how a simple statistics based model on top of the research problems extracted can find the saturated fields and grand challenges in any domain of computer science.

Abstract

Product placement in retail stores has a significant impact on the revenue of the retailer. Hence, research efforts are being made to propose approaches for improving item placement in retail stores based on the knowledge of utility patterns extracted from the log of customer purchase transactions. Another strategy to make any retail store interesting from a customer perspective is to cater to the varied requirements and preferences of customers. This can be achieved by placing a wider variety of items in the shelves of the retail store, thereby increasing the diversity of the items that are available for purchase. In this regard, the key contributions of our work are three-fold. First, we introduce the problem of concept hierarchy based diverse itemset placement in retail stores. Second, we present a framework and schemes for facilitating efficient retrieval of the diverse top-revenue itemsets based on a concept hierarchy. Third, we conducted a performance evaluation with a real dataset to demonstrate the overall effectiveness of our proposed schemes.