Abstract

Our everyday experiences are an excellent demonstration of the surprisingly adaptive and fluid learning behavior that is orchestrated by the human brain. Such a learning behavior is a hallmark of human cognitive ability and spans a broad spectrum of tasks. Ranging from complex tasks such as cycling and driving to seemingly simpler ones such as typing and grasping movements, all tasks involve the acquisition of skillful behavior. Skill learning is a natural behavioral phenomenon concerned with the acquisition of the ability to perform tasks proficiently. Motor skill learning refers to learning a specific subclass of skills that involve sequential motor movements such that they are executed accurately and quickly with practice (Newell, 1991; Clegg et al., 1998; Haibach et al., 2017; Schmidt et al., 2019). Much of the early interest in motor sequencing focused on investigating the typical behavioral phenomenon in sequence learning tasks (Lashley, 1951; Hebb, 1961; Fitts and Posner, 1967). This has led to the formulation of many serial order canonical experimental tasks such as the m × n task (Hikosaka et al., 1995; Bapi et al., 2000, 2006) and discrete sequence production (DSP) task (Verwey, 2001; Abrahamse et al., 2013; Verwey et al., 2015) in the explicit domain and serial reaction time (SRT) task (Nissen and Bullemer, 1987; Willingham, 1999; Robertson, 2007) in the implicit domain. While explicit learning involves conscious awareness of what is being learned, implicit learning occurs without conscious awareness of learning. Subsequent research has extensively used these paradigms to understand the brain processes involved in sequence learning, memory, attention, etc.

Abstract

India is known as the land of many tongues. There is no single language called Indian. India speaks hundreds of languages and dialects. Some are extinct, while some are still in use with considerable speakers. Despite having a lot of different scripts, most of the Indian languages still share a lot of lexical features which can be utilized to help improve the quality of Multilingual NMT systems trained on them. So, in this paper, we present an extensive study of Multilingual as well as Multilingual Multi Domain NMT involving languages of the Indian subcontinent. We draw four major conclusions from our experiments: (i) Multilingual Multi Domain models can significantly improve the accuracy of all the individual languages within their domains, resulting in improving the overall performance of the Multilingual Multi Domain system, (ii) Encoder representation of different languages based on their family helps Multilingual models gain an average improvement of 3.25 BLEU points, (iii) Our new technique of incorporating domain information into the language tokens results in getting a significant improvement of 6 BLEU points on an average as compared to the baselines, (iv) Multistage Fine-tuning further helps in improvement of (1-1.5) BLEU points.

Abstract

Social media platforms have democratized the publication process resulting into easy and viral propagation of information. However, spread of rumors via such media often results into undesired and extremely impactful political, economic, social, psychological and criminal consequences. Several manual as well as automated efforts have been undertaken in the past to solve this critical problem. Existing automated methods are text based, user credibility based or use signals from the tweet propagation tree. We aim at using the text, user, propagation tree and temporal information jointly for rumor detection on Twitter. This involves several challenges like how to handle text variations on Twitter, what signals from user profile could be useful, how to best encode the propagation tree information, and how to incorporate the temporal signal. Our novel architecture, T 3N (Text and Temporal Tree Network), leverages deep learning based architectures to encode text, user and tree information in a temporal-aware manner. Our extensive comparisons show that our proposed methods outperform the state-of-the-art techniques by ∼7 and ∼6 percent points respectively on two popular benchmark datasets, and also lead to better early detection results.

Abstract

The paper presents a sub-nW BJT based temperature sensor for ultra-low power microsystems. The sensor is based on amplifying the difference between baseemitter voltages of BJTs using gate-leakage transistors. Implemented in UMC 65nm technology, the sensor occupies an area of 0.005mm2 . It achieves a maximum non-linearity error of 0.12oC(3σ) over the temperature range of −55oC to 80oC. Without any trimming, a worst case inaccuracy of +0.36oC/ − 1.61oC is observed w.r.t process variations, depicting the process-invariant nature of the temperature sensor. It also achieves a low supply sensitivity of 0.56oC/V over a wide supply range of 0.7V-3V. The power consumption of the sensor is 419pW at 27oC and 0.7V supply.

Abstract

[0002] Resistors are the essential and common elements of any electronic circuits and extensively used in electronic equipment. They are frequently used in Integrated Circuits. The conventional on-chip resistance takes a large silicon area compared to metal oxide 15 semiconductor MOS transistor. In fabrication, the area is proportional to cost. If the silicon area is more, then the cost of the chip also increases. Therefore, large resistances are avoided to fabricate on the chip. For low power circuits, large resistances are often required but due to limited silicon area they are avoided. Hence the conventional resistor is replaced by a complementary metal-oxide-semiconductor CMOS circuit or a switched capacitor-based 20 resistor circuit to save area.

Abstract

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5% detection and 0.5% false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks.

Abstract

A secure authentication protocol plays a crucial role in securing communications over wireless and mobile networks. Due to resource-limitations and the nature of the wireless channel, the global mobile networks are highly susceptible to various attacks. Recently, an efficient authentication system for global roaming has been proposed in the literature. In this article, we first show that the analyzed authentication system is vulnerable man-in-the-middle attack, replay attack and Denial-of-Service (DoS) attack, and it does not ensure untraceability and local password-verification process to identify wrong passwords. To fix these security flaws, we propose a more efficient and robust authentication system for roaming in mobility networks. We use the formal verification tools like ProVerif, Automated Validation of Internet Security Protocols and Applications (AVISPA) and Burrows-Abadi-Needham (BAN) logic to check the regularity of the authentication protocol. Moreover, we prove the secrecy of a session key through the formal security using the random oracle model, known as Real-Or-Random (ROR) model. Finally, a detailed performance evaluation proves that the security protocol not only provides a security strength, but also preserves the low computational overhead. Thus, the proposed authentication protocol is secure and computationally efficient as compared to other relevant schemes.

Abstract

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (eg, crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyse Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users' context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme's security and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.

Abstract

Complete information about the class label is not given in many real-world classification problems. For example in a multiclass setting, instead of the ground-truth label, we can be given a set of candidate labels, assuming that the true label belongs to this set. This type of setting is called learning under partial labels. In this paper, we propose exact passive-aggressive online algorithms for multiclass classification using only partial labels. For updating the weights, we find the exact solution of a quadratic optimization problem under multiple class separability constraints. We obtain this by finding the active constraints using KKT conditions of the optimization problem. The set of support classes for which the weight vector is to be updated is determined by these constraints. The proposed algorithms are called PA, PA-I, and PA-II. We also provide a thorough theoretical analysis of the proposed algorithms including regret bounds. We provide extensive simulation results to show the effectiveness of the proposed approaches.

Abstract

This paper presents a methodology submitted to the FinSBD-2 shared task to extract well formed sentences, lists and items from noisy unstructured financial PDF documents in English language. The proposed architecture for document structure identification, is a combination of deep learning and heuristic based approaches. We use two unidirectional Long Short-Term Memory (LSTM) encoders to get the sentence split tokens from the set of all the possible split points. Further, the outputs are passed on to an attention based LSTM network to select only the well formed sentences from all the possible sentences. These outputs are merged to ultimately produce all possible well formed sentences. Apart from the sentences, lists and items are identified using a combination of heuristics which identify patterns in the data. The final F1 score, 0.217 on this task, is obtained by comparing the start and end indices of sentences, lists and items. We have presented another parameter, which is used to evaluate the class coverage by checking the overlap between the predicted and ground truth sentences and obtained an average 40% class coverage score. This metric is more useful for industry researchers who require coverage of the content rather than character level precision. The proposed approach will empower both academia and industry researchers in their effort to handle noisy documents for various NLP tasks by providing a simple, fast and robust approach to identify structure in their documents.