Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents.

Emotion detection (ED) plays a vital role in determining individual interest in any field. Humans use gestures, facial expressions, and voice pitch and choose words to describe their emotions. Significant work has been done to detect emotions from the textual data in English, French, Chinese, and other high-resource languages. However, emotion classification has not been well studied in low-resource languages (i.e., Urdu) due to the lack of labeled corpora. This article presents a publicly available Urdu Nastalique Emotions Dataset (UNED) of sentences and paragraphs annotated with different emotions and proposes a deep learning (DL)-based technique for classifying emotions in theUNEDcorpus. Our annotatedUNEDcorpus has six emotions for both paragraphs and sentences. We perform extensive experimentation to evaluate the quality of the corpus and further classify it using machine learning and DL approaches. Experimental results show that the developed DL-based model performs better than generic machine learning approaches with an F1 score of 85% on the UNED sentence-based corpus and 50% on the UNED paragraph-based corpus.

Abstract At the advent of advanced wireless technology and contemporary computing paradigms, Distributed Denial of Service (DDoS) attacks on Web-based services have not only increased exponentially in number, but also in the degree of sophistication; hence the need for detecting these attacks within the ocean of communication packets is extremely important. DDoS attacks were initially projected toward the network and transport layers. Over the years, attackers have shifted their offensive strategies toward the application layer. The application layer attacks are potentially more detrimental and stealthier because of the attack traffic and the benign traffic flows being indistinguishable. The distributed nature of these attacks is difficult to combat as they may affect tangible computing resources apart from network bandwidth consumption. In addition, smart devices connected to the Internet can be infected and used as botnets to launch DDoS attacks. In this paper, we propose a novel deep neural network-based detection mechanism that uses feed-forward back-propagation for accurately discovering multiple application layer DDoS attacks. The proposed neural network architecture can identify and use the most relevant high level features of packet flows with an accuracy of 98% on the state-of-the-art dataset containing various forms of DDoS attacks.

The capabilities of the Smart Grid coupled with dynamic pricing enables the Smart Grid to adaptively manage the electricity generation and distribution. Several dynamic real-time pricing schemes have been proposed in recent times but few have been successfully implemented despite their economic and environmental benefits. In particular, the current real-time pricing schemes have not been able to incentivize subscribers to respond to time-varying prices in order for the smart-grid to fully benefit from such pricing. Traditional pricing schemes failed to incorporate fairness for end-users because real-time data gathering was expensive and impractical before smart devices were incorporated into the grid. In this paper, we propose a novel dynamic pricing model, fair dynamic pricing (FDP), to maintain reliable power supply during times of peak demand. The proposed model is analyzed and evaluated using a real-time consumer load dataset from San Diego, CA, USA. We demonstrate that in periods of peak load, the burden of generating expensive electricity is placed on subscribers responsible for creating the peaks rather than being subsidized by the remaining subscribers. Our results show that FDP improves the rates of low-demand subscribers by 18.4% and charges a penalty of up to 34% to high-demand subscribers for 400 sample observations. This percentage varies with the number of subscribers in the system during an interval and real-time prices.

With the widespread use of mobile devices relying on limited battery power, the burden of optimizing applications for energy has shifted towards the application developers. In their quest to develop energy efficient applications, developers face the hurdle of measuring the effect of software change on energy consumption. A naive solution to this problem would be to have an exhaustive suite of test cases that are executed upon every change to measure their effect on energy consumption. This method is inefficient and also suffers from environment dependent inconsistencies. A more generalized method would be to relate software structural metrics with its energy consumption behavior. Previous attempts to relate change in objectoriented metrics to their effects on energy consumption have been inconclusive. We observe that structural information is global and executed tests are rarely comprehensive in their coverage, this approach is prone to errors. In this paper, we present a methodology to relate software energy consumption with software structural metrics considering the test case execution traces. Furthermore, we demonstrate that software structural metrics can be reliably related to energy consumption behavior of programs using several versions of three open-source iteratively developed android applications. We discover that by using our approach we are able to identify strong correlations between several software metrics and energy consumption behavior.

Caches were designed to amortize the cost of memory accesses by moving copies of frequently accessed data closer to the processor. Over the years the increasing gap between processor speed and memory access latency has made the cache a bottleneck for program performance. Enhancing cache performance has been instrumental in speeding up programs. For this reason several hardware and software techniques have been proposed by researchers to optimize the cache for minimizing the number of misses. Among these are compile-time data placement techniques in memory which improve cache performance. For the purpose of this work, we concern ourselves with the problem of laying out data in memory given the sequence of accesses on a finite set of data objects such that cache-misses are minimized. The problem has been shown to be hard to solve optimally even if the sequence of data accesses is known at compile time. In this paper we show that given a direct-mapped cache, its size, and the data access sequence, it is possible to identify the instances where there are no conflict misses. We describe an algorithm that can assign the data to cache for minimal number of misses if there exists a way in which conflict misses can be avoided altogether. We also describe the implementation of a heuristic for assigning data to cache for instances where the size of the cache forces conflict misses. Experiments show that our technique results in a 30% reduction in the number of cache misses compared to the original assignment.

The de facto service architecture of today's communication networks, in particular the Internet, is heterogeneous, complex, ad hoc, and not particularly well understood. With layering as the only means for functional abstraction, and even this violated by middle-boxes, the diversity of current technologies can barely be expressed, let alone analyzed. As a first step to remedying this problem, we present an axiomatic formulation of fundamental forwarding mechanisms in communication networks. This formulation allows us to express precisely and abstractly the concepts of naming and addressing and to specify a consistent set of control patterns and operational primitives, from which a variety of communication services can be composed. Importantly, this framework can be used to (1) formally analyze network protocols based on structural properties, and also to (2) derive working prototype implementations of these protocols. The prototype is implemented as a universal forwarding engine, a general framework and runtime environment based on the Click router.

No abstract available.