Reviewerly/exHarmony huggingface dataset:
Multiobjective Virtual Machine Placement Using Evolutionary Algorithm with Decomposition

Virtual machine placement in a distributed cloud environment becomes more challenging due to the tradeoffs between multiple objectives. Placing a virtual machine to an appropriate physical machine must simultaneously minimize the objectives such as resource wastage, power consumption and network transmission delay. Minimizing an objective may have negative impact on some other objectives. A multiobjective optimization problem is to find a solution where all objective will attain optimality. With the evolution of solutions, the Multiobjective Evolutionary Algorithms has the potential to converge towards pareto-optimal solutions. In this paper, VM placement problem with multi-objectives is addressed using Evolutionary algorithm with Decomposition. The proposed algorithm is compared based on performance indicator such as spacing and ONGV with existing multiobjective Elitist algorithms (SPEA and NSGA-II) and Non-elitist algorithms (VEGA and MOGA) shows significant improvement.
Learning and Adaptive Testing of Nondeterministic State Machines

The paper addresses the problems of active learning and conformance testing of systems modeled by nondeterministic Mealy machines (NFSM). It presents a unified SAT-based approach originally proposed by the authors for deterministic FSMs and now generalized to partial nondeterministic machines and checking experiments. Learning a nondeterministic black box, the approach neither needs a Teacher nor uses it a conformance tester to approximate equivalence queries. The idea behind this approach is to infer from a current set of traces not one, but two inequivalent conjectures, use an input sequence distinguishing them in an output query, and update the current trace set with an observed trace to obtain a new pair of distinguishable conjectures, if possible. The classical active learning problem is further generalized by adding a nondeterministic specification FSM, which defines the solution space. The setup unifies the learning and adaptive testing problems and makes them equisolvable with the proposed approach.
A Two-Flights Mutual Authentication for Energy-Constrained IoT Devices

Authentication is a fundamental part of essential security operations and is a cornerstone for the Internet of Things (IoT) security. In this work an energy-efficient and secure mutual authentication protocol is proposed for constrained IoT devices wherein a combination of RC5 (Rivest Cipher) and ECC (Elliptic Curve Cryptography) cryptosystems are used. The protocol is implemented, and its functionality is verified on Zolertia RE-mote IoT devices. It supports secure data transmission along with authentication. Unlike existing schemes, mutual authentication in the proposed protocol is achieved with only two flights between client and server. The security against most common attacks is analysed, furthermore energy consumption of our protocol is evaluated and compared with existing protocol e.g. DTLS handshake. Our protocol saves up to 57% energy compared to the DTLS handshake protocol per authentication cycle.
Deep Multi-view Learning from Sequential Data without Correspondence

Multi-view representation learning has become an active research topic in machine learning and data mining. One underlying assumption of the conventional methods is that training data of the views must be equal in size and sample-wise matching. However, in many real-world applications, such as video analysis, text streaming, and signal processing, data for the views often come in the form of sequences, that are different in length and misaligned, resulting in the failure of directly applying existing methods to such problems. In this paper, we first introduce a novel deep multi-view model that can implicitly discover sample correspondence while learning the representation. It can be shown that our method generalizes deep canonical correlation analysis - a popular multi-view learning method. We then extend our model by integrating the objective function with the reconstruction losses of autoencoders, forming a new variant of the proposed model. Extensively experimental results demonstrate the superior performances of our models over competing methods.
Recurrent Neural Network Based Password Generation for Group Attribute Context-Ware Applications

Password generation becomes an emerging issue to improve the efficiency of authentication in social engineering and plays an important role in checking the security vulnerability since the weak password may lead to possible attacks for different users. To determine the password vulnerability and to enhance user privacy, a recurrent neural network (i.e., long short-term memory (LSTM) and gated recurrent unit (GRU)) based password generation scheme is proposed for group attribute context-ware applications. In this work, different group attributes are considered for modeling training, natural language processing is adopted for password prediction, and password similarity computing is established for analyzing the group relations. The password strength evaluation is performed on the fixed length password based on generation probability. It indicates that group attributes based password generation model has more accuracy than the ordinary model.
Super-Resolution-Based Traffic Prohibitory Sign Recognition

Traffic signs occupy a small proportion in the image, which makes feature extraction difficult. This study focuses on small prohibitory signs. First, we introduce super resolution (SR), which can improve the quality of the image, to solve the recognition problem of small prohibitory signs. Second, we propose a new model that can detect and classify various prohibitory signs compared with a traditional joint model. The proposed model utilizes the color and shape of prohibitory signs in generating proposals and filters the negative samples. The retained proposals are divided into small, medium, and large objects based on the size of prohibitory signs, and the small and medium objects are reconstructed through SR. Finally, the proposals are classified by using support vector machine (SVM) algorithm. Experiments on Tsinghua-Tencent 100K and German Traffic Sign Recognition Benchmark (GTSRB) datasets demonstrate that the proposed method is feasible. The proposed method achieves recognition accuracies of 78% and 67% on the small objects of Tsinghua- Tencent 100K and GTSRB datasets, respectively, which exceed the recognition accuracy without SR.
Alexa, I'm in Need!

Due the involved counselling activities, the profession of social work might provide a promising, yet so far uninvestigated application field for voice assistants. To explore requirements and barriers for voice-based services in the field of social work, we conducted focus group sessions with 41 professionals. During the group discussions and through a questionnaire, we collected requirements for applying voice assistants in this professional setting and corresponding application ideas. Overall, the participants expected a positive impact of voice assistants on their profession. While the protection of the clients' sensitive data and the risk of their reduced social interactions need to be considered, the participants saw high potential in documentation services and the cooperative guidance of clients by human professionals and voice assistants. Future research should investigate the design of this interplay and study appropriate handover strategies between human experts and virtual assistants for specific social work services.
Convolutional LSTM Network with Hierarchical Attention for Relation Classification in Clinical Texts

Identifying relation from clinical texts is a complex and challenging task due to the specific biomedical knowledge. Existing methods for this work generally have the misclassification problem caused by sample class imbalance. In this paper, we propose a hierarchical attention-based convolutional long short-term memory (ConvLSTM) network model to solve this problem. We construct a sentence as multi-dimensional hierarchical sequence and directly learn local and global context information by a single-layer ConvLSTM network. Besides, a hierarchical attention-based pooling is built to capture the parts of a sentence that are relevant with the target semantic relation. Experiments on the 2010 i2b2/VA relation dataset show that our model outperforms several previous state-of-the-art models without relying on any external features.
Towards Digitally-Mediated Sign Language Communication

This paper presents our efforts towards building an architecture for digitally-mediated sign language communication. The architecture is based on a client-server model and enables a near real-time recognition of sign language signs on a mobile device. The paper describes the two main components of the architecture, a recognition engine (server-side) and a mobile application (client-side), and outlines directions for future work.
Real-Time Search Method for Large-Scale Regional Targets Based on Parallel Google S2 Algorithm

In recent years, with the rise of shared bicycles, shared vehicles and other moving objects, how to quickly find nearby objects has gradually become an urgent problem for people to travel. Aiming at the characteristics of fast, real-time and large data volume, this paper proposes a massive data real-time processing system based on Spark Streaming. The massive data generated in real time is sent to the Kafka cluster through the Flume NG cluster. The Spark platform analyzes the data in Kafka in real time, and saves the processed data to redis through our improved parallelized Google S2 algorithm. In this paper, the architecture and function of the system are introduced in detail, and the real-time processing capability of the system is verified by experiments. Experiments show that under the three nodes, when the amount of data is 300,000, our improved algorithm is 4.2% higher than the traditional method; when the amount of data is 500,000, the time is increased by 24%. At the same time, it also verifies the accurate search ability of Google S2 algorithm. Therefore, the architecture and algorithm designed in this paper can be applied to real-time query of objects under large-scale high concurrency.
Using the Doc2Vec Algorithm to Detect Semantically Similar Jira Issues in the Process of Resolving Customer Requests

The paper is devoted to research in the field of software maintenance automation. An approach to solving customer requests based on the use of the Doc2Vec algorithm is proposed. It consists of finding semantically related resolved requests, as well as identifying qualified software engineers in the Jira bug tracking system. The developed software tool implements the proposed approach and provides reports which help software engineers in solving unresolved customer requests. The experiment compares the automated approach to resolving customer requests with the manual one. The results show advantages of using the software tool in the maintenance process.
Research on Virtual Machine Layout Strategy Based on Improved Particle Swarm Optimization Algorithm

In the cloud computing center, the scheduling module allocates virtual machines to physical servers according to the virtual machine's resource usage, regardless of the physical server's overall and long-term resource utilization, which causes a large amount of energy loss in the cloud computing center. The virtual machine placement algorithm provides a way to save energy and improve resource management. This paper proposes a particle swarm optimization algorithm with crossover operator (CPSO) to maximize the use of resources and reduce energy consumption. In the article we designed a new fitness function, which optimizes the algorithm from three goals: load balancing, resource utilization and physical server usage. By adding the crossover operator in the genetic algorithm to the particle swarm optimization algorithm, the fitness value can be prevented from entering the local optimum too early. The algorithm can adaptively adjust the crossover probability and speed up the convergence of the algorithm. Finally, the algorithm is evaluated experimentally. The results show that CPSO is superior to the discrete particle swarm optimization (DPSO) and greedy algorithm (Best-Fit) in terms of resource utilization and physical machine usage. And the solution obtained by the algorithm is close to the optimal solution.
Disentangling Latent Emotions of Word Embeddings on Complex Emotional Narratives

Word embedding models such as GloVe are widely used in natural language processing (NLP) research to convert words into vectors. Here, we provide a preliminary guide to probe latent emotions in text through GloVe word vectors. First, we trained a neural network model to predict continuous emotion valence ratings by taking linguistic inputs from Stanford Emotional Narratives Dataset (SEND). After interpreting the weights in the model, we found that only a few dimensions of the word vectors contributed to expressing emotions in text, and words were clustered on the basis of their emotional polarities. Furthermore, we performed a linear transformation that projected high dimensional embedded vectors into an emotion space. Based on NRC Emotion Lexicon (EmoLex), we visualized the entanglement of emotions in the lexicon by using both projected and raw GloVe word vectors. We showed that, in the proposed emotion space, we were able to better disentangle emotions than using raw GloVe vectors alone. In addition, we found that the sum vectors of different pairs of emotion words successfully captured expressed human feelings in the EmoLex. For example, the sum of two embedded word vectors expressing Joy and Trust which express Love shared high similarity (similarity score .62) with the embedded vector expressing Optimism. On the contrary, this sum vector was dissimilar (similarity score −.19) with the embedded vector expressing Remorse. In this paper, we argue that through the proposed emotion space, arithmetic of emotions is preserved in the word vectors. The affective representation uncovered in emotion vector space could shed some light on how to help machines to disentangle emotion expressed in word embeddings.
Informative Instance Detection for Active Learning on Imbalanced Data

In imbalanced data classification, it is hard to learn the hidden pattern from the minority class due to its insufficient information. To solve this problem, a popular type of sampling methods is proposed based on Active Learning framework, but they still suffer from two key issues: how to keep the structure of the original data and avoid imbalance during the learning process. In this paper, we proposed a novel Active Learning framework (COAL) to select and generate informative instances. To keep the structure and enhance the diversity of the original data, we make use of Clustering-based uncertainty sampling to find informative instances. Meanwhile, to avoid imbalance in the active learning process, we make use of Oversampling method to balance the quantities between classes. Extensive experiments have been conducted by using real world datasets with a large range of imbalance ratio (from 2.78 to 66.67). The experimental results show that the proposed COAL outperforms state-of-the-art methods in terms of several well-known evaluation metrics.
A survey of signal processing based graph learning techniques

Recent advances in technology have led to easy data acquisition mechanisms in many fields, leading to massive datasets. It is often of interest to understand the inherent structure and learn the best representation of the given dataset. Graphs are a powerful way to model interrelationships between data features - well constructed meaningful graphs help in representing and processing the data effectively. The graph topology needs to be inferred from the observed data. In this survey, we briefly explore signal processing based graph learning approaches that have been proposed in the literature and propose new research directions.
Ensemble Attention For Text Recognition In Natural Images

Recognizing text from natural images is a challenging and hot research topic in computer vision, yet not completely solved. The recent methods regard this task as a sequence labeling problem. In this task, there is a strong correspondence between the position of the input image patches sequence and the output character sequence. However, most of the recent recognition systems rarely consider this local information of the input sequence when recognizing the current character. In contrast to this, we present a Local Restricted Attention (LRA) mechanism to encode the current vector by considering adjacent vectors of the input sequence. We propose an ensemble decoder block which combines LRA mechanism with a regular decoder mechanism. This block not only brings significant improvement of recognition results under shorter training time but also can be easily embedded in other recognition frameworks. In addition, we propose a scene text recognition network based on the ensemble decoder. The experimental performances show that the proposed model achieves the state-of-the-art on several benchmark datasets including IIIT-5K, SVT, CUTE80, SVT-Perspective and ICDARs.
Deep Residual Equivariant Mapping for Multi-angle Face Recognition

Face recognition has caught a lot of attention and plenty of valuable methods have been proposed during the past decades. However, because it is hard to learn geometrically invariant representations, existing face recognition methods still perform relatively poorly in conducting multi-angle face recognition. In this paper, we hypothesize that there is an inherent mapping between the frontal and non-frontal faces, and the non-frontal face representations can be converted into the frontal face representations by an equivariant mapping. To carry out the mapping, we propose a Multi-Angle Deep Residual Equivariant Mapping (MADREM) block which adaptively maps the non-frontal face representation to the frontal face representation. It can be considered the MADREM block carry out face alignment and face normalization in the feature space. The residual equivariant mapping block can enhance the discriminative power of the face representations. Finally, we achieve an accuracy of 99.78% on the LFW dataset and 94.25% on CFP-FP dataset based on proposed multiscale-convolution and residual equivariant mapping block.
The Common Approach to Determination of the Destructive Information Impacts and Negative Personal Tendencies of Young Generation Using the Neural Network Methods for the Internet Content Processing

The paper considers determination of destructive information impacts and personal tendencies of young generation that predispose them to uncritical comprehension of the content with destructive components. An application of traditional manual and semi-automatic methods seems ineffective because of the huge amount of information in the Internet space. The paper proposes an approach using the technologies of psychological examination and artificial intelligence. It incorporates the technique to determine the tendency of social networks’ users to acquire destructive information, the technique for classification of the social networks communities considering an existence of destructive impacts, and the technique for hypothetical detecting changes in the tendency of users to acquire information that may contain destructive components when interacting in social networks. The paper describes the experiments on highlighting the relation between the information that users provide in social networks and some of their psychological traits and states that may cause predisposition for non-critical acquisition and digestion of potentially destructive information.
Tools to Support Voice User Interface Design

As Voice User Interfaces gain popularity and appear in many devices in the commercial market (such as Google Home, Amazon Alexa, Siri, etc), designers must address the new usability challenges that come with using voice as a primary form of interaction. There is currently a lack of tools and resources that guide both current and new usability experts in building usable VUIs. My research seeks to identify and fill the gaps in VUI design resources in HCI. To date, I have conducted a series of meta-analyses and studies with both VUI and non-VUI usability experts that explore the gaps in design knowledge for Voice User Interfaces. I describe this currently completed research and how it guides my primary research goal of developing tools and resources to aid usability experts and designers in filling these design knowledge gaps.
Logistic Regression-Based Classification for Reviews Analysis on E-Commerce Based Applications

In recent years, many web applications play an important role that too online-commerce-based applications are ruling the web-based application by attracting the customer based on their products and providing more discount on the product. Customers buy the products online because of low-cost and the quick delivery of the products, and therefore for measuring the quality of the product, the user’s comments on the different products play an important role which can be used for getting useful information on the product. In this paper, we do extract the data set from various E-commerce applications then by applying various methods like data pre-processing, classification and clustering on the products API we can able to analyse the reviews of the customers on different products. In this paper, we have created the product API for 100 instances of products by considering different attributes like product id, product name, total number of comments, rating and the text analysis. In this paper, first we apply the data pre-processing to clean the data set and then apply the logistic regression-based classification on the attributes taken, then form a cluster based on positive and negative reviews provided by the users and determining the accuracy using logistic regression technique and then comparing with decision tree technique accuracy with proposed logistic regression technique we achieve an improved accuracy.
On Perfect Correctness in (Lockable) Obfuscation

In a lockable obfuscation scheme [28, 39] a party takes as input a program P, a lock value $$\alpha $$ , a message $$\mathsf {msg}$$ and produces an obfuscated program $$\tilde{P}$$ . The obfuscated program can be evaluated on an input x to learn the message $$\mathsf {msg}$$ if $$P(x)= \alpha $$ . The security of such schemes states that if $$\alpha $$ is randomly chosen (independent of P and $$\mathsf {msg}$$ ), then one cannot distinguish an obfuscation of P from a "dummy" obfuscation. Existing constructions of lockable obfuscation achieve provable security under the Learning with Errors assumption. One limitation of these constructions is that they achieve only statistical correctness and allow for a possible one sided error where the obfuscated program could output the $$\mathsf {msg}$$ on some value x where $$P(x) \ne \alpha $$ . In this work we motivate the problem of studying perfect correctness in lockable obfuscation for the case where the party performing the obfuscation might wish to inject a backdoor or hole in correctness. We begin by studying the existing constructions and identify two components that are susceptible to imperfect correctness. The first is in the LWE-based pseudo random generators (PRGs) that are non-injective, while the second is in the last level testing procedure of the core constructions. We address each in turn. First, we build upon previous work to design injective PRGs that are provably secure from the LWE assumption. Next, we design an alternative last level testing procedure that has additional structure to prevent correctness errors. We then provide a surgical proof of security (to avoid redundancy) that connects our construction to the construction by Goyal, Koppula, and Waters (GKW) [28]. Specifically, we show how for a random value $$\alpha $$ an obfuscation under our new construction is indistinguishable from an obfuscation under the existing GKW construction.