Reviewerly/exHarmony huggingface dataset:
Investigating Quick Response Code Application in Third-Party Logistics Companies

This research aims to ascertain the application of the quick response (QR) code technology as the marketing tools among third-party logistics (3PLs) companies in Malaysia. This study adopted a qualitative research approach by conducting an in-depth interview with five respondents who are experienced in 3PLs services. The supply chain management with the 7Ps marketing mix act was applied as the guiding principle. The findings revealed that the interaction between consumers and 3PLs companies made easy and swift by the enabling application of mobile technology related to QR code. QR code enables to support both 3PLs and logistics industry towards competitive advantage. Growth of the market share, profit-revenue, and sales by utilizing the application of QR code with the linkage of social media is imperative to establish the company brand and reputation. This study has a significant implication for both 3PLs companies and academicians to further explore the practices and its application in a different organization setting.
Policy Deployment and QFD: The Twin-Engines of Macrologistics

Policy Deployment is a top-down, bottoms-up method to ensure that plans and strategies are successfully implemented within the organization and throughout the value chain. Constructing a Quality Function Deployment (QFD) into a grid format requires detailed information on two elements: As mentioned earlier in the chapter, the model of the fundamental work force tasks realigns only how QFD is viewed, but also how Policy Deployment and logistics are viewed as well. State of art Information Systems, a highly automated physical distribution system, coupled with a well integrated Policy Deployment amd QFD system, became the cornerstone for advancing Florida Power and Light Company (FPL) successful bid for the Deming Prize in 1989. During the past seven years, FPL has maintained its Policy Deployment focus on the following IS related logistics activities: The influence of Macrologistics Management at FPL during the Deming Prize years was considerable.
Neutralizing manipulation of critical data by enforcing data-instruction dependency

In this paper, we propose a new approach to neutralize attacks that tamper with critical program data. Our technique uses a sequence of instructions as a trap against the illicit modification of the critical data. In a nutshell, we set up a dependency such that the continued execution of the program is contingent upon the successful execution of the instruction sequence and the successful execution of the instruction sequence is contingent upon the integrity of the critical data. In particular, we discuss a specific implementation of our technique focusing on a critical data that is often subject to malicious manipulation: the return address of a function. We show that our technique can be an effective countermeasure to defend against attacks that overwrite the return address to divert control to a malicious code. We further show that our technique offers significant protection without resorting to complementary defenses such as ASLR, DEP or StackGuard.
Behavior-Based Approach for Fog Data Analytics: An Approach Toward Security and Privacy

Fog computing is well known for its low edge to edge delay and service latency while providing better performance than cloud computing. It offloads the cloud servers and shifts the computation facility toward the edge of network. However, with the increasing demand of technology, security and privacy concerns are also increasing. As the new security mechanisms are devised, attacker’s approach toward getting access to information is also modified. Nowadays, attackers are more advanced in terms of attacking strategies. Therefore, there is a dire need of advanced security mechanisms for such advancing technology. While discussing about security, individual biometric seems to be a promising approach from the last few decades. There are a lot of biometric-based techniques that have been developed using face, palm print, fingers eyelids, etc. But there are a few works in the field of typing behavior characteristics. To this end, we design a security strategy for fog computing by analyzing user’s typing behavior pattern. We deployed nine behavior parameters for this study. We first implemented this strategy using four parameters and then added new parameters while evaluating error rates at each step. Results also show that Crossover Error Rate (CER) reduces to 2% for the final stage using the proposed strategy. The proposed scheme is validated by a simulator designed for registering new users and identifying the user requesting for service.
Hubungan antara Tayangan Video Merakit SIBI di Youtube dengan Kepercayaan Diri

Abstract— YouTube social media is a social media that is highly idolized by all circles of society including deaf people. However, not all video shows on YouTube can be enjoyed by people who are deaf, even not only in social media, the presence of deaf people is also very less visible because of difficulties in communicate verbally. This has the lowest self-confidence of deaf people so that they have low motivation in their alertness and motivation to achieve their dreams. The song Merakit was created by singer Yura Yunita which contains motivation for people with disabilities to stay enthusiastic and never give up in reaching their dreams. The song was then made into a video using the SIBI sign language and shown for deaf friends. The title, namely the correlations between video show Merakit SIBI on Youtube and self-confidence has the aim of seeing how the relationship between video shows Merakit SIBI in the aspects of communicators, messages and media on YouTube with the confidence of the deaf. This research uses quantitative methods and a correlational approach. The population of students in 6 SLB schools in Sumedang Regency used a total sampling technique so that the respondents got as many as 41 people. This study uses social cognitive theory with the variables studied, namely the aspects of communicators, messages and media with self-confidence. The study used questionnaires, literature and observation as data analysis techniques and used SPSS 23 to process the resulting data. The results of the study indicate that there is a significant correlations between the communicator aspect and the message aspect with self-confidence, and there is no significant correlations between the media aspect and self-confidence Keywords— video shows, youtube, confidence, deaf, communicators, messages, media.
Location Privacy-Preserving Method Based on Degree of Semantic Distribution Similarity

While enjoying the convenience brought by location-based services, mobile users also face the risk of leakage of location privacy. Therefore, it is necessary to protect location privacy. Most existing privacy-preserving methods are based on K-anonymous and L-segment diversity to construct an anonymous set, but lack consideration of the distribution of semantic location on the road segments. Thus, the number of various semantic location types in the anonymous set varies greatly, which leads to semantic inference attack and privacy disclosure. To solve this problem, a privacy-preserving method is proposed based on degree of semantic distribution similarity on the road segment, ensuring the privacy of the anonymous set. Finally, the feasibility and effectiveness of the method are proved by extensive experiments evaluations based on dataset of real road network.
Residual analysis in regression mixture model

We consider data in which each observed subject belongs to one of different subpopulations (components). The true number of component which a subject belongs to is unknown, but the researcher knows the probabilities that a subject belongs to a given component (concentration of the component in the mixture). The concentrations are different for different observations. So the distribution of the observed data is a mixture of components’ distributions with varying concentrations. A set of variables is observed for each subject. Dependence between these variables is described by a nonlinear regression model. The coefficients of this model are different for different components. An estimator is proposed for these regression coefficients estimation based on the least squares and generalized estimating equations. Consistency of this estimator is demonstrated under general assumptions. A mixture of logistic regression models with continuous response is considered as an example. It is shown that the general consistency conditions are satisfied for this model under very mild assumptions. Performance of the estimator is assessed by simulations and applied for sociological data analysis. Q-Q diagrams are built for visual comparison of residuals’ distributions.
A Survey: Movement of the Hand motion Trajectory for dependent and independent Recognition

HCI makes a evaluation on the use of depth for hand following and gesture recognition through multi stroke in free mid-air Trajectory. The survey examines 10 papers describing depth-based gesture trajectory scheme. Various methods used with different algorithm in single stroke of the hand localization and gesture classification methods; the purpose is to make process aqua-rate, faster with various parameters. The review is organized about a new model of extreme importance in designing an intelligent and efficient human-computer interface. In the reviewed literature, the enclosed issues include preprocessing, object tracing and recognition, human activity analysis. Hand Gesture plays a most significant role in HCI for providing interactive framework, movement of the hand gesture trajectory find favorable characteristics. Various inputs should be taken through depth camera of various activities of posture of hand behavior of alphabets and numeric values with single stroke with different method like Neural Network, HMMs and DTW. Now this paper an investigation and comparative results has been shown through various paper.
ACOL-GAN

In recent years, deep learning has achieved great success in many fields. As the most basic machine learning task, clustering has also become one of the research hotspots. However, clustering performance based on Variational Autoencoder is generally better than that based on Generative Adversarial Network, which is mainly because the former implements multi-modal learning and there are obvious boundaries between different categories, while the latter does not. In this paper, we propose a new clustering model named ACOL-GAN, which replaces the normal distribution that standard GAN relied on with sampling networks and adopts the Auto-clustering Output Layer as the output layer in discriminator. Due to Graph-based Activity Regularization terms, softmax nodes of parent-classes are specialized as the competition between each other during training. The experimental results show that ACOL-GAN achieved the state-of-the-art performance for clustering tasks on MNIST USPS and Fashion-MNIST, with the highest accuracy on Fashion-MNIST.
Simple Unsupervised Similarity-Based Aspect Extraction

In the context of sentiment analysis, there has been growing interest in performing a finer granularity analysis focusing on the specific aspects of the entities being evaluated. This is the goal of Aspect-Based Sentiment Analysis (ABSA) which basically involves two tasks: aspect extraction and polarity detection. The first task is responsible for discovering the aspects mentioned in the review text and the second task assigns a sentiment orientation (positive, negative, or neutral) to that aspect. Currently, the state-of-the-art in ABSA consists of the application of deep learning methods such as recurrent, convolutional and attention neural networks. The limitation of these techniques is that they require a lot of training data and are computationally expensive. In this paper, we propose a simple approach called SUAEx for aspect extraction. SUAEx is unsupervised and relies solely on the similarity of word embeddings. Experimental results on datasets from three different domains have shown that SUAEx achieves results that can outperform the state-of-the-art attention-based approach at a fraction of the time.
Semi-automatic Knowledge Base Expansion for Question Answering

In this paper we present a hybrid semi-automatic methodology for the construction of a Lexical Knowledge Base. The purpose of the work is to face the challenges related to synonymy in Question Answering systems. We talk about a “hybrid” method for two main reasons: firstly, it includes both a manual annotation process and an automatic expansion phase; secondly, the Knowledge Base data refers, a the same time, to both the syntactic and the semantic properties borrowed from the Lexicon-Grammar theoretical framework. The resulting Knowledge Base allows the automatic recognition of those nouns and adjectives which are not typically related into synonyms databases. In detail, we refer to nouns and adjectives that enter into a morph-phonological relation with verbs, in addition to the classic matching between words based on synset from the MultiWordNet synonym database.
Digital Photo Frame System Based on Hashing Image Retrieval in Smart Home

With the emergence of massive multimedia data, how to securely and effectively manage them in smart homes has become a crucial task. Although the digital photo frame can satisfy the display function, the current digital photo frame system cannot provide users with personalized services. Therefore, in this paper, we propose a digital photo frame system with online hashing image retrieval. The system is mainly divided into two parts: First, we leverage the image retrieval based on online hashing to manage photos. When a user selects a photo, we retrieve it and return the similar photos with background music based on the photo semantic information. Then, in order to enhance system security, we build the privacy mode, so that users can manage the data in security. At the end, we build a dataset for smart homes, and implement a multifunctional secure digital photo frame system.
Voice Conversion towards Arbitrary Speakers With Limited Data

Voice conversion towards a specific speaker requires a large number of target speaker's utterances, which is expensive in practice. This paper proposes a speaker-adaptive voice conversion (SAVC) system, which accomplishes voice conversion towards arbitrary speakers with limited data. First, a multi-speaker voice conversion (MSVC) model is trained to learn the shared information between speakers and build a speaker latent space. Second, utterances of a new target speaker are used to fine tune the MSVC model aiming to learn the voice of the target speaker. In the two steps, phonetic posteriorgrams (PPGs), a speaker-independent linguistic feature, and speaker embeddings such as i-vector or x-vector are encoded to train the model. In order to achieve better results, two different adaptive approaches are explored: adaptation on the whole MSVC model or additional linear-hidden layers (AHL). As the results show, both adaptive approaches significantly outperform the MSVC model without adaptation. Besides, the whole adapted model based on x-vector gets a higher similarity to target speaker within 10 utterances.
Multi-Task Learning Network for Document-level and Multi-aspect Sentiment Classification

Document-level sentiment classification aims to predict overall sentiment polarity in a document about a product, while multi-aspect sentiment classification aims at detecting sentiment polarities for different aspects of a product in a document. Most existing methods perform the two tasks separately and ignore the correlation between them. In this paper, we propose a multi-task framework called multi-sentiment hierarchical attention network (MSHAN) that jointly performs document-level and multi-aspect sentiment classification both. Specifically, MSHAN adopts hierarchical architecture and attention mechanism to predict aspect sentiments and aggregate those aspect sentiments into overall sentiment. Moreover, considering that aspect sentiment can not fully express overall sentiment, MSHAN adopts another hierarchical architecture to capture additional document sentiment information and add this sentiment to the overall sentiment. Experimental results on two real-world datasets show that the proposed method outperforms previous methods. To the best of our knowledge, this is the first study that performs document-level and multi-aspect sentiment classification in a unified model.
HCCN

Due to its computation efficiency and retrieval quality, hashing has been widely applied to approximate nearest neighbor search for large scale image retrieval, while deep hashing further improves the retrieval quality by end-to-end representation learning and hash coding. However, subject to the ill-posed gradient difficulty in the optimization with sign activations, existing pairwise similarity learning based hashing methods need to first learn continuous representations and then generate binary hash codes in a separated binarization step, which cannot learn similarity relationships adequately to generate sufficient good hash codes and suffer from substantial loss of retrieval quality. To overcome this limitation, this work presents a continuous learning method based on hash (HCCN). The main idea is to improve the recognizability of the hash code through central similarity learning. At the same time, continuous learning is used to optimize the deep network with symbolic function activation to solve the ill-posed gradient problem and reduce the feature loss of the data. Comprehensive experiments demonstrate that HCCN can generate cohesive hash codes and achieve noticeable boost in retrieval performance on three datasets, NUS-WIDE, CIFAR-10, and MS-COCO.
Realizing Macro Based Technique for Behavioral Attestation on Remote Platform

In Trusted Computing, the client platform is checked for its trustworthiness using Remote Attestation. Integrity Measurement Architecture (IMA) is a well-known technique of TCG based attestation. However, due to static nature of IMA, it cannot be aware of the runtime behavior of applications which leads to integrity problems. To overcome this problem several dynamic behavior-based attestation techniques have been proposed that can measure the run-time behavior of applications by capturing all system-calls produced by them. In this paper, we have proposed a system call based technique of intrusion detection for remote attestation in which macros are used for reporting. Macros are used to denote subsequences of system calls of variable length. The basic goal of this paper is to shorten the number of system calls by the concept of macros which ultimately reduces the processing time as well as network overhead.
Landing an Autonomous UAV on a Moving Platform Using only a Front Facing Camera

This use case chapter goes through a series of packages developed for the author’s MSc Dissertation that allows an Unmanned Aerial Vehicle (UAV) to land on a moving platform, using only a front facing camera. The implemented system was run and tested on both ROS Indigo and ROS Kinetic and includes software that implements landing zone detection [32], velocity estimation [35], global [30] and local planning [31], as well as simulation [33]. At first, a typical way of running a simulated environment is presented, to allow readers to test its functionality and decide if they want to deepen their understanding of the implemented system. Subsequently, each package is presented separately, highlighting the rationale behind each implementation. After all packages have been analyzed, Concluding, some future work is presented, highlighting the weakness of the implemented software and how it can be improved, along with a summary of the system’s features and advantages.
A context-aware tracking method for aerial videos

Remote sensing target tracking in the aerial videos from aerial platforms is one of the research hotspots in visual tracking. In this paper, we propose a remote sensing target tracking method for aerial video based on a context-aware multi-domain convolutional neural network (CAMD). The process can be divided into two main stages: (1) in the design of the tracking network structure, we fuse multiple convolutional layers using residual connections to improve the effectiveness of regression learning. (2) in the “fuzzy interval”, a response-adaptive context-aware correlation filter (RA-CACF) module is introduced into our tracking network to boost the tracking performance. This method can greatly improve both the tracking efficiency and stability. We test the proposed method on the UAV123 datasets, and the experimental results demonstrate that our tracker can achieve high accuracy and efficiency results compared to state-of-the-art trackers.
Ground Target Automatic Recognition and Tracking in Complex Battlefield Environment

With the developments of science and the changes of modern warfare modes, the precise guidance and attack towards hostile targets in complex battlefield environments has extremely important military needs and research significance. However, the existing guidance mode is often constrained by factors such as battlefield background interference, target appearance variation and limited data links, which in turn affects tracking and strike performance. To this end, we present an integrated recognition and tracking scheme for the ground targets in complex battlefield environment. Specifically, with the missile-target distance information, we design an adaptive scale estimation scheme to tackle the aspect ratio variation during tracking. Furthermore, an efficient tracking monitoring index is advocated to judge the tracking condition as well as forecast the potential tracking failure. Once tracking failure is determined, a deep-learning based detection module is activated to re-detect the target as well as correct the tracking drift. In the strike stage of the mission, the key region of the hostile target would be identified and locked by the missile in order to maximize the attack effect. The proposed recognition and tracking scheme are tested upon extensive actual videos and achieved a tracking accuracy over 85%, which demonstrates its effectiveness as well as provides a novel solution for accurate and robust attack on enemy targets in a complex battlefield environment.
Estimate Early and Estimate Often: How Your Concurrent Engineering Design Team Can Bootstrap Your Organizations Programmatic Capabilities

What do you do when it is necessary to generate reasonable cost estimates at the earliest Concept Maturity Levels and you have never flown any similar missions before? This paper describes the current and future Team X and A-Team cost processes and methods, how they are being used to expand our data frontiers, cost modeling capabilities and how this enables the ability to estimate early and estimate often.