Reviewerly/exHarmony huggingface dataset:
Embedding Quantum Key Distribution into Optical Telecom Communication Systems

Quantum key distribution (QKD) together with one-time pad cryptography provides unconditional security for the sensitive information. However, the lack of telecom compatibility hinders massive deployment of QKD. In this talk we will discuss the approaches of embedding QKD in optical communication systems and the recent progress.
A pre-silicon logic level security verification flow for higher-order masking schemes against glitches on FPGAs

Higher-order masking schemes have been proven in theory to be secure countermeasures against side-channel attacks in the algorithm level. The ISW framework is one of the most acceptable secure models of the existing higher-order masking schemes. However, a gap may exist between scheme and implementation. Several analyses have exhibited the weakness of masking in hardware designs on FPGAs. Firstly, we give the definition of leakage point and introduce three implementation logical flaws: glitch, EDA optimization and intermediate variable of scheme flaw. Secondly, we propose a leakage verification flow for implementing and verifying circuits realized higher-order masking schemes to avoid these leakage points. The flow provides an efficient evaluation method to locate and identify leakage points in masking hardware implementations. With the knowledge of the weaknesses of implementation, the implementation should be modified by corresponding methods to fix flaws, especially for glitch, which has been regarded as the main challenge of masking in hardware designs, we provide a method to remove the leakage point using Dijkstra algorithm with no extra time and area overheads. Finally, the design flow is evaluated on the implementation of Rivain&Prouff masking. Our experiments demonstrate how it automatically locates and protects the implementation. In addition, the experiments are also performed on flawed implementations due to EDA optimization and intermediate variables.
Generating Adversarial Samples With Constrained Wasserstein Distance

In recent years, deep neural network (DNN) approaches prove to be useful in many machine learning tasks, including classification. However, small perturbations that are carefully crafted by attackers can lead to the misclassification of the images. Previous studies have shown that adversarial subspaces lie off (but close to) the data submanifold and detection techniques based on the distributional difference between adversarial and normal samples have been proposed. These distribution-based detection techniques achieve excellent performance in characterizing adversarial samples. In this paper, We propose W-PGD to generate adversarial samples close to normal data distribution to bypass those detecting mechanisms. We trained a neural network WassNet to estimate the difference between two distributions using Wasserstein distance and use the gradient of WassNet to constrain the Wasserstein distance between adversarial and normal samples’ distribution. Our experiment shows that W-PGD can effectively decrease the detection rate of distribution-based detection techniques and generate adversarial samples with constrained Wasserstein distance.
Neuroevolution Strategies for Word Embedding Adaptation in Text Adventure Games

Word embeddings have gained popularity in many Natural Language Processing (NLP) tasks. They can encode general semantic relationship between words, and hence provide benefits in many downstream tasks when used as a knowledge base. However, they still suffer from morphological ambiguity as the trained vectors do not share representations at the subword level. In this report, we propose a new architecture that uses neuroevolution to fine-tune pre-trained word embeddings for the challenging task of playing text-based games. We fit this architecture into an existing game agent and evaluate its performance on six text-based games. Experimental results show that the proposed fine-tuning architecture may significantly mitigate the effect of morphological ambiguity to enable our game agent to reduce the total number of steps required to generate valid actions and perform well in these games.
Chat Response Generation Based on Semantic Prediction Using Distributed Representations of Words

In this paper, we propose a chat response generationFurumai, Kazuaki systemTakiguchi, Tetsuya usingAriki, Yasuo distributed expression of words by Word2vec. With the conventional one-hot representation method, there was a problem in that the model becomes complicated as the vocabulary increases, and only the words that appear in the dialogue corpus can be handled. We address these problems by using Word2vec and extend it to handle unknown words that did not appear in the conversation corpus. In a subjective evaluation experiment, we showed that various responses can be generated by estimating words using semantic prediction.
Research and Development of Automatic Assessment Strategy for ERP Practical Skills Based on MOOC

In recent years, teaching, testing, competing and training of ERP practical skills have been developed rapidly and the enterprises' demand of ERP practical and the qualified personnel is increasing. However, since the ERP software is developed for enterprise applications, it does not have the function of automatic assessment. In addition, because the MOOC platform breaks the time and space constraints, and can meet the basic functions of ERP software teaching and learning, therefore, according to the characteristics of ERP system, this paper develops a strategy of automatic assessment for ERP practical skills based on MOOC. The results applied to the actual ERP course show that the strategy has the functions of automatic marking criteria setting, automatic scoring and detailed feedback, which can greatly reduce the workload of teachers and the volume of scoring, and improve the quality of the feedback.
Universal D-Semifaithfull Coding for Countably Infinite Alphabets

The problem of fixed-distortion universal source coding (or universal D-semifaithfull source coding) for stationary and memoryless sources on countably infinite alphabets is investigated. The main result of this paper offers sufficient conditions to achieve strong and weak minimax universality for D-semifaithfull source coding of any memoryless source defined by an envelope function on infinite alphabets. It is also shown that universal D-semifaithfull source coding is not feasible for the complete family of memoryless sources. Furthermore, a sufficient condition for impossibility is presented for the family of envelope distributions.
System Design of Ultrasonic 2D Hand Gesture Trajectory Capture Using Variable Gain

This paper presents a technique for two dimensional (2D) hand gesture trajectory capture system which can be applied to the interaction of intelligent equipment. Firstly, the sensing principle and hardware are described. In the processing of gesture trajectory, an automatic calibration algorithm based on cross correlation operation is proposed to resolve the interference from environment; the dynamic threshold and variable gain methods are developed to extract distance and the finger positioning model is established basing on the geometric relation. Finally, we obtain the hand gesture trajectory with our hardware in real time and demonstrate the effectiveness of the system.
Advanced Image Encryption Scheme Using Snow Stream Cipher

Image encryption has evolved significantly over the years. This paper proposes an image encryption system using Arnold’s cat map transformation and Snow stream cipher. In our proposed scheme, the image is first scrambled using Arnold’s transformation and then the selected scrambled image pixels are XORed with the Snow cipher keystream sequence to produce the cipher image. Performance of the proposed scheme is analyzed based on several statistical properties. We have observed encouraging statistical properties of the cipher image bitstream making our encryption scheme suitable for applications that requires secured transportation of images.
Science Lab Repository Requirements Elicitation Based on Text Analytics

Requirements elicitation is an important task before any development of system repository can be conducted. Typically, traditional methods such as interview, questionnaire and observation are made to gauge the users’ needs. However, the users may not be able to spell out specifically of their need especially if there is no available system to compare resulting to outrageous demands and unrealistic expectations to the repository developer. An alternative approach to gauge the user needs from users’ reviews of the on-the-shelf software may be a good starting point. In this paper we attempt to extract requirements from the users’ independent reviews gathered from the internet using text analytics approach. The keywords are visualized based on its relevance and importance to the user. Then, it is used as a benchmark for the user to alter to their specific repository needs. From the experimental results, it is observed that there are functions that are very much needed by the user and yet there are also functions that are not used at all. Hence, this proposed approach may give insight to the user and developer about the actual needs of the respective system. It is envisaged that such approach can be a guide to the novice user and the developer in order to shorten the time to agree on the development of the repository system.
Displacement Error Analysis of 6-DoF Virtual Reality

Virtual view synthesis is a critical step in enabling Six-Degrees of Freedom (DoF) immersion experiences in Virtual Reality (VR). It comprises synthesis of virtual viewpoints for a user navigating the immersion environment, based on a small subset of captured viewpoints featuring texture and depth maps. We investigate the extreme values of the displacement error in view synthesis caused by depth map quantization, for a given 6DoF VR video dataset, particularly based on the camera settings, scene properties, and the depth map quantization error. We establish a linear relationship between the displacement error and the quantization error, scaled by the sine of the angle between the location of the object and the virtual view in the 3D scene, formed at the reference camera location. In the majority of cases the horizontal and vertical displacement errors induced at a pixel location of a reconstructed 360° viewpoint comprising the immersion environment are respectively proportional to 3/5 and 1/5 of the respective quantization error. Also, the distance between the reference view and the synthesized view severely increases the displacement error. Following these observations: displacement error values can be predicted for given pixel coordinates and quantization error, and this can serve as a first step towards modeling the relationship between the encoding rate of reference views and the quality of synthesized views.
READINESS OF A UNIVERSITY TEACHER TO KNOWLEDGE MANAGEMENT

The article deals with the problem of readiness of a university teacher for knowledge management in conditions of formation of a cognitive university. The modern process of development of the information society is continuously associated with the changing role of knowledge, which is a separately created public good and a separate resource. The development of knowledge economy transforms the conditions for the functioning of a higher school, orienting it to the training of graduates who would have an individual set of necessary competences on a multi- and metadisciplinary basis. The urgency of the problem is determined by new educational conditions that give the teacher the functions of a mentor, coach, consultant and manager-organizer of the educational process, which is identical to the process of knowledge management.&#x0D; In the course of changing the role of a teacher in an educational organization of higher education, his scientific activity acquires special significance. The authors presented the specificity of scientific activities of a university teacher in the context of the development of a fourth-generation university, revealed the concept of a university teacher's readiness to manage knowledge.&#x0D; The main research methods are the analysis and synthesis of scientific information, research results in the areas of knowledge management in educational institutions of higher education and teachers professional activities; generalization and systematization of scientific facts and provisions.&#x0D; Based on the principle of identification, to determine the structure of a university teacher's readiness for knowledge management, the types of scientific activity are correlated with the main stages of knowledge life cycle management and his official duties.&#x0D; As a result of the research, the authors proposed a knowledge management readiness structure that integrates value-motivational, instrumental-digital, research, appraisal-analytical, interactive-communication and academic-entrepreneurial components.
A new fog based security strategy (FBS2) for reliable image transmission

Fog computing has become a fast intermediate between the cloud and the internet of things (IOT) devices. So, the communication channel between the fog and IOT devices must be secured against attacks. Both Cryptography and steganography are well-known techniques for data security. Each of them has its own advantages and strength points, hence, combining Cryptography and steganography in a hybrid system certainly promotes the system’s level of security. Although several hybrid security strategies have been proposed, they suffer from many drawbacks especially when they are applied in fog environments. Fog computing requires not only high security level but also a real time transmission. Hence, recent hybrid security strategies cannot be applied directly in fog environments as they suffer from long processing time. This paper introduces a new Fog Based Security Strategy (FBS2). FBS2 is a hybrid security strategy as it combines Cryptography and steganography techniques. The proposed Cryptography Technique (PCT) is divided into two phases, namely; confusion and diffusion. The former scrambles the secret image pixels, while the latter changes the pixels values using new confusion and diffusion methodologies respectively. Confusion divides the secret images into closed loops of pixels, then rearrange the pixels into straight paths, while diffusion employs a Programmable Linear Feedback Shift Register (PLFSR). On the other hand, the Proposed Steganography Technique is implemented using the discrete wavelet packet transform. Embedding process depends on a new matching procedure, which is based on the most significant bits of both the encrypted secret image and the corresponding employed cover image pixels. Least significant bits (LSBs) are used as indicators to the matching process. At first an intermediate code is derived from the matching process, then, it is encrypted to represent the LSBs of the stego-image. Experimental results have shown that the proposed FBS2 outperforms the previous counterparts in terms of efficiency, security, and processing time, which proves its suitability for application on the fog.
Investigating Active Tangibles and Augmented Reality for Creativity Support in Remote Collaboration

Physical manipulation is a key part of externalizing representations of knowledge and the creative process. However, contemporary tools for remote collaboration ignore physical manipulation and the haptic modality. We are interested in exploring remote physical manipulation in the context of ideation and brainstorming. Augmented Reality provides much of the benefits of spatial representation of remote participants, yet AR does not allow for rich physical manipulation and haptic feedback. Thus, we propose to use pairs of multi-robot system to provide synchronized haptic proxies in conjunction with the AR system. These small, tangible robots can be used directly as handles for digital models. We share insights gathered during experimentation to help design platforms combining AR and actuated tangibles, and present several application scenarios to illustrate their potential for remote collaboration.
Dynamic linear threshold attenuation linking synaptic computing network for image enhancement

Dynamic linear threshold attenuation linking synaptic computing network (DLSCN) is proposed. The attenuation rate of neural threshold determines the propagation rate of the gamma band. DLSCN employs clustering to segment the external input stimuli of neurons in intensity. Different attenuation rates are set in different intensity stimulus regions to control the propagation rate of the gamma band. Therefore, the ignition timing of neurons can be delayed or advanced so as to obtain the expected distribution of linking synaptic. The intensity distribution of the output image of the network is more consistent with the characteristics of the human visual system. Experiments on four public datasets show that DLSCN achieves good results in enhancing image contrast and boosting image details.
A study on the reliability of equipment system through case-study on the manufacture of machinery/electronic equipment using practical QRM (quality, reliability, maintenance) process and evaluation index

Reliability evaluation approaches to equipment systems consisting of hundreds of parts and dozens of modules have limitations with respect to environmental testing, life testing, and accelerated life testing. Equipment systems cannot be tested with a statistically verifiable sample number. Therefore, screening methods used by manufacturers involve short operational tests after production; failures occurring in the field are dealt with after delivery. The frequency of maintenance checks is closely related to the company's reputation and life cycle costs (LCCs). The aim of this study is to investigate ways to reduce maintenance cost and high LCCs by the company's reliability related processor. First, we classified equipment industries into three categories: machine processing, semiconductor processing, and inspection. Then, we investigated user requirements for equipment systems. We found that there were different demands for each industry. For equipment used in the machine processing industry, capability (productivity, adaptability, flexibility, and precision) and reliability (reliability of drive system, bearing failure rate) are considered (Jo-Peng et al., 2010 [1]). Equipment used in the semiconductor process is required to be employed for immediate use; further, they are required to be highly accurate. Inspection equipment is usually manufactured by small companies; therefore, they have problems associated with quality and initial failures. Next, we proposed the practical quality, reliability, and maintenance (QRM) processes to reduce high LCCs for four years. Before implementing the processes, we developed and applied an evaluation index that evaluated the quality management status, reliability, and maintenance aspects of the company. The index was designed by the behaviorally anchored rating scale (BARS) technique, which is a quantitative calculation method (Martin-Raugh et al., 2016). The results showed that companies could reduce their LCCs and improve their reliability.
Provably Secure Proactive Secret Sharing Without the Adjacent Assumption

In secret sharing (SS), the secret is shared among a number of parties so that only a quorum of these parties can recover the secret, but a smaller set of parties cannot learn any information about the secret. However, the traditional SS technique is insufficient to protect the secret with a long lifetime, because the adversary may gradually compromise enough parties to retrieve the secret over the long time. To solve this issue, proactive secret sharing (PSS) divides the lifetime of the secret into many short time periods and the parties jointly update their secret shares in each time period. The benefit is that if the adversary cannot break into enough parties in a single time period, her compromised shares will become obsolete after the shares being updated. In the last two decades, many PSS schemes have been proposed and they are widely used in various security protocols. However, the majority of existing PSS schemes require the adjacent assumption, i.e. if a party is corrupted during an update phase, it is corrupted in both time periods adjacent to that update phase. Note that this assumption not only hinders the security model to capture the mobile adversary’s abilities, but also prevents PSS schemes being used in many real-world applications. In this paper, we revisit the research of PSS, and our work contributes in the following aspects. Firstly, we discuss why some existing schemes (including Herzberg’s PSS scheme) cannot maintain their security when the adjacent assumption is removed. Secondly, we use the polynomial truncation method to improve Herzberg’s PSS scheme. To the best of our knowledge, our proposed scheme is the first provably secure PSS scheme without the adjacent assumption.
A Comparison Study of Cybersecurity Workforce Frameworks and Future Directions

The cybersecurity industry is continuing to grow in workforce participation due to the growing popularity of its services in both commercial and government organizations. The government has recognized and created several frameworks that outline the work role standards for the multiple sectors. A comparison of these frameworks would not only provide a perspective of the purpose for maintaining different frameworks, but also provide a model of their relationships and the sectors that they cover. This paper will compare frameworks, outline the differences and similarities among them, and suggest ways to promote the workforce participation even more.
Goal-Oriented Knowledge-Driven Neural Dialog Generation System

In this paper, we propose a goal-oriented knowledge-driven neural dialog generation system, which leads the conversation based on a knowledge graph. During the conversation, the system has to actively integrate appropriate knowledge conditioned on current dialog state, and then generate coherent, fluent and meaningful responses. We use ERNIE as our backbone model, proposing a fine-tuning scheme to first pre-train on knowledge graph and dialog sequence, and then fine-tune to generate the next response. We extend multi-task learning in multi-turn dialog generation to improve consistency. We show that with well-designed transfer learning, ERNIE shows competitive performance on a knowledge-grounded dialog generation task. In the Baidu knowledge-driven dialog competition, our best single model achieved 4th in the automatic evaluation stage with 47.03 f1 score and 0.417/0.281 BLEU1/BLEU2 score, and ranked 1st in the final human evaluation stage, with descent topic completion performance(1.81/3) and highest coherence score(2.59/3).
Utilizing Passages in Fusion-based Document Retrieval

The usage of passage-level information has been successfully demonstrated in many core IR tasks, and among such tasks, the task of passage-based document retrieval. In this work, we study the merits of utilizing similar information for the fusion-based document retrieval task. Overall, we show that such information can be highly useful for this task as well. To this end, we propose three passage-based fusion methods and show that their performance can transcend that of strong document-level fusion methods.