Reviewerly/exHarmony huggingface dataset:
Transfer Learning-Based Model Protection With Secret Key

We propose a novel method for protecting trained models with a secret key so that unauthorized users without the correct key cannot get the correct inference. By taking advantage of transfer learning, the proposed method enables us to train a large protected model like a model trained with ImageNet by using a small subset of a training dataset. It utilizes a learnable encryption step with a secret key to generate learnable transformed images. Models with pre-trained weights are fine-tuned by using such transformed images. In experiments with the ImageNet dataset, it is shown that the performance of a protected model was close to that of a non-protected model when the correct key was given, while the accuracy tremendously dropped when an incorrect key was used. The protected model was also demonstrated to be robust against key estimation attacks.
Security Application of Encryption Technology in Network Communication

Abstract With the development and maturity of science and technology and the arrival of the Internet era, instant messaging software has become an indispensable communication method in people’s life due to its comprehensive functions, high performance and high stability. However, due to the open, interactive and global nature of the Internet, there are a series of security problems that cannot be ignored, such as information leakage and virus transmission. Information security is particularly important to us and directly affects whether our personal interests can be effectively protected. Therefore, strengthening information protection and avoiding information leakage are important issues that need to be solved urgently. This paper designs a system based on hybrid encryption by using symmetric encryption original AES and improved symmetric encryption AES to ensure the efficiency of people in the process of information transmission and communication.
ShuffleCount: Task-Specific Knowledge Distillation for Crowd Counting

One promising way to improve the performance of a small deep network is knowledge distillation. Performances of smaller student models with fewer parameters and lower computational cost can be comparable to that of larger teacher models in specific computer vision tasks. Knowledge distillation is especially attractive for the high-accuracy real-time crowd counting task in our daily lives, where the computational resource can be limited and the model efficiency is extremely important. In this paper, we propose a novel task-specific knowledge distillation framework for crowd counting, named ShuffleCount. Its main contributions are two-fold: First, different from existing frameworks, our task-specific ShuffleCount effectively learns from the teacher network through hierarchic feature regulation, and better avoids negative knowledge transferred from the teacher. Second, the proposed student network, i.e., the optimized Shufflenet, shows promising performances. When tested on the benchmark dataset Shanghai Tech A, it achieves a 15% higher accuracy yet keeps low computational cost when compared with the state-of-the-art MobileCount. Our code is available online at https://github.com/JiangMinyang/CC-KD.
Evaluation of Transformation Tools in the Context of NoSQL Databases

With the upcoming term of big data and enhancements in cloud technology a new kind of database arose - NoSQL databases. Concomitant with these new databases load processes changed from Extract-Transform-Load (ETL) to Extract-Load-Transform (ELT). Therefore new transformation tools are needed supporting the requirements of a transformation tool and NoSQL databases. Until now there are no evaluations available which give a review about the existing transformations tools as well as an analysis of them. In this context not only relevant work is presented but also the most popular NoSQL databases and the requirements of transformation tools. Furthermore the most known transformation tools fitting the requirements are introduced and evaluated. This work is not only a market overview for a new kind of transformation tools but also a decision guidance for choosing the right tool.
Intrinsic Robotic Introspection: Learning Internal States From Neuron Activations

We present an introspective framework inspired by the process of how humans perform introspection. Our working assumption is that neural network activations encode information, and building internal states from these activations can improve the performance of an actor-critic model. We perform experiments where we first train a Variational Autoencoder model to reconstruct the activations of a feature extraction network and use the latent space to improve the performance of an actor-critic when deciding which low-level robotic behaviour to execute. We show that internal states reduce the number of episodes needed by about 1300 episodes while training an actor-critic, denoting faster convergence to get a high success value while completing a robotic task.
Learning Quantum Computing

Though research on quantum computing continues at a good pace, learning quantum computing requires a deep and clear level of knowledge of a range of domains that diverge from classical computer science. There are not quantum computing interfaces that facilitate learning this new paradigm, only those to illustrate some quantum computations with some accompanying documentation. Moreover, in this documentation, science is mixed with technology without a clear distinction between the underlying mathematics and physics concepts. We consider that this must be addressed in the first place in order to create proper learning environments that go beyond performing calculations without facilitating the understanding of the core of quantum computing concepts and their implications. This is a new research line, dealing with the design of quantum computing interfaces integrating science and technology.
Control Effectiveness: a Capture-the-Flag Study

As cybersecurity breaches continue to increase in number and cost, and the demand for cyber-insurance rises, the ability to reason accurately about an organisation’s residual risk is of paramount importance. Security controls are integral to risk practice and decision-making: organisations deploy controls in order to reduce their risk exposure, and cyber-insurance companies provide coverage to these organisations based on their cybersecurity posture. Therefore, in order to reason about an organisation’s residual risk, it is critical to possess an accurate understanding of the controls organisations have in place and of the influence that these controls have on the likelihood that organisations will be harmed by a cyber-incident. Supporting evidence, however, for the effectiveness of controls is often lacking. With the aim of enriching internal threat data, in this article we explore a practical exercise in the form of a capture-the-flag (CTF) study. We experimented with a set of security controls and invited four professional penetration testers to solve the challenges. The results indicate that CTFs are a viable path for enriching threat intelligence and examining security controls, enabling us to begin to theorise about the relative effectiveness of certain risk controls on the face of threats, and to provide some recommendations for strengthening the cybersecurity posture.
Depth Optimized Synthesis of Symmetric Boolean Functions

Symmetric Boolean functions are characterized by the fact that their output can be determined by the Hamming weight of their inputs. Symmetric Boolean functions are essential for many complex systems and possess significant cryptographic properties.Due to their popularity and significance, research has been focusing on optimization of synthesis strategies for symmetric Boolean functions. However, none of them has targeted optimizing the depth of the final synthesis, yet.In this paper, we propose a synthesis scheme for symmetric Boolean functions which generates circuits with a worst-case depth of O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n). To the best of our knowledge we are the first to propose a synthesis scheme that aims at reducing the depth of the generated circuits for symmetric Boolean functions. In the experiments we show that our approach allows to reduce the depth of the final implementation by up to 25.93% compared to the state-of-the-art.
Implementing the plugin distribution system

Recent works proposed to dynamically extend protocol implementations through protocol plugins. While addressing deployment issues, they raise safety concerns (do they terminate, do they act maliciously,. . . ). To fill this gap, a system distributing trust in plugin's verification properties was proposed in the literature. However, it was not implemented. This poster demonstrates the feasibility of this approach by providing an open-source implementation of this system. We also extend the state-of-the-art verification works about protocol plugins by considering a new property called side-effects.
Real-time human-centric segmentation for complex video scenes

Most existing video tasks related to “human” focus on the segmentation of salient humans, ignoring the unspecified others in the video. Few studies have focused on segmenting and tracking all humans in a complex video, including pedestrians and humans of other states (e.g., seated, riding, or occluded). In this paper, we propose a novel framework, abbreviated as HVISNet, that segments and tracks all presented people in given videos based on a one-stage detector. To better evaluate complex scenes, we offer a new benchmark called HVIS (Human Video Instance Segmentation), which comprises 1447 human instance masks in 805 high-resolution videos in diverse scenes. Extensive experiments show that our proposed HVISNet outperforms the state-of-the-art methods in terms of accuracy at a real-time inference speed (30 FPS), especially on complex video scenes. We also notice that using the center of the bounding box to distinguish different individuals severely deteriorates the segmentation accuracy, especially in heavily occluded conditions. This common phenomenon is referred to as the ambiguous positive samples problem. To alleviate this problem, we propose a mechanism named Inner Center Sampling to improve the accuracy of instance segmentation. Such a plug-and-play inner center sampling mechanism can be incorporated in any instance segmentation model based on a one-stage detector to improve the performance. In particular, it gains 4.1 mAP improvement on the state-of-the-art method in the case of occluded humans. Code and data are available at https://github.com/IIGROUP/HVISNet.
Emotion based video player

One's work can be done efficiently only if their mood is good. Emotions is the index of mood. Here model capture one's image as an input, predict their mood and play a video of opposite genre as an output, in order to change their mood, which is the main goal of this project. Hence taking them through an emotional roller coaster. The solution makes use of CNN (convolutional neutral networks) for detecting one's mood. It uses OpenCV (open-source computer vision library) in-order to get user's image using their respective web camera. It is done by importing modules like web-browser and requests in-order to get access to YouTube to play videos accordingly. The average accuracy rate of the system has increased to 98.53 percent. Eight primary emotion classes have been effectively classified by the method. As a result, the proposed strategy has been shown to be effective in recognizing emotions.
Prioritization of complex heterogeneous queries using evolutionary and computational approach

It is well understood that the efficiency of query evaluation can be enhanced through different optimization mechanisms and somehow the query execution plans obtained through static implementation may not be optimal. In such situation, while focusing on query optimization based on virtual server, the strategic implementations many times permits to increase the efficiency of query execution plans. In fact the methodologies associated with query optimization in virtual environment are somehow uncommon as these are linked with search based and schema based applications. Indeed it is clearly known that the optimization criteria can be either static or dynamic. But the parameters associated with queries in virtual platforms can be computed focusing on I/O cost, CPU cost, server/receive response as well as ratio of mechanism towards optimal cost in different situations. Of course, the query execution plans should have provision to be stored in different locations in distributive manner, but many times it will not be feasible to explore and compute the linked query plans associated with large scaled databases. So it is required to focus on specific optimization criteria with proportionate measures to overcome the challenges associated with query optimization. In virtual platform, the sharing of computational resources is not confined only in specific location but it can be provisioned with diversified and distributed locations. Therefore, harmonization of data is very much required during auto access of data based on situation and application.
Machine Learning Attacks and Countermeasures on Hardware Binary Edwards Curve Scalar Multipliers

Machine Learning techniques have proven effective in Side Channel Analysis (SCA), enabling multiple improvements over the already-established profiling process of Template Attacks. Focusing on the need to mitigate their impact on embedded devices, a design model and strategy is proposed that can effectively be used as a backbone for introducing SCA countermeasures on Elliptic Curve Cryptography (ECC) scalar multipliers. The proposed design strategy is based on the decomposition of the round calculations of the Montgomery Power Ladder (MPL) algorithm and the Scalar Multiplication (SM) algorithm into the underlined finite field operations, and their restructuring into parallel-processed operation sets. Having as a basis the proposed design strategy, we showcase how advanced SCA countermeasures can be easily introduced, focusing on randomizing the projective coordinates of the MPL round’s ECC point results. To evaluate the design approach and its SCA countermeasures, several simple ML-based SCAs are performed, and an attack roadmap is provided. The proposed roadmap assumes attackers that do not have access to a huge number of leakage traces, and that have limited resources with which to mount Deep Learning attacks. The trained models’ performance reveals a high level of resistance against ML-based SCAs when including SCA countermeasures in the proposed design strategy.
Zero-Shot Learning Of A Conditional Generative Adversarial Network For Data-Free Network Quantization

We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-shot learning of a conditional generator only needs a pre-trained discriminative (classification) model and does not need any training data. In particular, the conditional generator is trained to produce labeled synthetic samples whose characteristics mimic the original training data by using the statistics stored in the batch normalization layers of the pretrained model. We show the usefulness of ZS-CGAN in data-free quantization of deep neural networks. We achieved the state-of-the-art data-free network quantization of the ResNet and MobileNet classification models trained on the ImageNet dataset. Data-free quantization using ZS-CGAN showed a minimal loss in accuracy compared to that obtained by conventional data-dependent quantization.
Literature Review of Finding Duplicate Bugs in Open Source Systems

Bug triage aimed at enhancing the quality of bug reports. Triaging also ensures that the bug report has enough information that makes sense to developers. It also checks for duplicates as well as ensuring that the report filled correctly in the associated project. Bug triage is important because it helps to prioritize and schedule the required set of actions aimed at fixing a bug. Searching and identifying duplicates in bug triage reporting is an essential element because it helps to save time and effort as well as enforcing version control. To detect and recommend duplicates in bug report triage various techniques are used. These techniques are essential because the automated process of bug reporting tends to point to the same root cause of an issue despite using different reporting parameters. An important technique used for checking duplicates in bug reporting triage involves the combination of non-repudiation classification and clustering.
K-Means Clustering Algorithm in Web-Based Applications for Grouping Data on Scholarship Selection Results

In our case the selection of scholarship recipients was done previously by a university, by assessing and scoring 7 variables: whether scholarship application form is submitted, whether student study plan is submitted, whether student transcript is submitted, whether student's curriculum vitae is submitted, what is the score of student's scientific article, what is the score of student's presentation on a given topic, and what is the score of student's GPA. This such selection process was done without computation, with the consequence of time consumption and potential human errors. This research aims to overcome that problem, by providing a computed selection process using the same 7 variables and applying a necessary algorithm. In our research the K-means Clustering algorithm is applied although it is understood that other algorithms can be used too. The tests are carried out using black box and white box methods. The result shows that K-means Clustering algorithm is successfully applied to the scholarship selection system, and the K-means Clustering algorithm is successful in grouping students who receive and who do not receive the scholarship.
Progressive Face Super-Resolution with Non-Parametric Facial Prior Enhancement

The main challenge of face super-resolution is to overcome facial distortions in an upscaling process. Recent works have utilized facial priors such as facial landmarks and component maps to generate a precise super-resolved image. However, the facial priors are estimated from the ground-truth and deep neural networks. Thus, recent works based on the facial priors are not only limited to specific datasets including the ground-truth, but also need sub-networks to extract facial priors. To solve these problems, we propose a progressive face super-resolution network with non-parametric facial prior enhancement, called as NPFNet, which extracts and highlights facial components without any tricks, such as the ground-truth and deep neural networks. The self-enhancement module facilitates our network to fully utilize facial distinct features to enhance super-resolved images with a parameter-free operation. Extensive experiments on CelebA and VGGFace2 demonstrate that the proposed method outperforms state-of-the-art face super-resolution methods in terms of visual quality and quantitative measurements.
Web-Based Integrated Disaster Information System For Optimizing The Role and Function of The Data and Information Center (PUSDATIN) Badan Penanggulangan Bencana Daerah Kabupaten Sidoarjo

The Data and Information Center (PUSDATIN) is the main facility for disaster management. Integrated data is the main support in determining disaster management policies. The purpose of research is to built an integrated disaster information system to optimize the function of PUSDATIN. The research method used was interviews and data collection. This system was developed using MySQL and Dreamweaver. There are 3 (three) user levels : user, admin and super admin. The result is an integrated disaster information system on web-based that can be accessed by anyone, according to the level of user, users can execute CRUD commands smoothly, data redundancies can be reduced, and optimization of BPBD resources by using PUSDATIN as 1 (one) gate data. The conclusion of this research is this web-based integrated disaster information system running well and has a positive effect on the performance of PUSDATIN as a traffic data center for disasters and fires.
Social engineering (SoE) attacks towards network security in higher learning institute: The partial least squares path modeling approach

The basic network security issues have changed very little over the past decade. Protecting the confidential institutional information, preventing unauthorized access defending the network against SoE attacks remain primary concerns of network security professional toady. Widespread remote access by the high number of increasing sophisticated SoE attacks is making network security significantly more challenging in the institute. Confidential information can reside in two states on a network. It can reside on institutional physical storage media, such as a hard drive or memory, or it can reside in transit across the physical network wire in the form of packets. These two information states present multiple opportunities for SoE attacks from users in higher leaning institute internal network, as well as those users on the internet. Given the dramatic rise in external security threats, couples with the rising cost of network intrusions. Institution are more pressured than ever to define and protect their network perimeter. There is no choice for the institute but to keep abreast of large number of security issues confronting in today’s world.
Towards Satellite-Suited Noise-Free Quantum Memories

Quantum memories on satellites will allow to circumvent transmission losses in long-distance quantum communication. We demonstrate a warm Caesium vapour memory based on EIT with an unconditional noise floor of 1 . 2 · 10 -3 due to collision-induced fluorescence.
VizInteract: Rapid Data Exploration Through Multi-touch Interaction with Multi-dimensional Visualizations

Creating and editing multi-dimensional data visualizations with current tools typically involves complex interactions. We present VizInteract, an interactive data visualization tool for touch-enabled displays. VizInteract supports efficient multi-touch data exploration through rapid construction of and interaction with multi-dimensional data visualizations. Building on primitive visualization idioms like histograms, VizInteract addresses the need for easy data exploration by facilitating the construction of multi-dimensional visualizations, such as scatter plots, parallel coordinate plots, radar charts, and scatter plot matrices, through simple multi-touch actions. Touch-based brushing-and-linking as well as attribute-based filter bubbles support "diving into the data" during analysis. We present the results of two explorative studies, one on a tablet and another on a large touchscreen and analyze the usage patterns that emerge while participants conducted visual analytics data exploration tasks in both conditions.