Reviewerly/exHarmony huggingface dataset:
TiSSA: A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors

Modeling user behaviors as sequences provides critical advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for personalized search and recommendation. Recently, recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences. However, most of the previous RNN-based work suffers from the complex dependency problem, which may lose the integrity of highly correlated behaviors and may introduce noises derived from unrelated behaviors. In this paper, we propose to integrate a novel Time Slice Self-Attention (TiSSA) mechanism into RNNs for better modeling sequential user behaviors, which utilizes the time-interval-based gated recurrent units to exploit the temporal dimension when encoding user actions, and has a specially designed time slice hierarchical self-attention function to characterize both local and global dependency of user actions, while the final context-aware user representations can be used for downstream applications. We have performed experiments on a huge dataset collected from one of the largest e-commerce platforms in the world. Experimental results show that the proposed TiSSA achieves significant improvement over the state-of-the-art. TiSSA is also adopted in this large e-commerce platform, and the results of online A/B test further indicate its practical value.
Reputation Deflation Through Dynamic Expertise Assessment in Online Labor Markets

Current reputation systems in online labor markets (e.g., Freelancer, PeoplePerHour) experience three major shortcomings: (1) reputation inflation (i.e., reputation scores are inflated to "above average" values) (2) reputation attribution (i.e., attribution of reputation scores to individual skills is unfeasible) and (3) reputation staticity (i.e., reputation scores are uniformly averaged over time). These shortcomings render online reputation systems uninformative, and sometimes even misleading. This work proposes a data-driven approach that deflates reputation scores by solving the problems of reputation attribution and saticity. The deflating process starts with projecting any random set of skills to a set of competency dimensions. For each competency dimension, a Hidden Markov Model estimates a contractor's current (but latent) competency-specific expertise. Aggregation of competency-specific estimates provides expertise predictions for any given set of required skills. Empirical analysis on 61,330 completed tasks from a major online labor market shows that the resulting estimates are deflated and they better predict contractor performance. These results suggest a series of implications for online (labor) markets and their users.
Panchromatic image compression based on improved post-transform for space optical remote sensors

Panchromatic images of space optical remote sensors effectively demonstrate the shape, structure, and texture of landscape objects. Conventional compressors, such as JPEG2000 and Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC), widely use discrete wavelet transforms (DWTs) as sparse multiresolution representations of remote sensing panchromatic images (RSPIs). However, many large amplitude and high-frequency coefficients can be produced when RSPIs project onto a DWT basis, which are detrimental to the subsequent encoding process. Therefore, using only DWT to perform sparse representation is not an optimal solution for RSPIs. In this study, we propose a low-complexity compression approach based on a dual-loop, double-bases post-transform combined with bit-plane encoding (BPE) for RSPIs. First, a DWT is applied to RSPIs to perform the transform operations. Then, from the computed DWT coefficients a post-transform with a double base (discrete cosine transform and Hadamard transform) is performed. One of the double bases can be used for the high bit rate and the other can be used for the low bit rate. The best post-transform is selected based on a p-norms approach. The post-transformed coefficients are reorganized into tree structures to perform encoding using BPE. We use dual loop (basis loop and rate loop) to control the coding process. BPE results are fed back to select the basis used by post-transform. The best post-transform results are fed forward to allocate the bit rate for each coding segment. The experimental results of on-board RSPIs show that the proposed approach improves the PSNR by 0.6 dB to 1.1 dB compared to that of the CCSDS-IDC method with a small increase in complexity. The compression performance of the proposed method obtains similar results, but lower complexity to that of JPEG2000-based coders.
Cyber-ARGUS - A mission assurance framework

The use of cyberspace as a platform for military operations has been growing at impressive rates. Yet, it is still a relatively new area that poses considerable research challenges. Security techniques are not sufficiently effective to protect IT systems and most fail to address the correlation between actions and effects across multiple domains. In other words, identifying how actions performed in the cyber domain affect the mission goals is yet an unsolved problem. This research presents a potential solution and proposes a framework that links the cyber and the operations domains, evaluating how actions in the first impact the effectiveness of missions in the latter. The framework, Cyber-ARGUS, is a Command and Control (C2) support system comprised of a set of tools that provides coherent and consistent mapping between the two domains. Relevant information about the nodes of a cyber infrastructure supporting an operation is stored in a knowledge base, and then used to build a Bayesian Network that provides impact assessment. The technique is illustrated through the simulation of an air transportation scenario in which the C2 infrastructure is subjected to various cyber-attacks, and Cyber-ARGUS is used to assess their associated impact to the air operations. The main contribution of this research is the methodology that enables assessing the cyber impact to ongoing missions. One major advantage of the technique is that it achieves this by calculating the combined effects produced by the attackers and defenders plans, without the requirement of knowing the hard-to-assess enemy's individual actions.
Pair wise training for stacked convolutional autoencoders using small scale images

Deep neural networks have dramatically gained immense potential in almost every field like computer vision, natural language processing, biomedical informatics etc. Among these networks, autoencoders are popular in performing dimensionality reduction task, while learning a representation for an unl abeled dataset. A usual way of dealing with such networks is to pre-train them in a layer-wise fashion, and consequently fine-tune the whole stack in a supervised manner. In this paper, a pair-wise training strategy is proposed to determine optimum model parameters by reducing training time as well as the complexity of training a convolutional autoencoder without compromising its accuracy. The proposed approach works in a fully unsupervised manner and has been tested on datasets like MNIST, CIFAR10, and CIFAR100 and it shows that the training time has improved by an average of 25% on these three datasets.
Group Low-Rank Representation-Based Discriminant Linear Regression

In this paper, a novel least square regression method, named group low-rank representation-based discriminant linear regression (GLRRDLR), is proposed for multi-class classification. Unlike the conventional linear regression methods, the proposed method aims to learn a more discriminative projection. Specially, two main techniques are adopted to improve the discriminability of the projection. The first approach is to make the transformed samples locate in their own subspace by introducing a group low-rank constraint to the model, such that the distance between samples from the same class can be decreased greatly. The second approach is to simultaneously learn a discriminative target matrix for regression. The extensive experimental results show that the proposed method performs much better than the state-of-the-art methods, which proves the effectiveness of the above two approaches in improving the discriminability of the projection.
Evaluation of information and communication technology sector in the teaching process and strategic collaboration between universities and industry

Abstract The main purpose of the study was to evaluate the information and communication technology (ICT) sector in the teaching process based on teachers’ attitudes and the strategic collaboration between universities and industry. The most common issue faced by the graduated students is to find a desired job at the end of the course of study. The main obstacle in finding a desired job is not enough ICT knowledge, which is not adequately learned at universities. On the other hand, the ICT teaching process at universities could be slow because many teachers do not have the right attitude toward ICT application in the learning process. In this study, a survey was performed among ICT working students and ICT teachers as well to reveal the relationship between the skills required for a job and the skills being taught at universities; whether the employability skills of fresh graduates increase hiring opportunities. The sample of examinees for this study consisted of the ICT sector in the European Union as well as ICT teachers based on the World Bank database. The proposed model was analyzed using structural equation modeling. It was found that the relationship between the academic program and job market along with ICT skills affected the employability factor. Hence the relationship between the academic program and job market significantly affects the employability factor. In effect, more focus on designing the academic curriculum is needed.
A Key-Policy Searchable Attribute-Based Encryption Scheme for Efficient Keyword Search and Fine-Grained Access Control over Encrypted Data

Attribute based encryption is a promising technique that achieves flexible and fine-grained data access control over encrypted data, which is very suitable for a secure data sharing environment such as the currently popular cloud computing. However, traditional attribute based encryption fails to provide an efficient keyword based search on encrypted data, which somewhat weakens the power of this encryption technique, as search is usually the most important approach to quickly obtain data of interest from large-scale dataset. To address this problem, attribute based encryption with keyword search (ABKS) is designed to achieve fine-grained data access control and keyword based search, simultaneously, by an ingenious combination of attribute based encryption and searchable encryption. Recently, several ABKS schemes have been constructed in secure cloud storage system for data access control and keyword search. Nonetheless, each of these schemes has some defects such as impractical computation overhead and insufficient access policy expression. To overcome these limitations, in this paper, we design a Key-Policy Searchable Attribute-based Encryption Scheme (KPSABES) based on the full-blown key-policy attribute-based encryption proposed by Vipul Goyal et al. By novel design, our scheme not only inherits all advantages of that scheme but also achieves efficient and secure keyword search over encrypted data. We provide the detailed performance analyses and security proofs for our scheme. Extensive experiments demonstrated that our proposed scheme is superior in many aspects to the similar work.
Secure user authentication scheme with novel server mutual verification for multiserver environments

Summary The fast growth of mobile services and devices has made the conventional single‐server architecture ineffective from the point of its functional requirements. To extend the scalability and availability of mobile services to various applications, it is required to deploy multiserver architecture. In 2016, Moon et al insisted that Lu et al's scheme is weak to insiders and impersonation attack, then they proposed a biometric‐based scheme for authentication and key agreement of users in multiserver environments. Unfortunately, we analyze Moon et al's scheme and demonstrate that their scheme does not withstand various attacks from a malicious registered server. We propose a user authentication scheme with server mutual verification to overcome these security drawbacks. The proposed scheme withstands an attack from malicious insiders in multiserver environments. We use a threshold cryptography to strengthen the process of server authorization and to provide better security functionalities. We then prove the authentication and session key of the proposed scheme using Burrows‐Abadi‐Needham (BAN) logic and show that our proposed scheme is secure against various attacks.
Complex reactive event processing for assisted living: The Habitat project case study

While the increasing average age of population is posing new challenges to societies and healthcare systems, the emergence of the Internet of Things research area is generating the hope for automated assisted environments, which could combine the advances in sensors networks with that of runtime monitoring systems, in order to create smart houses able to take care of their older inhabitants and delay the recourse to hospitals and nursing homes. However, although various assisted living systems have been proposed in the last decade, the goal of realizing an effective domestic support system for elderly is still far from reached. In this work, we present a project aiming to re-engineer a set of everyday life objects, equipping them with environmental and wearable sensors, thus to monitor the condition of older people in their domestic residences and provide security while preserving the autonomy and independence of the subjects. The main focus of the paper at hand is on the requirements and solutions implemented to realize the backbone infrastructure of such system as regards both the adopted semantic message routing mechanism and the newly conceived approach to event analysis, which combines Complex Event Processing and a reactive implementation of Event Calculus.
Redesigning a Software Development Course as a Preparation for a Capstone

Students need a wide variety of skills before they can successfully engage in real-world projects with paying customers. Some of these skills, such as the "soft skills" of communicating with a non-technical customer and working in a larger team to collaboratively develop a project, are generally not present in Computer Science courses. In this paper, we discuss our experience teaching these skills and explain the structure of our redesigned Software Development course and how it aligns with the Capstone experience. The main novelty of our approach is the specific implementation of the rotating project manager role and the introduction of the final portfolio reflection. We believe that the concepts we introduced during our redesign can be useful to any educator wanting to include collaborative project-based work in their courses. To enable faculty to apply and enhance these concepts, the resources that we used in this course, which include activities, templates, guidelines, rubrics, and questionnaires, are available as an open source project at https://team-repo.github.io/.
A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer

Denoising images subjected to Gaussian and Poisson noise has attracted attention in many areas of image processing.This paper introduces an image denoising framework using higher order fractional overlapping group sparsity prior to sparser image representation constraint.The proposed prior has a capability of avoiding staircase effects in both edges and oscillatory patterns (textures).We adopt the alternating direction method of multipliers for optimizing the proposed objective function by converting it into a constrained optimization problem using variable splitting approach.Finally, we conduct experiments on various degraded images and compare our results with those of several state-of-the-art methods.The numerical results show that the proposed fractional order image denoising framework improves the peak signal to noise ratio of an image by preserving the textures and eliminating the staircases effects.This leads to visually pleasant restored images which exhibit a higher value of Structural SIMilarity score when compared to that of other methods.
Securing Voice-Driven Interfaces Against Fake (Cloned) Audio Attacks

Voice cloning technologies have found applications in a variety of areas ranging from personalized speech interfaces to advertisement, robotics, and so on. Existing voice cloning systems are capable of learning speaker characteristics and use trained models to synthesize a person's voice from only a few audio samples. Advances in cloned speech generation technologies are capable of generating perceptually indistinguishable speech from a bona-fide speech. These advances pose new security and privacy threats to voice-driven interfaces and speech-based access control systems. The state-of-the-art speech synthesis technologies use trained or tuned generative models for cloned speech generation. Trained generative models rely on linear operations, learned weights, and excitation source for cloned speech synthesis. These systems leave characteristic artifacts in the synthesized speech. Higher-order spectral analysis is used to capture differentiating attributes between bona-fide and cloned audios. Specifically, quadrature phase coupling (QPC) in the estimated bicoherence, Gaussianity test statistics, and linearity test statistics are used to capture generative model artifacts. Performance of the proposed method is evaluated on cloned audios generated using speaker adaptation-and speaker encoding-based approaches. Experimental results for a dataset consisting of 126 cloned speech and 8 bona-fide speech samples indicate that the proposed method is capable of detecting bona-fide and cloned audios with close to a perfect detection rate.
Synthesizing tradeoff spaces with quantitative guarantees for families of software systems

Designing software in a way that guarantees run-time behavior while achieving an acceptable balance among multiple quality attributes is an open problem. Providing guarantees about the satisfaction of the same requirements under uncertain environments is even more challenging. Tools and techniques to inform engineers about poorly-understood design spaces in the presence of uncertainty are needed, so that engineers can explore the design space, especially when tradeoffs are crucial. To tackle this problem, we describe an approach that combines synthesis of spaces of system design alternatives from formal specifications of architectural styles with probabilistic formal verification. The main contribution of this paper is a formal framework for specification-driven synthesis and analysis of design spaces that provides formal guarantees about the correctness of system behaviors and satisfies quantitative properties (e.g., defined over system qualities) subject to uncertainty, which is treated as a first-class entity. We illustrate our approach in two case studies: a service-based adaptive system and a mobile robotics architecture. Our results show how the framework can provide useful insights into how average case probabilistic guarantees can differ from worst case guarantees, emphasizing the relevance of combining quantitative formal verification methods with structural synthesis, in contrast with techniques based on simulation and dynamic analysis that can only provide estimates about average case probabilistic properties.
Underwater Laser Triangulation Sensor Model With Flat Refractive Interfaces

Underwater 3-D measurement has many applications, for example, in the oil and gas industry, archeology, and biology. Systems with laser triangulation sensors (LTSs) are being currently used underwater, even though they face some strong environment influences. Among these challenging influences are poor image quality and refraction, due to optical window interfaces between water and air inside the camera chamber. The refraction effect can be modeled knowing the distance from the camera pinhole center to the surface of refraction, the axis of refraction, the refractive index of the mediums, and the thickness of the optical window. This paper analyses a method for underwater LTS measurement using real experiments. The proposed method is based on the pinhole camera model, a refraction modeling, and a fitted mathematical plane for the projected laser light plane. After the in-air calibration, a step standard is measured underwater and the window distance from the camera is optimized. The method is evaluated according to guidelines for optical systems evaluation (VDI/VDE 2634).
Secure Message Embedding in 3D Images

Due to the evolution in digital communications, sending a sheltered message, where interferers are prying for any useful piece of information, has become a rigid mission. In this paper, a multiple layer message security scheme is proposed, utilizing 3D images. The proposed scheme is robust against any means of eavesdropping or intruding as it is comprised of four layers of security as follows: encryption using AES-128, encoding using a repetition code, least significant bit (LSB) steganography and jamming through the addition of noise. The proposed scheme is compared with its counterparts from the literature and is shown to exhibit excellent traits, in terms of being reversible and its ability to carry out blind extraction of the data as well as withstanding geometrical attacks. Furthermore, the proposed scheme exhibits very good performance in terms of the mean squared error (MSE) and the peak signal to noise ratios (PSNR).
Keystroke Dynamics: Establishing Keyprints to Verify Users in Online Courses

This study examined the potential use of keystroke dynamics to create keyprints (typing fingerprints) to authenticate individuals in online courses. Previous studies have determined that keystroke typing patterns are unique; in addition to replicating these findings, this study explored best practices for implementing keyprint signatures in situations other than simple password or key phrase verification. While authentication can be difficult when attempting to correctly identify individual users, the results of this study indicate that keyprints can reliably indicate negative cases (a typing sample that is likely not the intended student). The results of this study suggest that complete keyprint signatures are better than keyprint profiles (reduced keyprints based on unique typing patterns only). Results also suggest that keyprint signatures are most reliable when a variety of methods are used to obtain keyprint data (i.e., both copy typing and free typing). Small comparison samples increase the challenge of identification but do not prevent the system from identifying negative cases.