Reviewerly/exHarmony huggingface dataset:
Enhanced Bert-Based Ranking Models for Spoken Document Retrieval

The Bidirectional Encoder Representations from Transformers (BERT) model has recently achieved record-breaking success on many natural language processing (NLP) tasks such as question answering and language understanding. However, relatively little work has been done on ad-hoc information retrieval (IR), especially for spoken document retrieval (SDR). This paper adopts and extends BERT for SDR, while its contributions are at least three-fold. First, we augment BERT with extra language features such as unigram and inverse document frequency (IDF) statistics to make it more applicable to SDR. Second, we also explore the incorporation of confidence scores into document representations to see if they could help alleviate the negative effects resulting from imperfect automatic speech recognition (ASR). Third, we conduct a comprehensive set of experiments to compare our BERT-based ranking methods with other state-of-the-art ones and investigate the synergy effect of them as well.
Orientation-based Ant colony algorithm for synthesizing the test scenarios in UML activity diagram

The model-based analysis is preferred over the code-based analysis as it speeds up the development process and directs the guiding effort. In the software industry, the Unified Modeling Language (UML) is a set standard followed by the developers as well as system analysts to extract all attainable paths of controls, usually known as scenarios under an activity diagram. In this manuscript, a bio-inspired methodology has been applied on concurrent sub-part of a UML activity diagram to fetch various feasible test scenarios. The food search pattern of an ant has been taken as a base heuristic. An orientation factor has been introduced in the existing ant colony optimization algorithm. Experiments have been performed using three student projects, five synthetic models and an openly available model repository named LINDHOLMEN data-set at Github. The statistical analysis has validated the results obtained through various existing approaches and the proposed approach. Experimentation shows that the orientation-based ant colony algorithm has produced better results as compared to the existing Genetic Algorithm (GA) and Ant Colony Optimization (ACO) on the basis of feasible test scenarios generated.
Soft Video Multicasting Using Adaptive Compressed Sensing

Recently, soft video multicasting has gained a lot of attention, especially in broadcast and mobile scenarios where the bit rate supported by the channel may differ across receivers, and may vary quickly over time. Unlike the conventional designs that force the source to use a single bit rate according to the receiver with the worst channel quality, soft video delivery schemes transmit the video such that the video quality at each receiver is commensurate with its specific instantaneous channel quality. In this paper, we present a soft video multicasting system using an adaptive block-based compressed sensing (BCS) method. The proposed system consists of an encoder, a transmission system, and a decoder. At the encoder side, each block in each frame of the input video is adaptively sampled with a rate that depends on the texture complexity and visual saliency of the block. The obtained BCS samples are then placed into several packets, and the packets are transmitted via a channel-aware OFDM (orthogonal frequency division multiplexing) transmission system with a number of subchannels. At the decoder side, the received BCS samples are first used to build an initial approximation of the transmitted frame. To further improve the reconstruction quality, an iterative BCS reconstruction algorithm is then proposed that uses an adaptive transform and an adaptive soft-thresholding operator, which exploits the temporal similarity between adjacent frames to achieve better reconstruction quality. The extensive objective and subjective experimental results indicate the superiority of the proposed system over the state-of-the-art soft video multicasting systems.
Deep hypersphere embedding for real-time face recognition

With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
Digging into user control

We explore predictability and control in interactive systems where controls are easy to validate. Human-in-the-loop techniques allow users to guide unsupervised algorithms by exposing and supporting interaction with underlying model representations, increasing transparency and promising fine-grained control. However, these models must balance user input and the underlying data, meaning they sometimes update slowly, poorly, or unpredictably---either by not incorporating user input as expected (adherence) or by making other unexpected changes (instability). While prior work exposes model internals and supports user feedback, less attention has been paid to users' reactions when transparent models limit control. Focusing on interactive topic models, we explore user perceptions of control using a study where 100 participants organize documents with one of three distinct topic modeling approaches. These approaches incorporate input differently, resulting in varied adherence, stability, update speeds, and model quality. Participants disliked slow updates most, followed by lack of adherence. Instability was polarizing: some participants liked it when it surfaced interesting information, while others did not. Across modeling approaches, participants differed only in whether they noticed adherence.
Semantic concept schema of the linear mixed model of experimental observations

Abstract In the information age, smart data modelling and data management can be carried out to address the wealth of data produced in scientific experiments. In this paper, we propose a semantic model for the statistical analysis of datasets by linear mixed models. We tie together disparate statistical concepts in an interdisciplinary context through the application of ontologies, in particular the Statistics Ontology (STATO), to produce FAIR data summaries. We hope to improve the general understanding of statistical modelling and thus contribute to a better description of the statistical conclusions from data analysis, allowing their efficient exploration and automated processing.
LDVAS: Lattice-Based Designated Verifier Auditing Scheme for Electronic Medical Data in Cloud-Assisted WBANs

Data integrity has become an extremely important security issue in cloud-assisted wireless body area networks (WBANs). As accurate medical diagnostic analysis is heavily based on outsourced electronic medical data in cloud storage, any corrupted data may lead to fateful consequences. Data auditing contributes to medical data integrity verification, but most of existing data auditing schemes are vulnerable to attackers equipped with practical quantum-computing devices, which are very likely to be invented in the near future. Besides, in many scenarios, a patient has no capability to execute medical data integrity verification personally, he/she has to specify a verifier to complete data auditing. To this end, we present an efficient lattice-based designated verifier auditing scheme (LDVAS), which could be well deployed in cloud-assisted WBANs. In particular, LDVAS enables a patient to designate a unique verifier to execute data auditing in post-quantum secure settings, and any others cannot fulfil such task without the approval of the patient. We formally prove the security of LDVAS based on the hardness assumptions of lattice-based problems. The performance evaluation demonstrates the high efficiency and feasibility of LDVAS on the side of the designated verifier.
Adaptive Hierarchical Translation-based Sequential Recommendation

We propose an adaptive hierarchical translation-based sequential recommendation called HierTrans that first extends traditional item-level relations to the category-level, to help capture dynamic sequence patterns that can generalize across users and time. Then unlike item-level based methods, we build a novel hierarchical temporal graph that contains item multi-relations at the category-level and user dynamic sequences at the item-level. Based on the graph, HierTrans adaptively aggregates the high-order multi-relations among items and dynamic user preferences to capture the dynamic joint influence for next-item recommendation. Specifically, the user translation vector in HierTrans can adaptively change based on both a user’s previous interacted items and the item relations inside the user’s sequences, as well as the user’s personal dynamic preference. Experiments on public datasets demonstrate the proposed model HierTrans consistently outperforms state-of-the-art sequential recommendation methods.
New Constructions of Statistical NIZKs: Dual-Mode DV-NIZKs and More

Non-interactive zero-knowledge proofs (NIZKs) are important primitives in cryptography. A major challenge since the early works on NIZKs has been to construct NIZKs with a statistical zero-knowledge guarantee against unbounded verifiers. In the common reference string (CRS) model, such “statistical NIZK arguments” are currently known from $$k\text {-}\mathsf {Lin} $$ in a pairing-group and from $$\mathsf {LWE}$$ . In the (reusable) designated-verifier model (DV-NIZK), where a trusted setup algorithm generates a reusable verification key for checking proofs, we also have a construction from $$\mathsf {DCR}$$ . If we relax our requirements to computational zero-knowledge, we additionally have NIZKs from factoring and $$\mathsf {CDH}$$ in a pairing group in the CRS model, and from nearly all assumptions that imply public-key encryption (e.g., $$\mathsf {CDH}$$ , $$\mathsf {LPN}$$ , $$\mathsf {LWE}$$ ) in the designated-verifier model. Thus, there still remains a gap in our understanding of statistical NIZKs in both the CRS and the designated-verifier models. In this work, we develop new techniques for constructing statistical NIZK arguments. First, we construct statistical DV-NIZK arguments from the $$k\text {-}\mathsf {Lin} $$ assumption in pairing-free groups, the $$\mathsf {QR}$$ assumption, and the $$\mathsf {DCR}$$ assumption. These are the first constructions in pairing-free groups and from $$\mathsf {QR}$$ that satisfy statistical zero-knowledge. All of our constructions are secure even if the verification key is chosen maliciously (i.e., they are “malicious-designated-verifier” NIZKs), and moreover, they satisfy a “dual-mode” property where the CRS can be sampled from two computationally indistinguishable distributions: one distribution yields statistical DV-NIZK arguments while the other yields computational DV-NIZK proofs. We then show how to adapt our $$k\text {-}\mathsf {Lin} $$ construction in a pairing group to obtain new publicly-verifiable statistical NIZK arguments from pairings with a qualitatively weaker assumption than existing constructions of pairing-based statistical NIZKs. Our constructions follow the classic paradigm of Feige, Lapidot, and Shamir (FLS). While the FLS framework has traditionally been used to construct computational (DV)-NIZK proofs, we newly show that the same framework can be leveraged to construct dual-mode (DV)-NIZKs.
Analytic Minimum Mean-Square Error Bounds in Linear Dynamic Systems With Gaussian Mixture Noise Statistics

For linear dynamic systems with Gaussian noise, the Kalman filter provides the Minimum Mean-Square Error (MMSE) state estimation by tracking the posterior. Similarly, for systems with Gaussian Mixture (GM) noise distributions, a bank of Kalman filters or the Gaussian Sum Filter (GSF), can provide the MMSE state estimation. However, the MMSE itself is not analytically tractable. Moreover, the general analytic bounds proposed in the literature are not tractable for GM noise statistics. Hence, in this work, we evaluate the MMSE of linear dynamic systems with GM noise statistics and propose its analytic lower and upper bounds. We provide two analytic upper bounds which are the Mean-Square Errors (MSE) of implementable filters, and we show that based on the shape of the GM noise distributions, the tighter upper bound can be selected. We also show that for highly multimodal GM noise distributions, the bounds and the MMSE converge. Simulation results support the validity of the proposed bounds and their behavior in limits.
Combined Cryptography and Steganography for Enhanced Security in Suboptimal Images

Technology has developed to a very great extent, and the use of smart systems has introduced an increasing threat to data security and privacy. Most of the applications are built-in unsecured operating systems, and so there is a growing threat to information cloning, forging tampering counterfeiting, etc.. This will lead to an un-compensatory loss for end-users particularly in banking applications and personal data in social media. A robust and effective algorithm based on elliptic curve cryptography combined with Hill cipher has been proposed to mitigate such threats and increase information security. In this method, ciphertext and DCT coefficients of an image, embedded into the base image based on LSB watermarking. The ciphertext is generated based on the Hill Cipher algorithm. Hill Cipher can, however, be easily broken and has weak security and to add complexity, Elliptic curve cryptography (ECC), is combined with Hill cipher. Based on the ECC algorithm, the key is produced, and this key is employed to generate ciphertext through the Hill cipher algorithm. This combination of both steganography and cryptography results in increased authority and ownership of the data for sub-optimal media applications. It is hard to extract the hidden data and the image without the proper key. The performance for hiding text and image into an image data have been analyzed for sub-optimal multimedia applications.
Theory as a Source of Software Requirements

Today, when undertaking requirements elicitation, engineers attend to the needs and wants of the user groups considered relevant for the software system. However, answers to some relevant question (e.g., how to improve adoption of the intended system) cannot always be addressed through direct need and want elicitation. Using an example of energy demand-response systems, this paper demonstrates that use of grounded theory analysis can help address such questions. The theory emerging from such analysis produces a set of additional requirements which cannot be directly elicited from individuals/groups, and would otherwise be missed out. Thus, we demonstrate that the theory generated through grounded theory analysis can serve as an additional valuable source of software system requirements.
Comparison of Prediction Model Performance Updating Protocols: Using a Data-Driven Testing Procedure to Guide Updating.

In evolving clinical environments, the accuracy of prediction models deteriorates over time. Guidance on the design of model updating policies is limited, and there is limited exploration of the impact of different policies on future model performance and across different model types. We implemented a new data-driven updating strategy based on a nonparametric testing procedure and compared this strategy to two baseline approaches in which models are never updated or fully refit annually. The test-based strategy generally recommended intermittent recalibration and delivered more highly calibrated predictions than either of the baseline strategies. The test-based strategy highlighted differences in the updating requirements between logistic regression, L1-regularized logistic regression, random forest, and neural network models, both in terms of the extent and timing of updates. These findings underscore the potential improvements in using a data-driven maintenance approach over "one-size fits all" to sustain more stable and accurate model performance over time.
The MuseLearn Platform: Personalized Content for Museum Visitors Assisted by Vision-Based Recognition and 3D Pose Estimation of Exhibits

MuseLearn is a platform that enhances the presentation of the exhibits of a museum with multimedia-rich content that is adapted and recommended for certain visitor profiles and playbacks on their mobile devices. The platform consists mainly of a content management system that stores and prepares multimedia material for the presentation of exhibits; a recommender system that monitors objectively the visitor’s behavior so that it can further adapt the content to their needs; and a pose estimation system that identifies an exhibit and links it to the additional content that is prepared for it. We present the systems and the initial results for a selected set of exhibits in Herakleidon Museum, a museum holding temporary exhibitions mainly about ancient Greek technology. The initial evaluation that we presented is encouraging for all systems. Thus, the plan is to use the developed systems for all museum exhibits as well as to enhance their functionality.
Nonclassicality and entanglement of single-photon catalysis-assisted two-mode squeezed coherent state

We theoretically study the nonclassicality and entanglement properties of the two-mode non-Gaussian entangled state, the single-photon catalysis (SPC)-assisted two-mode squeezed coherent (TMSC) state, prepared by performing two SPC operations on the TMSC. It is found that the success probability of our generated SPC-assisted TMSC at the high transmittance range presents a better performance. In addition, according to the low-order antibunching effect and the Wigner function, the analyses of the nonclassicality show that, when comparing to the conventional two-mode squeezed state, in the regime of low initial squeezing parameter, adding a small amount of coherent amplitudes is beneficial to the appearance of antibunching effect. Dramatically, with the increase of the coherent amplitudes, the negative volume of Wigner function becomes more and more obvious at a fixed low transmittance. Further, in terms of EPR correlation and HZ correlation, our results indicate that the entanglement for the output state can be occurred by taking appropriate values of squeezing parameter, transmittance and coherent amplitude. In particular, the improvement of the entanglement is located at low squeezing, low transmittance and small amplitude. These results show that the SPC operation on the TMSC can be used for effectively achieving the higher nonclassicality and improving the entanglement, which may have potential applications in quantum information.
Boosting Frank-Wolfe by Chasing Gradients

The Frank-Wolfe algorithm has become a popular first-order optimization algorithm for it is simple and projection-free, and it has been successfully applied to a variety of real-world problems. Its main drawback however lies in its convergence rate, which can be excessively slow due to naive descent directions. We propose to speed up the Frank-Wolfe algorithm by better aligning the descent direction with that of the negative gradient via a subroutine. This subroutine chases the negative gradient direction in a matching pursuit-style while still preserving the projection-free property. Although the approach is reasonably natural, it produces very significant results. We derive convergence rates $\mathcal{O}(1/t)$ to $\mathcal{O}(e^{-\omega t})$ of our method and we demonstrate its competitive advantage both per iteration and in CPU time over the state-of-the-art in a series of computational experiments.
PleBeuS: a Policy-based Blockchain Selection Framework

Due to the growing interest in the blockchain (BC), several applications are being developed, taking advantage of the benefits that such technology promises to deliver, such as removal of Trust Third Parties (TTP) to verify transactions and data immutability. However, these applications require certain aspects, such as high transaction throughput or data privacy, that early BC implementations (e.g., Bitcoin) did not provide. Thus, a myriad of novel BC implementations was developed, which introduced the issue of choosing the right implementation for a specific use-case. This paper presents a framework, called PleBeuS, to address this selection issue by allowing users to specify policies that rule the automatic selection of the BC that data will be stored. The selection process relies on a cost-aware approach and considers both public and private implementations and their technical characteristics. Moreover, PleBeuS communicates with a BC-agnostic interoperability API to enforce transactions. The evaluation of the PleBeuS prototype showed that it is possible to automatically select a BC-based on user policies, considering cost thresholds and technical details (e.g., BC throughput, deployment), and reduce manual interaction.
A Pairwise Fair and Community-preserving Approach to k-Center Clustering

Clustering is a foundational problem in machine learning with numerous applications. As machine learning increases in ubiquity as a backend for automated systems, concerns about fairness arise. Much of the current literature on fairness deals with discrimination against protected classes in supervised learning (group fairness). We define a different notion of fair clustering wherein the probability that two points (or a community of points) become separated is bounded by an increasing function of their pairwise distance (or community diameter). We capture the situation where data points represent people who gain some benefit from being clustered together. Unfairness arises when certain points are deterministically separated, either arbitrarily or by someone who intends to harm them as in the case of gerrymandering election districts. In response, we formally define two new types of fairness in the clustering setting, pairwise fairness and community preservation. To explore the practicality of our fairness goals, we devise an approach for extending existing $k$-center algorithms to satisfy these fairness constraints. Analysis of this approach proves that reasonable approximations can be achieved while maintaining fairness. In experiments, we compare the effectiveness of our approach to classical $k$-center algorithms/heuristics and explore the tradeoff between optimal clustering and fairness.