Reviewerly/exHarmony huggingface dataset:
AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems

Abstract Mobile health care systems highly depend on collected physiological data through medical sensors to provide high‐quality care services. However, inaccurate physiological data from sensors pose a major challenge for health care providers when making decisions, whereas an erroneous decision can affect the user's life. We propose, in this paper, an anomalous data detection and isolation approach for mobile health care systems. Our approach, called AUDIT, detects inaccurate measurements in real time and distinguishes between faults or errors and health events. To do so, we propose reduced time and space complexities algorithms based on dimension reduction within the context of resource constraints. Furthermore, a decision algorithm is proposed while exploring the spatio‐temporal correlation between physiological attributes. First, we describe our approach. Then, we give its implementation details. Finally, to demonstrate the effectiveness of our approach, we show different experiments related to its detection performances and its time and space complexities.
Visual object tracking via a manifold regularized discriminative dual dictionary model

Real-time object tracking plays an important role in many computer vision systems, yet in complex scenarios, it is still a very challenging problem. In this paper, we propose a new visual tracking algorithm via a manifold regularized discriminative dual dictionary (MD3) model. First, a dual dictionary is introduced to avoid the calculation of representation coefficient in distance function construction. Second, the local background templates are utilized to keep the learned dictionaries discriminative. Third, the manifold regularization on representation coefficient is proposed to ensure that MD3 model has a bit error tolerance on the object update. We formulate object tracking in a particle filter framework, in which the observation model is calculated as the reconstruction error between learned dictionaries and the candidate template. Extensive experiments in various tracking scenarios are performed to evaluate the proposed method, and the results interpret that the tracking accuracy as well as the computational cost can be improved as compared with the state-of-the-art approaches.
Distributional Collision Resistance Beyond One-Way Functions

Distributional collision resistance is a relaxation of collision resistance that only requires that it is hard to sample a collision (x, y) where x is uniformly random and y is uniformly random conditioned on colliding with x. The notion lies between one-wayness and collision resistance, but its exact power is still not well-understood. On one hand, distributional collision resistant hash functions cannot be built from one-way functions in a black-box way, which may suggest that they are stronger. On the other hand, so far, they have not yielded any applications beyond one-way functions. Assuming distributional collision resistant hash functions, we construct constant-round statistically hiding commitment scheme. Such commitments are not known based on one-way functions, and are impossible to obtain from one-way functions in a black-box way. Our construction relies on the reduction from inaccessible entropy generators to statistically hiding commitments by Haitner et al. (STOC '09). In the converse direction, we show that two-message statistically hiding commitments imply distributional collision resistance, thereby establishing a loose equivalence between the two notions. A corollary of the first result is that constant-round statistically hiding commitments are implied by average-case hardness in the class $${\textsf {SZK}}$$ (which is known to imply distributional collision resistance). This implication seems to be folklore, but to the best of our knowledge has not been proven explicitly. We provide yet another proof of this implication, which is arguably more direct than the one going through distributional collision resistance.
Quantum Discord, Thermal Discord, and Entropy Generation in the Minimum Error Discrimination Strategy

We study the classical and quantum correlations in the minimum error discrimination (ME) of two non-orthogonal pure quantum states. In particular, we consider quantum discord, thermal discord and entropy generation. We show that ME allows one to reach the accessible information between the two involved parties, Alice and Bob, in the discrimination process. We determine the amount of quantum discord that is consumed in the ME and show that the entropy generation is, in general, higher than the thermal discord. However, in certain cases the entropy generation is very close to thermal discord, which indicates that, in these cases, the process generates the least possible entropy. Moreover, we also study the ME process as a thermodynamic cycle and we show that it is in agreement with the second law of thermodynamics. Finally, we study the relation between the accessible information and the optimum success probability in ME.
Publicly Verifiable Proofs from Blockchains

A proof system is publicly verifiable, if anyone, by looking at the transcript of the proof, can be convinced that the corresponding theorem is true. Public verifiability is important in many applications since it allows to compute a proof only once while convincing an unlimited number of verifiers. Popular interactive proof systems (e.g., $$\varSigma $$ -protocols) protect the witness through various properties (e.g., witness indistinguishability (WI) and zero knowledge (ZK)) but typically they are not publicly verifiable since such proofs are convincing only for those verifiers who contributed to the transcripts of the proofs. The only known proof systems that are publicly verifiable rely on a non-interactive (NI) prover, through trust assumptions (e.g., NIZK in the CRS model), heuristic assumptions (e.g., NIZK in the random oracle model), specific number-theoretic assumptions on bilinear groups or relying on obfuscation assumptions (obtaining NIWI with no setups). In this work we construct publicly verifiable witness-indistinguishable proof systems from any $$\varSigma $$ -protocol, based only on the existence of a very generic blockchain. The novelty of our approach is in enforcing a non-interactive verification (thus guaranteeing public verifiability) while allowing the prover to be interactive and talk to the blockchain (this allows us to circumvent the need of strong assumptions and setups). This opens interesting directions for the design of cryptographic protocols leveraging on blockchain technology.
Subaperture stitching testing for fine flat mirrors with large apertures using an orthonormal polynomial fitting algorithm

Interferometry, which measures the difference between a reference surface and a test surface, is widely used in high-precision testing. Usually the reference surface is considered as perfect and the surface errors there can be ignored during the testing. Considering the interferometry for fine optics with large apertures where the error of the reference surface is non-ignorable, we propose a stitching algorithm based on an orthonormal polynomial fitting method that can be used to accomplish the testing of both the reference surface and the surface under test simultaneously. To evaluate the accuracy of the above algorithm, the performance of the proposed method was analyzed by testing the tertiary mirror for the Thirty-Meter Telescope project (TMT project) and utilizing the algorithm in the simulation. Further, a practical experiment was implemented to demonstrate the practicability of the proposed method.
Refinement of User Stories into Backlog Items: Linguistic Structure and Action Verbs

[Context and motivation] In agile system development methods, product backlog items (or tasks) play a prominent role in the refinement process of software requirements. Tasks are typically defined manually to operationalize how to implement a user story; tasks formulation often exhibits low quality, perhaps due to the tedious nature of decomposing user stories into tasks. [Question/Problem] We investigate the process through which user stories are refined into tasks. [Principal ideas/results] We study a large collection of backlog items (N = 1,593), expressed as user stories and sprint tasks, looking for linguistic patterns that characterize the required feature of the user story requirement. Through a linguistic analysis of sentence structures and action verbs (the main verb in the sentence that indicates the task), we discover patterns of labeling refinements, and explore new ways for refinement process improvement. [Contribution] By identifying a set of 7 elementary action verbs and a template for task labels, we make first steps towards comprehending the refinement of user stories to backlog items.
A Generalized Locally Linear Factorization Machine with Supervised Variational Encoding

Factorization Machines (FMs) learn weights for feature interactions, and achieve great success in many data mining tasks. Recently, Locally Linear Factorization Machines (LLFMs) have been proposed to capture the underlying structures of data for better performance. However, one obvious drawback of LLFM is that the local coding is only operated in the original feature space, which limits the model to be applied to high-dimensional and sparse data. In this work, we present a generalized LLFM (GLLFM) which overcomes this limitation by modeling the local coding procedure in a latent space. Moreover, a novel Supervised Variational Encoding (SVE) technique is proposed such that the distance can effectively describe the similarity between data points. Specifically, the proposed GLLFM-SVE trains several local FMs in the original space to model the higher order feature interactions effectively, where each FM associates to an anchor point in the latent space induced by SVE. The prediction for a data point is computed by a weighted sum of several local FMs, where the weights are determined by local coding coordinates with anchor points. Actually, GLLFM-SVE is quite flexible and other Neural Network (NN) based FMs can be easily embedded into this framework. Experimental results show that GLLFM-SVE significantly improves the performance of LLFM. By using NN-based FMs as local predictors, our model outperforms all the state-of-the-art methods on large-scale real-world benchmarks with similar number of parameters and comparable training time.
Assessment structures in psychological testing

Based on the notion of an assessment structure introduced by Falmagne (2015) the present paper characterizes the so-called quasi ordinal assessment spaces. It establishes a one-to-one correspondence to certain binary relations on an extended item set, containing a positive instance and a negative instance for each of the items. This result allows for building quasi ordinal assessment spaces from data through a generalized version of a well-established procedure, known as item tree analysis. Analyzing data from psychological testing constitutes a prototypical application that calls for this generalization of knowledge spaces, as it allows for describing dependencies between positive and negative answers to the items in a questionnaire. Empirical application of the approach is illustrated for a published data set on the Woodworth Psychoneurotic Inventory.
Biometric Systems and Their Applications

Nowadays, we are talking more and more about insecurity in various sectors as well as the computer techniques to be implemented to counter this trend: access control to computers, e-commerce, banking, etc. There are two traditional ways of identifying an individual. The first method is a knowledge-based method. It is based on the knowledge of an individual's information such as the PIN code to allow him/her to activate a mobile phone. The second method is based on the possession of token. It can be a piece of identification, a key, a badge, etc. These two methods of identification can be used in a complementary way to obtain increased security like in bank cards. However, they each have their weaknesses. In the first case, the password can be forgotten or guessed by a third party. In the second case, the badge (or ID or key) may be lost or stolen. Biometric features are an alternative solution to the two previous identification modes. The advantage of using the biometric features is that they are all universal, measurable, unique, and permanent. The interest of applications using biometrics can be summed up in two classes: to facilitate the way of life and to avoid fraud.
Single- and Multi-FPGA Acceleration of Dense Stereo Vision for Planetary Rovers

Increased mobile autonomy is a vital requisite for future planetary exploration rovers. Stereo vision is a key enabling technology in this regard, as it can passively reconstruct in three dimensions the surroundings of a rover and facilitate the selection of science targets and the planning of safe routes. Nonetheless, accurate dense stereo algorithms are computationally demanding. When executed on the low-performance, radiation-hardened CPUs typically installed on rovers, slow stereo processing severely limits the driving speed and hence the science that can be conducted in situ . Aiming to decrease execution time while increasing the accuracy of stereo vision embedded in future rovers, this article proposes HW/SW co-design and acceleration on resource-constrained, space-grade FPGAs. In a top-down approach, we develop a stereo algorithm based on the space sweep paradigm, design its parallel HW architecture, implement it with VHDL, and demonstrate feasible solutions even on small-sized devices with our multi-FPGA partitioning methodology. To meet all cost, accuracy, and speed requirements set by the European Space Agency for this system, we customize our HW/SW co-processor by design space exploration and testing on a Mars-like dataset. Implemented on Xilinx Virtex technology, or European NG-MEDIUM devices, the FPGA kernel processes a 1,120 × 1,120 stereo pair in 1.7s−3.1s, utilizing only 5.4−9.3 LUT6 and 200−312 RAMB18. The proposed system exhibits up to 32× speedup over desktop CPUs, or 2,810× over space-grade LEON3, and achieves a mean reconstruction error less than 2cm up to 4m depth. Excluding errors exceeding 2cm (which are less than 4% of the total), the mean error is under 8mm.
On the weight distribution of second order Reed–Muller codes and their relatives

The weight distribution of second order q-ary Reed–Muller codes have been determined by Sloane and Berlekamp (IEEE Trans. Inform. Theory, vol. IT-16, 1970) for $$q=2$$ and by McEliece (JPL Space Progr Summ 3:28–33, 1969) for general prime power q. Unfortunately, there were some mistakes in the computation of the latter one. This paper aims to provide a precise account for the weight distribution of second order q-ary Reed–Muller codes. In addition, the weight distributions of second order q-ary homogeneous Reed–Muller codes and second order q-ary projective Reed–Muller codes are also determined.
Detection and Tracking of Faces in 3D Using a Stereo Camera Arrangements

3D facial tracking has become vital to the continued integration of computers, technology, and human society.In recent decades, the integration of technology has increased, and the use of surveillance, conference calls, gaming components, and other similar applications has spurred demand for the ability to recognize the distinctive features of humans.However, in order for these new technologies to function effectively and reach their fullest potential, a great deal of work is still needed.The field of facial mapping and tracking is still in its early developmental stages, necessitating additional research into the best methods of tracking and monitoring specific human faces.To this end, an algorithm has been created that would allow for improvements in this area; however, a video was first required that could be used effectively for the algorithm.Two web cameras running on Raspberry Pi were used to gather the footage necessary for detecting and tracking specific facial features.While certain limitations were identified throughout the process, the algorithm still achieved significant successful tracking results.In spite of this success, further efforts are still needed to effectively explore the proposed algorithm and improve upon these initial results.
A lossless online Bayesian classifier

We are living in a world progressively driven by data. Besides the issue that big data cannot be entirely stored in the main memory as required by traditional offline learning methods, the problem of learning data that can only be collected over time is also very prevalent. Consequently, there is a need of online methods which can handle sequentially arriving data and offer the same accuracy as offline methods. In this paper, we introduce a new lossless online Bayesian-based classifier which uses the arriving data in a 1-by-1 manner and discards each data right after use. The lossless property of our proposed method guarantees that it can reach the same prediction performance as its offline counterpart regardless of the incremental training order. Experimental results demonstrate its superior performance over many well-known state-of-the-art online learning methods in the literature.
Critical Materiality

The miniaturization of electronic technologies, as well as advances in organic and material science, have contributed to the development of composite, smart, and computational materials that create promising narratives for the future of ubiquitous computing. The goal of this one-day studio is to develop tools to acquire a deeper conceptual and critical understanding of materiality in HCI. The studio will draw on strategies from a broad range of sources including critical making, speculative design, experiential prototyping, and indigenous ontologies, in order to map out key questions and concerns. The studio will give the participants the opportunity to discuss the concept of Critical Materiality as a framework for developing tangible, embedded, and embodied interfaces, by brainstorming narratives around the past lives, current uses, and the future imaginaries of materials. Participants will co-develop a shared vocabulary and theoretical framework for Critical Materiality as a strategy to be deployed in conceiving and implementing HCI artifacts and experiences. The studio will culminate in the design and production of a deck of cards that propose keywords, questions, concerns, and opportunities for Hybrid Materials within this Critical Materiality framework. The deck of cards will be made available during the conference and distributed online.
Route planning by evolutionary computing: an approach based on genetic algorithms

Route planning is a classical kind of problem that arises in different areas of knowledge, such as path scheduling, transportation, collision avoidance, robotics and game design. Due to its stochastic behaviour, the search for viable paths can benefit from the use of heuristic algorithms, such as those available in evolutionary computing. In this way, this work presents a procedure to evaluate a feasible route between two points, in a constrained environment, through the use of a genetic algorithm. The developed implementation starts by searching for the track from random generated paths, which will evolve towards the best possible solution. Results demonstrate the ability of the algorithm to learn and produce suitable routes without previous knowledge of the environment. It can be concluded that the algorithm is simple, produces reliable tracks and is fast enough to deal with problems in real time, thus allowing it to be applied in real world issues, such as the planning of transport routes capable of avoiding congestion points.
Data integrity verification: a novel approach for cloud computing

Data integrity verification (DIV) is one of the primary security checks for data stored on cloud servers. Cloud users have assured the safety of their data with frequent checking of data integrity. In this paper, we provide an efficient and effective DIV approach that offers effective and privacy-preserving audit structure. The main building components of our approach are a multi-power variant of the Paillier cryptography system with homomorphic tag. The Paillier cryptography system along with homomorphic tag assigns a unique and verifiable value to each data block, which helps performing dynamic data operations in cloud environment. To demonstrate our approach, we have implemented an application on Hadoop and MapReduce framework. We have tested this application with respect to the different parameters. The efficiency of the proposed method has been shown through the experimental results. Our method has shown a good improvement over those of the other modern systems.
Deep Neural Architecture with Character Embedding for Semantic Frame Detection

Semantic frame detection has been extensively used for language understanding tasks, such as in dialogue systems or more recently, in Chat-bots. Traditionally, this involves two separate tasks: the detection of the semantic frame (i.e. intent detection), and the detection of frame elements (i.e. slot-filling). Recent efforts have attempted to combine the two tasks using recurrent neural networks. However, there is still room for improvement as these efforts do not efficiently model temporal dependencies. In this paper we propose a deep neural network architecture that uses long-short term memory (RNN-LSTM) with character embedding for joint modeling of intent detection and slot filling. Our results show significant improvement in slot-filling and intent detection at the sentence level over state-of-the-art semantic frame detection methods.
Color Correction and Depth-Based Hierarchical Hole Filling in Free Viewpoint Generation

In this paper, a high-quality depth-image-based-rendering-based free viewpoint generation model is proposed, which consists of three parts. First, a boundary-aware 3-D warping is introduced for projecting the side views from original view plane to target virtual view plane. Specifically, a high-pass filter is used to remove the ghost contours along object boundaries. Second, a color correction-based image blending (CC-IB) procedure is put forward for synthesizing the two warped side views. During the proposed CC-IB, occluded regions are color-corrected and reconstructed in the first place, on the basis of an equivalent substitution thought and a polynomial fitting method, then color-continuous virtual view can be produced by blending the un-occluded regions and the reconstructed disocclusions. Finally, a depth guided hierarchical hole-filling method is presented for filling the remaining holes in the blended virtual view. With the assistance of depth, background pixels are preferentially selected for filling the holes. Experimental results show that the proposed model can produce high-quality virtual views with comfortable visual perceptions. Comparisons with other state-of-the-art models demonstrate that the proposed method can achieve good performance in both visual quality and quantitative evaluation.
The effectiveness of mobile-based interactive learning multimedia in science process skills

Science process skills can facilitate students to increase the curiosity and activity of students in learning by doing, experiencing, and associating the subject of science in real life. Scholars found different phenomenon to improve science process skill. Hence this research aimed to explore the effectiveness of mobile-based interactive learning multimedia to improve students’ science process skill. This research was a quasi-experimental research with the entire subject 80 elementary students’ in Salam Magelang. Data were collected through observation, interview, and test. The results showed that mobile-based interactive learning multimedia is more effective to improve science process skill between the students who learn using mobile-based interactive learning multimedia and classical model with students’ book.