Reviewerly/exHarmony huggingface dataset:
Design and Implementation of 5-D IIR Depth Velocity Filters for Light Field Video Processing

The design and hardware implementation of a low-complexity signal processing algorithm is proposed for real-time depth-velocity filtering in 5-D light field videos (LFVs). The proposed design is based on a stable 5-D infinite impulse response digital filter having three cascaded sections, each synthesized using the concept of multidimensional passive network resonance. A novel semi-systolic hardware implementation is proposed. Each section of the filter is implemented and tested on a Xilinx Virtex-7 FPGA platform using MATLAB-based hardware co-simulation with both synthetic and real LFV signals. A real-time processing throughput of 467 LFV frames/s is implied with each section of the filter operating at 204, 164, and 115 MHz for input LFV frames of size 9 × 9 × 220 × 360.
Design and leakage assessment of side channel attack resistant binary edwards Elliptic Curve digital signature algorithm architectures

Considering that Elliptic Curve Digital Signature Algorithm (ECDSA) implementations need to be efficient, flexible and Side Channel Attack (SCA) resistant, in this paper, a design approach and architecture for ECDSA and the underlined scalar multiplication operation is proposed for GF(2k), satisfying the above three directives. To achieve that, in the paper, Binary Edwards Curves (BECs) are adopted as an alternative to traditional Weierstrass Elliptic Curves (ECs) for GF(2k) since they offer intrinsic SCA resistance against simple attacks due to their uniformity, operation regularity and completeness. To achieve high performance and flexibility, we propose a hardware/software ECDSA codesign approach where scalar multiplication is implemented in hardware and integrated in the ECDSA functionality through appropriate drivers of an ECDSA software stack. To increase BEC scalar multiplier performance and introduce SCA resistance we adopt and expand a parallelism design strategy/methodology where GF(2k) operations of a scalar multiplier round for both point operations performed in this round are reordered and assigned into parallelism layer in order to be executed concurrently. Within this strategy we include hardware and software based SCA countermeasures that rely on masking/randomization and hiding. While scalar randomization is realized by the ECDSA software stack in an easy way, in order to achieve resistance using hardware means, we propose and introduce in every scalar multiplier round, within the parallelism layers, projective coordinates randomization of all the round’s output points. So, in our approach, considering that with the proposed parallelism plan in every scalar multiplier round BEC point operations are performed in parallel and that the round’s output points are randomized with a different number in each round, we manage to achieve maximum SCA resistance. To validate this resistance, we introduce and realize a leakage assessment process on BEC scalar multipliers for the first time in research literature. This process is based on real measurements collected from a controlled SAKURA X environment with a GF(2233) based scalar multiplier implementation. Using Welch’s t-test we investigate possible information leakage of the multiplier’s input point and scalar and after an extended analysis we find trivial leakage. Finally, we validate the ECDSA architecture and its scalar multiplier efficiency by implementing it on a Zynq 7000 series FPGA Avnet Zedboard and collecting very promising, well balanced, results on speed and hardware resources in comparison with other works.
Influence of social conversational features on language identification in highly multilingual online conversations

With the explosion of multilingual content on Web, particularly in social media platforms, identification of languages present in the text is becoming an important task for various applications. While automatic language identification (ALI) in social media text is considered to be a non-trivial task due to the presence of slang words, misspellings, creative spellings and special elements such as hashtags, user mentions etc., ALI in multilingual environment becomes even more challenging task. In a highly multilingual society, code-mixing without affecting the underlying language sense has become a natural phenomenon. In such a dynamic environment, conversational text alone often fails to identify the underlying languages present in the text. This paper proposes various methods of exploiting social conversational features for enhancing ALI performance. Although social conversational features for ALI have been explored previously using methods like probabilistic language modeling, these models often fail to address issues related to code-mixing, phonetic typing, out-of-vocabulary etc. which are prevalent in a highly multilingual environment. This paper differs in the way the social conversational features are used to propose text refinement strategies that are suitable for ALI in highly multilingual environment. The contributions in this paper therefore includes the following. First, this paper analyzes the characteristics of various social conversational features by exploiting language usage patterns. Second, various methods of text refinement suitable for language identification are proposed. Third, the effects of the proposed refinement methods are investigated using various sentence level language identification frameworks. From various experimental observations over three conversational datasets collected from Facebook, Youtube and Twitter social media platforms, it is evident that our proposed method of ALI using social conversational features outperforms the baseline counterparts.
Self-Managing: An Empirical Study of the Practice in Agile Teams

Self-management is of such importance that it constitutes one of the principles of agility. In fact, according to the agile manifesto and scrum principles, agile teams are self-managing and cross functional. Other agile methodologies, such as extreme programming, also include the self-management concept among their basics, as it can have a huge impact on the productivity and motivation of employees and organizations.
Optimized Tone Mapping Function for Contrast Enhancement Considering Human Visual Perception System

Conventional contrast enhancement methods, including global and local enhancements, produce enhanced images with some limitations. Global contrast enhancement does not take the local characteristics into consideration, and therefore, the enhancement performance could be limited. On the other hand, a local contrast enhancement method achieves a noticeable improvement, but it generates unnatural improvement results compared with the input image. Due to the complementary characteristics of these two methods, it is hard to achieve remarkable contrast enhancement without visual artifacts. To overcome the limitations, we propose a new tone mapping function for contrast enhancement using an optimization approach that is subject to constraints such as the output image needing to be enhanced naturally and noticeably. Since contrast enhancement without artificiality is possible when the enhancement process mimics the human eye, we model the human visual perception system, and then, the model is incorporated into the proposed tone mapping function. Consequently, the contrast of the image is adaptively enhanced according to a region that is more attractive to a person. The experimental results demonstrate that the proposed algorithm outperforms other contrast enhancement methods in terms of both objective and subjective criteria.
Label noise filtering techniques to improve monotonic classification

The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the problems with monotonicity constraints are very frequent. To construct predictive monotone models from those problems, many classifiers require as input a data set satisfying the monotonicity relationships among all samples. Changing the class labels of the data set (relabeling) is useful for this. Relabeling is assumed to be an important building block for the construction of monotone classifiers and it is proved that it can improve the predictive performance. In this paper, we will address the construction of monotone datasets considering as noise the cases that do not meet the monotonicity restrictions. For the first time in the specialized literature, we propose the use of noise filtering algorithms in a preprocessing stage with a double goal: to increase both the monotonicity index of the models and the accuracy of the predictions for different monotonic classifiers. The experiments are performed over 12 datasets coming from classification and regression problems and show that our scheme improves the prediction capabilities of the monotonic classifiers instead of being applied to original and relabeled datasets. In addition, we have included the analysis of noise filtering process in the particular case of wine quality classification to understand its effect in the predictive models generated.
Heuristics for the Constrained Incremental Graph Drawing Problem

Visualization of information is a relevant topic in Computer Science, where graphs have become a standard representation model, and graph drawing is now a well-established area. Within this context, edge crossing minimization is a widely studied problem given its importance in obtaining readable representations of graphs. In this paper, we focus on the so-called incremental graph drawing problem, in which we try to preserve the user’s mental map when obtaining successive drawings of the same graph. In particular, we minimize the number of edge crossings while satisfying some constraints required to preserve the position of vertices with respect to previous drawings. We propose heuristic methods to obtain high-quality solutions to this optimization problem in the short computational times required for graph drawing applications. We also propose a mathematical programming formulation and obtain the optimal solution for small and medium instances. Our extensive experimentation shows the merit of our proposal with respect to both optimal solutions obtained with CPLEX and heuristic solutions obtained with LocalSolver, a well-known black-box solver in combinatorial optimization.
Image Hybrid Encryption Based on Matrix Nonlinear Operation and Generalized Arnold Transformation

In this paper, we propose a scheme to implement hybrid encryption of images using generalized Arnold transformation and matrix nonlinear operations. This scheme consists of two stages. In the first stage, the pixels scrambling encryption is achieved by using generalized Arnold transformation. In the second stage, the pixels sequence encryption is carried out by a nonlinear matrix operation based on the key stream generated by a random matrix. Accordingly, the decryption process is completed by two steps inverse transformations. The hybrid encryption algorithm is one-time pad, and therefore has good anti-attack performance. The algorithm is featured by good confidentiality, low computational complexity, and easy-to-program processing. Moreover, the effectiveness, security and robustness of the algorithm are demonstrated by an image encryption simulation and the encryption performance analysis. Compared with the traditional Arnold scrambling encryption scheme and the chaotic encryption method based on the generalized standard mapping, the superiority of the proposed algorithm is demonstrated.
Programming language foundations in Agda

One of the leading textbooks for formal methods is Software Foundations (SF), written by Benjamin Pierce in collaboration with others, and based on Coq. After five years using SF in the classroom, we came to the conclusion that Coq is not the best vehicle for this purpose, as too much of the course needs to focus on learning tactics for proof derivation, to the cost of learning programming language theory. Accordingly, we have written a new textbook, Programming Language Foundations in Agda (PLFA). PLFA covers much of the same ground as SF, although it is not a slavish imitation. What did we learn from writing PLFA? First, that it is possible. One might expect that without proof tactics that the proofs become too long, but in fact proofs in PLFA are about the same length as those in SF. Proofs in Coq require an interactive environment to be understood, while proofs in Agda can be read on the page. Second, that constructive proofs of preservation and progress give immediate rise to a prototype evaluator. This fact is obvious in retrospect but it is not exploited in SF (which instead provides a separate normalise tactic) nor can we find it in the literature. Third, that using extrinsically-typed terms is far less perspicuous than using intrinsically-typed terms. SF uses the former presentation, while PLFA presents both; the former uses about 1.6 as many lines of Agda code as the latter, roughly the golden ratio. The textbook is written as a literate Agda script, and can be found here: http://plfa.inf.ed.ac.uk
On Smart Soccer Ball as a Head Impact Sensor

Heading the ball is a fundamental skill in soccer. However, recent studies have shown that players who headed the ball frequently were more vulnerable to concussion. To record head impacts and flag potential concussions, intraoral and head-mounted devices such as Vector MouthGuard and Biosystems xPatch have been developed. However, they are yet to gain wider acceptance among soccer players, perhaps because they are inconvenient and expensive. Since headers involve contact with the ball, we leverage this unique opportunity in soccer to explore using a smartball for monitoring headers. We develop a method to assess the impact of headers using the Adidas micoach soccer ball and compare its performance with that of the xPatch. We find that, while the micoach ball is somewhat limited in its sensing capabilities, there is enough promise that a similar ball with a more precise accelerometer can be an affordable and convenient alternative for monitoring header impacts in soccer.
Interpreting element addition and depletion at the Ann Mason porphyry-Cu deposit, Nevada, using mapped mass balance patterns

Elemental mass changes were calculated for 154 samples from four rock types around the Ann Mason porphyry-Cu-(MoAu) deposit, Yerington district, Nevada. The purpose of these calculations was to investigate addition and depletion patterns in two-dimensional (2-D) map space. Mass changes in Ca, Mg, Na, P, Cu, Mo, As, Sb, Sr, and Pb from samples in the pre- to syn-mineralisation Yerington batholith vary systematically with lateral and vertical distance from the Ann Mason deposit. At the core of the Ann Mason deposit, Cu and Mo were strongly added (>10,000% relative to average protolith values), with restricted lateral mass addition halos around the deposit. Copper is depleted in wall rocks surrounding the deposit. Calcium and Sr were removed (up to 98% depletion) from parts of the deposit centre due to the destruction of calcic-plagioclase and other calcium-bearing minerals during potassic alteration. Calcium (up to 1570%) and Sr (up to 315%) were added outside the deposit and were concentrated around 4 km from the deposit centre where epidote alteration is most intense. Arsenic and Sb were removed from the deposit centre (up to 99% depletion) where silicate-mineral alteration assemblages dominate. Antimony shows an addition halo (ranging from 25% up to 1900%) that extends from the deposit centre 500 m laterally away, and correlates spatially with the increased abundance of pyrite that typically surrounds porphyry deposits. Epidote-altered samples of Shamrock monzonite from outside the Casting Copper skarn show addition of Ca (up to 196%), Sb (up to 1100%), and depletion of Na (up to −98%). Samples from the central part of the post-mineralisation Shamrock monzonite record lower mass changes in Mg, Ca, P, and Mo, compared to other parts of the batholith. Some samples from the northern contact of the Shamrock batholith exhibit addition of Ca (up to 554%), Sr (up to 504%), and depletion in Na (up to −98%). These samples are coincident with strong epidote alteration. Our results provide a rock-normalised geochemical map that reflects quantified element addition and depletion patterns, which is useful for interrogating geological processes at a regional scale. Although interpreting relative element addition and depletion in map space is possible using conventional immobile element ratio approaches, mass balance calculations provide the benefit of quantified results.
Text Generation From Tables

This paper proposes a neural generative model, namely Table2Seq, to generate a natural language sentence based on a table. Specifically, the model maps a table to continuous vectors and then generates a natural language sentence by leveraging the semantics of a table. Since rare words, e.g., entities and values, usually appear in a table, we develop a flexible copying mechanism that selectively replicates contents from the table to the output sequence. We conduct extensive experiments to demonstrate the effectiveness of our Table2Seq model and the utility of the designed copying mechanism. On the WIKIBIO and SIMPLEQUESTIONS datasets, the Table2Seq model improves the state-of-the-art results from 34.70 to 40.26 and from 33.32 to 39.12 in terms of BLEU-4 scores, respectively. Moreover, we construct an open-domain dataset WIKITABLETEXT that includes 13 318 descriptive sentences for 4962 tables. Our Table2Seq model achieves a BLEU-4 score of 38.23 on WIKITABLETEXT outperforming template-based and language model based approaches. Furthermore, through experiments on 1 M table-query pairs from a search engine, our Table2Seq model considering the structured part of a table, i.e., table attributes and table cells, as additional information outperforms a sequence-to-sequence model considering only the sequential part of a table, i.e., table caption.
How different visualizations affect human reasoning about uncertainty: An analysis of visual behaviour

Humans find reasoning about uncertainty difficult. In decision support systems and software for intelligence analysis, graphical representations are commonly used to display uncertainty. Nevertheless, our understanding of how people use the information presented in graphs displaying uncertainty to make decisions is limited. As many artificial intelligent systems require a human-in-the-loop who is able to actively take part in the analysis process, the understanding of high-level cognition involved in human-graph interaction is essential in the design of better tools for analysis. In this research, we investigate the visual behaviour that is associated with participants responses to problems testing probabilistic reasoning represented through two different visualizations (tree and Venn diagrams). Using the data from visual fixations and transitions, we present a description of different reasoning strategies covering both accurate and inaccurate reasoning for different visualization formats. The results show that gaze behaviour is related to reasoning accuracy. Moreover, this study shows that different graphs representing the same problem evoke different reasoning strategies, suggesting that higher level cognition is influenced by the graphical representation in which uncertainty is encoded.
Improving (1+1) covariance matrix adaptation evolution strategy: A simple yet efficient approach

In recent years, part of the meta-heuristic optimisation research community has called for a simplification of the algorithmic design: indeed, while most of the state-of-the-art algorithms are characterised by a high level of complexity, complex algorithms are hard to understand and therefore tune for specific real-world applications. Here, we follow this reductionist approach by combining straightforwardly two methods recently proposed in the literature, namely the Re-sampling Inheritance Search (RIS) and the (1+1) Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We propose an RI-(1+1)-CMA-ES algorithm that on the one hand improves upon the original (1+1)-CMA-ES, on the other it keeps the original spirit of simplicity at the basis of RIS. We show with an extensive experimental campaign that the proposed algorithm efficiently solves a large number of benchmark functions and is competitive with several modern optimisation algorithms much more complex in terms of algorithmic design.
Integrating computation into the mechanistic hierarchy in the cognitive and neural sciences

It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system (e.g., an automaton) to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem (Sect. 2), we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences (Sects. 3 and 4). We then examine two possible solutions (Sect. 5). On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches (Sect. 6).
On Quasi-Isometry of Threshold-Based Sampling

The problem of isometry for threshold-based sampling such as integrate-and-fire (IF) or send-on-delta (SOD) is addressed. While for uniform sampling the Parseval theorem provides isometry and makes the Euclidean metric canonical, there is no analogy for threshold-based sampling. The relaxation of the isometric postulate to quasi-isometry, however, allows the discovery of the underlying metric structure of threshold-based sampling. This paper characterizes this metric structure making Hermann Weyl's discrepancy measure canonical for threshold-based sampling.