String Transformations Research Articles

Revealing symmetries in quantum computing for many-body systems

Abstract We develop a method to deduce the symmetry properties of many-body Hamiltonians when they are prepared in Jordan-Wigner form in which they can act on multi-qubit states. 
Symmetries, such as point-group symmetries in molecules, are apparent in the standard second quantized form of the Hamiltonian.
They are, however,
masked when the Hamiltonian is translated into a Pauli matrix representation required for its operation on qubits.
To reveal these symmetries we prove a general theorem that provides a straightforward method to calculate the transformation of Pauli tensor strings under symmetry operations.
They are a subgroup of the Clifford group transformations and induce a corresponding group representation inside the symplectic matrices.
We finally give a simplified derivation of an affine qubit encoding scheme which allows for the removal of qubits due to Boolean symmetries and thus reduces effort in quantum computations
for many-body systems.

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated “biochemical” language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications.Scientific contributionThe approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

Redesigning Android Development: Using Reactive Programming to Retrofit REST APIs and Concurrency

In this paper the current techniques and tools used by developers to create applications are described in detail and the use of REST APIs in Android applications as a case study. The first component is the installation of the APIs, which we will focus on the Retrofit library for network management, and the API Clients to manage requests. This study evaluates the nuances of JSON parsing, serialization, and deserialization through the Retrofit API client. We study the technique of data searching, the introduction of Tap Drawer using Firebase to improve the app functionality, and the string transformation method of Scalar with JSON Converter Factory.The tutorial explains how to set up an API, design an interface for Product ID query, use a Binding Adaptor for handling images and a Glide dependency to load images. As well, we consider how to create a Product Adaptor for a Recycler View which leverages Fragments to implement complex user interfaces. According to the plan of Android development, we absorb width, wrap content and verticality alignment as layout factors.The building of the “Adapter List” based on the “Create View Holder” function and the creation of Product Model class — which consists of fields for ratings and product details — as well as the JSON integration are presented in the article. Asynchronous processes are applied in the application as well as the `enqueue` technique has been used to provide effective network connection.We intend to acquire an experimental data by app scanning on the Android platform, by referring to Stack Overflow's most favourite pages and through a survey of mobile application developers in order to build a code collection associated with REST mobile client technologies. From our findings it follows that application communicating through the Internet phenomenon associated with JSON syntax implementation in comparison to XML data processing execution rather widespread and acceptable in terms of Android developers. We include the HTTP libraries standardized by third-party organizations in the paper which also explains how to use the practices.In unity, we discuss the perks of Android system uniqueness which facilitates the application customization, suppliers and the consumers, in terms of security, usability, and usefulness. Our app framework for Android offers coronary adjust to existing apps through behaviour simply without accessing or modifying app source code but sticks to the universal app-agnostic transformation objectives. This architecture is thus designed to allow the modified software version to function without any hindrance on any normal Android device. Keywords- REST API, Android, Retrofit, Firebase, Gson, Recycler View, JSON parsing, HTTP libraries, refactoring, Android programming principles.

A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images.

This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows-Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.

A message recovery attack on multivariate polynomial trapdoor function.

Cybersecurity guarantees the exchange of information through a public channel in a secure way. That is the data must be protected from unauthorized parties and transmitted to the intended parties with confidentiality and integrity. In this work, we mount an attack on a cryptosystem based on multivariate polynomial trapdoor function over the field of rational numbers Q. The developers claim that the security of their proposed scheme depends on the fact that a polynomial system consisting of 2n (where n is a natural number) equations and 3n unknowns constructed by using quasigroup string transformations, has infinitely many solutions and finding exact solution is not possible. We explain that the proposed trapdoor function is vulnerable to a Gröbner basis attack. Selected polynomials in the corresponding Gröbner basis can be used to recover the plaintext against a given ciphertext without the knowledge of the secret key.

A new class of string transformations for compressed text indexing

Introduced about thirty years ago in the field of data compression, the Burrows-Wheeler Transform (BWT) is a string transformation that, besides being a booster of the performance of memoryless compressors, plays a fundamental role in the design of efficient self-indexing compressed data structures. Finding other string transformations with the same remarkable properties of BWT has been a challenge for many researchers for a long time. In this paper, we introduce a whole class of new string transformations, called local orderings-based transformations, which have all the “myriad virtues” of BWT. As a further result, we show that such new string transformations can be used for the construction of the recently introduced r-index, which makes them suitable also for highly repetitive collections. In this context, we consider the problem of finding, for a given string, the BWT variant that minimizes the number of runs in the transformed string.

An ultra-lightweight block cipher with string transformations

Security efficiency and hardware efficiency are equally important when designing a block cipher. In this paper, we propose a new block cipher that uses string transformations and requires less memory and fewer computational resources, making it suitable for highly constrained environments. We compare the performance of our design with AES-128 and quasigroup-based block cipher INRU in the cipher block chaining (CBC) mode of operation using the National Institute of Standards and Technology (NIST) test suite. We then analyze our design against the standard linear, differential, and algebraic attacks. Choosing suitable S-boxes and a quasigroup of a smaller order does not compromise the security of the cipher, but the storage space is reduced compared with AES-128 and INRU.

Iterative genetic improvement: Scaling stochastic program synthesis

Program synthesis aims to automatically find programs from an underlying programming language that satisfy a given specification. While this has the potential to revolutionize computing, how to search over the vast space of programs efficiently is an unsolved challenge in program synthesis. In cases where large programs are required for a solution, it is generally believed that stochastic search has advantages over other classes of search techniques. Unfortunately, existing stochastic program synthesizers do not meet this expectation very well, suffering from the scalability issue. To overcome this problem, we propose a new framework for stochastic program synthesis, called iterative genetic improvement. The key idea is to apply genetic improvement to improve a current reference program, and then iteratively replace the reference program by the best program found. Compared to traditional stochastic synthesis approaches, iterative genetic improvement can build up the complexity of programs incrementally in a more robust way. We evaluate the approach on two program synthesis domains: list manipulation and string transformation, along with a number of general program synthesis problems. Our empirical results indicate that this method has considerable advantages over several representative stochastic program synthesizer techniques, both in terms of scalability and of solution quality.

FlashFill++: Scaling Programming by Example by Cutting to the Chase

Programming-by-Examples (PBE) involves synthesizing an "intended program" from a small set of user-provided input-output examples. A key PBE strategy has been to restrict the search to a carefully designed small domain-specific language (DSL) with "effectively-invertible" (EI) operators at the top and "effectively-enumerable" (EE) operators at the bottom. This facilitates an effective combination of top-down synthesis strategy (which backpropagates outputs over various paths in the DSL using inverse functions) with a bottom-up synthesis strategy (which propagates inputs over various paths in the DSL). We address the problem of scaling synthesis to large DSLs with several non-EI/EE operators. This is motivated by the need to support a richer class of transformations and the need for readable code generation. We propose a novel solution strategy that relies on propagating fewer values and over fewer paths. Our first key idea is that of "cut functions" that prune the set of values being propagated by using knowledge of the sub-DSL on the other side. Cuts can be designed to preserve completeness of synthesis; however, DSL designers may use incomplete cuts to have finer control over the kind of programs synthesized. In either case, cuts make search feasible for non-EI/EE operators and efficient for deep DSLs. Our second key idea is that of "guarded DSLs" that allow a precedence on DSL operators, which dynamically controls exploration of various paths in the DSL. This makes search efficient over grammars with large fanouts without losing recall. It also makes ranking simpler yet more effective in learning an intended program from very few examples. Both cuts and precedence provide a mechanism to the DSL designer to restrict search to a reasonable, and possibly incomplete, space of programs. Using cuts and gDSLs, we have built FlashFill++, an industrial-strength PBE engine for performing rich string transformations, including datetime and number manipulations. The FlashFill++ gDSL is designed to enable readable code generation in different target languages including Excel's formula language, PowerFx, and Python. We show FlashFill++ is more expressive, more performant, and generates better quality code than comparable existing PBE systems. FlashFill++ is being deployed in several mass-market products ranging from spreadsheet software to notebooks and business intelligence applications, each with millions of users.

B-Morpher: Automated Learning of Morphological Language Characteristics for Inflection and Morphological Analysis

Abstract The automated induction of inflection rules is an important research area for computational linguistics. In this paper, we present a novel morphological rule induction model called B-Morpher that can be used for both inflection analysis and morphological analysis. The core element of the engine is a modified Bayes classifier in which class categories correspond to general string transformation rules. Beside the core classification module, the engine contains a neural network module and verification unit to improve classification accuracy. For the evaluation, beside the large Hungarian dataset the tests include smaller non-Hungarian datasets from the SIGMORPHON shared task pools. Our evaluation shows that the efficiency of B-Morpher is comparable with the best results, and it outperforms the state-of-theart base models for some languages. The proposed system can be characterized by not only high accuracy, but also short training time and small knowledge base size.

Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

ABSTRACT Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.

Neural Network Approach to Program Synthesis for Tabular Transformation by Example

Data transformation is a laborious and time-consuming task for analysts. Programming by example (PBE) is a technique that can simplify this difficult task for data analysts by automatically generating programs for data transformation. Most of the previously proposed PBE methods are based on search algorithms, but recent improvements in machine learning (ML) have led to its application in PBE research. For example, RobustFill was proposed as an ML-based PBE method for string transformation by using long short-term memory (LSTM) as the sequential encoder–decoder model. However, an ML-based PBE method has not been developed for tabular transformations, which are used frequently in data analysis. Thus, in the present study, we propose an ML-based PBE method for tabular transformations. First, we consider the features of tabular transformations, which are more complex and data intensive than string transformations, and propose a new ML-based PBE method using the state-of-the-art Transformer sequential encoder–decoder model. To our knowledge, this is the first ML-based PBE method for tabular transformations. We also propose two decoding methods comprising multistep beam search and program validation-beam search, which are optimized for program generation, and thus generate correct programs with higher accuracy. Our evaluation results demonstrated that the Transformer-based PBE model performed much better than LSTM-based PBE when applied to tabular transformations. Furthermore, the Transformer-based model with the proposed decoding method performed better than the conventional PBE model using the search-based method.

Semantic programming by example with pre-trained models

The ability to learn programs from few examples is a powerful technology with disruptive applications in many domains, as it allows users to automate repetitive tasks in an intuitive way. Existing frameworks on inductive synthesis only perform syntactic manipulations, where they rely on the syntactic structure of the given examples and not their meaning. Any semantic manipulations, such as transforming dates, have to be manually encoded by the designer of the inductive programming framework. Recent advances in large language models have shown these models to be very adept at performing semantic transformations of its input by simply providing a few examples of the task at hand. When it comes to syntactic transformations, however, these models are limited in their expressive power. In this paper, we propose a novel framework for integrating inductive synthesis with few-shot learning language models to combine the strength of these two popular technologies. In particular, the inductive synthesis is tasked with breaking down the problem in smaller subproblems, among which those that cannot be solved syntactically are passed to the language model. We formalize three semantic operators that can be integrated with inductive synthesizers. To minimize invoking expensive semantic operators during learning, we introduce a novel deferred query execution algorithm that considers the operators to be oracles during learning. We evaluate our approach in the domain of string transformations: the combination methodology can automate tasks that cannot be handled using either technologies by themselves. Finally, we demonstrate the generality of our approach via a case study in the domain of string profiling.

Knowledge Refactoring for Inductive Program Synthesis

Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the knowledge refactoring problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. A key feature of Knorf is that, rather than simply removing knowledge, it also introduces new knowledge through predicate invention. We evaluate our approach on two domains: building Lego structures and real-world string transformations. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.

When a dollar makes a BWT

The Burrows-Wheeler-Transform (BWT) is a reversible string transformation which plays a central role in text compression and is fundamental in many modern bioinformatics applications. The BWT is a permutation of the characters, which is in general better compressible and allows to answer several different query types more efficiently than the original string.It is easy to see that not every string is a BWT image, and exact characterizations of BWT images are known. We investigate a related combinatorial question. In many applications, a sentinel character $ is added to mark the end of the string, and thus the BWT of a string ending with $ contains exactly one $-character. Given a string w, we ask in which positions, if any, the $-character can be inserted to turn w into the BWT image of a word ending with $. We show that this depends only on the standard permutation of w and present a O(nlog⁡n)-time algorithm for identifying all such positions, improving on the naive quadratic time algorithm. We also give a combinatorial characterization of such positions and develop bounds on their number and value. This is an extended version of [Giuliani et al. ICTCS 2019].

On the Identification of Common Fastenings of a Rectangular Plate

A rectangular plate pivotally mounted on two opposite edges is considered. It is shown that one of the plate fastenings at the other two edges is determined up to a permutation of the fastenings at these edges uniquely from five natural frequencies. It is also shown that four eigenfrequencies for such a recovery is not enough. The corresponding counterexample is given. It was previously shown that the general boundary conditions at the two edges of a rectangular plate can be uniquely determined by 9 eigenfrequencies. The method used earlier was based on the reconstruction of the matrix accurate to linear row transformations. Such a matrix is restored up to linear transformations of strings by a vector determined up to a constant factor and made up of 10 fourth-order minors of such a matrix. The method used in this work is based on the restoration of elastic canonical boundary conditions for which it is assumed that the stiffness coefficients of elastic fastenings can be zero or infinity.

Malware Detection by Risky Zone “Signature” Extraction from API Calls String Transformation using (AOSSR) Technique

Low accuracy of malware detection exists in malware detectors that are based on various malware representation architectures due to several problems as in case of API call graph construction and matching algorithms, where a major issue of building a precise call graph from the information collected about malware samples, also graph matching algorithms are having NP-Complete Problems and slow because of their computational complexity [1], [2]. Moreover, increasing the malware detection accuracy based on API call graphs by enhancing the graph matching and construction algorithms, experiences more computational time taken for matching process and in construction process as there are many graphs created which makes it so difficult to fetch or identify the malware (Elhadi et al., 2013) [3]. It has been further argued by (Li et al., 2018) that in case of malevolent activities, it is comparatively intricate to find whether the software will fall under the spell of malicious occurrences or not, since the duty to determine whether the conduct of a program is malicious is quite complex [4]. This research proposes enhancement of API String-based representation technique through the implementation of a malware detection framework that adopts String-based representation of the malware signature which is a compact representation of the malware risky zone where only the set of API calls representing the actual malware behaviour is accounted in the String using Absolute Order Signature String Representation technique (AOSSR) to represent the malware Strings resulting in a better performance of malware detection accuracy. The Methodology this research work follows mainly composed of three phases. The first phase deals with the conversion of the known malware samples from text format to string format. The second phase addresses the extractaction of the risky zone of an input file which is the file that needs to be checked, by the help of the file signatures already been presented in the database. The third phase addresses how to match two strings efficiency. The Application of the research is very clear as last experiment conducted on 515 malware samples demonstrates that the proposed malware detection architecture has 98% accuracy and 0 false positive rates. Comparing three families, using Analysis of Variance (ANOVA) test it is proven there is no significant difference (p>0.05) in the detection rate of algorithm across the three families of malwares. The algorithms performance is consistent. The result also shows that the Receiver Operating Characteristics (ROC) curves display a better True Positives Rate (TPR) for the proposed architecture over the previous attempts, which reflect significant improvement of TPR.

An attempt to self-organized polygon formation control of swarm robots under cyclic topologies

In this paper, we consider the problem of controlling a swarm of mobile robots to formulate a prescribed formation shape under cyclic topologies. By mimicking the shape transformation of elastic strings, we design a distributed control strategy using local sensing quantities. To implement this control algorithm, only a small number of robots need to be regulated to achieve the desired formation shape. It is shown that the desired polygon formation can be globally asymptotically stabilized under the proposed control strategy. Furthermore, in order to adapt to the situations where external influence is not exerted on the “right” robots, we propose a new self-organized control strategy that is capable of transferring the external influence through interacting with their nearest neighboring robots. Simulations are carried out to demonstrate the effectiveness of the proposed control strategies.

String Transformation Based Morphology Learning

There are several morphological methods that can solve the morphological rule induction problem. For different languages this task represents different difficulty levels. In this paper we propose a novel method that can learn prefix, infix and suffix transformations as well. The test language is Hungarian, and we chose a previously generated word pair set of accusative case for evaluating the method, comparing its training time, memory requirements, average inflection time and correctness ratio with some of the most popular models like dictionaries, finite state transducers, the tree of aligned suffix rules and a lattice based method. We also provide multiple training and searching strategies, introducing parallelism and the concept of prefix trees to optimize the number of rules that need to be processed for each input word. This newly created novel method can be applied not only for morphology, but also for any problems in the field of bioinformatics and data mining that can benefit from string transformations learning.

Logical reduction of metarules

Many forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.