Reviewerly/exHarmony huggingface dataset:
Complex-Valued Convolution and Frequency Global Filter for Automatic Modulation Recognition

Automatic modulation recognition (AMR) plays an important role in cognitive radio and dynamic spectrum access, which has been widely applied in military and civilian applications. Due to the breakthroughs in deep learning (DL), DL-based AMR methods are becoming extremely popular. However, most existing DL-based methods are unable to deal with complex format data, and learning the mappings from the time series or its transformed representation to the true modulation type directly is difficult. To address these difficulties, this letter presents a complex-valued convolution and frequency global filter unit (CGFU), and proposes a hybrid neural network, namely CGF-HNN, which can efficiently exploit features from different domains. We evaluate the recognition performance of the proposed model on two well-known datasets, i.e., RML2016.10a and RML2018.01a. Simulation results show that the proposed model outperforms the existing state-of-the-art models.
QCCA-Secure Generic Transformations in the Quantum Random Oracle Model

The post-quantum security of cryptographic schemes assumes that the quantum adversary only receives the classical result of computations with the secret key. Further, it is unknown whether the post-quantum secure schemes still remain secure if the adversary can obtain a superposition state of the results. In this paper, we formalize one class of public-key encryption schemes named oracle-masked schemes. Then we define the plaintext extraction procedure for those schemes and this procedure simulates the quantum-accessible decryption oracle with a certain loss. The construction of the plaintext extraction procedure does not need to take the secret key as input. Based on this property, we prove the IND-qCCA security of the Fujisaki-Okamoto (FO) transformation in the quantum random oracle model (QROM) and our security proof is tighter than the proof given by Zhandry (Crypto 2019). We also give the first IND-qCCA security proof of the REACT transformation in the QROM. Furthermore, our formalization can be applied to prove the IND-qCCA security of key encapsulation mechanisms with explicit rejection. As an example, we present the IND-qCCA security proof of $$\textsf {T}_{\textsf {CH}}$$ transformation, proposed by Huguenin-Dumittan and Vaudenay (Eurocrypt 2022), in the QROM.
Multi-Client Inner Product Encryption: Function-Hiding Instantiations Without Random Oracles

In a Multi-Client Functional Encryption (MCFE) scheme, n clients each obtain a secret encryption key from a trusted authority. During each time step t, each client i can encrypt its data using its secret key. The authority can use its master secret key to compute a functional key given a function f, and the functional key can be applied to a collection of n clients’ ciphertexts encrypted to the same time step, resulting in the outcome of f on the clients’ data. In this paper, we focus on MCFE for inner-product computations. If an MCFE scheme hides not only the clients’ data, but also the function f, we say it is function hiding. Although MCFE for inner-product computation has been extensively studied, how to achieve function privacy is still poorly understood. The very recent work of Agrawal et al. showed how to construct a function-hiding MCFE scheme for inner-product assuming standard bilinear group assumptions; however, they assume the existence of a random oracle and prove only a relaxed, selective security notion. An intriguing open question is whether we can achieve function-hiding MCFE for inner-product without random oracles. In this work, we are the first to show a function-hiding MCFE scheme for inner products, relying on standard bilinear group assumptions. Further, we prove adaptive security without the use of a random oracle. Our scheme also achieves succinct ciphertexts, that is, each coordinate in the plaintext vector encrypts to only O(1) group elements. Our main technical contribution is a new upgrade from single-input functional encryption for inner-products to a multi-client one. Our upgrade preserves function privacy, that is, if the original single-input scheme is function-hiding, so is the resulting multi-client construction. Further, this new upgrade allows us to obtain a conceptually simple construction.
GLUE: Generalizing Unbounded Attribute-Based Encryption for Flexible Efficiency Trade-Offs

Ciphertext-policy attribute-based encryption is a versatile primitive that has been considered extensively to securely manage data in practice. Especially completely unbounded schemes are attractive, because they do not restrict the sets of attributes and policies. So far, any such schemes that support negations in the access policy or that have online/offline extensions have an inefficient decryption algorithm. In this work, we propose GLUE (Generalized, Large-universe, Unbounded and Expressive), which is a novel scheme that allows for the efficient implementation of the decryption while allowing the support of both negations and online/offline extensions. We achieve these properties simultaneously by uncovering an underlying dependency between encryption and decryption, which allows for a flexible trade-off in their efficiency. For the security proof, we devise a new technique that enables us to generalize multiple existing schemes. As a result, we obtain a completely unbounded scheme supporting negations that, to the best of our knowledge, outperforms all existing such schemes in the decryption algorithm.
A Key-Recovery Attack Against Mitaka in the t-Probing Model

Mitaka is a lattice-based signature proposed at Eurocrypt 2022. A key advertised feature of Mitaka is that it can be masked at high orders efficiently, making it attractive in scenarios where side-channel attacks are a concern. Mitaka comes with a claimed security proof in the t-probing model. We uncover a flaw in the security proof of Mitaka, and subsequently show that it is not secure in the t-probing model. For any number of shares $$d \ge 4$$ , probing $$t < d$$ variables per execution allows an attacker to recover the private key efficiently with approximately $$2^{21}$$ executions. Our analysis shows that even a constant number of probes suffices ( $$t = 3$$ ), as long as the attacker has access to a number of executions that is linear in d/t.
Sender-binding Key Encapsulation

Secure communication is gained by combining encryption with authentication. In real-world applications encryption commonly takes the form of KEM-DEM hybrid encryption, which is combined with ideal authentication. The pivotal question is how weak the employed key encapsulation mechanism (KEM) is allowed to be to still yield universally composable (UC) secure communication when paired with symmetric encryption and ideal authentication. This question has so far been addressed for public-key encryption (PKE) only, showing that encryption does not need to be stronger than sender-binding CPA, which binds the CPA secure ciphertext non-malleably to the sender ID. For hybrid encryption, prior research unanimously reaches for CCA2 secure encryption which is unnecessarily strong. Answering this research question is vital to develop more efficient and feasible protocols for real-world secure communication and thus enable more communication to be conducted securely. In this paper we use ideas from the PKE setting to develop new answers for hybrid encryption. We develop a new and significantly weaker security notion—sender-binding CPA for KEMs—which is still strong enough for secure communication. By using game-based notions as building blocks, we attain secure communication in the form of ideal functionalities with proofs in the UC-framework. Secure communication is reached in both the classic as well as session context by adding authentication and one-time/replayable CCA secure symmetric encryption respectively. We furthermore provide an efficient post-quantum secure LWE-based construction in the standard model giving an indication of the real-world benefit resulting from our new security notion. Overall we manage to make significant progress on discovering the minimal security requirements for hybrid encryption components to facilitate secure communication.
Post-quantum Anonymity of Kyber

Kyber is a key-encapsulation mechanism (KEM) that was recently selected by NIST in its PQC standardization process; it is also the only scheme to be selected in the context of public-key encryption (PKE) and key establishment. The main security target for KEMs, and their associated PKE schemes, in the NIST PQC context has been IND-CCA security. However, some important modern applications also require their underlying KEMs/PKE schemes to provide anonymity (Bellare et al., ASIACRYPT 2001). Examples of such applications include anonymous credential systems, cryptocurrencies, broadcast encryption schemes, authenticated key exchange, and auction protocols. It is hence important to analyze the compatibility of NIST’s new PQC standard in such “beyond IND-CCA” applications. Some starting steps were taken by Grubbs et al. (EUROCRYPT 2022) and Xagawa (EUROCRYPT 2022) wherein they studied the anonymity properties of most NIST PQC third round candidate KEMs. Unfortunately, they were unable to show the anonymity of Kyber because of certain technical barriers. In this paper, we overcome said barriers and resolve the open problems posed by Grubbs et al. (EUROCRYPT 2022) and Xagawa (EUROCRYPT 2022) by establishing the anonymity of Kyber, and the (hybrid) PKE schemes derived from it, in a post-quantum setting. Along the way, we also provide an approach to obtain tight IND-CCA security proofs for Kyber with concrete bounds; this resolves another issue identified by the aforementioned works related to the post-quantum IND-CCA security claims of Kyber from a provable security point-of-view. Our results also extend to Saber, a NIST PQC third round finalist, in a similar fashion.
Extendable Threshold Ring Signatures with Enhanced Anonymity

Threshold ring signatures are digital signatures that allow t parties to sign a message while hiding their identity in a larger set of n users called “ring”. Recently, Aranha et al. [PKC 2022] introduced the notion of extendable threshold ring signatures ( $$\textsf{ETRS}$$ ). $$\textsf{ETRS}$$ allow one to update, in a non-interactive manner, a threshold ring signature on a certain message so that the updated signature has a greater threshold, and/or an augmented set of potential signers. An application of this primitive is anonymous count me in. A first signer creates a ring signature with a sufficiently large ring announcing a proposition in the signed message. After such cause becomes public, other parties can anonymously decide to support that proposal by producing an updated signature. Crucially, such applications rely on partial signatures being posted on a publicly accessible bulletin board since users may not know/trust each other. In this paper, we first point out that even if anonymous count me in was suggested as an application of $$\textsf{ETRS}$$ , the anonymity notion proposed in the previous work is insufficient in many application scenarios. Indeed, the existing notion guarantees anonymity only against adversaries who just see the last signature, and are not allowed to access the “full evolution” of an $$\textsf{ETRS}$$ . This is in stark contrast with applications where partial signatures are posted in a public bulletin board. We therefore propose stronger anonymity definitions and construct a new $$\textsf{ETRS}$$ that satisfies such definitions. Interestingly, while satisfying stronger anonymity properties, our $$\textsf{ETRS}$$ asymptotically improves on the two $$\textsf{ETRS}$$ presented in prior work [PKC 2022] in terms of both time complexity and signature size. Our $$\textsf{ETRS}$$ relies on extendable non-interactive witness-indistinguishable proof of knowledge ( $$\textsf{ENIWI}$$ PoK), a novel technical tool that we formalize and construct, and that may be of independent interest. We build our constructions from pairing groups under the SXDH assumption.
Recognizing Filipino Sign Language with InceptionV3, LSTM, and GRU

Sign language Recognition is a field in artificial intelligence that is gaining traction in recent years. Although there have been significant advances in both Artificial intelligence methods and Sign language recognition methods for sign languages, Filipino Sign Language is still lacking in this field. The problem lies in the dataset. With this in mind, the authors of this created a video dataset of 20 introductory Filipino Sign language words and then utilized InceptionV3 and Recurrent Neural Network (RNN), specifically Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), to develop a pipeline model that can classify the FSL signs. The author was able to create a 20-sign FSL dataset of 414 videos (at least 20 samples each). The models were trained using the dataset. Results show that GRU at 16 units performed best at this dataset with an accuracy of 86.74% Further studies on the generalizability of the architecture as well as other models trained on this dataset is recommended
Implementation of YOLOv3 and YOLOv4 CNN Algorithm in Detecting Struvite Crystals in Canine Urine Sediments

A microscopic examination is performed by a veterinary lab personnel to detect entities present in urine sediments. Some of these entities are casts, white blood cells, red blood cells, and crystals. You Only Look Once (YOLO) is a multi-scale algorithm suitable for detecting small objects – like struvite crystals. The major objective for this study is to make a performance comparison between YOLOv3 and YOLOv4 in detecting struvite crystals in canine urine sediments. The testing had a total of 20 canine urine sediment samples. The system’s accuracy has been determined using confusion matrix. YOLOv3 had an accuracy of 77.05%, while YOLOv4 had 73.77%. A ttest was used to compare the struvite crystals detected by the two versions, and the null hypothesis was not rejected. Thus, there is no significant difference between the two versions. Ttest was also used for the processing time of the two versions, which resulted in the rejection of the null hypothesis, revealing a significant difference in the processing time, with YOLOv3 having the faster average processing time of 45.24 seconds compared to the YOLOv4’s 50.37 seconds.
A Lightweight Image Encryption Algorithm Based on Elliptic Curves and Chaotic In Parallel

the use of lightweight encryption algorithms in a parallel environment helps to improve the performance and efficiency of the system that handles large-sized images. In a parallel environment the encryption and decryption images task is completed faster and with less strain on system resources. This is particularly useful for applications that require fast processing of large images, such as in websites or high-traffic applications. The main aim of this paper is offer a lightweight image coding technique that is implemented in a parallel pixel-level environment based on using chaotic sequences with curve points. The suggested system is built using highly parallel encryption and decryption operations. This results in the creation of a strong key value with a minimum bit length that may be used for lightweight encryption. By combining the Rubik's Cube and image density with elliptic curve cryptography (ECC), image colors are combined and distorted, and by using a Chaotic Logistics Map and Image Density with a secret key, Rubik's Cube is encrypted for the image, obtaining a secure image against attacks. The test results indicate that the highest value for PSNR, UACI, and NPCR are 8.3865, 34.4914, and 100, respectively. While encoding and decoding speeds reach 0.0062018 and 0.0069293 seconds, respectively.
A Machine Vision Measurement Method for Large Plates Based on Reference Point Assistance

In this work, a coordinate positioning method based on machine vision with reference point assistance is proposed to apply to the size measurement for large rectangular plate parts. This method first obtains the coordinate information of the reference point through circular detection, and uses the line detection and mean-shift clustering algorithm to fit the plate vertex information. Then the pixel coordinate relationship between the reference point and the plate vertex is used to solve the world coordinate of the plate vertex. Finally, the world coordinate of the plate vertex is used to calculate the length and width dimensions of the plate. Instead of other methods which unify coordinate system by moving cameras, this method uses reference point assistance system on measuring plate to unify the coordinate system, simplifying the difficulty of the unification of coordinate system and ensuring accuracy. Effectiveness of such method is verified by measuring rectangular plates with scales from centimeters to meters. The results showed that the measurement error could be controlled within 5 millimeters. In practical applications, the measurement accuracy has been much higher than the processing accuracy during the plate manufacturing, meeting the needs of size measurement.
Recognition and Counting of Materials Used in Fast Food Restaurants Based on YOLO Object Detection for Inventory System

Inventory Systems are used to track the available supply stocks of a particular product. An automated inventory system is helpful for easier access and finding specific products. A problem encountered in an inventory system is a mismatch of the actual number of stocks compared to the listed stocks in the inventory. This study uses materials in fast-food restaurants such as aprons, bussing belts, nameplates, spray bottles, and thermal bags. This study covers an inventory system with object recognition, detection, and counting features for the said objects. The system’s hardware uses both the Raspberry Pi and Pi Camera Module. YOLO (You Only Look Once) is the algorithm used for this study. The said algorithm can detect objects from a captured image. The researchers train custom datasets using Darknet and Google Colab. There are 725 total datasets used for training, which are annotated using the LabelImg tool. The researchers are able to add a counting algorithm based on the outputs of the YOLO algorithm. The outputs are saved on the system’s database, and SQLite is used as the database engine. The researchers use a confusion matrix to evaluate the system’s performance. The system’s computed overall accuracy is 89.55%.
Bubble Sheet Multiple Choice Mobile Checker with Test Grader using Optical Mark Recognition (OMR) Algorithm

This paper describes a mobile phone-based system of image processing for automatically checking a student's bubble sheet paper using OMR sheet layout with high speed and precision. This mobile application is designed to aid teachers' workload for checking the exam and is a hassle-free mobile app that can be used anytime. Image processing, database, and user interface are the interconnected modules of the system. Python and the open CV library power the backend, while Android controls the frontend. The program was created using open-source technology, and there was no hardware necessary for the system to function. Using an image analysis technique that uses gradient extraction, the acquired image is processed, then transported, and the result is obtained using a mobile phone. Homography (ROI) surveyed the area of interest. Before data extraction begins, the background and foreground are corrected using image correction algorithms. Upon conducting the system analysis for its functionality, the researchers tested one hundred fifty-two sheets and obtained a total accuracy of 90.1% by checking the bubble sheets.
Design of a Rule-Based Tuned PID Controller for Tether Management of a Suspended Tethered UAV

Unmanned Aerial Vehicles (UAVs) have become more popular in recent years for various uses. Despite their popularity, UAVs have one major disadvantage: a short flight time. This is undesirable for critical applications that require prolonged periods of UAV operation. Recent research has found a solution, and that is to use a tether to connect the UAV to a ground station that supplies power.Recent innovations have led to the development of the suspended or hanging tethered UAV, where the tether source is located above the UAV rather than below. This setup is useful for situations where a high, elevated area is more accessible than the base area of the point of interest. The use of tether cables, however, also presents one problem, which is a drag. Too much tension or slack on the cable can impede the flight of the UAV, and the cable might also get entangled with the spinning rotors. Therefore, a fast and responsive tether-cable management controller must be devised as a solution.This study focuses on developing a PID-based controller for the tether management system of a suspended tether-powered drone. This study employs a modified Ziegler-Nichols’ rule-based tuning method to identify the potential gains of Kp and Kd. The study utilizes a heuristic approach to determine the optimum Ki value. Several tests were conducted to determine the appropriate PID gains. Finally, the performance of the PID-based controller was verified through an actual flight test.
Accelerating Black Box Testing with Light-Weight Learning

Black box testing can employ randomness for generating test sequences. Often, even a large number of test sequences may sample a minuscule portion of the overall behaviors, thus missing failures of the system under test. The challenge is to reconcile the tradeoff between good coverage and high complexity. Combining black box testing with learning (a sequence of increasingly more accurate) models for the tested system was suggested for improving the coverage of black box testing. The learned models can be used to perform more comprehensive exploration, e.g., using model checking. We present a light-weight approach that employs machine learning ideas in order to improve the coverage and accelerate the testing process. Rather than focus on constructing a complete model for the tested system, we construct a kernel, whose nodes are consistent with prefixes of test sequences that were examined so far; as part of the testing process, we keep refining and expanding the kernel. We detect whether the kernel itself contains faulty executions. Otherwise, we exploit the kernel to generate further test sequences that use only a reduced set of representative prefixes.
Efficient Implementation of LIMDDs for Quantum Circuit Simulation

Realizing the promised advantage of quantum computers over classical computers requires both physical devices and corresponding methods for the design, verification and analysis of quantum circuits. In this regard, decision diagrams have proven themselves to be an indispensable tool due to their capability to represent both quantum states and unitaries (circuits) compactly. Nonetheless, recent results show that decision diagrams can grow to exponential size even for the ubiquitous stabilizer states, which are generated by Clifford circuits. Since Clifford circuits can be efficiently simulated classically, this is surprising. Moreover, since Clifford circuits play a crucial role in many quantum computing applications, from networking, to error correction, this limitation forms a major obstacle for using decision diagrams for the design, verification and analysis of quantum circuits. The recently proposed Local Invertible Map Decision Diagram (LIMDD) solves this problem by combining the strengths of decision diagrams and the stabilizer formalism that enables efficient simulation of Clifford circuits. However, LIMDDs have only been introduced on paper thus far and have not been implemented yet—preventing an investigation of their practical capabilities through experiments. In this work, we present the first implementation of LIMDDs for quantum circuit simulation. A case study confirms the improved performance in both worlds for the Quantum Fourier Transform applied to a stabilizer state. The resulting package is available under a free license at https://github.com/cda-tum/ddsim/tree/limdd .