In this paper, we consider the transmission needs of communication networks for two classes of secondary users (SUs), named SU1 and SU2 (lowest priority) in cognitive radio networks (CRNs). In such CRNs, primary users (PUs) have preemptive priority over both SU1's users (SU1s) and SU2's users (SU2s). We propose a preemptive scheme (referred to as the P Scheme) and a non-preemptive scheme (referred to as the Non-P Scheme) when considering the interactions between SU1s and SU2s. Focusing on the transmission interruptions to SU2 packets, we present a probabilistic returning scheme with a returning probability to realize feedback control for SU2 packets. We present a Markov chain model to develop some formulas for SU1 and SU2 packets, and compare the influences of the P Scheme and the Non-P Scheme in the proposed probabilistic returning scheme. Numerical analyses compare the impact of the returning probability on the P Scheme and the Non-P Scheme. Furthermore, we optimize the returning probability and compare the optimal numerical results yielded by the P Scheme and the Non-P Scheme.

This letter investigates the information-theoretic privacy-utility tradeoff. We analyze the minimum information leakage (f-leakage) under the utility constraint that the excess distortion probability is allowed up to ε∈[0, 1). The derived upper bound is characterized by the ε-cutoff random transformation and a distortion ball.

In this paper, a linear array of 4 leaf-shaped bowtie slot antennas is proposed for use in quasi-millimeter wave band. The slot antennas array is designed to operate at 28GHz frequency band. The leaf-shaped bowtie slot antenna is a type of self-complementary antenna with low profile and low cost of fabrication. The proposed antenna structure offers improvement in radiation pattern, gain, and -10dB impedance bandwidth. Through out of this paper radiation pattern, actual gain, and -10dB impedance bandwidth are evaluated by Finite Different Time Domain (FDTD) simulation. Antenna characteristics are analyzed in the frequency range of 27GHz to 29GHz. To improve antenna characteristics such as actual gain and -10dB impedance bandwidth, a dielectric superstrate layer with relative permittivity of 10.2 is placed on top of ground plane of the slot antennas array. Three antenna structures are introduced and compared. With two layers of dielectric superstrate on top of the antennas ground plane, analysis results show that -10dB impedance bandwidth occupies the frequency range of 27.17GHz to 28.39GHz. Therefore, the operational impedance bandwidth is 1.22GHz. Maximum actual gain of the slot antennas array with two dielectric superstrate layers is 20.49dBi and -3dB gain bandwidth occupies the frequency range of 27.02GHz to 28.57GHz. To validate the analysis results, prototype of the designed slot antennas array is fabricated. Characteristics of the slot antennas array are measured and compared with the analysis results.

Developing well-designed presentation slides is challenging for many people, especially novices. The ability to build high quality slideshows is becoming more important in society. In this study, a neural network was used to identify novice vs. well-designed presentation slides based on visual and structural features. For such a purpose, a dataset containing 1,080 slide pairs was newly constructed. One of each pair was created by a novice, and the other was the improved one by the same person according to the experts' advice. Ten checkpoints frequently pointed out by professional consultants were extracted and set as prediction targets. The intrinsic problem was that the label distribution was imbalanced, because only a part of the samples had corresponding design problems. Therefore, re-sampling methods for addressing class imbalance were applied to improve the accuracy of the proposed model. Furthermore, we combined the target task with an assistant task for transfer and multi-task learning, which helped the proposed model achieve better performance. After the optimal settings were used for each checkpoint, the average accuracy of the proposed model rose up to 81.79%. With the advice provided by our assessment system, the novices significantly improved their slide design.

Low power consumption is important in edge artificial intelligence (AI) chips, where power supply is limited. Therefore, we propose reconfigurable neural network accelerator (ReNA), an AI chip that can process both a convolutional layer and fully connected layer with the same structure by reconfiguring the circuit. In addition, we developed tools for pre-evaluation of the performance when a deep neural network (DNN) model is implemented on ReNA. With this approach, we established the flow for the implementation of DNN models on ReNA and evaluated its power consumption. ReNA achieved 1.51TOPS/W in the convolutional layer and 1.38TOPS/W overall in a VGG16 model with a 70% pruning rate.

To prove the graph relations such as the connectivity and isolation for a certified graph, a system of a graph signature and proofs has been proposed. In this system, an issuer generates a signature certifying the topology of an undirected graph, and issues the signature to a prover. The prover can prove the knowledge of the signature and the graph in the zero-knowledge, i.e., the signature and the signed graph are hidden. In addition, the prover can prove relations on the certified graph such as the connectivity and isolation between two vertexes. In the previous system, using integer commitments on RSA modulus, the graph relations are proved. However, the RSA modulus needs a longer size for each element. Furthermore, the proof size and verification cost depend on the total numbers of vertexes and edges. In this paper, we propose a graph signature and proof system, where these are computed on bilinear groups without the RSA modulus. Moreover, using a bilinear map accumulator, the prover can prove the connectivity and isolation on a graph, where the proof size and verification cost become independent from the total numbers of vertexes and edges.

We study a new reconfiguration problem inspired by classic mechanical puzzles: a colored token is placed on each vertex of a given graph; we are also given a set of distinguished cycles on the graph. We are tasked with rearranging the tokens from a given initial configuration to a final one by using cyclic shift operations along the distinguished cycles. We call this a cyclic shift puzzle. We first investigate a large class of graphs, which generalizes several classic cyclic shift puzzles, and we give a characterization of which final configurations can be reached from a given initial configuration. Our proofs are constructive, and yield efficient methods for shifting tokens to reach the desired configurations. On the other hand, when the goal is to find a shortest sequence of shifting operations, we show that the problem is NP-hard, even for puzzles with tokens of only two different colors.

Deep learning applications have often been processed in the cloud or on servers. Still, for applications that require privacy protection and real-time processing, the execution environment is moved to edge devices. Edge devices that implement a neural network (NN) are physically accessible to an attacker. Therefore, physical attacks are a risk. Fault attacks on these devices are capable of misleading classification results and can lead to serious accidents. Therefore, we focus on the softmax function and evaluate a fault attack using a clock glitch against NN implemented in an 8-bit microcontroller. The clock glitch is used for fault injection, and the injection timing is controlled by monitoring the power waveform. The specific waveform is enrolled in advance, and the glitch timing pulse is generated by the sum of absolute difference (SAD) matching algorithm. Misclassification can be achieved by appropriately injecting glitches triggered by pattern detection. We propose a countermeasure against fault injection attacks that utilizes the randomization of power waveforms. The SAD matching is disabled by random number initialization on the summation register of the softmax function.

Social Skills Training (SST) has been used for years to improve individuals' social skills toward building a better daily life. In SST carried out by humans, the social skills level is usually evaluated through a verbal interview conducted by the trainer. Although this evaluation is based on psychiatric knowledge and professional experience, its quality depends on the trainer's capabilities. Therefore, to standardize such evaluations, quantifiable metrics are required. To meet this need, the second edition of the Social Responsiveness Scale (SRS-2) offers a viable solution because it has been extensively tested and standardized by empirical research works. This paper describes the development of an automated method to evaluate a person's social skills level based on SRS-2. We use multimodal features, including BERT-based features, and perform score estimation with a 0.76 Pearson correlation coefficient while using feature selection. In addition, we examine the linguistic aspects of BERT-based features through subjective evaluations. Consequently, the BERT-based features show a strong negative correlation with human subjective scores of fluency, appropriate word choice, and understandable speech structure.

The construction of a Huffman code can be understood as the problem of finding a full binary tree such that each leaf is associated with a linear function of the depth of the leaf and the sum of the function values is minimized. Fujiwara and Jacobs extended this to a general function and proved the extended problem to be NP-hard. The authors also showed the case where the functions associated with leaves are each non-decreasing and convex is solvable in polynomial time. However, the complexity of the case of non-decreasing non-convex functions remains unknown. In this paper we try to reveal the complexity by considering non-decreasing non-convex functions each of which takes the smaller value of either a linear function or a constant. As a result, we provide a polynomial-time algorithm for two subclasses of such functions.

Hansen, Kaplan, Zamir and Zwick (STOC 2019) introduced a systematic way to use “bias” for predicting an assignment to a Boolean variable in the process of PPSZ and showed that their biased PPSZ algorithm achieves a relatively large success probability improvement of PPSZ for Unique 3SAT. We propose an additional way to use “bias” and show by numerical analysis that the improvement gets increased further.

We study the gathering problem requiring a team of mobile agents to gather at a single node in arbitrary networks. The team consists of k agents with unique identifiers (IDs), and f of them are weakly Byzantine agents, which behave arbitrarily except falsifying their identifiers. The agents move in synchronous rounds and cannot leave any information on nodes. If the number of nodes n is given to agents, the existing fastest algorithm tolerates any number of weakly Byzantine agents and achieves gathering with simultaneous termination in O(n4·|Λgood|·X(n)) rounds, where |Λgood| is the length of the maximum ID of non-Byzantine agents and X(n) is the number of rounds required to explore any network composed of n nodes. In this paper, we ask the question of whether we can reduce the time complexity if we have a strong team, i.e., a team with a few Byzantine agents, because not so many agents are subject to faults in practice. We give a positive answer to this question by proposing two algorithms in the case where at least 4f2+9f+4 agents exist. Both the algorithms assume that the upper bound N of n is given to agents. The first algorithm achieves gathering with non-simultaneous termination in O((f+|&Lambdagood|)·X(N)) rounds. The second algorithm achieves gathering with simultaneous termination in O((f+|&Lambdaall|)·X(N)) rounds, where |&Lambdaall| is the length of the maximum ID of all agents. The second algorithm significantly reduces the time complexity compared to the existing one if n is given to agents and |&Lambdaall|=O(|&Lambdagood|) holds.

In the rendezvous problem, two computing entities (called agents) located at different vertices in a graph have to meet at the same vertex. In this paper, we consider the synchronous neighborhood rendezvous problem, where the agents are initially located at two adjacent vertices. While this problem can be trivially solved in O(Δ) rounds (Δ is the maximum degree of the graph), it is highly challenging to reveal whether that problem can be solved in o(Δ) rounds, even assuming the rich computational capability of agents. The only known result is that the time complexity of O($O(sqrt{n})$) rounds is achievable if the graph is complete and agents are probabilistic, asymmetric, and can use whiteboards placed at vertices. Our main contribution is to clarify the situation (with respect to computational models and graph classes) admitting such a sublinear-time rendezvous algorithm. More precisely, we present two algorithms achieving fast rendezvous additionally assuming bounded minimum degree, unique vertex identifier, accessibility to neighborhood IDs, and randomization. The first algorithm runs within $ ilde{O}(sqrt{nDelta/delta} + n/delta)$ rounds for graphs of the minimum degree larger than $sqrt{n}$, where n is the number of vertices in the graph, and δ is the minimum degree of the graph. The second algorithm assumes that the largest vertex ID is O(n), and achieves $ ilde{O}left( rac{n}{sqrt{delta}} ight)$-round time complexity without using whiteboards. These algorithms attain o(Δ)-round complexity in the case of $delta = {omega}(sqrt{n} log n)$ and δ=ω(n2/3log4/3n) respectively. We also prove that four unconventional assumptions of our algorithm, bounded minimum degree, accessibility to neighborhood IDs, initial distance one, and randomization are all inherently necessary for attaining fast rendezvous. That is, one can obtain the Ω(n)-round lower bound if either one of them is removed.

We propose a novel contention window management algorithm that adjusts contention window size in dense wireless network environments. In the algorithm, a station estimates the number of neighboring stations by observing its number of freezes while attempting wireless channel accesses. Then, station adopts a new contention window size for further frame transmissions. We evaluate the proposed algorithm with the NS-3 simulator. The simulation results show that our algorithm outperforms existing works in terms of delay, throughput, collision rate, and frame delivery ratio.

Binary sequences with high linear complexity and high 2-adic complexity have important applications in communication and cryptography. In this paper, the 2-adic complexity of a class of balanced Whiteman generalized cyclotomic sequences which have high linear complexity is considered. Through calculating the determinant of the circulant matrix constructed by one of these sequences, the result shows that the 2-adic complexity of this class of sequences is large enough to resist the attack of the rational approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).

In this paper, we study Hermitian linear complementary dual (abbreviated Hermitian LCD) rank metric codes. A class of Hermitian LCD generalized Gabidulin codes are constructed by qm-self-dual bases of Fq2m over Fq2. Moreover, the exact number of qm-self-dual bases of Fq2m over Fq2 is derived. As a consequence, an upper bound and a lower bound of the number of the constructed Hermitian LCD generalized Gabidulin codes are determined.

This paper proposes a network tomography scheme for information-centric networking (ICN), which we call ICN tomography. When content is received over a conventional IP network, the communication occurs after converting the content name into an IP address, which is the locator, so as to identify the position of the network. By contrast, in ICN, communication is achieved by directly specifying the content name or content ID. The content is sent to the requesting user by a nearby node having the content or cache, making it difficult to apply a conventional network tomography that uses end-to-end quality of service (QoS) measurements and routing information between the source and destination node pairs as input to the ICN. This is because, in ICN, the end-to-end flow for an end host receiving some content can take various routes; therefore, the intermediate and source nodes can vary. In this paper, we first describe the technical challenges of applying network tomography to ICN. We then propose ICN tomography, where we use the content name as an endpoint to define an end-to-end QoS measurement and a routing matrix. In defining the routing matrix, we assume that the end-to-end flow follows a probabilistic routing. Finally, the effectiveness of the proposed method is evaluated through a numerical analysis and simulation.

In this paper, two-stage BP detection is proposed for overloaded MIMO. The proposal combines BP with the MMSE pre-cancellation algorithm followed by normal BP detection. In overloaded MIMO systems, the loops in a factor graph degrade the demodulation performance of BP detection. MMSE pre-cancellation reduces the number of connections or coefficient values in the factor graph which improves the convergence characteristics of posteriori probabilities. Numerical results obtained through computer simulation show that the BERs of the proposed two-stage BP detection outperforms the conventional BP with MMSE pre-cancellation in a low bit energy range when the MMSE block size is four and the number of MMSE blocks is one. When the pre-cancellation is applied for complexity reduction, the proposed scheme reduces multiplication operations and summation operations by the same factor of 0.7 though the amount of the performance improvement to the conventional scheme is limited.

Some textbooks of formal languages and automata theory implicitly state the structural equality of the binary n-dimensional de Bruijn graph and the state diagram of minimum state deterministic finite automaton which accepts regular language (0+1)*1(0+1)n-1. By introducing special finite automata whose accepting states are refined with two or more colors, we extend this fact to both k-ary versions. That is, we prove that k-ary n-dimensional de Brujin graph and the state diagram for minimum state deterministic colored finite automaton which accepts the (k-1)-tuple of the regular languages (0+1+…+k-1)*1(0+1+…+k-1)n-1,...,and(0+1+…+k-1)*(k-1)(0+1+…+k-1)n-1 are isomorphic for arbitrary k more than or equal to 2. We also investigate the properties of colored finite automata themselves and give computational complexity results on three decision problems concerning color unmixedness of nondeterminisitic ones.

Given a set P of n points and an integer k, we wish to place k facilities on points in P so that the minimum distance between facilities is maximized. The problem is called the k-dispersion problem, and the set of such k points is called a k-dispersion of P. Note that the 2-dispersion problem corresponds to the computation of the diameter of P. Thus, the k-dispersion problem is a natural generalization of the diameter problem. In this paper, we consider the case of k=3, which is the 3-dispersion problem, when P is in convex position. We present an O(n2)-time algorithm to compute a 3-dispersion of P.