We propose two systematic constructions of deletion-correcting codes for protecting quantum inforomation. The first one works with qudits of any dimension l, which is referred to as l-adic, but only one deletion is corrected and the constructed codes are asymptotically bad. The second one corrects multiple deletions and can construct asymptotically good codes. The second one also allows conversion of stabilizer-based quantum codes to deletion-correcting codes, and entanglement assistance.

Reducing power consumption is a crucial factor making industrial designs, such as mobile SoCs, competitive. Voltage scaling (VS) is the classical yet most effective technique that contributes to quadratic power reduction. A recent design technique called activation-aware slack assignment (ASA) enhances the voltage-scaling by allocating the timing margin of critical paths with a stochastic mean-time-to-failure (MTTF) analysis. Meanwhile, such stochastic treatment of timing errors is accepted in limited application domains, such as image processing. This paper proposes a design optimization methodology that achieves a mode-wise voltage-scalable (MWVS) design guaranteeing no timing errors in each mode operation. This work formulates the MWVS design as an optimization problem that minimizes the overall power consumption considering each mode duration, achievable voltage lowering and accompanied circuit overhead explicitly, and explores the solution space with the downhill simplex algorithm that does not require numerical derivation and frequent objective function evaluations. For obtaining a solution, i.e., a design, in the optimization process, we exploit the multi-corner multi-mode design flow in a commercial tool for performing mode-wise ASA with sets of false paths dedicated to individual modes. We applied the proposed design methodology to RISC-V design. Experimental results show that the proposed methodology saves 13% to 20% more power compared to the conventional VS approach and attains 8% to 15% gain from the conventional single-mode ASA. We also found that cycle-by-cycle fine-grained false path identification reduced leakage power by 31% to 42%.

Currently there has been increasing demand for real-time training on resource-limited IoT devices such as smart sensors, which realizes standalone online adaptation for streaming data without data transfers to remote servers. OS-ELM (Online Sequential Extreme Learning Machine) has been one of promising neural-network-based online algorithms for on-chip learning because it can perform online training at low computational cost and is easy to implement as a digital circuit. Existing OS-ELM digital circuits employ fixed-point data format and the bit-widths are often manually tuned, however, this may cause overflow or underflow which can lead to unexpected behavior of the circuit. For on-chip learning systems, an overflow/underflow-free design has a great impact since online training is continuously performed and the intervals of intermediate variables will dynamically change as time goes by. In this paper, we propose an overflow/underflow-free bit-width optimization method for fixed-point digital circuits of OS-ELM. Experimental results show that our method realizes overflow/underflow-free OS-ELM digital circuits with 1.0x - 1.5x more area cost compared to the baseline simulation method where overflow or underflow can happen.

Line detection is the fundamental image processing technique which has various applications in the field of computer vision. For example, lane keeping required to realize autonomous vehicles can be implemented based on line detection technique. For such purposes, however, low detection latency and power consumption are essential. Using hardware-based stream processing is considered as an effective way to achieve such properties since it eliminates the need of storing the whole frame into energy-consuming external memory. In addition, adopting FPGAs enables us to keep flexibility of software processing. The line segment detector (LSD) is the algorithm based on intensity gradient, and performs better than the well-known Hough transform in terms of processing speed and accuracy. However, implementing the original LSD on FPGAs as a pipeline structure is difficult mainly because of its iterative region growing approach. Therefore, we propose a simple and stream-friendly line segment detection algorithm based on the concept of LSD. The whole system is implemented on a Xilinx Zynq-7000 XC7Z020-1CLG400C FPGA without any external memory. Evaluation results reveal that the implemented system is able to detect line segments successfully and is compact with 7.5% of Block RAM and less than 7.0% of the other resources used, while maintaining 60 fps throughput for VGA videos. It is also shown that the system is power-efficient compared to software processing on CPUs.

Multiparty computation (MPC) is the technology that computes an arbitrary function represented as a circuit without revealing input values. Typical MPC uses secret sharing (SS) schemes, garbled circuit (GC), and homomorphic encryption (HE). These cryptographic technologies have a trade-off relationship for the computation cost, communication cost, and type of computable circuit. Hence, the optimal choice depends on the computing resources, communication environment, and function related to applications. The private decision tree evaluation (PDTE) is one of the important applications of secure computation. There exist several PDTE protocols with constant communication rounds using GC, HE, and SS-MPC over the field. However, to the best of our knowledge, PDTE protocols with constant communication rounds using MPC based on SS over the ring (requiring only lower computation costs and communication complexity) are non-trivial and still missing. In this paper, we propose a PDTE protocol based on a three-party computation (3PC) protocol over the ring with one corruption. We also propose another three-party PDTE protocol over the field with one corruption that is more efficient than the naive construction.

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.

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.

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.

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.

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.

In this paper, a macro cell switching scheme for distributed antennas is proposed. In conventional distributed antenna transmission (DAT), the macro cell to which each antenna belongs is fixed. Though a cell-free system has been investigated because of its higher system throughput, the implementation cost of front-hauls can be excessive. To increase the flexibility of resource allocation in the DAT with moderate front-haul complexity, we propose the macro cell switching of distributed antennas (DAs). In the proposed scheme, DAs switch their attribution macro cells depending on the amount of pre-assigned connections. Numerical results obtained through computer simulation show that the proposed scheme realizes a better system throughput than the conventional system, especially when the number of user equipments (UEs) is smaller and the distance between DAs are larger.

In this paper, we propose the first private decision tree evaluation (PDTE) schemes which are suitable for use in Machine Learning as a Service (MLaaS) scenarios. In our schemes, a user and a model owner send the ciphertexts of a sample and a decision tree model, respectively, and a single server classifies the sample without knowing the sample nor the decision tree. Although many PDTE schemes have been proposed so far, most of them require to reveal the decision tree to the server. This is undesirable because the classification model is the intellectual property of the model owner, and/or it may include sensitive information used to train the model, and therefore the model also should be hidden from the server. In other PDTE schemes, multiple servers jointly conduct the classification process and the decision tree is kept secret from the servers under the assumption they do not collude. Unfortunately, this assumption may not hold because MLaaS is usually provided by a single company. In contrast, our schemes do not have such problems. In principle, fully homomorphic encryption allows us to classify an encrypted sample based on an encrypted decision tree, and in fact, the existing non-interactive PDTE scheme can be modified so that the server classifies only handling ciphertexts. However, the resulting scheme is less efficient than ours. We also show the experimental results for our schemes.

We consider the coding problem for lossy source coding with side information at the decoder, which is known as the Wyner-Ziv source coding problem. The goal of the coding problem is to find the minimum rate such that the probability of exceeding a given distortion threshold is less than the desired level. We give an equivalent expression of the minimum rate by using the chromatic number and notions of covering of a set. This allows us to analyze the coding problem in terms of graph coloring and covering.

In this study, Bayesian image denoising, in which the prior distribution is assumed to be a Gaussian Markov random field (GMRF), is considered. Recently, an effective algorithm for Bayesian image denoising with a standard GMRF prior has been proposed, which can help implement the overall procedure and optimize its parameters in O(n)-time, where n is the size of the image. A new GMRF-type prior, referred to as a hierarchical GMRF (HGMRF) prior, is proposed, which is obtained by applying a hierarchical Bayesian approach to the standard GMRF prior; in addition, an effective denoising algorithm based on the HGMRF prior is proposed. The proposed HGMRF method can help implement the overall procedure and optimize its parameters in O(n)-time, as well as the previous GMRF method. The restoration quality of the proposed method is found to be significantly higher than that of the previous GMRF method as well as that of a non-local means filter in several cases. Furthermore, numerical evidence implies that the proposed HGMRF prior is more suitable for the image prior than the standard GMRF prior.

In this paper, we construct the first efficient leakage-resilient CCA2 (LR-CCA2)-secure attribute-based encryption (ABE) schemes. We also construct the first efficient LR-CCA2-secure identity-based encryption (IBE) scheme with optimal leakage rate. To obtain our results, we develop a new quasi-adaptive non-interactive zero-knowledge (QA-NIZK) argument for the ciphertext consistency of the LR-CPA-secure schemes. Our ABE schemes are obtained by boosting the LR-CPA-security of some existing schemes to the LR-CCA2-security by using our QA-NIZK arguments. The schemes are almost as efficient as the underlying LR-CPA-secure schemes.

It is known that quasi-cyclic (QC) codes over the finite field Fq correspond to certain Fq[x]-modules. A QC code C is specified by a generator polynomial matrix G whose rows generate C as an Fq[x]-module. The reversed code of C, denoted by R, is the code obtained by reversing all codewords of C while the dual code of C is denoted by C⊥. We call C reversible, self-orthogonal, and self-dual if R = C, C⊥ ⊇ C, and C⊥ = C, respectively. In this study, for a given C, we find an explicit formula for a generator polynomial matrix of R. A necessary and sufficient condition for C to be reversible is derived from this formula. In addition, we reveal the relations among C, R, and C⊥. Specifically, we give conditions on G corresponding to C⊥ ⊇ R, C⊥ ⊆ R, and C = R = C⊥. As an application, we employ these theoretical results to the construction of QC codes with best parameters. Computer search is used to show that there exist various binary reversible self-orthogonal QC codes that achieve the upper bounds on the minimum distance of linear codes.

Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to first detect human body parts, and then utilize the parts information to estimate the pedestrians' existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottleneck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by fine-tuning that CNN. In experiments, it shows astonishing effectiveness of optimization and robustness of occlusion handling.

In ball games, acquiring skills to change the direction becomes necessary. For revealing the mechanism of skill acquisition in terms of the relevant field, it would be necessary to take an approach regarding players' cognition as well as body movements measurable from outside. In the phase of change-of-direction performance that this study focuses on, cognitive factors including the prediction of opposite players' movements and judgements of the situation have significance. The purpose of this study was to reveal cognitive transformation in the skill acquisition process for change-of-direction performance. The survey was conducted for three months from August 29 to November 28, 2020, and those surveyed were seven university freshmen belonging to women's basketball club of M University. The way to analyze verbal reports collected in order to explore the changes in the players' cognition is described in Sect.2. In Sect.3, we made a plot graph showing temporal changes in respective factors based on coding outcomes for verbal reports. Consequently, as cognitive transformation in the skill acquisition process for change-of-direction performance, four items such as (1) goal setting for skill acquisition, (2) experience of change in running direction, (3) experience of speed and acceleration, and (4) experience of the movement of lower extremities such as legs and hip joints were suggested as common cognitive transformation. In addition, cognitive transformation varied by the degree of skill acquisition for change-of-direction performance. It was indicated that paying too much attention to body feelings including the position of and shift in the center of gravity in the body posed an obstacle to the skill acquisition for change-of-direction performance.

In the upcoming Internet of Things era, reducing energy consumption of embedded processors is highly desired. Minimum Energy Point Tracking (MEPT) is one of the most efficient methods to reduce both dynamic and static energy consumption of a processor. Previous works proposed a variety of MEPT methods over the past years. However, none of them incorporate their algorithms with practical real-time operating systems, although edge computing applications often require low energy task execution with guaranteeing real-time properties. The difficulty comes from the time complexity for identifying an MEP and changing voltages, which often prevents real-time task scheduling. The conventional Dynamic Voltage and Frequency Scaling (DVFS) only scales the supply voltage. On the other hand, MEPT needs to adjust the body bias voltage in addition. This additional tuning knob makes MEPT much more complicated. This paper proposes an approximate MEPT algorithm, which reduces the complexity of identifying an MEP down to that of DVFS. The key idea is to linearly approximate the relationship between the processor frequency, supply voltage, and body bias voltage. Thanks to the approximation, optimal voltages for a specified clock frequency can be derived immediately. We also propose a task scheduling algorithm, which adjusts processor performance to the workload and then provides a soft real-time capability to the system. The operating system stochastically adjusts the average response time of the processor to be equal to a specified deadline. MEPT will be performed as a general task, and its overhead is considered in the calculation of the frequency. The experiments using a fabricated test chip and on-chip sensors show that the proposed algorithm is a maximum of 16 times more energy-efficient than DVFS. Also, the energy loss induced by the approximation is only 3% at most, and the algorithm does not sacrifice the fundamental real-time properties.